diff --git a/README.md b/README.md
index 9a81698b71..82674523ed 100644
--- a/README.md
+++ b/README.md
@@ -55,14 +55,13 @@ can be found [here](https://www.aeon-toolkit.org/en/stable/developer_guide/dev_i
The best place to get started for all `aeon` packages is our [getting started guide](https://www.aeon-toolkit.org/en/stable/getting_started.html).
-Below we provide a quick example of how to use `aeon` for forecasting,
-classification and clustering.
+Below we provide a quick example of how to use `aeon` for classification and clustering.
### Classification
*It's worth mentioning that the classifier used in the example can easily be
swapped out for a regressor, and the labels for numeric targets. This flexibility
-allowing for seamless adaptation to different tasks and datasets while preserving
+allows for seamless adaptation to different tasks and datasets while preserving
API consistency.*
```python
diff --git a/aeon/anomaly_detection/__init__.py b/aeon/anomaly_detection/__init__.py
index 11da46fb1c..a02aa61dad 100644
--- a/aeon/anomaly_detection/__init__.py
+++ b/aeon/anomaly_detection/__init__.py
@@ -1,6 +1,8 @@
"""Time Series Anomaly Detection."""
__all__ = [
+ "CBLOF",
+ "COPOD",
"DWT_MLEAD",
"IsolationForest",
"KMeansAD",
@@ -12,7 +14,8 @@
"STRAY",
]
-
+from aeon.anomaly_detection._cblof import CBLOF
+from aeon.anomaly_detection._copod import COPOD
from aeon.anomaly_detection._dwt_mlead import DWT_MLEAD
from aeon.anomaly_detection._iforest import IsolationForest
from aeon.anomaly_detection._kmeans import KMeansAD
diff --git a/aeon/anomaly_detection/_cblof.py b/aeon/anomaly_detection/_cblof.py
new file mode 100644
index 0000000000..53f244c6c0
--- /dev/null
+++ b/aeon/anomaly_detection/_cblof.py
@@ -0,0 +1,162 @@
+"""CBLOF for Anomaly Detection."""
+
+__maintainer__ = []
+__all__ = ["CBLOF"]
+
+from typing import Optional, Union
+
+import numpy as np
+
+from aeon.anomaly_detection._pyodadapter import PyODAdapter
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+class CBLOF(PyODAdapter):
+ r"""CBLOF for Anomaly Detection.
+
+ This class implements the CBLOF algorithm for anomaly detection
+ using PyODAdadpter to be used in the aeon framework. All parameters are passed to
+ the PyOD model ``CBLOF`` except for `window_size` and `stride`, which are used to
+ construct the sliding windows.
+
+ .. list-table:: Capabilities
+ :stub-columns: 1
+
+ * - Input data format
+ - univariate and multivariate
+ * - Output data format
+ - anomaly scores
+ * - Learning Type
+ - unsupervised or semi-supervised
+
+ The documentation for parameters has been adapted from the
+ [PyOD documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#id117).
+ Here, `X` refers to the set of sliding windows extracted from the time series
+ using :func:`aeon.utils.windowing.sliding_windows` with the parameters
+ ``window_size`` and ``stride``. The internal `X` has the shape
+ `(n_windows, window_size * n_channels)`.
+
+ Parameters
+ ----------
+ n_clusters : int, default=8
+ The number of clusters to form as well as the number of
+ centroids to generate.
+
+ clustering_estimator : Estimator or None, default=None
+ The base clustering algorithm for performing data clustering.
+ A valid clustering algorithm should be passed in. The estimator should
+ have standard sklearn APIs, fit() and predict(). The estimator should
+ have attributes ``labels_`` and ``cluster_centers_``.
+ If ``cluster_centers_`` is not in the attributes once the model is fit,
+ it is calculated as the mean of the samples in a cluster.
+
+ If not set, CBLOF uses KMeans for scalability. See
+ https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
+
+ aeon clustering estimators are not supported.
+
+ alpha : float in (0.5, 1), default=0.9
+ Coefficient for deciding small and large clusters. The ratio
+ of the number of samples in large clusters to the number of samples in
+ small clusters.
+
+ beta : int or float in (1,), default=5
+ Coefficient for deciding small and large clusters. For a list
+ sorted clusters by size `|C1|, \|C2|, ..., |Cn|, beta = |Ck|/|Ck-1|`
+
+ use_weights : bool, default=False
+ If set to True, the size of clusters are used as weights in
+ outlier score calculation.
+
+ check_estimator : bool, default=False
+ If set to True, check whether the base estimator is consistent with
+ sklearn standard.
+
+ random_state : int, np.RandomState or None, default=None
+ If int, random_state is the seed used by the random
+ number generator; If RandomState instance, random_state is the random
+ number generator; If None, the random number generator is the
+ RandomState instance used by `np.random`.
+
+ window_size : int, default=10
+ Size of the sliding window.
+
+ stride : int, default=1
+ Stride of the sliding window.
+ """
+
+ _tags = {
+ "capability:multivariate": True,
+ "capability:univariate": True,
+ "capability:missing_values": False,
+ "fit_is_empty": False,
+ "python_dependencies": ["pyod"],
+ }
+
+ def __init__(
+ self,
+ n_clusters: int = 8,
+ clustering_estimator=None,
+ alpha: float = 0.9,
+ beta: Union[int, float] = 5,
+ use_weights: bool = False,
+ check_estimator: bool = False,
+ random_state: Optional[Union[int, np.random.RandomState]] = None,
+ window_size: int = 10,
+ stride: int = 1,
+ ):
+ _check_soft_dependencies(*self._tags["python_dependencies"])
+ from pyod.models.cblof import CBLOF
+
+ model = CBLOF(
+ n_clusters=n_clusters,
+ clustering_estimator=clustering_estimator,
+ alpha=alpha,
+ beta=beta,
+ use_weights=use_weights,
+ check_estimator=check_estimator,
+ random_state=random_state,
+ )
+ self.n_clusters = n_clusters
+ self.clustering_estimator = clustering_estimator
+ self.alpha = alpha
+ self.beta = beta
+ self.use_weights = use_weights
+ self.check_estimator = check_estimator
+ self.random_state = random_state
+ super().__init__(model, window_size, stride)
+
+ def _fit(self, X: np.ndarray, y: Union[np.ndarray, None] = None) -> None:
+ super()._fit(X, y)
+
+ def _predict(self, X: np.ndarray) -> np.ndarray:
+ return super()._predict(X)
+
+ def _fit_predict(
+ self, X: np.ndarray, y: Union[np.ndarray, None] = None
+ ) -> np.ndarray:
+ return super()._fit_predict(X, y)
+
+ @classmethod
+ def _get_test_params(cls, parameter_set="default"):
+ """Return testing parameter settings for the estimator.
+
+ Parameters
+ ----------
+ parameter_set : str, default="default"
+ Name of the set of test parameters to return, for use in tests. If no
+ special parameters are defined for a value, will return `"default"` set.
+
+ Returns
+ -------
+ params : dict
+ Parameters to create testing instances of the class.
+ Each dict are parameters to construct an "interesting" test instance, i.e.,
+ `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+ `create_test_instance` uses the first (or only) dictionary in `params`.
+ """
+ return {
+ "n_clusters": 4,
+ "alpha": 0.75,
+ "beta": 3,
+ }
diff --git a/aeon/anomaly_detection/_copod.py b/aeon/anomaly_detection/_copod.py
new file mode 100644
index 0000000000..1194f98b94
--- /dev/null
+++ b/aeon/anomaly_detection/_copod.py
@@ -0,0 +1,87 @@
+"""COPOD for anomaly detection."""
+
+__maintainer__ = []
+__all__ = ["COPOD"]
+
+from typing import Union
+
+import numpy as np
+
+from aeon.anomaly_detection._pyodadapter import PyODAdapter
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+class COPOD(PyODAdapter):
+ """COPOD for anomaly detection.
+
+ This class implements the COPOD using PyODAdadpter to be used in the aeon framework.
+ The parameter `n_jobs` is passed to COPOD model from PyOD, `window_size` and
+ `stride` are used to construct the sliding windows.
+
+ .. list-table:: Capabilities
+ :stub-columns: 1
+ * - Input data format
+ - univariate and multivariate
+ * - Output data format
+ - anomaly scores
+ * - Learning Type
+ - unsupervised or semi-supervised
+
+ Parameters
+ ----------
+ n_jobs : int, default=1
+ The number of jobs to run in parallel for the COPOD model.
+
+ window_size : int, default=10
+ Size of the sliding window.
+
+ stride : int, default=1
+ Stride of the sliding window.
+ """
+
+ _tags = {
+ "capability:multivariate": True,
+ "capability:univariate": True,
+ "capability:missing_values": False,
+ "fit_is_empty": False,
+ "python_dependencies": ["pyod"],
+ }
+
+ def __init__(self, n_jobs: int = 1, window_size: int = 10, stride: int = 1):
+ _check_soft_dependencies(*self._tags["python_dependencies"])
+ from pyod.models.copod import COPOD
+
+ model = COPOD(n_jobs=n_jobs)
+ self.n_jobs = n_jobs
+ super().__init__(model, window_size=window_size, stride=stride)
+
+ def _fit(self, X: np.ndarray, y: Union[np.ndarray, None] = None) -> None:
+ super()._fit(X, y)
+
+ def _predict(self, X: np.ndarray) -> np.ndarray:
+ return super()._predict(X)
+
+ def _fit_predict(
+ self, X: np.ndarray, y: Union[np.ndarray, None] = None
+ ) -> np.ndarray:
+ return super()._fit_predict(X, y)
+
+ @classmethod
+ def _get_test_params(cls, parameter_set="default") -> dict:
+ """Return testing parameter settings for the estimator.
+
+ Parameters
+ ----------
+ parameter_set : str, default="default"
+ Name of the set of test parameters to return, for use in tests. If no
+ special parameters are defined for a value, will return `"default"` set.
+
+ Returns
+ -------
+ params : dict or list of dict, default={}
+ Parameters to create testing instances of the class.
+ Each dict are parameters to construct an "interesting" test instance, i.e.,
+ `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+ `create_test_instance` uses the first (or only) dictionary in `params`.
+ """
+ return {}
diff --git a/aeon/anomaly_detection/_dwt_mlead.py b/aeon/anomaly_detection/_dwt_mlead.py
index 73772ace5c..2d9a036b67 100644
--- a/aeon/anomaly_detection/_dwt_mlead.py
+++ b/aeon/anomaly_detection/_dwt_mlead.py
@@ -236,7 +236,7 @@ def _push_anomaly_counts_down_to_points(
return counter[:n]
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Only supports 'default'-parameter set.
@@ -253,7 +253,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"start_level": 2,
diff --git a/aeon/anomaly_detection/_iforest.py b/aeon/anomaly_detection/_iforest.py
index 54469c0244..c098030920 100644
--- a/aeon/anomaly_detection/_iforest.py
+++ b/aeon/anomaly_detection/_iforest.py
@@ -137,7 +137,7 @@ def _fit_predict(
return super()._fit_predict(X, y)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -152,7 +152,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`IsolationForest(**params)` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"n_estimators": 10,
diff --git a/aeon/anomaly_detection/_kmeans.py b/aeon/anomaly_detection/_kmeans.py
index 403aee5f45..07b98ebe48 100644
--- a/aeon/anomaly_detection/_kmeans.py
+++ b/aeon/anomaly_detection/_kmeans.py
@@ -171,7 +171,7 @@ def _inner_predict(self, X: np.ndarray, padding: int) -> np.ndarray:
return point_anomaly_scores
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -186,7 +186,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"n_clusters": 5,
diff --git a/aeon/anomaly_detection/_merlin.py b/aeon/anomaly_detection/_merlin.py
index e86020d061..3647455353 100644
--- a/aeon/anomaly_detection/_merlin.py
+++ b/aeon/anomaly_detection/_merlin.py
@@ -208,7 +208,7 @@ def _drag(X, length, discord_range):
return C[d_max], np.sqrt(D[d_max])
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -223,6 +223,5 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {"min_length": 4, "max_length": 7}
diff --git a/aeon/anomaly_detection/_pyodadapter.py b/aeon/anomaly_detection/_pyodadapter.py
index e492d71839..637e9340a5 100644
--- a/aeon/anomaly_detection/_pyodadapter.py
+++ b/aeon/anomaly_detection/_pyodadapter.py
@@ -158,7 +158,7 @@ def _inner_predict(self, X: np.ndarray, padding: int) -> np.ndarray:
return point_anomaly_scores
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -173,7 +173,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
_check_soft_dependencies(*cls._tags["python_dependencies"])
diff --git a/aeon/anomaly_detection/_stomp.py b/aeon/anomaly_detection/_stomp.py
index d6f988a547..93d89c4400 100644
--- a/aeon/anomaly_detection/_stomp.py
+++ b/aeon/anomaly_detection/_stomp.py
@@ -118,7 +118,7 @@ def _check_params(self, X: np.ndarray) -> None:
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -133,7 +133,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"window_size": 10,
diff --git a/aeon/anomaly_detection/_stray.py b/aeon/anomaly_detection/_stray.py
index cf82893671..3ebce07a08 100644
--- a/aeon/anomaly_detection/_stray.py
+++ b/aeon/anomaly_detection/_stray.py
@@ -66,13 +66,10 @@ class STRAY(BaseAnomalyDetector):
--------
>>> from aeon.anomaly_detection import STRAY
>>> from aeon.datasets import load_airline
- >>> from sklearn.preprocessing import MinMaxScaler
>>> import numpy as np
- >>> X = load_airline().to_frame().to_numpy()
- >>> scaler = MinMaxScaler()
- >>> X = scaler.fit_transform(X)
+ >>> X = load_airline()
>>> detector = STRAY(k=3)
- >>> y = detector.fit_predict(X, axis=0)
+ >>> y = detector.fit_predict(X)
>>> y[:5]
array([False, False, False, False, False])
"""
diff --git a/aeon/anomaly_detection/base.py b/aeon/anomaly_detection/base.py
index 934f2ef6da..87ca40aa19 100644
--- a/aeon/anomaly_detection/base.py
+++ b/aeon/anomaly_detection/base.py
@@ -115,11 +115,11 @@ def fit(self, X, y=None, axis=1):
BaseAnomalyDetector
The fitted estimator, reference to self.
"""
- if self.get_class_tag("fit_is_empty"):
+ if self.get_tag("fit_is_empty"):
self.is_fitted = True
return self
- if self.get_class_tag("requires_y"):
+ if self.get_tag("requires_y"):
if y is None:
raise ValueError("Tag requires_y is true, but fit called with y=None")
@@ -159,7 +159,7 @@ def predict(self, X, axis=1) -> np.ndarray:
A boolean, int or float array of length len(X), where each element indicates
whether the corresponding subsequence is anomalous or its anomaly score.
"""
- fit_empty = self.get_class_tag("fit_is_empty")
+ fit_empty = self.get_tag("fit_is_empty")
if not fit_empty:
self._check_is_fitted()
@@ -194,7 +194,7 @@ def fit_predict(self, X, y=None, axis=1) -> np.ndarray:
A boolean, int or float array of length len(X), where each element indicates
whether the corresponding subsequence is anomalous or its anomaly score.
"""
- if self.get_class_tag("requires_y"):
+ if self.get_tag("requires_y"):
if y is None:
raise ValueError("Tag requires_y is true, but fit called with y=None")
@@ -203,7 +203,7 @@ def fit_predict(self, X, y=None, axis=1) -> np.ndarray:
X = self._preprocess_series(X, axis, True)
- if self.get_class_tag("fit_is_empty"):
+ if self.get_tag("fit_is_empty"):
self.is_fitted = True
return self._predict(X)
@@ -230,7 +230,7 @@ def _check_y(self, y: VALID_INPUT_TYPES) -> np.ndarray:
# Remind user if y is not required for this estimator on failure
req_msg = (
f"{self.__class__.__name__} does not require a y input."
- if self.get_class_tag("requires_y")
+ if self.get_tag("requires_y")
else ""
)
new_y = y
diff --git a/aeon/anomaly_detection/tests/test_cblof.py b/aeon/anomaly_detection/tests/test_cblof.py
new file mode 100644
index 0000000000..c8d9f5d9c8
--- /dev/null
+++ b/aeon/anomaly_detection/tests/test_cblof.py
@@ -0,0 +1,93 @@
+"""Tests for the CBLOF class."""
+
+import numpy as np
+import pytest
+
+from aeon.anomaly_detection import CBLOF
+from aeon.testing.data_generation import make_example_1d_numpy
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies("pyod", severity="none"),
+ reason="required soft dependency PyOD not available",
+)
+def test_cblof_default():
+ """Test CBLOF."""
+ series = make_example_1d_numpy(n_timepoints=80, random_state=0)
+ series[50:58] -= 2
+
+ cblof = CBLOF(window_size=10, stride=1, random_state=2)
+ pred = cblof.fit_predict(series, axis=0)
+
+ assert pred.shape == (80,)
+ assert pred.dtype == np.float_
+ assert 50 <= np.argmax(pred) <= 60
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies("pyod", severity="none"),
+ reason="required soft dependency PyOD not available",
+)
+def test_cblof_pyod_parameters():
+ """Test parameters are correctly passed to the CBLOF PyOD model."""
+ params = {
+ "n_clusters": 3,
+ "alpha": 0.5,
+ "beta": 2,
+ }
+ cblof = CBLOF(**params)
+
+ assert cblof.pyod_model.n_clusters == params["n_clusters"]
+ assert cblof.pyod_model.alpha == params["alpha"]
+ assert cblof.pyod_model.beta == params["beta"]
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies("pyod", severity="none"),
+ reason="required soft dependency PyOD not available",
+)
+def test_aeon_cblof_with_pyod_cblof():
+ """Test CBLOF with PyOD CBLOF."""
+ from pyod.models.cblof import CBLOF as PyODCBLOF
+
+ series = make_example_1d_numpy(n_timepoints=100, random_state=0)
+ series[20:30] -= 2
+
+ # fit and predict with aeon CBLOF
+ cblof = CBLOF(window_size=1, stride=1, random_state=2)
+ cblof_preds = cblof.fit_predict(series)
+
+ # fit and predict with PyOD CBLOF
+ _series = series.reshape(-1, 1)
+ pyod_cblof = PyODCBLOF(random_state=2)
+ pyod_cblof.fit(_series)
+ pyod_cblof_preds = pyod_cblof.decision_function(_series)
+
+ np.testing.assert_allclose(cblof_preds, pyod_cblof_preds)
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies("pyod", severity="none"),
+ reason="required soft dependency PyOD not available",
+)
+def test_custom_clustering_estimator():
+ """Test custom clustering estimator."""
+ from sklearn.cluster import Birch
+
+ series = make_example_1d_numpy(n_timepoints=100, random_state=0)
+ series[22:28] -= 2
+
+ estimator = Birch(n_clusters=2)
+ cblof = CBLOF(
+ n_clusters=2,
+ clustering_estimator=estimator,
+ window_size=5,
+ stride=1,
+ random_state=2,
+ )
+
+ preds = cblof.fit_predict(series)
+
+ assert preds.shape == (100,)
+ assert 20 <= np.argmax(preds) <= 30
diff --git a/aeon/anomaly_detection/tests/test_copod.py b/aeon/anomaly_detection/tests/test_copod.py
new file mode 100644
index 0000000000..b1cddaa4dc
--- /dev/null
+++ b/aeon/anomaly_detection/tests/test_copod.py
@@ -0,0 +1,61 @@
+"""Tests for the COPOD class."""
+
+import numpy as np
+import pytest
+
+from aeon.anomaly_detection import COPOD
+from aeon.testing.data_generation import make_example_1d_numpy
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies("pyod", severity="none"),
+ reason="required soft dependency PyOD not available",
+)
+def test_copod_default():
+ """Test COPOD."""
+ series = make_example_1d_numpy(n_timepoints=80, random_state=0)
+ series[50:58] -= 2
+
+ copod = COPOD(window_size=10, stride=1)
+ pred = copod.fit_predict(series, axis=0)
+
+ assert pred.shape == (80,)
+ assert pred.dtype == np.float_
+ assert 50 <= np.argmax(pred) <= 60
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies("pyod", severity="none"),
+ reason="required soft dependency PyOD not available",
+)
+def test_copod_pyod_parameters():
+ """Test parameters are correctly passed to the PyOD model."""
+ params = {"n_jobs": 2}
+ copod = COPOD(**params)
+
+ assert copod.pyod_model.n_jobs == params["n_jobs"]
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies("pyod", severity="none"),
+ reason="required soft dependency PyOD not available",
+)
+def test_aeon_copod_with_pyod_copod():
+ """Test COPOD with PyOD COPOD."""
+ from pyod.models.copod import COPOD as PyODCOPOD
+
+ series = make_example_1d_numpy(n_timepoints=100, random_state=0)
+ series[20:30] -= 2
+
+ # fit and predict with aeon COPOD
+ copod = COPOD(window_size=1, stride=1)
+ copod_preds = copod.fit_predict(series)
+
+ # fit and predict with PyOD COPOD
+ _series = series.reshape(-1, 1)
+ pyod_copod = PyODCOPOD()
+ pyod_copod.fit(_series)
+ pyod_copod_preds = pyod_copod.decision_function(_series)
+
+ assert np.allclose(copod_preds, pyod_copod_preds)
diff --git a/aeon/base/__init__.py b/aeon/base/__init__.py
index 66241c6b93..a9edf83e52 100644
--- a/aeon/base/__init__.py
+++ b/aeon/base/__init__.py
@@ -4,10 +4,10 @@
"BaseAeonEstimator",
"BaseCollectionEstimator",
"BaseSeriesEstimator",
- "_HeterogenousMetaEstimator",
+ "ComposableEstimatorMixin",
]
from aeon.base._base import BaseAeonEstimator
from aeon.base._base_collection import BaseCollectionEstimator
from aeon.base._base_series import BaseSeriesEstimator
-from aeon.base._meta import _HeterogenousMetaEstimator
+from aeon.base._compose import ComposableEstimatorMixin
diff --git a/aeon/base/_base.py b/aeon/base/_base.py
index 402cd9eace..6a9f7dbb70 100644
--- a/aeon/base/_base.py
+++ b/aeon/base/_base.py
@@ -19,18 +19,19 @@ class BaseAeonEstimator(BaseEstimator, ABC):
Contains the following methods:
- reset estimator to post-init - reset(keep)
- clonee stimator (copy) - clone(random_state)
- inspect tags (class method) - get_class_tags()
- inspect tags (one tag, class) - get_class_tag(tag_name, tag_value_default,
+ - reset estimator to post-init - reset(keep)
+ - clone stimator (copy) - clone(random_state)
+ - inspect tags (class method) - get_class_tags()
+ - inspect tags (one tag, class) - get_class_tag(tag_name, tag_value_default,
raise_error)
- inspect tags (all) - get_tags()
- inspect tags (one tag) - get_tag(tag_name, tag_value_default, raise_error)
- setting dynamic tags - set_tags(**tag_dict)
+ - inspect tags (all) - get_tags()
+ - inspect tags (one tag) - get_tag(tag_name, tag_value_default, raise_error)
+ - setting dynamic tags - set_tags(**tag_dict)
+ - get fitted parameters - get_fitted_params(deep)
All estimators have the attribute:
- fitted state flag - is_fitted
+ - fitted state flag - is_fitted
"""
_tags = {
@@ -62,7 +63,7 @@ def reset(self, keep=None):
hyper-parameters (arguments of ``__init__``)
object attributes containing double-underscores, i.e., the string "__"
runs ``__init__`` with current values of hyperparameters (result of
- get_params)
+ ``get_params``)
Not affected by the reset are:
object attributes containing double-underscores
@@ -72,13 +73,13 @@ class and object methods, class attributes
Parameters
----------
keep : None, str, or list of str, default=None
- If None, all attributes are removed except hyper-parameters.
+ If None, all attributes are removed except hyperparameters.
If str, only the attribute with this name is kept.
If list of str, only the attributes with these names are kept.
Returns
-------
- self
+ self : object
Reference to self.
"""
# retrieve parameters to copy them later
@@ -162,7 +163,12 @@ def get_class_tags(cls):
return deepcopy(collected_tags)
@classmethod
- def get_class_tag(cls, tag_name, tag_value_default=None, raise_error=False):
+ def get_class_tag(
+ cls,
+ tag_name,
+ raise_error=True,
+ tag_value_default=None,
+ ):
"""
Get tag value from estimator class (only class tags).
@@ -170,22 +176,22 @@ def get_class_tag(cls, tag_name, tag_value_default=None, raise_error=False):
----------
tag_name : str
Name of tag value.
- tag_value_default : any type
- Default/fallback value if tag is not found.
- raise_error : bool
+ raise_error : bool, default=True
Whether a ValueError is raised when the tag is not found.
+ tag_value_default : any type, default=None
+ Default/fallback value if tag is not found and error is not raised.
Returns
-------
tag_value
- Value of the ``tag_name`` tag in self.
- If not found, returns an error if raise_error is True, otherwise it
- returns `tag_value_default`.
+ Value of the ``tag_name`` tag in cls.
+ If not found, returns an error if ``raise_error`` is True, otherwise it
+ returns ``tag_value_default``.
Raises
------
ValueError
- if raise_error is ``True`` and ``tag_name`` is not in
+ if ``raise_error`` is True and ``tag_name`` is not in
``self.get_tags().keys()``
Examples
@@ -220,7 +226,7 @@ def get_tags(self):
collected_tags.update(self._tags_dynamic)
return deepcopy(collected_tags)
- def get_tag(self, tag_name, tag_value_default=None, raise_error=True):
+ def get_tag(self, tag_name, raise_error=True, tag_value_default=None):
"""
Get tag value from estimator class.
@@ -230,17 +236,17 @@ def get_tag(self, tag_name, tag_value_default=None, raise_error=True):
----------
tag_name : str
Name of tag to be retrieved.
- tag_value_default : any type, default=None
- Default/fallback value if tag is not found.
- raise_error : bool
+ raise_error : bool, default=True
Whether a ValueError is raised when the tag is not found.
+ tag_value_default : any type, default=None
+ Default/fallback value if tag is not found and error is not raised.
Returns
-------
tag_value
Value of the ``tag_name`` tag in self.
- If not found, returns an error if raise_error is True, otherwise it
- returns `tag_value_default`.
+ If not found, returns an error if ``raise_error`` is True, otherwise it
+ returns ``tag_value_default``.
Raises
------
@@ -275,7 +281,7 @@ def set_tags(self, **tag_dict):
Returns
-------
- self
+ self : object
Reference to self.
"""
tag_update = deepcopy(tag_dict)
@@ -291,26 +297,13 @@ def get_fitted_params(self, deep=True):
Parameters
----------
deep : bool, default=True
- Whether to return fitted parameters of components.
-
- * If True, will return a dict of parameter name : value for this object,
- including fitted parameters of fittable components
- (= BaseAeonEstimator-valued parameters).
- * If False, will return a dict of parameter name : value for this object,
- but not include fitted parameters of components.
+ If True, will return the fitted parameters for this estimator and
+ contained subobjects that are estimators.
Returns
-------
- fitted_params : dict with str-valued keys
- Dictionary of fitted parameters, paramname : paramvalue
- keys-value pairs include:
-
- * always: all fitted parameters of this object
- * if ``deep=True``, also contains keys/value pairs of component parameters
- parameters of components are indexed as ``[componentname]__[paramname]``
- all parameters of ``componentname`` appear as ``paramname`` with its value
- * if ``deep=True``, also contains arbitrary levels of component recursion,
- e.g., ``[componentname]__[componentcomponentname]__[paramname]``, etc.
+ fitted_params : dict
+ Fitted parameter names mapped to their values.
"""
self._check_is_fitted()
return self._get_fitted_params(self, deep)
@@ -324,7 +317,13 @@ def _get_fitted_params(self, est, deep):
out = dict()
for key in fitted_params:
- value = getattr(est, key)
+ # some of these can be properties and can make assumptions which may not be
+ # true in aeon i.e. sklearn Pipeline feature_names_in_
+ try:
+ value = getattr(est, key)
+ except AttributeError:
+ continue
+
if deep and isinstance(value, BaseEstimator):
deep_items = self._get_fitted_params(value, deep).items()
out.update((key + "__" + k, val) for k, val in deep_items)
@@ -349,7 +348,7 @@ def _check_is_fitted(self):
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""
Return testing parameter settings for the estimator.
@@ -365,17 +364,16 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class. Each dict are
parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
# default parameters = empty dict
return {}
@classmethod
- def create_test_instance(cls, parameter_set="default", return_first=True):
+ def _create_test_instance(cls, parameter_set="default", return_first=True):
"""
Construct Estimator instance if possible.
- Calls the `get_test_params` method and returns an instance or list of instances
+ Calls the `_get_test_params` method and returns an instance or list of instances
using the returned dict or list of dict.
Parameters
@@ -393,7 +391,7 @@ def create_test_instance(cls, parameter_set="default", return_first=True):
Instance of the class with default parameters. If return_first
is False, returns list of instances.
"""
- params = cls.get_test_params(parameter_set=parameter_set)
+ params = cls._get_test_params(parameter_set=parameter_set)
if isinstance(params, list):
if return_first:
@@ -418,6 +416,22 @@ def _validate_data(self, **kwargs):
"aeon estimators do not have a _validate_data method."
)
+ def get_metadata_routing(self):
+ """Sklearn metadata routing.
+
+ Not supported by ``aeon`` estimators.
+ """
+ raise NotImplementedError(
+ "aeon estimators do not have a get_metadata_routing method."
+ )
+
+ @classmethod
+ def _get_default_requests(cls):
+ """Sklearn metadata request defaults."""
+ from sklearn.utils._metadata_requests import MetadataRequest
+
+ return MetadataRequest(None)
+
def _clone_estimator(base_estimator, random_state=None):
"""Clone an estimator."""
diff --git a/aeon/base/_base_collection.py b/aeon/base/_base_collection.py
index da64b2f797..d3d298e859 100644
--- a/aeon/base/_base_collection.py
+++ b/aeon/base/_base_collection.py
@@ -55,7 +55,7 @@ def _preprocess_collection(self, X, store_metadata=True):
Parameters
----------
X : collection
- See aeon.utils.registry.COLLECTIONS_DATA_TYPES for details
+ See aeon.utils.COLLECTIONS_DATA_TYPES for details
on aeon supported data structures.
store_metadata : bool, default=True
Whether to store metadata about X in self.metadata_.
@@ -107,7 +107,7 @@ def _check_X(self, X):
Parameters
----------
X : data structure
- See aeon.utils.registry.COLLECTIONS_DATA_TYPES for details
+ See aeon.utils.COLLECTIONS_DATA_TYPES for details
on aeon supported data structures.
Returns
@@ -174,7 +174,7 @@ def _convert_X(self, X):
Parameters
----------
X : data structure
- Must be of type aeon.utils.registry.COLLECTIONS_DATA_TYPES.
+ Must be of type aeon.utils.COLLECTIONS_DATA_TYPES.
Returns
-------
diff --git a/aeon/base/_compose.py b/aeon/base/_compose.py
new file mode 100644
index 0000000000..0995e85de6
--- /dev/null
+++ b/aeon/base/_compose.py
@@ -0,0 +1,279 @@
+"""Implements meta estimator for estimators composed of other estimators."""
+
+__maintainer__ = ["MatthewMiddlehurst"]
+__all__ = ["ComposableEstimatorMixin"]
+
+from abc import ABC, abstractmethod
+
+from aeon.base import BaseAeonEstimator
+from aeon.base._base import _clone_estimator
+
+
+class ComposableEstimatorMixin(ABC):
+ """Handles parameter management for estimators composed of named estimators.
+
+ Parts (i.e. get_params and set_params) adapted or copied from the scikit-learn
+ ``_BaseComposition`` class in utils/metaestimators.py.
+ """
+
+ # Attribute name containing an iterable of processed (str, estimator) tuples
+ # with unfitted estimators and unique names. Used in get_params and set_params
+ _estimators_attr = "_estimators"
+ # Attribute name containing an iterable of fitted (str, estimator) tuples.
+ # Used in get_fitted_params
+ _fitted_estimators_attr = "estimators_"
+
+ @abstractmethod
+ def __init__(self):
+ super().__init__()
+
+ def get_params(self, deep=True):
+ """Get parameters for this estimator.
+
+ Returns the parameters given in the constructor as well as the
+ estimators contained within the composable estimator if deep.
+
+ Parameters
+ ----------
+ deep : bool, default=True
+ If True, will return the parameters for this estimator and
+ contained subobjects that are estimators.
+
+ Returns
+ -------
+ params : mapping of string to any
+ Parameter names mapped to their values.
+ """
+ out = super().get_params(deep=deep)
+ if not deep:
+ return out
+
+ estimators = getattr(self, self._estimators_attr)
+ out.update(estimators)
+
+ for name, estimator in estimators:
+ for key, value in estimator.get_params(deep=True).items():
+ out[f"{name}__{key}"] = value
+ return out
+
+ def set_params(self, **params):
+ """Set the parameters of this estimator.
+
+ Valid parameter keys can be listed with ``get_params()``. Note that
+ you can directly set the parameters of the estimators contained composable
+ estimator using their assigned name.
+
+ Parameters
+ ----------
+ **kwargs : dict
+ Parameters of this estimator or parameters of estimators contained
+ within the composable estimator. Parameters of the estimators may be set
+ using its name and the parameter name separated by a '__'.
+
+ Returns
+ -------
+ self : estimator instance
+ Estimator instance.
+ """
+ # Ensure strict ordering of parameter setting:
+ # 1. All steps
+ if self._estimators_attr in params:
+ setattr(self, self._estimators_attr, params.pop(self._estimators_attr))
+
+ # 2. Replace items with estimators in params
+ items = getattr(self, self._estimators_attr)
+ if isinstance(items, list) and items:
+ # Get item names used to identify valid names in params
+ item_names, _ = zip(*items)
+ for name in list(params.keys()):
+ if "__" not in name and name in item_names:
+ self._replace_estimator(
+ self._estimators_attr, name, params.pop(name)
+ )
+
+ # 3. Step parameters and other initialisation arguments
+ super().set_params(**params)
+ return self
+
+ def _replace_estimator(self, attr, name, new_val):
+ # assumes `name` is a valid estimator name
+ new_estimators = list(getattr(self, attr))
+ for i, (estimator_name, _) in enumerate(new_estimators):
+ if estimator_name == name:
+ new_estimators[i] = (name, new_val)
+ break
+ setattr(self, attr, new_estimators)
+
+ def get_fitted_params(self, deep=True):
+ """Get fitted parameters.
+
+ State required:
+ Requires state to be "fitted".
+
+ Parameters
+ ----------
+ deep : bool, default=True
+ If True, will return the fitted parameters for this estimator and
+ contained subobjects that are estimators.
+
+ Returns
+ -------
+ fitted_params : dict
+ Fitted parameter names mapped to their values.
+ """
+ self._check_is_fitted()
+
+ out = super().get_fitted_params(deep=deep)
+ if not deep:
+ return out
+
+ estimators = getattr(self, self._fitted_estimators_attr)
+ out.update(estimators)
+
+ for name, estimator in estimators:
+ for key, value in self._get_fitted_params(estimator, deep=True).items():
+ out[f"{name}__{key}"] = value
+ return out
+
+ def _check_estimators(
+ self,
+ estimators,
+ attr_name="estimators",
+ class_type=BaseAeonEstimator,
+ allow_tuples=True,
+ allow_single_estimators=True,
+ unique_names=True,
+ invalid_names=None,
+ ):
+ """Check that estimators is a list of estimators or list of str/est tuples.
+
+ Parameters
+ ----------
+ estimators : list
+ A list of estimators or list of (str, estimator) tuples.
+ attr_name : str, optional. Default = "steps"
+ Name of checked attribute in error messages
+ class_type : class, tuple of class or None, default=BaseAeonEstimator.
+ Class(es) that all estimators in ``estimators`` are checked to be an
+ instance of.
+ allow_tuples : boolean, default=True.
+ Whether tuples of (str, estimator) are allowed in ``estimators``.
+ Generally, the end-state we want is a list of tuples, so this should be True
+ in most cases.
+ allow_single_estimators : boolean, default=True.
+ Whether non-tuple estimator classes are allowed in ``estimators``.
+ unique_names : boolean, default=True.
+ Whether to check that all tuple strings in `estimators` are unique.
+ invalid_names : str, list of str or None, default=None.
+ Names that are invalid for estimators in ``estimators``.
+
+ Raises
+ ------
+ TypeError
+ If estimators not valid for the given configuration.
+ """
+ if (
+ estimators is None
+ or len(estimators) == 0
+ or not isinstance(estimators, list)
+ ):
+ raise TypeError(
+ f"Invalid {attr_name} attribute, {attr_name} should be a list."
+ )
+
+ if invalid_names is not None and isinstance(invalid_names, str):
+ invalid_names = [invalid_names]
+
+ param_names = self.get_params(deep=False).keys()
+ names = []
+ for obj in estimators:
+ if isinstance(obj, tuple):
+ if not allow_tuples:
+ raise ValueError(
+ f"{attr_name} should only contain singular estimators instead "
+ f"of (str, estimator) tuples."
+ )
+ if not len(obj) == 2 or not isinstance(obj[0], str):
+ raise ValueError(
+ f"All tuples in {attr_name} must be of form (str, estimator)."
+ )
+ if not isinstance(obj[1], class_type):
+ raise ValueError(
+ f"All estimators in {attr_name} must be an instance "
+ f"of {class_type}."
+ )
+ if obj[0] in param_names:
+ raise ValueError(
+ f"Estimator name conflicts with constructor arguments: {obj[0]}"
+ )
+ if "__" in obj[0]:
+ raise ValueError(f"Estimator name must not contain __: {obj[0]}")
+ if invalid_names is not None and obj[0] in invalid_names:
+ raise ValueError(f"Estimator name is invalid: {obj[0]}")
+ if unique_names:
+ if obj[0] in names:
+ raise ValueError(
+ f"Names in {attr_name} must be unique. Found duplicate "
+ f"name: {obj[0]}."
+ )
+ else:
+ names.append(obj[0])
+ elif isinstance(obj, class_type):
+ if not allow_single_estimators:
+ raise ValueError(
+ f"{attr_name} should only contain (str, estimator) tuples "
+ f"instead of singular estimators."
+ )
+ else:
+ raise TypeError(
+ f"All elements in {attr_name} must be a (str, estimator) tuple or "
+ f"estimator type of {class_type}."
+ )
+
+ def _convert_estimators(self, estimators, clone_estimators=True):
+ """Convert estimators to list of (str, estimator) tuples.
+
+ Assumes ``_check_estimators`` has already been called on ``estimators``.
+
+ Parameters
+ ----------
+ estimators : list of estimators, or list of (str, estimator tuples)
+ A list of estimators or list of (str, estimator) tuples to be converted.
+ clone_estimators : boolean, default=True.
+ Whether to return clone of estimators in ``estimators`` (True) or
+ references (False).
+
+ Returns
+ -------
+ estimator_tuples : list of (str, estimator) tuples
+ If estimators was a list of (str, estimator) tuples, then identical/cloned
+ to ``estimators``.
+ if was a list of estimators or mixed, then unique str are generated to
+ create tuples.
+ """
+ cloned_ests = []
+ names = []
+ name_dict = {}
+ for est in estimators:
+ if isinstance(est, tuple):
+ name = est[0]
+ cloned_ests.append(
+ _clone_estimator(est[1]) if clone_estimators else est[1]
+ )
+ else:
+ name = est.__class__.__name__
+ cloned_ests.append(_clone_estimator(est) if clone_estimators else est)
+
+ if name not in name_dict and name in names:
+ name_dict[name] = 0
+ names.append(name)
+
+ estimator_tuples = []
+ for i, est in enumerate(cloned_ests):
+ if names[i] in name_dict:
+ estimator_tuples.append((f"{names[i]}_{name_dict[names[i]]}", est))
+ name_dict[names[i]] += 1
+ else:
+ estimator_tuples.append((names[i], est))
+
+ return estimator_tuples
diff --git a/aeon/base/_meta.py b/aeon/base/_meta.py
deleted file mode 100644
index 08e915bd93..0000000000
--- a/aeon/base/_meta.py
+++ /dev/null
@@ -1,801 +0,0 @@
-"""Implements meta estimator for estimators composed of other estimators."""
-
-__maintainer__ = []
-__all__ = ["_HeterogenousMetaEstimator"]
-
-from inspect import isclass
-
-from sklearn import clone
-
-from aeon.base import BaseAeonEstimator
-
-
-class _HeterogenousMetaEstimator:
- """Handles parameter management for estimators composed of named estimators.
-
- Partly adapted from sklearn utils.metaestimator.py.
- """
-
- # for default get_params/set_params from _HeterogenousMetaEstimator
- # _steps_attr points to the attribute of self
- # which contains the heterogeneous set of estimators
- # this must be an iterable of (name: str, estimator, ...) tuples for the default
- _steps_attr = "_steps"
- # if the estimator is fittable, _HeterogenousMetaEstimator also
- # provides an override for get_fitted_params for params from the fitted estimators
- # the fitted estimators should be in a different attribute, _steps_fitted_attr
- # this must be an iterable of (name: str, estimator, ...) tuples for the default
- _steps_fitted_attr = "steps_"
-
- def get_params(self, deep=True):
- """Get parameters of estimator.
-
- Parameters
- ----------
- deep : boolean, optional
- If True, will return the parameters for this estimator and
- contained sub-objects that are estimators.
-
- Returns
- -------
- params : mapping of string to any
- Parameter names mapped to their values.
- """
- steps = self._steps_attr
- return self._get_params(steps, deep=deep)
-
- def set_params(self, **kwargs):
- """Set the parameters of estimator.
-
- Valid parameter keys can be listed with ``get_params()``.
-
- Returns
- -------
- self : returns an instance of self.
- """
- steps_attr = self._steps_attr
- self._set_params(steps_attr, **kwargs)
- return self
-
- def get_fitted_params(self):
- """Get fitted parameters.
-
- private _get_fitted_params, called from get_fitted_params
-
- State required:
- Requires state to be "fitted".
-
- Returns
- -------
- fitted_params : dict with str keys
- fitted parameters, keyed by names of fitted parameter
- """
- self._check_is_fitted()
-
- fitted_params = self._get_fitted_params_default()
-
- steps = self._steps_fitted_attr
- steps_params = self._get_params(steps, fitted=True)
-
- fitted_params.update(steps_params)
-
- return fitted_params
-
- def _get_fitted_params_default(self, obj=None):
- """Obtain fitted params of object, per sklearn convention.
-
- Extracts a dict with {paramstr : paramvalue} contents,
- where paramstr are all string names of "fitted parameters".
-
- A "fitted attribute" of obj is one that ends in "_" but does not start with "_".
- "fitted parameters" are names of fitted attributes, minus the "_" at the end.
-
- Parameters
- ----------
- obj : any object, optional, default=self.
-
- Returns
- -------
- fitted_params : dict with str keys
- fitted parameters, keyed by names of fitted parameter.
- """
- obj = obj if obj else self
-
- # default retrieves all self attributes ending in "_"
- # and returns them with keys that have the "_" removed
- fitted_params = [attr for attr in dir(obj) if attr.endswith("_")]
- fitted_params = [x for x in fitted_params if not x.startswith("_")]
- fitted_params = [x for x in fitted_params if hasattr(obj, x)]
- fitted_param_dict = {p[:-1]: getattr(obj, p) for p in fitted_params}
-
- return fitted_param_dict
-
- def is_composite(self):
- """Check if the object is composite.
-
- A composite object is an object which contains objects, as parameters.
- Called on an instance, since this may differ by instance.
-
- Returns
- -------
- composite: bool, whether self contains a parameter which is BaseAeonEstimator
- """
- # children of this class are always composite
- return True
-
- def _get_params(self, attr, deep=True, fitted=False):
- if fitted:
- method = "get_fitted_params"
- deepkw = {}
- else:
- method = "get_params"
- deepkw = {"deep": deep}
-
- out = getattr(super(), method)(**deepkw)
- if deep and hasattr(self, attr):
- estimators = getattr(self, attr)
- estimators = [(x[0], x[1]) for x in estimators]
- out.update(estimators)
- for name, estimator in estimators:
- if hasattr(estimator, "get_params"):
- for key, value in getattr(estimator, method)(**deepkw).items():
- out[f"{name}__{key}"] = value
- return out
-
- def _set_params(self, attr, **params):
- # Ensure strict ordering of parameter setting:
- # 1. All steps
- if attr in params:
- setattr(self, attr, params.pop(attr))
- # 2. Step replacement
- items = getattr(self, attr)
- names = []
- if items:
- names, _ = zip(*items)
- for name in list(params.keys()):
- if "__" not in name and name in names:
- self._replace_estimator(attr, name, params.pop(name))
- # 3. Step parameters and other initialisation arguments
- super().set_params(**params)
- return self
-
- def _replace_estimator(self, attr, name, new_val):
- # assumes `name` is a valid estimator name
- new_estimators = list(getattr(self, attr))
- for i, (estimator_name, _) in enumerate(new_estimators):
- if estimator_name == name:
- new_estimators[i] = (name, new_val)
- break
- setattr(self, attr, new_estimators)
-
- def _check_names(self, names):
- if len(set(names)) != len(names):
- raise ValueError(f"Names provided are not unique: {list(names)!r}")
- invalid_names = [name for name in names if "__" in name]
- if invalid_names:
- raise ValueError(
- "Estimator names must not contain __: got " "{!r}".format(invalid_names)
- )
- invalid_names = set(names).intersection(self.get_params(deep=False))
- if invalid_names:
- raise ValueError(
- "Estimator names conflict with constructor "
- "arguments: {!r}".format(sorted(invalid_names))
- )
-
- def _subset_dict_keys(self, dict_to_subset, keys, prefix=None):
- """Subset dictionary d to keys in keys.
-
- Subsets `dict_to_subset` to keys in iterable `keys`.
-
- If `prefix` is passed, subsets to `f"{prefix}__{key}"` for all `key` in `keys`.
- The prefix is then removed from the keys of the return dict, i.e.,
- return has keys `{key}` where `f"{prefix}__{key}"` was key in `dict_to_subset`.
- Note that passing `prefix` will turn non-str keys into str keys.
-
- Parameters
- ----------
- dict_to_subset : dict
- dictionary to subset by keys
- keys : iterable
- prefix : str or None, optional
-
- Returns
- -------
- `subsetted_dict` : dict
- `dict_to_subset` subset to keys in `keys` described as above
- """
-
- def rem_prefix(x):
- if prefix is None:
- return x
- prefix__ = f"{prefix}__"
- if x.startswith(prefix__):
- return x[len(prefix__) :]
- else:
- return x
-
- if prefix is not None:
- keys = [f"{prefix}__{key}" for key in keys]
- keys_in_both = set(keys).intersection(dict_to_subset.keys())
- subsetted_dict = {rem_prefix(k): dict_to_subset[k] for k in keys_in_both}
- return subsetted_dict
-
- @staticmethod
- def _is_name_and_est(obj, cls_type=None):
- """Check whether obj is a tuple of type (str, cls_type).
-
- Parameters
- ----------
- cls_type : class or tuple of class, optional. Default = BaseAeonEstimator.
- class(es) that all estimators are checked to be an instance of
-
- Returns
- -------
- bool : True if obj is (str, cls_type) tuple, False otherise
- """
- if cls_type is None:
- cls_type = BaseAeonEstimator
- if not isinstance(obj, tuple) or len(obj) != 2:
- return False
- if not isinstance(obj[0], str) or not isinstance(obj[1], cls_type):
- return False
- return True
-
- def _check_estimators(
- self,
- estimators,
- attr_name="steps",
- cls_type=None,
- allow_mix=True,
- clone_ests=True,
- ):
- """Check that estimators is a list of estimators or list of str/est tuples.
-
- Parameters
- ----------
- estimators : any object
- should be list of estimators or list of (str, estimator) tuples
- estimators should inherit from cls_type class
- attr_name : str, optional. Default = "steps"
- Name of checked attribute in error messages
- cls_type : class or tuple of class, optional. Default = BaseAeonEstimator.
- class(es) that all estimators are checked to be an instance of
- allow_mix : boolean, optional. Default = True.
- whether mix of estimator and (str, estimator) is allowed in `estimators`
- clone_ests : boolean, optional. Default = True.
- whether estimators in return are cloned (True) or references (False).
-
- Returns
- -------
- est_tuples : list of (str, estimator) tuples
- if estimators was a list of (str, estimator) tuples, then identical/cloned
- if was a list of estimators, then str are generated via _get_estimator_names
-
- Raises
- ------
- TypeError, if estimators is not a list of estimators or (str, estimator) tuples
- TypeError, if estimators in the list are not instances of cls_type
- """
- msg = (
- f"Invalid {attr_name!r} attribute, {attr_name!r} should be a list"
- " of estimators, or a list of (string, estimator) tuples. "
- )
- if cls_type is None:
- msg += f"All estimators in {attr_name!r} must be of type BaseAeonEstimator."
- cls_type = BaseAeonEstimator
- elif isclass(cls_type) or isinstance(cls_type, tuple):
- msg += (
- f"All estimators in {attr_name!r} must be of type "
- f"{cls_type.__name__}."
- )
- else:
- raise TypeError("cls_type must be a class or tuple of classes")
-
- if (
- estimators is None
- or len(estimators) == 0
- or not isinstance(estimators, list)
- ):
- raise TypeError(msg)
-
- def is_est_is_tuple(obj):
- """Check whether obj is estimator of right type, or (str, est) tuple."""
- is_est = isinstance(obj, cls_type)
- is_tuple = self._is_name_and_est(obj, cls_type)
-
- return is_est, is_tuple
-
- if not all(any(is_est_is_tuple(x)) for x in estimators):
- raise TypeError(msg)
-
- msg_no_mix = (
- f"elements of {attr_name} must either all be estimators, "
- f"or all (str, estimator) tuples, mix of the two is not allowed"
- )
-
- if not allow_mix and not all(is_est_is_tuple(x)[0] for x in estimators):
- if not all(is_est_is_tuple(x)[1] for x in estimators):
- raise TypeError(msg_no_mix)
-
- return self._get_estimator_tuples(estimators, clone_ests=clone_ests)
-
- def _coerce_estimator_tuple(self, obj, clone_est=False):
- """Coerce estimator or (str, estimator) tuple to (str, estimator) tuple.
-
- Parameters
- ----------
- obj : estimator or (str, estimator) tuple
- assumes that this has been checked, no checks are performed
- clone_est : boolean, optional. Default = False.
- Whether to return clone of estimator in obj (True) or a reference (False).
-
- Returns
- -------
- est_tuple : (str, estimator tuple)
- obj if obj was (str, estimator) tuple
- (obj class name, obj) if obj was estimator
- """
- if isinstance(obj, tuple):
- est = obj[1]
- name = obj[0]
- else:
- est = obj
- name = type(obj).__name__
-
- if clone_est:
- return (name, est.clone())
- else:
- return (name, est)
-
- def _get_estimator_list(self, estimators):
- """Return list of estimators, from a list or tuple.
-
- Parameters
- ----------
- estimators : list of estimators, or list of (str, estimator tuples)
-
- Returns
- -------
- list of estimators - identical with estimators if list of estimators
- if list of (str, estimator) tuples, the str get removed
- """
- return [self._coerce_estimator_tuple(x)[1] for x in estimators]
-
- def _get_estimator_names(self, estimators, make_unique=False):
- """Return names for the estimators, optionally made unique.
-
- Parameters
- ----------
- estimators : list of estimators, or list of (str, estimator tuples)
- make_unique : bool, optional, default=False
- whether names should be made unique in the return
-
- Returns
- -------
- names : list of str, unique entries, of equal length as estimators
- names for estimators in estimators
- if make_unique=True, made unique using _make_strings_unique
- """
- names = [self._coerce_estimator_tuple(x)[0] for x in estimators]
- if make_unique:
- names = self._make_strings_unique(names)
- return names
-
- def _get_estimator_tuples(self, estimators, clone_ests=False):
- """Return list of estimator tuples, from a list or tuple.
-
- Parameters
- ----------
- estimators : list of estimators, or list of (str, estimator tuples)
- clone_ests : bool, optional, default=False.
- whether estimators of the return are cloned (True) or references (False)
-
- Returns
- -------
- est_tuples : list of (str, estimator) tuples
- if estimators was a list of (str, estimator) tuples, then identical/cloned
- if was a list of estimators, then str are generated via _get_estimator_names
- """
- ests = self._get_estimator_list(estimators)
- if clone_ests:
- ests = [
- e.clone() if isinstance(e, BaseAeonEstimator) else clone(e)
- for e in ests
- ]
- unique_names = self._get_estimator_names(estimators, make_unique=True)
- est_tuples = list(zip(unique_names, ests))
- return est_tuples
-
- def _make_strings_unique(self, strlist):
- """Make a list or tuple of strings unique by appending _int of occurrence.
-
- Parameters
- ----------
- strlist : nested list/tuple structure with string elements
-
- Returns
- -------
- uniquestr : nested list/tuple structure with string elements
- has same bracketing as `strlist`
- string elements, if not unique, are replaced by unique strings
- if any duplicates, _integer of occurrence is appended to non-uniques
- e.g., "abc", "abc", "bcd" becomes "abc_1", "abc_2", "bcd"
- in case of clashes, process is repeated until it terminates
- e.g., "abc", "abc", "abc_1" becomes "abc_0", "abc_1_0", "abc_1_1"
- """
- # recursions to guarantee that strlist is flat list of strings
- ##############################################################
-
- # if strlist is not flat, flatten and apply, then unflatten
- if not is_flat(strlist):
- flat_strlist = flatten(strlist)
- unique_flat_strlist = self._make_strings_unique(flat_strlist)
- uniquestr = unflatten(unique_flat_strlist, strlist)
- return uniquestr
-
- # now we can assume that strlist is flat
-
- # if strlist is a tuple, convert to list, apply this function, then convert back
- if isinstance(strlist, tuple):
- uniquestr = self._make_strings_unique(list(strlist))
- uniquestr = tuple(strlist)
- return uniquestr
-
- # end of recursions
- ###################
- # now we can assume that strlist is a flat list
-
- # if already unique, just return
- if len(set(strlist)) == len(strlist):
- return strlist
-
- from collections import Counter
-
- strcount = Counter(strlist)
-
- # if any duplicates, we append _integer of occurrence to non-uniques
- nowcount = Counter()
- uniquestr = strlist
- for i, x in enumerate(uniquestr):
- if strcount[x] > 1:
- nowcount.update([x])
- uniquestr[i] = x + "_" + str(nowcount[x])
-
- # repeat until all are unique
- # the algorithm recurses, but will always terminate
- # because potential clashes are lexicographically increasing
- return self._make_strings_unique(uniquestr)
-
- def _dunder_concat(
- self,
- other,
- base_class,
- composite_class,
- attr_name="steps",
- concat_order="left",
- composite_params=None,
- ):
- """Concatenate pipelines for dunder parsing, helper function.
-
- This is used in concrete heterogeneous meta-estimators that implement
- dunders for easy concatenation of pipeline-like composites.
- Examples: TransformerPipeline, MultiplexForecaster, FeatureUnion
-
- Parameters
- ----------
- self : `aeon` estimator, instance of composite_class (when this is invoked)
- other : `aeon` estimator, should inherit from composite_class or base_class
- otherwise, `NotImplemented` is returned
- base_class : estimator base class assumed as base class for self, other,
- and estimator components of composite_class, in case of concatenation
- composite_class : estimator class that has attr_name attribute in instances
- attr_name attribute should contain list of base_class estimators,
- list of (str, base_class) tuples, or a mixture thereof
- attr_name : str, optional, default="steps"
- name of the attribute that contains estimator or (str, estimator) list
- concatenation is done for this attribute, see below
- concat_order : str, one of "left" and "right", optional, default="left"
- if "left", result attr_name will be like self.attr_name + other.attr_name
- if "right", result attr_name will be like other.attr_name + self.attr_name
- composite_params : dict, optional, default=None; else, pairs strname-value
- if not None, parameters of the composite are always set accordingly
- i.e., contains key-value pairs, and composite_class has key set to value
-
- Returns
- -------
- instance of composite_class, where attr_name is a concatenation of
- self.attr_name and other.attr_name, if other was of composite_class
- if other is of base_class, then composite_class(attr_name=other) is used
- in place of other, for the concatenation
- concat_order determines which list is first, see above
- "concatenation" means: resulting instance's attr_name contains
- list of (str, est), a direct result of concat self.attr_name and other.attr_name
- if str are all the class names of est, list of est only is used instead
- """
- # input checks
- if not isinstance(concat_order, str):
- raise TypeError(f"concat_order must be str, but found {type(concat_order)}")
- if concat_order not in ["left", "right"]:
- raise ValueError(
- f'concat_order must be one of "left", "right", but found '
- f"{concat_order!r}"
- )
- if not isinstance(attr_name, str):
- raise TypeError(f"attr_name must be str, but found {type(attr_name)}")
- if not isclass(composite_class):
- raise TypeError("composite_class must be a class")
- if not isclass(base_class):
- raise TypeError("base_class must be a class")
- if not issubclass(composite_class, base_class):
- raise ValueError("composite_class must be a subclass of base_class")
- if not isinstance(self, composite_class):
- raise TypeError("self must be an instance of composite_class")
-
- def concat(x, y):
- if concat_order == "left":
- return x + y
- else:
- return y + x
-
- # get attr_name from self and other
- # can be list of ests, list of (str, est) tuples, or list of miture
- self_attr = getattr(self, attr_name)
-
- # from that, obtain ests, and original names (may be non-unique)
- # we avoid _make_strings_unique call too early to avoid blow-up of string
- ests_s = tuple(self._get_estimator_list(self_attr))
- names_s = tuple(self._get_estimator_names(self_attr))
- if isinstance(other, composite_class):
- other_attr = getattr(other, attr_name)
- ests_o = tuple(other._get_estimator_list(other_attr))
- names_o = tuple(other._get_estimator_names(other_attr))
- new_names = concat(names_s, names_o)
- new_ests = concat(ests_s, ests_o)
- elif isinstance(other, base_class):
- new_names = concat(names_s, (type(other).__name__,))
- new_ests = concat(ests_s, (other,))
- elif self._is_name_and_est(other, base_class):
- other_name = other[0]
- other_est = other[1]
- new_names = concat(names_s, (other_name,))
- new_ests = concat(ests_s, (other_est,))
- else:
- return NotImplemented
-
- # create the "steps" param for the composite
- # if all the names are equal to class names, we eat them away
- if all(type(x[1]).__name__ == x[0] for x in zip(new_names, new_ests)):
- step_param = {attr_name: list(new_ests)}
- else:
- step_param = {attr_name: list(zip(new_names, new_ests))}
-
- # retrieve other parameters, from composite_params attribute
- if composite_params is None:
- composite_params = {}
- else:
- composite_params = composite_params.copy()
-
- # construct the composite with both step and additional params
- composite_params.update(step_param)
- return composite_class(**composite_params)
-
- def _anytagis(self, tag_name, value, estimators):
- """Return whether any estimator in list has tag `tag_name` of value `value`.
-
- Parameters
- ----------
- tag_name : str, name of the tag to check
- value : value of the tag to check for
- estimators : list of (str, estimator) pairs to query for the tag/value
-
- Returns
- -------
- bool : True iff at least one estimator in the list has value in tag tag_name
- """
- tagis = [est.get_tag(tag_name, value) == value for _, est in estimators]
- return any(tagis)
-
- def _anytagis_then_set(self, tag_name, value, value_if_not, estimators):
- """Set self's `tag_name` tag to `value` if any estimator on the list has it.
-
- Writes to self:
- sets the tag `tag_name` to `value` if `_anytagis(tag_name, value)` is True
- otherwise sets the tag `tag_name` to `value_if_not`
-
- Parameters
- ----------
- tag_name : str, name of the tag
- value : value to check and to set tag to if one of the tag values is `value`
- value_if_not : value to set in self if none of the tag values is `value`
- estimators : list of (str, estimator) pairs to query for the tag/value
- """
- if self._anytagis(tag_name=tag_name, value=value, estimators=estimators):
- self.set_tags(**{tag_name: value})
- else:
- self.set_tags(**{tag_name: value_if_not})
-
- def _anytag_notnone_val(self, tag_name, estimators):
- """Return first non-'None' value of tag `tag_name` in estimator list.
-
- Parameters
- ----------
- tag_name : str, name of the tag
- estimators : list of (str, estimator) pairs to query for the tag/value
-
- Returns
- -------
- tag_val : first non-'None' value of tag `tag_name` in estimator list.
- """
- for _, est in estimators:
- tag_val = est.get_tag(tag_name)
- if tag_val != "None":
- return tag_val
- return tag_val
-
- def _anytag_notnone_set(self, tag_name, estimators):
- """Set self's `tag_name` tag to first non-'None' value in estimator list.
-
- Writes to self:
- tag with name tag_name, sets to _anytag_notnone_val(tag_name, estimators)
-
- Parameters
- ----------
- tag_name : str, name of the tag
- estimators : list of (str, estimator) pairs to query for the tag/value
- """
- tag_val = self._anytag_notnone_val(tag_name=tag_name, estimators=estimators)
- if tag_val != "None":
- self.set_tags(**{tag_name: tag_val})
-
- def _tagchain_is_linked(
- self,
- left_tag_name,
- mid_tag_name,
- estimators,
- left_tag_val=True,
- mid_tag_val=True,
- ):
- """Check whether all tags left of the first mid_tag/val are left_tag/val.
-
- Useful to check, for instance, whether all instances of estimators
- left of the first missing value imputer can deal with missing values.
-
- Parameters
- ----------
- left_tag_name : str, name of the left tag
- mid_tag_name : str, name of the middle tag
- estimators : list of (str, estimator) pairs to query for the tag/value
- left_tag_val : value of the left tag, optional, default=True
- mid_tag_val : value of the middle tag, optional, default=True
-
- Returns
- -------
- chain_is_linked : bool,
- True iff all "left" tag instances `left_tag_name` have value `left_tag_val`
- a "left" tag instance is an instance in estimators which is earlier
- than the first occurrence of `mid_tag_name` with value `mid_tag_val`
- chain_is_complete : bool,
- True iff chain_is_linked is True, and
- there is an occurrence of `mid_tag_name` with value `mid_tag_val`
- """
- for _, est in estimators:
- if est.get_tag(mid_tag_name) == mid_tag_val:
- return True, True
- if not est.get_tag(left_tag_name) == left_tag_val:
- return False, False
- return True, False
-
- def _tagchain_is_linked_set(
- self,
- left_tag_name,
- mid_tag_name,
- estimators,
- left_tag_val=True,
- mid_tag_val=True,
- left_tag_val_not=False,
- mid_tag_val_not=False,
- ):
- """Check if _tagchain_is_linked, then set self left_tag_name and mid_tag_name.
-
- Writes to self:
- tag with name left_tag_name, sets to left_tag_val if _tag_chain_is_linked[0]
- otherwise sets to left_tag_val_not
- tag with name mid_tag_name, sets to mid_tag_val if _tag_chain_is_linked[1]
- otherwise sets to mid_tag_val_not
-
- Parameters
- ----------
- left_tag_name : str, name of the left tag
- mid_tag_name : str, name of the middle tag
- estimators : list of (str, estimator) pairs to query for the tag/value
- left_tag_val : value of the left tag, optional, default=True
- mid_tag_val : value of the middle tag, optional, default=True
- left_tag_val_not : value to set if not linked, optional, default=False
- mid_tag_val_not : value to set if not linked, optional, default=False
- """
- linked, complete = self._tagchain_is_linked(
- left_tag_name=left_tag_name,
- mid_tag_name=mid_tag_name,
- estimators=estimators,
- left_tag_val=left_tag_val,
- mid_tag_val=mid_tag_val,
- )
- if linked:
- self.set_tags(**{left_tag_name: left_tag_val})
- else:
- self.set_tags(**{left_tag_name: left_tag_val_not})
- if complete:
- self.set_tags(**{mid_tag_name: mid_tag_val})
- else:
- self.set_tags(**{mid_tag_name: mid_tag_val_not})
-
-
-def flatten(obj):
- """Flatten nested list/tuple structure.
-
- Parameters
- ----------
- obj: nested list/tuple structure
-
- Returns
- -------
- list or tuple, tuple if obj was tuple, list otherwise
- flat iterable, containing non-list/tuple elements in obj in same order as in obj
-
- Examples
- --------
- >>> flatten([1, 2, [3, (4, 5)], 6])
- [1, 2, 3, 4, 5, 6]
- """
- if not isinstance(obj, (list, tuple)):
- return [obj]
- else:
- return type(obj)([y for x in obj for y in flatten(x)])
-
-
-def unflatten(obj, template):
- """Invert flattening, given template for nested list/tuple structure.
-
- Parameters
- ----------
- obj : list or tuple of elements
- template : nested list/tuple structure
- number of non-list/tuple elements of obj and template must be equal
-
- Returns
- -------
- rest : list or tuple of elements
- has element bracketing exactly as `template`
- and elements in sequence exactly as `obj`
-
- Examples
- --------
- >>> unflatten([1, 2, 3, 4, 5, 6], [6, 3, [5, (2, 4)], 1])
- [1, 2, [3, (4, 5)], 6]
- """
- if not isinstance(template, (list, tuple)):
- return obj[0]
-
- list_or_tuple = type(template)
- ls = [unflat_len(x) for x in template]
- for i in range(1, len(ls)):
- ls[i] += ls[i - 1]
- ls = [0] + ls
-
- res = [unflatten(obj[ls[i] : ls[i + 1]], template[i]) for i in range(len(ls) - 1)]
-
- return list_or_tuple(res)
-
-
-def unflat_len(obj):
- """Return number of non-list/tuple elements in obj."""
- if not isinstance(obj, (list, tuple)):
- return 1
- else:
- return sum([unflat_len(x) for x in obj])
-
-
-def is_flat(obj):
- """Check whether list or tuple is flat, returns true if yes, false if nested."""
- return not any(isinstance(x, (list, tuple)) for x in obj)
diff --git a/aeon/base/estimator/__init__.py b/aeon/base/estimators/__init__.py
similarity index 100%
rename from aeon/base/estimator/__init__.py
rename to aeon/base/estimators/__init__.py
diff --git a/aeon/base/estimator/compose/__init__.py b/aeon/base/estimators/compose/__init__.py
similarity index 100%
rename from aeon/base/estimator/compose/__init__.py
rename to aeon/base/estimators/compose/__init__.py
diff --git a/aeon/base/estimators/compose/collection_channel_ensemble.py b/aeon/base/estimators/compose/collection_channel_ensemble.py
new file mode 100644
index 0000000000..4164536f19
--- /dev/null
+++ b/aeon/base/estimators/compose/collection_channel_ensemble.py
@@ -0,0 +1,254 @@
+"""Base class for composable channel ensembles in series collection modules.
+
+i.e. classification, regression and clustering.
+"""
+
+__maintainer__ = ["MatthewMiddlehurst"]
+__all__ = ["BaseCollectionChannelEnsemble"]
+
+import numpy as np
+from sklearn.base import BaseEstimator
+from sklearn.utils import check_random_state
+
+from aeon.base import (
+ BaseAeonEstimator,
+ BaseCollectionEstimator,
+ ComposableEstimatorMixin,
+)
+from aeon.base._base import _clone_estimator
+
+
+class BaseCollectionChannelEnsemble(ComposableEstimatorMixin, BaseCollectionEstimator):
+ """Applies estimators to channels of an array.
+
+ Parameters
+ ----------
+ _ensemble : list of aeon and/or sklearn estimators or list of tuples
+ Estimators to be used in the ensemble.
+ A list of tuples (str, estimator) can also be passed, where the str is used to
+ name the estimator.
+ The objects are cloned prior. As such, the state of the input will not be
+ modified by fitting the ensemble.
+ channels : list of int, array-like of int, slice, "all", "all-split" or callable
+ Channel(s) to be used with the estimator. Must be the same length as
+ ``_ensemble``.
+ If "all", all channels are used for the estimator. "all-split" will create a
+ separate estimator for each channel.
+ int, array-like of int and slice are used as indices to select channels. If a
+ callable is passed, the input data should return the channel indices to be used.
+ remainder : BaseEstimator or None, default=None
+ By default, only the specified channels in ``channels`` are
+ used and combined in the output, and the non-specified
+ channels are dropped.
+ By setting `remainder` to be an estimator, the remaining
+ non-specified columns will use the ``remainder`` estimator. The
+ estimator must support ``fit`` and ``predict``.
+ random_state : int, RandomState instance or None, default=None
+ Random state used to fit the estimators. If None, no random state is set for
+ ensemble members (but they may still be seeded prior to input).
+ If `int`, random_state is the seed used by the random number generator;
+ If `RandomState` instance, random_state is the random number generator;
+ _ensemble_input_name : str, default="estimators"
+ Name of the input parameter for the ensemble of estimators.
+
+ Attributes
+ ----------
+ ensemble_ : list of tuples (str, estimator) of estimators
+ Clones of estimators in _ensemble which are fitted in the ensemble.
+ Will always be in (str, estimator) format regardless of _ensemble input.
+ channels_ : list
+ The channel indices for each estimator in ``ensemble_``.
+
+ See Also
+ --------
+ ClassifierChannelEnsemble : A channel ensemble for classification tasks.
+ """
+
+ # Attribute name containing an iterable of processed (str, estimator) tuples
+ # with unfitted estimators and unique names. Used in get_params and set_params
+ _estimators_attr = "_ensemble"
+ # Attribute name containing an iterable of fitted (str, estimator) tuples.
+ # Used in get_fitted_params
+ _fitted_estimators_attr = "ensemble_"
+
+ def __init__(
+ self,
+ _ensemble,
+ channels,
+ remainder=None,
+ random_state=None,
+ _ensemble_input_name="estimators",
+ ):
+ self._ensemble = _ensemble
+ self.channels = channels
+ self.remainder = remainder
+ self.random_state = random_state
+ self._ensemble_input_name = _ensemble_input_name
+
+ self._check_estimators(
+ self._ensemble,
+ attr_name=_ensemble_input_name,
+ class_type=BaseEstimator,
+ invalid_names=["Remainder"],
+ )
+ self._ensemble = self._convert_estimators(
+ self._ensemble, clone_estimators=False
+ )
+
+ super().__init__()
+
+ # can handle missing values if all estimators can
+ missing = all(
+ [
+ (
+ e[1].get_tag(
+ "capability:missing_values",
+ raise_error=False,
+ tag_value_default=False,
+ )
+ if isinstance(e[1], BaseAeonEstimator)
+ else False
+ )
+ for e in self._ensemble
+ ]
+ )
+ remainder_missing = remainder is None or (
+ isinstance(remainder, BaseAeonEstimator)
+ and remainder.get_tag(
+ "capability:missing_values", raise_error=False, tag_value_default=False
+ )
+ )
+
+ # can handle unequal length if all estimators can
+ unequal = all(
+ [
+ (
+ e[1].get_tag(
+ "capability:unequal_length",
+ raise_error=False,
+ tag_value_default=False,
+ )
+ if isinstance(e[1], BaseAeonEstimator)
+ else False
+ )
+ for e in self._ensemble
+ ]
+ )
+ remainder_unequal = remainder is None or (
+ isinstance(remainder, BaseAeonEstimator)
+ and remainder.get_tag(
+ "capability:unequal_length", raise_error=False, tag_value_default=False
+ )
+ )
+
+ tags_to_set = {
+ "capability:missing_values": missing and remainder_missing,
+ "capability:unequal_length": unequal and remainder_unequal,
+ }
+ self.set_tags(**tags_to_set)
+
+ def _fit(self, X, y):
+ n_channels = X[0].shape[0]
+ rng = check_random_state(self.random_state)
+
+ # clone estimators
+ self.ensemble_ = [
+ (
+ step[0],
+ _clone_estimator(
+ step[1], random_state=rng.randint(np.iinfo(np.int32).max)
+ ),
+ )
+ for step in self._ensemble
+ ]
+
+ # verify channels list
+ if not isinstance(self.channels, list):
+ raise ValueError("channels must be a list of valid inputs, see docstring.")
+ if len(self.channels) != len(self._ensemble):
+ raise ValueError(
+ "The number of channels must be the same as the number of estimators."
+ )
+
+ # process channels options
+ msg = (
+ "Selected estimator channels must be a int, list/tuple of ints, "
+ "slice, 'all' or 'all-split' (or a callable resulting in one of these)."
+ )
+ splits = []
+ self.channels_ = []
+ for i, channel in enumerate(self.channels):
+ if callable(channel):
+ channel = channel(X)
+
+ if channel == "all":
+ channel = list(range(n_channels))
+ elif channel == "all-split":
+ splits.append(i)
+ elif isinstance(channel, slice):
+ if not isinstance(channel.start, (int, type(None))) or not isinstance(
+ channel.stop, (int, type(None))
+ ):
+ raise ValueError(msg)
+ elif isinstance(channel, (list, tuple)):
+ if not all(isinstance(x, int) for x in channel):
+ raise ValueError(msg)
+ elif not isinstance(channel, int):
+ raise ValueError(msg)
+
+ self.channels_.append(channel)
+
+ # if any channels are all-split, create a separate estimator for each channel
+ for i in splits:
+ self.ensemble_[i] = (self.ensemble_[i][0] + "-0", self.ensemble_[i][1])
+ self.channels_[i] = 0
+ for n in range(1, n_channels):
+ self.ensemble_.append(
+ (
+ self.ensemble_[i][0] + "-" + str(n),
+ _clone_estimator(
+ self.ensemble_[i][1],
+ random_state=rng.randint(np.iinfo(np.int32).max),
+ ),
+ )
+ )
+ self.channels_.append(n)
+
+ # process remainder if not None
+ if self.remainder is not None:
+ current_channels = []
+ all_channels = np.arange(n_channels)
+ for channels in self._channels:
+ if isinstance(channels, int):
+ channels = [channels]
+ current_channels.extend(all_channels[channels])
+ remaining_idx = sorted(list(set(all_channels) - set(current_channels)))
+
+ if remaining_idx:
+ self.ensemble_.append(
+ (
+ "Remainder",
+ _clone_estimator(
+ self.remainder,
+ random_state=rng.randint(np.iinfo(np.int32).max),
+ ),
+ )
+ )
+ self.channels_.append(remaining_idx)
+
+ # fit estimators
+ for i, (_, estimator) in enumerate(self.ensemble_):
+ estimator.fit(self._get_channel(X, self.channels_[i]), y)
+
+ return self
+
+ @staticmethod
+ def _get_channel(X, key):
+ """Get time series channel(s) from input data X."""
+ if isinstance(X, np.ndarray):
+ return X[:, key]
+ else:
+ li = [x[key] for x in X]
+ if li[0].ndim == 1:
+ li = [x.reshape(1, -1) for x in li]
+ return li
diff --git a/aeon/base/estimator/compose/collection_ensemble.py b/aeon/base/estimators/compose/collection_ensemble.py
similarity index 69%
rename from aeon/base/estimator/compose/collection_ensemble.py
rename to aeon/base/estimators/compose/collection_ensemble.py
index 47abcb93a6..1223414ae9 100644
--- a/aeon/base/estimator/compose/collection_ensemble.py
+++ b/aeon/base/estimators/compose/collection_ensemble.py
@@ -1,4 +1,10 @@
-"""Base class for collection ensembles."""
+"""Base class for composable ensembles in series collection modules.
+
+i.e. classification, regression and clustering.
+"""
+
+__maintainer__ = ["MatthewMiddlehurst"]
+__all__ = ["BaseCollectionEnsemble"]
import numpy as np
from sklearn.base import BaseEstimator, is_classifier
@@ -9,25 +15,25 @@
from aeon.base import (
BaseAeonEstimator,
BaseCollectionEstimator,
- _HeterogenousMetaEstimator,
+ ComposableEstimatorMixin,
)
from aeon.base._base import _clone_estimator
-class BaseCollectionEnsemble(_HeterogenousMetaEstimator, BaseCollectionEstimator):
+class BaseCollectionEnsemble(ComposableEstimatorMixin, BaseCollectionEstimator):
"""Weighted ensemble of collection estimators with fittable ensemble weight.
Parameters
----------
- _estimators : list of aeon and/or sklearn estimators or list of tuples
- Estimators to be used in the ensemble. The str is used to name the estimator.
- List of tuples (str, estimator) of estimators can also be passed, where
- the str is used to name the estimator.
- The objects are cloned prior, as such the state of the input will not be
- modified by fitting the pipeline.
+ _ensemble : list of aeon and/or sklearn estimators or list of tuples
+ Estimators to be used in the ensemble.
+ A list of tuples (str, estimator) can also be passed, where the str is used to
+ name the estimator.
+ The objects are cloned prior. As such, the state of the input will not be
+ modified by fitting the ensemble.
weights : float, or iterable of float, default=None
If float, ensemble weight for estimator i will be train score to this power.
- If iterable of float, must be equal length as _estimators. Ensemble weight for
+ If iterable of float, must be equal length as _ensemble. Ensemble weight for
_estimator i will be weights[i].
If None, all estimators have equal weight.
cv : None, int, or sklearn cross-validation object, default=None
@@ -48,42 +54,55 @@ class BaseCollectionEnsemble(_HeterogenousMetaEstimator, BaseCollectionEstimator
ensemble members (but they may still be seeded prior to input).
If `int`, random_state is the seed used by the random number generator;
If `RandomState` instance, random_state is the random number generator;
+ _ensemble_input_name : str, default="estimators"
+ Name of the input parameter for the ensemble of estimators.
Attributes
----------
ensemble_ : list of tuples (str, estimator) of estimators
- Clones of estimators in _estimators which are fitted in the ensemble.
- Will always be in (str, estimator) format regardless of _estimators input.
+ Clones of estimators in _ensemble which are fitted in the ensemble.
+ Will always be in (str, estimator) format regardless of _ensemble input.
weights_ : dict
Weights of estimators using the str names as keys.
See Also
--------
- ClassifierEnsemble : A pipeline for classification tasks.
- RegressorEnsemble : A pipeline for regression tasks.
+ ClassifierEnsemble : An ensemble for classification tasks.
+ RegressorEnsemble : An ensemble for regression tasks.
"""
+ # Attribute name containing an iterable of processed (str, estimator) tuples
+ # with unfitted estimators and unique names. Used in get_params and set_params
+ _estimators_attr = "_ensemble"
+ # Attribute name containing an iterable of fitted (str, estimator) tuples.
+ # Used in get_fitted_params
+ _fitted_estimators_attr = "ensemble_"
+
def __init__(
self,
- _estimators,
+ _ensemble,
weights=None,
cv=None,
metric=None,
metric_probas=False,
random_state=None,
+ _ensemble_input_name="estimators",
):
- self._estimators = _estimators
+ self._ensemble = _ensemble
self.weights = weights
self.cv = cv
self.metric = metric
self.metric_probas = metric_probas
self.random_state = random_state
+ self._ensemble_input_name = _ensemble_input_name
- self.ensemble_ = self._check_estimators(
- self._estimators,
- attr_name="_estimators",
- cls_type=BaseEstimator,
- clone_ests=False,
+ self._check_estimators(
+ self._ensemble,
+ attr_name=_ensemble_input_name,
+ class_type=BaseEstimator,
+ )
+ self._ensemble = self._convert_estimators(
+ self._ensemble, clone_estimators=False
)
super().__init__()
@@ -92,11 +111,15 @@ def __init__(
multivariate = all(
[
(
- e[1].get_tag("capability:multivariate", False, raise_error=False)
+ e[1].get_tag(
+ "capability:multivariate",
+ raise_error=False,
+ tag_value_default=False,
+ )
if isinstance(e[1], BaseAeonEstimator)
else False
)
- for e in self.ensemble_
+ for e in self._ensemble
]
)
@@ -104,11 +127,15 @@ def __init__(
missing = all(
[
(
- e[1].get_tag("capability:missing_values", False, raise_error=False)
+ e[1].get_tag(
+ "capability:missing_values",
+ raise_error=False,
+ tag_value_default=False,
+ )
if isinstance(e[1], BaseAeonEstimator)
else False
)
- for e in self.ensemble_
+ for e in self._ensemble
]
)
@@ -116,11 +143,15 @@ def __init__(
unequal = all(
[
(
- e[1].get_tag("capability:unequal_length", False, raise_error=False)
+ e[1].get_tag(
+ "capability:unequal_length",
+ raise_error=False,
+ tag_value_default=False,
+ )
if isinstance(e[1], BaseAeonEstimator)
else False
)
- for e in self.ensemble_
+ for e in self._ensemble
]
)
@@ -132,7 +163,21 @@ def __init__(
self.set_tags(**tags_to_set)
def _fit(self, X, y):
- self._clone_steps()
+ if self.random_state is not None:
+ rng = check_random_state(self.random_state)
+ self.ensemble_ = [
+ (
+ step[0],
+ _clone_estimator(
+ step[1], random_state=rng.randint(np.iinfo(np.int32).max)
+ ),
+ )
+ for step in self._ensemble
+ ]
+ else:
+ self.ensemble_ = [
+ (step[0], _clone_estimator(step[1])) for step in self._ensemble
+ ]
msg = (
"weights must be a float, dict, or iterable of floats of length equal "
@@ -184,20 +229,3 @@ def _fit(self, X, y):
self.weights_[name] = metric(y, preds) ** self.weights
return self
-
- def _clone_steps(self):
- if self.random_state is not None:
- rng = check_random_state(self.random_state)
- self.ensemble_ = [
- (
- step[0],
- _clone_estimator(
- step[1], random_state=rng.randint(np.iinfo(np.int32).max)
- ),
- )
- for step in self.ensemble_
- ]
- else:
- self.ensemble_ = [
- (step[0], _clone_estimator(step[1])) for step in self.ensemble_
- ]
diff --git a/aeon/base/estimator/compose/collection_pipeline.py b/aeon/base/estimators/compose/collection_pipeline.py
similarity index 77%
rename from aeon/base/estimator/compose/collection_pipeline.py
rename to aeon/base/estimators/compose/collection_pipeline.py
index f96553ab9e..48e333d431 100644
--- a/aeon/base/estimator/compose/collection_pipeline.py
+++ b/aeon/base/estimators/compose/collection_pipeline.py
@@ -1,10 +1,10 @@
-"""Base class for pipelines in collection data based modules.
+"""Base class for pipelines in series collection modules.
i.e. classification, regression and clustering.
"""
__maintainer__ = ["MatthewMiddlehurst"]
-
+__all__ = ["BaseCollectionPipeline"]
import numpy as np
from sklearn.base import BaseEstimator
@@ -13,12 +13,12 @@
from aeon.base import (
BaseAeonEstimator,
BaseCollectionEstimator,
- _HeterogenousMetaEstimator,
+ ComposableEstimatorMixin,
)
from aeon.base._base import _clone_estimator
-class BaseCollectionPipeline(_HeterogenousMetaEstimator, BaseCollectionEstimator):
+class BaseCollectionPipeline(ComposableEstimatorMixin, BaseCollectionEstimator):
"""Base class for composable pipelines in collection based modules.
Parameters
@@ -52,6 +52,13 @@ class BaseCollectionPipeline(_HeterogenousMetaEstimator, BaseCollectionEstimator
RegressorPipeline : A pipeline for regression tasks.
"""
+ # Attribute name containing an iterable of processed (str, estimator) tuples
+ # with unfitted estimators and unique names. Used in get_params and set_params
+ _estimators_attr = "_steps"
+ # Attribute name containing an iterable of fitted (str, estimator) tuples.
+ # Used in get_fitted_params
+ _fitted_estimators_attr = "steps_"
+
def __init__(self, transformers, _estimator, random_state=None):
self.transformers = transformers
self._estimator = _estimator
@@ -64,12 +71,13 @@ def __init__(self, transformers, _estimator, random_state=None):
)
if _estimator is not None:
self._steps.append(_estimator)
- self._steps = self._check_estimators(
+
+ self._check_estimators(
self._steps,
attr_name="_steps",
- cls_type=BaseEstimator,
- clone_ests=False,
+ class_type=BaseEstimator,
)
+ self._steps = self._convert_estimators(self._steps, clone_estimators=False)
super().__init__()
@@ -77,7 +85,11 @@ def __init__(self, transformers, _estimator, random_state=None):
# *or* transformer chain removes multivariate
multivariate_tags = [
(
- e[1].get_tag("capability:multivariate", False, raise_error=False)
+ e[1].get_tag(
+ "capability:multivariate",
+ raise_error=False,
+ tag_value_default=False,
+ )
if isinstance(e[1], BaseAeonEstimator)
else False
)
@@ -88,13 +100,17 @@ def __init__(self, transformers, _estimator, random_state=None):
for e in self._steps:
if (
isinstance(e[1], BaseAeonEstimator)
- and e[1].get_tag("capability:multivariate", False, raise_error=False)
+ and e[1].get_tag(
+ "capability:multivariate",
+ raise_error=False,
+ tag_value_default=False,
+ )
and e[1].get_tag("output_data_type", raise_error=False) == "Tabular"
):
multivariate_rm_tag = True
break
elif not isinstance(e[1], BaseAeonEstimator) or not e[1].get_tag(
- "capability:multivariate", False, raise_error=False
+ "capability:multivariate", raise_error=False, tag_value_default=False
):
break
@@ -104,7 +120,11 @@ def __init__(self, transformers, _estimator, random_state=None):
# *or* transformer chain removes missing data
missing_tags = [
(
- e[1].get_tag("capability:missing_values", False, raise_error=False)
+ e[1].get_tag(
+ "capability:missing_values",
+ raise_error=False,
+ tag_value_default=False,
+ )
if isinstance(e[1], BaseAeonEstimator)
else False
)
@@ -115,13 +135,19 @@ def __init__(self, transformers, _estimator, random_state=None):
for e in self._steps:
if (
isinstance(e[1], BaseAeonEstimator)
- and e[1].get_tag("capability:missing_values", False, raise_error=False)
- and e[1].get_tag("removes_missing_values", False, raise_error=False)
+ and e[1].get_tag(
+ "capability:missing_values",
+ raise_error=False,
+ tag_value_default=False,
+ )
+ and e[1].get_tag(
+ "removes_missing_values", raise_error=False, tag_value_default=False
+ )
):
missing_rm_tag = True
break
elif not isinstance(e[1], BaseAeonEstimator) or not e[1].get_tag(
- "capability:missing_values", False, raise_error=False
+ "capability:missing_values", raise_error=False, tag_value_default=False
):
break
@@ -132,7 +158,11 @@ def __init__(self, transformers, _estimator, random_state=None):
# *or* transformer chain transforms the series to a tabular format
unequal_tags = [
(
- e[1].get_tag("capability:unequal_length", False, raise_error=False)
+ e[1].get_tag(
+ "capability:unequal_length",
+ raise_error=False,
+ tag_value_default=False,
+ )
if isinstance(e[1], BaseAeonEstimator)
else False
)
@@ -143,16 +173,24 @@ def __init__(self, transformers, _estimator, random_state=None):
for e in self._steps:
if (
isinstance(e[1], BaseAeonEstimator)
- and e[1].get_tag("capability:unequal_length", False, raise_error=False)
+ and e[1].get_tag(
+ "capability:unequal_length",
+ raise_error=False,
+ tag_value_default=False,
+ )
and (
- e[1].get_tag("removes_unequal_length", False, raise_error=False)
+ e[1].get_tag(
+ "removes_unequal_length",
+ raise_error=False,
+ tag_value_default=False,
+ )
or e[1].get_tag("output_data_type", raise_error=False) == "Tabular"
)
):
unequal_rm_tag = True
break
elif not isinstance(e[1], BaseAeonEstimator) or not e[1].get_tag(
- "capability:unequal_length", False, raise_error=False
+ "capability:unequal_length", raise_error=False, tag_value_default=False
):
break
diff --git a/aeon/base/estimator/hybrid/__init__.py b/aeon/base/estimators/hybrid/__init__.py
similarity index 57%
rename from aeon/base/estimator/hybrid/__init__.py
rename to aeon/base/estimators/hybrid/__init__.py
index 164aee492a..642a5cc0bc 100644
--- a/aeon/base/estimator/hybrid/__init__.py
+++ b/aeon/base/estimators/hybrid/__init__.py
@@ -2,4 +2,4 @@
__all__ = ["BaseRIST"]
-from aeon.base.estimator.hybrid.base_rist import BaseRIST
+from aeon.base.estimators.hybrid.base_rist import BaseRIST
diff --git a/aeon/base/estimator/hybrid/base_rist.py b/aeon/base/estimators/hybrid/base_rist.py
similarity index 100%
rename from aeon/base/estimator/hybrid/base_rist.py
rename to aeon/base/estimators/hybrid/base_rist.py
diff --git a/aeon/base/estimator/hybrid/tests/__init__.py b/aeon/base/estimators/hybrid/tests/__init__.py
similarity index 100%
rename from aeon/base/estimator/hybrid/tests/__init__.py
rename to aeon/base/estimators/hybrid/tests/__init__.py
diff --git a/aeon/base/estimator/hybrid/tests/test_base_rist.py b/aeon/base/estimators/hybrid/tests/test_base_rist.py
similarity index 100%
rename from aeon/base/estimator/hybrid/tests/test_base_rist.py
rename to aeon/base/estimators/hybrid/tests/test_base_rist.py
diff --git a/aeon/base/estimator/interval_based/__init__.py b/aeon/base/estimators/interval_based/__init__.py
similarity index 52%
rename from aeon/base/estimator/interval_based/__init__.py
rename to aeon/base/estimators/interval_based/__init__.py
index 1c499261fc..4a65216eed 100644
--- a/aeon/base/estimator/interval_based/__init__.py
+++ b/aeon/base/estimators/interval_based/__init__.py
@@ -2,4 +2,4 @@
__all__ = ["BaseIntervalForest"]
-from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
+from aeon.base.estimators.interval_based.base_interval_forest import BaseIntervalForest
diff --git a/aeon/base/estimator/interval_based/base_interval_forest.py b/aeon/base/estimators/interval_based/base_interval_forest.py
similarity index 100%
rename from aeon/base/estimator/interval_based/base_interval_forest.py
rename to aeon/base/estimators/interval_based/base_interval_forest.py
diff --git a/aeon/base/estimator/interval_based/tests/__init__.py b/aeon/base/estimators/interval_based/tests/__init__.py
similarity index 100%
rename from aeon/base/estimator/interval_based/tests/__init__.py
rename to aeon/base/estimators/interval_based/tests/__init__.py
diff --git a/aeon/base/estimator/interval_based/tests/test_base_interval_forest.py b/aeon/base/estimators/interval_based/tests/test_base_interval_forest.py
similarity index 97%
rename from aeon/base/estimator/interval_based/tests/test_base_interval_forest.py
rename to aeon/base/estimators/interval_based/tests/test_base_interval_forest.py
index fd0f20f830..d255632555 100644
--- a/aeon/base/estimator/interval_based/tests/test_base_interval_forest.py
+++ b/aeon/base/estimators/interval_based/tests/test_base_interval_forest.py
@@ -11,10 +11,7 @@
from aeon.classification.sklearn import ContinuousIntervalTree
from aeon.testing.data_generation import make_example_3d_numpy
from aeon.transformations.collection import AutocorrelationFunctionTransformer
-from aeon.transformations.collection.feature_based import (
- Catch22,
- SevenNumberSummaryTransformer,
-)
+from aeon.transformations.collection.feature_based import Catch22, SevenNumberSummary
from aeon.utils.numba.stats import row_mean, row_numba_min
@@ -56,7 +53,7 @@ def test_interval_forest_invalid_feature_skipping():
est = IntervalForestClassifier(
n_estimators=2,
n_intervals=2,
- interval_features=SevenNumberSummaryTransformer(),
+ interval_features=SevenNumberSummary(),
)
est.fit(X, y)
@@ -159,7 +156,7 @@ def test_interval_forest_invalid_attribute_subsample():
n_estimators=2,
n_intervals=2,
att_subsample_size=2,
- interval_features=SevenNumberSummaryTransformer(),
+ interval_features=SevenNumberSummary(),
)
with pytest.raises(ValueError):
diff --git a/aeon/base/tests/test_base.py b/aeon/base/tests/test_base.py
index 15b185e99d..1caafa0cdf 100644
--- a/aeon/base/tests/test_base.py
+++ b/aeon/base/tests/test_base.py
@@ -1,319 +1,334 @@
-"""
-Tests for BaseAeonEstimator universal base class.
+"""Tests for BaseAeonEstimator universal base class."""
-tests in this module:
+import pytest
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.utils._metadata_requests import MetadataRequest
- test_get_class_tags - tests get_class_tags inheritance logic
- test_get_class_tag - tests get_class_tag logic, incl default value
- test_get_tags - tests get_tags inheritance logic
- test_get_tag - tests get_tag logic, incl default value
- test_set_tags - tests set_tags logic and related get_tags inheritance
+from aeon.base import BaseAeonEstimator
+from aeon.base._base import _clone_estimator
+from aeon.classification import BaseClassifier
+from aeon.classification.feature_based import SummaryClassifier
+from aeon.testing.mock_estimators import MockClassifier
+from aeon.testing.mock_estimators._mock_classifiers import (
+ MockClassifierComposite,
+ MockClassifierFullTags,
+ MockClassifierParams,
+)
+from aeon.testing.testing_data import EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION
+from aeon.transformations.collection import Tabularizer
- test_reset - tests reset logic on a simple, non-composite estimator
- test_reset_composite - tests reset logic on a composite estimator
- test_components - tests retrieval of list of components via _components
- test_get_fitted_params - tests get_fitted_params logic, nested and non-nested
-"""
+def test_reset():
+ """Tests reset method for correct behaviour, on a simple estimator."""
+ X, y = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION["numpy3D"]["train"]
-__maintainer__ = []
+ clf = MockClassifierParams(return_ones=True)
+ clf.fit(X, y)
-__all__ = [
- "test_get_class_tags",
- "test_get_class_tag",
- "test_get_tags",
- "test_get_tag",
- "test_set_tags",
- "test_reset",
- "test_reset_composite",
- "test_get_fitted_params",
-]
+ assert clf.return_ones is True
+ assert clf.value == 50
+ assert clf.foo_ == "bar"
+ assert clf.is_fitted is True
+ clf.__secret_att = 42
-from copy import deepcopy
+ clf.reset()
-import pytest
+ assert hasattr(clf, "return_ones") and clf.return_ones is True
+ assert hasattr(clf, "value") and clf.value == 50
+ assert hasattr(clf, "_tags") and clf._tags == MockClassifierParams._tags
+ assert hasattr(clf, "is_fitted") and clf.is_fitted is False
+ assert hasattr(clf, "__secret_att") and clf.__secret_att == 42
+ assert hasattr(clf, "fit")
+ assert not hasattr(clf, "foo_")
-from aeon.base import BaseAeonEstimator
+ clf.fit(X, y)
+ clf.reset(keep="foo_")
+ assert hasattr(clf, "is_fitted") and clf.is_fitted is False
+ assert hasattr(clf, "foo_") and clf.foo_ == "bar"
-# Fixture class for testing tag system
-class FixtureClassParent(BaseAeonEstimator):
- _tags = {"A": "1", "B": 2, "C": 1234, 3: "D"}
+ clf.fit(X, y)
+ clf.random_att = 60
+ clf.unwanted_att = 70
+ clf.reset(keep=["foo_", "random_att"])
+ assert hasattr(clf, "is_fitted") and clf.is_fitted is False
+ assert hasattr(clf, "foo_") and clf.foo_ == "bar"
+ assert hasattr(clf, "random_att") and clf.random_att == 60
+ assert not hasattr(clf, "unwanted_att")
-# Fixture class for testing tag system, child overrides tags
-class FixtureClassChild(FixtureClassParent):
- _tags = {"A": 42, 3: "E"}
+def test_reset_composite():
+ """Test reset method for correct behaviour, on a composite estimator."""
+ X, y = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION["numpy3D"]["train"]
-FIXTURE_CLASSCHILD = FixtureClassChild
+ clf = MockClassifierComposite(mock=MockClassifierParams(return_ones=True))
+ clf.fit(X, y)
-FIXTURE_CLASSCHILD_TAGS = {
- "python_version": None,
- "python_dependencies": None,
- "cant_pickle": False,
- "non_deterministic": False,
- "algorithm_type": None,
- "capability:missing_values": False,
- "capability:multithreading": False,
- "A": 42,
- "B": 2,
- "C": 1234,
- 3: "E",
-}
+ assert clf.foo_ == "bar"
+ assert clf.mock_.foo_ == "bar"
+ assert clf.mock.return_ones is True
+ assert clf.mock_.return_ones is True
-# Fixture class for testing tag system, object overrides class tags
-FIXTURE_OBJECT = FixtureClassChild()
-FIXTURE_OBJECT._tags_dynamic = {"A": 42424241, "B": 3}
+ clf.reset()
-FIXTURE_OBJECT_TAGS = {
- "python_version": None,
- "python_dependencies": None,
- "cant_pickle": False,
- "non_deterministic": False,
+ assert hasattr(clf.mock, "return_ones") and clf.mock.return_ones is True
+ assert not hasattr(clf, "mock_")
+ assert not hasattr(clf, "foo_")
+ assert not hasattr(clf.mock, "foo_")
+
+ clf.fit(X, y)
+ clf.reset(keep="mock_")
+
+ assert not hasattr(clf, "foo_")
+ assert hasattr(clf, "mock_")
+ assert hasattr(clf.mock_, "foo_") and clf.mock_.foo_ == "bar"
+ assert hasattr(clf.mock_, "return_ones") and clf.mock_.return_ones is True
+
+
+def test_reset_invalid():
+ """Tests that reset method raises error for invalid keep argument."""
+ clf = MockClassifier()
+ with pytest.raises(TypeError, match=r"keep must be a string or list"):
+ clf.reset(keep=1)
+
+
+def test_clone():
+ """Tests that clone method correctly clones an estimator."""
+ X, y = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION["numpy3D"]["train"]
+
+ clf = MockClassifierParams(return_ones=True)
+ clf.fit(X, y)
+
+ clf_clone = clf.clone()
+ assert clf_clone.return_ones is True
+ assert not hasattr(clf_clone, "foo_")
+
+ clf = SummaryClassifier(random_state=100)
+
+ clf_clone = clf.clone(random_state=42)
+ assert clf_clone.random_state == 1608637542
+
+
+def test_clone_function():
+ """Tests that _clone_estimator function correctly clones an estimator."""
+ X, y = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION["numpy3D"]["train"]
+
+ clf = MockClassifierParams(return_ones=True)
+ clf.fit(X, y)
+
+ clf_clone = _clone_estimator(clf)
+ assert clf_clone.return_ones is True
+ assert not hasattr(clf_clone, "foo_")
+
+ clf = SummaryClassifier(random_state=100)
+
+ clf_clone = _clone_estimator(clf, random_state=42)
+ assert clf_clone.random_state == 1608637542
+
+
+EXPECTED_MOCK_TAGS = {
+ "X_inner_type": ["np-list", "numpy3D"],
"algorithm_type": None,
- "capability:missing_values": False,
+ "cant_pickle": False,
+ "capability:contractable": False,
+ "capability:missing_values": True,
"capability:multithreading": False,
- "A": 42424241,
- "B": 3,
- "C": 1234,
- 3: "E",
+ "capability:multivariate": True,
+ "capability:train_estimate": False,
+ "capability:unequal_length": True,
+ "capability:univariate": True,
+ "fit_is_empty": False,
+ "non_deterministic": False,
+ "python_dependencies": None,
+ "python_version": None,
}
def test_get_class_tags():
- """Tests get_class_tags class method of BaseAeonEstimator for correctness.
-
- Raises
- ------
- AssertError if inheritance logic in get_class_tags is incorrect
- """
- child_tags = FIXTURE_CLASSCHILD.get_class_tags()
-
- msg = "Inheritance logic in BaseAeonEstimator.get_class_tags is incorrect"
-
- assert child_tags == FIXTURE_CLASSCHILD_TAGS, msg
+ """Tests get_class_tags class method of BaseAeonEstimator for correctness."""
+ child_tags = MockClassifierFullTags.get_class_tags()
+ assert child_tags == EXPECTED_MOCK_TAGS
def test_get_class_tag():
- """Tests get_class_tag class method of BaseAeonEstimator for correctness.
+ """Tests get_class_tag class method of BaseAeonEstimator for correctness."""
+ for key in EXPECTED_MOCK_TAGS.keys():
+ assert EXPECTED_MOCK_TAGS[key] == MockClassifierFullTags.get_class_tag(key)
- Raises
- ------
- AssertError if inheritance logic in get_tag is incorrect
- AssertError if default override logic in get_tag is incorrect
- """
- child_tags = dict()
- child_tags_keys = FIXTURE_CLASSCHILD_TAGS.keys()
+ # these should be true for inherited class above, but false for the parent class
+ assert BaseClassifier.get_class_tag("capability:missing_values") is False
+ assert BaseClassifier.get_class_tag("capability:multivariate") is False
+ assert BaseClassifier.get_class_tag("capability:unequal_length") is False
- for key in child_tags_keys:
- child_tags[key] = FIXTURE_CLASSCHILD.get_class_tag(key)
+ assert (
+ BaseAeonEstimator.get_class_tag(
+ "invalid_tag", raise_error=False, tag_value_default=50
+ )
+ == 50
+ )
- child_tag_default = FIXTURE_CLASSCHILD.get_class_tag("foo", "bar")
- child_tag_defaultNone = FIXTURE_CLASSCHILD.get_class_tag("bar")
+ with pytest.raises(ValueError, match=r"Tag with name invalid_tag"):
+ BaseAeonEstimator.get_class_tag("invalid_tag")
- msg = "Inheritance logic in BaseAeonEstimator.get_class_tag is incorrect"
- for key in child_tags_keys:
- assert child_tags[key] == FIXTURE_CLASSCHILD_TAGS[key], msg
+def test_get_tags():
+ """Tests get_tags method of BaseAeonEstimator for correctness."""
+ child_tags = MockClassifierFullTags().get_tags()
+ assert child_tags == EXPECTED_MOCK_TAGS
- msg = "Default override logic in BaseAeonEstimator.get_class_tag is incorrect"
- assert child_tag_default == "bar", msg
- assert child_tag_defaultNone is None, msg
+def test_get_tag():
+ """Tests get_tag method of BaseAeonEstimator for correctness."""
+ clf = MockClassifierFullTags()
+ for key in EXPECTED_MOCK_TAGS.keys():
+ assert EXPECTED_MOCK_TAGS[key] == clf.get_tag(key)
+ # these should be true for class above which overrides, but false for this which
+ # does not
+ clf = MockClassifier()
+ assert clf.get_tag("capability:missing_values") is False
+ assert clf.get_tag("capability:multivariate") is False
+ assert clf.get_tag("capability:unequal_length") is False
-def test_get_tags():
- """Tests get_tags method of BaseAeonEstimator for correctness.
+ assert clf.get_tag("invalid_tag", raise_error=False, tag_value_default=50) == 50
- Raises
- ------
- AssertError if inheritance logic in get_tags is incorrect
- """
- object_tags = FIXTURE_OBJECT.get_tags()
+ with pytest.raises(ValueError, match=r"Tag with name invalid_tag"):
+ clf.get_tag("invalid_tag")
- msg = "Inheritance logic in BaseAeonEstimator.get_tags is incorrect"
- assert object_tags == FIXTURE_OBJECT_TAGS, msg
+def test_set_tags():
+ """Tests set_tags method of BaseAeonEstimator for correctness."""
+ clf = MockClassifier()
+ tags_to_set = {
+ "capability:multivariate": True,
+ "capability:missing_values": True,
+ "capability:unequal_length": True,
+ }
+ clf.set_tags(**tags_to_set)
-def test_get_tag():
- """Tests get_tag method of BaseAeonEstimator for correctness.
+ assert clf.get_tag("capability:missing_values") is True
+ assert clf.get_tag("capability:multivariate") is True
+ assert clf.get_tag("capability:unequal_length") is True
- Raises
- ------
- AssertError if inheritance logic in get_tag is incorrect
- AssertError if default override logic in get_tag is incorrect
- """
- object_tags = dict()
- object_tags_keys = FIXTURE_OBJECT_TAGS.keys()
+ clf.reset()
- for key in object_tags_keys:
- object_tags[key] = FIXTURE_OBJECT.get_tag(key, raise_error=False)
+ assert clf.get_tag("capability:missing_values") is False
+ assert clf.get_tag("capability:multivariate") is False
+ assert clf.get_tag("capability:unequal_length") is False
- object_tag_default = FIXTURE_OBJECT.get_tag("foo", "bar", raise_error=False)
- object_tag_defaultNone = FIXTURE_OBJECT.get_tag("bar", raise_error=False)
- msg = "Inheritance logic in BaseAeonEstimator.get_tag is incorrect"
+def test_get_fitted_params():
+ """Tests fitted parameter retrieval."""
+ X, y = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION["numpy3D"]["train"]
- for key in object_tags_keys:
- assert object_tags[key] == FIXTURE_OBJECT_TAGS[key], msg
+ non_composite = MockClassifier()
+ non_composite.fit(X, y)
+ composite = MockClassifierComposite()
+ composite.fit(X, y)
- msg = "Default override logic in BaseAeonEstimator.get_tag is incorrect"
+ params = non_composite.get_fitted_params()
+ comp_params = composite.get_fitted_params()
- assert object_tag_default == "bar", msg
- assert object_tag_defaultNone is None, msg
+ expected = {
+ "fit_time_",
+ "foo_",
+ "classes_",
+ "metadata_",
+ "n_classes_",
+ }
+ assert isinstance(params, dict)
+ assert set(params.keys()) == expected
+ assert params["foo_"] is composite.foo_
-def test_get_tag_raises():
- """Tests that get_tag method raises error for unknown tag.
+ assert isinstance(comp_params, dict)
+ assert set(comp_params.keys()) == expected.union(
+ {
+ "mock_",
+ "mock___classes_",
+ "mock___fit_time_",
+ "mock___foo_",
+ "mock___metadata_",
+ "mock___n_classes_",
+ }
+ )
+ assert comp_params["foo_"] is composite.foo_
+ assert comp_params["mock___foo_"] is composite.mock_.foo_
- Raises
- ------
- AssertError if get_tag does not raise error for unknown tag.
- """
- with pytest.raises(ValueError, match=r"Tag with name"):
- FIXTURE_OBJECT.get_tag("bar")
+ params_shallow = non_composite.get_fitted_params(deep=False)
+ comp_params_shallow = composite.get_fitted_params(deep=False)
+ assert isinstance(params_shallow, dict)
+ assert set(params_shallow.keys()) == set(params.keys())
-FIXTURE_TAG_SET = {"A": 42424243, "E": 3}
-FIXTURE_OBJECT_SET = deepcopy(FIXTURE_OBJECT).set_tags(**FIXTURE_TAG_SET)
-FIXTURE_OBJECT_SET_TAGS = {
- "python_version": None,
- "python_dependencies": None,
- "cant_pickle": False,
- "non_deterministic": False,
- "algorithm_type": None,
- "capability:missing_values": False,
- "capability:multithreading": False,
- "A": 42424243,
- "B": 3,
- "C": 1234,
- 3: "E",
- "E": 3,
-}
-FIXTURE_OBJECT_SET_DYN = {"A": 42424243, "B": 3, "E": 3}
+ assert isinstance(comp_params_shallow, dict)
+ assert set(comp_params_shallow.keys()) == set(params.keys()).union({"mock_"})
-def test_set_tags():
- """Tests set_tags method of BaseAeonEstimator for correctness.
+def test_get_fitted_params_sklearn():
+ """Tests fitted parameter retrieval with sklearn components."""
+ X, y = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION["numpy3D"]["train"]
- Raises
- ------
- AssertionError if override logic in set_tags is incorrect
- """
- msg = "Setter/override logic in BaseAeonEstimator.set_tags is incorrect"
+ clf = SummaryClassifier(estimator=DecisionTreeClassifier())
+ clf.fit(X, y)
- assert FIXTURE_OBJECT_SET._tags_dynamic == FIXTURE_OBJECT_SET_DYN, msg
- assert FIXTURE_OBJECT_SET.get_tags() == FIXTURE_OBJECT_SET_TAGS, msg
+ params = clf.get_fitted_params()
+ assert "estimator_" in params.keys()
+ assert "transformer_" in params.keys()
+ assert "estimator___tree_" in params.keys()
+ assert "estimator___max_features_" in params.keys()
-class CompositionDummy(BaseAeonEstimator):
- """Potentially composite object, for testing."""
+ # pipeline
+ pipe = make_pipeline(Tabularizer(), StandardScaler(), DecisionTreeClassifier())
+ clf = SummaryClassifier(estimator=pipe)
+ clf.fit(X, y)
- def __init__(self, foo, bar=84):
- self.foo = foo
- self.foo_ = deepcopy(foo)
- self.bar = bar
+ params = clf.get_fitted_params()
+ assert "estimator_" in params.keys()
+ assert "transformer_" in params.keys()
-class ResetTester(BaseAeonEstimator):
- clsvar = 210
- def __init__(self, a, b=42):
- self.a = a
- self.b = b
- self.c = 84
+def test_check_is_fitted():
+ """Test _check_is_fitted works correctly."""
+ X, y = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION["numpy3D"]["train"]
- def foo(self, d=126):
- self.d = deepcopy(d)
- self._d = deepcopy(d)
- self.d_ = deepcopy(d)
- self.f__o__o = 252
+ clf = MockClassifier()
+ with pytest.raises(ValueError, match=r"has not been fitted yet"):
+ clf._check_is_fitted()
-def test_reset():
- """Tests reset method for correct behaviour, on a simple estimator.
-
- Raises
- ------
- AssertionError if logic behind reset is incorrect, logic tested:
- reset should remove any object attributes that are not hyper-parameters,
- with the exception of attributes containing double-underscore "__"
- reset should not remove class attributes or methods
- reset should set hyper-parameters as in pre-reset state
- """
- x = ResetTester(168)
- x.foo()
-
- x.reset()
-
- assert hasattr(x, "a") and x.a == 168
- assert hasattr(x, "b") and x.b == 42
- assert hasattr(x, "c") and x.c == 84
- assert hasattr(x, "clsvar") and x.clsvar == 210
- assert not hasattr(x, "d")
- assert not hasattr(x, "_d")
- assert not hasattr(x, "d_")
- assert hasattr(x, "f__o__o") and x.f__o__o == 252
- assert hasattr(x, "foo")
+ clf.fit(X, y)
+ clf._check_is_fitted()
-def test_reset_composite():
- """Test reset method for correct behaviour, on a composite estimator."""
- y = ResetTester(42)
- x = ResetTester(a=y)
- x.foo(y)
- x.d.foo()
+def test_create_test_instance():
+ """Test _create_test_instance works as expected."""
+ clf = SummaryClassifier._create_test_instance()
- x.reset()
+ assert isinstance(clf, SummaryClassifier)
+ assert clf.estimator.n_estimators == 2
- assert hasattr(x, "a")
- assert not hasattr(x, "d")
- assert not hasattr(x.a, "d")
+def test_overridden_sklearn():
+ """Tests that overridden sklearn components return expected outputs."""
+ X, y = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION["numpy3D"]["train"]
-class FittableCompositionDummy(BaseAeonEstimator):
- """Potentially composite object, for testing."""
+ clf = MockClassifier()
+ clf.fit(X, y)
- def __init__(self, foo, bar=84):
- self.foo = foo
- self.foo_ = deepcopy(foo)
- self.bar = bar
+ assert clf.__sklearn_is_fitted__() == clf.is_fitted
- def fit(self):
- if hasattr(self.foo_, "fit"):
- self.foo_.fit()
- self.is_fitted = True
+ assert isinstance(clf._get_default_requests(), MetadataRequest)
+ with pytest.raises(NotImplementedError):
+ clf._validate_data()
-def test_get_fitted_params():
- """Tests fitted parameter retrieval.
-
- Raises
- ------
- AssertionError if logic behind get_fitted_params is incorrect, logic tested:
- calling get_fitted_params on a non-composite fittable returns the fitted param
- calling get_fitted_params on a composite returns all nested params
- """
- non_composite = FittableCompositionDummy(foo=42)
- composite = FittableCompositionDummy(foo=deepcopy(non_composite))
-
- non_composite.fit()
- composite.fit()
-
- non_comp_f_params = non_composite.get_fitted_params()
- comp_f_params = composite.get_fitted_params()
- comp_f_params_shallow = composite.get_fitted_params(deep=False)
-
- assert isinstance(non_comp_f_params, dict)
- assert set(non_comp_f_params.keys()) == {"foo_"}
-
- assert isinstance(comp_f_params, dict)
- assert set(comp_f_params) == {"foo_", "foo___foo_"}
- assert set(comp_f_params_shallow) == {"foo_"}
- assert comp_f_params["foo_"] is composite.foo_
- assert comp_f_params["foo_"] is not composite.foo
- assert comp_f_params_shallow["foo_"] is composite.foo_
- assert comp_f_params_shallow["foo_"] is not composite.foo
+ with pytest.raises(NotImplementedError):
+ clf.get_metadata_routing()
diff --git a/aeon/base/tests/test_base_aeon.py b/aeon/base/tests/test_base_aeon.py
deleted file mode 100644
index f9d3b57481..0000000000
--- a/aeon/base/tests/test_base_aeon.py
+++ /dev/null
@@ -1,46 +0,0 @@
-"""Tests for universal base class that require aeon or sklearn imports."""
-
-__maintainer__ = []
-
-from sklearn.preprocessing import StandardScaler
-from sklearn.tree import DecisionTreeClassifier
-
-from aeon.classification.feature_based import SummaryClassifier
-from aeon.pipeline import make_pipeline
-from aeon.testing.data_generation import make_example_3d_numpy
-from aeon.transformations.collection import Tabularizer
-
-
-def test_get_fitted_params_sklearn():
- """Tests fitted parameter retrieval with sklearn components.
-
- Raises
- ------
- AssertionError if logic behind get_fitted_params is incorrect, logic tested:
- calling get_fitted_params on obj aeon component returns expected nested params
- """
- X, y = make_example_3d_numpy()
- clf = SummaryClassifier(estimator=DecisionTreeClassifier())
- clf.fit(X, y)
-
- # params = clf.get_fitted_params()
-
- # todo v1.0.0 fix this
-
-
-def test_get_fitted_params_sklearn_nested():
- """Tests fitted parameter retrieval with sklearn components.
-
- Raises
- ------
- AssertionError if logic behind get_fitted_params is incorrect, logic tested:
- calling get_fitted_params on obj aeon component returns expected nested params
- """
- X, y = make_example_3d_numpy()
- pipe = make_pipeline(Tabularizer(), StandardScaler(), DecisionTreeClassifier())
- clf = SummaryClassifier(estimator=pipe)
- clf.fit(X, y)
-
- # params = clf.get_fitted_params()
-
- # todo v1.0.0 fix this
diff --git a/aeon/base/tests/test_compose.py b/aeon/base/tests/test_compose.py
new file mode 100644
index 0000000000..55ba965e72
--- /dev/null
+++ b/aeon/base/tests/test_compose.py
@@ -0,0 +1,174 @@
+"""Test composable estimator mixin."""
+
+import pytest
+
+from aeon.classification.compose import ClassifierEnsemble
+from aeon.testing.mock_estimators import MockClassifier, MockClassifierParams
+from aeon.testing.testing_data import EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION
+
+
+def test_get_params():
+ """Tst get_params retrieval for composable estimators."""
+ ens = [("clf1", MockClassifierParams()), ("clf2", MockClassifierParams())]
+ clf = ClassifierEnsemble(ens)
+
+ params = clf.get_params(deep=False)
+
+ expected = {
+ "classifiers",
+ "cv",
+ "majority_vote",
+ "metric",
+ "metric_probas",
+ "random_state",
+ "weights",
+ }
+
+ assert isinstance(params, dict)
+ assert set(params.keys()) == expected
+ assert params["classifiers"] == ens
+
+ params = clf.get_params()
+
+ expected = expected.union(
+ {
+ "clf1",
+ "clf2",
+ "clf1__return_ones",
+ "clf1__value",
+ "clf2__return_ones",
+ "clf2__value",
+ }
+ )
+
+ assert isinstance(params, dict)
+ assert set(params.keys()) == expected
+ assert params["clf1__value"] == 50
+
+
+def test_set_params():
+ """Test set_params for composable estimators."""
+ clf = ClassifierEnsemble(
+ [("clf1", MockClassifierParams()), ("clf2", MockClassifierParams())]
+ )
+
+ ens = [("clf3", MockClassifierParams()), ("clf4", MockClassifierParams())]
+ params = {"_ensemble": ens, "clf3__value": 100, "clf4__return_ones": True}
+ clf.set_params(**params)
+
+ assert clf._ensemble[0][1].value == 100
+ assert clf._ensemble[1][1].return_ones is True
+
+
+def test_get_fitted_params():
+ """Test get_fitted_params for composable estimators."""
+ X, y = EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION["numpy3D"]["train"]
+
+ clf = ClassifierEnsemble(
+ [("clf1", MockClassifierParams()), ("clf2", MockClassifierParams())]
+ )
+ clf.fit(X, y)
+
+ params = clf.get_fitted_params(deep=False)
+
+ expected = {
+ "classes_",
+ "ensemble_",
+ "fit_time_",
+ "metadata_",
+ "n_classes_",
+ "weights_",
+ }
+
+ assert isinstance(params, dict)
+ assert set(params.keys()) == expected
+ assert params["n_classes_"] == clf.n_classes_
+
+ params = clf.get_fitted_params()
+
+ expected = expected.union(
+ {
+ "clf1",
+ "clf1__classes_",
+ "clf1__fit_time_",
+ "clf1__foo_",
+ "clf1__metadata_",
+ "clf1__n_classes_",
+ "clf2",
+ "clf2__classes_",
+ "clf2__fit_time_",
+ "clf2__foo_",
+ "clf2__metadata_",
+ "clf2__n_classes_",
+ }
+ )
+
+ assert isinstance(params, dict)
+ assert set(params.keys()) == expected
+ assert params["clf1__n_classes_"] == 2
+
+
+def test_check_estimators():
+ """Test check_estimators for composable estimators."""
+ ens = [("clf1", MockClassifier()), MockClassifier()]
+ clf = ClassifierEnsemble(ens)
+
+ clf._check_estimators(ens, unique_names=False)
+
+ with pytest.raises(ValueError, match="estimators should only contain singular"):
+ clf._check_estimators(ens, allow_tuples=False)
+
+ with pytest.raises(ValueError, match="should only contain"):
+ clf._check_estimators(ens, allow_single_estimators=False)
+
+ with pytest.raises(ValueError, match="must be an instance of"):
+ clf._check_estimators([("class", MockClassifier)])
+
+ with pytest.raises(ValueError, match="must be of form"):
+ clf._check_estimators([(MockClassifier(),)])
+
+ with pytest.raises(ValueError, match="must be of form"):
+ clf._check_estimators([(MockClassifier, "class")])
+
+ with pytest.raises(ValueError, match="conflicts with constructor arguments"):
+ clf._check_estimators([("classifiers", MockClassifier())])
+
+ with pytest.raises(ValueError, match="Estimator name must not contain"):
+ clf._check_estimators([("__clf", MockClassifier())])
+
+ with pytest.raises(ValueError, match="must be unique"):
+ clf._check_estimators(
+ [("clf", MockClassifier()), ("clf", MockClassifier())], unique_names=True
+ )
+
+ with pytest.raises(ValueError, match="name is invalid"):
+ clf._check_estimators(ens, invalid_names=["clf1"])
+
+ with pytest.raises(ValueError, match="name is invalid"):
+ clf._check_estimators(ens, invalid_names="clf1")
+
+ with pytest.raises(TypeError, match="tuple or estimator"):
+ clf._check_estimators(["invalid"])
+
+ with pytest.raises(TypeError, match="Invalid estimators attribute"):
+ clf._check_estimators([])
+
+
+def test_convert_estimators():
+ """Test convert_estimators for composable estimators."""
+ ens = [
+ ("clf1", MockClassifierParams()),
+ MockClassifierParams(),
+ MockClassifierParams(),
+ ]
+ clf = ClassifierEnsemble(ens)
+ ens2 = clf._convert_estimators(ens)
+
+ assert isinstance(ens2, list)
+ assert len(ens2) == 3
+ assert ens2[0][0] == "clf1"
+ assert ens2[1][0] == "MockClassifierParams_0"
+ assert ens2[2][0] == "MockClassifierParams_1"
+ assert isinstance(ens2[0][1], MockClassifierParams)
+ assert isinstance(ens2[1][1], MockClassifierParams)
+ assert isinstance(ens2[2][1], MockClassifierParams)
diff --git a/aeon/base/tests/test_meta.py b/aeon/base/tests/test_meta.py
deleted file mode 100644
index bb3d3dcbe4..0000000000
--- a/aeon/base/tests/test_meta.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""Tests for _HeterogenousMetaEstimator."""
-
-import pytest
-
-from aeon.base._meta import _HeterogenousMetaEstimator
-from aeon.classification import DummyClassifier
-from aeon.classification.compose._channel_ensemble import ChannelEnsembleClassifier
-
-
-def test_hetero_meta():
- """Test _HeterogenousMetaEstimator."""
- h = _HeterogenousMetaEstimator()
- assert h.is_composite()
- with pytest.raises(ValueError, match="Names provided are not unique"):
- h._check_names(["FOO", "FOO"])
- bce = ChannelEnsembleClassifier(estimators=[("Dummy", DummyClassifier(), 0)])
- with pytest.raises(ValueError, match="Estimator names must not contain"):
- bce._check_names(["__FOO"])
- names = ["FOO", "estimators"]
- with pytest.raises(ValueError, match="Estimator names conflict with constructor"):
- bce._check_names(names)
- names = ["DummyClassifier"]
- bce._check_names(names)
- assert not h._is_name_and_est("Single")
- assert not h._is_name_and_est(("Single", "Tuple"))
- with pytest.raises(TypeError, match="must be of type BaseAeonEstimator"):
- h._check_estimators(estimators="FOO")
- h._check_estimators(estimators=None)
- with pytest.raises(TypeError, match="cls_type must be a class"):
- h._check_estimators(estimators="FOO", cls_type="BAR")
- x = h._coerce_estimator_tuple(obj=bce, clone_est=True)
- assert isinstance(x, tuple)
- assert isinstance(x[0], str)
- assert isinstance(x[1], ChannelEnsembleClassifier)
- list = h._make_strings_unique([("49", "49")])
- assert list[0][0] != list[0][1]
- list = h._make_strings_unique(("49", "49"))
- assert list[0][0] != list[0][1]
- with pytest.raises(TypeError, match="concat_order must be str"):
- h._dunder_concat(
- other=None, base_class=None, composite_class=None, concat_order=49
- )
- with pytest.raises(ValueError, match="concat_order must be one of"):
- h._dunder_concat(
- other=None, base_class=None, composite_class=None, concat_order="up"
- )
- with pytest.raises(TypeError, match="attr_name must be str"):
- h._dunder_concat(
- other=None, base_class=None, composite_class=None, attr_name=49
- )
- with pytest.raises(TypeError, match="composite_class must be a class"):
- h._dunder_concat(other=None, base_class=None, composite_class=None)
- with pytest.raises(TypeError, match="base_class must be a class"):
- h._dunder_concat(
- other=None, base_class=None, composite_class=ChannelEnsembleClassifier
- )
- with pytest.raises(TypeError, match="self must be an instance of composite_class"):
- _HeterogenousMetaEstimator._dunder_concat(
- str,
- other=None,
- base_class=_HeterogenousMetaEstimator,
- composite_class=ChannelEnsembleClassifier,
- )
diff --git a/aeon/benchmarking/__init__.py b/aeon/benchmarking/__init__.py
index 31a112da32..92c2d4559f 100644
--- a/aeon/benchmarking/__init__.py
+++ b/aeon/benchmarking/__init__.py
@@ -1,25 +1 @@
"""Benchmarking."""
-
-__all__ = [
- "get_available_estimators",
- "get_estimator_results",
- "get_estimator_results_as_array",
- "get_bake_off_2017_results",
- "get_bake_off_2021_results",
- "get_bake_off_2023_results",
- "uni_classifiers_2017",
- "multi_classifiers_2021",
- "uni_classifiers_2023",
-]
-
-from aeon.benchmarking.results_loaders import (
- get_available_estimators,
- get_bake_off_2017_results,
- get_bake_off_2021_results,
- get_bake_off_2023_results,
- get_estimator_results,
- get_estimator_results_as_array,
- multi_classifiers_2021,
- uni_classifiers_2017,
- uni_classifiers_2023,
-)
diff --git a/aeon/benchmarking/published_results.py b/aeon/benchmarking/published_results.py
new file mode 100644
index 0000000000..7879b433d2
--- /dev/null
+++ b/aeon/benchmarking/published_results.py
@@ -0,0 +1,321 @@
+"""Functions to load published results."""
+
+__maintainer__ = ["TonyBagnall", "MatthewMiddlehurst"]
+__all__ = [
+ "load_classification_bake_off_2017_results",
+ "load_classification_bake_off_2021_results",
+ "load_classification_bake_off_2023_results",
+]
+
+from aeon.benchmarking.results_loaders import _load_to_dict, _results_dict_to_array
+from aeon.datasets.tsc_datasets import (
+ multivariate_equal_length,
+ univariate2015,
+ univariate_equal_length,
+)
+
+
+def load_classification_bake_off_2017_results(
+ num_resamples=100, as_array=False, ignore_nan=False
+):
+ """Fetch all the results of the 2017 univariate TSC bake off.
+
+ Basic utility function to recover legacy results from [1]_. Loads results for 85
+ univariate UCR data sets for classifiers used in the publication. Can load either
+ the default train/test split, or the resampled results up to 100 resamples.
+
+ Parameters
+ ----------
+ num_resamples : int or None, default=1
+ The number of data resamples to return scores for. The first resample
+ is the default train/test split for the dataset.
+ For 1, only the score for the default train/test split of the dataset is
+ returned.
+ For 2 or more, a np.ndarray of scores for all resamples up to num_resamples are
+ returned.
+ If None, the scores of all resamples are returned.
+
+ If as_array is true, the scores are averaged instead of being returned as a
+ np.ndarray.
+ as_array : bool, default=False
+ If True, return the results as a tuple containing a np.ndarray of (averaged)
+ scores for each classifier. Also returns a list of dataset names for each
+ row of the np.ndarray, and classifier names for each column.
+ ignore_nan : bool, default=False
+ Ignore the error raised when NaN values are present in the results. Ignores
+ NaN values when averaging when as_array is True.
+
+ Returns
+ -------
+ results: dict or tuple
+ Dictionary with estimator name keys containing another dictionary.
+ Sub-dictionary consists of dataset name keys and contains of scores for each
+ dataset.
+ If as_array is true, instead returns a tuple of: An array of scores. Each
+ column is a results for a classifier, each row a dataset. A list of dataset
+ names for each row. A list of classifier names for each column.
+
+ References
+ ----------
+ .. [1] A Bagnall, J Lines, A Bostrom, J Large, E Keogh, "The great time series
+ classification bake off: a review and experimental evaluation of recent
+ algorithmic advances", Data mining and knowledge discovery 31, 606-660, 2017.
+
+ Examples
+ --------
+ >>> from aeon.benchmarking.published_results import (
+ ... load_classification_bake_off_2017_results
+ ... )
+ >>> from aeon.visualisation import plot_critical_difference
+ >>> # Load the results
+ >>> results, data, cls = load_classification_bake_off_2023_results(
+ ... num_resamples=100, as_array=True
+ ... ) # doctest: +SKIP
+ >>> # Select a subset of classifiers
+ >>> cls = ["MSM_1NN","TSF","DTW_F","EE","BOSS","ST","FlatCOTE"] # doctest: +SKIP
+ >>> index = [cls.index(i) for i in cls] # doctest: +SKIP
+ >>> selected = results[:,index] # doctest: +SKIP
+ >>> # Plot the critical difference diagram
+ >>> plot = plot_critical_difference(selected, cls) # doctest: +SKIP
+ >>> plot.show() # doctest: +SKIP
+ """
+ path = "https://timeseriesclassification.com/results/PublishedResults/Bakeoff2017/"
+ classifiers = [
+ "ACF",
+ "BOSS",
+ "CID_DTW",
+ "CID_ED",
+ "DDTW_R1_1NN",
+ "DDTW_Rn_1NN",
+ "DTW_F",
+ "EE",
+ "ERP_1NN",
+ "Euclidean_1NN",
+ "FlatCOTE",
+ "FS",
+ "LCSS_1NN",
+ "LPS",
+ "LS",
+ "MSM_1NN",
+ "PS",
+ "RotF",
+ "SAXVSM",
+ "ST",
+ "TSBF",
+ "TSF",
+ "TWE_1NN",
+ "WDDTW_1NN",
+ "WDTW_1NN",
+ ]
+ res = _load_to_dict(
+ path=path,
+ estimators=classifiers,
+ datasets=univariate2015,
+ num_resamples=num_resamples,
+ file_suffix=".csv",
+ est_alias=False,
+ csv_header=None,
+ ignore_nan=True,
+ )
+ if as_array:
+ res, datasets = _results_dict_to_array(res, classifiers, univariate2015, False)
+ return res, datasets, classifiers
+ return res
+
+
+def load_classification_bake_off_2021_results(num_resamples=30, as_array=False):
+ """Pull down all the results of the 2021 multivariate bake off.
+
+ Basic utility function to recover legacy results from [1]_. Loads results for 26
+ tsml data sets for classifiers used in the publication. Can load either
+ the default train/test split, or the resampled results up to 30 resamples.
+
+ Parameters
+ ----------
+ num_resamples : int or None, default=1
+ The number of data resamples to return scores for. The first resample
+ is the default train/test split for the dataset.
+ For 1, only the score for the default train/test split of the dataset is
+ returned.
+ For 2 or more, a np.ndarray of scores for all resamples up to num_resamples are
+ returned.
+ If None, the scores of all resamples are returned.
+
+ If as_array is true, the scores are averaged instead of being returned as a
+ np.ndarray.
+ as_array : bool, default=False
+ If True, return the results as a tuple containing a np.ndarray of (averaged)
+ scores for each classifier. Also returns a list of dataset names for each
+ row of the np.ndarray, and classifier names for each column.
+
+ Returns
+ -------
+ results: dict or tuple
+ Dictionary with estimator name keys containing another dictionary.
+ Sub-dictionary consists of dataset name keys and contains of scores for each
+ dataset.
+ If as_array is true, instead returns a tuple of: An array of scores. Each
+ column is a results for a classifier, each row a dataset. A list of dataset
+ names for each row. A list of classifier names for each column.
+
+ References
+ ----------
+ .. [1] AP Ruiz, M Flynn, J Large, M Middlehurst, A Bagnall, "The great multivariate
+ time series classification bake off: a review and experimental evaluation of
+ recent algorithmic advances", Data mining and knowledge discovery 35, 401-449,
+ 2021.
+
+ Examples
+ --------
+ >>> from aeon.benchmarking.published_results import (
+ ... load_classification_bake_off_2021_results
+ ... )
+ >>> from aeon.visualisation import plot_critical_difference
+ >>> # Load the results
+ >>> results, data, cls = load_classification_bake_off_2023_results(
+ ... num_resamples=30, as_array=True
+ ... ) # doctest: +SKIP
+ >>> # Plot the critical difference diagram
+ >>> plot = plot_critical_difference(results, cls) # doctest: +SKIP
+ >>> plot.show() # doctest: +SKIP
+ """
+ path = "https://timeseriesclassification.com/results/PublishedResults/Bakeoff2021/"
+ classifiers = [
+ "CBOSS",
+ "CIF",
+ "DTW_D",
+ "DTW_I",
+ "gRSF",
+ "HIVE-COTEv1",
+ "ResNet",
+ "RISE",
+ "ROCKET",
+ "STC",
+ "TSF",
+ ]
+ res = _load_to_dict(
+ path=path,
+ estimators=classifiers,
+ datasets=multivariate_equal_length,
+ num_resamples=num_resamples,
+ file_suffix="_TESTFOLDS.csv",
+ est_alias=False,
+ )
+ if as_array:
+ res, datasets = _results_dict_to_array(
+ res, classifiers, multivariate_equal_length, False
+ )
+ return res, datasets, classifiers
+ return res
+
+
+def load_classification_bake_off_2023_results(num_resamples=30, as_array=False):
+ """Pull down all the results of the 2023 univariate bake off.
+
+ Basic utility function to recover legacy results from [1]_. Loads results for 112
+ UCR/tsml data sets for classifiers used in the publication. Can load either
+ the default train/test split, or the resampled results up to 30 resamples.
+
+ Parameters
+ ----------
+ num_resamples : int or None, default=1
+ The number of data resamples to return scores for. The first resample
+ is the default train/test split for the dataset.
+ For 1, only the score for the default train/test split of the dataset is
+ returned.
+ For 2 or more, a np.ndarray of scores for all resamples up to num_resamples are
+ returned.
+ If None, the scores of all resamples are returned.
+
+ If as_array is true, the scores are averaged instead of being returned as a
+ np.ndarray.
+ as_array : bool, default=False
+ If True, return the results as a tuple containing a np.ndarray of (averaged)
+ scores for each classifier. Also returns a list of dataset names for each
+ row of the np.ndarray, and classifier names for each column.
+
+ Returns
+ -------
+ results: dict or tuple
+ Dictionary with estimator name keys containing another dictionary.
+ Sub-dictionary consists of dataset name keys and contains of scores for each
+ dataset.
+ If as_array is true, instead returns a tuple of: An array of scores. Each
+ column is a results for a classifier, each row a dataset. A list of dataset
+ names for each row. A list of classifier names for each column.
+
+ References
+ ----------
+ .. [1] M Middlehurst, P Schaefer, A Bagnall, "Bake off redux: a review and
+ experimental evaluation of recent time series classification algorithms",
+ arXiv preprint arXiv:2304.13029, 2023.
+
+ Examples
+ --------
+ >>> from aeon.benchmarking.published_results import (
+ ... load_classification_bake_off_2023_results
+ ... )
+ >>> from aeon.visualisation import plot_critical_difference
+ >>> # Load the results
+ >>> results, data, cls = load_classification_bake_off_2023_results(
+ ... num_resamples=30, as_array=True
+ ... ) # doctest: +SKIP
+ >>> # Select a subset of classifiers
+ >>> cls = ["HC2","MR-Hydra","InceptionT","FreshPRINCE","RDST"] # doctest: +SKIP
+ >>> index = [cls.index(i) for i in cls] # doctest: +SKIP
+ >>> selected = results[:,index] # doctest: +SKIP
+ >>> # Plot the critical difference diagram
+ >>> plot = plot_critical_difference(selected, cls) # doctest: +SKIP
+ >>> plot.show() # doctest: +SKIP
+ """
+ path = "https://timeseriesclassification.com/results/PublishedResults/Bakeoff2023/"
+ classifiers = [
+ "Arsenal",
+ "BOSS",
+ "CIF",
+ "CNN",
+ "Catch22",
+ "DrCIF",
+ "EE",
+ "FreshPRINCE",
+ "HC1",
+ "HC2",
+ "Hydra-MR",
+ "Hydra",
+ "InceptionT",
+ "Mini-R",
+ "MrSQM",
+ "Multi-R",
+ "PF",
+ "RDST",
+ "RISE",
+ "ROCKET",
+ "RSF",
+ "RSTSF",
+ "ResNet",
+ "STC",
+ "ShapeDTW",
+ "Signatures",
+ "TDE",
+ "TS-CHIEF",
+ "TSF",
+ "TSFresh",
+ "WEASEL-D",
+ "WEASEL",
+ "cBOSS",
+ "1NN-DTW",
+ ]
+ res = _load_to_dict(
+ path=path,
+ estimators=classifiers,
+ datasets=univariate_equal_length,
+ num_resamples=num_resamples,
+ file_suffix="_TESTFOLDS.csv",
+ est_alias=False,
+ )
+ if as_array:
+ res, datasets = _results_dict_to_array(
+ res, classifiers, univariate_equal_length, False
+ )
+ return res, datasets, classifiers
+ return res
diff --git a/aeon/benchmarking/results_loaders.py b/aeon/benchmarking/results_loaders.py
index a83639b17d..fae88b6919 100644
--- a/aeon/benchmarking/results_loaders.py
+++ b/aeon/benchmarking/results_loaders.py
@@ -10,19 +10,17 @@
from http.client import IncompleteRead, RemoteDisconnected
-from typing import Union
+from typing import Optional, Union
from urllib.error import HTTPError, URLError
import numpy as np
import pandas as pd
-from aeon.datasets.tsc_datasets import univariate as UCR
-
VALID_TASK_TYPES = ["classification", "clustering", "regression"]
VALID_RESULT_MEASURES = {
"classification": ["accuracy", "auroc", "balacc", "f1", "logloss"],
- "clustering": ["clacc", "ami", "ari", "mi", "ri"],
+ "clustering": ["clacc", "ami", "ari", "mi"],
"regression": ["mse", "mae", "r2", "mape", "rmse"],
}
@@ -64,7 +62,14 @@
"cBOSS": ["CBOSSClassifier", "ContractableBOSS"],
"TDE": ["TDEClassifier", "TemporalDictionaryEnsemble"],
"WEASEL-1.0": ["WEASEL", "WEASEL1", "WEASEL 1.0"],
- "WEASEL-2.0": ["WEASEL-D", "WEASEL-Dilation", "WEASEL2", "WEASEL 2.0", "WEASEL_V2"],
+ "WEASEL-2.0": [
+ "WEASEL-D",
+ "WEASEL-Dilation",
+ "WEASEL2",
+ "WEASEL 2.0",
+ "WEASEL_V2",
+ "W 2.0",
+ ],
"MrSQM": ["MrSQMClassifier"],
# distance based
"1NN-DTW": [
@@ -151,14 +156,14 @@
"XGBoost": ["XGBoostRegressor"],
}
-CONNECTION_ERRORS = [
+CONNECTION_ERRORS = (
HTTPError,
URLError,
RemoteDisconnected,
IncompleteRead,
ConnectionResetError,
TimeoutError,
-]
+)
def estimator_alias(name: str) -> str:
@@ -229,68 +234,59 @@ def get_available_estimators(
return data.iloc[:, 0].tolist() if as_list else data
-# temporary function due to legacy format
-def _load_results(
- estimators, datasets, default_only, path, suffix, probs_names, task, measure
-):
- path = f"{path}/{task}/{measure}/"
- all_results = {}
- for cls in estimators:
- alias_cls = estimator_alias(cls)
- url = path + alias_cls + suffix
- data = pd.read_csv(url)
- cls_results = {}
- problems = data[probs_names].str.replace(r"_.*", "", regex=True)
- results = data.iloc[:, 1:].to_numpy()
- p = list(problems)
- for problem in datasets:
- if problem in p:
- pos = p.index(problem)
- if default_only:
- cls_results[problem] = results[pos][0]
- else:
- cls_results[problem] = results[pos]
- all_results[cls] = cls_results
- return all_results
-
-
def get_estimator_results(
- estimators: list,
- datasets=UCR,
- default_only=True,
- task="classification",
- measure="accuracy",
- path="http://timeseriesclassification.com/results/ReferenceResults",
+ estimators: Union[str, list[str]],
+ datasets: Optional[list[str]] = None,
+ num_resamples: Optional[int] = 1,
+ task: str = "classification",
+ measure: str = "accuracy",
+ remove_dataset_modifiers: bool = False,
+ path: str = "http://timeseriesclassification.com/results/ReferenceResults",
):
"""Look for results for given estimators for a list of datasets.
This function loads or pulls down a CSV of results, scans it for datasets and
- returns any results found. If a dataset is not present, it is ignored.
+ returns any results found as a dictionary. If a dataset is not present, it is
+ ignored.
Parameters
----------
- estimators : list of str
- list of estimators to search for.
- datasets : list of str, default = UCR
- list of problem names to search for. Default is to look for the 112 UCR
- datasets listed in aeon.datasets.tsc_datasets.
- default_only : boolean, default = True
- Whether to recover just the default test results, or 30 resamples.
+ estimators : str ot list of str
+ Estimator name or list of estimator names to search for. See
+ get_available_estimators, aeon.benchmarking.results_loading.NAME_ALIASES or
+ the directory at path for valid options.
+ datasets : list of str or None, default=None
+ List of problem names to search for. If the dataset is not present in the
+ results, it is ignored.
+ If None, all datasets the estimator has results for is returned.
+ num_resamples : int or None, default=1
+ The number of data resamples to return scores for. The first resample
+ is the default train/test split for the dataset.
+ For 1, only the score for the default train/test split of the dataset is
+ returned.
+ For 2 or more, a np.ndarray of scores for all resamples up to num_resamples are
+ returned.
+ If None, the scores of all resamples are returned.
task : str, default="classification"
- Should be one of VALID_TASK_TYPES.
- measure : str, default = "accuracy"
- Should be one of VALID_RESULT_MEASURES[task].
+ Should be one of aeon.benchmarking.results_loading.VALID_TASK_TYPES. i.e.
+ "classification", "clustering", "regression".
+ measure : str, default="accuracy"
+ Should be one of aeon.benchmarking.results_loading.VALID_RESULT_MEASURES[task].
+ Dependent on the task, i.e. for classification, "accuracy", "auroc", "balacc",
+ and regression, "mse", "mae", "r2".
+ remove_dataset_modifiers: bool, default=False
+ If True, will remove any dataset modifier (anything after the first underscore)
+ from the dataset names in the loaded results file.
+ i.e. a loaded result row for "Dataset_eq" will be converted to just "Dataset".
path : str, default="https://timeseriesclassification.com/results/ReferenceResults/"
- Path where to read results from, default to tsc.com
- suffix : str, default="_TESTFOLDS.csv"
- String added to dataset name to load.
+ Path where to read results from. Defaults to timeseriesclassification.com.
Returns
-------
- list of dictionaries of dictionaries
- list len(estimators) of dictionaries, each of which is a dictionary of
- dataset names for keys and results as the value. If default only is an
- np.ndarray.
+ results: dict
+ Dictionary with estimator name keys containing another dictionary.
+ Sub-dictionary consists of dataset name keys and contains of scores for each
+ dataset.
Examples
--------
@@ -309,57 +305,79 @@ def get_estimator_results(
f"Error in get_estimator_results, {measure} is not a valid type of "
f"results for task {task}"
)
- suffix = "_" + measure + ".csv"
- probs_names = "Resamples:"
+ if not isinstance(estimators, list):
+ estimators = [estimators]
+ path = f"{path}/{task}/{measure}/"
- return _load_results(
+ return _load_to_dict(
+ path=path,
estimators=estimators,
datasets=datasets,
- default_only=default_only,
- path=path,
- suffix=suffix,
- probs_names=probs_names,
- task=task,
- measure=measure,
+ num_resamples=num_resamples,
+ file_suffix=f"_{measure}.csv",
+ est_alias=True,
+ remove_data_modifier=remove_dataset_modifiers,
)
def get_estimator_results_as_array(
- estimators: list,
- datasets=UCR,
- default_only=True,
- task="Classification",
- measure="accuracy",
- include_missing=False,
- path="http://timeseriesclassification.com/results/ReferenceResults",
+ estimators: Union[str, list[str]],
+ datasets: Optional[list[str]] = None,
+ num_resamples: Optional[int] = 1,
+ task: str = "classification",
+ measure: str = "accuracy",
+ remove_dataset_modifiers: bool = False,
+ path: str = "http://timeseriesclassification.com/results/ReferenceResults",
+ include_missing: bool = False,
):
"""Look for results for given estimators for a list of datasets.
- This function pulls down a CSV of results, scans it for datasets and returns any
- results found. If a dataset is not present, it is ignored if include_missing is
- False, set to NaN if include_missing is True.
+ This function loads or pulls down a CSV of results, scans it for datasets and
+ returns any results found as an array. If a dataset is not present, it is ignored.
Parameters
----------
estimators : list of str
- List of estimators to search for.
- datasets : list of str, default = UCR.
- List of problem names to search for. Default is to look for the 112 UCR
- datasets listed in aeon.datasets.tsc_datasets.
- default_only : boolean, default = True
- Whether to recover just the default test results, or 30 resamples. If false,
- values are averaged to get a 2D array.
- include_missing : boolean, default = False
- If a classifier does not have results for a given problem, either the whole
- problem is ignored when include_missing is False, or NaN.
- path : str, default https://timeseriesclassification.com/results/ReferenceResults/
- Path where to read results from, default to tsc.com.
+ Estimator name or list of estimator names to search for. See
+ get_available_estimators, aeon.benchmarking.results_loading.NAME_ALIASES or
+ the directory at path for valid options.
+ datasets : list of or None, default=1
+ List of problem names to search for.
+ If None, all datasets the estimator has results for is returned.
+ If the dataset is not present in any of the results, it is ignored unless
+ include_missing is true.
+ num_resamples : int or None, default=None
+ The number of data resamples to average over for all scores. The first resample
+ is the default train/test split for the dataset.
+ For 1, only the score for the default train/test split of the dataset is
+ returned.
+ For 2 or more, the scores of all resamples up to num_resamples are averaged and
+ returned.
+ If None, the scores of all resamples are averaged and returned.
+ task : str, default="classification"
+ Should be one of aeon.benchmarking.results_loading.VALID_TASK_TYPES. i.e.
+ "classification", "clustering", "regression".
+ measure : str, default="accuracy"
+ Should be one of aeon.benchmarking.results_loading.VALID_RESULT_MEASURES[task].
+ Dependent on the task, i.e. for classification, "accuracy", "auroc", "balacc",
+ and regression, "mse", "mae", "r2".
+ remove_dataset_modifiers: bool, default=False
+ If True, will remove any dataset modifier (anything after the first underscore)
+ from the dataset names in the loaded results file.
+ i.e. a loaded result row for "Dataset_eq" will be converted to just "Dataset".
+ path : str, default="https://timeseriesclassification.com/results/ReferenceResults/"
+ Path where to read results from. Defaults to timeseriesclassification.com.
+ include_missing : bool, default=False
+ Whether to include datasets with missing results in the output.
+ If False, the whole problem is ignored if any estimator is missing results it.
+ If True, NaN is returned instead of a score in missing cases.
Returns
-------
- 2D numpy array
- Each column is a results for a classifier, each row a dataset.
- if include_missing == false, returns names: an aligned list of names of included.
+ results: 2D numpy array
+ Array of scores. Each column is a results for a classifier, each row a dataset.
+ names: list of str
+ List of dataset names that were retained.
Examples
--------
@@ -370,310 +388,87 @@ def get_estimator_results_as_array(
(array([[0.98250729, 0.98250729],
[0.81074169, 0.84143223]]), ['Chinatown', 'Adiac'])
"""
- res_dicts = get_estimator_results(
+ if not isinstance(estimators, list):
+ estimators = [estimators]
+
+ res_dict = get_estimator_results(
estimators=estimators,
datasets=datasets,
- default_only=default_only,
+ num_resamples=num_resamples,
task=task,
measure=measure,
+ remove_dataset_modifiers=remove_dataset_modifiers,
path=path,
)
- all_res = []
+ if datasets is None:
+ datasets = []
+ for cls in res_dict:
+ datasets.extend(res_dict[cls].keys())
+ datasets = set(datasets)
+
+ return _results_dict_to_array(res_dict, estimators, datasets, include_missing)
+
+
+def _load_to_dict(
+ path,
+ estimators,
+ datasets,
+ num_resamples,
+ file_suffix,
+ est_alias=True,
+ remove_data_modifier=False,
+ csv_header="infer",
+ ignore_nan=False,
+):
+ results = {}
+ for est in estimators:
+ est_name = estimator_alias(est) if est_alias else est
+ url = path + est_name + file_suffix
+ data = pd.read_csv(url, header=csv_header)
+ problems = (
+ list(data.iloc[:, 0].str.replace(r"_.*", "", regex=True))
+ if remove_data_modifier
+ else list(data.iloc[:, 0])
+ )
+ dsets = problems if datasets is None else datasets
+ res_arr = data.iloc[:, 1:].to_numpy()
+
+ est_results = {}
+ for data in dsets:
+ if data in problems:
+ pos = problems.index(data)
+ if num_resamples == 1:
+ est_results[data] = res_arr[pos][0]
+ elif num_resamples is None:
+ est_results[data] = res_arr[pos]
+ else:
+ est_results[data] = res_arr[pos][:num_resamples]
+
+ if not ignore_nan and np.isnan(est_results[data]).any():
+ raise ValueError(
+ f"Missing resamples for {data} in {est}: {est_results[data]}"
+ )
+
+ results[est] = est_results
+ return results
+
+
+def _results_dict_to_array(res_dict, estimators, datasets, include_missing):
+ results = []
names = []
- for d in datasets:
+ for data in datasets:
r = np.zeros(len(estimators))
include = True
for i in range(len(estimators)):
- temp = res_dicts[estimators[i]]
- if d in temp:
- if default_only:
- r[i] = temp[d]
- else:
- r[i] = np.average(temp[d])
- elif not include_missing: # Skip whole problem
+ if data in res_dict[estimators[i]]:
+ r[i] = np.nanmean(res_dict[estimators[i]][data])
+ elif not include_missing: # Skip the whole problem
include = False
+ break
else:
- r[i] = False
+ r[i] = np.nan
if include:
- all_res.append(r)
- names.append(d)
-
- if include_missing:
- return np.array(all_res)
- else:
- return np.array(all_res), names
-
-
-def _get_published_results(
- directory, classifiers, resamples, suffix, default_only, header, n_data
-):
- path = (
- "https://timeseriesclassification.com/results/PublishedResults/"
- + directory
- + "/"
- )
- estimators = classifiers
- all_results = {}
- for cls in estimators:
- url = path + cls + suffix
- try:
- data = pd.read_csv(url, header=header)
- except Exception:
- print(" Error trying to load from url", url) # noqa
- print(" Check results for ", cls, " are on the website") # noqa
- raise
- problems = data.iloc[:, 0].tolist()
- results = data.iloc[:, 1:].to_numpy()
- cls_results = np.zeros(shape=len(problems))
- if results.shape[1] != resamples:
- results = results[:, :resamples]
- for i in range(len(problems)):
- if default_only:
- cls_results[i] = results[i][0]
- else:
- cls_results[i] = np.nanmean(results[i])
- all_results[cls] = cls_results
- arrays = [v[:n_data] for v in all_results.values()]
- data_array = np.stack(arrays, axis=-1)
- return data_array
-
-
-# Classifiers used in the original 2017 univariate TSC bake off
-uni_classifiers_2017 = {
- "ACF": 0,
- "BOSS": 1,
- "CID_DTW": 2,
- "CID_ED": 3,
- "DDTW_R1_1NN": 4,
- "DDTW_Rn_1NN": 5,
- "DTW_F": 6,
- "EE": 7,
- "ERP_1NN": 8,
- "Euclidean_1NN": 9,
- "FlatCOTE": 10,
- "FS": 11,
- "LCSS_1NN": 12,
- "LPS": 13,
- "LS": 14,
- "MSM_1NN": 15,
- "PS": 16,
- "RotF": 17,
- "SAXVSM": 18,
- "ST": 19,
- "TSBF": 20,
- "TSF": 21,
- "TWE_1NN": 22,
- "WDDTW_1NN": 23,
- "WDTW_1NN": 24,
-}
-
-# Classifiers used in the 2021 multivariate TSC bake off
-multi_classifiers_2021 = {
- "CBOSS": 0,
- "CIF": 1,
- "DTW_D": 2,
- "DTW_I": 3,
- "gRSF": 4,
- "HIVE-COTEv1": 5,
- "ResNet": 6,
- "RISE": 7,
- "ROCKET": 8,
- "STC": 9,
- "TSF": 10,
-}
-
-uni_classifiers_2023 = {
- "Arsenal": 0,
- "BOSS": 1,
- "CIF": 2,
- "CNN": 3,
- "Catch22": 4,
- "DrCIF": 5,
- "EE": 6,
- "FreshPRINCE": 7,
- "HC1": 8,
- "HC2": 9,
- "Hydra-MR": 10,
- "Hydra": 11,
- "InceptionT": 12,
- "Mini-R": 13,
- "MrSQM": 14,
- "Multi-R": 15,
- "PF": 16,
- "RDST": 17,
- "RISE": 18,
- "ROCKET": 19,
- "RSF": 20,
- "RSTSF": 21,
- "ResNet": 22,
- "STC": 23,
- "ShapeDTW": 24,
- "Signatures": 25,
- "TDE": 26,
- "TS-CHIEF": 27,
- "TSF": 28,
- "TSFresh": 29,
- "WEASEL-D": 30,
- "WEASEL": 31,
- "cBOSS": 32,
- "1NN-DTW": 33,
-}
-
-
-def get_bake_off_2017_results(default_only=True):
- """Fetch all the results of the 2017 univariate TSC bake off [1]_ from tsc.com.
-
- Basic utility function to recover legacy results. Loads results for 85
- univariate UCR data sets for all the classifiers listed in ``classifiers_2017``.
- Can load either the
- default train/test split, or the results averaged over 100 resamples.
-
- Parameters
- ----------
- default_only : boolean, default = True
- Whether to return the results for the default train/test split, or results
- averaged over resamples.
-
- Returns
- -------
- 2D numpy array
- Each column is a results for a classifier, each row a dataset.
-
- References
- ----------
- .. [1] A Bagnall, J Lines, A Bostrom, J Large, E Keogh, "The great time series
- classification bake off: a review and experimental evaluation of recent
- algorithmic advances", Data mining and knowledge discovery 31, 606-660, 2017.
-
- Examples
- --------
- >>> from aeon.benchmarking import get_bake_off_2017_results, uni_classifiers_2017
- >>> from aeon.visualisation import plot_critical_difference
- >>> default_results = get_bake_off_2017_results(default_only=True) # doctest: +SKIP
- >>> classifiers = ["MSM_1NN","LPS","TSBF","TSF","DTW_F","EE","BOSS","ST","FlatCOTE"]
- >>> # Get column positions of classifiers in results
- >>> cls = uni_classifiers_2017
- >>> index =[cls[key] for key in classifiers if key in cls]
- >>> selected =default_results[:,index] # doctest: +SKIP
- >>> plot = plot_critical_difference(selected, classifiers)# doctest: +SKIP
- >>> plot.show()# doctest: +SKIP
- >>> average_results = get_bake_off_2017_results(default_only=True) # doctest: +SKIP
- >>> selected =average_results[:,index] # doctest: +SKIP
- >>> plot = plot_critical_difference(selected, cls)# doctest: +SKIP
- >>> plot.show()# doctest: +SKIP
- """
- return _get_published_results(
- directory="Bakeoff2017",
- classifiers=uni_classifiers_2017,
- resamples=100,
- suffix=".csv",
- default_only=default_only,
- header=None,
- n_data=85,
- )
-
-
-def get_bake_off_2021_results(default_only=True):
- """Pull down all the results of the 2020 multivariate bake off [1]_ from tsc.com.
-
- Basic utility function to recover legacy results. Loads results for 26 tsml
- data sets for all the classifiers listed in ``classifiers_2021``. Can load either
- the default train/test split, or the results averaged over 30 resamples.
-
- Parameters
- ----------
- default_only : boolean, default = True
- Whether to return the results for the default train/test split, or results
- averaged over resamples.
-
- Returns
- -------
- 2D numpy array
- Each column is a results for a classifier, each row a dataset.
-
- References
- ----------
- .. [1] AP Ruiz, M Flynn, J Large, M Middlehurst, A Bagnall, "The great multivariate
- time series classification bake off: a review and experimental evaluation of
- recent algorithmic advances", Data mining and knowledge discovery 35, 401-449, 2021.
-
- Examples
- --------
- >>> from aeon.benchmarking import get_bake_off_2021_results, multi_classifiers_2021
- >>> from aeon.visualisation import plot_critical_difference
- >>> default_results = get_bake_off_2021_results(default_only=True) # doctest: +SKIP
- >>> cls = list(multi_classifiers_2021.keys()) # doctest: +SKIP
- >>> selected =default_results # doctest: +SKIP
- >>> plot = plot_critical_difference(selected, cls)# doctest: +SKIP
- >>> plot.show()# doctest: +SKIP
- >>> average_results = get_bake_off_2021_results(default_only=False) # doctest: +SKIP
- >>> selected =average_results # doctest: +SKIP
- >>> plot = plot_critical_difference(selected, cls)# doctest: +SKIP
- >>> plot.show()# doctest: +SKIP
- """
- return _get_published_results(
- directory="Bakeoff2021",
- classifiers=multi_classifiers_2021,
- resamples=30,
- suffix="_TESTFOLDS.csv",
- default_only=default_only,
- header="infer",
- n_data=26,
- )
-
-
-def get_bake_off_2023_results(default_only=True):
- """Pull down all the results of the 2023 univariate bake off [1]_ from tsc.com.
-
- Basic utility function to recover legacy results. Loads results for 112 UCR/tsml
- data sets for all the classifiers listed in ``classifiers_2023``. Can load
- either the default train/test split, or the results averaged over 30 resamples.
- Please note this paper is under review, and there are more extensive results on
- new datasets we will make more generally avaiable once published.
-
- Parameters
- ----------
- default_only : boolean, default = True
- Whether to return the results for the default train/test split, or results
- averaged over resamples.
-
- Returns
- -------
- 2D numpy array
- Each column is a results for a classifier, each row a dataset.
-
- References
- ----------
- .. [1] M Middlehurst, P Schaefer, A Bagnall, "Bake off redux: a review and
- experimental evaluation of recent time series classification algorithms",
- arXiv preprint arXiv:2304.13029, 2023.
-
- Examples
- --------
- >>> from aeon.benchmarking import get_bake_off_2023_results, uni_classifiers_2023
- >>> from aeon.visualisation import plot_critical_difference
- >>> default_results = get_bake_off_2023_results(default_only=True) # doctest: +SKIP
- >>> classifiers = ["HC2","MR-Hydra","InceptionT", "FreshPRINCE","WEASEL-D","RDST"]
- >>> # Get column positions of classifiers in results
- >>> cls = uni_classifiers_2023
- >>> index =[cls[key] for key in classifiers if key in cls]
- >>> selected =default_results[:,index] # doctest: +SKIP
- >>> plot = plot_critical_difference(selected, classifiers)# doctest: +SKIP
- >>> plot.show()# doctest: +SKIP
- >>> average_results = get_bake_off_2023_results(default_only=False) # doctest: +SKIP
- >>> selected =average_results[:,index] # doctest: +SKIP
- >>> plot = plot_critical_difference(selected, classifiers)# doctest: +SKIP
- >>> plot.show()# doctest: +SKIP
-
-
- """
- return _get_published_results(
- directory="Bakeoff2023",
- classifiers=uni_classifiers_2023,
- resamples=30,
- suffix="_TESTFOLDS.csv",
- default_only=default_only,
- header="infer",
- n_data=112,
- )
+ results.append(r)
+ names.append(data)
+ return np.array(results), names
diff --git a/aeon/benchmarking/tests/test_published_results.py b/aeon/benchmarking/tests/test_published_results.py
new file mode 100644
index 0000000000..fe79537c46
--- /dev/null
+++ b/aeon/benchmarking/tests/test_published_results.py
@@ -0,0 +1,68 @@
+"""Test published result loaders."""
+
+import pytest
+
+from aeon.benchmarking.published_results import (
+ load_classification_bake_off_2017_results,
+ load_classification_bake_off_2021_results,
+ load_classification_bake_off_2023_results,
+)
+from aeon.benchmarking.results_loaders import CONNECTION_ERRORS
+from aeon.testing.testing_config import PR_TESTING
+
+
+@pytest.mark.skipif(
+ PR_TESTING,
+ reason="Only run on overnights because it relies on external website.",
+)
+@pytest.mark.xfail(raises=CONNECTION_ERRORS)
+def test_load_classification_bake_off_2017_results():
+ """Test original bake off results."""
+ default_results, _, _ = load_classification_bake_off_2017_results(
+ num_resamples=1, as_array=True
+ )
+ assert default_results.shape == (85, 25)
+ assert default_results[0][0] == 0.6649616368286445
+ assert default_results[84][24] == 0.853
+ average_results, _, _ = load_classification_bake_off_2017_results(as_array=True)
+ assert average_results.shape == (85, 25)
+ assert average_results[0][0] == 0.6575447570332481
+ assert average_results[84][24] == 0.8578933333100001
+
+
+@pytest.mark.skipif(
+ PR_TESTING,
+ reason="Only run on overnights because it relies on external website.",
+)
+@pytest.mark.xfail(raises=CONNECTION_ERRORS)
+def test_load_classification_bake_off_2021_results():
+ """Test multivariate bake off results."""
+ default_results, _, _ = load_classification_bake_off_2021_results(
+ num_resamples=1, as_array=True
+ )
+ assert default_results.shape == (26, 11)
+ assert default_results[0][0] == 0.99
+ assert default_results[25][10] == 0.775
+ average_results, _, _ = load_classification_bake_off_2021_results(as_array=True)
+ assert average_results.shape == (26, 11)
+ assert average_results[0][0] == 0.9755555555555556
+ assert average_results[25][10] == 0.8505208333333333
+
+
+@pytest.mark.skipif(
+ PR_TESTING,
+ reason="Only run on overnights because it relies on external website.",
+)
+@pytest.mark.xfail(raises=CONNECTION_ERRORS)
+def test_load_classification_bake_off_2023_results():
+ """Test bake off redux results."""
+ default_results, _, _ = load_classification_bake_off_2023_results(
+ num_resamples=1, as_array=True
+ )
+ assert default_results.shape == (112, 34)
+ assert default_results[0][0] == 0.88
+ assert default_results[111][33] == 0.8363333333333334
+ average_results, _, _ = load_classification_bake_off_2023_results(as_array=True)
+ assert average_results.shape == (112, 34)
+ assert average_results[0][0] == 0.8056666666666666
+ assert average_results[111][33] == 0.8465888888888888
diff --git a/aeon/benchmarking/tests/test_results_loaders.py b/aeon/benchmarking/tests/test_results_loaders.py
index 76a68c7e36..dcc271df09 100644
--- a/aeon/benchmarking/tests/test_results_loaders.py
+++ b/aeon/benchmarking/tests/test_results_loaders.py
@@ -2,22 +2,22 @@
import os
+import numpy as np
import pandas as pd
import pytest
from pytest import raises
from aeon.benchmarking.results_loaders import (
+ CONNECTION_ERRORS,
NAME_ALIASES,
+ VALID_RESULT_MEASURES,
estimator_alias,
get_available_estimators,
- get_bake_off_2017_results,
- get_bake_off_2021_results,
- get_bake_off_2023_results,
get_estimator_results,
get_estimator_results_as_array,
)
-from aeon.datasets._data_loaders import CONNECTION_ERRORS
from aeon.testing.testing_config import PR_TESTING
+from aeon.testing.utils.deep_equals import deep_equals
def test_name_alias_unique():
@@ -70,8 +70,8 @@ def test_get_available_estimators():
get_available_estimators(task="smiling")
-cls = ["HC2", "FreshPRINCE", "InceptionT"]
-data = ["Chinatown", "Tools"]
+cls = ["HIVECOTEV2", "FreshPRINCE", "InceptionTime"]
+data = ["Chinatown", "ItalyPowerDemand", "Tools"]
test_path = os.path.dirname(__file__)
data_path = os.path.join(test_path, "../example_results/")
@@ -81,21 +81,34 @@ def test_get_available_estimators():
reason="Only run on overnights because of intermittent fail for read/write",
)
@pytest.mark.xfail(raises=CONNECTION_ERRORS)
-def test_get_estimator_results():
- """Test loading results returned in a dict.
-
- Tests with baked in examples to avoid reliance on external website.
- """
- res = get_estimator_results(estimators=cls, datasets=data, path=data_path)
- assert res["HC2"]["Chinatown"] == 0.9825072886297376
- res = get_estimator_results(
- estimators=cls, datasets=data, path=data_path, default_only=False
- )
- assert res["HC2"]["Chinatown"][0] == 0.9825072886297376
+@pytest.mark.parametrize(
+ "path", [data_path, "http://timeseriesclassification.com/results/ReferenceResults"]
+)
+def test_get_estimator_results(path):
+ """Test loading results returned in a dict."""
+ res = get_estimator_results(cls, datasets=data, path=path)
+ assert isinstance(res, dict)
+ assert len(res) == 3
+ assert all(len(v) == 2 for v in res.values())
+ assert res["HIVECOTEV2"]["Chinatown"] == 0.9825072886297376
+
+ # test resamples
+ res2 = get_estimator_results(cls, datasets=data, num_resamples=30, path=path)
+ assert isinstance(res2, dict)
+ assert len(res2) == 3
+ assert all(len(v) == 2 for v in res2.values())
+ assert isinstance(res2["HIVECOTEV2"]["Chinatown"], np.ndarray)
+ assert len(res2["HIVECOTEV2"]["Chinatown"]) == 30
+ assert res2["HIVECOTEV2"]["Chinatown"][0] == 0.9825072886297376
+ assert np.average(res2["HIVECOTEV2"]["ItalyPowerDemand"]) == 0.9630385487528345
+
+ res3 = get_estimator_results(cls, datasets=data, num_resamples=None, path=path)
+ assert deep_equals(res3, res2)
+
with pytest.raises(ValueError, match="not a valid task"):
- get_estimator_results(estimators=cls, task="skipping")
- with pytest.raises(ValueError, match="not a valid type "):
- get_estimator_results(estimators=cls, measure="madness")
+ get_estimator_results(cls, datasets=data, task="invalid")
+ with pytest.raises(ValueError, match="not a valid type"):
+ get_estimator_results(cls, datasets=data, measure="invalid")
@pytest.mark.skipif(
@@ -103,74 +116,65 @@ def test_get_estimator_results():
reason="Only run on overnights because of intermittent fail for read/write",
)
@pytest.mark.xfail(raises=CONNECTION_ERRORS)
-def test_get_estimator_results_as_array():
- """Test loading results returned in an array.
+@pytest.mark.parametrize(
+ "path", [data_path, "http://timeseriesclassification.com/results/ReferenceResults"]
+)
+def test_get_estimator_results_as_array(path):
+ """Test loading results returned in an array."""
+ res, names = get_estimator_results_as_array(
+ cls,
+ datasets=data,
+ path=path,
+ )
+ assert isinstance(res, np.ndarray)
+ assert res.shape == (2, 3)
+ assert res[0][0] == 0.9825072886297376
+ assert isinstance(names, list)
+ assert len(names) == 2
+ assert names == ["Chinatown", "ItalyPowerDemand"]
- Tests with baked in examples to avoid reliance on external website.
- """
- res = get_estimator_results_as_array(
- estimators=cls,
+ res2, names2 = get_estimator_results_as_array(
+ cls,
datasets=data,
- path=data_path,
+ path=path,
include_missing=True,
- default_only=True,
)
- assert res[0][0] == 0.9825072886297376
- res = get_estimator_results_as_array(
- estimators=cls,
+ assert isinstance(res2, np.ndarray)
+ assert res2.shape == (3, 3)
+ assert res2[0][0] == 0.9825072886297376
+ assert np.isnan(res2[2][2])
+ assert len(names2) == 3
+ assert names2 == data
+
+ # test resamples
+ res3, names3 = get_estimator_results_as_array(
+ cls,
datasets=data,
- path=data_path,
+ num_resamples=10,
+ path=path,
include_missing=True,
- default_only=False,
)
- assert res[0][0] == 0.968901846452867
-
-
-# Tests for the results loaders that should not be part of the general CI.
-# Add to this list if new results are added
-CLASSIFIER_NAMES = {
- "Arsenal",
- "BOSS",
- "cBOSS",
- "CIF",
- "CNN",
- "Catch22",
- "DrCIF",
- "EE",
- "FreshPRINCE",
- "FP",
- "GRAIL",
- "HC1",
- "HC2",
- "Hydra",
- "H-InceptionTime",
- "InceptionTime",
- "LiteTime",
- "MR",
- "MiniROCKET",
- "MrSQM",
- "MR-Hydra",
- "PF",
- "QUANT",
- "RDST",
- "RISE",
- "RIST",
- "ROCKET",
- "RSF",
- "R-STSF",
- "ResNet",
- "STC",
- "STSF",
- "ShapeDTW",
- "Signatures",
- "TDE",
- "TS-CHIEF",
- "TSF",
- "TSFresh",
- "WEASEL-1.0",
- "WEASEL-2.0",
- "1NN-DTW",
-}
+ assert isinstance(res3, np.ndarray)
+ assert res3.shape == (3, 3)
+ assert res3[1][1] == 0.9524781341107872
+ assert np.isnan(res3[2][0])
+ assert names3 == names2
+
+ res4, names4 = get_estimator_results_as_array(
+ cls, datasets=data, num_resamples=None, path=path, include_missing=True
+ )
+ assert isinstance(res4, np.ndarray)
+ assert res4.shape == (3, 3)
+ assert res4[1][0] == 0.9630385487528345
+
+ # all datasets
+ res5, names5 = get_estimator_results_as_array(
+ "HIVECOTEV2", datasets=None, path=path
+ )
+ assert isinstance(res5, np.ndarray)
+ assert res5.shape == (112, 1)
+ assert isinstance(names5, list)
+ assert len(names5) == 112
@pytest.mark.skipif(
@@ -178,76 +182,16 @@ def test_get_estimator_results_as_array():
reason="Only run on overnights because of intermittent fail for read/write",
)
@pytest.mark.xfail(raises=CONNECTION_ERRORS)
-def test_load_all_classifier_results():
- """Run through all classifiers in CLASSIFIER_NAMES."""
- for measure in ["accuracy", "auroc", "balacc", "logloss"]:
- for name_key in CLASSIFIER_NAMES:
+@pytest.mark.parametrize("task", ["classification", "regression", "clustering"])
+def test_load_all_estimator_results(task):
+ """Run through estimators from get_available_estimators and load results."""
+ estimators = get_available_estimators(task=task, as_list=True)
+ for measure in VALID_RESULT_MEASURES[task]:
+ for est in estimators:
res, names = get_estimator_results_as_array(
- estimators=[name_key],
- include_missing=False,
+ est,
+ task=task,
measure=measure,
- default_only=False,
)
- assert res.shape[0] >= 112
+ assert res.shape[0] > 25
assert res.shape[1] == 1
- res = get_estimator_results_as_array(
- estimators=[name_key],
- include_missing=True,
- measure=measure,
- default_only=False,
- )
- from aeon.datasets.tsc_datasets import univariate as UCR
-
- assert res.shape[0] == len(UCR)
- assert res.shape[1] == 1
-
-
-@pytest.mark.skipif(
- PR_TESTING,
- reason="Only run on overnights because it relies on external website.",
-)
-@pytest.mark.xfail(raises=CONNECTION_ERRORS)
-def test_get_bake_off_2017_results():
- """Test original bake off results."""
- default_results = get_bake_off_2017_results()
- assert default_results.shape == (85, 25)
- assert default_results[0][0] == 0.6649616368286445
- assert default_results[84][24] == 0.853
- average_results = get_bake_off_2017_results(default_only=False)
- assert average_results.shape == (85, 25)
- assert average_results[0][0] == 0.6575447570332481
- assert average_results[84][24] == 0.8578933333100001
-
-
-@pytest.mark.skipif(
- PR_TESTING,
- reason="Only run on overnights because it relies on external website.",
-)
-@pytest.mark.xfail(raises=CONNECTION_ERRORS)
-def test_get_bake_off_2020_results():
- """Test multivariate bake off results."""
- default_results = get_bake_off_2021_results()
- assert default_results.shape == (26, 11)
- assert default_results[0][0] == 0.99
- assert default_results[25][10] == 0.775
- average_results = get_bake_off_2021_results(default_only=False)
- assert average_results.shape == (26, 11)
- assert average_results[0][0] == 0.9755555555555556
- assert average_results[25][10] == 0.8505208333333333
-
-
-@pytest.mark.skipif(
- PR_TESTING,
- reason="Only run on overnights because it relies on external website.",
-)
-@pytest.mark.xfail(raises=CONNECTION_ERRORS)
-def test_get_bake_off_2023_results():
- """Test bake off redux results."""
- default_results = get_bake_off_2023_results()
- assert default_results.shape == (112, 34)
- assert default_results[0][0] == 0.7774936061381074
- assert default_results[111][32] == 0.9504373177842566
- average_results = get_bake_off_2023_results(default_only=False)
- assert average_results.shape == (112, 34)
- assert average_results[0][0] == 0.7692242114236999
- assert average_results[111][32] == 0.9428571428571431
diff --git a/aeon/classification/compose/__init__.py b/aeon/classification/compose/__init__.py
index 6d9d66f378..5f441f81c1 100644
--- a/aeon/classification/compose/__init__.py
+++ b/aeon/classification/compose/__init__.py
@@ -1,11 +1,11 @@
"""Compositions for classifiers."""
__all__ = [
- "ChannelEnsembleClassifier",
- "WeightedEnsembleClassifier",
+ "ClassifierChannelEnsemble",
+ "ClassifierEnsemble",
"ClassifierPipeline",
]
-from aeon.classification.compose._channel_ensemble import ChannelEnsembleClassifier
-from aeon.classification.compose._ensemble import WeightedEnsembleClassifier
+from aeon.classification.compose._channel_ensemble import ClassifierChannelEnsemble
+from aeon.classification.compose._ensemble import ClassifierEnsemble
from aeon.classification.compose._pipeline import ClassifierPipeline
diff --git a/aeon/classification/compose/_channel_ensemble.py b/aeon/classification/compose/_channel_ensemble.py
index 1a967a7559..a1ddc71e81 100644
--- a/aeon/classification/compose/_channel_ensemble.py
+++ b/aeon/classification/compose/_channel_ensemble.py
@@ -1,254 +1,135 @@
-"""ChannelEnsembleClassifier: For Multivariate Time Series Classification.
+"""ClassifierChannelEnsemble for multivariate time series classification.
Builds classifiers on each channel (dimension) independently.
"""
-__maintainer__ = []
-__all__ = ["ChannelEnsembleClassifier"]
+__maintainer__ = ["MatthewMiddlehurst"]
+__all__ = ["ClassifierChannelEnsemble"]
-from itertools import chain
import numpy as np
-import pandas as pd
-from sklearn.preprocessing import LabelEncoder
+from sklearn.utils import check_random_state
-from aeon.base import _HeterogenousMetaEstimator
+from aeon.base.estimators.compose.collection_channel_ensemble import (
+ BaseCollectionChannelEnsemble,
+)
from aeon.classification.base import BaseClassifier
-class _BaseChannelEnsembleClassifier(_HeterogenousMetaEstimator, BaseClassifier):
- """Base Class for channel ensemble."""
+class ClassifierChannelEnsemble(BaseCollectionChannelEnsemble, BaseClassifier):
+ """Applies estimators to channels of an array.
+
+ Parameters
+ ----------
+ classifiers : list of aeon and/or sklearn estimators or list of tuples
+ Estimators to be used in the ensemble.
+ A list of tuples (str, estimator) can also be passed, where the str is used to
+ name the estimator.
+ The objects are cloned prior. As such, the state of the input will not be
+ modified by fitting the ensemble.
+ channels : list of int, array-like of int, slice, "all", "all-split" or callable
+ Channel(s) to be used with the estimator. Must be the same length as
+ ``_estimators``.
+ If "all", all channels are used for the estimator. "all-split" will create a
+ separate estimator for each channel.
+ int, array-like of int and slice are used as indices to select channels. If a
+ callable is passed, the input data should return the channel indices to be used.
+ remainder : BaseEstimator or None, default=None
+ By default, only the specified channels in ``channels`` are
+ used and combined in the output, and the non-specified
+ channels are dropped.
+ By setting `remainder` to be an estimator, the remaining
+ non-specified columns will use the ``remainder`` estimator. The
+ estimator must support ``fit`` and ``predict``.
+ majority_vote : bool, default=False
+ If True, the ensemble predictions are the class with the majority of class
+ votes from the ensemble.
+ If False, the ensemble predictions are the class with the highest probability
+ summed from ensemble members.
+ random_state : int, RandomState instance or None, default=None
+ Random state used to fit the estimators. If None, no random state is set for
+ ensemble members (but they may still be seeded prior to input).
+ If `int`, random_state is the seed used by the random number generator;
+ If `RandomState` instance, random_state is the random number generator;
+
+ Attributes
+ ----------
+ ensemble_ : list of tuples (str, estimator) of estimators
+ Clones of estimators in classifiers which are fitted in the ensemble.
+ Will always be in (str, estimator) format regardless of classifiers input.
+ channels_ : list
+ The channel indices for each estimator in ``ensemble_``.
+ """
_tags = {
+ "X_inner_type": ["np-list", "numpy3D"],
"capability:multivariate": True,
}
- def __init__(self, estimators, verbose=False):
- self.verbose = verbose
- self.estimators = estimators
- self.remainder = "drop"
- super().__init__()
- self._anytagis_then_set(
- "capability:unequal_length", False, True, self._estimators
- )
- self._anytagis_then_set(
- "capability:missing_values", False, True, self._estimators
- )
-
- @property
- def _estimators(self):
- return [(name, estimator) for name, estimator, _ in self.estimators]
-
- @_estimators.setter
- def _estimators(self, value):
- self.estimators = [
- (name, estimator, col)
- for ((name, estimator), (_, _, col)) in zip(value, self.estimators)
- ]
-
- def _validate_estimators(self):
- if not self.estimators:
- return
-
- names, estimators, _ = zip(*self.estimators)
-
- self._check_names(names)
-
- # validate estimators
- for t in estimators:
- if t == "drop":
- continue
- if not (hasattr(t, "fit") or hasattr(t, "predict_proba")):
- raise TypeError(
- "All estimators should implement fit and predict proba"
- "or can be 'drop' "
- "specifiers. '%s' (type %s) doesn't." % (t, type(t))
- )
-
- def _validate_channel_callables(self, X):
- """Convert callable channel specifications."""
- channels = []
- for _, _, channel in self.estimators:
- if callable(channel):
- channel = channel(X)
- channels.append(channel)
- self._channels = channels
-
- def _validate_remainder(self, X):
- """Validate ``remainder`` and defines ``_remainder``."""
- is_estimator = hasattr(self.remainder, "fit") or hasattr(
- self.remainder, "predict_proba"
+ def __init__(
+ self,
+ classifiers,
+ channels,
+ remainder=None,
+ majority_vote=False,
+ random_state=None,
+ ):
+ self.classifiers = classifiers
+ self.majority_vote = majority_vote
+
+ super().__init__(
+ _ensemble=classifiers,
+ channels=channels,
+ remainder=remainder,
+ random_state=random_state,
+ _ensemble_input_name="classifiers",
)
- if self.remainder != "drop" and not is_estimator:
- raise ValueError(
- "The remainder keyword needs to be 'drop', '%s' was passed "
- "instead" % self.remainder
- )
- n_channels = X.shape[1]
- cols = []
- for channels in self._channels:
- cols.extend(_get_channel_indices(X, channels))
- remaining_idx = sorted(list(set(range(n_channels)) - set(cols))) or None
-
- self._remainder = ("remainder", self.remainder, remaining_idx)
-
- def _iter(self, replace_strings=False):
- """Generate (name, estimator, channel) tuples.
-
- If fitted=True, use the fitted transformations, else use the
- user specified transformations updated with converted channel names
- and potentially appended with transformer for remainder.
- """
- if self.is_fitted:
- estimators = self.estimators_
- else:
- # interleave the validated channel specifiers
- estimators = [
- (name, estimator, channel)
- for (name, estimator, _), channel in zip(
- self.estimators, self._channels
- )
+ def _predict(self, X) -> np.ndarray:
+ """Predicts labels for sequences in X."""
+ rng = check_random_state(self.random_state)
+ return np.array(
+ [
+ self.classes_[int(rng.choice(np.flatnonzero(prob == prob.max())))]
+ for prob in self.predict_proba(X)
]
+ )
- # add transformer tuple for remainder
- if self._remainder[2] is not None:
- estimators = chain(estimators, [self._remainder])
-
- for name, estimator, channel in estimators:
- if replace_strings and (
- estimator == "drop"
- or estimator != "drop"
- and _is_empty_channel_selection(channel)
- ):
- continue
- yield name, estimator, channel
-
- def _fit(self, X, y):
- """Fit all estimators, fit the data.
+ def _predict_proba(self, X) -> np.ndarray:
+ """Predicts labels probabilities for sequences in X.
Parameters
----------
X : 3D np.ndarray of shape = [n_cases, n_channels, n_timepoints]
+ The data to make predict probabilities for.
- y : array-like, shape = [n_cases]
- The class labels.
-
+ Returns
+ -------
+ y : array-like, shape = [n_cases, n_classes_]
+ Predicted probabilities using the ordering in classes_.
"""
- if self.estimators is None or len(self.estimators) == 0:
- raise AttributeError(
- "Invalid `estimators` attribute, `estimators`"
- " should be a list of (string, estimator)"
- " tuples"
- )
-
- self._validate_estimators()
- self._validate_channel_callables(X)
- self._validate_remainder(X)
-
- self.le_ = LabelEncoder().fit(y)
- self.classes_ = self.le_.classes_
- transformed_y = self.le_.transform(y)
-
- estimators_ = []
- for name, estimator, channel in self._iter(replace_strings=True):
- estimator = estimator.clone()
- estimator.fit(_get_channel(X, channel), transformed_y)
- estimators_.append((name, estimator, channel))
-
- self.estimators_ = estimators_
- return self
-
- def _collect_probas(self, X):
- return np.asarray(
- [
- estimator.predict_proba(_get_channel(X, channel))
- for (name, estimator, channel) in self._iter(replace_strings=True)
- ]
- )
-
- def _predict_proba(self, X) -> np.ndarray:
- """Predict class probabilities for X using 'soft' voting."""
- return np.average(self._collect_probas(X), axis=0)
-
- def _predict(self, X) -> np.ndarray:
- maj = np.argmax(self.predict_proba(X), axis=1)
- return self.le_.inverse_transform(maj)
-
-
-class ChannelEnsembleClassifier(_BaseChannelEnsembleClassifier):
- """Applies estimators to channels of an array.
-
- This estimator allows different channels or channel subsets of the input
- to be transformed separately and the features generated by each
- transformer will be ensembled to form a single output.
-
- Parameters
- ----------
- estimators : list of tuples
- List of (name, estimator, channel(s)) tuples specifying the transformer
- objects to be applied to subsets of the data.
- name : string
- Like in Pipeline and FeatureUnion, this allows the
- transformer and its parameters to be set using ``set_params`` and searched
- in grid search.
- estimator : or {'drop'}
- Estimator must support `fit` and `predict_proba`. Special-cased
- strings 'drop' and 'passthrough' are accepted as well, to
- indicate to drop the channels.
- channels(s) : array-like of int, slice, boolean mask array
- Integer channels are indexed from 0.
- remainder : {'drop', 'passthrough'} or estimator, default 'drop'
- By default, only the specified channels in `transformations` are
- transformed and combined in the output, and the non-specified
- channels are dropped. (default of ``'drop'``).
- By specifying ``remainder='passthrough'``, all remaining channels
- that were not specified in `transformations` will be automatically passed
- through. This subset of channels is concatenated with the output of
- the transformations.
- By setting ``remainder`` to be an estimator, the remaining
- non-specified channels will use the ``remainder`` estimator. The
- estimator must support `fit` and `transform`.
- verbose : bool, default=False
- Whether to print debug info.
-
- Examples
- --------
- >>> from aeon.classification.dictionary_based import ContractableBOSS
- >>> from aeon.classification.interval_based import CanonicalIntervalForestClassifier
- >>> from aeon.datasets import load_basic_motions
- >>> X_train, y_train = load_basic_motions(split="train")
- >>> X_test, y_test = load_basic_motions(split="test")
- >>> cboss = ContractableBOSS(
- ... n_parameter_samples=4, max_ensemble_size=2, random_state=0
- ... )
- >>> cif = CanonicalIntervalForestClassifier(
- ... n_estimators=2, n_intervals=4, att_subsample_size=4, random_state=0
- ... )
- >>> estimators = [("cBOSS", cboss, 5), ("CIF", cif, [3, 4])]
- >>> channel_ens = ChannelEnsembleClassifier(estimators=estimators)
- >>> channel_ens.fit(X_train, y_train)
- ChannelEnsembleClassifier(...)
- >>> y_pred = channel_ens.predict(X_test)
- """
+ dists = np.zeros((len(X), self.n_classes_))
+
+ if self.majority_vote:
+ # Call predict on each classifier, add the predictions to the
+ # current probabilities
+ for i, (_, clf) in enumerate(self.ensemble_):
+ preds = clf.predict(X=self._get_channel(X, self.channels_[i]))
+ for n in range(X.shape[0]):
+ dists[n, self._class_dictionary[preds[n]]] += 1
+ else:
+ # Call predict_proba on each classifier, then add them to the current
+ # probabilities
+ for i, (_, clf) in enumerate(self.ensemble_):
+ dists += clf.predict_proba(X=self._get_channel(X, self.channels_[i]))
- # for default get_params/set_params from _HeterogenousMetaEstimator
- # _steps_attr points to the attribute of self
- # which contains the heterogeneous set of estimators
- # this must be an iterable of (name: str, estimator, ...) tuples for the default
- _steps_attr = "_estimators"
- # if the estimator is fittable, _HeterogenousMetaEstimator also
- # provides an override for get_fitted_params for params from the fitted estimators
- # the fitted estimators should be in a different attribute, _steps_fitted_attr
- # this must be an iterable of (name: str, estimator, ...) tuples for the default
- _steps_fitted_attr = "estimators_"
+ # Make each instances probability array sum to 1 and return
+ y_proba = dists / dists.sum(axis=1, keepdims=True)
- def __init__(self, estimators, remainder="drop", verbose=False):
- self.remainder = remainder
- super().__init__(estimators, verbose=verbose)
+ return y_proba
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -256,7 +137,7 @@ def get_test_params(cls, parameter_set="default"):
parameter_set : str, default="default"
Name of the set of test parameters to return, for use in tests. If no
special parameters are defined for a value, will return `"default"` set.
- ChannelEnsembleClassifier provides the following special sets:
+ ClassifierChannelEnsemble provides the following special sets:
- "results_comparison" - used in some classifiers to compare against
previously generated results where the default set of parameters
cannot produce suitable probability estimates
@@ -267,160 +148,29 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.classification.dictionary_based import ContractableBOSS
- from aeon.classification.interval_based import CanonicalIntervalForestClassifier
from aeon.classification.interval_based import (
- TimeSeriesForestClassifier as TSFC,
+ CanonicalIntervalForestClassifier,
+ TimeSeriesForestClassifier,
)
- if parameter_set != "results_comparison":
+ if parameter_set == "results_comparison":
+ cboss = ContractableBOSS(
+ n_parameter_samples=4, max_ensemble_size=2, random_state=0
+ )
+ cif = CanonicalIntervalForestClassifier(
+ n_estimators=2, n_intervals=4, att_subsample_size=4, random_state=0
+ )
return {
- "estimators": [
- ("tsf1", TSFC(n_estimators=2), 0),
- ("tsf2", TSFC(n_estimators=2), 0),
- ]
+ "classifiers": [("cBOSS", cboss), ("CIF", cif)],
+ "channels": [5, [3, 4]],
}
- cboss = ContractableBOSS(
- n_parameter_samples=4, max_ensemble_size=2, random_state=0
- )
- cif = CanonicalIntervalForestClassifier(
- n_estimators=2, n_intervals=4, att_subsample_size=4, random_state=0
- )
- return {"estimators": [("cBOSS", cboss, 5), ("CIF", cif, [3, 4])]}
-
-
-def _get_channel(X, key):
- """Get time series channel(s) from input data X.
-
- Supported input types (X): numpy arrays
-
- Supported key types (key):
- - scalar: output is 1D
- - lists, slices, boolean masks: output is 2D
- - callable that returns any of the above
-
- Supported key data types:
-
- - integer or boolean mask (positional):
- - supported for arrays and sparse matrices
- - string (key-based):
- - only supported for dataframes
- - So no keys other than strings are allowed (while in principle you
- can use any hashable object as key).
- """
- # check whether we have string channel names or integers
- if _check_key_type(key, int):
- channel_names = False
- elif hasattr(key, "dtype") and np.issubdtype(key.dtype, np.bool_):
- # boolean mask
- channel_names = True
- else:
- raise ValueError(
- "No valid specification of the channels. Only a "
- "scalar, list or slice of all integers or all "
- "strings, or boolean mask is allowed"
- )
-
- if isinstance(key, (int, str)):
- key = [key]
-
- if not channel_names:
- return X[:, key] if isinstance(X, np.ndarray) else X.iloc[:, key]
- if not isinstance(X, pd.DataFrame):
- raise ValueError(
- f"X must be a pd.DataFrame if channel names are "
- f"specified, but found: {type(X)}"
- )
- return X.loc[:, key]
-
-
-def _check_key_type(key, superclass):
- """Check that scalar, list or slice is of a certain type.
-
- This is only used in _get_channel and _get_channel_indices to check
- if the `key` (channel specification) is fully integer or fully string-like.
-
- Parameters
- ----------
- key : scalar, list, slice, array-like
- The channel specification to check
- superclass : int or str
- The type for which to check the `key`
- """
- if isinstance(key, superclass):
- return True
- if isinstance(key, slice):
- return isinstance(key.start, (superclass, type(None))) and isinstance(
- key.stop, (superclass, type(None))
- )
- if isinstance(key, list):
- return all(isinstance(x, superclass) for x in key)
- if hasattr(key, "dtype"):
- if superclass is int:
- return key.dtype.kind == "i"
else:
- # superclass = str
- return key.dtype.kind in ("O", "U", "S")
- return False
-
-
-def _get_channel_indices(X, key):
- """Get feature channel indices for input data X and key.
-
- For accepted values of `key`, see the docstring of _get_channel
- """
- n_channels = X.shape[1]
-
- if (
- _check_key_type(key, int)
- or hasattr(key, "dtype")
- and np.issubdtype(key.dtype, np.bool_)
- ):
- # Convert key into positive indexes
- idx = np.arange(n_channels)[key]
- return np.atleast_1d(idx).tolist()
- elif _check_key_type(key, str):
- try:
- all_columns = list(X.columns)
- except AttributeError as e:
- raise ValueError(
- "Specifying the columns using strings is only "
- "supported for pandas DataFrames"
- ) from e
- if isinstance(key, str):
- columns = [key]
- elif isinstance(key, slice):
- start, stop = key.start, key.stop
- if start is not None:
- start = all_columns.index(start)
- if stop is not None:
- # pandas indexing with strings is endpoint included
- stop = all_columns.index(stop) + 1
- else:
- stop = n_channels + 1
- return list(range(n_channels)[slice(start, stop)])
- else:
- columns = list(key)
-
- return [all_columns.index(col) for col in columns]
- else:
- raise ValueError(
- "No valid specification of the columns. Only a "
- "scalar, list or slice of all integers or all "
- "strings, or boolean mask is allowed"
- )
-
-
-def _is_empty_channel_selection(column):
- """Check if column selection is empty.
-
- Both an empty list or all-False boolean array are considered empty.
- """
- if hasattr(column, "dtype") and np.issubdtype(column.dtype, np.bool_):
- return not column.any()
- elif hasattr(column, "__len__"):
- return len(column) == 0
- else:
- return False
+ return {
+ "classifiers": [
+ ("tsf1", TimeSeriesForestClassifier(n_estimators=2)),
+ ("tsf2", TimeSeriesForestClassifier(n_estimators=2)),
+ ],
+ "channels": [0, 0],
+ }
diff --git a/aeon/classification/compose/_ensemble.py b/aeon/classification/compose/_ensemble.py
index a8df525386..d409adaab7 100644
--- a/aeon/classification/compose/_ensemble.py
+++ b/aeon/classification/compose/_ensemble.py
@@ -5,16 +5,10 @@
import numpy as np
-from deprecated.sphinx import deprecated
-from sklearn.metrics import accuracy_score
-from sklearn.model_selection import cross_val_predict
from sklearn.utils import check_random_state
-from aeon.base import _HeterogenousMetaEstimator
-from aeon.base.estimator.compose.collection_ensemble import BaseCollectionEnsemble
-from aeon.classification import DummyClassifier
+from aeon.base.estimators.compose.collection_ensemble import BaseCollectionEnsemble
from aeon.classification.base import BaseClassifier
-from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
from aeon.classification.sklearn._wrapper import SklearnClassifierWrapper
from aeon.utils.sklearn import is_sklearn_classifier
@@ -25,11 +19,11 @@ class ClassifierEnsemble(BaseCollectionEnsemble, BaseClassifier):
Parameters
----------
classifiers : list of aeon and/or sklearn classifiers or list of tuples
- Estimators to be used in the ensemble. The str is used to name the estimator.
- List of tuples (str, estimator) of estimators can also be passed, where
- the str is used to name the estimator.
- The objects are cloned prior, as such the state of the input will not be
- modified by fitting the pipeline.
+ Estimators to be used in the ensemble.
+ A list of tuples (str, estimator) can also be passed, where the str is used to
+ name the estimator.
+ The objects are cloned prior. As such, the state of the input will not be
+ modified by fitting the ensemble.
weights : float, or iterable of float, default=None
If float, ensemble weight for estimator i will be train score to this power.
If iterable of float, must be equal length as _estimators. Ensemble weight for
@@ -63,14 +57,14 @@ class ClassifierEnsemble(BaseCollectionEnsemble, BaseClassifier):
Attributes
----------
ensemble_ : list of tuples (str, estimator) of estimators
- Clones of estimators in _estimators which are fitted in the ensemble.
- Will always be in (str, estimator) format regardless of _estimators input.
+ Clones of estimators in classifiers which are fitted in the ensemble.
+ Will always be in (str, estimator) format regardless of classifiers input.
weights_ : dict
Weights of estimators using the str names as keys.
See Also
--------
- RegressorEnsemble : A pipeline for regression tasks.
+ RegressorEnsemble : An ensemble for regression tasks.
"""
_tags = {
@@ -93,12 +87,13 @@ def __init__(
wclf = [self._wrap_sklearn(clf) for clf in self.classifiers]
super().__init__(
- _estimators=wclf,
+ _ensemble=wclf,
weights=weights,
cv=cv,
metric=metric,
metric_probas=metric_probas,
random_state=random_state,
+ _ensemble_input_name="classifiers",
)
def _predict(self, X) -> np.ndarray:
@@ -124,7 +119,7 @@ def _predict_proba(self, X) -> np.ndarray:
y : array-like, shape = [n_cases, n_classes_]
Predicted probabilities using the ordering in classes_.
"""
- dists = np.zeros((X.shape[0], self.n_classes_))
+ dists = np.zeros((len(X), self.n_classes_))
if self.majority_vote:
# Call predict on each classifier, add the weighted predictions to the
@@ -160,7 +155,7 @@ def _wrap_sklearn(clf):
return clf
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -175,273 +170,14 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
- """
- return {
- "classifiers": [
- KNeighborsTimeSeriesClassifier.create_test_instance(),
- DummyClassifier.create_test_instance(),
- ],
- "weights": [2, 1],
- }
-
-
-# TODO: remove v1.0.0
-@deprecated(
- version="1.0.0",
- reason="WeightedEnsembleClassifier will be removed in 1.0.0, use "
- "ClassifierEnsemble instead.",
- category=FutureWarning,
-)
-class WeightedEnsembleClassifier(_HeterogenousMetaEstimator, BaseClassifier):
- """Weighted ensemble of classifiers with fittable ensemble weight.
-
- Produces a probabilistic prediction which is the weighted average of
- predictions of individual classifiers.
- Classifier with name `name` has ensemble weight in `weights_[name]`.
- `weights_` is fitted in `fit`, if `weights` is a scalar, otherwise fixed.
-
- If `weights` is a scalar, empirical training loss is computed for each classifier.
- In this case, ensemble weights of classifier is empirical loss,
- to the power of `weights` (a scalar).
-
- The evaluation for the empirical training loss can be selected
- through the `metric` and `metric_type` parameters.
-
- The in-sample empirical training loss is computed in-sample or out-of-sample,
- depending on the `cv` parameter. None = in-sample; other = cross-validated oos.
-
- Parameters
- ----------
- classifiers : list of tuples (str, classifier) of aeon classifiers
- Classifiers to apply to the input series.
- weights : float, or iterable of float, optional, default=None
- if float, ensemble weight for classifier i will be train score to this power
- if iterable of float, must be equal length as classifiers
- ensemble weight for classifier i will be weights[i]
- if None, ensemble weights are equal (uniform average)
- cv : None, int, or sklearn cross-validation object, optional, default=None
- determines whether in-sample or which cross-validated predictions used in fit
- None : predictions are in-sample, equivalent to fit(X, y).predict(X)
- cv : predictions are equivalent to fit(X_train, y_train).predict(X_test)
- where multiple X_train, y_train, X_test are obtained from cv folds
- returned y is union over all test fold predictions
- cv test folds must be non-intersecting
- int : equivalent to cv=KFold(cv, shuffle=True, random_state=x),
- i.e., k-fold cross-validation predictions out-of-sample
- random_state x is taken from self if exists, otherwise x=None
- metric : sklearn metric for computing training score, default=accuracy_score
- only used if weights is a float
- metric_type : str, one of "point" or "proba", default="point"
- type of sklearn metric, point prediction ("point") or probabilistic ("proba")
- if "point", most probable class is passed as y_pred
- if "proba", probability of most probable class is passed as y_pred
- random_state : int, RandomState instance or None, default=None
- If `int`, random_state is the seed used by the random number generator;
- If `RandomState` instance, random_state is the random number generator;
- If `None`, the random number generator is the `RandomState` instance used
- by `np.random`.
-
- Attributes
- ----------
- classifiers_ : list of tuples (str, classifier) of aeon classifiers
- clones of classifies in `classifiers` which are fitted in the ensemble
- is always in (str, classifier) format, even if `classifiers` is just a list
- strings not passed in `classifiers` are replaced by unique generated strings
- i-th classifier in `classifier_` is clone of i-th in `classifier`
- weights_ : dict with str being classifier names as in `classifiers_`
- value at key is ensemble weights of classifier with name key
- ensemble weights are fitted in `fit` if `weights` is a scalar
-
- Examples
- --------
- >>> from aeon.classification import DummyClassifier
- >>> from aeon.classification.convolution_based import RocketClassifier
- >>> from aeon.datasets import load_unit_test
- >>> X_train, y_train = load_unit_test(split="train")
- >>> X_test, y_test = load_unit_test(split="test")
- >>> clf = WeightedEnsembleClassifier(
- ... [DummyClassifier(), RocketClassifier(num_kernels=100)],
- ... weights=2,
- ... )
- >>> clf.fit(X_train, y_train)
- WeightedEnsembleClassifier(...)
- >>> y_pred = clf.predict(X_test)
- """
-
- # for default get_params/set_params from _HeterogenousMetaEstimator
- # _steps_attr points to the attribute of self
- # which contains the heterogeneous set of estimators
- # this must be an iterable of (name: str, estimator, ...) tuples for the default
- _steps_attr = "_classifiers"
- # if the estimator is fittable, _HeterogenousMetaEstimator also
- # provides an override for get_fitted_params for params from the fitted estimators
- # the fitted estimators should be in a different attribute, _steps_fitted_attr
- # this must be an iterable of (name: str, estimator, ...) tuples for the default
- _steps_fitted_attr = "classifiers_"
-
- def __init__(
- self,
- classifiers,
- weights=None,
- cv=None,
- metric=None,
- metric_type="point",
- random_state=None,
- ):
- self.classifiers = classifiers
- self.weights = weights
- self.cv = cv
- self.metric = metric
- self.metric_type = metric_type
- self.random_state = random_state
-
- # make the copies that are being fitted
- self.classifiers_ = self._check_estimators(
- self.classifiers, cls_type=BaseClassifier
- )
-
- # pass on random state
- for _, clf in self.classifiers_:
- params = clf.get_params()
- if "random_state" in params and params["random_state"] is None:
- clf.set_params(random_state=random_state)
-
- if weights is None:
- self.weights_ = {x[0]: 1 for x in self.classifiers_}
- elif isinstance(weights, (float, int)):
- self.weights_ = {}
- elif isinstance(weights, dict):
- self.weights_ = {x[0]: weights[x[0]] for x in self.classifiers_}
- else:
- self.weights_ = {x[0]: weights[i] for i, x in enumerate(self.classifiers_)}
-
- if metric is None:
- self._metric = accuracy_score
- else:
- self._metric = metric
-
- super().__init__()
-
- # set property tags based on tags of components
- ests = self.classifiers_
- self._anytagis_then_set("capability:multivariate", False, True, ests)
- self._anytagis_then_set("capability:missing_values", False, True, ests)
-
- @property
- def _classifiers(self):
- return self._get_estimator_tuples(self.classifiers, clone_ests=False)
-
- @_classifiers.setter
- def _classifiers(self, value):
- self.classifiers = value
-
- def _fit(self, X, y):
- """Fit time series classifier to training data.
-
- Parameters
- ----------
- X : 3D np.ndarray of shape = [n_cases, n_channels, n_timepoints]
- y : 1D np.array of int, of shape [n_cases] - class labels for fitting
- indices correspond to instance indices in X
-
- Returns
- -------
- self : Reference to self.
- """
- # if weights are fixed, we only fit
- if not isinstance(self.weights, (float, int)):
- for _, classifier in self.classifiers_:
- classifier.fit(X=X, y=y)
- # if weights are calculated by training loss, we fit_predict and evaluate
- else:
- exponent = self.weights
- for clf_name, clf in self.classifiers_:
- # learn cross-val accuracy of the model
- train_probs = cross_val_predict(
- clf, X=X, y=y, cv=self.cv, method="predict_proba"
- )
-
- # train final model
- clf.fit(X, y)
- train_preds = clf.classes_[np.argmax(train_probs, axis=1)]
-
- if self.metric_type == "proba":
- for i in range(len(train_preds)):
- train_preds[i] = train_probs[i, np.argmax(train_probs[i, :])]
- metric = self._metric
- self.weights_[clf_name] = metric(y, train_preds) ** exponent
-
- return self
-
- def _predict(self, X) -> np.ndarray:
- """Predicts labels for sequences in X."""
- y_proba = self._predict_proba(X)
- y_pred = y_proba.argmax(axis=1)
-
- return y_pred
-
- def _predict_proba(self, X) -> np.ndarray:
- """Predicts labels probabilities for sequences in X.
-
- Parameters
- ----------
- X : 3D np.ndarray of shape = [n_cases, n_channels, n_timepoints]
- The data to make predict probabilities for.
-
- Returns
- -------
- y : array-like, shape = [n_cases, n_classes_]
- Predicted probabilities using the ordering in classes_.
- """
- dists = np.zeros((X.shape[0], self.n_classes_))
-
- # Call predict proba on each classifier, multiply the probabilities by the
- # classifiers weight then add them to the current HC2 probabilities
- for clf_name, clf in self.classifiers_:
- y_proba = clf.predict_proba(X=X)
- dists += y_proba * self.weights_[clf_name]
-
- # Make each instances probability array sum to 1 and return
- y_proba = dists / dists.sum(axis=1, keepdims=True)
-
- return y_proba
-
- @classmethod
- def get_test_params(cls, parameter_set="default"):
- """Return testing parameter settings for the estimator.
-
- Parameters
- ----------
- parameter_set : str, default="default"
- Name of the set of test parameters to return, for use in tests. If no
- special parameters are defined for a value, will return `"default"` set.
-
- Returns
- -------
- params : dict or list of dict, default={}
- Parameters to create testing instances of the class.
- Each dict are parameters to construct an "interesting" test instance, i.e.,
- `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.classification import DummyClassifier
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
- params1 = {
- "classifiers": [
- KNeighborsTimeSeriesClassifier.create_test_instance(),
- DummyClassifier.create_test_instance(),
- ],
- "weights": [42, 1],
- }
-
- params2 = {
+ return {
"classifiers": [
- KNeighborsTimeSeriesClassifier.create_test_instance(),
- DummyClassifier.create_test_instance(),
+ KNeighborsTimeSeriesClassifier._create_test_instance(),
+ DummyClassifier._create_test_instance(),
],
- "weights": 2,
- "cv": 3,
+ "weights": [2, 1],
}
- return [params1, params2]
diff --git a/aeon/classification/compose/_pipeline.py b/aeon/classification/compose/_pipeline.py
index 9b2ec98c6f..7a2fb2d076 100644
--- a/aeon/classification/compose/_pipeline.py
+++ b/aeon/classification/compose/_pipeline.py
@@ -4,7 +4,7 @@
__all__ = ["ClassifierPipeline"]
-from aeon.base.estimator.compose.collection_pipeline import BaseCollectionPipeline
+from aeon.base.estimators.compose.collection_pipeline import BaseCollectionPipeline
from aeon.classification.base import BaseClassifier
@@ -87,7 +87,7 @@ def __init__(self, transformers, classifier, random_state=None):
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -102,18 +102,15 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
from aeon.transformations.collection import Truncator
- from aeon.transformations.collection.feature_based import (
- SevenNumberSummaryTransformer,
- )
+ from aeon.transformations.collection.feature_based import SevenNumberSummary
return {
"transformers": [
Truncator(truncated_length=5),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
"classifier": KNeighborsTimeSeriesClassifier(distance="euclidean"),
}
diff --git a/aeon/classification/compose/tests/test_pipeline.py b/aeon/classification/compose/tests/test_pipeline.py
index 2d1e607fbb..3641ae3c88 100644
--- a/aeon/classification/compose/tests/test_pipeline.py
+++ b/aeon/classification/compose/tests/test_pipeline.py
@@ -24,20 +24,20 @@
Tabularizer,
TimeSeriesScaler,
)
-from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer
+from aeon.transformations.collection.feature_based import SevenNumberSummary
@pytest.mark.parametrize(
"transformers",
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
[Padder(pad_length=15), Tabularizer(), StandardScaler()],
- [Padder(pad_length=15), SevenNumberSummaryTransformer()],
- [Tabularizer(), StandardScaler(), SevenNumberSummaryTransformer()],
+ [Padder(pad_length=15), SevenNumberSummary()],
+ [Tabularizer(), StandardScaler(), SevenNumberSummary()],
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
],
)
@@ -68,14 +68,14 @@ def test_classifier_pipeline(transformers):
"transformers",
[
[Padder(pad_length=15), Tabularizer()],
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
[Tabularizer(), StandardScaler()],
[Padder(pad_length=15), Tabularizer(), StandardScaler()],
- [Padder(pad_length=15), SevenNumberSummaryTransformer()],
- [Tabularizer(), StandardScaler(), SevenNumberSummaryTransformer()],
+ [Padder(pad_length=15), SevenNumberSummary()],
+ [Tabularizer(), StandardScaler(), SevenNumberSummary()],
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
],
)
@@ -108,7 +108,7 @@ def test_unequal_tag_inference():
n_cases=10, min_n_timepoints=8, max_n_timepoints=12
)
- t1 = SevenNumberSummaryTransformer()
+ t1 = SevenNumberSummary()
t2 = Padder()
t3 = TimeSeriesScaler()
t4 = AutocorrelationFunctionTransformer(n_lags=5)
@@ -229,7 +229,7 @@ def test_multivariate_tag_inference():
"""Test that ClassifierPipeline infers multivariate tag correctly."""
X, y = make_example_3d_numpy(n_cases=10, n_channels=2, n_timepoints=12)
- t1 = SevenNumberSummaryTransformer()
+ t1 = SevenNumberSummary()
t2 = TimeSeriesScaler()
t3 = HOG1DTransformer()
t4 = StandardScaler()
diff --git a/aeon/classification/convolution_based/_arsenal.py b/aeon/classification/convolution_based/_arsenal.py
index f55afc5272..a24a57d9ab 100644
--- a/aeon/classification/convolution_based/_arsenal.py
+++ b/aeon/classification/convolution_based/_arsenal.py
@@ -395,7 +395,7 @@ def _train_probas_for_estimator(self, Xt, y, idx, rng):
return results, weight, oob
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -417,7 +417,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"num_kernels": 20, "n_estimators": 5}
diff --git a/aeon/classification/convolution_based/_minirocket.py b/aeon/classification/convolution_based/_minirocket.py
index bb20bb39dc..dc3ef18a7a 100644
--- a/aeon/classification/convolution_based/_minirocket.py
+++ b/aeon/classification/convolution_based/_minirocket.py
@@ -194,7 +194,7 @@ def _predict_proba(self, X) -> np.ndarray:
return dists
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -213,7 +213,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"num_kernels": 100}
diff --git a/aeon/classification/convolution_based/_multirocket.py b/aeon/classification/convolution_based/_multirocket.py
index 7ca894b9bf..a0c1767eff 100644
--- a/aeon/classification/convolution_based/_multirocket.py
+++ b/aeon/classification/convolution_based/_multirocket.py
@@ -198,7 +198,7 @@ def _predict_proba(self, X) -> np.ndarray:
return dists
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -217,7 +217,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"num_kernels": 100}
diff --git a/aeon/classification/convolution_based/_rocket.py b/aeon/classification/convolution_based/_rocket.py
index c8f828643f..8509fde22a 100644
--- a/aeon/classification/convolution_based/_rocket.py
+++ b/aeon/classification/convolution_based/_rocket.py
@@ -194,7 +194,7 @@ def _predict_proba(self, X) -> np.ndarray:
return dists
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -213,7 +213,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"num_kernels": 100}
diff --git a/aeon/classification/deep_learning/_cnn.py b/aeon/classification/deep_learning/_cnn.py
index 771e58d005..5b87cab9b8 100644
--- a/aeon/classification/deep_learning/_cnn.py
+++ b/aeon/classification/deep_learning/_cnn.py
@@ -302,7 +302,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -321,7 +321,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_encoder.py b/aeon/classification/deep_learning/_encoder.py
index 2765c4cbbe..1773d1ea1c 100644
--- a/aeon/classification/deep_learning/_encoder.py
+++ b/aeon/classification/deep_learning/_encoder.py
@@ -285,7 +285,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -304,7 +304,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 8,
diff --git a/aeon/classification/deep_learning/_fcn.py b/aeon/classification/deep_learning/_fcn.py
index 5484f45bdb..e79ea65270 100644
--- a/aeon/classification/deep_learning/_fcn.py
+++ b/aeon/classification/deep_learning/_fcn.py
@@ -306,7 +306,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -325,7 +325,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_inception_time.py b/aeon/classification/deep_learning/_inception_time.py
index 135ced4a81..d56f3659a8 100644
--- a/aeon/classification/deep_learning/_inception_time.py
+++ b/aeon/classification/deep_learning/_inception_time.py
@@ -132,6 +132,14 @@ class InceptionTimeClassifier(BaseClassifier):
Notes
-----
+ Adapted from the implementation from Fawaz et. al
+ https://github.com/hfawaz/InceptionTime/blob/master/classifiers/inception.py
+
+ and Ismail-Fawaz et al.
+ https://github.com/MSD-IRIMAS/CF-4-TSC
+
+ References
+ ----------
..[1] Fawaz et al. InceptionTime: Finding AlexNet for Time Series
Classification, Data Mining and Knowledge Discovery, 34, 2020
@@ -140,12 +148,6 @@ class InceptionTimeClassifier(BaseClassifier):
Hand-Crafted Convolution Filters, 2022 IEEE International
Conference on Big Data.
- Adapted from the implementation from Fawaz et. al
- https://github.com/hfawaz/InceptionTime/blob/master/classifiers/inception.py
-
- and Ismail-Fawaz et al.
- https://github.com/MSD-IRIMAS/CF-4-TSC
-
Examples
--------
>>> from aeon.classification.deep_learning import InceptionTimeClassifier
@@ -342,7 +344,7 @@ def _predict_proba(self, X) -> np.ndarray:
return probs
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -361,7 +363,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_classifiers": 1,
@@ -477,18 +478,20 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
Notes
-----
- ..[1] Fawaz et al. InceptionTime: Finding AlexNet for Time Series
- Classification, Data Mining and Knowledge Discovery, 34, 2020
-
- ..[2] Ismail-Fawaz et al. Deep Learning For Time Series Classification Using New
- Hand-Crafted Convolution Filters, 2022 IEEE International Conference on Big Data.
-
Adapted from the implementation from Fawaz et. al
https://github.com/hfawaz/InceptionTime/blob/master/classifiers/inception.py
and Ismail-Fawaz et al.
https://github.com/MSD-IRIMAS/CF-4-TSC
+ References
+ ----------
+ ..[1] Fawaz et al. InceptionTime: Finding AlexNet for Time Series
+ Classification, Data Mining and Knowledge Discovery, 34, 2020
+
+ ..[2] Ismail-Fawaz et al. Deep Learning For Time Series Classification Using New
+ Hand-Crafted Convolution Filters, 2022 IEEE International Conference on Big Data.
+
Examples
--------
>>> from aeon.classification.deep_learning import IndividualInceptionClassifier
@@ -725,7 +728,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -744,7 +747,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_lite_time.py b/aeon/classification/deep_learning/_lite_time.py
index 43f58dfd1c..b53397939b 100644
--- a/aeon/classification/deep_learning/_lite_time.py
+++ b/aeon/classification/deep_learning/_lite_time.py
@@ -17,16 +17,24 @@
class LITETimeClassifier(BaseClassifier):
- """LITETime ensemble classifier.
+ """LITETime or LITEMVTime ensemble classifier.
- Ensemble of IndividualLITETimeClassifier objects, as described in [1]_.
+ Ensemble of IndividualLITETimeClassifier objects, as described in [1]_
+ and [2]_. For using LITEMV, simply set the `use_litemv`
+ bool parameter to True.
Parameters
----------
n_classifiers : int, default = 5,
- the number of LITE models used for the
+ the number of LITE or LITEMV models used for the
Ensemble in order to create
- LITETime.
+ LITETime or LITEMVTime.
+ use_litemv : bool, default = False
+ The boolean value to control which version of the
+ network to use. If set to `False`, then LITE is used,
+ if set to `True` then LITEMV is used. LITEMV is the
+ same architecture as LITE but specifically designed
+ to better handle multivariate time series.
n_filters : int or list of int32, default = 32
The number of filters used in one lite layer, if not a list, the same
number of filters is used in all lite layers.
@@ -87,12 +95,17 @@ class LITETimeClassifier(BaseClassifier):
metrics : keras metrics, default = None,
will be set to accuracy as default if None
- Notes
- -----
+ References
+ ----------
..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing
tEchniques for Time Series Classification, IEEE International
Conference on Data Science and Advanced Analytics, 2023.
+ ..[2] Ismail-Fawaz, Ali, et al. "Look Into the LITE
+ in Deep Learning for Time Series Classification."
+ arXiv preprint arXiv:2409.02869 (2024).
+ Notes
+ -----
Adapted from the implementation from Ismail-Fawaz et. al
https://github.com/MSD-IRIMAS/LITE
@@ -118,6 +131,7 @@ class LITETimeClassifier(BaseClassifier):
def __init__(
self,
n_classifiers=5,
+ use_litemv=False,
n_filters=32,
kernel_size=40,
strides=1,
@@ -141,6 +155,8 @@ def __init__(
):
self.n_classifiers = n_classifiers
+ self.use_litemv = use_litemv
+
self.strides = strides
self.activation = activation
self.n_filters = n_filters
@@ -189,6 +205,7 @@ def _fit(self, X, y):
for n in range(0, self.n_classifiers):
cls = IndividualLITEClassifier(
+ use_litemv=self.use_litemv,
n_filters=self.n_filters,
kernel_size=self.kernel_size,
file_path=self.file_path,
@@ -258,7 +275,7 @@ def _predict_proba(self, X) -> np.ndarray:
return probs
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -277,26 +294,43 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_classifiers": 1,
- "n_epochs": 10,
+ "n_epochs": 2,
+ "batch_size": 4,
+ "kernel_size": 4,
+ }
+ param2 = {
+ "n_classifiers": 1,
+ "use_litemv": True,
+ "n_epochs": 2,
"batch_size": 4,
"kernel_size": 4,
+ "metrics": ["accuracy"],
+ "verbose": True,
+ "use_mini_batch_size": True,
}
- return [param1]
+ return [param1, param2]
class IndividualLITEClassifier(BaseDeepClassifier):
- """Single LITETime classifier.
+ """Single LITE or LITEMV classifier.
- One LITE deep model, as described in [1]_.
+ One LITE or LITEMV deep model, as described in [1]_
+ and [2]_. For using LITEMV, simply set the `use_litemv`
+ bool parameter to True.
Parameters
----------
- n_filters : int or list of int32, default = 32
+ use_litemv : bool, default = False
+ The boolean value to control which version of the
+ network to use. If set to `False`, then LITE is used,
+ if set to `True` then LITEMV is used. LITEMV is the
+ same architecture as LITE but specifically designed
+ to better handle multivariate time series.
+ n_filters : int or list of int32, default = 32
The number of filters used in one lite layer, if not a list, the same
number of filters is used in all lite layers.
kernel_size : int or list of int, default = 40
@@ -356,12 +390,17 @@ class IndividualLITEClassifier(BaseDeepClassifier):
metrics : keras metrics, default = None,
will be set to accuracy as default if None
- Notes
- -----
+ References
+ ----------
..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing
tEchniques for Time Series Classificaion, IEEE International
Conference on Data Science and Advanced Analytics, 2023.
+ ..[2] Ismail-Fawaz, Ali, et al. "Look Into the LITE
+ in Deep Learning for Time Series Classification."
+ arXiv preprint arXiv:2409.02869 (2024).
+ Notes
+ -----
Adapted from the implementation from Ismail-Fawaz et. al
https://github.com/MSD-IRIMAS/LITE
@@ -378,6 +417,7 @@ class IndividualLITEClassifier(BaseDeepClassifier):
def __init__(
self,
+ use_litemv=False,
n_filters=32,
kernel_size=40,
strides=1,
@@ -399,7 +439,7 @@ def __init__(
metrics=None,
optimizer=None,
):
- # predefined
+ self.use_litemv = use_litemv
self.n_filters = n_filters
self.strides = strides
self.activation = activation
@@ -429,6 +469,7 @@ def __init__(
)
self._network = LITENetwork(
+ use_litemv=self.use_litemv,
n_filters=self.n_filters,
kernel_size=self.kernel_size,
strides=self.strides,
@@ -568,7 +609,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -587,12 +628,20 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
- "n_epochs": 10,
+ "n_epochs": 2,
+ "batch_size": 4,
+ "kernel_size": 4,
+ }
+ param2 = {
+ "use_litemv": True,
+ "n_epochs": 2,
"batch_size": 4,
"kernel_size": 4,
+ "metrics": ["accuracy"],
+ "verbose": True,
+ "use_mini_batch_size": True,
}
- return [param1]
+ return [param1, param2]
diff --git a/aeon/classification/deep_learning/_mlp.py b/aeon/classification/deep_learning/_mlp.py
index 48eb8f711e..0f0d538cb9 100644
--- a/aeon/classification/deep_learning/_mlp.py
+++ b/aeon/classification/deep_learning/_mlp.py
@@ -271,7 +271,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -290,7 +290,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_resnet.py b/aeon/classification/deep_learning/_resnet.py
index 963faec26b..fe754548aa 100644
--- a/aeon/classification/deep_learning/_resnet.py
+++ b/aeon/classification/deep_learning/_resnet.py
@@ -317,7 +317,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -336,7 +336,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param = {
"n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_tapnet.py b/aeon/classification/deep_learning/_tapnet.py
index 2ad048827b..48cc486fb6 100644
--- a/aeon/classification/deep_learning/_tapnet.py
+++ b/aeon/classification/deep_learning/_tapnet.py
@@ -246,7 +246,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -261,7 +261,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 20,
diff --git a/aeon/classification/dictionary_based/_boss.py b/aeon/classification/dictionary_based/_boss.py
index da821b72a2..856074b226 100644
--- a/aeon/classification/dictionary_based/_boss.py
+++ b/aeon/classification/dictionary_based/_boss.py
@@ -396,7 +396,7 @@ def _individual_train_acc(self, boss, y, train_size, lowest_acc, keep_train_pred
return correct / train_size
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -418,7 +418,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/classification/dictionary_based/_cboss.py b/aeon/classification/dictionary_based/_cboss.py
index a649741b83..652b4a76ff 100644
--- a/aeon/classification/dictionary_based/_cboss.py
+++ b/aeon/classification/dictionary_based/_cboss.py
@@ -420,7 +420,7 @@ def _individual_train_acc(self, boss, y, train_size, lowest_acc, keep_train_pred
return correct / train_size
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -445,7 +445,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_parameter_samples": 10, "max_ensemble_size": 5}
diff --git a/aeon/classification/dictionary_based/_mrseql.py b/aeon/classification/dictionary_based/_mrseql.py
index 1426844f7f..b515d31e82 100644
--- a/aeon/classification/dictionary_based/_mrseql.py
+++ b/aeon/classification/dictionary_based/_mrseql.py
@@ -106,7 +106,9 @@ def _predict_proba(self, X) -> np.ndarray:
return self.clf_.predict_proba(_X)
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+ def _get_test_params(
+ cls, parameter_set: str = "default"
+ ) -> Union[dict, list[dict]]:
"""Return testing parameter settings for the estimator.
Parameters
@@ -125,6 +127,5 @@ def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dic
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {}
diff --git a/aeon/classification/dictionary_based/_mrsqm.py b/aeon/classification/dictionary_based/_mrsqm.py
index ce793f5424..4cf980148b 100644
--- a/aeon/classification/dictionary_based/_mrsqm.py
+++ b/aeon/classification/dictionary_based/_mrsqm.py
@@ -123,7 +123,9 @@ def _predict_proba(self, X) -> np.ndarray:
return self.clf_.predict_proba(X)
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+ def _get_test_params(
+ cls, parameter_set: str = "default"
+ ) -> Union[dict, list[dict]]:
"""Return testing parameter settings for the estimator.
Parameters
@@ -142,7 +144,6 @@ def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dic
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"features_per_rep": 50,
diff --git a/aeon/classification/dictionary_based/_muse.py b/aeon/classification/dictionary_based/_muse.py
index f4f0feea2a..105948219d 100644
--- a/aeon/classification/dictionary_based/_muse.py
+++ b/aeon/classification/dictionary_based/_muse.py
@@ -345,7 +345,7 @@ def _add_first_order_differences(self, X):
return X_new
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -360,7 +360,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"window_inc": 4,
diff --git a/aeon/classification/dictionary_based/_redcomets.py b/aeon/classification/dictionary_based/_redcomets.py
index f85109450a..6ddbb7cfa1 100644
--- a/aeon/classification/dictionary_based/_redcomets.py
+++ b/aeon/classification/dictionary_based/_redcomets.py
@@ -596,7 +596,7 @@ def _sax_wrapper(sax):
return sax_parallel_res
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -610,9 +610,7 @@ def get_test_params(cls, parameter_set="default"):
dict
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
- ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
- instance.``create_test_instance`` uses the first (or only) dictionary in
- `params``.
+ `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
"""
return {
"variant": 3,
diff --git a/aeon/classification/dictionary_based/_tde.py b/aeon/classification/dictionary_based/_tde.py
index 97a98ecbcb..f8af016b54 100644
--- a/aeon/classification/dictionary_based/_tde.py
+++ b/aeon/classification/dictionary_based/_tde.py
@@ -530,7 +530,7 @@ def _individual_train_acc(self, tde, y, train_size, lowest_acc, keep_train_preds
return correct / train_size
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -555,7 +555,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/classification/dictionary_based/_weasel.py b/aeon/classification/dictionary_based/_weasel.py
index dee61e3b33..03b86a8c17 100644
--- a/aeon/classification/dictionary_based/_weasel.py
+++ b/aeon/classification/dictionary_based/_weasel.py
@@ -316,7 +316,7 @@ def _compute_window_inc(self):
return 1 if self.n_timepoints < 100 else self.window_inc
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -331,7 +331,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"window_inc": 4,
diff --git a/aeon/classification/dictionary_based/_weasel_v2.py b/aeon/classification/dictionary_based/_weasel_v2.py
index 0dd7539b01..b8d014a089 100644
--- a/aeon/classification/dictionary_based/_weasel_v2.py
+++ b/aeon/classification/dictionary_based/_weasel_v2.py
@@ -236,7 +236,7 @@ def _predict_proba(self, X) -> np.ndarray:
return super()._predict_proba(X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -251,7 +251,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {"feature_selection": "none"}
diff --git a/aeon/classification/distance_based/_elastic_ensemble.py b/aeon/classification/distance_based/_elastic_ensemble.py
index 8bd0d6d3f5..c2b159f827 100644
--- a/aeon/classification/distance_based/_elastic_ensemble.py
+++ b/aeon/classification/distance_based/_elastic_ensemble.py
@@ -491,7 +491,9 @@ def get_inclusive(min_val: float, max_val: float, num_vals: float):
)
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+ def _get_test_params(
+ cls, parameter_set: str = "default"
+ ) -> Union[dict, list[dict]]:
"""Return testing parameter settings for the estimator.
Parameters
@@ -510,7 +512,6 @@ def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dic
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/classification/distance_based/_proximity_forest.py b/aeon/classification/distance_based/_proximity_forest.py
index 44871a2500..40fca3624d 100644
--- a/aeon/classification/distance_based/_proximity_forest.py
+++ b/aeon/classification/distance_based/_proximity_forest.py
@@ -44,7 +44,7 @@ class ProximityForest(BaseClassifier):
n_jobs : int, default = 1
The number of parallel jobs to run for neighbors search.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
- ``-1`` means using all processors. See :term:`Glossary `
+ ``-1`` means using all processors.
for more details. Parameter for compatibility purposes, still unimplemented.
parallel_backend : str, ParallelBackendBase instance or None, default=None
Specify the parallelisation backend implementation in joblib, if None a 'prefer'
diff --git a/aeon/classification/distance_based/_proximity_tree.py b/aeon/classification/distance_based/_proximity_tree.py
index f2c3cd36e6..e3db90864d 100644
--- a/aeon/classification/distance_based/_proximity_tree.py
+++ b/aeon/classification/distance_based/_proximity_tree.py
@@ -235,7 +235,7 @@ def _get_best_splitter(self, X, y):
X[j],
splitter[0][labels[k]],
metric=measure,
- kwargs=splitter[1][measure],
+ **splitter[1][measure],
)
if dist < min_dist:
min_dist = dist
@@ -321,7 +321,7 @@ def _build_tree(self, X, y, depth, node_id, parent_target_value=None):
X[i],
splitter[0][labels[j]],
metric=measure,
- kwargs=splitter[1][measure],
+ **splitter[1][measure],
)
if dist < min_dist:
min_dist = dist
@@ -405,7 +405,7 @@ def _classify(self, treenode, x):
x,
treenode.splitter[0][branches[i]],
metric=measure,
- kwargs=treenode.splitter[1][measure],
+ **treenode.splitter[1][measure],
)
if dist < min_dist:
min_dist = dist
diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py
index 870dded920..40d65f9a47 100644
--- a/aeon/classification/distance_based/_time_series_neighbors.py
+++ b/aeon/classification/distance_based/_time_series_neighbors.py
@@ -49,7 +49,7 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier):
n_jobs : int, default = None
The number of parallel jobs to run for neighbors search.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
- ``-1`` means using all processors. See :term:`Glossary `
+ ``-1`` means using all processors.
for more details. Parameter for compatibility purposes, still unimplemented.
Examples
@@ -219,7 +219,9 @@ def _kneighbors(self, X):
return closest_idx, ws
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+ def _get_test_params(
+ cls, parameter_set: str = "default"
+ ) -> Union[dict, list[dict]]:
"""Return testing parameter settings for the estimator.
Parameters
@@ -234,7 +236,6 @@ def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dic
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
# non-default distance and algorithm
params1 = {"distance": "euclidean"}
diff --git a/aeon/classification/early_classification/_probability_threshold.py b/aeon/classification/early_classification/_probability_threshold.py
index f34d8cd13a..79d2f49812 100644
--- a/aeon/classification/early_classification/_probability_threshold.py
+++ b/aeon/classification/early_classification/_probability_threshold.py
@@ -460,7 +460,7 @@ def compute_harmonic_mean(self, state_info, y) -> tuple[float, float, float]:
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -479,7 +479,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.classification.feature_based import SummaryClassifier
from aeon.classification.interval_based import TimeSeriesForestClassifier
diff --git a/aeon/classification/early_classification/_teaser.py b/aeon/classification/early_classification/_teaser.py
index aded30f648..8a01bbba3b 100644
--- a/aeon/classification/early_classification/_teaser.py
+++ b/aeon/classification/early_classification/_teaser.py
@@ -622,7 +622,7 @@ def compute_harmonic_mean(self, state_info, y) -> tuple[float, float, float]:
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -641,7 +641,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.classification.feature_based import SummaryClassifier
from aeon.classification.interval_based import TimeSeriesForestClassifier
diff --git a/aeon/classification/feature_based/_catch22.py b/aeon/classification/feature_based/_catch22.py
index c99475cf33..1683422053 100644
--- a/aeon/classification/feature_based/_catch22.py
+++ b/aeon/classification/feature_based/_catch22.py
@@ -234,7 +234,7 @@ def _predict_proba(self, X) -> np.ndarray:
return dists
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -253,7 +253,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/classification/feature_based/_fresh_prince.py b/aeon/classification/feature_based/_fresh_prince.py
index 2760c226f2..df0c27cf36 100644
--- a/aeon/classification/feature_based/_fresh_prince.py
+++ b/aeon/classification/feature_based/_fresh_prince.py
@@ -12,7 +12,7 @@
from aeon.classification.base import BaseClassifier
from aeon.classification.sklearn import RotationForestClassifier
-from aeon.transformations.collection.feature_based import TSFreshFeatureExtractor
+from aeon.transformations.collection.feature_based import TSFresh
class FreshPRINCEClassifier(BaseClassifier):
@@ -59,7 +59,7 @@ class FreshPRINCEClassifier(BaseClassifier):
See Also
--------
- TSFreshFeatureExtractor, TSFreshClassifier, RotationForestClassifier
+ TSFresh, TSFreshClassifier, RotationForestClassifier
TSFresh related classes.
References
@@ -189,7 +189,7 @@ def _fit_fp_shared(self, X, y):
n_jobs=self._n_jobs,
random_state=self.random_state,
)
- self._tsfresh = TSFreshFeatureExtractor(
+ self._tsfresh = TSFresh(
default_fc_parameters=self.default_fc_parameters,
n_jobs=self._n_jobs,
chunksize=self.chunksize,
@@ -200,7 +200,7 @@ def _fit_fp_shared(self, X, y):
return self._tsfresh.fit_transform(X, y)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -222,7 +222,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/classification/feature_based/_signature_classifier.py b/aeon/classification/feature_based/_signature_classifier.py
index 445efb7b40..88308436f5 100644
--- a/aeon/classification/feature_based/_signature_classifier.py
+++ b/aeon/classification/feature_based/_signature_classifier.py
@@ -193,7 +193,7 @@ def _predict_proba(self, X) -> np.ndarray:
return self.pipeline.predict_proba(X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -212,7 +212,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"estimator": RandomForestClassifier(n_estimators=10)}
diff --git a/aeon/classification/feature_based/_summary.py b/aeon/classification/feature_based/_summary.py
index 43e6b33d9f..a4f34ff688 100644
--- a/aeon/classification/feature_based/_summary.py
+++ b/aeon/classification/feature_based/_summary.py
@@ -11,7 +11,7 @@
from aeon.base._base import _clone_estimator
from aeon.classification.base import BaseClassifier
-from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer
+from aeon.transformations.collection.feature_based import SevenNumberSummary
class SummaryClassifier(BaseClassifier):
@@ -19,7 +19,7 @@ class SummaryClassifier(BaseClassifier):
Summary statistic classifier.
This classifier simply transforms the input data using the
- SevenNumberSummaryTransformer transformer and builds a provided estimator using the
+ SevenNumberSummary transformer and builds a provided estimator using the
transformed data.
Parameters
@@ -50,6 +50,10 @@ class SummaryClassifier(BaseClassifier):
Number of classes. Extracted from the data.
classes_ : ndarray of shape (n_classes)
Holds the label for each class.
+ estimator_ : sklearn classifier
+ The fitted estimator.
+ transformer_ : SevenNumberSummary
+ The fitted transformer.
See Also
--------
@@ -88,9 +92,6 @@ def __init__(
self.n_jobs = n_jobs
self.random_state = random_state
- self._transformer = None
- self._estimator = None
-
super().__init__()
def _fit(self, X, y):
@@ -113,11 +114,11 @@ def _fit(self, X, y):
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
- self._transformer = SevenNumberSummaryTransformer(
+ self.transformer_ = SevenNumberSummary(
summary_stats=self.summary_stats,
)
- self._estimator = _clone_estimator(
+ self.estimator_ = _clone_estimator(
(
RandomForestClassifier(n_estimators=200)
if self.estimator is None
@@ -126,12 +127,12 @@ def _fit(self, X, y):
self.random_state,
)
- m = getattr(self._estimator, "n_jobs", None)
+ m = getattr(self.estimator_, "n_jobs", None)
if m is not None:
- self._estimator.n_jobs = self._n_jobs
+ self.estimator_.n_jobs = self._n_jobs
- X_t = self._transformer.fit_transform(X, y)
- self._estimator.fit(X_t, y)
+ X_t = self.transformer_.fit_transform(X, y)
+ self.estimator_.fit(X_t, y)
return self
@@ -148,7 +149,7 @@ def _predict(self, X) -> np.ndarray:
y : array-like, shape = [n_cases]
Predicted class labels.
"""
- return self._estimator.predict(self._transformer.transform(X))
+ return self.estimator_.predict(self.transformer_.transform(X))
def _predict_proba(self, X) -> np.ndarray:
"""Predict class probabilities for n instances in X.
@@ -163,18 +164,18 @@ def _predict_proba(self, X) -> np.ndarray:
y : array-like, shape = [n_cases, n_classes_]
Predicted probabilities using the ordering in classes_.
"""
- m = getattr(self._estimator, "predict_proba", None)
+ m = getattr(self.estimator_, "predict_proba", None)
if callable(m):
- return self._estimator.predict_proba(self._transformer.transform(X))
+ return self.estimator_.predict_proba(self.transformer_.transform(X))
else:
dists = np.zeros((X.shape[0], self.n_classes_))
- preds = self._estimator.predict(self._transformer.transform(X))
+ preds = self.estimator_.predict(self.transformer_.transform(X))
for i in range(0, X.shape[0]):
dists[i, self._class_dictionary[preds[i]]] = 1
return dists
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -193,7 +194,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"estimator": RandomForestClassifier(n_estimators=10)}
diff --git a/aeon/classification/feature_based/_tsfresh.py b/aeon/classification/feature_based/_tsfresh.py
index fcb9159f2b..5eefe77865 100644
--- a/aeon/classification/feature_based/_tsfresh.py
+++ b/aeon/classification/feature_based/_tsfresh.py
@@ -13,10 +13,7 @@
from aeon.base._base import _clone_estimator
from aeon.classification.base import BaseClassifier
-from aeon.transformations.collection.feature_based import (
- TSFreshFeatureExtractor,
- TSFreshRelevantFeatureExtractor,
-)
+from aeon.transformations.collection.feature_based import TSFresh, TSFreshRelevant
class TSFreshClassifier(BaseClassifier):
@@ -59,8 +56,8 @@ class TSFreshClassifier(BaseClassifier):
See Also
--------
- TSFreshFeatureExtractor
- TSFreshRelevantFeatureExtractor
+ TSFresh
+ TSFreshRelevant
TSFreshRegressor
References
@@ -125,13 +122,13 @@ def _fit(self, X, y):
ending in "_" and sets is_fitted flag to True.
"""
self._transformer = (
- TSFreshRelevantFeatureExtractor(
+ TSFreshRelevant(
default_fc_parameters=self.default_fc_parameters,
n_jobs=self._n_jobs,
chunksize=self.chunksize,
)
if self.relevant_feature_extractor
- else TSFreshFeatureExtractor(
+ else TSFresh(
default_fc_parameters=self.default_fc_parameters,
n_jobs=self._n_jobs,
chunksize=self.chunksize,
@@ -220,7 +217,7 @@ def _predict_proba(self, X) -> np.ndarray:
return dists
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -239,7 +236,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/classification/hybrid/_hivecote_v1.py b/aeon/classification/hybrid/_hivecote_v1.py
index 71f05fe2ed..22925487a6 100644
--- a/aeon/classification/hybrid/_hivecote_v1.py
+++ b/aeon/classification/hybrid/_hivecote_v1.py
@@ -292,7 +292,7 @@ def _predict_proba(self, X) -> np.ndarray:
return dists / dists.sum(axis=1, keepdims=True)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -311,7 +311,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from sklearn.ensemble import RandomForestClassifier
diff --git a/aeon/classification/hybrid/_hivecote_v2.py b/aeon/classification/hybrid/_hivecote_v2.py
index 1b77f32967..53cd94ef30 100644
--- a/aeon/classification/hybrid/_hivecote_v2.py
+++ b/aeon/classification/hybrid/_hivecote_v2.py
@@ -325,7 +325,7 @@ def _predict_proba(self, X, return_component_probas=False) -> np.ndarray:
return dists / dists.sum(axis=1, keepdims=True)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -347,7 +347,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from sklearn.ensemble import RandomForestClassifier
diff --git a/aeon/classification/hybrid/_rist.py b/aeon/classification/hybrid/_rist.py
index 2a036cd8e9..f098a6b9c6 100644
--- a/aeon/classification/hybrid/_rist.py
+++ b/aeon/classification/hybrid/_rist.py
@@ -6,7 +6,7 @@
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.preprocessing import FunctionTransformer
-from aeon.base.estimator.hybrid import BaseRIST
+from aeon.base.estimators.hybrid import BaseRIST
from aeon.classification import BaseClassifier
from aeon.utils.numba.general import first_order_differences_3d
@@ -134,7 +134,7 @@ def __init__(
}
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return unit test parameter settings for the estimator.
Parameters
diff --git a/aeon/classification/interval_based/_cif.py b/aeon/classification/interval_based/_cif.py
index 92ee2f3ca5..c46a23dc9a 100644
--- a/aeon/classification/interval_based/_cif.py
+++ b/aeon/classification/interval_based/_cif.py
@@ -8,7 +8,7 @@
import numpy as np
-from aeon.base.estimator.interval_based import BaseIntervalForest
+from aeon.base.estimators.interval_based import BaseIntervalForest
from aeon.classification import BaseClassifier
from aeon.classification.sklearn import ContinuousIntervalTree
from aeon.transformations.collection.feature_based import Catch22
@@ -233,7 +233,7 @@ def _fit_predict_proba(self, X, y) -> np.ndarray:
return super()._fit_predict_proba(X, y)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -258,7 +258,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10, "n_intervals": 2, "att_subsample_size": 4}
diff --git a/aeon/classification/interval_based/_drcif.py b/aeon/classification/interval_based/_drcif.py
index af3ed64765..90811f2539 100644
--- a/aeon/classification/interval_based/_drcif.py
+++ b/aeon/classification/interval_based/_drcif.py
@@ -10,7 +10,7 @@
import numpy as np
from sklearn.preprocessing import FunctionTransformer
-from aeon.base.estimator.interval_based import BaseIntervalForest
+from aeon.base.estimators.interval_based import BaseIntervalForest
from aeon.classification.base import BaseClassifier
from aeon.classification.sklearn._continuous_interval_tree import ContinuousIntervalTree
from aeon.transformations.collection import PeriodogramTransformer
@@ -260,7 +260,7 @@ def _fit_predict_proba(self, X, y) -> np.ndarray:
return super()._fit_predict_proba(X, y)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -285,7 +285,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10, "n_intervals": 2, "att_subsample_size": 4}
diff --git a/aeon/classification/interval_based/_interval_forest.py b/aeon/classification/interval_based/_interval_forest.py
index e7729ceeec..9cf6f33d43 100644
--- a/aeon/classification/interval_based/_interval_forest.py
+++ b/aeon/classification/interval_based/_interval_forest.py
@@ -5,7 +5,7 @@
import numpy as np
-from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
+from aeon.base.estimators.interval_based.base_interval_forest import BaseIntervalForest
from aeon.classification.base import BaseClassifier
@@ -225,7 +225,7 @@ def _fit_predict_proba(self, X, y) -> np.ndarray:
return super()._fit_predict_proba(X, y)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -250,7 +250,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10, "n_intervals": 2}
diff --git a/aeon/classification/interval_based/_interval_pipelines.py b/aeon/classification/interval_based/_interval_pipelines.py
index 5b71b57dbd..d29044e40b 100644
--- a/aeon/classification/interval_based/_interval_pipelines.py
+++ b/aeon/classification/interval_based/_interval_pipelines.py
@@ -211,7 +211,7 @@ def _predict_proba(self, X) -> np.ndarray:
return dists
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -230,7 +230,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.utils.numba.stats import row_mean, row_numba_min
@@ -450,7 +449,7 @@ def _predict_proba(self, X) -> np.ndarray:
return dists
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -469,7 +468,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.utils.numba.stats import row_mean, row_numba_min
diff --git a/aeon/classification/interval_based/_rise.py b/aeon/classification/interval_based/_rise.py
index ef3235bd2a..e17ce0ff7f 100644
--- a/aeon/classification/interval_based/_rise.py
+++ b/aeon/classification/interval_based/_rise.py
@@ -5,7 +5,7 @@
import numpy as np
-from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
+from aeon.base.estimators.interval_based.base_interval_forest import BaseIntervalForest
from aeon.classification import BaseClassifier
from aeon.classification.sklearn import ContinuousIntervalTree
from aeon.transformations.collection import (
@@ -195,7 +195,7 @@ def _fit_predict_proba(self, X, y) -> np.ndarray:
return super()._fit_predict_proba(X, y)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -221,7 +221,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10}
diff --git a/aeon/classification/interval_based/_rstsf.py b/aeon/classification/interval_based/_rstsf.py
index 19cb902f78..9280a4a03f 100644
--- a/aeon/classification/interval_based/_rstsf.py
+++ b/aeon/classification/interval_based/_rstsf.py
@@ -172,7 +172,7 @@ def _predict_transform(self, X):
return Xt
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -191,7 +191,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"n_estimators": 2,
diff --git a/aeon/classification/interval_based/_stsf.py b/aeon/classification/interval_based/_stsf.py
index b6bf3c924c..4642be7e11 100644
--- a/aeon/classification/interval_based/_stsf.py
+++ b/aeon/classification/interval_based/_stsf.py
@@ -11,7 +11,7 @@
import numpy as np
from sklearn.preprocessing import FunctionTransformer
-from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
+from aeon.base.estimators.interval_based.base_interval_forest import BaseIntervalForest
from aeon.classification.base import BaseClassifier
from aeon.transformations.collection import PeriodogramTransformer
from aeon.utils.numba.general import first_order_differences_3d
@@ -186,7 +186,7 @@ def _fit_predict_proba(self, X, y) -> np.ndarray:
return super()._fit_predict_proba(X, y)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -211,7 +211,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10}
diff --git a/aeon/classification/interval_based/_tsf.py b/aeon/classification/interval_based/_tsf.py
index a624fc9960..ae827c6950 100644
--- a/aeon/classification/interval_based/_tsf.py
+++ b/aeon/classification/interval_based/_tsf.py
@@ -8,7 +8,7 @@
import numpy as np
-from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
+from aeon.base.estimators.interval_based.base_interval_forest import BaseIntervalForest
from aeon.classification import BaseClassifier
from aeon.classification.sklearn import ContinuousIntervalTree
@@ -198,7 +198,7 @@ def _fit_predict_proba(self, X, y) -> np.ndarray:
return super()._fit_predict_proba(X, y)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -223,7 +223,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10}
diff --git a/aeon/classification/interval_based/tests/test_interval_pipelines.py b/aeon/classification/interval_based/tests/test_interval_pipelines.py
index ca4b4dfb7e..4e0f120f4e 100644
--- a/aeon/classification/interval_based/tests/test_interval_pipelines.py
+++ b/aeon/classification/interval_based/tests/test_interval_pipelines.py
@@ -27,9 +27,11 @@ def test_random_interval_classifier(cls):
def test_parameter_sets():
"""Test results comparison parameter sets."""
- paras = SupervisedIntervalClassifier.get_test_params(
+ paras = SupervisedIntervalClassifier._get_test_params(
parameter_set="results_comparison"
)
assert paras["n_intervals"] == 2
- paras = RandomIntervalClassifier.get_test_params(parameter_set="results_comparison")
+ paras = RandomIntervalClassifier._get_test_params(
+ parameter_set="results_comparison"
+ )
assert paras["n_intervals"] == 3
diff --git a/aeon/classification/ordinal_classification/_ordinal_tde.py b/aeon/classification/ordinal_classification/_ordinal_tde.py
index 982aab1f1e..886ff4707a 100644
--- a/aeon/classification/ordinal_classification/_ordinal_tde.py
+++ b/aeon/classification/ordinal_classification/_ordinal_tde.py
@@ -514,7 +514,7 @@ def _individual_train_mae(self, tde, y, train_size, highest_mae, keep_train_pred
return mae
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -533,7 +533,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/classification/shapelet_based/_ls.py b/aeon/classification/shapelet_based/_ls.py
index 06cc9fbc86..1398054ae4 100644
--- a/aeon/classification/shapelet_based/_ls.py
+++ b/aeon/classification/shapelet_based/_ls.py
@@ -209,7 +209,9 @@ def get_locations(self, X):
return self.clf_.locate(self.transformed_data_)
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+ def _get_test_params(
+ cls, parameter_set: str = "default"
+ ) -> Union[dict, list[dict]]:
"""Return testing parameter settings for the estimator.
Parameters
@@ -228,6 +230,5 @@ def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dic
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {"max_iter": 50, "batch_size": 10}
diff --git a/aeon/classification/shapelet_based/_rdst.py b/aeon/classification/shapelet_based/_rdst.py
index 4288686f68..cd1756c985 100644
--- a/aeon/classification/shapelet_based/_rdst.py
+++ b/aeon/classification/shapelet_based/_rdst.py
@@ -265,7 +265,9 @@ def _predict_proba(self, X) -> np.ndarray:
return dists
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+ def _get_test_params(
+ cls, parameter_set: str = "default"
+ ) -> Union[dict, list[dict]]:
"""Return testing parameter settings for the estimator.
Parameters
@@ -284,6 +286,5 @@ def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dic
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {"max_shapelets": 20}
diff --git a/aeon/classification/shapelet_based/_sast.py b/aeon/classification/shapelet_based/_sast.py
index 096a845529..fa16b267ca 100644
--- a/aeon/classification/shapelet_based/_sast.py
+++ b/aeon/classification/shapelet_based/_sast.py
@@ -180,6 +180,9 @@ def plot_most_important_feature_on_ts(self, ts, feature_importance, limit: int =
"""
import matplotlib.pyplot as plt
+ # get overall importance irrespective of class
+ feature_importance = [abs(x) for x in feature_importance]
+
features = zip(self._transformer._kernel_orig, feature_importance)
sorted_features = sorted(features, key=itemgetter(1), reverse=True)
diff --git a/aeon/classification/shapelet_based/_stc.py b/aeon/classification/shapelet_based/_stc.py
index 3add6f524d..c0ac9df290 100644
--- a/aeon/classification/shapelet_based/_stc.py
+++ b/aeon/classification/shapelet_based/_stc.py
@@ -318,7 +318,9 @@ def _fit_stc_shared(self, X, y):
return self._transformer.fit_transform(X, y)
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+ def _get_test_params(
+ cls, parameter_set: str = "default"
+ ) -> Union[dict, list[dict]]:
"""Return testing parameter settings for the estimator.
Parameters
@@ -343,7 +345,6 @@ def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dic
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from sklearn.ensemble import RandomForestClassifier
diff --git a/aeon/classification/sklearn/_wrapper.py b/aeon/classification/sklearn/_wrapper.py
index 79362af2be..889f181108 100644
--- a/aeon/classification/sklearn/_wrapper.py
+++ b/aeon/classification/sklearn/_wrapper.py
@@ -47,7 +47,7 @@ def _predict_proba(self, X):
return self.classifier_.predict_proba(X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -62,7 +62,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"classifier": RandomForestClassifier(n_estimators=5),
diff --git a/aeon/classification/sklearn/tests/test_rotation_forest_classifier.py b/aeon/classification/sklearn/tests/test_rotation_forest_classifier.py
index 605203d6f7..bbaea82166 100644
--- a/aeon/classification/sklearn/tests/test_rotation_forest_classifier.py
+++ b/aeon/classification/sklearn/tests/test_rotation_forest_classifier.py
@@ -1,10 +1,12 @@
"""Rotation Forest test code."""
import numpy as np
+import pytest
from sklearn.metrics import accuracy_score
from aeon.classification.sklearn import RotationForestClassifier
from aeon.datasets import load_unit_test
+from aeon.testing.data_generation import make_example_3d_numpy
def test_rotf_output():
@@ -81,3 +83,19 @@ def test_rotf_fit_predict():
y_proba = rotf.predict_proba(X_train)
assert isinstance(y_proba, np.ndarray)
assert y_proba.shape == (len(y_train), 2)
+
+
+def test_rotf_input():
+ """Test RotF with incorrect input."""
+ rotf = RotationForestClassifier()
+ X2 = rotf._check_X(np.random.random((10, 1, 100)))
+ assert X2.shape == (10, 100)
+ with pytest.raises(
+ ValueError, match="RotationForestClassifier is not a time series classifier"
+ ):
+ rotf._check_X(np.random.random((10, 10, 100)))
+ with pytest.raises(
+ ValueError, match="RotationForestClassifier is not a time series classifier"
+ ):
+ rotf._check_X([[1, 2, 3], [4, 5], [6, 7, 8]])
+ X, y = make_example_3d_numpy()
diff --git a/aeon/classification/tests/test_sklearn_compatability.py b/aeon/classification/tests/test_sklearn_compatability.py
index 0e084fe216..e6b6668459 100644
--- a/aeon/classification/tests/test_sklearn_compatability.py
+++ b/aeon/classification/tests/test_sklearn_compatability.py
@@ -61,18 +61,18 @@
Pipeline(
[
("transform", Resizer(length=10)),
- ("clf", CanonicalIntervalForestClassifier.create_test_instance()),
+ ("clf", CanonicalIntervalForestClassifier._create_test_instance()),
]
),
VotingClassifier(
estimators=[
- ("clf1", CanonicalIntervalForestClassifier.create_test_instance()),
- ("clf2", CanonicalIntervalForestClassifier.create_test_instance()),
- ("clf3", CanonicalIntervalForestClassifier.create_test_instance()),
+ ("clf1", CanonicalIntervalForestClassifier._create_test_instance()),
+ ("clf2", CanonicalIntervalForestClassifier._create_test_instance()),
+ ("clf3", CanonicalIntervalForestClassifier._create_test_instance()),
]
),
CalibratedClassifierCV(
- estimator=CanonicalIntervalForestClassifier.create_test_instance(),
+ estimator=CanonicalIntervalForestClassifier._create_test_instance(),
cv=2,
),
]
@@ -80,7 +80,7 @@
def test_sklearn_cross_validation():
"""Test sklearn cross-validation works with aeon data and classifiers."""
- clf = CanonicalIntervalForestClassifier.create_test_instance()
+ clf = CanonicalIntervalForestClassifier._create_test_instance()
X, y = make_example_3d_numpy(n_cases=20, n_channels=2, n_timepoints=30)
scores = cross_val_score(clf, X, y=y, cv=KFold(n_splits=2))
assert isinstance(scores, np.ndarray)
@@ -99,7 +99,7 @@ def test_sklearn_cross_validation_iterators(cross_validation_method):
@pytest.mark.parametrize("parameter_tuning_method", PARAMETER_TUNING_METHODS)
def test_sklearn_parameter_tuning(parameter_tuning_method):
"""Test if sklearn parameter tuners can handle aeon data and classifiers."""
- clf = CanonicalIntervalForestClassifier.create_test_instance()
+ clf = CanonicalIntervalForestClassifier._create_test_instance()
param_grid = {"n_intervals": [2, 3], "att_subsample_size": [2, 3]}
X, y = make_example_3d_numpy(n_cases=20, n_channels=2, n_timepoints=30)
diff --git a/aeon/clustering/_clara.py b/aeon/clustering/_clara.py
index 0121922fbb..4f44f5adab 100644
--- a/aeon/clustering/_clara.py
+++ b/aeon/clustering/_clara.py
@@ -211,7 +211,7 @@ def _score(self, X, y=None):
return -self.inertia_
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -227,7 +227,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
diff --git a/aeon/clustering/_clarans.py b/aeon/clustering/_clarans.py
index 0d15bc7b81..f1c9eff87b 100644
--- a/aeon/clustering/_clarans.py
+++ b/aeon/clustering/_clarans.py
@@ -185,7 +185,7 @@ def _fit(self, X: np.ndarray, y=None):
self.n_iter_ = 0
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -200,7 +200,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
diff --git a/aeon/clustering/_elastic_som.py b/aeon/clustering/_elastic_som.py
index 6b8899e29c..e7d7d34682 100644
--- a/aeon/clustering/_elastic_som.py
+++ b/aeon/clustering/_elastic_som.py
@@ -382,7 +382,7 @@ def _first_center_initializer(self, X: np.ndarray) -> np.ndarray:
return X[list(range(self.n_clusters))]
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -398,7 +398,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
diff --git a/aeon/clustering/_k_means.py b/aeon/clustering/_k_means.py
index c947be4a40..550d38944e 100644
--- a/aeon/clustering/_k_means.py
+++ b/aeon/clustering/_k_means.py
@@ -408,7 +408,7 @@ def _handle_empty_cluster(
return curr_pw, curr_labels, curr_inertia, cluster_centres
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -424,7 +424,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
diff --git a/aeon/clustering/_k_medoids.py b/aeon/clustering/_k_medoids.py
index 9a0a91ae9c..1f36f75ebe 100644
--- a/aeon/clustering/_k_medoids.py
+++ b/aeon/clustering/_k_medoids.py
@@ -541,7 +541,7 @@ def _pam_build_center_initializer(
return np.array(medoid_idxs)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -557,7 +557,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
diff --git a/aeon/clustering/_k_sc.py b/aeon/clustering/_k_sc.py
index 24caa8e96b..1ace94b245 100644
--- a/aeon/clustering/_k_sc.py
+++ b/aeon/clustering/_k_sc.py
@@ -130,7 +130,7 @@ def _check_params(self, X: np.ndarray) -> None:
self._average_params["max_shift"] = temp_max_shift
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -146,7 +146,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
diff --git a/aeon/clustering/_k_shape.py b/aeon/clustering/_k_shape.py
index 9bcf4d160c..3da2aca0cf 100644
--- a/aeon/clustering/_k_shape.py
+++ b/aeon/clustering/_k_shape.py
@@ -153,7 +153,7 @@ def _predict(self, X, y=None) -> np.ndarray:
return self._tslearn_k_shapes.predict(_X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -169,7 +169,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
diff --git a/aeon/clustering/_k_shapes.py b/aeon/clustering/_k_shapes.py
index 7d323c9a89..cdad58032a 100644
--- a/aeon/clustering/_k_shapes.py
+++ b/aeon/clustering/_k_shapes.py
@@ -154,7 +154,7 @@ def _predict(self, X, y=None) -> np.ndarray:
return self._tslearn_k_shapes.predict(_X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -170,7 +170,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py
index 23ab843004..6511c6a393 100644
--- a/aeon/clustering/_kernel_k_means.py
+++ b/aeon/clustering/_kernel_k_means.py
@@ -176,7 +176,7 @@ def _predict(self, X, y=None) -> np.ndarray:
return self._tslearn_kernel_k_means.predict(_X)
@classmethod
- def get_test_params(cls, parameter_set="default") -> dict:
+ def _get_test_params(cls, parameter_set="default") -> dict:
"""Return testing parameter settings for the estimator.
Parameters
@@ -192,7 +192,6 @@ def get_test_params(cls, parameter_set="default") -> dict:
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py
index 17231fdf1f..3cdb48e996 100644
--- a/aeon/clustering/base.py
+++ b/aeon/clustering/base.py
@@ -67,7 +67,7 @@ def fit(self, X, y=None) -> BaseCollectionEstimator:
return self
@final
- def predict(self, X, y=None) -> np.ndarray:
+ def predict(self, X) -> np.ndarray:
"""Predict the closest cluster each sample in X belongs to.
Parameters
@@ -81,7 +81,6 @@ def predict(self, X, y=None) -> np.ndarray:
of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``,
where ``n_timepoints_i`` is length of series ``i``. Other types are
allowed and converted into one of the above.
- y: ignored, exists for API consistency reasons.
Returns
-------
@@ -211,7 +210,7 @@ def _predict_proba(self, X) -> np.ndarray:
def _score(self, X, y=None): ...
@abstractmethod
- def _predict(self, X, y=None) -> np.ndarray:
+ def _predict(self, X) -> np.ndarray:
"""Predict the closest cluster each sample in X belongs to.
Parameters
@@ -219,7 +218,6 @@ def _predict(self, X, y=None) -> np.ndarray:
X : np.ndarray (2d or 3d array of shape (n_cases, n_timepoints) or shape
(n_cases,n_channels,n_timepoints)).
Time series instances to predict their cluster indexes.
- y: ignored, exists for API consistency reasons.
Returns
-------
diff --git a/aeon/clustering/compose/_pipeline.py b/aeon/clustering/compose/_pipeline.py
index 63f9c80534..bb972a3d68 100644
--- a/aeon/clustering/compose/_pipeline.py
+++ b/aeon/clustering/compose/_pipeline.py
@@ -4,7 +4,7 @@
__all__ = ["ClustererPipeline"]
-from aeon.base.estimator.compose.collection_pipeline import BaseCollectionPipeline
+from aeon.base.estimators.compose.collection_pipeline import BaseCollectionPipeline
from aeon.clustering import BaseClusterer
@@ -67,7 +67,7 @@ class ClustererPipeline(BaseCollectionPipeline, BaseClusterer):
>>> X_train, y_train = load_unit_test(split="train")
>>> X_test, y_test = load_unit_test(split="test")
>>> pipeline = ClustererPipeline(
- ... Resizer(length=10), TimeSeriesKMeans.create_test_instance()
+ ... Resizer(length=10), TimeSeriesKMeans._create_test_instance()
... )
>>> pipeline.fit(X_train, y_train)
ClustererPipeline(...)
@@ -92,7 +92,7 @@ def _score(self, X, y=None):
raise NotImplementedError("Pipeline does not support scoring.")
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -107,18 +107,15 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.clustering import TimeSeriesKMeans
from aeon.transformations.collection import Truncator
- from aeon.transformations.collection.feature_based import (
- SevenNumberSummaryTransformer,
- )
+ from aeon.transformations.collection.feature_based import SevenNumberSummary
return {
"transformers": [
Truncator(truncated_length=5),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
- "clusterer": TimeSeriesKMeans.create_test_instance(),
+ "clusterer": TimeSeriesKMeans._create_test_instance(),
}
diff --git a/aeon/clustering/compose/tests/test_pipeline.py b/aeon/clustering/compose/tests/test_pipeline.py
index 5225e1d2d8..b15633f62b 100644
--- a/aeon/clustering/compose/tests/test_pipeline.py
+++ b/aeon/clustering/compose/tests/test_pipeline.py
@@ -22,20 +22,20 @@
Tabularizer,
TimeSeriesScaler,
)
-from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer
+from aeon.transformations.collection.feature_based import SevenNumberSummary
@pytest.mark.parametrize(
"transformers",
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
[Padder(pad_length=15), Tabularizer(), StandardScaler()],
- [Padder(pad_length=15), SevenNumberSummaryTransformer()],
- [Tabularizer(), StandardScaler(), SevenNumberSummaryTransformer()],
+ [Padder(pad_length=15), SevenNumberSummary()],
+ [Tabularizer(), StandardScaler(), SevenNumberSummary()],
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
],
)
@@ -44,7 +44,7 @@ def test_clusterer_pipeline(transformers):
X_train, y_train = make_example_3d_numpy(n_cases=10, n_timepoints=12)
X_test, _ = make_example_3d_numpy(n_cases=10, n_timepoints=12)
- c = TimeSeriesKMeans.create_test_instance()
+ c = TimeSeriesKMeans._create_test_instance()
pipeline = ClustererPipeline(transformers=transformers, clusterer=c)
pipeline.fit(X_train, y_train)
c.fit(X_train, y_train)
@@ -67,14 +67,14 @@ def test_clusterer_pipeline(transformers):
"transformers",
[
[Padder(pad_length=15), Tabularizer()],
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
[Tabularizer(), StandardScaler()],
[Padder(pad_length=15), Tabularizer(), StandardScaler()],
- [Padder(pad_length=15), SevenNumberSummaryTransformer()],
- [Tabularizer(), StandardScaler(), SevenNumberSummaryTransformer()],
+ [Padder(pad_length=15), SevenNumberSummary()],
+ [Tabularizer(), StandardScaler(), SevenNumberSummary()],
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
],
)
@@ -107,7 +107,7 @@ def test_unequal_tag_inference():
n_cases=10, min_n_timepoints=8, max_n_timepoints=12
)
- t1 = SevenNumberSummaryTransformer()
+ t1 = SevenNumberSummary()
t2 = Padder()
t3 = TimeSeriesScaler()
t4 = AutocorrelationFunctionTransformer(n_lags=5)
@@ -228,7 +228,7 @@ def test_multivariate_tag_inference():
"""Test that ClustererPipeline infers multivariate tag correctly."""
X, y = make_example_3d_numpy(n_cases=10, n_channels=2, n_timepoints=12)
- t1 = SevenNumberSummaryTransformer()
+ t1 = SevenNumberSummary()
t2 = TimeSeriesScaler()
t3 = HOG1DTransformer()
t4 = StandardScaler()
@@ -240,7 +240,7 @@ def test_multivariate_tag_inference():
assert not t3.get_tag("capability:multivariate")
# todo revisit with mock clusterer
- c1 = TimeSeriesKMeans.create_test_instance()
+ c1 = TimeSeriesKMeans._create_test_instance()
# c2 = ContractableBOSS(n_parameter_samples=5, max_ensemble_size=3)
c3 = KMeans(n_clusters=2, max_iter=3, random_state=0)
diff --git a/aeon/clustering/deep_learning/__init__.py b/aeon/clustering/deep_learning/__init__.py
index 933aa34733..9194c1417e 100644
--- a/aeon/clustering/deep_learning/__init__.py
+++ b/aeon/clustering/deep_learning/__init__.py
@@ -1,6 +1,12 @@
"""Deep learning based clusterers."""
-__all__ = ["BaseDeepClusterer", "AEFCNClusterer", "AEResNetClusterer"]
+__all__ = [
+ "BaseDeepClusterer",
+ "AEBiGRUClusterer",
+ "AEFCNClusterer",
+ "AEResNetClusterer",
+]
+from aeon.clustering.deep_learning._ae_bgru import AEBiGRUClusterer
from aeon.clustering.deep_learning._ae_fcn import AEFCNClusterer
from aeon.clustering.deep_learning._ae_resnet import AEResNetClusterer
from aeon.clustering.deep_learning.base import BaseDeepClusterer
diff --git a/aeon/clustering/deep_learning/_ae_bgru.py b/aeon/clustering/deep_learning/_ae_bgru.py
new file mode 100644
index 0000000000..9b7df32716
--- /dev/null
+++ b/aeon/clustering/deep_learning/_ae_bgru.py
@@ -0,0 +1,322 @@
+"""Deep Learning Auto-Encoder using Bidirectional GRU Network."""
+
+__maintainer__ = []
+__all__ = ["AEBiGRUClusterer"]
+
+import gc
+import os
+import time
+from copy import deepcopy
+
+from sklearn.utils import check_random_state
+
+from aeon.clustering import DummyClusterer
+from aeon.clustering.deep_learning.base import BaseDeepClusterer
+from aeon.networks import AEBiGRUNetwork
+
+
+class AEBiGRUClusterer(BaseDeepClusterer):
+ """Auto-Encoder based Bidirectional GRU Network.
+
+ Parameters
+ ----------
+ n_clusters : int, default=None
+ Number of clusters for the deep learnign model.
+ clustering_algorithm : str, default="deprecated"
+ Use 'estimator' parameter instead.
+ clustering_params : dict, default=None
+ Use 'estimator' parameter instead.
+ estimator : aeon clusterer, default=None
+ An aeon estimator to be built using the transformed data.
+ Defaults to aeon TimeSeriesKMeans() with euclidean distance
+ and mean averaging method and n_clusters set to 2.
+ latent_space_dim : int, default=128
+ Dimension of the latent space of the auto-encoder.
+ temporal_latent_space : bool, default = False
+ Flag to choose whether the latent space is an MTS or Euclidean space.
+ n_layers : int, default = 2
+ Number of Bidirectional GRU Layers.
+ activation : str or list of str, default = "relu"
+ Activation used after the Bidirectional GRU Layer.
+ n_epochs : int, default = 2000
+ The number of epochs to train the model.
+ batch_size : int, default = 16
+ The number of samples per gradient update.
+ use_mini_batch_size : bool, default = True,
+ Whether or not to use the mini batch size formula.
+ random_state : int, RandomState instance or None, default=None
+ If `int`, random_state is the seed used by the random number generator;
+ If `RandomState` instance, random_state is the random number generator;
+ If `None`, the random number generator is the `RandomState` instance used
+ by `np.random`.
+ Seeded random number generation can only be guaranteed on CPU processing,
+ GPU processing will be non-deterministic.
+ verbose : boolean, default = False
+ Whether to output extra information.
+ loss : str, default="mean_squared_error"
+ Fit parameter for the keras model.
+ metrics : str, default=["mean_squared_error"]
+ Metrics to evaluate model predictions.
+ optimizer : keras.optimizers object, default = Adam(lr=0.01)
+ Specify the optimizer and the learning rate to be used.
+ file_path : str, default = "./"
+ File path to save best model.
+ save_best_model : bool, default = False
+ Whether or not to save the best model, if the
+ modelcheckpoint callback is used by default,
+ this condition, if True, will prevent the
+ automatic deletion of the best saved model from
+ file and the user can choose the file name.
+ save_last_model : bool, default = False
+ Whether or not to save the last model, last
+ epoch trained, using the base class method
+ save_last_model_to_file.
+ best_file_name : str, default = "best_model"
+ The name of the file of the best model, if
+ save_best_model is set to False, this parameter
+ is discarded.
+ last_file_name : str, default = "last_model"
+ The name of the file of the last model, if
+ save_last_model is set to False, this parameter
+ is discarded.
+ callbacks : keras.callbacks, default = None
+ List of keras callbacks.
+
+
+ Examples
+ --------
+ >>> from aeon.clustering.deep_learning import AEBiGRUClusterer
+ >>> from aeon.clustering import DummyClusterer
+ >>> from aeon.datasets import load_unit_test
+ >>> X_train, y_train = load_unit_test(split="train")
+ >>> X_test, y_test = load_unit_test(split="test")
+ >>> _clst = DummyClusterer(n_clusters=2)
+ >>> aebgru=AEBiGRUClusterer( estimator=_clst, n_epochs=20,
+ ... batch_size=4 ) # doctest: +SKIP
+ >>> aebgru.fit(X_train) # doctest: +SKIP
+ AEBiGRUClusterer(...)
+ """
+
+ def __init__(
+ self,
+ n_clusters=None,
+ clustering_algorithm="deprecated",
+ estimator=None,
+ clustering_params=None,
+ latent_space_dim=128,
+ temporal_latent_space=False,
+ n_layers=2,
+ n_units=None,
+ activation="relu",
+ n_epochs=2000,
+ batch_size=32,
+ use_mini_batch_size=False,
+ random_state=None,
+ verbose=False,
+ loss="mse",
+ metrics=None,
+ optimizer="Adam",
+ file_path="./",
+ save_best_model=False,
+ save_last_model=False,
+ best_file_name="best_model",
+ last_file_name="last_file",
+ callbacks=None,
+ ):
+ self.latent_space_dim = latent_space_dim
+ self.temporal_latent_space = temporal_latent_space
+ self.n_layers = n_layers
+ self.n_units = n_units
+ self.activation = activation
+ self.optimizer = optimizer
+ self.loss = loss
+ self.metrics = metrics
+ self.verbose = verbose
+ self.use_mini_batch_size = use_mini_batch_size
+ self.callbacks = callbacks
+ self.file_path = file_path
+ self.n_epochs = n_epochs
+ self.save_best_model = save_best_model
+ self.save_last_model = save_last_model
+ self.best_file_name = best_file_name
+ self.random_state = random_state
+ self.estimator = estimator
+
+ super().__init__(
+ n_clusters=n_clusters,
+ estimator=estimator,
+ batch_size=batch_size,
+ last_file_name=last_file_name,
+ )
+
+ self._network = AEBiGRUNetwork(
+ latent_space_dim=self.latent_space_dim,
+ n_layers=self.n_layers,
+ n_units=self.n_units,
+ activation=self.activation,
+ temporal_latent_space=self.temporal_latent_space,
+ )
+
+ def build_model(self, input_shape, **kwargs):
+ """Construct a compiled, un-trained, keras model that is ready for training.
+
+ In aeon, time series are stored in numpy arrays of shape
+ (n_channels,n_timepoints). Keras/tensorflow assume
+ data is in shape (n_timepoints,n_channels). This method also assumes
+ (n_timepoints,n_channels). Transpose should happen in fit.
+
+ Parameters
+ ----------
+ input_shape : tuple
+ The shape of the data fed into the input layer, should be
+ (n_timepoints,n_channels).
+
+ Returns
+ -------
+ output : a compiled Keras Model.
+ """
+ import numpy as np
+ import tensorflow as tf
+
+ rng = check_random_state(self.random_state)
+ self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
+ tf.keras.utils.set_random_seed(self.random_state_)
+ encoder, decoder = self._network.build_network(input_shape, **kwargs)
+
+ input_layer = tf.keras.layers.Input(input_shape, name="input layer")
+ encoder_output = encoder(input_layer)
+ decoder_output = decoder(encoder_output)
+ output_layer = tf.keras.layers.Reshape(
+ target_shape=input_shape, name="outputlayer"
+ )(decoder_output)
+
+ model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
+
+ self.optimizer_ = (
+ tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
+ )
+
+ if self.metrics is None:
+ self._metrics = ["mean_squared_error"]
+ elif isinstance(self.metrics, list):
+ self._metrics = self.metrics
+ elif isinstance(self.metrics, str):
+ self._metrics = [self.metrics]
+ else:
+ raise ValueError("Metrics should be a list, string, or None.")
+
+ model.compile(optimizer=self.optimizer_, loss=self.loss, metrics=self._metrics)
+
+ return model
+
+ def _fit(self, X):
+ """Fit the classifier on the training set (X, y).
+
+ Parameters
+ ----------
+ X : np.ndarray of shape = (n_cases (n), n_channels (d), n_timepoints (m))
+ The training input samples.
+
+ Returns
+ -------
+ self : object
+ """
+ import tensorflow as tf
+
+ # Transpose to conform to Keras input style.
+ X = X.transpose(0, 2, 1)
+
+ self.input_shape = X.shape[1:]
+ self.training_model_ = self.build_model(self.input_shape)
+
+ if self.verbose:
+ self.training_model_.summary()
+
+ if self.use_mini_batch_size:
+ mini_batch_size = min(self.batch_size, X.shape[0] // 10)
+ else:
+ mini_batch_size = self.batch_size
+
+ self.file_name_ = (
+ self.best_file_name if self.save_best_model else str(time.time_ns())
+ )
+
+ if self.callbacks is None:
+ self.callbacks_ = [
+ tf.keras.callbacks.ReduceLROnPlateau(
+ monitor="loss", factor=0.5, patience=50, min_lr=0.0001
+ ),
+ tf.keras.callbacks.ModelCheckpoint(
+ filepath=self.file_path + self.file_name_ + ".keras",
+ monitor="loss",
+ save_best_only=True,
+ ),
+ ]
+ else:
+ self.callbacks_ = self._get_model_checkpoint_callback(
+ callbacks=self.callbacks,
+ file_path=self.file_path,
+ file_name=self.file_name_,
+ )
+
+ self.history = self.training_model_.fit(
+ X,
+ X,
+ batch_size=mini_batch_size,
+ epochs=self.n_epochs,
+ verbose=self.verbose,
+ callbacks=self.callbacks_,
+ )
+
+ try:
+ self.model_ = tf.keras.models.load_model(
+ self.file_path + self.file_name_ + ".keras", compile=False
+ )
+ if not self.save_best_model:
+ os.remove(self.file_path + self.file_name_ + ".keras")
+ except FileNotFoundError:
+ self.model_ = deepcopy(self.training_model_)
+
+ self._fit_clustering(X=X)
+
+ gc.collect()
+
+ return self
+
+ def _score(self, X, y=None):
+ # Transpose to conform to Keras input style.
+ X = X.transpose(0, 2, 1)
+ latent_space = self.model_.layers[1].predict(X)
+ return self._estimator.score(latent_space)
+
+ @classmethod
+ def get_test_params(cls, parameter_set="default"):
+ """Return testing parameter settings for the estimator.
+
+ Parameters
+ ----------
+ parameter_set : str, default="default"
+ Name of the set of test parameters to return, for use in tests. If no
+ special parameters are defined for a value, will return `"default"` set.
+ For classifiers, a "default" set of parameters should be provided for
+ general testing, and a "results_comparison" set for comparing against
+ previously recorded results if the general set does not produce suitable
+ probabilities to compare against.
+
+ Returns
+ -------
+ params : dict or list of dict, default={}
+ Parameters to create testing instances of the class.
+ Each dict are parameters to construct an "interesting" test instance, i.e.,
+ `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+ `create_test_instance` uses the first (or only) dictionary in `params`.
+ """
+ param1 = {
+ "estimator": DummyClusterer(n_clusters=2),
+ "n_epochs": 1,
+ "batch_size": 4,
+ "n_layers": 1,
+ "n_units": 2,
+ }
+
+ return [param1]
diff --git a/aeon/clustering/deep_learning/_ae_fcn.py b/aeon/clustering/deep_learning/_ae_fcn.py
index 50089a6b05..70b55bb420 100644
--- a/aeon/clustering/deep_learning/_ae_fcn.py
+++ b/aeon/clustering/deep_learning/_ae_fcn.py
@@ -5,6 +5,7 @@
import gc
import os
+import sys
import time
from copy import deepcopy
@@ -66,6 +67,10 @@ class AEFCNClusterer(BaseDeepClusterer):
verbose : boolean, default = False
Whether to output extra information.
loss : string, default="mean_squared_error"
+ Fit parameter for the keras model. "multi_rec" for multiple mse loss.
+ Multiple mse loss computes mean squared error between all embeddings
+ of encoder layers with the corresponding reconstructions of the
+ decoder layers.
Fit parameter for the keras model.
metrics : keras metrics, default = ["mean_squared_error"]
will be set to mean_squared_error as default if None
@@ -296,18 +301,29 @@ def _fit(self, X):
file_name=self.file_name_,
)
- self.history = self.training_model_.fit(
- X,
- X,
- batch_size=mini_batch_size,
- epochs=self.n_epochs,
- verbose=self.verbose,
- callbacks=self.callbacks_,
- )
+ if not self.loss == "multi_rec":
+ self.history = self.training_model_.fit(
+ X,
+ X,
+ batch_size=mini_batch_size,
+ epochs=self.n_epochs,
+ verbose=self.verbose,
+ callbacks=self.callbacks_,
+ )
+
+ elif self.loss == "multi_rec":
+ self.history = self._fit_multi_rec_model(
+ autoencoder=self.training_model_,
+ inputs=X,
+ outputs=X,
+ batch_size=mini_batch_size,
+ epochs=self.n_epochs,
+ )
try:
self.model_ = tf.keras.models.load_model(
- self.file_path + self.file_name_ + ".keras", compile=False
+ self.file_path + self.file_name_ + ".keras",
+ compile=False,
)
if not self.save_best_model:
os.remove(self.file_path + self.file_name_ + ".keras")
@@ -326,8 +342,134 @@ def _score(self, X, y=None):
latent_space = self.model_.layers[1].predict(X)
return self._estimator.score(latent_space)
+ def _fit_multi_rec_model(
+ self,
+ autoencoder,
+ inputs,
+ outputs,
+ batch_size,
+ epochs,
+ ):
+ import tensorflow as tf
+
+ train_dataset = tf.data.Dataset.from_tensor_slices((inputs, outputs))
+ train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)
+
+ if isinstance(self.optimizer_, str):
+ self.optimizer_ = tf.keras.optimizers.get(self.optimizer_)
+
+ history = {"loss": []}
+
+ def layerwise_mse_loss(autoencoder, inputs, outputs):
+ def loss(y_true, y_pred):
+ # Calculate MSE for each layer in the encoder and decoder
+ mse = 0
+
+ _encoder_intermediate_outputs = (
+ []
+ ) # Store embeddings of each layer in the Encoder
+ _decoder_intermediate_outputs = (
+ []
+ ) # Store embeddings of each layer in the Decoder
+
+ encoder = autoencoder.layers[1] # Returns Functional API Models.
+ decoder = autoencoder.layers[2] # Returns Functional API Models.
+
+ # Run the models since the below given loop misses the latent space
+ # layer which doesn't contribute to the loss.
+ logits = encoder(inputs)
+ __dec_outputs = decoder(logits)
+
+ # Encoder
+ for i in range(self.n_layers):
+ _activation_layer = encoder.get_layer(f"__act_encoder_block{i}")
+ _model = tf.keras.models.Model(
+ inputs=encoder.input, outputs=_activation_layer.output
+ )
+ __output = _model(inputs, training=True)
+ _encoder_intermediate_outputs.append(__output)
+
+ # Decoder
+ for i in range(self.n_layers):
+ _activation_layer = decoder.get_layer(f"__act_decoder_block{i}")
+ _model = tf.keras.models.Model(
+ inputs=decoder.input, outputs=_activation_layer.output
+ )
+ __output = _model(logits, training=True)
+ _decoder_intermediate_outputs.append(__output)
+
+ if not (
+ len(_encoder_intermediate_outputs)
+ == len(_decoder_intermediate_outputs)
+ ):
+ raise ValueError("The Auto-Encoder must be symmetric in nature.")
+
+ # # Append normal mean_squared_error
+
+ for enc_output, dec_output in zip(
+ _encoder_intermediate_outputs, _decoder_intermediate_outputs
+ ):
+ mse += tf.keras.backend.mean(
+ tf.keras.backend.square(enc_output - dec_output)
+ )
+
+ inputs_casted = tf.cast(inputs, dtype=tf.float64)
+ __dec_outputs_casted = tf.cast(__dec_outputs, dtype=tf.float64)
+ return tf.cast(mse, dtype=tf.float64) + tf.cast(
+ tf.reduce_mean(tf.square(inputs_casted - __dec_outputs_casted)),
+ dtype=tf.float64,
+ )
+
+ return loss
+
+ # Initialize callbacks
+ for callback in self.callbacks_:
+ callback.set_model(autoencoder)
+ callback.on_train_begin()
+
+ for epoch in range(epochs):
+ epoch_loss = 0
+ num_batches = 0
+ for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
+ with tf.GradientTape() as tape:
+ # Calculate the actual loss by calling the loss function
+ loss_func = layerwise_mse_loss(
+ autoencoder=autoencoder,
+ inputs=x_batch_train,
+ outputs=y_batch_train,
+ )
+ loss_value = loss_func(y_batch_train, autoencoder(x_batch_train))
+
+ grads = tape.gradient(loss_value, autoencoder.trainable_weights)
+ self.optimizer_.apply_gradients(
+ zip(grads, autoencoder.trainable_weights)
+ )
+
+ epoch_loss += float(loss_value)
+ num_batches += 1
+
+ # Update callbacks on batch end
+ for callback in self.callbacks_:
+ callback.on_batch_end(step, {"loss": float(loss_value)})
+
+ epoch_loss /= num_batches
+ history["loss"].append(epoch_loss)
+
+ sys.stdout.write(
+ "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss))
+ )
+
+ for callback in self.callbacks_:
+ callback.on_epoch_end(epoch, {"loss": float(epoch_loss)})
+
+ # Finalize callbacks
+ for callback in self.callbacks_:
+ callback.on_train_end()
+
+ return history
+
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -346,7 +488,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 1,
diff --git a/aeon/clustering/deep_learning/_ae_resnet.py b/aeon/clustering/deep_learning/_ae_resnet.py
index 170e57b268..d9f3ebd52e 100644
--- a/aeon/clustering/deep_learning/_ae_resnet.py
+++ b/aeon/clustering/deep_learning/_ae_resnet.py
@@ -5,6 +5,7 @@
import gc
import os
+import sys
import time
from copy import deepcopy
@@ -99,7 +100,10 @@ class method save_last_model_to_file.
verbose : boolean, default = False
whether to output extra information
loss : string, default = "mean_squared_error"
- fit parameter for the keras model.
+ fit parameter for the keras model. "multi_rec" for multiple mse loss.
+ Multiple mse loss computes mean squared error between all embeddings
+ of encoder layers with the corresponding reconstructions of the
+ decoder layers.
optimizer : keras.optimizer, default = keras.optimizers.Adam()
metrics : list of strings, default = ["mean_squared_error"]
will be set to mean_squared_error as default if None
@@ -310,14 +314,24 @@ def _fit(self, X):
else:
mini_batch_size = self.batch_size
- self.history = self.training_model_.fit(
- X,
- X,
- batch_size=mini_batch_size,
- epochs=self.n_epochs,
- verbose=self.verbose,
- callbacks=self.callbacks_,
- )
+ if not self.loss == "multi_rec":
+ self.history = self.training_model_.fit(
+ X,
+ X,
+ batch_size=mini_batch_size,
+ epochs=self.n_epochs,
+ verbose=self.verbose,
+ callbacks=self.callbacks_,
+ )
+
+ elif self.loss == "multi_rec":
+ self.history = self._fit_multi_rec_model(
+ autoencoder=self.training_model_,
+ inputs=X,
+ outputs=X,
+ batch_size=mini_batch_size,
+ epochs=self.n_epochs,
+ )
try:
self.model_ = tf.keras.models.load_model(
@@ -342,8 +356,132 @@ def _score(self, X, y=None):
latent_space = self.model_.layers[1].predict(X)
return self._estimator.score(latent_space)
+ def _fit_multi_rec_model(
+ self,
+ autoencoder,
+ inputs,
+ outputs,
+ batch_size,
+ epochs,
+ ):
+ import tensorflow as tf
+
+ train_dataset = tf.data.Dataset.from_tensor_slices((inputs, outputs))
+ train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)
+
+ if isinstance(self.optimizer_, str):
+ self.optimizer_ = tf.keras.optimizers.get(self.optimizer_)
+
+ history = {"loss": []}
+
+ def layerwise_mse_loss(autoencoder, inputs, outputs):
+ def loss(y_true, y_pred):
+ # Calculate MSE for each layer in the encoder and decoder
+ mse = 0
+
+ _encoder_intermediate_outputs = (
+ []
+ ) # Store embeddings of each layer in the Encoder
+ _decoder_intermediate_outputs = (
+ []
+ ) # Store embeddings of each layer in the Decoder
+
+ encoder = autoencoder.layers[1] # Returns Functional API Models.
+ decoder = autoencoder.layers[2] # Returns Functional API Models.
+
+ # Run the models since the below given loop misses the latent space
+ # layer which doesn't contribute to the loss.
+ logits = encoder(inputs)
+ __dec_outputs = decoder(logits)
+
+ # Encoder
+ for i in range(self.n_residual_blocks):
+ _activation_layer = encoder.get_layer(f"__act_encoder_block{i}")
+ _model = tf.keras.models.Model(
+ inputs=encoder.input, outputs=_activation_layer.output
+ )
+ __output = _model(inputs, training=True)
+ _encoder_intermediate_outputs.append(__output)
+
+ # Decoder
+ for i in range(self.n_residual_blocks):
+ _activation_layer = decoder.get_layer(f"__act_decoder_block{i}")
+ _model = tf.keras.models.Model(
+ inputs=decoder.input, outputs=_activation_layer.output
+ )
+ __output = _model(logits, training=True)
+ _decoder_intermediate_outputs.append(__output)
+
+ if not (
+ len(_encoder_intermediate_outputs)
+ == len(_decoder_intermediate_outputs)
+ ):
+ raise ValueError("The Auto-Encoder must be symmetric in nature.")
+
+ for enc_output, dec_output in zip(
+ _encoder_intermediate_outputs, _decoder_intermediate_outputs
+ ):
+ mse += tf.keras.backend.mean(
+ tf.keras.backend.square(enc_output - dec_output)
+ )
+
+ inputs_casted = tf.cast(inputs, tf.float64)
+ __dec_outputs_casted = tf.cast(__dec_outputs, tf.float64)
+ return tf.cast(mse, tf.float64) + tf.cast(
+ tf.reduce_mean(tf.square(inputs_casted - __dec_outputs_casted)),
+ tf.float64,
+ )
+
+ return loss
+
+ # Initialize callbacks
+ for callback in self.callbacks_:
+ callback.set_model(autoencoder)
+ callback.on_train_begin()
+
+ for epoch in range(epochs):
+ epoch_loss = 0
+ num_batches = 0
+ for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
+ with tf.GradientTape() as tape:
+ # Calculate the actual loss by calling the loss function
+ loss_func = layerwise_mse_loss(
+ autoencoder=autoencoder,
+ inputs=x_batch_train,
+ outputs=y_batch_train,
+ )
+ loss_value = loss_func(y_batch_train, autoencoder(x_batch_train))
+
+ grads = tape.gradient(loss_value, autoencoder.trainable_weights)
+ self.optimizer_.apply_gradients(
+ zip(grads, autoencoder.trainable_weights)
+ )
+
+ epoch_loss += float(loss_value)
+ num_batches += 1
+
+ # Update callbacks on batch end
+ for callback in self.callbacks_:
+ callback.on_batch_end(step, {"loss": float(loss_value)})
+
+ epoch_loss /= num_batches
+ history["loss"].append(epoch_loss)
+
+ sys.stdout.write(
+ "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss))
+ )
+
+ for callback in self.callbacks_:
+ callback.on_epoch_end(epoch, {"loss": float(epoch_loss)})
+
+ # Finalize callbacks
+ for callback in self.callbacks_:
+ callback.on_train_end()
+
+ return history
+
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -362,7 +500,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param = {
"n_epochs": 1,
diff --git a/aeon/clustering/deep_learning/tests/test_clusterer_features.py b/aeon/clustering/deep_learning/tests/test_clusterer_features.py
new file mode 100644
index 0000000000..4305f128cf
--- /dev/null
+++ b/aeon/clustering/deep_learning/tests/test_clusterer_features.py
@@ -0,0 +1,26 @@
+"""Tests whether various clusterer params work well."""
+
+import numpy as np
+import pytest
+
+from aeon.clustering.deep_learning import AEFCNClusterer, AEResNetClusterer
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="Tensorflow soft dependency not found.",
+)
+def test_multi_rec_fcn():
+ """Tests whether multi-rec loss works fine or not."""
+ X = np.random.random((100, 5, 2))
+ clst = AEFCNClusterer(
+ n_clusters=2, n_epochs=10, n_filters=[2, 3, 4], loss="multi_rec"
+ )
+ clst.fit(X)
+ assert (
+ clst.history["loss"][0] > clst.history["loss"][9]
+ ) # Check if loss is decreasing.
+ clst = AEResNetClusterer(n_clusters=2, n_epochs=10, loss="multi_rec")
+ clst.fit(X)
+ assert clst.history["loss"][0] > clst.history["loss"][9]
diff --git a/aeon/clustering/feature_based/_catch22.py b/aeon/clustering/feature_based/_catch22.py
index 6c716e249d..0b6b2e32fa 100644
--- a/aeon/clustering/feature_based/_catch22.py
+++ b/aeon/clustering/feature_based/_catch22.py
@@ -218,7 +218,7 @@ def _score(self, X, y=None):
raise NotImplementedError("Catch22Clusterer does not support scoring.")
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -233,7 +233,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"features": (
diff --git a/aeon/clustering/feature_based/_summary.py b/aeon/clustering/feature_based/_summary.py
index 8c68142ea1..26bb296f0e 100644
--- a/aeon/clustering/feature_based/_summary.py
+++ b/aeon/clustering/feature_based/_summary.py
@@ -11,7 +11,7 @@
from aeon.base._base import _clone_estimator
from aeon.clustering import BaseClusterer
-from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer
+from aeon.transformations.collection.feature_based import SevenNumberSummary
class SummaryClusterer(BaseClusterer):
@@ -19,7 +19,7 @@ class SummaryClusterer(BaseClusterer):
Summary statistic clusterer.
This clusterer simply transforms the input data using the
- SevenNumberSummaryTransformer transformer and builds a provided estimator using the
+ SevenNumberSummary transformer and builds a provided estimator using the
transformed data.
Parameters
@@ -105,7 +105,7 @@ def _fit(self, X, y=None):
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
- self._transformer = SevenNumberSummaryTransformer(
+ self._transformer = SevenNumberSummary(
summary_stats=self.summary_stats,
)
diff --git a/aeon/clustering/feature_based/_tsfresh.py b/aeon/clustering/feature_based/_tsfresh.py
index d708afa678..503638e239 100644
--- a/aeon/clustering/feature_based/_tsfresh.py
+++ b/aeon/clustering/feature_based/_tsfresh.py
@@ -7,12 +7,14 @@
__all__ = ["TSFreshClusterer"]
+from typing import Optional
+
import numpy as np
from sklearn.cluster import KMeans
from aeon.base._base import _clone_estimator
from aeon.clustering import BaseClusterer
-from aeon.transformations.collection.feature_based import TSFreshFeatureExtractor
+from aeon.transformations.collection.feature_based import TSFresh
class TSFreshClusterer(BaseClusterer):
@@ -43,10 +45,12 @@ class TSFreshClusterer(BaseClusterer):
If `RandomState` instance, random_state is the random number generator;
If `None`, the random number generator is the `RandomState` instance used
by `np.random`.
+ n_clusters : int, default=8
+ Number of clusters for KMeans (or other estimators that support n_clusters).
See Also
--------
- TSFreshFeatureExtractor
+ TSFresh
References
----------
@@ -76,12 +80,13 @@ class TSFreshClusterer(BaseClusterer):
def __init__(
self,
- default_fc_parameters="efficient",
+ default_fc_parameters: str = "efficient",
estimator=None,
- verbose=0,
- n_jobs=1,
- chunksize=None,
- random_state=None,
+ verbose: int = 0,
+ n_jobs: int = 1,
+ chunksize: Optional[int] = None,
+ random_state: Optional[int] = None,
+ n_clusters: int = 8, # Default value as 8
):
self.default_fc_parameters = default_fc_parameters
self.estimator = estimator
@@ -90,13 +95,14 @@ def __init__(
self.n_jobs = n_jobs
self.chunksize = chunksize
self.random_state = random_state
+ self.n_clusters = n_clusters
self._transformer = None
self._estimator = None
super().__init__()
- def _fit(self, X, y=None):
+ def _fit(self, X: np.ndarray, y: Optional[np.ndarray] = None):
"""Fit a pipeline on cases X.
Parameters
@@ -116,15 +122,26 @@ def _fit(self, X, y=None):
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
- self._transformer = TSFreshFeatureExtractor(
+ self._transformer = TSFresh(
default_fc_parameters=self.default_fc_parameters,
n_jobs=self._n_jobs,
chunksize=self.chunksize,
)
- self._estimator = _clone_estimator(
- (KMeans() if self.estimator is None else self.estimator),
- self.random_state,
- )
+
+ n_clusters = 8 if self.n_clusters is None else self.n_clusters
+
+ if self.estimator is None:
+ self._estimator = _clone_estimator(
+ KMeans(n_clusters=n_clusters), self.random_state
+ )
+ else:
+ if (
+ hasattr(self.estimator, "n_clusters")
+ and self.estimator.n_clusters is None
+ ):
+ self.estimator.n_clusters = self.n_clusters
+
+ self._estimator = _clone_estimator(self.estimator, self.random_state)
if self.verbose < 2:
self._transformer.show_warnings = False
@@ -147,7 +164,7 @@ def _fit(self, X, y=None):
return self
- def _predict(self, X) -> np.ndarray:
+ def _predict(self, X: np.ndarray) -> np.ndarray:
"""Predict class values of n instances in X.
Parameters
@@ -162,7 +179,7 @@ def _predict(self, X) -> np.ndarray:
"""
return self._estimator.predict(self._transformer.transform(X))
- def _predict_proba(self, X) -> np.ndarray:
+ def _predict_proba(self, X: np.ndarray) -> np.ndarray:
"""Predict class values of n instances in X.
Parameters
@@ -194,11 +211,11 @@ def _predict_proba(self, X) -> np.ndarray:
dists[i, preds[i]] = 1
return dists
- def _score(self, X, y=None):
+ def _score(self, X: np.ndarray, y: Optional[np.ndarray] = None):
raise NotImplementedError("TSFreshClusterer does not support scoring.")
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set: str = "default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -213,8 +230,8 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"default_fc_parameters": "minimal",
+ "n_clusters": 3,
}
diff --git a/aeon/clustering/tests/test_elastic_som.py b/aeon/clustering/tests/test_elastic_som.py
index 71a223409c..5d5ef47630 100644
--- a/aeon/clustering/tests/test_elastic_som.py
+++ b/aeon/clustering/tests/test_elastic_som.py
@@ -5,7 +5,7 @@
from aeon.clustering import ElasticSOM
from aeon.distances import dtw_distance, msm_alignment_path
-from aeon.distances._distance import DISTANCES
+from aeon.distances._distance import ELASTIC_DISTANCES
from aeon.testing.data_generation import make_example_3d_numpy
@@ -245,7 +245,7 @@ def custom_neighborhood_function(neuron_position, c, sigma):
clst.fit(X)
-@pytest.mark.parametrize("dist", DISTANCES)
+@pytest.mark.parametrize("dist", ELASTIC_DISTANCES)
def test_elastic_som_distances(dist):
"""Test ElasticSOM distances."""
if "distance" not in dist:
diff --git a/aeon/datasets/__init__.py b/aeon/datasets/__init__.py
index 911838f28c..c81fa896a6 100644
--- a/aeon/datasets/__init__.py
+++ b/aeon/datasets/__init__.py
@@ -36,7 +36,6 @@
"load_gun_point_segmentation",
"load_electric_devices_segmentation",
"load_acsf1",
- "load_macroeconomic",
"load_unit_test_tsf",
"load_solar",
"load_cardano_sentiment",
@@ -73,7 +72,6 @@
load_japanese_vowels,
load_longley,
load_lynx,
- load_macroeconomic,
load_osuleaf,
load_PBS_dataset,
load_plaid,
diff --git a/aeon/datasets/_single_problem_loaders.py b/aeon/datasets/_single_problem_loaders.py
index 3d0a48379a..b073823df9 100644
--- a/aeon/datasets/_single_problem_loaders.py
+++ b/aeon/datasets/_single_problem_loaders.py
@@ -20,7 +20,6 @@
"load_PBS_dataset",
"load_gun_point_segmentation",
"load_electric_devices_segmentation",
- "load_macroeconomic",
"load_unit_test_tsf",
"load_covid_3month",
]
@@ -32,7 +31,6 @@
from aeon.datasets import load_from_tsf_file
from aeon.datasets._data_loaders import _load_saved_dataset, _load_tsc_dataset
-from aeon.utils.validation._dependencies import _check_soft_dependencies
DIRNAME = "data"
MODULE = os.path.dirname(__file__)
@@ -509,25 +507,149 @@ def load_cardano_sentiment(split=None, return_type="numpy3d"):
return X, y
+def load_gun_point_segmentation():
+ """Load the GunPoint time series segmentation problem and returns X.
+
+ We group TS of the UCR GunPoint dataset by class label and concatenate
+ all TS to create segments with repeating temporal patterns and
+ characteristics. The location at which different classes were
+ concatenated are marked as change points.
+
+ We resample the resulting TS to control the TS resolution.
+ The window sizes for these datasets are hand-selected to capture
+ temporal patterns but are approximate and limited to the values
+ [10,20,50,100] to avoid over-fitting.
+
+ Returns
+ -------
+ X : pd.Series
+ Single time series for segmentation
+ period_length : int
+ The annotated period length by a human expert
+ change_points : numpy array
+ The change points annotated within the dataset
+
+ Examples
+ --------
+ >>> from aeon.datasets import load_gun_point_segmentation
+ >>> X, period_length, change_points = load_gun_point_segmentation()
+ """
+ dir = "segmentation"
+ name = "GunPoint"
+ fname = name + ".csv"
+
+ period_length = int(10)
+ change_points = np.int32([900])
+
+ path = os.path.join(MODULE, DIRNAME, dir, fname)
+ ts = pd.read_csv(path, index_col=0, header=None).squeeze("columns")
+
+ return ts, period_length, change_points
+
+
+def load_electric_devices_segmentation():
+ """Load the Electric Devices segmentation problem and returns X.
+
+ We group TS of the UCR Electric Devices dataset by class label and concatenate
+ all TS to create segments with repeating temporal patterns and
+ characteristics. The location at which different classes were
+ concatenated are marked as change points.
+
+ We resample the resulting TS to control the TS resolution.
+ The window sizes for these datasets are hand-selected to capture
+ temporal patterns but are approximate and limited to the values
+ [10,20,50,100] to avoid over-fitting.
+
+ Returns
+ -------
+ X : pd.Series
+ Single time series for segmentation
+ period_length : int
+ The annotated period length by a human expert
+ change_points : numpy array
+ The change points annotated within the dataset
+
+ Examples
+ --------
+ >>> from aeon.datasets import load_electric_devices_segmentation
+ >>> X, period_length, change_points = load_electric_devices_segmentation()
+ """
+ dir = "segmentation"
+ name = "ElectricDevices"
+ fname = name + ".csv"
+
+ period_length = int(10)
+ change_points = np.int32([1090, 4436, 5712, 7923])
+
+ path = os.path.join(MODULE, DIRNAME, dir, fname)
+ ts = pd.read_csv(path, index_col=0, header=None).squeeze("columns")
+
+ return ts, period_length, change_points
+
+
+def load_unit_test_tsf(return_type="tsf_default"):
+ """
+ Load tsf UnitTest dataset.
+
+ Parameters
+ ----------
+ return_type : str - "pd_multiindex_hier" or "tsf_default" (default)
+ - "tsf_default" = container that faithfully mirrors tsf format from the original
+ implementation in: https://github.com/rakshitha123/TSForecasting/
+ blob/master/utils/data_loader.py.
+
+ Returns
+ -------
+ loaded_data: pd.DataFrame
+ The converted dataframe containing the time series.
+ frequency: str
+ The frequency of the dataset.
+ forecast_horizon: int
+ The expected forecast horizon of the dataset.
+ contain_missing_values: bool
+ Whether the dataset contains missing values or not.
+ contain_equal_length: bool
+ Whether the series have equal lengths or not.
+ """
+ path = os.path.join(MODULE, DIRNAME, "UnitTest", "UnitTest_Tsf_Loader.tsf")
+ data, meta = load_from_tsf_file(path, return_type=return_type)
+ return (
+ data,
+ meta["frequency"],
+ meta["forecast_horizon"],
+ meta["contain_missing_values"],
+ meta["contain_equal_length"],
+ )
+
+
# forecasting data sets
-def load_shampoo_sales():
+def load_shampoo_sales(return_array=True):
"""Load the shampoo sales univariate time series dataset for forecasting.
+ Parameters
+ ----------
+ return_array : bool, default=True
+ return series as an np.ndarray if True, else as a pd.Series.
+
Returns
-------
- y : pd.Series/DataFrame
+ np.ndarray or pd.Series
Shampoo sales dataset
Examples
--------
>>> from aeon.datasets import load_shampoo_sales
>>> y = load_shampoo_sales()
+ >>> type(y)
+
+ >>> y = load_shampoo_sales(return_array=False)
+ >>> type(y)
+
Notes
-----
This dataset describes the monthly number of sales of shampoo over a 3
- year period.
- The units are a sales count.
+ year period. The units are a sales count.
Dimensionality: univariate
Series length: 36
@@ -544,82 +666,35 @@ def load_shampoo_sales():
fname = name + ".csv"
path = os.path.join(MODULE, DIRNAME, name, fname)
y = pd.read_csv(path, index_col=0, dtype={1: float}).squeeze("columns")
+ if return_array:
+ return y.values
y.index = pd.PeriodIndex(y.index, freq="M", name="Period")
y.name = "Number of shampoo sales"
return y
-def load_longley(y_name="TOTEMP"):
- """Load the Longley dataset for forecasting with exogenous variables.
+def load_lynx(return_array=True):
+ """Load the lynx univariate time series dataset for forecasting.
Parameters
----------
- y_name: str, default="TOTEMP"
- Name of target variable (y)
-
- Returns
- -------
- y: pd.Series
- The target series to be predicted.
- X: pd.DataFrame
- The exogenous time series data for the problem.
-
- Examples
- --------
- >>> from aeon.datasets import load_longley
- >>> y, X = load_longley()
-
- Notes
- -----
- This mulitvariate time series dataset contains various US macroeconomic
- variables from 1947 to 1962 that are known to be highly collinear.
-
- Dimensionality: multivariate, 6
- Series length: 16
- Frequency: Yearly
- Number of cases: 1
-
- Variable description:
-
- TOTEMP - Total employment
- GNPDEFL - Gross national product deflator
- GNP - Gross national product
- UNEMP - Number of unemployed
- ARMED - Size of armed forces
- POP - Population
-
- References
- ----------
- .. [1] Longley, J.W. (1967) "An Appraisal of Least Squares Programs for the
- Electronic Computer from the Point of View of the User." Journal of
- the American Statistical Association. 62.319, 819-41.
- (https://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat)
- """
- name = "Longley"
- fname = name + ".csv"
- path = os.path.join(MODULE, DIRNAME, name, fname)
- data = pd.read_csv(path, index_col=0)
- data = data.set_index("YEAR")
- data.index = pd.PeriodIndex(data.index, freq="Y", name="Period")
- data = data.astype(float)
-
- # Get target series
- y = data.pop(y_name)
- return y, data
-
-
-def load_lynx():
- """Load the lynx univariate time series dataset for forecasting.
+ return_array : bool, default=True
+ return series as an np.ndarray if True, else as a pd.Series.
Returns
-------
- y : pd.Series/DataFrame
+ np.ndarray or pd.Series/DataFrame
Lynx sales dataset
Examples
--------
>>> from aeon.datasets import load_lynx
>>> y = load_lynx()
+ >>> type(y)
+
+ >>> y = load_lynx(return_array=False)
+ >>> type(y)
+
Notes
-----
@@ -653,23 +728,35 @@ def load_lynx():
fname = name + ".csv"
path = os.path.join(MODULE, DIRNAME, name, fname)
y = pd.read_csv(path, index_col=0, dtype={1: float}).squeeze("columns")
+ if return_array:
+ return y.values
y.index = pd.PeriodIndex(y.index, freq="Y", name="Period")
y.name = "Number of Lynx trappings"
return y
-def load_airline():
+def load_airline(return_array=True):
"""Load the airline univariate time series dataset [1].
+ Parameters
+ ----------
+ return_array : bool, default=True
+ return series as an np.ndarray if True, else as a pd.Series.
+
Returns
-------
- y : pd.Series
- Time series
+ np.ndarray or pd.Series
+ Airline time series
Examples
--------
>>> from aeon.datasets import load_airline
>>> y = load_airline()
+ >>> type(y)
+
+ >>> y = load_airline(return_array=False)
+ >>> type(y)
+
Notes
-----
@@ -694,155 +781,67 @@ def load_airline():
fname = name + ".csv"
path = os.path.join(MODULE, DIRNAME, name, fname)
y = pd.read_csv(path, index_col=0, dtype={1: float}).squeeze("columns")
-
+ if return_array:
+ return y.values
# make sure time index is properly formatted
y.index = pd.PeriodIndex(y.index, freq="M", name="Period")
y.name = "Number of airline passengers"
return y
-def load_uschange(y_name="Consumption"):
- """Load MTS dataset for forecasting Growth rates of personal consumption and income.
-
- Returns
- -------
- y : pd.Series
- selected column, default consumption
- X : pd.DataFrame
- columns with explanatory variables
-
- Examples
- --------
- >>> from aeon.datasets import load_uschange
- >>> y, X = load_uschange()
-
- Notes
- -----
- Percentage changes in quarterly personal consumption expenditure,
- personal disposable income, production, savings and the
- unemployment rate for the US, 1960 to 2016.
-
+def load_solar(return_array=True):
+ """Get national solar estimates for GB from Sheffield Solar PV_Live API.
- Dimensionality: multivariate
- Columns: ['Quarter', 'Consumption', 'Income', 'Production',
- 'Savings', 'Unemployment']
- Series length: 188
- Frequency: Quarterly
- Number of cases: 1
+ This function calls the Sheffield Solar PV_Live API to extract national solar data
+ for the GB eletricity network. Note that these are estimates of the true solar
+ generation, since the true values are "behind the meter" and essentially
+ unknown.
- This data shows an increasing trend, non-constant (increasing) variance
- and periodic, seasonal patterns.
+ The returned time series is half hourly. For more information please refer
+ to [1]_.
- References
+ Parameters
----------
- .. [1] Data for "Forecasting: Principles and Practice" (2nd Edition)
- """
- name = "Uschange"
- fname = name + ".csv"
- path = os.path.join(MODULE, DIRNAME, name, fname)
- data = pd.read_csv(path, index_col=0).squeeze("columns")
-
- # Sort by Quarter then set simple numeric index
- # TODO add support for period/datetime indexing
- # data.index = pd.PeriodIndex(data.index, freq='Y')
- data = data.sort_values("Quarter")
- data = data.reset_index(drop=True)
- data.index = pd.Index(data.index, dtype=int)
- data.name = name
- y = data[y_name]
- if y_name != "Quarter":
- data = data.drop("Quarter", axis=1)
- X = data.drop(y_name, axis=1)
- return y, X
-
-
-def load_gun_point_segmentation():
- """Load the GunPoint time series segmentation problem and returns X.
-
- We group TS of the UCR GunPoint dataset by class label and concatenate
- all TS to create segments with repeating temporal patterns and
- characteristics. The location at which different classes were
- concatenated are marked as change points.
-
- We resample the resulting TS to control the TS resolution.
- The window sizes for these datasets are hand-selected to capture
- temporal patterns but are approximate and limited to the values
- [10,20,50,100] to avoid over-fitting.
+ return_array : bool, default=True
+ return series as an np.ndarray if True, else as a pd.Series.
Returns
-------
- X : pd.Series
- Single time series for segmentation
- period_length : int
- The annotated period length by a human expert
- change_points : numpy array
- The change points annotated within the dataset
-
- Examples
- --------
- >>> from aeon.datasets import load_gun_point_segmentation
- >>> X, period_length, change_points = load_gun_point_segmentation()
- """
- dir = "segmentation"
- name = "GunPoint"
- fname = name + ".csv"
-
- period_length = int(10)
- change_points = np.int32([900])
-
- path = os.path.join(MODULE, DIRNAME, dir, fname)
- ts = pd.read_csv(path, index_col=0, header=None).squeeze("columns")
-
- return ts, period_length, change_points
-
-
-def load_electric_devices_segmentation():
- """Load the Electric Devices segmentation problem and returns X.
-
- We group TS of the UCR Electric Devices dataset by class label and concatenate
- all TS to create segments with repeating temporal patterns and
- characteristics. The location at which different classes were
- concatenated are marked as change points.
+ np.ndarray or pd.Series
+ Example Sheffield solar time series
- We resample the resulting TS to control the TS resolution.
- The window sizes for these datasets are hand-selected to capture
- temporal patterns but are approximate and limited to the values
- [10,20,50,100] to avoid over-fitting.
-
- Returns
- -------
- X : pd.Series
- Single time series for segmentation
- period_length : int
- The annotated period length by a human expert
- change_points : numpy array
- The change points annotated within the dataset
+ References
+ ----------
+ .. [1] https://www.solar.sheffield.ac.uk/pvlive/
Examples
--------
- >>> from aeon.datasets import load_electric_devices_segmentation
- >>> X, period_length, change_points = load_electric_devices_segmentation()
+ >>> from aeon.datasets import load_solar # doctest: +SKIP
+ >>> y = load_solar() # doctest: +SKIP
"""
- dir = "segmentation"
- name = "ElectricDevices"
+ name = "solar"
fname = name + ".csv"
-
- period_length = int(10)
- change_points = np.int32([1090, 4436, 5712, 7923])
-
- path = os.path.join(MODULE, DIRNAME, dir, fname)
- ts = pd.read_csv(path, index_col=0, header=None).squeeze("columns")
-
- return ts, period_length, change_points
+ path = os.path.join(MODULE, DIRNAME, name, fname)
+ y = pd.read_csv(path, index_col=0, parse_dates=["datetime_gmt"], dtype={1: float})
+ y = y.asfreq("30min")
+ y = y.squeeze("columns")
+ if return_array:
+ return y.values
+ return y
-def load_PBS_dataset():
+def load_PBS_dataset(return_array=True):
"""Load the Pharmaceutical Benefit Scheme univariate time series dataset [1]_.
+ Parameters
+ ----------
+ return_array : bool, default=True
+ return series as an np.ndarray if True, else as a pd.Series.
+
Returns
-------
- y : pd.Series
- Time series
+ np.ndarray or pd.Series
+ PBS time series
Examples
--------
@@ -874,125 +873,121 @@ def load_PBS_dataset():
fname = name + ".csv"
path = os.path.join(MODULE, DIRNAME, name, fname)
y = pd.read_csv(path, index_col=0, dtype={1: float}).squeeze("columns")
-
+ if return_array:
+ return y.values
# make sure time index is properly formatted
y.index = pd.PeriodIndex(y.index, freq="M", name="Period")
y.name = "Number of scripts"
return y
-def load_macroeconomic():
- """
- Load the US Macroeconomic Data [1]_.
+def load_uschange(return_array=True):
+ """Load US Change forecasting dataset.
+
+ An example of a single multivariate time series. The data is the percentage
+ changes in quarterly personal consumption expenditure, personal disposable
+ income, production, savings and the unemployment rate for the US, 1960 to 2016.
+
+ This data shows an increasing trend, non-constant (increasing) variance
+ and periodic, seasonal patterns.
+
+ Channels: ['Consumption', 'Income', 'Production',
+ 'Savings', 'Unemployment']
+ Series length: 187
+ Frequency: Quarterly
+
+ Parameters
+ ----------
+ return_array : bool, default=True
+ return series as an np.ndarray if True, else as a pd.DataFrame in wide format.
Returns
-------
- y : pd.DataFrame
- Time series
+ np.ndarray or pd.DataFrame
+ US Change dataset, shape (5,187).
Examples
--------
- >>> from aeon.datasets import load_macroeconomic
- >>> y = load_macroeconomic() # doctest: +SKIP
-
- Notes
- -----
- US Macroeconomic Data for 1959Q1 - 2009Q3.
-
- Dimensionality: multivariate, 14
- Series length: 203
- Frequency: Quarterly
- Number of cases: 1
-
- This data is kindly wrapped via `statsmodels.datasets.macrodata`.
+ >>> from aeon.datasets import load_uschange
+ >>> data = load_uschange()
+ >>> data.shape
+ (5, 187)
+ >>> data = load_uschange(return_array=False)
+ >>> data.shape
+ (5, 187)
References
----------
- .. [1] Wrapped via statsmodels:
- https://www.statsmodels.org/dev/datasets/generated/macrodata.html
- .. [2] Data Source: FRED, Federal Reserve Economic Data, Federal Reserve
- Bank of St. Louis; http://research.stlouisfed.org/fred2/;
- accessed December 15, 2009.
- .. [3] Data Source: Bureau of Labor Statistics, U.S. Department of Labor;
- http://www.bls.gov/data/; accessed December 15, 2009.
+ .. [1] Data for "Forecasting: Principles and Practice" (2nd Edition)
"""
- _check_soft_dependencies("statsmodels")
- import statsmodels.api as sm
-
- y = sm.datasets.macrodata.load_pandas().data
- y["year"] = y["year"].astype(int).astype(str)
- y["quarter"] = y["quarter"].astype(int).astype(str).apply(lambda x: "Q" + x)
- y["time"] = y["year"] + "-" + y["quarter"]
- y.index = pd.PeriodIndex(data=y["time"], freq="Q", name="Period")
- y = y.drop(columns=["year", "quarter", "time"])
- y.name = "US Macroeconomic Data"
- return y
+ name = "Uschange"
+ fname = name + ".csv"
+ path = os.path.join(MODULE, DIRNAME, name, fname)
+ data = pd.read_csv(path, index_col=0).squeeze("columns")
+ data = data.sort_values("Quarter")
+ data = data.reset_index(drop=True)
+ data.index = pd.Index(data.index, dtype=int)
+ data.name = name
+ data = data.drop("Quarter", axis=1)
+ if return_array:
+ return data.to_numpy().T
+ return data.T
-def load_unit_test_tsf(return_type="tsf_default"):
- """
- Load tsf UnitTest dataset.
-
- Parameters
- ----------
- return_type : str - "pd_multiindex_hier" or "tsf_default" (default)
- - "tsf_default" = container that faithfully mirrors tsf format from the original
- implementation in: https://github.com/rakshitha123/TSForecasting/
- blob/master/utils/data_loader.py.
+def load_longley(return_array=True):
+ """Load the Longley multivariate time series.
- Returns
- -------
- loaded_data: pd.DataFrame
- The converted dataframe containing the time series.
- frequency: str
- The frequency of the dataset.
- forecast_horizon: int
- The expected forecast horizon of the dataset.
- contain_missing_values: bool
- Whether the dataset contains missing values or not.
- contain_equal_length: bool
- Whether the series have equal lengths or not.
- """
- path = os.path.join(MODULE, DIRNAME, "UnitTest", "UnitTest_Tsf_Loader.tsf")
- data, meta = load_from_tsf_file(path, return_type=return_type)
- return (
- data,
- meta["frequency"],
- meta["forecast_horizon"],
- meta["contain_missing_values"],
- meta["contain_equal_length"],
- )
+ This time series contains six US macroeconomic
+ variables from 1947 to 1962 that are known to be highly collinear.
+ Dimensionality: multivariate, 6
+ Series length: 16
+ Frequency: Yearly
+ Number of cases: 1
-def load_solar():
- """Get national solar estimates for GB from Sheffield Solar PV_Live API.
+ Variable description:
- This function calls the Sheffield Solar PV_Live API to extract national solar data
- for the GB eletricity network. Note that these are estimates of the true solar
- generation, since the true values are "behind the meter" and essentially
- unknown.
+ TOTEMP - Total employment
+ GNPDEFL - Gross national product deflator
+ GNP - Gross national product
+ UNEMP - Number of unemployed
+ ARMED - Size of armed forces
+ POP - Population
- The returned time series is half hourly. For more information please refer
- to [1, 2]_.
+ Parameters
+ ----------
+ return_array : bool, default=True
+ return series as an np.ndarray if True, else as a pd.DataFrame in wide format.
Returns
-------
- pd.Series
-
- References
- ----------
- .. [1] https://www.solar.sheffield.ac.uk/pvlive/
- .. [2] https://www.solar.sheffield.ac.uk/pvlive/api/
+ np.ndarray or pd.DataFrame
+ US Change dataset, shape (6, 16).
Examples
--------
- >>> from aeon.datasets import load_solar # doctest: +SKIP
- >>> y = load_solar() # doctest: +SKIP
+ >>> from aeon.datasets import load_longley
+ >>> data = load_longley()
+ >>> data.shape
+ (6, 16)
+ >>> data = load_longley(return_array=False)
+ >>> data.shape
+ (6, 16)
+
+ References
+ ----------
+ .. [1] Longley, J.W. (1967) "An Appraisal of Least Squares Programs for the
+ Electronic Computer from the Point of View of the User." Journal of
+ the American Statistical Association. 62.319, 819-41.
+ (https://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat)
"""
- name = "solar"
+ name = "Longley"
fname = name + ".csv"
path = os.path.join(MODULE, DIRNAME, name, fname)
- y = pd.read_csv(path, index_col=0, parse_dates=["datetime_gmt"], dtype={1: float})
- y = y.asfreq("30T")
- y = y.squeeze("columns")
- return y
+ data = pd.read_csv(path, index_col=0)
+ data = data.set_index("YEAR")
+ data.index = pd.PeriodIndex(data.index, freq="Y", name="Period")
+ data = data.astype(float)
+ if return_array:
+ return data.to_numpy().T
+ return data.T
diff --git a/aeon/datasets/dataset_collections.py b/aeon/datasets/dataset_collections.py
index 71c40ff7d0..3dd870c9f4 100644
--- a/aeon/datasets/dataset_collections.py
+++ b/aeon/datasets/dataset_collections.py
@@ -61,9 +61,9 @@ def get_available_tser_datasets(name="tser_soton", return_list=True):
"""
if name == "tser_soton": # List them all
if return_list:
- return sorted(list(tser_soton.union(tser_monash)))
+ return sorted(list(set(tser_soton).union(set(tser_monash))))
else:
- return tser_soton
+ return set(tser_soton)
if name == "tser_monash":
if return_list:
return sorted(list(tser_monash))
@@ -95,7 +95,7 @@ def get_available_tsc_datasets(name=None):
return True if name is in either multivariate or univaraite
"""
if name is None: # List them all
- merged_set = univariate.union(multivariate)
+ merged_set = set(univariate).union(set(multivariate))
return sorted(list(merged_set))
return name in univariate or name in multivariate
diff --git a/aeon/datasets/tests/test_load_forecasting.py b/aeon/datasets/tests/test_load_forecasting.py
index cf90835307..e1ed0999af 100644
--- a/aeon/datasets/tests/test_load_forecasting.py
+++ b/aeon/datasets/tests/test_load_forecasting.py
@@ -7,66 +7,9 @@
from pandas.testing import assert_frame_equal
import aeon
-from aeon.datasets import load_forecasting, load_from_tsf_file, load_uschange
+from aeon.datasets import load_forecasting, load_from_tsf_file
from aeon.testing.testing_config import PR_TESTING
-_CHECKS = {
- "uschange": {
- "columns": ["Income", "Production", "Savings", "Unemployment"],
- "len_y": 187,
- "len_X": 187,
- "data_types_X": {
- "Income": "float64",
- "Production": "float64",
- "Savings": "float64",
- "Unemployment": "float64",
- },
- "data_type_y": "float64",
- "data": load_uschange(),
- },
-}
-
-
-@pytest.mark.skipif(
- PR_TESTING,
- reason="Only run on overnights because of intermittent fail for read/write",
-)
-@pytest.mark.parametrize("dataset", sorted(_CHECKS.keys()))
-def test_forecasting_data_loaders(dataset):
- """
- Assert if datasets are loaded correctly.
-
- dataset: dictionary with values to assert against should contain:
- 'columns' : list with column names in correct order,
- 'len_y' : length of the y series (int),
- 'len_X' : length of the X series/dataframe (int),
- 'data_types_X' : dictionary with column name keys and dtype as value,
- 'data_type_y' : dtype if y column (string)
- 'data' : tuple with y series and X series/dataframe if one is not
- applicable fill with None value,
- """
- checks = _CHECKS[dataset]
- y = checks["data"][0]
- X = checks["data"][1]
-
- if y is not None:
- assert isinstance(y, pd.Series)
- assert len(y) == checks["len_y"]
- assert y.dtype == checks["data_type_y"]
-
- if X is not None:
- if len(checks["data_types_X"]) > 1:
- assert isinstance(X, pd.DataFrame)
- else:
- assert isinstance(X, pd.Series)
-
- assert X.columns.values.tolist() == checks["columns"]
-
- for col, dt in checks["data_types_X"].items():
- assert X[col].dtype == dt
-
- assert len(X) == checks["len_X"]
-
@pytest.mark.skipif(
PR_TESTING,
diff --git a/aeon/datasets/tests/test_single_problem_loaders.py b/aeon/datasets/tests/test_single_problem_loaders.py
index 9436c37964..6f895afeca 100644
--- a/aeon/datasets/tests/test_single_problem_loaders.py
+++ b/aeon/datasets/tests/test_single_problem_loaders.py
@@ -9,20 +9,24 @@
import aeon
from aeon.datasets import ( # Univariate; Unequal length; Multivariate
load_acsf1,
+ load_airline,
load_arrow_head,
load_basic_motions,
load_covid_3month,
load_from_tsf_file,
load_italy_power_demand,
load_japanese_vowels,
- load_macroeconomic,
+ load_longley,
+ load_lynx,
load_osuleaf,
+ load_PBS_dataset,
load_plaid,
+ load_shampoo_sales,
load_solar,
load_unit_test,
load_unit_test_tsf,
+ load_uschange,
)
-from aeon.utils.validation._dependencies import _check_soft_dependencies
UNIVARIATE_PROBLEMS = [
load_acsf1,
@@ -41,7 +45,7 @@
@pytest.mark.parametrize("loader", UNEQUAL_LENGTH_PROBLEMS)
-def test_load_dataframe(loader):
+def test_load_unequal_length(loader):
"""Test unequal length baked in TSC problems load into List of numpy."""
# should work for all
X, y = loader()
@@ -63,7 +67,7 @@ def test_load_numpy3d(loader):
@pytest.mark.parametrize("loader", UNIVARIATE_PROBLEMS)
def test_load_numpy2d_uni(loader):
- """Test equal length TSC problems load into numpy3d."""
+ """Test equal length univariate TSC problems can be loaded into numpy2d."""
X, y = loader(return_type="numpy2d")
assert isinstance(X, np.ndarray)
assert isinstance(y, np.ndarray)
@@ -98,24 +102,6 @@ def test_basic_load_tsf_to_dataframe():
assert metadata["contain_equal_length"] is False
-def test_load_solar():
- """Test function to load solar data."""
- solar = load_solar()
- assert type(solar) is pd.Series
- assert solar.shape == (289,)
-
-
-@pytest.mark.skipif(
- not _check_soft_dependencies("statsmodels", severity="none"),
- reason="skip test if required soft dependency statsmodels not available",
-)
-def test_load_macroeconomic():
- """Test load macroeconomic."""
- y = load_macroeconomic()
- assert isinstance(y, pd.DataFrame)
- assert y.shape == (203, 12)
-
-
def test_load_covid_3month():
"""Test load covid 3 month."""
X, y = load_covid_3month()
@@ -123,3 +109,44 @@ def test_load_covid_3month():
assert len(X) == len(y)
assert X.shape == (201, 1, 84)
assert isinstance(y, np.ndarray)
+
+
+FORECASTING_DATA = {
+ "shampoo_sales": [load_shampoo_sales, (36,)],
+ "lynx": [load_lynx, (114,)],
+ "airline": [load_airline, (144,)],
+ "solar": [load_solar, (289,)],
+ "PBS": [load_PBS_dataset, (204,)],
+}
+
+
+@pytest.mark.parametrize("data", FORECASTING_DATA.keys())
+def test_univariate_forecasting_loaders(data):
+ """Test baked in loaders of univariate forecasting data."""
+ y = FORECASTING_DATA[data][0]()
+ assert isinstance(y, np.ndarray)
+ y2 = FORECASTING_DATA[data][0](return_array=False)
+ assert isinstance(y2, pd.Series)
+ assert y2.shape == FORECASTING_DATA[data][1]
+ assert y.shape == y2.shape
+
+
+def test_uschange():
+ """Test if multivariate uschange dataset is loaded correctly."""
+ data = load_uschange()
+ assert isinstance(data, np.ndarray)
+ assert data.shape == (5, 187)
+ X = load_uschange(return_array=False)
+ assert isinstance(X, pd.DataFrame)
+ assert X.shape == data.shape
+
+
+def test_longley():
+ """Test if multivariate longley dataset is loaded correctly."""
+ data = load_longley()
+ assert isinstance(data, np.ndarray)
+ assert data.shape == (6, 16)
+ X = load_longley(return_array=False)
+
+ assert isinstance(X, pd.DataFrame)
+ assert X.shape == data.shape
diff --git a/aeon/datasets/tsad_datasets.py b/aeon/datasets/tsad_datasets.py
index 8f10af3eaf..4372772dc5 100644
--- a/aeon/datasets/tsad_datasets.py
+++ b/aeon/datasets/tsad_datasets.py
@@ -67,7 +67,7 @@ def tsad_collections() -> dict[str, list[str]]:
df = _load_indexfile()
return (
df.groupby("collection_name")
- .apply(lambda x: x["dataset_name"].to_list())
+ .apply(lambda x: x["dataset_name"].to_list(), include_groups=False)
.to_dict()
)
diff --git a/aeon/datasets/tsc_datasets.py b/aeon/datasets/tsc_datasets.py
index 105aa6d7a9..1408ba222e 100644
--- a/aeon/datasets/tsc_datasets.py
+++ b/aeon/datasets/tsc_datasets.py
@@ -35,7 +35,7 @@
"""
# The 85 UCR univariate time series classification problems in the 2015 version
-univariate2015 = {
+univariate2015 = [
"Adiac",
"ArrowHead",
"Beef",
@@ -121,11 +121,11 @@
"Worms",
"WormsTwoClass",
"Yoga",
-}
+]
# 128 UCR univariate time series classification problems [1]
-univariate = {
+univariate = [
"ACSF1",
"Adiac",
"AllGestureWiimoteX",
@@ -254,10 +254,10 @@
"Worms",
"WormsTwoClass",
"Yoga",
-}
+]
# 30 UEA multivariate time series classification problems [2]
-multivariate = {
+multivariate = [
"ArticularyWordRecognition",
"AtrialFibrillation",
"BasicMotions",
@@ -288,10 +288,10 @@
"SpokenArabicDigits",
"StandWalkJump",
"UWaveGestureLibrary",
-}
+]
# 112 equal length/no missing univariate time series classification problems [3]
-univariate_equal_length = {
+univariate_equal_length = [
"ACSF1",
"Adiac",
"ArrowHead",
@@ -404,10 +404,10 @@
"Worms",
"WormsTwoClass",
"Yoga",
-}
+]
# 11 variable length univariate time series classification problems [3]
-univariate_variable_length = {
+univariate_variable_length = [
"AllGestureWiimoteX",
"AllGestureWiimoteY",
"AllGestureWiimoteZ",
@@ -419,18 +419,18 @@
"PickupGestureWiimoteZ",
"PLAID",
"ShakeGestureWiimoteZ",
-}
+]
# 4 fixed length univariate time series classification problems with missing values"""
-univariate_missing_values = {
+univariate_missing_values = [
"DodgerLoopDay",
"DodgerLoopGame",
"DodgerLoopWeekend",
"MelbournePedestrian",
-}
+]
# 26 equal length multivariate time series classification problems [4]"""
-multivariate_equal_length = {
+multivariate_equal_length = [
"ArticularyWordRecognition",
"AtrialFibrillation",
"BasicMotions",
@@ -457,10 +457,10 @@
"SelfRegulationSCP2",
"StandWalkJump",
"UWaveGestureLibrary",
-}
+]
# 7 variable length multivariate time series classification problems [4]"""
-multivariate_unequal_length = {
+multivariate_unequal_length = [
"AsphaltObstaclesCoordinates",
"AsphaltPavementTypeCoordinates",
"AsphaltRegularityCoordinates",
@@ -468,7 +468,7 @@
"InsectWingbeat",
"JapaneseVowels",
"SpokenArabicDigits",
-}
+]
# 158 tsml time series classification problems
tsc_zenodo = {
@@ -635,7 +635,7 @@
# 30 new univariate classification problems used in the bake off [5]. Some are new,
# some are discrete versions of regression problems, some are equal length versions
# of the current UCR problems and some are no missing versions of the current 128 UCR.
-univariate_bake_off_2024 = {
+univariate_bake_off_2024 = [
"AconityMINIPrinterLarge", # AconityMINIPrinterLarge_eq
"AconityMINIPrinterSmall", # AconityMINIPrinterSmall_eq
"AllGestureWiimoteX", # AllGestureWiimoteX_eq
@@ -666,4 +666,4 @@
"ShakeGestureWiimoteZ", # ShakeGestureWiimoteZ_eq
"SharePriceIncrease", # SharePriceIncrease
"Tools", # Tools
-}
+]
diff --git a/aeon/datasets/tser_datasets.py b/aeon/datasets/tser_datasets.py
index a46c0a70ff..6716cc9c91 100644
--- a/aeon/datasets/tser_datasets.py
+++ b/aeon/datasets/tser_datasets.py
@@ -23,7 +23,7 @@
"Covid3Month": 3902690,
}
-tser_soton = {
+tser_soton = [
"AcousticContaminationMadrid",
"AluminiumConcentration",
"AppliancesEnergy",
@@ -87,13 +87,79 @@
"WaveDataTension",
"WindTurbinePower",
"ZincConcentration",
-}
+]
+
+tser_soton_clean = [
+ "AcousticContaminationMadrid_nmv",
+ "AluminiumConcentration",
+ "AppliancesEnergy",
+ "AustraliaRainfall",
+ "BarCrawl6min",
+ "BeijingIntAirportPM25Quality",
+ "BeijingPM10Quality_nmv",
+ "BeijingPM25Quality_nmv",
+ "BenzeneConcentration_nmv",
+ "BIDMC32HR",
+ "BIDMC32RR",
+ "BIDMC32SpO2",
+ "BinanceCoinSentiment",
+ "BitcoinSentiment",
+ "BoronConcentration",
+ "CalciumConcentration",
+ "CardanoSentiment",
+ "ChilledWaterPredictor",
+ "CopperConcentration",
+ "Covid19Andalusia",
+ "Covid3Month",
+ "DailyOilGasPrices",
+ "DailyTemperatureLatitude",
+ "DhakaHourlyAirQuality",
+ "ElectricityPredictor",
+ "ElectricMotorTemperature",
+ "EthereumSentiment",
+ "FloodModeling1",
+ "FloodModeling2",
+ "FloodModeling3",
+ "GasSensorArrayAcetone",
+ "GasSensorArrayEthanol",
+ "HotwaterPredictor",
+ "HouseholdPowerConsumption1_nmv",
+ "HouseholdPowerConsumption2_nmv",
+ "IEEEPPG",
+ "IronConcentration",
+ "LiveFuelMoistureContent",
+ "LPGasMonitoringHomeActivity",
+ "MadridPM10Quality_nmv",
+ "MagnesiumConcentration",
+ "ManganeseConcentration",
+ "MethaneMonitoringHomeActivity",
+ "MetroInterstateTrafficVolume",
+ "NaturalGasPricesSentiment",
+ "NewsHeadlineSentiment",
+ "NewsTitleSentiment",
+ "OccupancyDetectionLight",
+ "ParkingBirmingham_eq",
+ "PhosphorusConcentration",
+ "PotassiumConcentration",
+ "PPGDalia_eq",
+ "PrecipitationAndalusia_nmv",
+ "SierraNevadaMountainsSnow",
+ "SodiumConcentration",
+ "SolarRadiationAndalusia_nmv",
+ "SteamPredictor",
+ "SulphurConcentration",
+ "TetuanEnergyConsumption",
+ "VentilatorPressure",
+ "WaveDataTension",
+ "WindTurbinePower",
+ "ZincConcentration",
+]
-tser_soton_unequal_length = {
+tser_soton_unequal_length = [
"ParkingBirmingham",
"PPGDalia",
-}
-tser_soton_missing_values = {
+]
+tser_soton_missing_values = [
"AcousticContaminationMadrid",
"BeijingPM10Quality",
"BeijingPM25Quality",
@@ -103,4 +169,4 @@
"MadridPM10Quality",
"PrecipitationAndalusia",
"SolarRadiationAndalusia",
-}
+]
diff --git a/aeon/distances/__init__.py b/aeon/distances/__init__.py
index 07f57b1a06..e1d3205ef2 100644
--- a/aeon/distances/__init__.py
+++ b/aeon/distances/__init__.py
@@ -65,12 +65,12 @@
"shape_dtw_pairwise_distance",
"sbd_distance",
"sbd_pairwise_distance",
- "mpdist",
- "mpdist_pairwise_distance",
- "paa_sax_mindist",
- "sax_mindist",
- "sfa_mindist",
- "dft_sfa_mindist",
+ "mp_distance",
+ "mp_pairwise_distance",
+ "mindist_paa_sax_distance",
+ "mindist_sax_distance",
+ "mindist_sfa_distance",
+ "mindist_dft_sfa_distance",
"shift_scale_invariant_distance",
"shift_scale_invariant_pairwise_distance",
"shift_scale_invariant_best_shift",
@@ -80,7 +80,6 @@
"soft_dtw_cost_matrix",
]
-from aeon.distances._dft_sfa_mindist import dft_sfa_mindist
from aeon.distances._distance import (
alignment_path,
cost_matrix,
@@ -92,20 +91,13 @@
get_pairwise_distance_function,
pairwise_distance,
)
-from aeon.distances._euclidean import euclidean_distance, euclidean_pairwise_distance
-from aeon.distances._manhattan import manhattan_distance, manhattan_pairwise_distance
-from aeon.distances._minkowski import minkowski_distance, minkowski_pairwise_distance
-from aeon.distances._mpdist import mpdist, mpdist_pairwise_distance
-from aeon.distances._paa_sax_mindist import paa_sax_mindist
-from aeon.distances._sax_mindist import sax_mindist
+from aeon.distances._mpdist import mp_distance, mp_pairwise_distance
from aeon.distances._sbd import sbd_distance, sbd_pairwise_distance
-from aeon.distances._sfa_mindist import sfa_mindist
from aeon.distances._shift_scale_invariant import (
shift_scale_invariant_best_shift,
shift_scale_invariant_distance,
shift_scale_invariant_pairwise_distance,
)
-from aeon.distances._squared import squared_distance, squared_pairwise_distance
from aeon.distances.elastic import (
adtw_alignment_path,
adtw_cost_matrix,
@@ -157,3 +149,23 @@
wdtw_distance,
wdtw_pairwise_distance,
)
+from aeon.distances.mindist._dft_sfa import mindist_dft_sfa_distance
+from aeon.distances.mindist._paa_sax import mindist_paa_sax_distance
+from aeon.distances.mindist._sax import mindist_sax_distance
+from aeon.distances.mindist._sfa import mindist_sfa_distance
+from aeon.distances.pointwise._euclidean import (
+ euclidean_distance,
+ euclidean_pairwise_distance,
+)
+from aeon.distances.pointwise._manhattan import (
+ manhattan_distance,
+ manhattan_pairwise_distance,
+)
+from aeon.distances.pointwise._minkowski import (
+ minkowski_distance,
+ minkowski_pairwise_distance,
+)
+from aeon.distances.pointwise._squared import (
+ squared_distance,
+ squared_pairwise_distance,
+)
diff --git a/aeon/distances/_distance.py b/aeon/distances/_distance.py
index ea535af957..6b3c9d91a3 100644
--- a/aeon/distances/_distance.py
+++ b/aeon/distances/_distance.py
@@ -6,16 +6,12 @@
import numpy as np
from typing_extensions import Unpack
-from aeon.distances._euclidean import euclidean_distance, euclidean_pairwise_distance
-from aeon.distances._manhattan import manhattan_distance, manhattan_pairwise_distance
-from aeon.distances._minkowski import minkowski_distance, minkowski_pairwise_distance
-from aeon.distances._mpdist import mpdist
+from aeon.distances._mpdist import mp_distance, mp_pairwise_distance
from aeon.distances._sbd import sbd_distance, sbd_pairwise_distance
from aeon.distances._shift_scale_invariant import (
shift_scale_invariant_distance,
shift_scale_invariant_pairwise_distance,
)
-from aeon.distances._squared import squared_distance, squared_pairwise_distance
from aeon.distances.elastic import (
adtw_alignment_path,
adtw_cost_matrix,
@@ -66,6 +62,26 @@
wdtw_distance,
wdtw_pairwise_distance,
)
+from aeon.distances.mindist import (
+ mindist_dft_sfa_distance,
+ mindist_dft_sfa_pairwise_distance,
+ mindist_paa_sax_distance,
+ mindist_paa_sax_pairwise_distance,
+ mindist_sax_distance,
+ mindist_sax_pairwise_distance,
+ mindist_sfa_distance,
+ mindist_sfa_pairwise_distance,
+)
+from aeon.distances.pointwise import (
+ euclidean_distance,
+ euclidean_pairwise_distance,
+ manhattan_distance,
+ manhattan_pairwise_distance,
+ minkowski_distance,
+ minkowski_pairwise_distance,
+ squared_distance,
+ squared_pairwise_distance,
+)
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
@@ -144,116 +160,11 @@ def distance(
>>> distance(x, y, metric="dtw")
768.0
"""
- if metric == "squared":
- return squared_distance(x, y)
- elif metric == "euclidean":
- return euclidean_distance(x, y)
- elif metric == "manhattan":
- return manhattan_distance(x, y)
- elif metric == "minkowski":
- return minkowski_distance(x, y, kwargs.get("p", 2.0), kwargs.get("w", None))
- elif metric == "dtw":
- return dtw_distance(x, y, kwargs.get("window"), kwargs.get("itakura_max_slope"))
- elif metric == "ddtw":
- return ddtw_distance(
- x, y, kwargs.get("window"), kwargs.get("itakura_max_slope")
- )
- elif metric == "wdtw":
- return wdtw_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.05),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "shape_dtw":
- return shape_dtw_distance(
- x,
- y,
- window=kwargs.get("window"),
- itakura_max_slope=kwargs.get("itakura_max_slope"),
- descriptor=kwargs.get("descriptor", "identity"),
- reach=kwargs.get("reach", 30),
- transformation_precomputed=kwargs.get("transformation_precomputed", False),
- transformed_x=kwargs.get("transformed_x", None),
- transformed_y=kwargs.get("transformed_y", None),
- )
- elif metric == "wddtw":
- return wddtw_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.05),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "lcss":
- return lcss_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("epsilon", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "erp":
- return erp_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.0),
- kwargs.get("g_arr", None),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "edr":
- return edr_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("epsilon"),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "twe":
- return twe_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("nu", 0.001),
- kwargs.get("lmbda", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "msm":
- return msm_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("independent", True),
- kwargs.get("c", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "mpdist":
- return mpdist(x, y, kwargs.get("m", 0))
- elif metric == "adtw":
- return adtw_distance(
- x,
- y,
- itakura_max_slope=kwargs.get("itakura_max_slope"),
- window=kwargs.get("window"),
- warp_penalty=kwargs.get("warp_penalty", 1.0),
- )
- elif metric == "sbd":
- return sbd_distance(x, y, kwargs.get("standardize", True))
- elif metric == "shift_scale":
- return shift_scale_invariant_distance(x, y, kwargs.get("max_shift", None))
- elif metric == "soft_dtw":
- return soft_dtw_distance(
- x,
- y,
- gamma=kwargs.get("gamma", 1.0),
- itakura_max_slope=kwargs.get("itakura_max_slope"),
- window=kwargs.get("window"),
- )
+ if metric in DISTANCES_DICT:
+ return DISTANCES_DICT[metric]["distance"](x, y, **kwargs)
+ elif isinstance(metric, Callable):
+ return metric(x, y, **kwargs)
else:
- if isinstance(metric, Callable):
- return metric(x, y, **kwargs)
raise ValueError("Metric must be one of the supported strings or a callable")
@@ -328,124 +239,13 @@ def pairwise_distance(
[147.],
[ 48.]])
"""
- if metric == "squared":
- return squared_pairwise_distance(x, y)
- elif metric == "euclidean":
- return euclidean_pairwise_distance(x, y)
- elif metric == "manhattan":
- return manhattan_pairwise_distance(x, y)
- elif metric == "minkowski":
- return minkowski_pairwise_distance(
- x, y, kwargs.get("p", 2.0), kwargs.get("w", None)
- )
- elif metric == "dtw":
- return dtw_pairwise_distance(
- x, y, kwargs.get("window"), kwargs.get("itakura_max_slope")
- )
- elif metric == "shape_dtw":
- return shape_dtw_pairwise_distance(
- x,
- y,
- window=kwargs.get("window"),
- itakura_max_slope=kwargs.get("itakura_max_slope"),
- descriptor=kwargs.get("descriptor", "identity"),
- reach=kwargs.get("reach", 30),
- transformation_precomputed=kwargs.get("transformation_precomputed", False),
- transformed_x=kwargs.get("transformed_x", None),
- transformed_y=kwargs.get("transformed_y", None),
- )
- elif metric == "ddtw":
- return ddtw_pairwise_distance(
- x, y, kwargs.get("window"), kwargs.get("itakura_max_slope")
- )
- elif metric == "wdtw":
- return wdtw_pairwise_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.05),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "wddtw":
- return wddtw_pairwise_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.05),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "lcss":
- return lcss_pairwise_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("epsilon", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "erp":
- return erp_pairwise_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.0),
- kwargs.get("g_arr", None),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "edr":
- return edr_pairwise_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("epsilon"),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "twe":
- return twe_pairwise_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("nu", 0.001),
- kwargs.get("lmbda", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "msm":
- return msm_pairwise_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("independent", True),
- kwargs.get("c", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "mpdist":
- return _custom_func_pairwise(x, y, mpdist, **kwargs)
- elif metric == "adtw":
- return adtw_pairwise_distance(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("itakura_max_slope"),
- kwargs.get("warp_penalty", 1.0),
- )
- elif metric == "sbd":
- return sbd_pairwise_distance(x, y, kwargs.get("standardize", True))
- elif metric == "shift_scale":
- return shift_scale_invariant_pairwise_distance(
- x, y, kwargs.get("max_shift", None)
- )
- elif metric == "soft_dtw":
- return soft_dtw_pairwise_distance(
- x,
- y,
- gamma=kwargs.get("gamma", 1.0),
- itakura_max_slope=kwargs.get("itakura_max_slope"),
- window=kwargs.get("window"),
- )
+ if metric in PAIRWISE_DISTANCE:
+ return DISTANCES_DICT[metric]["pairwise_distance"](x, y, **kwargs)
+ elif isinstance(metric, Callable):
+ if y is None and not symmetric:
+ return _custom_func_pairwise(x, x, metric, **kwargs)
+ return _custom_func_pairwise(x, y, metric, **kwargs)
else:
- if isinstance(metric, Callable):
- if y is None and not symmetric:
- return _custom_func_pairwise(x, x, metric, **kwargs)
- return _custom_func_pairwise(x, y, metric, **kwargs)
raise ValueError("Metric must be one of the supported strings or a callable")
@@ -502,7 +302,7 @@ def _custom_from_multiple_to_multiple_distance(
def alignment_path(
x: np.ndarray,
y: np.ndarray,
- metric: str,
+ metric: Union[str, DistanceFunction, None] = None,
**kwargs: Unpack[DistanceKwargs],
) -> tuple[list[tuple[int, int]], float]:
"""Compute the alignment path and distance between two time series.
@@ -513,7 +313,7 @@ def alignment_path(
First time series.
y : np.ndarray, of shape (m_channels, m_timepoints) or (m_timepoints,)
Second time series.
- metric : str
+ metric : str or Callable
The distance metric to use.
A list of valid distance metrics can be found in the documentation for
:func:`aeon.distances.get_distance_function` or by calling the function
@@ -546,101 +346,10 @@ def alignment_path(
>>> alignment_path(x, y, metric='dtw')
([(0, 0), (1, 1), (2, 2), (3, 3)], 4.0)
"""
- if metric == "dtw":
- return dtw_alignment_path(
- x, y, kwargs.get("window"), kwargs.get("itakura_max_slope")
- )
- elif metric == "shape_dtw":
- return shape_dtw_alignment_path(
- x,
- y,
- window=kwargs.get("window"),
- itakura_max_slope=kwargs.get("itakura_max_slope"),
- descriptor=kwargs.get("descriptor", "identity"),
- reach=kwargs.get("reach", 30),
- transformation_precomputed=kwargs.get("transformation_precomputed", False),
- transformed_x=kwargs.get("transformed_x", None),
- transformed_y=kwargs.get("transformed_y", None),
- )
- elif metric == "ddtw":
- return ddtw_alignment_path(
- x, y, kwargs.get("window"), kwargs.get("itakura_max_slope")
- )
- elif metric == "wdtw":
- return wdtw_alignment_path(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.05),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "wddtw":
- return wddtw_alignment_path(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.05),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "lcss":
- return lcss_alignment_path(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("epsilon", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "erp":
- return erp_alignment_path(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.0),
- kwargs.get("g_arr", None),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "edr":
- return edr_alignment_path(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("epsilon"),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "twe":
- return twe_alignment_path(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("nu", 0.001),
- kwargs.get("lmbda", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "msm":
- return msm_alignment_path(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("independent", True),
- kwargs.get("c", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "adtw":
- return adtw_alignment_path(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("itakura_max_slope"),
- kwargs.get("warp_penalty", 1.0),
- )
- elif metric == "soft_dtw":
- return soft_dtw_alignment_path(
- x,
- y,
- gamma=kwargs.get("gamma", 1.0),
- itakura_max_slope=kwargs.get("itakura_max_slope"),
- window=kwargs.get("window"),
- )
+ if metric in ALIGNMENT_PATH:
+ return DISTANCES_DICT[metric]["alignment_path"](x, y, **kwargs)
+ elif isinstance(metric, Callable):
+ return metric(x, y, **kwargs)
else:
raise ValueError("Metric must be one of the supported strings")
@@ -648,7 +357,7 @@ def alignment_path(
def cost_matrix(
x: np.ndarray,
y: np.ndarray,
- metric: str,
+ metric: Union[str, DistanceFunction, None] = None,
**kwargs: Unpack[DistanceKwargs],
) -> np.ndarray:
"""Compute the alignment path and distance between two time series.
@@ -697,101 +406,10 @@ def cost_matrix(
[204., 140., 91., 55., 30., 14., 5., 1., 0., 1.],
[285., 204., 140., 91., 55., 30., 14., 5., 1., 0.]])
"""
- if metric == "dtw":
- return dtw_cost_matrix(
- x, y, kwargs.get("window"), kwargs.get("itakura_max_slope")
- )
- elif metric == "shape_dtw":
- return shape_dtw_cost_matrix(
- x,
- y,
- window=kwargs.get("window"),
- itakura_max_slope=kwargs.get("itakura_max_slope"),
- descriptor=kwargs.get("descriptor", "identity"),
- reach=kwargs.get("reach", 30),
- transformation_precomputed=kwargs.get("transformation_precomputed", False),
- transformed_x=kwargs.get("transformed_x", None),
- transformed_y=kwargs.get("transformed_y", None),
- )
- elif metric == "ddtw":
- return ddtw_cost_matrix(
- x, y, kwargs.get("window"), kwargs.get("itakura_max_slope")
- )
- elif metric == "wdtw":
- return wdtw_cost_matrix(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.05),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "wddtw":
- return wddtw_cost_matrix(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.05),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "lcss":
- return lcss_cost_matrix(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("epsilon", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "erp":
- return erp_cost_matrix(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("g", 0.0),
- kwargs.get("g_arr", None),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "edr":
- return edr_cost_matrix(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("epsilon"),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "twe":
- return twe_cost_matrix(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("nu", 0.001),
- kwargs.get("lmbda", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "msm":
- return msm_cost_matrix(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("independent", True),
- kwargs.get("c", 1.0),
- kwargs.get("itakura_max_slope"),
- )
- elif metric == "adtw":
- return adtw_cost_matrix(
- x,
- y,
- kwargs.get("window"),
- kwargs.get("itakura_max_slope"),
- kwargs.get("warp_penalty", 1.0),
- )
- elif metric == "soft_dtw":
- return soft_dtw_cost_matrix(
- x,
- y,
- gamma=kwargs.get("gamma", 1.0),
- itakura_max_slope=kwargs.get("itakura_max_slope"),
- window=kwargs.get("window"),
- )
+ if metric in COST_MATRIX:
+ return DISTANCES_DICT[metric]["cost_matrix"](x, y, **kwargs)
+ elif isinstance(metric, Callable):
+ return metric(x, y, **kwargs)
else:
raise ValueError("Metric must be one of the supported strings")
@@ -1047,7 +665,7 @@ def _resolve_key_from_distance(metric: Union[str, Callable], key: str) -> Any:
if isinstance(metric, Callable):
return metric
if metric == "mpdist":
- return mpdist
+ return mp_distance
dist = DISTANCES_DICT.get(metric)
if dist is None:
raise ValueError(f"Unknown metric {metric}")
@@ -1063,6 +681,8 @@ class DistanceType(Enum):
POINTWISE = "pointwise"
ELASTIC = "elastic"
CROSS_CORRELATION = "cross-correlation"
+ MIN_DISTANCE = "min-dist"
+ MATRIX_PROFILE = "matrix-profile"
DISTANCES = [
@@ -1234,18 +854,66 @@ class DistanceType(Enum):
"symmetric": False,
"unequal_support": False,
},
+ {
+ "name": "dft_sfa",
+ "distance": mindist_dft_sfa_distance,
+ "pairwise_distance": mindist_dft_sfa_pairwise_distance,
+ "type": DistanceType.MIN_DISTANCE,
+ "symmetric": True,
+ "unequal_support": True,
+ },
+ {
+ "name": "paa_sax",
+ "distance": mindist_paa_sax_distance,
+ "pairwise_distance": mindist_paa_sax_pairwise_distance,
+ "type": DistanceType.MIN_DISTANCE,
+ "symmetric": True,
+ "unequal_support": True,
+ },
+ {
+ "name": "sax",
+ "distance": mindist_sax_distance,
+ "pairwise_distance": mindist_sax_pairwise_distance,
+ "type": DistanceType.MIN_DISTANCE,
+ "symmetric": True,
+ "unequal_support": True,
+ },
+ {
+ "name": "sfa",
+ "distance": mindist_sfa_distance,
+ "pairwise_distance": mindist_sfa_pairwise_distance,
+ "type": DistanceType.MIN_DISTANCE,
+ "symmetric": True,
+ "unequal_support": True,
+ },
+ {
+ "name": "mpdist",
+ "distance": mp_distance,
+ "pairwise_distance": mp_pairwise_distance,
+ "type": DistanceType.MATRIX_PROFILE,
+ "symmetric": True,
+ "unequal_support": True,
+ },
]
DISTANCES_DICT = {d["name"]: d for d in DISTANCES}
+COST_MATRIX = [d["name"] for d in DISTANCES if "cost_matrix" in d]
+ALIGNMENT_PATH = [d["name"] for d in DISTANCES if "alignment_path" in d]
+PAIRWISE_DISTANCE = [d["name"] for d in DISTANCES if "pairwise_distance" in d]
SYMMETRIC_DISTANCES = [d["name"] for d in DISTANCES if d["symmetric"]]
ASYMMETRIC_DISTANCES = [d["name"] for d in DISTANCES if not d["symmetric"]]
+UNEQUAL_LENGTH_SUPPORT_DISTANCES = [
+ d["name"] for d in DISTANCES if d["unequal_support"]
+]
+
ELASTIC_DISTANCES = [d["name"] for d in DISTANCES if d["type"] == DistanceType.ELASTIC]
POINTWISE_DISTANCES = [
d["name"] for d in DISTANCES if d["type"] == DistanceType.POINTWISE
]
-UNEQUAL_LENGTH_SUPPORT_DISTANCES = [
- d["name"] for d in DISTANCES if d["unequal_support"]
+MP_DISTANCES = [
+ d["name"] for d in DISTANCES if d["type"] == DistanceType.MATRIX_PROFILE
]
+MIN_DISTANCES = [d["name"] for d in DISTANCES if d["type"] == DistanceType.MIN_DISTANCE]
# This is a very specific list for testing where a time series of length 1 is not
# supported
diff --git a/aeon/distances/_mpdist.py b/aeon/distances/_mpdist.py
index 18646f857e..b3ca9e2b8f 100644
--- a/aeon/distances/_mpdist.py
+++ b/aeon/distances/_mpdist.py
@@ -10,7 +10,7 @@
from aeon.utils.validation.collection import _is_numpy_list_multivariate
-def mpdist(x: np.ndarray, y: np.ndarray, m: int = 0) -> float:
+def mp_distance(x: np.ndarray, y: np.ndarray, m: int = 0) -> float:
r"""Matrix Profile Distance.
MPdist [2]_ is a distance measure based on the matrix profile [1]_. Given a
@@ -57,11 +57,11 @@ def mpdist(x: np.ndarray, y: np.ndarray, m: int = 0) -> float:
Examples
--------
>>> import numpy as np
- >>> from aeon.distances import mpdist
+ >>> from aeon.distances import mp_distance
>>> x = np.array([5, 9, 16, 23, 19, 13, 7])
>>> y = np.array([3, 7, 13, 19, 23, 31, 36, 40, 48, 55, 63])
>>> m = 4
- >>> mpdist(x, y, m) # doctest: +SKIP
+ >>> mp_distance(x, y, m) # doctest: +SKIP
0.05663764013361034
"""
x = np.squeeze(x)
@@ -283,7 +283,7 @@ def _stomp_ab(
return mp, ip
-def mpdist_pairwise_distance(
+def mp_pairwise_distance(
X: Union[np.ndarray, list[np.ndarray]],
y: Optional[Union[np.ndarray, list[np.ndarray]]] = None,
m: int = 0,
@@ -317,24 +317,24 @@ def mpdist_pairwise_distance(
Examples
--------
>>> import numpy as np
- >>> from aeon.distances import mpdist_pairwise_distance
+ >>> from aeon.distances import mp_pairwise_distance
>>> # Distance between each time series in a collection of time series
>>> X = np.array([[16, 23, 19, 13],[48, 55, 63, 67]])
- >>> mpdist_pairwise_distance(X, m = 3)
+ >>> mp_pairwise_distance(X, m = 3)
array([[0. , 1.56786235],
[1.56786235, 0. ]])
>>> # Distance between two collections of time series
>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]])
>>> y = np.array([[[21, 13, 9]],[[19, 14, 5]], [[17, 11, 6]]])
- >>> mpdist_pairwise_distance(X, y, m = 2)
+ >>> mp_pairwise_distance(X, y, m = 2)
array([[2.82842712, 2.82842712, 2.82842712],
[2.82842712, 2.82842712, 2.82842712],
[2.82842712, 2.82842712, 2.82842712]])
>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]])
>>> y_univariate = np.array([[22, 18, 12]])
- >>> mpdist_pairwise_distance(X, y_univariate, m = 2)
+ >>> mp_pairwise_distance(X, y_univariate, m = 2)
array([[2.82842712],
[2.82842712],
[2.82842712]])
@@ -363,7 +363,7 @@ def _mpdist_pairwise_distance_single(x: NumbaList[np.ndarray], m: int) -> np.nda
for i in range(n_cases):
for j in range(i + 1, n_cases):
- distances[i, j] = mpdist(x[i], x[j], m)
+ distances[i, j] = mp_distance(x[i], x[j], m)
distances[j, i] = distances[i, j]
return distances
@@ -379,5 +379,5 @@ def _mpdist_pairwise_distance(
for i in range(n_cases):
for j in range(m_cases):
- distances[i, j] = mpdist(x[i], y[j], m)
+ distances[i, j] = mp_distance(x[i], y[j], m)
return distances
diff --git a/aeon/distances/elastic/_adtw.py b/aeon/distances/elastic/_adtw.py
index b55608c0d2..feab2b4c18 100644
--- a/aeon/distances/elastic/_adtw.py
+++ b/aeon/distances/elastic/_adtw.py
@@ -8,9 +8,9 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._squared import _univariate_squared_distance
from aeon.distances.elastic._alignment_paths import compute_min_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
+from aeon.distances.pointwise._squared import _univariate_squared_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/_alignment_paths.py b/aeon/distances/elastic/_alignment_paths.py
index 4286a08085..f70a374cb2 100644
--- a/aeon/distances/elastic/_alignment_paths.py
+++ b/aeon/distances/elastic/_alignment_paths.py
@@ -3,7 +3,7 @@
import numpy as np
from numba import njit
-from aeon.distances._euclidean import _univariate_euclidean_distance
+from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance
@njit(cache=True, fastmath=True)
diff --git a/aeon/distances/elastic/_dtw.py b/aeon/distances/elastic/_dtw.py
index 467d6d5937..85a5c3a6aa 100644
--- a/aeon/distances/elastic/_dtw.py
+++ b/aeon/distances/elastic/_dtw.py
@@ -8,9 +8,9 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._squared import _univariate_squared_distance
from aeon.distances.elastic._alignment_paths import compute_min_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
+from aeon.distances.pointwise._squared import _univariate_squared_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/_edr.py b/aeon/distances/elastic/_edr.py
index 3a2c8bc44b..e14996ef7a 100644
--- a/aeon/distances/elastic/_edr.py
+++ b/aeon/distances/elastic/_edr.py
@@ -8,9 +8,9 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._euclidean import _univariate_euclidean_distance
from aeon.distances.elastic._alignment_paths import compute_min_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
+from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/_erp.py b/aeon/distances/elastic/_erp.py
index e9ab54776f..179b2f24f4 100644
--- a/aeon/distances/elastic/_erp.py
+++ b/aeon/distances/elastic/_erp.py
@@ -8,9 +8,9 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._euclidean import _univariate_euclidean_distance
from aeon.distances.elastic._alignment_paths import compute_min_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
+from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/_lcss.py b/aeon/distances/elastic/_lcss.py
index 85304b740e..23e1eb9fe2 100644
--- a/aeon/distances/elastic/_lcss.py
+++ b/aeon/distances/elastic/_lcss.py
@@ -8,9 +8,9 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._euclidean import _univariate_euclidean_distance
from aeon.distances.elastic._alignment_paths import compute_lcss_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
+from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/_msm.py b/aeon/distances/elastic/_msm.py
index 009ca8caf4..956c674d9d 100644
--- a/aeon/distances/elastic/_msm.py
+++ b/aeon/distances/elastic/_msm.py
@@ -8,9 +8,9 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._squared import _univariate_squared_distance
from aeon.distances.elastic._alignment_paths import compute_min_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
+from aeon.distances.pointwise._squared import _univariate_squared_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/_shape_dtw.py b/aeon/distances/elastic/_shape_dtw.py
index 11fcee09b0..04106f4e6f 100644
--- a/aeon/distances/elastic/_shape_dtw.py
+++ b/aeon/distances/elastic/_shape_dtw.py
@@ -8,10 +8,10 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._squared import _univariate_squared_distance
from aeon.distances.elastic._alignment_paths import compute_min_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
from aeon.distances.elastic._dtw import _dtw_cost_matrix
+from aeon.distances.pointwise._squared import _univariate_squared_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/_soft_dtw.py b/aeon/distances/elastic/_soft_dtw.py
index 921a5ba4ba..31b8743599 100644
--- a/aeon/distances/elastic/_soft_dtw.py
+++ b/aeon/distances/elastic/_soft_dtw.py
@@ -8,10 +8,10 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._squared import _univariate_squared_distance
from aeon.distances.elastic._alignment_paths import compute_min_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
from aeon.distances.elastic._dtw import _dtw_cost_matrix
+from aeon.distances.pointwise._squared import _univariate_squared_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/_twe.py b/aeon/distances/elastic/_twe.py
index 1a09f4d98b..f8a5f10896 100644
--- a/aeon/distances/elastic/_twe.py
+++ b/aeon/distances/elastic/_twe.py
@@ -8,9 +8,9 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._euclidean import _univariate_euclidean_distance
from aeon.distances.elastic._alignment_paths import compute_min_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
+from aeon.distances.pointwise._euclidean import _univariate_euclidean_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/_wdtw.py b/aeon/distances/elastic/_wdtw.py
index f9a24234e4..3ad1767c9e 100644
--- a/aeon/distances/elastic/_wdtw.py
+++ b/aeon/distances/elastic/_wdtw.py
@@ -8,9 +8,9 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._squared import _univariate_squared_distance
from aeon.distances.elastic._alignment_paths import compute_min_return_path
from aeon.distances.elastic._bounding_matrix import create_bounding_matrix
+from aeon.distances.pointwise._squared import _univariate_squared_distance
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/elastic/tests/test_alignment_path.py b/aeon/distances/elastic/tests/test_alignment_path.py
index 2305a0338b..ade31d9ecc 100644
--- a/aeon/distances/elastic/tests/test_alignment_path.py
+++ b/aeon/distances/elastic/tests/test_alignment_path.py
@@ -5,7 +5,11 @@
from numpy.testing import assert_almost_equal
from aeon.distances import alignment_path as compute_alignment_path
-from aeon.distances._distance import DISTANCES, SINGLE_POINT_NOT_SUPPORTED_DISTANCES
+from aeon.distances._distance import (
+ DISTANCES,
+ DISTANCES_DICT,
+ SINGLE_POINT_NOT_SUPPORTED_DISTANCES,
+)
from aeon.testing.data_generation._legacy import make_series
@@ -20,11 +24,14 @@ def _validate_alignment_path_result(
original_x = x.copy()
original_y = y.copy()
alignment_path_result = alignment_path(x, y)
+ callable_alignment_path = DISTANCES_DICT[name]["alignment_path"](x, y)
assert isinstance(alignment_path_result, tuple)
assert isinstance(alignment_path_result[0], list)
assert isinstance(alignment_path_result[1], float)
assert compute_alignment_path(x, y, metric=name) == alignment_path_result
+ # Test a callable being passed
+ assert callable_alignment_path == alignment_path_result
distance_result = distance(x, y)
assert_almost_equal(alignment_path_result[1], distance_result)
diff --git a/aeon/distances/elastic/tests/test_cost_matrix.py b/aeon/distances/elastic/tests/test_cost_matrix.py
index 7903b0705a..79db314f07 100644
--- a/aeon/distances/elastic/tests/test_cost_matrix.py
+++ b/aeon/distances/elastic/tests/test_cost_matrix.py
@@ -5,7 +5,11 @@
from numpy.testing import assert_almost_equal
from aeon.distances import cost_matrix as compute_cost_matrix
-from aeon.distances._distance import DISTANCES, SINGLE_POINT_NOT_SUPPORTED_DISTANCES
+from aeon.distances._distance import (
+ DISTANCES,
+ DISTANCES_DICT,
+ SINGLE_POINT_NOT_SUPPORTED_DISTANCES,
+)
from aeon.testing.data_generation._legacy import make_series
@@ -30,9 +34,11 @@ def _validate_cost_matrix_result(
original_x = x.copy()
original_y = y.copy()
cost_matrix_result = cost_matrix(x, y)
+ cost_matrix_callable_result = DISTANCES_DICT[name]["cost_matrix"](x, y)
assert isinstance(cost_matrix_result, np.ndarray)
assert_almost_equal(cost_matrix_result, compute_cost_matrix(x, y, metric=name))
+ assert_almost_equal(cost_matrix_callable_result, cost_matrix_result)
if name == "ddtw" or name == "wddtw":
assert cost_matrix_result.shape == (x.shape[-1] - 2, y.shape[-1] - 2)
elif name == "lcss":
diff --git a/aeon/distances/mindist/__init__.py b/aeon/distances/mindist/__init__.py
new file mode 100644
index 0000000000..b08e4cfea0
--- /dev/null
+++ b/aeon/distances/mindist/__init__.py
@@ -0,0 +1,28 @@
+"""Mindist module."""
+
+__all__ = [
+ "mindist_dft_sfa_distance",
+ "mindist_dft_sfa_pairwise_distance",
+ "mindist_paa_sax_distance",
+ "mindist_paa_sax_pairwise_distance",
+ "mindist_sax_distance",
+ "mindist_sax_pairwise_distance",
+ "mindist_sfa_distance",
+ "mindist_sfa_pairwise_distance",
+]
+from aeon.distances.mindist._dft_sfa import (
+ mindist_dft_sfa_distance,
+ mindist_dft_sfa_pairwise_distance,
+)
+from aeon.distances.mindist._paa_sax import (
+ mindist_paa_sax_distance,
+ mindist_paa_sax_pairwise_distance,
+)
+from aeon.distances.mindist._sax import (
+ mindist_sax_distance,
+ mindist_sax_pairwise_distance,
+)
+from aeon.distances.mindist._sfa import (
+ mindist_sfa_distance,
+ mindist_sfa_pairwise_distance,
+)
diff --git a/aeon/distances/_dft_sfa_mindist.py b/aeon/distances/mindist/_dft_sfa.py
similarity index 89%
rename from aeon/distances/_dft_sfa_mindist.py
rename to aeon/distances/mindist/_dft_sfa.py
index 4fb5d26cdb..deb20141a4 100644
--- a/aeon/distances/_dft_sfa_mindist.py
+++ b/aeon/distances/mindist/_dft_sfa.py
@@ -10,7 +10,7 @@
@njit(cache=True, fastmath=True)
-def dft_sfa_mindist(
+def mindist_dft_sfa_distance(
x_dft: np.ndarray, y_sfa: np.ndarray, breakpoints: np.ndarray
) -> float:
r"""Compute the DFT-SFA lower bounding distance between DFT and SFA representation.
@@ -43,7 +43,7 @@ def dft_sfa_mindist(
Examples
--------
>>> import numpy as np
- >>> from aeon.distances import dft_sfa_mindist
+ >>> from aeon.distances import mindist_dft_sfa_distance
>>> from aeon.transformations.collection.dictionary_based import SFAFast
>>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
>>> y = np.array([[11, 12, 13, 14, 15, 16, 17, 18, 19, 20]])
@@ -59,15 +59,15 @@ def dft_sfa_mindist(
>>> x_sfa = transform.transform_words(x).squeeze()
>>> y_sfa = transform.transform_words(y).squeeze()
>>> x_dft = transform.transform_mft(x).squeeze()
- >>> dist = dft_sfa_mindist(x_dft, y_sfa, transform.breakpoints)
+ >>> dist = mindist_dft_sfa_distance(x_dft, y_sfa, transform.breakpoints)
"""
if x_dft.ndim == 1 and y_sfa.ndim == 1:
- return _univariate_DFT_SFA_distance(x_dft, y_sfa, breakpoints)
+ return _univariate_dft_sfa_distance(x_dft, y_sfa, breakpoints)
raise ValueError("x and y must be 1D")
@njit(cache=True, fastmath=True)
-def _univariate_DFT_SFA_distance(
+def _univariate_dft_sfa_distance(
x_dft: np.ndarray, y_sfa: np.ndarray, breakpoints: np.ndarray
) -> float:
dist = 0.0
@@ -90,10 +90,10 @@ def _univariate_DFT_SFA_distance(
return np.sqrt(2 * dist)
-def sfa_pairwise_distance(
+def mindist_dft_sfa_pairwise_distance(
X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray
) -> np.ndarray:
- """Compute the SFA pairwise distance between a set of SFA representations.
+ """Compute the DFT SFA pairwise distance between a set of SFA representations.
Parameters
----------
@@ -138,7 +138,7 @@ def _dft_sfa_from_multiple_to_multiple_distance(
for i in prange(n_instances):
for j in prange(i + 1, n_instances):
- distances[i, j] = _univariate_DFT_SFA_distance(X[i], X[j], breakpoints)
+ distances[i, j] = _univariate_dft_sfa_distance(X[i], X[j], breakpoints)
distances[j, i] = distances[i, j]
else:
n_instances = X.shape[0]
@@ -147,6 +147,6 @@ def _dft_sfa_from_multiple_to_multiple_distance(
for i in prange(n_instances):
for j in prange(m_instances):
- distances[i, j] = _univariate_DFT_SFA_distance(X[i], y[j], breakpoints)
+ distances[i, j] = _univariate_dft_sfa_distance(X[i], y[j], breakpoints)
return distances
diff --git a/aeon/distances/_paa_sax_mindist.py b/aeon/distances/mindist/_paa_sax.py
similarity index 89%
rename from aeon/distances/_paa_sax_mindist.py
rename to aeon/distances/mindist/_paa_sax.py
index df201f3da2..a53a8b35aa 100644
--- a/aeon/distances/_paa_sax_mindist.py
+++ b/aeon/distances/mindist/_paa_sax.py
@@ -8,7 +8,7 @@
@njit(cache=True, fastmath=True)
-def paa_sax_mindist(
+def mindist_paa_sax_distance(
x_paa: np.ndarray, y_sax: np.ndarray, breakpoints: np.ndarray, n: int
) -> float:
r"""Compute the PAA-SAX lower bounding distance between PAA and SAX representation.
@@ -42,7 +42,7 @@ def paa_sax_mindist(
Examples
--------
>>> import numpy as np
- >>> from aeon.distances import paa_sax_mindist
+ >>> from aeon.distances import mindist_paa_sax_distance
>>> from aeon.transformations.collection.dictionary_based import SAX
>>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
>>> y = np.array([[11, 12, 13, 14, 15, 16, 17, 18, 19, 20]])
@@ -50,15 +50,17 @@ def paa_sax_mindist(
>>> x_sax = transform.fit_transform(x).squeeze()
>>> x_paa = transform._get_paa(x).squeeze()
>>> y_sax = transform.transform(y).squeeze()
- >>> dist = paa_sax_mindist(x_paa, y_sax, transform.breakpoints, x.shape[-1])
+ >>> dist = mindist_paa_sax_distance(
+ ... x_paa, y_sax, transform.breakpoints, x.shape[-1]
+ ... )
"""
if x_paa.ndim == 1 and y_sax.ndim == 1:
- return _univariate_PAA_SAX_distance(x_paa, y_sax, breakpoints, n)
+ return _univariate_paa_sax_distance(x_paa, y_sax, breakpoints, n)
raise ValueError("x and y must be 1D")
@njit(cache=True, fastmath=True)
-def _univariate_PAA_SAX_distance(
+def _univariate_paa_sax_distance(
x_paa: np.ndarray, y_sax: np.ndarray, breakpoints: np.ndarray, n: int
) -> float:
dist = 0.0
@@ -88,10 +90,10 @@ def _univariate_PAA_SAX_distance(
return np.sqrt(dist)
-def sax_pairwise_distance(
+def mindist_paa_sax_pairwise_distance(
X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n: int
) -> np.ndarray:
- """Compute the SAX pairwise distance between a set of SAX representations.
+ """Compute the PAA SAX pairwise distance between a set of SAX representations.
Parameters
----------
@@ -138,7 +140,7 @@ def _paa_sax_from_multiple_to_multiple_distance(
for i in prange(n_instances):
for j in prange(i + 1, n_instances):
- distances[i, j] = _univariate_PAA_SAX_distance(
+ distances[i, j] = _univariate_paa_sax_distance(
X[i], X[j], breakpoints, n
)
distances[j, i] = distances[i, j]
@@ -149,7 +151,7 @@ def _paa_sax_from_multiple_to_multiple_distance(
for i in prange(n_instances):
for j in prange(m_instances):
- distances[i, j] = _univariate_PAA_SAX_distance(
+ distances[i, j] = _univariate_paa_sax_distance(
X[i], y[j], breakpoints, n
)
diff --git a/aeon/distances/_sax_mindist.py b/aeon/distances/mindist/_sax.py
similarity index 88%
rename from aeon/distances/_sax_mindist.py
rename to aeon/distances/mindist/_sax.py
index b71a6bc454..cdecfb2ebc 100644
--- a/aeon/distances/_sax_mindist.py
+++ b/aeon/distances/mindist/_sax.py
@@ -10,7 +10,9 @@
@njit(cache=True, fastmath=True)
-def sax_mindist(x: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n: int) -> float:
+def mindist_sax_distance(
+ x: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n: int
+) -> float:
r"""Compute the SAX lower bounding distance between two SAX representations.
Parameters
@@ -42,22 +44,24 @@ def sax_mindist(x: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n: int) -
Examples
--------
>>> import numpy as np
- >>> from aeon.distances import paa_sax_mindist
+ >>> from aeon.distances import mindist_paa_sax_distance
>>> from aeon.transformations.collection.dictionary_based import SAX
>>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
>>> y = np.array([[11, 12, 13, 14, 15, 16, 17, 18, 19, 20]])
>>> transform = SAX(n_segments=8, alphabet_size=8)
>>> x_sax = transform.fit_transform(x).squeeze()
>>> y_sax = transform.transform(y).squeeze()
- >>> dist = paa_sax_mindist(x_sax, y_sax, transform.breakpoints, x.shape[-1])
+ >>> dist = mindist_paa_sax_distance(
+ ... x_sax, y_sax, transform.breakpoints, x.shape[-1]
+ ... )
"""
if x.ndim == 1 and y.ndim == 1:
- return _univariate_SAX_distance(x, y, breakpoints, n)
+ return _univariate_sax_distance(x, y, breakpoints, n)
raise ValueError("x and y must be 1D")
@njit(cache=True, fastmath=True)
-def _univariate_SAX_distance(
+def _univariate_sax_distance(
x: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n: int
) -> float:
dist = 0.0
@@ -80,7 +84,7 @@ def _univariate_SAX_distance(
return np.sqrt(dist)
-def sax_pairwise_distance(
+def mindist_sax_pairwise_distance(
X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray, n: int
) -> np.ndarray:
"""Compute the SAX pairwise distance between a set of SAX representations.
@@ -131,7 +135,7 @@ def _sax_from_multiple_to_multiple_distance(
for i in prange(n_instances):
for j in prange(i + 1, n_instances):
- distances[i, j] = _univariate_SAX_distance(X[i], X[j], breakpoints, n)
+ distances[i, j] = _univariate_sax_distance(X[i], X[j], breakpoints, n)
distances[j, i] = distances[i, j]
else:
n_instances = X.shape[0]
@@ -140,6 +144,6 @@ def _sax_from_multiple_to_multiple_distance(
for i in prange(n_instances):
for j in prange(m_instances):
- distances[i, j] = _univariate_SAX_distance(X[i], y[j], breakpoints, n)
+ distances[i, j] = _univariate_sax_distance(X[i], y[j], breakpoints, n)
return distances
diff --git a/aeon/distances/_sfa_mindist.py b/aeon/distances/mindist/_sfa.py
similarity index 87%
rename from aeon/distances/_sfa_mindist.py
rename to aeon/distances/mindist/_sfa.py
index 6b277fcb19..601b83f1e6 100644
--- a/aeon/distances/_sfa_mindist.py
+++ b/aeon/distances/mindist/_sfa.py
@@ -10,7 +10,9 @@
@njit(cache=True, fastmath=True)
-def sfa_mindist(x: np.ndarray, y: np.ndarray, breakpoints: np.ndarray) -> float:
+def mindist_sfa_distance(
+ x: np.ndarray, y: np.ndarray, breakpoints: np.ndarray
+) -> float:
r"""Compute the SFA lower bounding distance between two SFA representations.
Parameters
@@ -41,7 +43,7 @@ def sfa_mindist(x: np.ndarray, y: np.ndarray, breakpoints: np.ndarray) -> float:
Examples
--------
>>> import numpy as np
- >>> from aeon.distances import sfa_mindist
+ >>> from aeon.distances import mindist_sfa_distance
>>> from aeon.transformations.collection.dictionary_based import SFAFast
>>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
>>> y = np.array([[11, 12, 13, 14, 15, 16, 17, 18, 19, 20]])
@@ -56,15 +58,15 @@ def sfa_mindist(x: np.ndarray, y: np.ndarray, breakpoints: np.ndarray) -> float:
SFAFast(...)
>>> x_sfa = transform.transform_words(x).squeeze()
>>> y_sfa = transform.transform_words(y).squeeze()
- >>> dist = sfa_mindist(x_sfa, y_sfa, transform.breakpoints)
+ >>> dist = mindist_sfa_distance(x_sfa, y_sfa, transform.breakpoints)
"""
if x.ndim == 1 and y.ndim == 1:
- return _univariate_SFA_distance(x, y, breakpoints)
+ return _univariate_sfa_distance(x, y, breakpoints)
raise ValueError("x and y must be 1D")
@njit(cache=True, fastmath=True)
-def _univariate_SFA_distance(
+def _univariate_sfa_distance(
x: np.ndarray, y: np.ndarray, breakpoints: np.ndarray
) -> float:
dist = 0.0
@@ -79,10 +81,10 @@ def _univariate_SFA_distance(
return np.sqrt(2 * dist)
-def sfa_pairwise_distance(
+def mindist_sfa_pairwise_distance(
X: np.ndarray, y: np.ndarray, breakpoints: np.ndarray
) -> np.ndarray:
- """Compute the SFA pairwise distance between a set of SFA representations.
+ """Compute the SFA mindist pairwise distance between a set of SFA representations.
Parameters
----------
@@ -128,7 +130,7 @@ def _sfa_from_multiple_to_multiple_distance(
for i in prange(n_instances):
for j in prange(i + 1, n_instances):
- distances[i, j] = _univariate_SFA_distance(X[i], X[j], breakpoints)
+ distances[i, j] = _univariate_sfa_distance(X[i], X[j], breakpoints)
distances[j, i] = distances[i, j]
else:
n_instances = X.shape[0]
@@ -137,6 +139,6 @@ def _sfa_from_multiple_to_multiple_distance(
for i in prange(n_instances):
for j in prange(m_instances):
- distances[i, j] = _univariate_SFA_distance(X[i], y[j], breakpoints)
+ distances[i, j] = _univariate_sfa_distance(X[i], y[j], breakpoints)
return distances
diff --git a/aeon/distances/pointwise/__init__.py b/aeon/distances/pointwise/__init__.py
new file mode 100644
index 0000000000..6d34d44376
--- /dev/null
+++ b/aeon/distances/pointwise/__init__.py
@@ -0,0 +1,29 @@
+"""Pointwise distances."""
+
+__all__ = [
+ "euclidean_distance",
+ "euclidean_pairwise_distance",
+ "manhattan_distance",
+ "manhattan_pairwise_distance",
+ "minkowski_distance",
+ "minkowski_pairwise_distance",
+ "squared_distance",
+ "squared_pairwise_distance",
+]
+
+from aeon.distances.pointwise._euclidean import (
+ euclidean_distance,
+ euclidean_pairwise_distance,
+)
+from aeon.distances.pointwise._manhattan import (
+ manhattan_distance,
+ manhattan_pairwise_distance,
+)
+from aeon.distances.pointwise._minkowski import (
+ minkowski_distance,
+ minkowski_pairwise_distance,
+)
+from aeon.distances.pointwise._squared import (
+ squared_distance,
+ squared_pairwise_distance,
+)
diff --git a/aeon/distances/_euclidean.py b/aeon/distances/pointwise/_euclidean.py
similarity index 98%
rename from aeon/distances/_euclidean.py
rename to aeon/distances/pointwise/_euclidean.py
index b9f03aba8f..f7f0a640d4 100644
--- a/aeon/distances/_euclidean.py
+++ b/aeon/distances/pointwise/_euclidean.py
@@ -6,7 +6,10 @@
from numba import njit
from numba.typed import List as NumbaList
-from aeon.distances._squared import _univariate_squared_distance, squared_distance
+from aeon.distances.pointwise._squared import (
+ _univariate_squared_distance,
+ squared_distance,
+)
from aeon.utils.conversion._convert_collection import _convert_collection_to_numba_list
from aeon.utils.validation.collection import _is_numpy_list_multivariate
diff --git a/aeon/distances/_manhattan.py b/aeon/distances/pointwise/_manhattan.py
similarity index 100%
rename from aeon/distances/_manhattan.py
rename to aeon/distances/pointwise/_manhattan.py
diff --git a/aeon/distances/_minkowski.py b/aeon/distances/pointwise/_minkowski.py
similarity index 100%
rename from aeon/distances/_minkowski.py
rename to aeon/distances/pointwise/_minkowski.py
diff --git a/aeon/distances/_squared.py b/aeon/distances/pointwise/_squared.py
similarity index 100%
rename from aeon/distances/_squared.py
rename to aeon/distances/pointwise/_squared.py
diff --git a/aeon/distances/tests/test_distances.py b/aeon/distances/tests/test_distances.py
index e351df4315..43d27be27d 100644
--- a/aeon/distances/tests/test_distances.py
+++ b/aeon/distances/tests/test_distances.py
@@ -9,6 +9,8 @@
from aeon.distances import get_distance_function_names, pairwise_distance
from aeon.distances._distance import (
DISTANCES,
+ MIN_DISTANCES,
+ MP_DISTANCES,
SINGLE_POINT_NOT_SUPPORTED_DISTANCES,
UNEQUAL_LENGTH_SUPPORT_DISTANCES,
_custom_func_pairwise,
@@ -67,6 +69,10 @@ def _validate_distance_result(
@pytest.mark.parametrize("dist", DISTANCES)
def test_distances(dist):
"""Test distance functions."""
+ # For now skipping mpdist and mindist
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
+
# ================== Test equal length ==================
# Test univariate of shape (n_timepoints,)
_validate_distance_result(
diff --git a/aeon/distances/tests/test_mpdist.py b/aeon/distances/tests/test_mpdist.py
index 1cee8ce0d4..84adbacd9a 100644
--- a/aeon/distances/tests/test_mpdist.py
+++ b/aeon/distances/tests/test_mpdist.py
@@ -5,7 +5,7 @@
import numpy as np
import pytest
-from aeon.distances._mpdist import mpdist
+from aeon.distances._mpdist import mp_distance
def test_mpdist():
@@ -18,27 +18,27 @@ def test_mpdist():
ValueError,
match=re.escape("x and y must be a 1D array of shape (n_timepoints,)"),
):
- mpdist(y, y)
+ mp_distance(y, y)
# Test for ValueError if ts2 is not a 1D array
with pytest.raises(
ValueError,
match=re.escape("x and y must be a 1D array of shape (n_timepoints,)"),
):
- mpdist(x, y)
+ mp_distance(x, y)
y = np.random.randn(1, 10)
with pytest.raises(
ValueError,
match=re.escape("subseries length must be less than or equal to the length"),
):
- mpdist(x, y, m=11)
+ mp_distance(x, y, m=11)
with pytest.raises(
ValueError,
match=re.escape("subseries length must be greater than 0 or zero"),
):
- mpdist(x, y, m=-1)
+ mp_distance(x, y, m=-1)
# Test MPDist function with valid inputs
- d = mpdist(x, y)
+ d = mp_distance(x, y)
assert isinstance(d, float) # Check if the result is a float
assert d >= 0 # Check if the distance is non-negative
diff --git a/aeon/distances/tests/test_numba_distance_parameters.py b/aeon/distances/tests/test_numba_distance_parameters.py
index 1c1655957c..f42b6016cf 100644
--- a/aeon/distances/tests/test_numba_distance_parameters.py
+++ b/aeon/distances/tests/test_numba_distance_parameters.py
@@ -6,7 +6,7 @@
import pytest
from aeon.distances import distance
-from aeon.distances._distance import DISTANCES
+from aeon.distances._distance import DISTANCES, MIN_DISTANCES, MP_DISTANCES
from aeon.distances.elastic._shape_dtw import _pad_ts_edges, _transform_subsequences
from aeon.testing.data_generation._legacy import make_series
from aeon.testing.expected_results.expected_distance_results import (
@@ -133,6 +133,10 @@ def _test_distance_params(
@pytest.mark.parametrize("dist", DISTANCES)
def test_new_distance_params(dist):
"""Test function to check the parameters of distance functions."""
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
+
if dist["name"] in DIST_PARAMS:
_test_distance_params(
DIST_PARAMS[dist["name"]],
diff --git a/aeon/distances/tests/test_pairwise.py b/aeon/distances/tests/test_pairwise.py
index 7a7558b47a..88170d6f4a 100644
--- a/aeon/distances/tests/test_pairwise.py
+++ b/aeon/distances/tests/test_pairwise.py
@@ -7,6 +7,8 @@
from aeon.distances import pairwise_distance as compute_pairwise_distance
from aeon.distances._distance import (
DISTANCES,
+ MIN_DISTANCES,
+ MP_DISTANCES,
SINGLE_POINT_NOT_SUPPORTED_DISTANCES,
SYMMETRIC_DISTANCES,
)
@@ -234,6 +236,9 @@ def _supports_nonequal_length(dist) -> bool:
@pytest.mark.parametrize("dist", DISTANCES)
def test_pairwise_distance(dist):
"""Test pairwise distance function."""
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
# ================== Test equal length ==================
# Test collection of univariate time series in the shape (n_cases, n_timepoints)
_validate_pairwise_result(
@@ -304,6 +309,9 @@ def test_pairwise_distance(dist):
@pytest.mark.parametrize("dist", DISTANCES)
def test_multiple_to_multiple_distances(dist):
"""Test multiple to multiple distances."""
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
# ================== Test equal length ==================
# Test passing two singular univariate time series of shape (n_timepoints,)
if dist["name"] != "scale_shift":
@@ -412,6 +420,9 @@ def test_multiple_to_multiple_distances(dist):
@pytest.mark.parametrize("dist", DISTANCES)
def test_single_to_multiple_distances(dist):
"""Test single to multiple distances."""
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
# ================== Test equal length ==================
# Test passing a singular univariate time series of shape (n_timepoints,) compared
# to a collection of univariate time series of shape (n_cases, n_timepoints)
@@ -548,6 +559,9 @@ def test_single_to_multiple_distances(dist):
@pytest.mark.parametrize("dist", DISTANCES)
def test_pairwise_distance_non_negative(dist, seed):
"""Most estimators require distances to be non-negative."""
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
X = make_example_3d_numpy(
n_cases=5, n_channels=1, n_timepoints=10, random_state=seed, return_y=False
)
diff --git a/aeon/distances/tests/test_sklearn_compatibility.py b/aeon/distances/tests/test_sklearn_compatibility.py
index 8752b7d75d..5d68e4114f 100644
--- a/aeon/distances/tests/test_sklearn_compatibility.py
+++ b/aeon/distances/tests/test_sklearn_compatibility.py
@@ -9,7 +9,7 @@
from sklearn.svm import SVR
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
-from aeon.distances._distance import DISTANCES
+from aeon.distances._distance import DISTANCES, MIN_DISTANCES, MP_DISTANCES
from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor
from aeon.testing.data_generation import make_example_3d_numpy
@@ -17,6 +17,9 @@
@pytest.mark.parametrize("dist", DISTANCES)
def test_function_transformer(dist):
"""Test all distances work with FunctionTransformer in a pipeline."""
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
X = make_example_3d_numpy(
n_cases=5, n_channels=1, n_timepoints=10, return_y=False, random_state=1
)
@@ -36,6 +39,9 @@ def test_function_transformer(dist):
@pytest.mark.parametrize("dist", DISTANCES)
def test_distance_based(dist):
"""Test all distances work with KNN in a pipeline."""
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
X, y = make_example_3d_numpy(
n_cases=6, n_channels=1, n_timepoints=10, regression_target=True
)
@@ -58,6 +64,9 @@ def test_distance_based(dist):
@pytest.mark.parametrize("dist", DISTANCES)
def test_clusterer(dist):
"""Test all distances work with DBSCAN."""
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
X = make_example_3d_numpy(n_cases=5, n_channels=1, n_timepoints=10, return_y=False)
db = DBSCAN(metric="precomputed", eps=2.5)
preds = db.fit_predict(dist["pairwise_distance"](X))
@@ -76,7 +85,11 @@ def test_clusterer(dist):
def test_univariate(dist, k, task):
"""Test all distances work with sklearn nearest neighbours."""
# TODO: when solved the issue with lcss and edr, remove this condition
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
# https://github.com/aeon-toolkit/aeon/issues/882
+
if dist["name"] in ["lcss", "edr"]:
return
@@ -141,6 +154,9 @@ def test_univariate(dist, k, task):
)
def test_multivariate(dist, k, task):
"""Test all distances work with sklearn nearest neighbours."""
+ # Skip for now
+ if dist["name"] in MIN_DISTANCES or dist["name"] in MP_DISTANCES:
+ return
# TODO: when solved the issue with lcss and edr, remove this condition
# https://github.com/aeon-toolkit/aeon/issues/882
if dist["name"] in ["lcss", "edr"]:
diff --git a/aeon/distances/tests/test_symbolic_mindist.py b/aeon/distances/tests/test_symbolic_mindist.py
index 6aa518db4e..197a9dbb42 100644
--- a/aeon/distances/tests/test_symbolic_mindist.py
+++ b/aeon/distances/tests/test_symbolic_mindist.py
@@ -4,10 +4,10 @@
from scipy.stats import zscore
from aeon.datasets import load_unit_test
-from aeon.distances._dft_sfa_mindist import dft_sfa_mindist
-from aeon.distances._paa_sax_mindist import paa_sax_mindist
-from aeon.distances._sax_mindist import sax_mindist
-from aeon.distances._sfa_mindist import sfa_mindist
+from aeon.distances.mindist._dft_sfa import mindist_dft_sfa_distance
+from aeon.distances.mindist._paa_sax import mindist_paa_sax_distance
+from aeon.distances.mindist._sax import mindist_sax_distance
+from aeon.distances.mindist._sfa import mindist_sfa_distance
from aeon.transformations.collection.dictionary_based import SAX, SFA, SFAFast
@@ -32,12 +32,12 @@ def test_sax_mindist():
Y = X_test[i].reshape(1, -1)
# SAX Min-Distance
- mindist_sax = sax_mindist(
+ mindist_sax = mindist_sax_distance(
SAX_train[i], SAX_test[i], SAX_transform.breakpoints, X_train.shape[-1]
)
# SAX-PAA Min-Distance
- mindist_paa_sax = paa_sax_mindist(
+ mindist_paa_sax = mindist_paa_sax_distance(
PAA_train[i], SAX_test[i], SAX_transform.breakpoints, X_train.shape[-1]
)
@@ -95,12 +95,12 @@ def test_sfa_mindist():
Y = X_test[i].reshape(1, -1)
# SFA Min-Distance
- mindist_sfa = sfa_mindist(
+ mindist_sfa = mindist_sfa_distance(
X_train_words[i], Y_train_words[i], sfa.breakpoints
)
# DFT-SFA Min-Distance
- mindist_dft_sfa = dft_sfa_mindist(
+ mindist_dft_sfa = mindist_dft_sfa_distance(
SFA_train_dfts[i], Y_train_words[i], sfa.breakpoints
)
diff --git a/aeon/networks/__init__.py b/aeon/networks/__init__.py
index 30cc6b24ef..5d8a87f2a8 100644
--- a/aeon/networks/__init__.py
+++ b/aeon/networks/__init__.py
@@ -13,17 +13,20 @@
"AEFCNNetwork",
"AEResNetNetwork",
"LITENetwork",
+ "DCNNNetwork",
+ "AEDCNNNetwork",
"AEAttentionBiGRUNetwork",
"AEDRNNNetwork",
"AEBiGRUNetwork",
]
-
from aeon.networks._ae_abgru import AEAttentionBiGRUNetwork
from aeon.networks._ae_bgru import AEBiGRUNetwork
+from aeon.networks._ae_dcnn import AEDCNNNetwork
from aeon.networks._ae_drnn import AEDRNNNetwork
from aeon.networks._ae_fcn import AEFCNNetwork
from aeon.networks._ae_resnet import AEResNetNetwork
from aeon.networks._cnn import TimeCNNNetwork
+from aeon.networks._dcnn import DCNNNetwork
from aeon.networks._encoder import EncoderNetwork
from aeon.networks._fcn import FCNNetwork
from aeon.networks._inception import InceptionNetwork
diff --git a/aeon/networks/_ae_dcnn.py b/aeon/networks/_ae_dcnn.py
new file mode 100644
index 0000000000..2f47851f45
--- /dev/null
+++ b/aeon/networks/_ae_dcnn.py
@@ -0,0 +1,291 @@
+"""Auto-Encoder based on Dilated Convolutional Nerual Networks (DCNN) Model."""
+
+__maintainer__ = []
+
+import warnings
+
+import numpy as np
+
+from aeon.networks.base import BaseDeepLearningNetwork
+
+
+class AEDCNNNetwork(BaseDeepLearningNetwork):
+ """Establish the Auto-Encoder based structure for a DCN Network.
+
+ Dilated Convolutional Neural (DCN) Network based Model
+ for low-rank embeddings.
+
+ Parameters
+ ----------
+ latent_space_dim: int, default=128
+ Dimension of the models's latent space.
+ temporal_latent_space : bool, default = False
+ Flag to choose whether the latent space is an MTS or Euclidean space.
+ n_layers: int, default=4
+ Number of convolution layers in the autoencoder.
+ kernel_size: Union[int, List[int]], default=3
+ Size of the 1D Convolutional Kernel of the encoder. Defaults to a
+ list of length `n_layers` with `kernel_size` value.
+ activation: Union[str, List[str]], default="relu"
+ The activation function used by convolution layers of the encoder.
+ Defaults to a list of "relu" for `n_layers` elements.
+ n_filters: Union[int, List[int]], default=None
+ Number of filters used in convolution layers of the encoder. Defaults
+ to a list of multiples of `32` for `n_layers` elements.
+ dilation_rate: Union[int, List[int]], default=1
+ The dilation rate for convolution of the encoder. Defaults to a list
+ of powers of `2` for `n_layers` elements. `dilation_rate` greater than
+ `1` is not supported on `Conv1DTranspose` for some devices/OS.
+ padding_encoder: Union[str, List[str]], default="same"
+ The padding string for the encoder layers. Defaults to a list of "same"
+ for `n_layers` elements. Valid strings are "causal", "valid", "same" or
+ any other Keras compatible string.
+ padding_decoder: Union[str, List[str]], default="same"
+ The padding string for the decoder layers. Defaults to a list of "same"
+ for `n_layers` elements.
+
+ References
+ ----------
+ .. [1] Franceschi, J. Y., Dieuleveut, A., & Jaggi, M. (2019). Unsupervised
+ scalable representation learning for multivariate time series. Advances in
+ neural information processing systems, 32.
+
+ """
+
+ _config = {
+ "python_dependencies": ["tensorflow"],
+ "python_version": "<3.12",
+ "structure": "auto-encoder",
+ }
+
+ def __init__(
+ self,
+ latent_space_dim=128,
+ temporal_latent_space=False,
+ n_layers=4,
+ kernel_size=3,
+ activation="relu",
+ n_filters=None,
+ dilation_rate=1,
+ padding_encoder="same",
+ padding_decoder="same",
+ ):
+ super().__init__()
+
+ self.latent_space_dim = latent_space_dim
+ self.kernel_size = kernel_size
+ self.n_filters = n_filters
+ self.n_layers = n_layers
+ self.dilation_rate = dilation_rate
+ self.activation = activation
+ self.temporal_latent_space = temporal_latent_space
+ self.padding_encoder = padding_encoder
+ self.padding_decoder = padding_decoder
+
+ def build_network(self, input_shape):
+ """Construct a network and return its input and output layers.
+
+ Arguments
+ ---------
+ input_shape : tuple of shape = (n_timepoints (m), n_channels (d))
+ The shape of the data fed into the input layer.
+
+ Returns
+ -------
+ model : a keras Model.
+ """
+ import tensorflow as tf
+
+ if self.n_filters is None:
+ self._n_filters_encoder = [32 * i for i in range(1, self.n_layers + 1)]
+ elif isinstance(self.n_filters, int):
+ self._n_filters_encoder = [self.n_filters for _ in range(self.n_layers)]
+ elif isinstance(self.n_filters, list):
+ self._n_filters_encoder = self.n_filters
+ assert len(self.n_filters) == self.n_layers
+
+ if self.dilation_rate is None:
+ self._dilation_rate_encoder = [
+ 2**layer_num for layer_num in range(1, self.n_layers + 1)
+ ]
+ elif isinstance(self.dilation_rate, int):
+ self._dilation_rate_encoder = [
+ self.dilation_rate for _ in range(self.n_layers)
+ ]
+ else:
+ self._dilation_rate_encoder = self.dilation_rate
+ assert isinstance(self.dilation_rate, list)
+ assert len(self.dilation_rate) == self.n_layers
+
+ if self.kernel_size is None:
+ self._kernel_size_encoder = [3 for _ in range(self.n_layers)]
+ elif isinstance(self.kernel_size, int):
+ self._kernel_size_encoder = [self.kernel_size for _ in range(self.n_layers)]
+ elif isinstance(self.kernel_size, list):
+ self._kernel_size_encoder = self.kernel_size
+ assert len(self.kernel_size) == self.n_layers
+
+ if self.activation is None:
+ self._activation_encoder = ["relu" for _ in range(self.n_layers)]
+ elif isinstance(self.activation, str):
+ self._activation_encoder = [self.activation for _ in range(self.n_layers)]
+ elif isinstance(self.activation, list):
+ self._activation_encoder = self.activation
+ assert len(self._activation_encoder) == self.n_layers
+
+ if self.padding_encoder is None:
+ self._padding_encoder = ["same" for _ in range(self.n_layers)]
+ elif isinstance(self.padding_encoder, str):
+ self._padding_encoder = [self.padding_encoder for _ in range(self.n_layers)]
+ elif isinstance(self.padding_encoder, list):
+ self._padding_encoder = self.padding_encoder
+ assert len(self._padding_encoder) == self.n_layers
+
+ if self.padding_decoder is None:
+ self._padding_decoder = ["same" for _ in range(self.n_layers)]
+ elif isinstance(self.padding_decoder, str):
+ self._padding_decoder = [self.padding_decoder for _ in range(self.n_layers)]
+ elif isinstance(self.padding_decoder, list):
+ self._padding_decoder = self.padding_decoder
+ assert len(self._padding_decoder) == self.n_layers
+
+ if self.dilation_rate == 1 or np.all(
+ np.array(self._dilation_rate_encoder) == 1
+ ):
+ warnings.warn(
+ """Currently, the dilation rate has been set to `1` which is
+ different from the original paper of the `AEDCNNNetwork` due to CPU
+ Implementation issues with `tensorflow.keras.layers.Conv1DTranspose`
+ & `dilation_rate` > 1 on some Hardwares & OS combinations. You
+ can use the dilation rates as specified in the paper by passing
+ `dilation_rate=None` to the Network/Clusterer.""",
+ UserWarning,
+ stacklevel=2,
+ )
+
+ if np.any(np.array(self._dilation_rate_encoder) > 1):
+ warnings.warn(
+ """Current network configuration contains `dilation_rate`
+ more than 1, which is not supported by
+ `tensorflow.keras.layers.Conv1DTranspose` layer for certain
+ hardware architectures and/or Operating Systems.""",
+ UserWarning,
+ stacklevel=2,
+ )
+
+ input_layer = tf.keras.layers.Input(input_shape)
+
+ x = input_layer
+ for i in range(0, self.n_layers):
+ x = self._dcnn_layer(
+ x,
+ self._n_filters_encoder[i],
+ self._dilation_rate_encoder[i],
+ _activation=self._activation_encoder[i],
+ _kernel_size=self._kernel_size_encoder[i],
+ _padding_encoder=self._padding_encoder[i],
+ )
+
+ if not self.temporal_latent_space:
+ shape_before_flatten = x.shape[1:]
+ x = tf.keras.layers.Flatten()(x)
+ output_layer = tf.keras.layers.Dense(self.latent_space_dim)(x)
+
+ elif self.temporal_latent_space:
+ output_layer = tf.keras.layers.Conv1D(
+ filters=self.latent_space_dim,
+ kernel_size=1,
+ )(x)
+
+ encoder = tf.keras.Model(inputs=input_layer, outputs=output_layer)
+
+ if self.temporal_latent_space:
+ input_layer_decoder = tf.keras.layers.Input(x.shape[1:])
+ temp = input_layer_decoder
+ elif not self.temporal_latent_space:
+ input_layer_decoder = tf.keras.layers.Input((self.latent_space_dim,))
+ dense_layer = tf.keras.layers.Dense(units=np.prod(shape_before_flatten))(
+ input_layer_decoder
+ )
+
+ reshape_layer = tf.keras.layers.Reshape(target_shape=shape_before_flatten)(
+ dense_layer
+ )
+ temp = reshape_layer
+
+ y = temp
+
+ for i in range(0, self.n_layers):
+ y = self._dcnn_layer_decoder(
+ y,
+ self._n_filters_encoder[::-1][i],
+ self._dilation_rate_encoder[::-1][i],
+ _activation=self._activation_encoder[::-1][i],
+ _kernel_size=self._kernel_size_encoder[::-1][i],
+ _padding_decoder=self._padding_decoder[i],
+ )
+
+ last_layer = tf.keras.layers.Conv1D(filters=input_shape[-1], kernel_size=1)(y)
+ decoder = tf.keras.Model(inputs=input_layer_decoder, outputs=last_layer)
+
+ return encoder, decoder
+
+ def _dcnn_layer(
+ self,
+ _inputs,
+ _num_filters,
+ _dilation_rate,
+ _activation,
+ _kernel_size,
+ _padding_encoder,
+ ):
+ import tensorflow as tf
+
+ _add = tf.keras.layers.Conv1D(_num_filters, kernel_size=1)(_inputs)
+ x = tf.keras.layers.Conv1D(
+ _num_filters,
+ kernel_size=_kernel_size,
+ dilation_rate=_dilation_rate,
+ padding=_padding_encoder,
+ kernel_regularizer="l2",
+ )(_inputs)
+ x = tf.keras.layers.Conv1D(
+ _num_filters,
+ kernel_size=_kernel_size,
+ dilation_rate=_dilation_rate,
+ padding=_padding_encoder,
+ kernel_regularizer="l2",
+ )(x)
+ output = tf.keras.layers.Add()([x, _add])
+ output = tf.keras.layers.Activation(_activation)(output)
+ return output
+
+ def _dcnn_layer_decoder(
+ self,
+ _inputs,
+ _num_filters,
+ _dilation_rate,
+ _activation,
+ _kernel_size,
+ _padding_decoder,
+ ):
+ import tensorflow as tf
+
+ _add = tf.keras.layers.Conv1DTranspose(_num_filters, kernel_size=1)(_inputs)
+ x = tf.keras.layers.Conv1DTranspose(
+ _num_filters,
+ kernel_size=_kernel_size,
+ dilation_rate=_dilation_rate,
+ padding=_padding_decoder,
+ kernel_regularizer="l2",
+ )(_inputs)
+ x = tf.keras.layers.Conv1DTranspose(
+ _num_filters,
+ kernel_size=_kernel_size,
+ dilation_rate=_dilation_rate,
+ padding=_padding_decoder,
+ kernel_regularizer="l2",
+ )(x)
+ output = tf.keras.layers.Add()([x, _add])
+ output = tf.keras.layers.Activation(_activation)(output)
+ return output
diff --git a/aeon/networks/_ae_fcn.py b/aeon/networks/_ae_fcn.py
index 1c1c2bef41..e64569e0d6 100644
--- a/aeon/networks/_ae_fcn.py
+++ b/aeon/networks/_ae_fcn.py
@@ -199,7 +199,9 @@ def build_network(self, input_shape, **kwargs):
)(x)
conv = tf.keras.layers.BatchNormalization()(conv)
- conv = tf.keras.layers.Activation(activation=self._activation[i])(conv)
+ conv = tf.keras.layers.Activation(
+ activation=self._activation[i], name=f"__act_encoder_block{i}"
+ )(conv)
x = conv
@@ -251,7 +253,9 @@ def build_network(self, input_shape, **kwargs):
)(x)
conv = tf.keras.layers.BatchNormalization()(conv)
- conv = tf.keras.layers.Activation(activation=self._activation[i])(conv)
+ conv = tf.keras.layers.Activation(
+ activation=self._activation[i], name=f"__act_decoder_block{i}"
+ )(conv)
x = conv
diff --git a/aeon/networks/_ae_resnet.py b/aeon/networks/_ae_resnet.py
index 90d7c0e696..a5464e2f6d 100644
--- a/aeon/networks/_ae_resnet.py
+++ b/aeon/networks/_ae_resnet.py
@@ -236,7 +236,14 @@ def build_network(self, input_shape, **kwargs):
input_tensor=input_block_tensor, output_tensor=conv
)
- conv = tf.keras.layers.Activation(activation=self._activation[c])(conv)
+ if c == self.n_conv_per_residual_block - 1:
+ conv = tf.keras.layers.Activation(
+ activation=self._activation[c], name=f"__act_encoder_block{d}"
+ )(conv)
+ else:
+ conv = tf.keras.layers.Activation(activation=self._activation[c])(
+ conv
+ )
x = conv
if not self.temporal_latent_space:
@@ -294,7 +301,14 @@ def build_network(self, input_shape, **kwargs):
input_tensor=input_block_tensor, output_tensor=conv
)
- conv = tf.keras.layers.Activation(activation=self._activation[c])(conv)
+ if c == self.n_conv_per_residual_block - 1:
+ conv = tf.keras.layers.Activation(
+ activation=self._activation[c], name=f"__act_decoder_block{d}"
+ )(conv)
+ else:
+ conv = tf.keras.layers.Activation(activation=self._activation[c])(
+ conv
+ )
x = conv
diff --git a/aeon/networks/_dcnn.py b/aeon/networks/_dcnn.py
new file mode 100644
index 0000000000..243340c30e
--- /dev/null
+++ b/aeon/networks/_dcnn.py
@@ -0,0 +1,167 @@
+"""Dilated Convolutional Nerual Networks (DCNN) Model."""
+
+__maintainer__ = []
+
+from aeon.networks.base import BaseDeepLearningNetwork
+
+
+class DCNNNetwork(BaseDeepLearningNetwork):
+ """Establish the network structure for a DCNN-Model.
+
+ Dilated Convolutional Neural Network based Model
+ for low-rank embeddings.
+
+ Parameters
+ ----------
+ latent_space_dim: int, default=128
+ Dimension of the models's latent space.
+ n_layers: int, default=4
+ Number of convolution layers.
+ kernel_size: Union[int, List[int]], default=3
+ Size of the 1D Convolutional Kernel. Defaults
+ to a list of three's for `n_layers` elements.
+ activation: Union[str, List[str]], default="relu"
+ The activation function used by convolution layers.
+ Defaults to a list of "relu" for `n_layers` elements.
+ n_filters: Union[int, List[int]], default=None
+ Number of filters used in convolution layers. Defaults
+ to a list of multiple's of 32 for `n_layers` elements.
+ dilation_rate: Union[int, List[int]], default=None
+ The dilation rate for convolution. Defaults to a list of
+ powers of 2 for `n_layers` elements.
+ padding: Union[str, List[str]], default="causal"
+ Padding to be used in each DCNN Layer. Defaults to a list
+ of causal paddings for `n_layers` elements.
+
+ References
+ ----------
+ .. [1] Franceschi, J. Y., Dieuleveut, A., & Jaggi, M. (2019).
+ Unsupervised scalable representation learning for multivariate
+ time series. Advances in neural information processing systems, 32.
+ """
+
+ _config = {
+ "python_dependencies": ["tensorflow"],
+ "python_version": "<3.12",
+ "structure": "encoder",
+ }
+
+ def __init__(
+ self,
+ latent_space_dim=128,
+ n_layers=4,
+ kernel_size=3,
+ activation="relu",
+ n_filters=None,
+ dilation_rate=None,
+ padding="causal",
+ ):
+ super().__init__()
+
+ self.latent_space_dim = latent_space_dim
+ self.kernel_size = kernel_size
+ self.n_filters = n_filters
+ self.n_layers = n_layers
+ self.dilation_rate = dilation_rate
+ self.activation = activation
+ self.padding = padding
+
+ def build_network(self, input_shape):
+ """Construct a network and return its input and output layers.
+
+ Parameters
+ ----------
+ input_shape : tuple of shape = (n_timepoints (m), n_channels (d))
+ The shape of the data fed into the input layer.
+
+ Returns
+ -------
+ model : a keras Model.
+ """
+ import tensorflow as tf
+
+ if self.n_filters is None:
+ self._n_filters = [32 * i for i in range(1, self.n_layers + 1)]
+ elif isinstance(self.n_filters, int):
+ self._n_filters = [self.n_filters for _ in range(self.n_layers)]
+ elif isinstance(self.n_filters, list):
+ self._n_filters = self.n_filters
+ assert len(self.n_filters) == self.n_layers
+
+ if self.dilation_rate is None:
+ self._dilation_rate = [
+ 2**layer_num for layer_num in range(1, self.n_layers + 1)
+ ]
+ elif isinstance(self.dilation_rate, int):
+ self._dilation_rate = [self.dilation_rate for _ in range(self.n_layers)]
+ else:
+ self._dilation_rate = self.dilation_rate
+ assert isinstance(self.dilation_rate, list)
+ assert len(self.dilation_rate) == self.n_layers
+
+ if self.kernel_size is None:
+ self._kernel_size = [3 for _ in range(self.n_layers)]
+ elif isinstance(self.kernel_size, int):
+ self._kernel_size = [self.kernel_size for _ in range(self.n_layers)]
+ elif isinstance(self.kernel_size, list):
+ self._kernel_size = self.kernel_size
+ assert len(self.kernel_size) == self.n_layers
+
+ if self.activation is None:
+ self._activation = ["relu" for _ in range(self.n_layers)]
+ elif isinstance(self.activation, str):
+ self._activation = [self.activation for _ in range(self.n_layers)]
+ elif isinstance(self.activation, list):
+ self._activation = self.activation
+ assert len(self._activation) == self.n_layers
+
+ if self.padding is None:
+ self._padding = ["causal" for _ in range(self.n_layers)]
+ elif isinstance(self.padding, str):
+ self._padding = [self.padding for _ in range(self.n_layers)]
+ elif isinstance(self.padding, list):
+ self._padding = self.padding
+ assert len(self._padding) == self.n_layers
+
+ input_layer = tf.keras.layers.Input(input_shape)
+
+ x = input_layer
+ for i in range(0, self.n_layers):
+ x = self._dcnn_layer(
+ x,
+ self._n_filters[i],
+ self._dilation_rate[i],
+ _activation=self._activation[i],
+ _kernel_size=self._kernel_size[i],
+ _padding=self._padding[i],
+ )
+
+ x = tf.keras.layers.GlobalMaxPool1D()(x)
+ output_layer = tf.keras.layers.Dense(self.latent_space_dim)(x)
+
+ return input_layer, output_layer
+
+ def _dcnn_layer(
+ self, _inputs, _n_filters, _dilation_rate, _activation, _kernel_size, _padding
+ ):
+ import tensorflow as tf
+
+ _add = tf.keras.layers.Conv1D(_n_filters, kernel_size=1)(_inputs)
+ x = tf.keras.layers.Conv1D(
+ _n_filters,
+ kernel_size=_kernel_size,
+ dilation_rate=_dilation_rate,
+ padding=_padding,
+ kernel_regularizer="l2",
+ )(_inputs)
+ x = tf.keras.layers.Conv1D(
+ _n_filters,
+ kernel_size=_kernel_size,
+ dilation_rate=_dilation_rate,
+ padding="causal",
+ kernel_regularizer="l2",
+ activation=_activation,
+ )(x)
+ output = tf.keras.layers.Add()([x, _add])
+ output = tf.keras.layers.Activation(_activation)(output)
+ return output
diff --git a/aeon/networks/_lite.py b/aeon/networks/_lite.py
index df19fba0d0..8730d54890 100644
--- a/aeon/networks/_lite.py
+++ b/aeon/networks/_lite.py
@@ -7,12 +7,21 @@
class LITENetwork(BaseDeepLearningNetwork):
- """LITE Network.
+ """LITE and LITE Multivariate (LITEMV) Networks.
- LITE deep neural network architecture from [1]_.
+ LITE deep neural network architecture from [1]_ and its
+ multivariate adaptation LITEMV from [2]_. For using
+ LITEMV, simply set the `use_litemv` bool parameter to
+ True.
Parameters
----------
+ use_litemv : bool, default = False
+ The boolean value to control which version of the
+ network to use. If set to `False`, then LITE is used,
+ if set to `True` then LITEMV is used. LITEMV is the
+ same architecture as LITE but specifically designed
+ to better handle multivariate time series.
n_filters : int or list of int32, default = 32
The number of filters used in one lite layer, if not a list, the same
number of filters is used in all lite layers.
@@ -28,22 +37,30 @@ class LITENetwork(BaseDeepLearningNetwork):
Notes
-----
+ Adapted from the implementation from Ismail-Fawaz et. al
+
+ https://github.com/MSD-IRIMAS/LITE
+
+ References
+ ----------
..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing
tEchniques for Time Series Classificaion, IEEE International
Conference on Data Science and Advanced Analytics, 2023.
- Adapted from the implementation from Ismail-Fawaz et. al
-
- https://github.com/MSD-IRIMAS/LITE
+ ..[2] Ismail-Fawaz, Ali, et al. "Look Into the LITE
+ in Deep Learning for Time Series Classification."
+ arXiv preprint arXiv:2409.02869 (2024).
"""
def __init__(
self,
+ use_litemv=False,
n_filters=32,
kernel_size=40,
strides=1,
activation="relu",
):
+ self.use_litemv = use_litemv
self.n_filters = n_filters
self.kernel_size = kernel_size
self.activation = activation
@@ -97,22 +114,41 @@ def hybrid_layer(self, input_tensor, input_channels, kernel_sizes=None):
filter_[indices_ % 2 == 0] *= -1 # formula of increasing detection filter
- # Create a Conv1D layer with non trainable option and no
- # biases and set the filter weights that were calculated in the
- # line above as the initialization
-
- conv = tf.keras.layers.Conv1D(
- filters=1,
- kernel_size=kernel_size,
- padding="same",
- use_bias=False,
- kernel_initializer=tf.keras.initializers.Constant(filter_.tolist()),
- trainable=False,
- name="hybrid-increasse-"
- + str(self.keep_track)
- + "-"
- + str(kernel_size),
- )(input_tensor)
+ if not self.use_litemv:
+ # Create a Conv1D layer with non trainable option and no
+ # biases and set the filter weights that were calculated in the
+ # line above as the initialization
+
+ conv = tf.keras.layers.Conv1D(
+ filters=1,
+ kernel_size=kernel_size,
+ padding="same",
+ use_bias=False,
+ kernel_initializer=tf.keras.initializers.Constant(filter_.tolist()),
+ trainable=False,
+ name="hybrid-increasse-"
+ + str(self.keep_track)
+ + "-"
+ + str(kernel_size),
+ )(input_tensor)
+ else:
+ # Create a DepthwiseConv1D layer with non trainable option and no
+ # biases and set the filter weights that were calculated in the
+ # line above as the initialization
+
+ conv = tf.keras.layers.DepthwiseConv1D(
+ kernel_size=kernel_size,
+ padding="same",
+ use_bias=False,
+ depthwise_initializer=tf.keras.initializers.Constant(
+ filter_.tolist()
+ ),
+ trainable=False,
+ name="hybrid-increasse-"
+ + str(self.keep_track)
+ + "-"
+ + str(kernel_size),
+ )(input_tensor)
conv_list.append(conv) # add the conv layer to the list
@@ -130,19 +166,41 @@ def hybrid_layer(self, input_tensor, input_channels, kernel_sizes=None):
filter_[indices_ % 2 > 0] *= -1 # formula of decreasing detection filter
- # Create a Conv1D layer with non trainable option
- # and no biases and set the filter weights that were
- # calculated in the line above as the initialization
+ if not self.use_litemv:
+ # Create a Conv1D layer with non trainable option
+ # and no biases and set the filter weights that were
+ # calculated in the line above as the initialization
- conv = tf.keras.layers.Conv1D(
- filters=1,
- kernel_size=kernel_size,
- padding="same",
- use_bias=False,
- kernel_initializer=tf.keras.initializers.Constant(filter_.tolist()),
- trainable=False,
- name="hybrid-decrease-" + str(self.keep_track) + "-" + str(kernel_size),
- )(input_tensor)
+ conv = tf.keras.layers.Conv1D(
+ filters=1,
+ kernel_size=kernel_size,
+ padding="same",
+ use_bias=False,
+ kernel_initializer=tf.keras.initializers.Constant(filter_.tolist()),
+ trainable=False,
+ name="hybrid-decrease-"
+ + str(self.keep_track)
+ + "-"
+ + str(kernel_size),
+ )(input_tensor)
+ else:
+ # Create a DepthwiseConv1D layer with non trainable option
+ # and no biases and set the filter weights that were
+ # calculated in the line above as the initialization
+
+ conv = tf.keras.layers.DepthwiseConv1D(
+ kernel_size=kernel_size,
+ padding="same",
+ use_bias=False,
+ depthwise_initializer=tf.keras.initializers.Constant(
+ filter_.tolist()
+ ),
+ trainable=False,
+ name="hybrid-decrease-"
+ + str(self.keep_track)
+ + "-"
+ + str(kernel_size),
+ )(input_tensor)
conv_list.append(conv) # add the conv layer to the list
@@ -171,19 +229,41 @@ def hybrid_layer(self, input_tensor, input_channels, kernel_sizes=None):
filter_[kernel_size : 5 * kernel_size // 4] = -filter_left
filter_[5 * kernel_size // 4 :] = -filter_right
- # Create a Conv1D layer with non trainable option and
- # no biases and set the filter weights that were
- # calculated in the line above as the initialization
+ if not self.use_litemv:
+ # Create a Conv1D layer with non trainable option and
+ # no biases and set the filter weights that were
+ # calculated in the line above as the initialization
- conv = tf.keras.layers.Conv1D(
- filters=1,
- kernel_size=kernel_size + kernel_size // 2,
- padding="same",
- use_bias=False,
- kernel_initializer=tf.keras.initializers.Constant(filter_.tolist()),
- trainable=False,
- name="hybrid-peeks-" + str(self.keep_track) + "-" + str(kernel_size),
- )(input_tensor)
+ conv = tf.keras.layers.Conv1D(
+ filters=1,
+ kernel_size=kernel_size + kernel_size // 2,
+ padding="same",
+ use_bias=False,
+ kernel_initializer=tf.keras.initializers.Constant(filter_.tolist()),
+ trainable=False,
+ name="hybrid-peeks-"
+ + str(self.keep_track)
+ + "-"
+ + str(kernel_size),
+ )(input_tensor)
+ else:
+ # Create a DepthwiseConv1D layer with non trainable option and
+ # no biases and set the filter weights that were
+ # calculated in the line above as the initialization
+
+ conv = tf.keras.layers.DepthwiseConv1D(
+ kernel_size=kernel_size + kernel_size // 2,
+ padding="same",
+ use_bias=False,
+ depthwise_initializer=tf.keras.initializers.Constant(
+ filter_.tolist()
+ ),
+ trainable=False,
+ name="hybrid-peeks-"
+ + str(self.keep_track)
+ + "-"
+ + str(kernel_size),
+ )(input_tensor)
conv_list.append(conv) # add the conv layer to the list
@@ -224,17 +304,30 @@ def _inception_module(
conv_list = []
for i in range(len(kernel_size_s)):
- conv_list.append(
- tf.keras.layers.Conv1D(
- filters=n_filters,
- kernel_size=kernel_size_s[i],
- strides=stride,
- padding="same",
- dilation_rate=dilation_rate,
- activation=activation,
- use_bias=False,
- )(input_inception)
- )
+ if not self.use_litemv:
+ conv_list.append(
+ tf.keras.layers.Conv1D(
+ filters=n_filters,
+ kernel_size=kernel_size_s[i],
+ strides=stride,
+ padding="same",
+ dilation_rate=dilation_rate,
+ activation=activation,
+ use_bias=False,
+ )(input_inception)
+ )
+ else:
+ conv_list.append(
+ tf.keras.layers.SeparableConv1D(
+ filters=n_filters,
+ kernel_size=kernel_size_s[i],
+ strides=stride,
+ padding="same",
+ dilation_rate=dilation_rate,
+ activation=activation,
+ use_bias=False,
+ )(input_inception)
+ )
if use_custom_filters:
hybrid_layer = self.hybrid_layer(
diff --git a/aeon/networks/tests/test_ae_dcnn.py b/aeon/networks/tests/test_ae_dcnn.py
new file mode 100644
index 0000000000..f2d583aedd
--- /dev/null
+++ b/aeon/networks/tests/test_ae_dcnn.py
@@ -0,0 +1,90 @@
+"""Tests for the AEDCNN Model."""
+
+import pytest
+
+from aeon.networks import AEDCNNNetwork
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="skip test if required soft dependency not available",
+)
+def test_default_initialization():
+ """Test if the network initializes with proper attributes."""
+ model = AEDCNNNetwork()
+ assert model.latent_space_dim == 128
+ assert model.kernel_size == 3
+ assert model.n_layers == 4
+ assert model.dilation_rate == 1
+ assert model.activation == "relu"
+ assert not model.temporal_latent_space
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="skip test if required soft dependency not available",
+)
+def test_custom_initialization():
+ """Test whether custom kwargs are correctly set."""
+ model = AEDCNNNetwork(
+ latent_space_dim=64,
+ temporal_latent_space=True,
+ n_layers=3,
+ kernel_size=5,
+ activation="sigmoid",
+ dilation_rate=[1, 2, 4],
+ )
+ model.build_network((100, 5))
+ assert model.latent_space_dim == 64
+ assert model._kernel_size_encoder == [5 for _ in range(model.n_layers)]
+ assert model.n_layers == 3
+ assert model.dilation_rate == [1, 2, 4]
+ assert model.activation == "sigmoid"
+ assert model.temporal_latent_space
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="skip test if required soft dependency not available",
+)
+def test_edge_case_initialization():
+ """Tests edge cases are correct or not."""
+ model = AEDCNNNetwork(
+ latent_space_dim=0,
+ n_layers=0,
+ kernel_size=0,
+ dilation_rate=[],
+ )
+ assert model.latent_space_dim == 0
+ assert model.kernel_size == 0
+ assert model.n_layers == 0
+ assert model.dilation_rate == []
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="skip test if required soft dependency not available",
+)
+def test_invalid_initialization():
+ """Test if the network raises valid exceptions or not."""
+ with pytest.raises(AssertionError):
+ AEDCNNNetwork(n_filters=[32, 64], n_layers=3).build_network((100, 10))
+
+ with pytest.raises(AssertionError):
+ AEDCNNNetwork(dilation_rate=[1, 2], n_layers=3).build_network((100, 10))
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="skip test if required soft dependency not available",
+)
+def test_build_network():
+ """Test call to the build_network method."""
+ model = AEDCNNNetwork()
+ input_shape = (100, 10) # Example input shape
+ encoder, decoder = model.build_network(input_shape)
+ assert encoder is not None
+ assert decoder is not None
+ assert encoder.input_shape == (None, 100, 10)
+ assert decoder.input_shape is not None
diff --git a/aeon/networks/tests/test_dcnn.py b/aeon/networks/tests/test_dcnn.py
new file mode 100644
index 0000000000..70ba35173d
--- /dev/null
+++ b/aeon/networks/tests/test_dcnn.py
@@ -0,0 +1,50 @@
+"""Tests for the DCNN Model."""
+
+import random
+
+import pytest
+
+from aeon.networks import DCNNNetwork
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+ "latent_space_dim,n_layers",
+ [
+ (32, 1),
+ (128, 2),
+ (256, 3),
+ (64, 4),
+ ],
+)
+def test_dcnnnetwork_init(latent_space_dim, n_layers):
+ """Test whether DCNNNetwork initializes correctly for various parameters."""
+ dcnnnet = DCNNNetwork(
+ latent_space_dim=latent_space_dim,
+ n_layers=n_layers,
+ activation=random.choice(["relu", "tanh"]),
+ n_filters=[random.choice([50, 25, 100]) for _ in range(n_layers)],
+ )
+ model = dcnnnet.build_network((1000, 5))
+ assert model is not None
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize("activation", ["relu", "tanh"])
+def test_dcnnnetwork_activations(activation):
+ """Test whether DCNNNetwork initializes correctly with different activations."""
+ dcnnnet = DCNNNetwork(
+ latent_space_dim=64,
+ n_layers=2,
+ activation=activation,
+ n_filters=[50, 50],
+ )
+ model = dcnnnet.build_network((150, 5))
+ assert model is not None
diff --git a/aeon/performance_metrics/forecasting/__init__.py b/aeon/performance_metrics/forecasting/__init__.py
deleted file mode 100644
index 53a84d8723..0000000000
--- a/aeon/performance_metrics/forecasting/__init__.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""Metrics to assess performance on forecasting task.
-
-Functions named as ``*_score`` return a scalar value to maximize: the higher
-the better.
-Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
-the lower the better.
-"""
-
-__all__ = [
- "mean_absolute_scaled_error",
- "median_absolute_scaled_error",
- "mean_squared_scaled_error",
- "median_squared_scaled_error",
- "mean_absolute_error",
- "mean_squared_error",
- "median_absolute_error",
- "median_squared_error",
- "geometric_mean_absolute_error",
- "geometric_mean_squared_error",
- "mean_absolute_percentage_error",
- "median_absolute_percentage_error",
- "mean_squared_percentage_error",
- "median_squared_percentage_error",
- "mean_relative_absolute_error",
- "median_relative_absolute_error",
- "geometric_mean_relative_absolute_error",
- "geometric_mean_relative_squared_error",
- "mean_asymmetric_error",
- "mean_linex_error",
- "relative_loss",
-]
-
-from aeon.performance_metrics.forecasting._functions import (
- geometric_mean_absolute_error,
- geometric_mean_relative_absolute_error,
- geometric_mean_relative_squared_error,
- geometric_mean_squared_error,
- mean_absolute_error,
- mean_absolute_percentage_error,
- mean_absolute_scaled_error,
- mean_asymmetric_error,
- mean_linex_error,
- mean_relative_absolute_error,
- mean_squared_error,
- mean_squared_percentage_error,
- mean_squared_scaled_error,
- median_absolute_error,
- median_absolute_percentage_error,
- median_absolute_scaled_error,
- median_relative_absolute_error,
- median_squared_error,
- median_squared_percentage_error,
- median_squared_scaled_error,
- relative_loss,
-)
diff --git a/aeon/performance_metrics/forecasting/_functions.py b/aeon/performance_metrics/forecasting/_functions.py
deleted file mode 100644
index c1086f482a..0000000000
--- a/aeon/performance_metrics/forecasting/_functions.py
+++ /dev/null
@@ -1,2732 +0,0 @@
-"""Metrics functions to assess performance on forecasting task.
-
-Functions named as ``*_score`` return a value to maximize: the higher the better.
-Function named as ``*_error`` or ``*_loss`` return a value to minimize:
-the lower the better.
-"""
-
-import numpy as np
-from scipy.stats import gmean
-from sklearn.metrics import mean_absolute_error as _mean_absolute_error
-from sklearn.metrics import mean_squared_error as _mean_squared_error
-from sklearn.metrics import median_absolute_error as _median_absolute_error
-from sklearn.metrics._regression import _check_reg_targets
-from sklearn.utils.stats import _weighted_percentile
-from sklearn.utils.validation import check_consistent_length
-
-from aeon.utils.validation.series import check_series
-from aeon.utils.weighted_metrics import weighted_geometric_mean
-
-__maintainer__ = []
-__all__ = [
- "relative_loss",
- "mean_linex_error",
- "mean_asymmetric_error",
- "mean_absolute_scaled_error",
- "median_absolute_scaled_error",
- "mean_squared_scaled_error",
- "median_squared_scaled_error",
- "mean_absolute_error",
- "mean_squared_error",
- "median_absolute_error",
- "median_squared_error",
- "geometric_mean_absolute_error",
- "geometric_mean_squared_error",
- "mean_absolute_percentage_error",
- "median_absolute_percentage_error",
- "mean_squared_percentage_error",
- "median_squared_percentage_error",
- "mean_relative_absolute_error",
- "median_relative_absolute_error",
- "geometric_mean_relative_absolute_error",
- "geometric_mean_relative_squared_error",
-]
-
-EPS = np.finfo(np.float64).eps
-
-
-def _get_kwarg(kwarg, metric_name="Metric", **kwargs):
- """Pop a kwarg from kwargs and raise warning if kwarg not present."""
- kwarg_ = kwargs.pop(kwarg, None)
- if kwarg_ is None:
- msg = "".join(
- [
- f"{metric_name} requires `{kwarg}`. ",
- f"Pass `{kwarg}` as a keyword argument when calling the metric.",
- ]
- )
- raise ValueError(msg)
- return kwarg_
-
-
-def mean_linex_error(
- y_true,
- y_pred,
- a=1.0,
- b=1.0,
- horizon_weight=None,
- multioutput="uniform_average",
- **kwargs,
-):
- """Calculate mean linex error.
-
- Output is non-negative floating point. The best value is 0.0.
-
- Many forecasting loss functions (like those discussed in [1]_) assume that
- over- and under- predictions should receive an equal penalty. However, this
- may not align with the actual cost faced by users' of the forecasts.
- Asymmetric loss functions are useful when the cost of under- and over-
- prediction are not the same.
-
- The linex error function accounts for this by penalizing errors on one side
- of a threshold approximately linearly, while penalizing errors on the other
- side approximately exponentially.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
- a : int or float
- Controls whether over- or under- predictions receive an approximately
- linear or exponential penalty. If `a` > 0 then negative errors
- (over-predictions) are penalized approximately linearly and positive errors
- (under-predictions) are penalized approximately exponentially. If `a` < 0
- the reverse is true.
- b : int or float
- Multiplicative penalty to apply to calculated errors.
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- asymmetric_loss : float
- Loss using asymmetric penalty of on errors.
- If multioutput is 'raw_values', then asymmetric loss is returned for
- each output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average asymmetric loss of all output errors is returned.
-
- See Also
- --------
- mean_asymmetric_error
-
- Notes
- -----
- Calculated as b * (np.exp(a * error) - a * error - 1), where a != 0 and b > 0
- according to formula in [2]_.
-
- References
- ----------
- .. [1] Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- .. [1] Diebold, Francis X. (2007). "Elements of Forecasting (4th ed.)",
- Thomson, South-Western: Ohio, US.
-
- Examples
- --------
- >>> import numpy as np
- >>> from aeon.performance_metrics.forecasting import mean_linex_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> mean_linex_error(y_true, y_pred) # doctest: +SKIP
- 0.19802627763937575
- >>> mean_linex_error(y_true, y_pred, b=2) # doctest: +SKIP
- 0.3960525552787515
- >>> mean_linex_error(y_true, y_pred, a=-1) # doctest: +SKIP
- 0.2391800623225643
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> mean_linex_error(y_true, y_pred) # doctest: +SKIP
- 0.2700398392309829
- >>> mean_linex_error(y_true, y_pred, a=-1) # doctest: +SKIP
- 0.49660966225813563
- >>> mean_linex_error(y_true, y_pred, multioutput='raw_values') # doctest: +SKIP
- array([0.17220024, 0.36787944])
- >>> mean_linex_error(y_true, y_pred, multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.30917568000716666
- """
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- if horizon_weight is not None:
- check_consistent_length(y_true, horizon_weight)
-
- linex_error = _linex_error(y_true, y_pred, a=a, b=b)
- output_errors = np.average(linex_error, weights=horizon_weight, axis=0)
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def mean_asymmetric_error(
- y_true,
- y_pred,
- asymmetric_threshold=0.0,
- left_error_function="squared",
- right_error_function="absolute",
- left_error_penalty=1.0,
- right_error_penalty=1.0,
- horizon_weight=None,
- multioutput="uniform_average",
- **kwargs,
-):
- """Calculate mean of asymmetric loss function.
-
- Output is non-negative floating point. The best value is 0.0.
-
- Error values that are less than the asymmetric threshold have
- `left_error_function` applied. Error values greater than or equal to
- asymmetric threshold have `right_error_function` applied.
-
- Many forecasting loss functions (like those discussed in [1]_) assume that
- over- and under- predictions should receive an equal penalty. However, this
- may not align with the actual cost faced by users' of the forecasts.
- Asymmetric loss functions are useful when the cost of under- and over-
- prediction are not the same.
-
- Setting `asymmetric_threshold` to zero, `left_error_function` to 'squared'
- and `right_error_function` to 'absolute` results in a greater penalty
- applied to over-predictions (y_true - y_pred < 0). The opposite is true
- for `left_error_function` set to 'absolute' and `right_error_function`
- set to 'squared`.
-
- The left_error_penalty and right_error_penalty can be used to add differing
- multiplicative penalties to over-predictions and under-predictions.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
- asymmetric_threshold : float, default = 0.0
- The value used to threshold the asymmetric loss function. Error values
- that are less than the asymmetric threshold have `left_error_function`
- applied. Error values greater than or equal to asymmetric threshold
- have `right_error_function` applied.
- left_error_function : {'squared', 'absolute'}, default='squared'
- Loss penalty to apply to error values less than the asymmetric threshold.
- right_error_function : {'squared', 'absolute'}, default='absolute'
- Loss penalty to apply to error values greater than or equal to the
- asymmetric threshold.
- left_error_penalty : int or float, default=1.0
- An additional multiplicative penalty to apply to error values less than
- the asymetric threshold.
- right_error_penalty : int or float, default=1.0
- An additional multiplicative penalty to apply to error values greater
- than the asymmetric threshold.
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- asymmetric_loss : float
- Loss using asymmetric penalty of on errors.
- If multioutput is 'raw_values', then asymmetric loss is returned for
- each output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average asymmetric loss of all output errors is returned.
-
- See Also
- --------
- mean_linex_error
-
- Notes
- -----
- Setting `left_error_function` and `right_error_function` to "aboslute", but
- choosing different values for `left_error_penalty` and `right_error_penalty`
- results in the "lin-lin" error function discussed in [2]_.
-
- References
- ----------
- .. [1] Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- .. [2] Diebold, Francis X. (2007). "Elements of Forecasting (4th ed.)",
- Thomson, South-Western: Ohio, US.
-
- Examples
- --------
- >>> import numpy as np
- >>> from aeon.performance_metrics.forecasting import mean_asymmetric_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> mean_asymmetric_error(y_true, y_pred) # doctest: +SKIP
- 0.5
- >>> mean_asymmetric_error(y_true, y_pred, left_error_function='absolute', \
- right_error_function='squared') # doctest: +SKIP
- 0.4625
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> mean_asymmetric_error(y_true, y_pred) # doctest: +SKIP
- 0.75
- >>> mean_asymmetric_error(y_true, y_pred, left_error_function='absolute', \
- right_error_function='squared') # doctest: +SKIP
- 0.7083333333333334
- >>> mean_asymmetric_error(y_true, y_pred, multioutput='raw_values') # doctest: +SKIP
- array([0.5, 1. ])
- >>> mean_asymmetric_error(y_true, y_pred, multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.85
- """
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
-
- if horizon_weight is not None:
- check_consistent_length(y_true, horizon_weight)
-
- asymmetric_errors = _asymmetric_error(
- y_true,
- y_pred,
- asymmetric_threshold=asymmetric_threshold,
- left_error_function=left_error_function,
- right_error_function=right_error_function,
- left_error_penalty=left_error_penalty,
- right_error_penalty=right_error_penalty,
- )
- output_errors = np.average(asymmetric_errors, weights=horizon_weight, axis=0)
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def mean_absolute_scaled_error(
- y_true, y_pred, sp=1, horizon_weight=None, multioutput="uniform_average", **kwargs
-):
- """Mean absolute scaled error (MASE).
-
- MASE output is non-negative floating point. The best value is 0.0.
-
- Like other scaled performance metrics, this scale-free error metric can be
- used to compare forecast methods on a single series and also to compare
- forecast accuracy between series.
-
- This metric is well suited to intermittent-demand series because it
- will not give infinite or undefined values unless the training data
- is a flat timeseries. In this case the function returns a large value
- instead of inf.
-
- Works with multioutput (multivariate) timeseries data
- with homogeneous seasonal periodicity.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- y_train : pd.Series, pd.DataFrame or np.array of shape (n_timepoints,) or \
- (n_timepoints, n_outputs), default = None
- Observed training values.
-
- sp : int
- Seasonal periodicity of training data.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
-
- Returns
- -------
- loss : float or ndarray of floats
- MASE loss.
- If multioutput is 'raw_values', then MASE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MASE of all output errors is returned.
-
- See Also
- --------
- median_absolute_scaled_error
- mean_squared_scaled_error
- median_squared_scaled_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Hyndman, R. J. (2006). "Another look at forecast accuracy metrics
- for intermittent demand", Foresight, Issue 4.
-
- Makridakis, S., Spiliotis, E. and Assimakopoulos, V. (2020)
- "The M4 Competition: 100,000 time series and 61 forecasting methods",
- International Journal of Forecasting, Volume 3.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import mean_absolute_scaled_error
- >>> y_train = np.array([5, 0.5, 4, 6, 3, 5, 2])
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> mean_absolute_scaled_error(y_true, y_pred, y_train=y_train) # doctest: +SKIP
- 0.18333333333333335
- >>> y_train = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> mean_absolute_scaled_error(y_true, y_pred, y_train=y_train) # doctest: +SKIP
- 0.18181818181818182
- >>> mean_absolute_scaled_error(y_true, y_pred, y_train=y_train, \
- multioutput='raw_values') # doctest: +SKIP
- array([0.10526316, 0.28571429])
- >>> mean_absolute_scaled_error(y_true, y_pred, y_train=y_train, \
- multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.21935483870967742
- """
- y_train = _get_kwarg("y_train", metric_name="mean_absolute_scaled_error", **kwargs)
-
- # Other input checks
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- if horizon_weight is not None:
- check_consistent_length(y_true, horizon_weight)
- y_train = check_series(y_train, enforce_univariate=False)
- # _check_reg_targets converts 1-dim y_true,y_pred to 2-dim so need to match
- if y_train.ndim == 1:
- y_train = np.expand_dims(y_train, 1)
-
- # Check test and train have same dimensions
- if y_true.ndim != y_train.ndim:
- raise ValueError("Equal dimension required for y_true and y_train")
-
- if (y_true.ndim > 1) and (y_true.shape[1] != y_train.shape[1]):
- raise ValueError("Equal number of columns required for y_true and y_train")
-
- # naive seasonal prediction
- y_train = np.asarray(y_train)
- y_pred_naive = y_train[:-sp]
-
- # mean absolute error of naive seasonal prediction
- mae_naive = mean_absolute_error(y_train[sp:], y_pred_naive, multioutput=multioutput)
-
- mae_pred = mean_absolute_error(
- y_true, y_pred, horizon_weight=horizon_weight, multioutput=multioutput
- )
- return mae_pred / np.maximum(mae_naive, EPS)
-
-
-def median_absolute_scaled_error(
- y_true, y_pred, sp=1, horizon_weight=None, multioutput="uniform_average", **kwargs
-):
- """Median absolute scaled error (MdASE).
-
- MdASE output is non-negative floating point. The best value is 0.0.
-
- Taking the median instead of the mean of the test and train absolute errors
- makes this metric more robust to error outliers since the median tends
- to be a more robust measure of central tendency in the presence of outliers.
-
- Like MASE and other scaled performance metrics this scale-free metric can be
- used to compare forecast methods on a single series or between series.
-
- Also like MASE, this metric is well suited to intermittent-demand series
- because it will not give infinite or undefined values unless the training
- data is a flat timeseries. In this case the function returns a large value
- instead of inf.
-
- Works with multioutput (multivariate) timeseries data
- with homogeneous seasonal periodicity.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- y_train : pd.Series, pd.DataFrame or np.array of shape (n_timepoints,) or \
- (n_timepoints, n_outputs), default = None
- Observed training values.
-
- sp : int
- Seasonal periodicity of training data.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- See Also
- --------
- mean_absolute_scaled_error
- mean_squared_scaled_error
- median_squared_scaled_error
-
- Returns
- -------
- loss : float or ndarray of floats
- MdASE loss.
- If multioutput is 'raw_values', then MdASE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MdASE of all output errors is returned.
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Hyndman, R. J. (2006). "Another look at forecast accuracy metrics
- for intermittent demand", Foresight, Issue 4.
-
- Makridakis, S., Spiliotis, E. and Assimakopoulos, V. (2020)
- "The M4 Competition: 100,000 time series and 61 forecasting methods",
- International Journal of Forecasting, Volume 3.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import median_absolute_scaled_error
- >>> y_train = np.array([5, 0.5, 4, 6, 3, 5, 2])
- >>> y_true = [3, -0.5, 2, 7]
- >>> y_pred = [2.5, 0.0, 2, 8]
- >>> median_absolute_scaled_error(y_true, y_pred, y_train=y_train) # doctest: +SKIP
- 0.16666666666666666
- >>> y_train = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> median_absolute_scaled_error(y_true, y_pred, y_train=y_train) # doctest: +SKIP
- 0.18181818181818182
- >>> median_absolute_scaled_error(y_true, y_pred, y_train=y_train, \
- multioutput='raw_values') # doctest: +SKIP
- array([0.10526316, 0.28571429])
- >>> median_absolute_scaled_error( y_true, y_pred, y_train=y_train, \
- multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.21935483870967742
- """
- y_train = _get_kwarg(
- "y_train", metric_name="median_absolute_scaled_error", **kwargs
- )
-
- # Other input checks
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- if horizon_weight is not None:
- check_consistent_length(y_true, horizon_weight)
- y_train = check_series(y_train, enforce_univariate=False)
- if y_train.ndim == 1:
- y_train = np.expand_dims(y_train, 1)
-
- # Check test and train have same dimensions
- if y_true.ndim != y_train.ndim:
- raise ValueError("Equal dimension required for y_true and y_train")
-
- if (y_true.ndim > 1) and (y_true.shape[1] != y_train.shape[1]):
- raise ValueError("Equal number of columns required for y_true and y_train")
-
- # naive seasonal prediction
- y_train = np.asarray(y_train)
- y_pred_naive = y_train[:-sp]
-
- # mean absolute error of naive seasonal prediction
- mdae_naive = median_absolute_error(
- y_train[sp:], y_pred_naive, multioutput=multioutput
- )
-
- mdae_pred = median_absolute_error(
- y_true, y_pred, horizon_weight=horizon_weight, multioutput=multioutput
- )
- return mdae_pred / np.maximum(mdae_naive, EPS)
-
-
-def mean_squared_scaled_error(
- y_true,
- y_pred,
- sp=1,
- horizon_weight=None,
- multioutput="uniform_average",
- square_root=False,
- **kwargs,
-):
- """Mean squared scaled error (MSSE) or root mean squared scaled error (RMSSE).
-
- If `square_root` is False then calculates MSSE, otherwise calculates RMSSE if
- `square_root` is True. Both MSSE and RMSSE output is non-negative floating
- point. The best value is 0.0.
-
- This is a squared varient of the MASE loss metric. Like MASE and other
- scaled performance metrics this scale-free metric can be used to compare
- forecast methods on a single series or between series.
-
- This metric is also suited for intermittent-demand series because it
- will not give infinite or undefined values unless the training data
- is a flat timeseries. In this case the function returns a large value
- instead of inf.
-
- Works with multioutput (multivariate) timeseries data
- with homogeneous seasonal periodicity.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- y_train : pd.Series, pd.DataFrame or np.array of shape (n_timepoints,) or \
- (n_timepoints, n_outputs), default = None
- Observed training values.
-
- sp : int
- Seasonal periodicity of training data.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- square_root : bool, default=False
- Whether to take the square root of the mean squared scaled error.
- If True, returns root mean squared scaled error (RMSSE)
- If False, returns mean squared scaled error (MSSE)
-
- Returns
- -------
- loss : float
- RMSSE loss.
- If multioutput is 'raw_values', then MSSE or RMSSE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MSSE or RMSSE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_scaled_error
- median_absolute_scaled_error
- median_squared_scaled_error
-
- References
- ----------
- M5 Competition Guidelines.
- https://mofc.unic.ac.cy/wp-content/uploads/2020/03/M5-Competitors-Guide-Final-10-March-2020.docx
-
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import mean_squared_scaled_error
- >>> y_train = np.array([5, 0.5, 4, 6, 3, 5, 2])
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> mean_squared_scaled_error(y_true, y_pred, y_train=y_train, \
- square_root=True) # doctest: +SKIP
- 0.20568833780186058
- >>> y_train = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> mean_squared_scaled_error(y_true, y_pred, y_train=y_train, \
- square_root=True) # doctest: +SKIP
- 0.15679361328058636
- >>> mean_squared_scaled_error(y_true, y_pred, y_train=y_train, \
- multioutput='raw_values', square_root=True) # doctest: +SKIP
- array([0.11215443, 0.20203051])
- >>> mean_squared_scaled_error(y_true, y_pred, y_train=y_train, \
- multioutput=[0.3, 0.7], square_root=True) # doctest: +SKIP
- 0.17451891814894502
- """
- y_train = _get_kwarg("y_train", metric_name="mean_squared_scaled_error", **kwargs)
-
- # Other input checks
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- if horizon_weight is not None:
- check_consistent_length(y_true, horizon_weight)
- y_train = check_series(y_train, enforce_univariate=False)
- if y_train.ndim == 1:
- y_train = np.expand_dims(y_train, 1)
-
- # Check test and train have same dimensions
- if y_true.ndim != y_train.ndim:
- raise ValueError("Equal dimension required for y_true and y_train")
-
- if (y_true.ndim > 1) and (y_true.shape[1] != y_train.shape[1]):
- raise ValueError("Equal number of columns required for y_true and y_train")
-
- # naive seasonal prediction
- y_train = np.asarray(y_train)
- y_pred_naive = y_train[:-sp]
-
- # mean squared error of naive seasonal prediction
- mse_naive = mean_squared_error(y_train[sp:], y_pred_naive, multioutput=multioutput)
-
- mse = mean_squared_error(
- y_true, y_pred, horizon_weight=horizon_weight, multioutput=multioutput
- )
-
- if square_root:
- loss = np.sqrt(mse / np.maximum(mse_naive, EPS))
- else:
- loss = mse / np.maximum(mse_naive, EPS)
-
- return loss
-
-
-def median_squared_scaled_error(
- y_true,
- y_pred,
- sp=1,
- horizon_weight=None,
- multioutput="uniform_average",
- square_root=False,
- **kwargs,
-):
- """Median squared scaled error (MdSSE) or root median squared scaled error (RMdSSE).
-
- If `square_root` is False then calculates MdSSE, otherwise calculates RMdSSE if
- `square_root` is True. Both MdSSE and RMdSSE output is non-negative floating
- point. The best value is 0.0.
-
- This is a squared varient of the MdASE loss metric. Like MASE and other
- scaled performance metrics this scale-free metric can be used to compare
- forecast methods on a single series or between series.
-
- This metric is also suited for intermittent-demand series because it
- will not give infinite or undefined values unless the training data
- is a flat timeseries. In this case the function returns a large value
- instead of inf.
-
- Works with multioutput (multivariate) timeseries data
- with homogeneous seasonal periodicity.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
- y_train : pd.Series, pd.DataFrame or np.array of shape (n_timepoints,) or \
- (n_timepoints, n_outputs), default = None
- Observed training values.
- sp : int
- Seasonal periodicity of training data.
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- loss : float
- RMdSSE loss.
- If multioutput is 'raw_values', then RMdSSE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average RMdSSE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_scaled_error
- median_absolute_scaled_error
- mean_squared_scaled_error
-
- References
- ----------
- M5 Competition Guidelines.
- https://mofc.unic.ac.cy/wp-content/uploads/2020/03/M5-Competitors-Guide-Final-10-March-2020.docx
-
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import median_squared_scaled_error
- >>> y_train = np.array([5, 0.5, 4, 6, 3, 5, 2])
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> median_squared_scaled_error(y_true, y_pred, y_train=y_train, \
- square_root=True) # doctest: +SKIP
- 0.16666666666666666
- >>> y_train = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> median_squared_scaled_error(y_true, y_pred, y_train=y_train, \
- square_root=True) # doctest: +SKIP
- 0.1472819539849714
- >>> median_squared_scaled_error(y_true, y_pred, y_train=y_train, \
- multioutput='raw_values', square_root=True) # doctest: +SKIP
- array([0.08687445, 0.20203051])
- >>> median_squared_scaled_error(y_true, y_pred, y_train=y_train, \
- multioutput=[0.3, 0.7], square_root=True) # doctest: +SKIP
- 0.16914781383660782
- """
- y_train = _get_kwarg("y_train", metric_name="median_squared_scaled_error", **kwargs)
-
- # Other input checks
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- if horizon_weight is not None:
- check_consistent_length(y_true, horizon_weight)
- y_train = check_series(y_train, enforce_univariate=False)
- if y_train.ndim == 1:
- y_train = np.expand_dims(y_train, 1)
-
- # Check test and train have same dimensions
- if y_true.ndim != y_train.ndim:
- raise ValueError("Equal dimension required for y_true and y_train")
-
- if (y_true.ndim > 1) and (y_true.shape[1] != y_train.shape[1]):
- raise ValueError("Equal number of columns required for y_true and y_train")
-
- # naive seasonal prediction
- y_train = np.asarray(y_train)
- y_pred_naive = y_train[:-sp]
-
- # median squared error of naive seasonal prediction
- mdse_naive = median_squared_error(
- y_train[sp:], y_pred_naive, multioutput=multioutput
- )
-
- mdse = median_squared_error(
- y_true, y_pred, horizon_weight=horizon_weight, multioutput=multioutput
- )
-
- if square_root:
- loss = np.sqrt(mdse / np.maximum(mdse_naive, EPS))
- else:
- loss = mdse / np.maximum(mdse_naive, EPS)
- return loss
-
-
-def mean_absolute_error(
- y_true, y_pred, horizon_weight=None, multioutput="uniform_average", **kwargs
-):
- """Mean absolute error (MAE).
-
- MAE output is non-negative floating point. The best value is 0.0.
-
- MAE is on the same scale as the data. Because MAE takes the absolute value
- of the forecast error rather than squaring it, MAE penalizes large errors
- to a lesser degree than MSE or RMSE.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- loss : float or ndarray of floats
- MAE loss.
- If multioutput is 'raw_values', then MAE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MAE of all output errors is returned.
-
- See Also
- --------
- median_absolute_error
- mean_squared_error
- median_squared_error
- geometric_mean_absolute_error
- geometric_mean_squared_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import mean_absolute_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> mean_absolute_error(y_true, y_pred) # doctest: +SKIP
- 0.55
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> mean_absolute_error(y_true, y_pred) # doctest: +SKIP
- 0.75
- >>> mean_absolute_error(y_true, y_pred, \
- multioutput='raw_values') # doctest: +SKIP
- array([0.5, 1. ])
- >>> mean_absolute_error(y_true, y_pred, \
- multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.85
- """
- return _mean_absolute_error(
- y_true, y_pred, sample_weight=horizon_weight, multioutput=multioutput
- )
-
-
-def mean_squared_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- square_root=False,
- **kwargs,
-):
- """Mean squared error (MSE) or root mean squared error (RMSE).
-
- If `square_root` is False then calculates MSE and if `square_root` is True
- then RMSE is calculated. Both MSE and RMSE are both non-negative floating
- point. The best value is 0.0.
-
- MSE is measured in squared units of the input data, and RMSE is on the
- same scale as the data. Because MSE and RMSE square the forecast error
- rather than taking the absolute value, they penalize large errors more than
- MAE.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- square_root : bool, default=False
- Whether to take the square root of the mean squared error.
- If True, returns root mean squared error (RMSE)
- If False, returns mean squared error (MSE)
-
- Returns
- -------
- loss : float or ndarray of floats
- MSE loss.
- If multioutput is 'raw_values', then MSE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MSE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_error
- median_absolute_error
- median_squared_error
- geometric_mean_absolute_error
- geometric_mean_squared_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import mean_squared_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> mean_squared_error(y_true, y_pred) # doctest: +SKIP
- 0.4125
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> mean_squared_error(y_true, y_pred) # doctest: +SKIP
- 0.7083333333333334
- >>> mean_squared_error(y_true, y_pred, square_root=True) # doctest: +SKIP
- 0.8227486121839513
- >>> mean_squared_error(y_true, y_pred, \
- multioutput='raw_values') # doctest: +SKIP
- array([0.41666667, 1. ])
- >>> mean_squared_error(y_true, y_pred, multioutput='raw_values', \
- square_root=True) # doctest: +SKIP
- array([0.64549722, 1. ])
- >>> mean_squared_error(y_true, y_pred, \
- multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.825
- >>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- square_root=True) # doctest: +SKIP
- 0.8936491673103708
- """
- # Scikit-learn argument `squared` returns MSE when True and RMSE when False
- # Scikit-time argument `square_root` returns RMSE when True and MSE when False
- # Therefore need to pass the opposite of square_root as squared argument
- # to the scikit-learn function being wrapped
- squared = not square_root
- return _mean_squared_error(
- y_true,
- y_pred,
- sample_weight=horizon_weight,
- multioutput=multioutput,
- squared=squared,
- )
-
-
-def median_absolute_error(
- y_true, y_pred, horizon_weight=None, multioutput="uniform_average", **kwargs
-):
- """Median absolute error (MdAE).
-
- MdAE output is non-negative floating point. The best value is 0.0.
-
- Like MAE, MdAE is on the same scale as the data. Because MAE takes the
- absolute value of the forecast error rather than squaring it, MAE penalizes
- large errors to a lesser degree than MdSE or RdMSE.
-
- Taking the median instead of the mean of the absolute errors also makes
- this metric more robust to error outliers since the median tends
- to be a more robust measure of central tendency in the presence of outliers.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- loss : float
- MdAE loss.
- If multioutput is 'raw_values', then MdAE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MdAE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_error
- mean_squared_error
- median_squared_error
- geometric_mean_absolute_error
- geometric_mean_squared_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import median_absolute_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> median_absolute_error(y_true, y_pred) # doctest: +SKIP
- 0.5
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> median_absolute_error(y_true, y_pred) # doctest: +SKIP
- 0.75
- >>> median_absolute_error(y_true, y_pred, \
- multioutput='raw_values') # doctest: +SKIP
- array([0.5, 1. ])
- >>> median_absolute_error(y_true, y_pred, \
- multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.85
- """
- return _median_absolute_error(
- y_true, y_pred, sample_weight=horizon_weight, multioutput=multioutput
- )
-
-
-def median_squared_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- square_root=False,
- **kwargs,
-):
- """Median squared error (MdSE) or root median squared error (RMdSE).
-
- If `square_root` is False then calculates MdSE and if `square_root` is True
- then RMdSE is calculated. Both MdSE and RMdSE return non-negative floating
- point. The best value is 0.0.
-
- Like MSE, MdSE is measured in squared units of the input data. RMdSE is
- on the same scale as the input data like RMSE. Because MdSE and RMdSE
- square the forecast error rather than taking the absolute value, they
- penalize large errors more than MAE or MdAE.
-
- Taking the median instead of the mean of the squared errors makes
- this metric more robust to error outliers relative to a meean based metric
- since the median tends to be a more robust measure of central tendency in
- the presence of outliers.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- square_root : bool, default=False
- Whether to take the square root of the mean squared error.
- If True, returns root mean squared error (RMSE)
- If False, returns mean squared error (MSE)
-
- Returns
- -------
- loss : float
- MdSE loss.
- If multioutput is 'raw_values', then MdSE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MdSE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_error
- median_absolute_error
- mean_squared_error
- geometric_mean_absolute_error
- geometric_mean_squared_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import median_squared_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> median_squared_error(y_true, y_pred) # doctest: +SKIP
- 0.25
- >>> median_squared_error(y_true, y_pred, square_root=True) # doctest: +SKIP
- 0.5
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> median_squared_error(y_true, y_pred) # doctest: +SKIP
- 0.625
- >>> median_squared_error(y_true, y_pred, square_root=True) # doctest: +SKIP
- 0.75
- >>> median_squared_error(y_true, y_pred, \
- multioutput='raw_values') # doctest: +SKIP
- array([0.25, 1. ])
- >>> median_squared_error(y_true, y_pred, multioutput='raw_values', \
- square_root=True) # doctest: +SKIP
- array([0.5, 1. ])
- >>> median_squared_error(y_true, y_pred, multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.7749999999999999
- >>> median_squared_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- square_root=True) # doctest: +SKIP
- 0.85
- """
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- if horizon_weight is None:
- output_errors = np.median(np.square(y_pred - y_true), axis=0)
-
- else:
- check_consistent_length(y_true, horizon_weight)
- output_errors = _weighted_percentile(
- np.square(y_pred - y_true), sample_weight=horizon_weight
- )
-
- if square_root:
- output_errors = np.sqrt(output_errors)
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def geometric_mean_absolute_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- **kwargs,
-):
- """Geometric mean absolute error (GMAE).
-
- GMAE output is non-negative floating point. The best value is approximately
- zero, rather than zero.
-
- Like MAE and MdAE, GMAE is measured in the same units as the input data.
- Because GMAE takes the absolute value of the forecast error rather than
- squaring it, MAE penalizes large errors to a lesser degree than squared error
- varients like MSE, RMSE or GMSE or RGMSE.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- loss : float
- GMAE loss. If multioutput is 'raw_values', then GMAE is returned for each
- output separately. If multioutput is 'uniform_average' or an ndarray
- of weights, then the weighted average GMAE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_error
- median_absolute_error
- mean_squared_error
- median_squared_error
- geometric_mean_squared_error
-
- Notes
- -----
- The geometric mean uses the product of values in its calculation. The presence
- of a zero value will result in the result being zero, even if all the other
- values of large. To partially account for this in the case where elements
- of `y_true` and `y_pred` are equal (zero error), the resulting zero error
- values are replaced in the calculation with a small value. This results in
- the smallest value the metric can take (when `y_true` equals `y_pred`)
- being close to but not exactly zero.
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> import numpy as np
- >>> from aeon.performance_metrics.forecasting import \
- geometric_mean_absolute_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> geometric_mean_absolute_error(y_true, y_pred) # doctest: +SKIP
- 0.000529527232030127
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> geometric_mean_absolute_error(y_true, y_pred) # doctest: +SKIP
- 0.5000024031086919
- >>> geometric_mean_absolute_error(y_true, y_pred, \
- multioutput='raw_values') # doctest: +SKIP
- array([4.80621738e-06, 1.00000000e+00])
- >>> geometric_mean_absolute_error(y_true, y_pred, \
- multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.7000014418652152
- """
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- errors = y_true - y_pred
- errors = np.where(errors == 0.0, EPS, errors)
- if horizon_weight is None:
- output_errors = gmean(np.abs(errors), axis=0)
- else:
- check_consistent_length(y_true, horizon_weight)
- output_errors = weighted_geometric_mean(
- np.abs(errors),
- weights=horizon_weight,
- axis=0,
- )
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def geometric_mean_squared_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- square_root=False,
- **kwargs,
-):
- """Geometric mean squared error (GMSE) or Root geometric mean squared error (RGMSE).
-
- If `square_root` is False then calculates GMSE and if `square_root` is True
- then RGMSE is calculated. Both GMSE and RGMSE return non-negative floating
- point. The best value is approximately zero, rather than zero.
-
- Like MSE and MdSE, GMSE is measured in squared units of the input data. RMdSE is
- on the same scale as the input data like RMSE and RdMSE. Because GMSE and RGMSE
- square the forecast error rather than taking the absolute value, they
- penalize large errors more than GMAE.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- square_root : bool, default=False
- Whether to take the square root of the mean squared error.
- If True, returns root geometric mean squared error (RGMSE)
- If False, returns geometric mean squared error (GMSE)
-
- Returns
- -------
- loss : float
- GMSE or RGMSE loss. If multioutput is 'raw_values', then loss is returned
- for each output separately. If multioutput is 'uniform_average' or an ndarray
- of weights, then the weighted average MdSE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_error
- median_absolute_error
- mean_squared_error
- median_squared_error
- geometric_mean_absolute_error
-
- Notes
- -----
- The geometric mean uses the product of values in its calculation. The presence
- of a zero value will result in the result being zero, even if all the other
- values of large. To partially account for this in the case where elements
- of `y_true` and `y_pred` are equal (zero error), the resulting zero error
- values are replaced in the calculation with a small value. This results in
- the smallest value the metric can take (when `y_true` equals `y_pred`)
- being close to but not exactly zero.
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> import numpy as np
- >>> from aeon.performance_metrics.forecasting import \
- geometric_mean_squared_error as gmse
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> gmse(y_true, y_pred) # doctest: +SKIP
- 2.80399089461488e-07
- >>> gmse(y_true, y_pred, square_root=True) # doctest: +SKIP
- 0.000529527232030127
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> gmse(y_true, y_pred) # doctest: +SKIP
- 0.5000000000115499
- >>> gmse(y_true, y_pred, square_root=True) # doctest: +SKIP
- 0.5000024031086919
- >>> gmse(y_true, y_pred, multioutput='raw_values') # doctest: +SKIP
- array([2.30997255e-11, 1.00000000e+00])
- >>> gmse(y_true, y_pred, multioutput='raw_values', \
- square_root=True) # doctest: +SKIP
- array([4.80621738e-06, 1.00000000e+00])
- >>> gmse(y_true, y_pred, multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.7000000000069299
- >>> gmse(y_true, y_pred, multioutput=[0.3, 0.7], \
- square_root=True) # doctest: +SKIP
- 0.7000014418652152
- """
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- errors = y_true - y_pred
- errors = np.where(errors == 0.0, EPS, errors)
- if horizon_weight is None:
- output_errors = gmean(np.square(errors), axis=0)
- else:
- check_consistent_length(y_true, horizon_weight)
- output_errors = weighted_geometric_mean(
- np.square(errors),
- weights=horizon_weight,
- axis=0,
- )
-
- if square_root:
- output_errors = np.sqrt(output_errors)
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def mean_absolute_percentage_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- symmetric=False,
- **kwargs,
-):
- """Mean absolute percentage error (MAPE) or symmetric version.
-
- If `symmetric` is False then calculates MAPE and if `symmetric` is True
- then calculates symmetric mean absolute percentage error (sMAPE). Both
- MAPE and sMAPE output is non-negative floating point. The best value is 0.0.
-
- sMAPE is measured in percentage error relative to the test data. Because it
- takes the absolute value rather than square the percentage forecast
- error, it penalizes large errors less than MSPE, RMSPE, MdSPE or RMdSPE.
-
- There is no limit on how large the error can be, particulalrly when `y_true`
- values are close to zero. In such cases the function returns a large value
- instead of `inf`.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- symmetric : bool, default=False
- Calculates symmetric version of metric if True.
-
- Returns
- -------
- loss : float
- MAPE or sMAPE loss.
- If multioutput is 'raw_values', then MAPE or sMAPE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MAPE or sMAPE of all output errors is returned.
-
- See Also
- --------
- median_absolute_percentage_error
- mean_squared_percentage_error
- median_squared_percentage_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import \
- mean_absolute_percentage_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> mean_absolute_percentage_error(y_true, y_pred, symmetric=False) # doctest: +SKIP
- 0.33690476190476193
- >>> mean_absolute_percentage_error(y_true, y_pred, symmetric=True) # doctest: +SKIP
- 0.5553379953379953
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> mean_absolute_percentage_error(y_true, y_pred, symmetric=False) # doctest: +SKIP
- 0.5515873015873016
- >>> mean_absolute_percentage_error(y_true, y_pred, symmetric=True) # doctest: +SKIP
- 0.6080808080808081
- >>> mean_absolute_percentage_error(y_true, y_pred, multioutput='raw_values', \
- symmetric=False) # doctest: +SKIP
- array([0.38095238, 0.72222222])
- >>> mean_absolute_percentage_error(y_true, y_pred, multioutput='raw_values', \
- symmetric=True) # doctest: +SKIP
- array([0.71111111, 0.50505051])
- >>> mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- symmetric=False) # doctest: +SKIP
- 0.6198412698412699
- >>> mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- symmetric=True) # doctest: +SKIP
- 0.5668686868686869
- """
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- if horizon_weight is not None:
- check_consistent_length(y_true, horizon_weight)
-
- output_errors = np.average(
- np.abs(_percentage_error(y_true, y_pred, symmetric=symmetric)),
- weights=horizon_weight,
- axis=0,
- )
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def median_absolute_percentage_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- symmetric=False,
- **kwargs,
-):
- """Median absolute percentage error (MdAPE) or symmetric version.
-
- If `symmetric` is False then calculates MdAPE and if `symmetric` is True
- then calculates symmetric median absolute percentage error (sMdAPE). Both
- MdAPE and sMdAPE output is non-negative floating point. The best value is 0.0.
-
- MdAPE and sMdAPE are measured in percentage error relative to the test data.
- Because it takes the absolute value rather than square the percentage forecast
- error, it penalizes large errors less than MSPE, RMSPE, MdSPE or RMdSPE.
-
- Taking the median instead of the mean of the absolute percentage errors also
- makes this metric more robust to error outliers since the median tends
- to be a more robust measure of central tendency in the presence of outliers.
-
- There is no limit on how large the error can be, particulalrly when `y_true`
- values are close to zero. In such cases the function returns a large value
- instead of `inf`.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- symmetric : bool, default=False
- Calculates symmetric version of metric if True.
-
- Returns
- -------
- loss : float
- MdAPE or sMdAPE loss.
- If multioutput is 'raw_values', then MdAPE or sMdAPE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MdAPE or sMdAPE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_percentage_error
- mean_squared_percentage_error
- median_squared_percentage_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import \
- median_absolute_percentage_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> median_absolute_percentage_error(y_true, y_pred, \
- symmetric=False) # doctest: +SKIP
- 0.16666666666666666
- >>> median_absolute_percentage_error(y_true, y_pred, \
- symmetric=True) # doctest: +SKIP
- 0.18181818181818182
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> median_absolute_percentage_error(y_true, y_pred, \
- symmetric=False) # doctest: +SKIP
- 0.5714285714285714
- >>> median_absolute_percentage_error(y_true, y_pred, \
- symmetric=True) # doctest: +SKIP
- 0.39999999999999997
- >>> median_absolute_percentage_error(y_true, y_pred, multioutput='raw_values', \
- symmetric=False) # doctest: +SKIP
- array([0.14285714, 1. ])
- >>> median_absolute_percentage_error(y_true, y_pred, multioutput='raw_values', \
- symmetric=True) # doctest: +SKIP
- array([0.13333333, 0.66666667])
- >>> median_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- symmetric=False) # doctest: +SKIP
- 0.7428571428571428
- >>> median_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- symmetric=True) # doctest: +SKIP
- 0.5066666666666666
- """
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- if horizon_weight is None:
- output_errors = np.median(
- np.abs(_percentage_error(y_true, y_pred, symmetric=symmetric)), axis=0
- )
- else:
- check_consistent_length(y_true, horizon_weight)
- output_errors = _weighted_percentile(
- np.abs(_percentage_error(y_pred, y_true, symmetric=symmetric)),
- sample_weight=horizon_weight,
- )
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def mean_squared_percentage_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- square_root=False,
- symmetric=False,
- **kwargs,
-):
- """Mean squared percentage error (MSPE) or square root version.
-
- If `square_root` is False then calculates MSPE and if `square_root` is True
- then calculates root mean squared percentage error (RMSPE). If `symmetric`
- is True then calculates sMSPE or sRMSPE. Output is non-negative floating
- point. The best value is 0.0.
-
- MSPE is measured in squared percentage error relative to the test data and
- RMSPE is measured in percentage error relative to the test data.
- Because the calculation takes the square rather than absolute value of
- the percentage forecast error, large errors are penalized more than
- MAPE, sMAPE, MdAPE or sMdAPE.
-
- There is no limit on how large the error can be, particulalrly when `y_true`
- values are close to zero. In such cases the function returns a large value
- instead of `inf`.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- square_root : bool, default=False
- Whether to take the square root of the mean squared error.
- If True, returns root mean squared error (RMSPE)
- If False, returns mean squared error (MSPE)
-
- symmetric : bool, default=False
- Calculates symmetric version of metric if True.
-
- Returns
- -------
- loss : float
- MSPE or RMSPE loss.
- If multioutput is 'raw_values', then MSPE or RMSPE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MSPE or RMSPE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_percentage_error
- median_absolute_percentage_error
- median_squared_percentage_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import mean_squared_percentage_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> mean_squared_percentage_error(y_true, y_pred, symmetric=False) # doctest: +SKIP
- 0.23776218820861678
- >>> mean_squared_percentage_error(y_true, y_pred, square_root=True, \
- symmetric=False) # doctest: +SKIP
- 0.48760864246710883
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> mean_squared_percentage_error(y_true, y_pred, symmetric=False) # doctest: +SKIP
- 0.5080309901738473
- >>> mean_squared_percentage_error(y_true, y_pred, square_root=True, \
- symmetric=False) # doctest: +SKIP
- 0.7026794936195895
- >>> mean_squared_percentage_error(y_true, y_pred, multioutput='raw_values', \
- symmetric=False) # doctest: +SKIP
- array([0.34013605, 0.67592593])
- >>> mean_squared_percentage_error(y_true, y_pred, multioutput='raw_values', \
- square_root=True, symmetric=False) # doctest: +SKIP
- array([0.58321184, 0.82214714])
- >>> mean_squared_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- symmetric=False) # doctest: +SKIP
- 0.5751889644746787
- >>> mean_squared_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- square_root=True, symmetric=False) # doctest: +SKIP
- 0.7504665536595034
- """
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- if horizon_weight is not None:
- check_consistent_length(y_true, horizon_weight)
-
- output_errors = np.average(
- np.square(_percentage_error(y_true, y_pred, symmetric=symmetric)),
- weights=horizon_weight,
- axis=0,
- )
-
- if square_root:
- output_errors = np.sqrt(output_errors)
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def median_squared_percentage_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- square_root=False,
- symmetric=False,
- **kwargs,
-):
- """Median squared percentage error (MdSPE) or square root version.
-
- If `square_root` is False then calculates MdSPE and if `square_root` is True
- then calculates root median squared percentage error (RMdSPE). If `symmetric`
- is True then calculates sMdSPE or sRMdSPE. Output is non-negative floating
- point. The best value is 0.0.
-
- MdSPE is measured in squared percentage error relative to the test data.
- RMdSPE is measured in percentage error relative to the test data.
- Because the calculation takes the square rather than absolute value of
- the percentage forecast error, large errors are penalized more than
- MAPE, sMAPE, MdAPE or sMdAPE.
-
- Taking the median instead of the mean of the absolute percentage errors also
- makes this metric more robust to error outliers since the median tends
- to be a more robust measure of central tendency in the presence of outliers.
-
- There is no limit on how large the error can be, particulalrly when `y_true`
- values are close to zero. In such cases the function returns a large value
- instead of `inf`.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- square_root : bool, default=False
- Whether to take the square root of the mean squared error.
- If True, returns root mean squared error (RMSPE)
- If False, returns mean squared error (MSPE)
-
- symmetric : bool, default=False
- Calculates symmetric version of metric if True.
-
- Returns
- -------
- loss : float
- MdSPE or RMdSPE loss.
- If multioutput is 'raw_values', then MdSPE or RMdSPE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MdSPE or RMdSPE of all output errors is returned.
-
- See Also
- --------
- mean_absolute_percentage_error
- median_absolute_percentage_error
- mean_squared_percentage_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import \
- median_squared_percentage_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> median_squared_percentage_error(y_true, y_pred, \
- symmetric=False) # doctest: +SKIP
- 0.027777777777777776
- >>> median_squared_percentage_error(y_true, y_pred, square_root=True, \
- symmetric=False) # doctest: +SKIP
- 0.16666666666666666
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> median_squared_percentage_error(y_true, y_pred, \
- symmetric=False) # doctest: +SKIP
- 0.5102040816326531
- >>> median_squared_percentage_error(y_true, y_pred, square_root=True, \
- symmetric=False) # doctest: +SKIP
- 0.5714285714285714
- >>> median_squared_percentage_error(y_true, y_pred, multioutput='raw_values', \
- symmetric=False) # doctest: +SKIP
- array([0.02040816, 1. ])
- >>> median_squared_percentage_error(y_true, y_pred, multioutput='raw_values', \
- square_root=True, symmetric=False) # doctest: +SKIP
- array([0.14285714, 1. ])
- >>> median_squared_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- symmetric=False) # doctest: +SKIP
- 0.7061224489795918
- >>> median_squared_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7], \
- square_root=True, symmetric=False) # doctest: +SKIP
- 0.7428571428571428
- """
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- perc_err = _percentage_error(y_true, y_pred, symmetric=symmetric)
- if horizon_weight is None:
- output_errors = np.median(np.square(perc_err), axis=0)
- else:
- check_consistent_length(y_true, horizon_weight)
- output_errors = _weighted_percentile(
- np.square(perc_err),
- sample_weight=horizon_weight,
- )
-
- if square_root:
- output_errors = np.sqrt(output_errors)
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def mean_relative_absolute_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- **kwargs,
-):
- """Mean relative absolute error (MRAE).
-
- In relative error metrics, relative errors are first calculated by
- scaling (dividing) the individual forecast errors by the error calculated
- using a benchmark method at the same index position. If the error of the
- benchmark method is zero then a large value is returned.
-
- MRAE applies mean absolute error (MAE) to the resulting relative errors.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- y_pred_benchmark : pd.Series, pd.DataFrame or np.array of shape (fh,) or \
- (fh, n_outputs) where fh is the forecasting horizon, default=None
- Forecasted values from benchmark method. Passed by kwargs.
-
- Returns
- -------
- loss : float
- MRAE loss.
- If multioutput is 'raw_values', then MRAE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MRAE of all output errors is returned.
-
- See Also
- --------
- median_relative_absolute_error
- geometric_mean_relative_absolute_error
- geometric_mean_relative_squared_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import mean_relative_absolute_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> y_pred_benchmark = y_pred*1.1
- >>> mean_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 0.9511111111111111
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> y_pred_benchmark = y_pred*1.1
- >>> mean_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 0.8703703703703702
- >>> mean_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark, multioutput='raw_values') # doctest: +SKIP
- array([0.51851852, 1.22222222])
- >>> mean_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark, multioutput=[0.3, 0.7]) # doctest: +SKIP
- 1.0111111111111108
- """
- y_pred_benchmark = _get_kwarg(
- "y_pred_benchmark", metric_name="mean_relative_absolute_error", **kwargs
- )
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- _, y_true, y_pred_benchmark, multioutput = _check_reg_targets(
- y_true, y_pred_benchmark, multioutput
- )
-
- if horizon_weight is None:
- output_errors = np.mean(
- np.abs(_relative_error(y_true, y_pred, y_pred_benchmark)), axis=0
- )
- else:
- check_consistent_length(y_true, horizon_weight)
- output_errors = np.average(
- np.abs(_relative_error(y_true, y_pred, y_pred_benchmark)),
- weights=horizon_weight,
- axis=0,
- )
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def median_relative_absolute_error(
- y_true, y_pred, horizon_weight=None, multioutput="uniform_average", **kwargs
-):
- """Median relative absolute error (MdRAE).
-
- In relative error metrics, relative errors are first calculated by
- scaling (dividing) the individual forecast errors by the error calculated
- using a benchmark method at the same index position. If the error of the
- benchmark method is zero then a large value is returned.
-
- MdRAE applies medan absolute error (MdAE) to the resulting relative errors.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- y_pred_benchmark : pd.Series, pd.DataFrame or np.array of shape (fh,) or \
- (fh, n_outputs) where fh is the forecasting horizon, default=None
- Forecasted values from benchmark method.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- loss : float
- MdRAE loss.
- If multioutput is 'raw_values', then MdRAE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average MdRAE of all output errors is returned.
-
- See Also
- --------
- mean_relative_absolute_error
- geometric_mean_relative_absolute_error
- geometric_mean_relative_squared_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import \
- median_relative_absolute_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> y_pred_benchmark = y_pred*1.1
- >>> median_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 1.0
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> y_pred_benchmark = y_pred*1.1
- >>> median_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 0.6944444444444443
- >>> median_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark, multioutput='raw_values') # doctest: +SKIP
- array([0.55555556, 0.83333333])
- >>> median_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark, multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.7499999999999999
- """
- y_pred_benchmark = _get_kwarg(
- "y_pred_benchmark", metric_name="median_relative_absolute_error", **kwargs
- )
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- _, y_true, y_pred_benchmark, multioutput = _check_reg_targets(
- y_true, y_pred_benchmark, multioutput
- )
-
- if horizon_weight is None:
- output_errors = np.median(
- np.abs(_relative_error(y_true, y_pred, y_pred_benchmark)), axis=0
- )
- else:
- check_consistent_length(y_true, horizon_weight)
- output_errors = _weighted_percentile(
- np.abs(_relative_error(y_true, y_pred, y_pred_benchmark)),
- sample_weight=horizon_weight,
- )
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def geometric_mean_relative_absolute_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- **kwargs,
-):
- """Geometric mean relative absolute error (GMRAE).
-
- In relative error metrics, relative errors are first calculated by
- scaling (dividing) the individual forecast errors by the error calculated
- using a benchmark method at the same index position. If the error of the
- benchmark method is zero then a large value is returned.
-
- GMRAE applies geometric mean absolute error (GMAE) to the resulting relative
- errors.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- y_pred_benchmark : pd.Series, pd.DataFrame or np.array of shape (fh,) or \
- (fh, n_outputs) where fh is the forecasting horizon, default=None
- Forecasted values from benchmark method.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- loss : float
- GMRAE loss.
- If multioutput is 'raw_values', then GMRAE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average GMRAE of all output errors is returned.
-
- See Also
- --------
- mean_relative_absolute_error
- median_relative_absolute_error
- geometric_mean_relative_squared_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import \
- geometric_mean_relative_absolute_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> y_pred_benchmark = y_pred*1.1
- >>> geometric_mean_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 0.0007839273064064755
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> y_pred_benchmark = y_pred*1.1
- >>> geometric_mean_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 0.5578632807409556
- >>> geometric_mean_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark, multioutput='raw_values') # doctest: +SKIP
- array([4.97801163e-06, 1.11572158e+00])
- >>> geometric_mean_relative_absolute_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark, multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.7810066018326863
- """
- y_pred_benchmark = _get_kwarg(
- "y_pred_benchmark",
- metric_name="geometric_mean_relative_absolute_error",
- **kwargs,
- )
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- _, y_true, y_pred_benchmark, multioutput = _check_reg_targets(
- y_true, y_pred_benchmark, multioutput
- )
-
- relative_errors = np.abs(_relative_error(y_true, y_pred, y_pred_benchmark))
- if horizon_weight is None:
- output_errors = gmean(
- np.where(relative_errors == 0.0, EPS, relative_errors), axis=0
- )
- else:
- check_consistent_length(y_true, horizon_weight)
- output_errors = weighted_geometric_mean(
- np.where(relative_errors == 0.0, EPS, relative_errors),
- weights=horizon_weight,
- axis=0,
- )
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def geometric_mean_relative_squared_error(
- y_true,
- y_pred,
- horizon_weight=None,
- multioutput="uniform_average",
- square_root=False,
- **kwargs,
-):
- """Geometric mean relative squared error (GMRSE).
-
- If `square_root` is False then calculates GMRSE and if `square_root` is True
- then calculates root geometric mean relative squared error (RGMRSE).
-
- In relative error metrics, relative errors are first calculated by
- scaling (dividing) the individual forecast errors by the error calculated
- using a benchmark method at the same index position. If the error of the
- benchmark method is zero then a large value is returned.
-
- GMRSE applies geometric mean squared error (GMSE) to the resulting relative
- errors. RGMRSE applies root geometric mean squared error (RGMSE) to the
- resulting relative errors.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- y_pred_benchmark : pd.Series, pd.DataFrame or np.array of shape (fh,) or \
- (fh, n_outputs) where fh is the forecasting horizon, default=None
- Forecasted values from benchmark method.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- square_root : bool, default=False
- Whether to take the square root of the mean squared error.
- If True, returns root mean squared error (RMSPE)
- If False, returns mean squared error (MSPE)
-
- Returns
- -------
- loss : float
- GMRSE or RGMRSE loss.
- If multioutput is 'raw_values', then GMRSE or RGMRSE is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average GMRSE or RGMRSE of all output errors is returned.
-
- See Also
- --------
- mean_relative_absolute_error
- median_relative_absolute_error
- geometric_mean_relative_absolute_error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> from aeon.performance_metrics.forecasting import \
- geometric_mean_relative_squared_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> y_pred_benchmark = y_pred*1.1
- >>> geometric_mean_relative_squared_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 0.0008303544925949156
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> y_pred_benchmark = y_pred*1.1
- >>> geometric_mean_relative_squared_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 0.622419372049448
- >>> geometric_mean_relative_squared_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark, multioutput='raw_values') # doctest: +SKIP
- array([4.09227746e-06, 1.24483465e+00])
- >>> geometric_mean_relative_squared_error(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark, multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.8713854839582426
- """
- y_pred_benchmark = _get_kwarg(
- "y_pred_benchmark",
- metric_name="geometric_mean_relative_squared_error",
- **kwargs,
- )
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
- _, y_true, y_pred_benchmark, multioutput = _check_reg_targets(
- y_true, y_pred_benchmark, multioutput
- )
- relative_errors = np.square(_relative_error(y_true, y_pred, y_pred_benchmark))
- if horizon_weight is None:
- output_errors = gmean(
- np.where(relative_errors == 0.0, EPS, relative_errors), axis=0
- )
- else:
- check_consistent_length(y_true, horizon_weight)
- output_errors = weighted_geometric_mean(
- np.where(relative_errors == 0.0, EPS, relative_errors),
- weights=horizon_weight,
- axis=0,
- )
-
- if square_root:
- output_errors = np.sqrt(output_errors)
-
- if isinstance(multioutput, str):
- if multioutput == "raw_values":
- return output_errors
- elif multioutput == "uniform_average":
- # pass None as weights to np.average: uniform mean
- multioutput = None
-
- return np.average(output_errors, weights=multioutput)
-
-
-def relative_loss(
- y_true,
- y_pred,
- relative_loss_function=mean_absolute_error,
- horizon_weight=None,
- multioutput="uniform_average",
- **kwargs,
-):
- """Relative loss of forecast versus benchmark forecast for a given metric.
-
- Applies a forecasting performance metric to a set of forecasts and
- benchmark forecasts and reports ratio of the metric from the forecasts to
- the the metric from the benchmark forecasts. Relative loss output is
- non-negative floating point. The best value is 0.0.
-
- If the score of the benchmark predictions for a given loss function is zero
- then a large value is returned.
-
- This function allows the calculation of scale-free relative loss metrics.
- Unlike mean absolute scaled error (MASE) the function calculates the
- scale-free metric relative to a defined loss function on a benchmark
- method instead of the in-sample training data. Like MASE, metrics created
- using this function can be used to compare forecast methods on a single
- series and also to compare forecast accuracy between series.
-
- This is useful when a scale-free comparison is beneficial but the training
- data used to generate some (or all) predictions is unknown such as when
- comparing the loss of 3rd party forecasts or surveys of professional
- forecasters.
-
- Only metrics that do not require y_train are curretnly supported.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- y_pred_benchmark : pd.Series, pd.DataFrame or np.array of shape (fh,) or \
- (fh, n_outputs) where fh is the forecasting horizon, default=None
- Forecasted values from benchmark method.
-
- relative_loss_function : function, default=mean_absolute_error
- Function to use in calculation relative loss.
-
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
-
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- relative_loss : float
- Loss for a method relative to loss for a benchmark method for a given
- loss metric.
- If multioutput is 'raw_values', then relative loss is returned for each
- output separately.
- If multioutput is 'uniform_average' or an ndarray of weights, then the
- weighted average relative loss of all output errors is returned.
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Examples
- --------
- >>> import numpy as np
- >>> from aeon.performance_metrics.forecasting import relative_loss
- >>> from aeon.performance_metrics.forecasting import mean_squared_error
- >>> y_true = np.array([3, -0.5, 2, 7, 2])
- >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- >>> y_pred_benchmark = y_pred*1.1
- >>> relative_loss(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 0.8148148148148147
- >>> relative_loss(y_true, y_pred, y_pred_benchmark=y_pred_benchmark, \
- relative_loss_function=mean_squared_error) # doctest: +SKIP
- 0.5178095088655261
- >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- >>> y_pred_benchmark = y_pred*1.1
- >>> relative_loss(y_true, y_pred, \
- y_pred_benchmark=y_pred_benchmark) # doctest: +SKIP
- 0.8490566037735847
- >>> relative_loss(y_true, y_pred, y_pred_benchmark=y_pred_benchmark, \
- multioutput='raw_values') # doctest: +SKIP
- array([0.625 , 1.03448276])
- >>> relative_loss(y_true, y_pred, y_pred_benchmark=y_pred_benchmark, \
- multioutput=[0.3, 0.7]) # doctest: +SKIP
- 0.927272727272727
- """
- y_pred_benchmark = _get_kwarg(
- "y_pred_benchmark", metric_name="relative_loss", **kwargs
- )
- _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
-
- if horizon_weight is not None:
- check_consistent_length(y_true, horizon_weight)
-
- loss_preds = relative_loss_function(
- y_true, y_pred, horizon_weight=horizon_weight, multioutput=multioutput
- )
- loss_benchmark = relative_loss_function(
- y_true,
- y_pred_benchmark,
- horizon_weight=horizon_weight,
- multioutput=multioutput,
- )
- return np.divide(loss_preds, np.maximum(loss_benchmark, EPS))
-
-
-def _asymmetric_error(
- y_true,
- y_pred,
- asymmetric_threshold=0.0,
- left_error_function="squared",
- right_error_function="absolute",
- left_error_penalty=1.0,
- right_error_penalty=1.0,
-):
- """Calculate asymmetric error.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
- asymmetric_threshold : float, default = 0.0
- The value used to threshold the asymmetric loss function. Error values
- that are less than the asymmetric threshold have `left_error_function`
- applied. Error values greater than or equal to asymmetric threshold
- have `right_error_function` applied.
- left_error_function : {'squared', 'absolute'}, default='squared'
- Loss penalty to apply to error values less than the asymmetric threshold.
- right_error_function : {'squared', 'absolute'}, default='absolute'
- Loss penalty to apply to error values greater than or equal to the
- asymmetric threshold.
- left_error_penalty : int or float, default=1.0
- An additional multiplicative penalty to apply to error values less than
- the asymetric threshold.
- right_error_penalty : int or float, default=1.0
- An additional multiplicative penalty to apply to error values greater
- than the asymmetric threshold.
-
- Returns
- -------
- asymmetric_errors : float
- Array of assymetric errors.
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
-
- Diebold, Francis X. (2007). "Elements of Forecasting (4th ed.)",
- Thomson, South-Western: Ohio, US.
- """
- functions = {"squared": np.square, "absolute": np.abs}
- left_func, right_func = (
- functions[left_error_function],
- functions[right_error_function],
- )
-
- if not (
- isinstance(left_error_penalty, (int, float))
- and isinstance(right_error_penalty, (int, float))
- ):
- msg = "`left_error_penalty` and `right_error_penalty` must be int or float."
- raise ValueError(msg)
-
- errors = np.where(
- y_true - y_pred < asymmetric_threshold,
- left_error_penalty * left_func(y_true - y_pred),
- right_error_penalty * right_func(y_true - y_pred),
- )
- return errors
-
-
-def _linex_error(y_true, y_pred, a=1.0, b=1.0):
- """Calculate mean linex error.
-
- Output is non-negative floating point. The best value is 0.0.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
- horizon_weight : array-like of shape (fh,), default=None
- Forecast horizon weights.
- multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
- (n_outputs,), default='uniform_average'
- Defines how to aggregate metric for multivariate (multioutput) data.
- If array-like, values used as weights to average the errors.
- If 'raw_values', returns a full set of errors in case of multioutput input.
- If 'uniform_average', errors of all outputs are averaged with uniform weight.
-
- Returns
- -------
- linex_error : float
- Array of linex errors.
-
- References
- ----------
- Diebold, Francis X. (2007). "Elements of Forecasting (4th ed.)",
- Thomson, South-Western: Ohio, US.
- """
- if not (isinstance(a, (int, float)) and a != 0):
- raise ValueError("`a` must be int or float not equal to zero.")
- if not (isinstance(b, (int, float)) and b > 0):
- raise ValueError("`b` must be an int or float greater than zero.")
- error = y_true - y_pred
- a_error = a * error
- linex_error = b * (np.exp(a_error) - a_error - 1)
- return linex_error
-
-
-def _relative_error(y_true, y_pred, y_pred_benchmark):
- """Relative error for observations to benchmark method.
-
- Parameters
- ----------
- y_true : pandas Series, pandas DataFrame or NumPy array of
- shape (fh,) or (fh, n_outputs) where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pandas Series, pandas DataFrame or NumPy array of
- shape (fh,) or (fh, n_outputs) where fh is the forecasting horizon
- Forecasted values.
-
- y_pred_benchmark : pd.Series, pd.DataFrame or np.array of shape (fh,) or \
- (fh, n_outputs) where fh is the forecasting horizon, default=None
- Forecasted values from benchmark method.
-
- Returns
- -------
- relative_error : float
- relative error
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of \
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
- """
- denominator = np.where(
- y_true - y_pred_benchmark >= 0,
- np.maximum((y_true - y_pred_benchmark), EPS),
- np.minimum((y_true - y_pred_benchmark), -EPS),
- )
- return (y_true - y_pred) / denominator
-
-
-def _percentage_error(y_true, y_pred, symmetric=False):
- """Percentage error.
-
- Parameters
- ----------
- y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Ground truth (correct) target values.
-
- y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
- where fh is the forecasting horizon
- Forecasted values.
-
- symmetric : bool, default = False
- Whether to calculate symmetric percentage error.
-
- Returns
- -------
- percentage_error : float
-
- References
- ----------
- Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of \
- forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
- """
- if symmetric:
- # Alternatively could use np.abs(y_true + y_pred) in denom
- # Results will be different if y_true and y_pred have different signs
- percentage_error = (
- 2
- * np.abs(y_true - y_pred)
- / np.maximum(np.abs(y_true) + np.abs(y_pred), EPS)
- )
- else:
- percentage_error = (y_true - y_pred) / np.maximum(np.abs(y_true), EPS)
- return percentage_error
diff --git a/aeon/performance_metrics/forecasting/tests/__init__.py b/aeon/performance_metrics/forecasting/tests/__init__.py
deleted file mode 100644
index bfc89ff117..0000000000
--- a/aeon/performance_metrics/forecasting/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Tests for aeon performance metrics module."""
diff --git a/aeon/performance_metrics/forecasting/tests/test_metrics.py b/aeon/performance_metrics/forecasting/tests/test_metrics.py
deleted file mode 100644
index 1d7fef49bb..0000000000
--- a/aeon/performance_metrics/forecasting/tests/test_metrics.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""Tests for some metrics."""
-
-__maintainer__ = []
-
-import numpy as np
-
-from aeon.performance_metrics.forecasting import (
- geometric_mean_squared_error,
- mean_linex_error,
-)
-
-
-def test_gmse_function():
- """Doctest from geometric_mean_squared_error."""
- gmse = geometric_mean_squared_error
- y_true = np.array([3, -0.5, 2, 7, 2])
- y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- assert np.allclose(gmse(y_true, y_pred), 2.80399089461488e-07)
- assert np.allclose(gmse(y_true, y_pred, square_root=True), 0.000529527232030127)
- y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- assert np.allclose(gmse(y_true, y_pred), 0.5000000000115499)
- assert np.allclose(gmse(y_true, y_pred, square_root=True), 0.5000024031086919)
- assert np.allclose(
- gmse(y_true, y_pred, multioutput="raw_values"),
- np.array([2.30997255e-11, 1.00000000e00]),
- )
- assert np.allclose(
- gmse(y_true, y_pred, multioutput="raw_values", square_root=True),
- np.array([4.80621738e-06, 1.00000000e00]),
- )
- assert np.allclose(gmse(y_true, y_pred, multioutput=[0.3, 0.7]), 0.7000000000069299)
- assert np.allclose(
- gmse(y_true, y_pred, multioutput=[0.3, 0.7], square_root=True),
- 0.7000014418652152,
- )
-
-
-def test_linex_function():
- """Doctest from mean_linex_error."""
- y_true = np.array([3, -0.5, 2, 7, 2])
- y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
- assert np.allclose(mean_linex_error(y_true, y_pred), 0.19802627763937575)
- assert np.allclose(mean_linex_error(y_true, y_pred, b=2), 0.3960525552787515)
- assert np.allclose(mean_linex_error(y_true, y_pred, a=-1), 0.2391800623225643)
- y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
- y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
- assert np.allclose(mean_linex_error(y_true, y_pred), 0.2700398392309829)
- assert np.allclose(mean_linex_error(y_true, y_pred, a=-1), 0.49660966225813563)
- assert np.allclose(
- mean_linex_error(y_true, y_pred, multioutput="raw_values"),
- np.array([0.17220024, 0.36787944]),
- )
- assert np.allclose(
- mean_linex_error(y_true, y_pred, multioutput=[0.3, 0.7]), 0.30917568000716666
- )
diff --git a/aeon/performance_metrics/forecasting/tests/test_performance_measures.py b/aeon/performance_metrics/forecasting/tests/test_performance_measures.py
deleted file mode 100644
index 3d1c819e79..0000000000
--- a/aeon/performance_metrics/forecasting/tests/test_performance_measures.py
+++ /dev/null
@@ -1,495 +0,0 @@
-"""Tests for forecasting performance metrics."""
-
-__maintainer__ = []
-
-import numpy as np
-import pandas as pd
-import pytest
-from pandas.api.types import is_numeric_dtype
-
-from aeon.performance_metrics.forecasting import (
- geometric_mean_relative_absolute_error,
- geometric_mean_relative_squared_error,
- mean_absolute_error,
- mean_absolute_percentage_error,
- mean_absolute_scaled_error,
- mean_asymmetric_error,
- mean_relative_absolute_error,
- mean_squared_error,
- mean_squared_percentage_error,
- mean_squared_scaled_error,
- median_absolute_error,
- median_absolute_percentage_error,
- median_absolute_scaled_error,
- median_relative_absolute_error,
- median_squared_error,
- median_squared_percentage_error,
- median_squared_scaled_error,
- relative_loss,
-)
-from aeon.testing.data_generation._legacy import make_series
-
-RANDOM_SEED = 42
-
-# For multiple comparisons of equality between functions and classes
-rng = np.random.default_rng(RANDOM_SEED)
-RANDOM_STATES = rng.integers(0, 1000000, size=5).tolist()
-
-# Create specific test series to verify calculated performance metrics match
-# those calculated externally
-Y1 = np.array(
- [
- 0.626832772836215,
- 0.783382993377663,
- 0.745780385700732,
- 1.06737808331213,
- 1.69664933579028,
- 2.08627141338732,
- 1.78023192557434,
- 1.58568920200064,
- 2.08902410668301,
- 2.51472070324453,
- 2.47425419784015,
- 2.27275916300358,
- 1.92803852608368,
- 1.64662766528414,
- 1.7028471682496,
- 1.62051042240568,
- 2.03642032341352,
- 2.36019377457168,
- 2.39730479510699,
- 2.69699728045652,
- 2.41172828049954,
- 2.37679353181132,
- 1.99603448413176,
- 2.53946033171028,
- 2.16285521091308,
- 1.70889477546947,
- 1.52488156869114,
- 1.8369477471545,
- 1.8225935878131,
- 1.64685504990138,
- 1.36106553603259,
- 1.20252674753628,
- 1.33235953453508,
- 1.70560866839458,
- 2.25722026784685,
- 1.84446872239422,
- ]
-)
-
-Y2 = pd.Series(
- [
- 0.982136629140069,
- 1.45950325745833,
- 1.42708285946536,
- 2.10474124388042,
- 2.12958738712948,
- 1.94254184770726,
- 2.24111458763484,
- 2.68784805815518,
- 2.97248086366361,
- 3.27426914233203,
- 3.16674535150384,
- 2.933698752984,
- 3.18393847027259,
- 3.43030921792323,
- 3.21901076902567,
- 2.51266154720592,
- 2.52702260323378,
- 2.4241798970835,
- 1.91495784087606,
- 1.49993972682056,
- 1.66460722130508,
- 1.72380847201769,
- 1.45265679700175,
- 1.54961689438936,
- 1.40262473301413,
- 1.50833698230433,
- 1.17807171492728,
- 1.37642259034361,
- 1.19122274092639,
- 1.72766650406602,
- 2.01019283258555,
- 1.70144149287405,
- 1.40552850108184,
- 1.22336047820607,
- 1.58882703694742,
- 1.68674857175401,
- ]
-)
-# Data for this test case borrower from Rob Hyndman's excel workbook
-# demonstrating how to calculate MASE
-Y3 = np.array(
- [
- 0,
- 2,
- 0,
- 1,
- 0,
- 11,
- 0,
- 0,
- 0,
- 0,
- 2,
- 0,
- 6,
- 3,
- 0,
- 0,
- 0,
- 0,
- 0,
- 7,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 3,
- 1,
- 0,
- 0,
- 1,
- 0,
- 1,
- 0,
- 0,
- ]
-)
-Y1_TRAIN, Y1_TEST = Y1[:24], Y1[24:]
-Y2_TRAIN, Y2_TEST = Y2[:24], Y2[24:]
-Y3_TRAIN, Y3_TEST = Y3[:24], Y3[24:]
-
-Y_TEST_CASES = {
- "test_case_1": {"train": Y1_TRAIN, "test": Y1_TEST},
- "test_case_2": {"train": Y2_TRAIN, "test": Y2_TEST},
- "test_case_3": {"train": Y3_TRAIN, "test": Y3_TEST},
- # Multivariate test case
- "test_case_4": {
- "train": np.vstack([Y1_TRAIN, Y2_TRAIN]),
- "test": np.vstack([Y1_TEST, Y2_TEST]),
- },
-}
-
-# Dictionary mapping functions to the true loss values to verify the aeon
-# metrics are performing as expected. True loss values were calculated
-# manually outside of aeon in Excel.
-LOSS_RESULTS = {
- "mean_absolute_scaled_error": {
- "test_case_1": 1.044427857,
- "test_case_2": 0.950832524,
- "test_case_3": 0.33045977,
- "func": mean_absolute_scaled_error,
- },
- "median_absolute_scaled_error": {
- "test_case_1": 0.997448587,
- "test_case_2": 0.975921875,
- "test_case_3": 1.0,
- "func": median_absolute_scaled_error,
- },
- "root_mean_squared_scaled_error": {
- "test_case_1": 1.001351033,
- "test_case_2": 0.854561506,
- "test_case_3": 0.289374954,
- "func": mean_squared_scaled_error,
- },
- "root_median_squared_scaled_error": {
- "test_case_1": 0.998411526,
- "test_case_2": 0.990760662,
- "test_case_3": 1.0,
- "func": median_squared_scaled_error,
- },
- "mean_absolute_error": {
- "test_case_1": 0.285709251,
- "test_case_2": 0.252975912,
- "test_case_3": 0.833333333,
- "func": mean_absolute_error,
- },
- "mean_squared_error": {
- "test_case_1": 0.103989049,
- "test_case_2": 0.07852696,
- "test_case_3": 1.5,
- "func": mean_squared_error,
- },
- "root_mean_squared_error": {
- "test_case_1": 0.322473331,
- "test_case_2": 0.280226623,
- "test_case_3": 1.224744871,
- "func": mean_squared_error,
- },
- "median_absolute_error": {
- "test_case_1": 0.298927846,
- "test_case_2": 0.240438602,
- "test_case_3": 1.0,
- "func": median_absolute_error,
- },
- "median_squared_error": {
- "test_case_1": 0.089530473,
- "test_case_2": 0.059582098,
- "test_case_3": 1.0,
- "func": median_squared_error,
- },
- "root_median_squared_error": {
- "test_case_1": 0.299216432,
- "test_case_2": 0.244094445,
- "test_case_3": 1.0,
- "func": median_squared_error,
- },
- "symmetric_mean_absolute_percentage_error": {
- "test_case_1": 0.16206745335345693,
- "test_case_2": 0.17096048184064724,
- "test_case_3": 1.0833333333333333,
- "func": mean_absolute_percentage_error,
- },
- "symmetric_median_absolute_percentage_error": {
- "test_case_1": 0.17291559217102262,
- "test_case_2": 0.15323286657516913,
- "test_case_3": 1.5,
- "func": median_absolute_percentage_error,
- },
- "mean_absolute_percentage_error": {
- "test_case_1": 0.16426360194846226,
- "test_case_2": 0.16956968442429066,
- "test_case_3": 1125899906842624.2,
- "func": mean_absolute_percentage_error,
- },
- "median_absolute_percentage_error": {
- "test_case_1": 0.17200352348889714,
- "test_case_2": 0.1521891319356885,
- "test_case_3": 1.0,
- "func": median_absolute_percentage_error,
- },
- "mean_squared_percentage_error": {
- "test_case_1": 0.03203423036447087,
- "test_case_2": 0.03427486821803671,
- "test_case_3": 5.070602400912918e30,
- "func": mean_squared_percentage_error,
- },
- "median_squared_percentage_error": {
- "test_case_1": 0.029589708748632582,
- "test_case_2": 0.023172298452886965,
- "test_case_3": 1.0,
- "func": median_squared_percentage_error,
- },
- "root_mean_squared_percentage_error": {
- "test_case_1": 0.17898108940463758,
- "test_case_2": 0.18513472990780716,
- "test_case_3": 2251799813685248.0,
- "func": mean_squared_percentage_error,
- },
- "root_median_squared_percentage_error": {
- "test_case_1": 0.17201659439900727,
- "test_case_2": 0.15222450017289255,
- "test_case_3": 1.0,
- "func": median_squared_percentage_error,
- },
- "mean_relative_absolute_error": {
- "test_case_1": 0.485695805,
- "test_case_2": 0.477896036,
- "test_case_3": 0.875,
- "func": mean_relative_absolute_error,
- },
- "median_relative_absolute_error": {
- "test_case_1": 0.411364556,
- "test_case_2": 0.453437859,
- "test_case_3": 1.0,
- "func": median_relative_absolute_error,
- },
- "geometric_mean_relative_absolute_error": {
- "test_case_1": 0.363521894,
- "test_case_2": 0.402438951,
- "test_case_3": 3.6839e-07,
- "func": geometric_mean_relative_absolute_error,
- },
- "geometric_mean_relative_squared_error": {
- "test_case_1": 0.132148167,
- "test_case_2": 0.161957109,
- "test_case_3": 4.517843023201426e-07,
- "func": geometric_mean_relative_squared_error,
- },
- "mean_aymmetric_error": {
- "test_case_1": 0.17139968,
- "test_case_2": 0.163956601,
- "test_case_3": 1.000000,
- "func": mean_asymmetric_error,
- },
- "relative_loss": {
- "test_case_1": 0.442644622,
- "test_case_2": 0.416852592,
- "test_case_3": 1.315789474,
- "func": relative_loss,
- },
-}
-
-
-@pytest.mark.parametrize("metric_func_name", LOSS_RESULTS.keys())
-@pytest.mark.parametrize("n_test_case", [1, 2, 3])
-def test_univariate_loss_expected_zero(n_test_case, metric_func_name):
- """Test cases where the expected loss is zero for perfect forecast."""
- metric_func = LOSS_RESULTS[metric_func_name]["func"]
-
- y_true = Y_TEST_CASES[f"test_case_{n_test_case}"]["test"]
- y_train = Y_TEST_CASES[f"test_case_{n_test_case}"]["train"]
-
- # Setting test case of perfect forecast and benchmark
- true_loss = 0
- y_pred = y_true
- y_pred_benchmark = y_true
-
- if metric_func_name.startswith("root_"):
- function_loss = metric_func(
- y_true,
- y_pred,
- y_train=y_train,
- y_pred_benchmark=y_pred_benchmark,
- square_root=True,
- )
- elif metric_func_name.startswith("symmetric_"):
- function_loss = metric_func(
- y_true,
- y_pred,
- y_train=y_train,
- y_pred_benchmark=y_pred_benchmark,
- symmetric=True,
- )
- else:
- function_loss = metric_func(
- y_true,
- y_pred,
- y_train=y_train,
- y_pred_benchmark=y_pred_benchmark,
- )
-
- # Assertion for functions
- assert np.isclose(function_loss, true_loss), " ".join(
- [
- f"Loss function {metric_func.__name__} returned {function_loss}",
- f"loss, but {true_loss} loss expected",
- ]
- )
-
-
-@pytest.mark.parametrize("metric_func_name", LOSS_RESULTS.keys())
-@pytest.mark.parametrize("n_test_case", [1, 2, 3])
-def test_univariate_loss_against_expected_value(n_test_case, metric_func_name):
- """Test univariate loss against expected value."""
- metric_func = LOSS_RESULTS[metric_func_name]["func"]
- true_loss = LOSS_RESULTS[metric_func_name][f"test_case_{n_test_case}"]
- y_true = Y_TEST_CASES[f"test_case_{n_test_case}"]["test"]
- y_train = Y_TEST_CASES[f"test_case_{n_test_case}"]["train"]
-
- # Use last value as naive forecast to test function
- y_pred = np.concatenate([y_train, y_true])[23:35]
-
- # Just using this nonsensical approach to generate benchmark for testing
- y_pred_benchmark = 0.6 * y_pred
- if metric_func_name.startswith("root_"):
- function_loss = metric_func(
- y_true,
- y_pred,
- y_train=y_train,
- y_pred_benchmark=y_pred_benchmark,
- square_root=True,
- )
- elif metric_func_name.startswith("symmetric_"):
- function_loss = metric_func(
- y_true,
- y_pred,
- symmetric=True,
- y_train=y_train,
- y_pred_benchmark=y_pred_benchmark,
- )
- else:
- function_loss = metric_func(
- y_true,
- y_pred,
- y_pred_benchmark=y_pred_benchmark,
- y_train=y_train,
- )
- # Assertion for functions
- assert np.isclose(function_loss, true_loss), " ".join(
- [
- f"Loss function {metric_func.__name__} returned {function_loss}",
- f"loss, but {true_loss} loss expected",
- ]
- )
-
-
-@pytest.mark.parametrize("random_state", RANDOM_STATES)
-@pytest.mark.parametrize("metric_func_name", LOSS_RESULTS.keys())
-def test_univariate_function_output_type(metric_func_name, random_state):
- """Test univariate loss function for output type."""
- metric_func = LOSS_RESULTS[metric_func_name]["func"]
- y = make_series(n_timepoints=75, random_state=random_state)
- y_train, y_true = y.iloc[:50], y.iloc[50:]
- y_pred = y.shift(1).iloc[50:]
- y_pred_benchmark = y.rolling(2).mean().iloc[50:]
-
- function_loss = metric_func(
- y_true, y_pred, y_train=y_train, y_pred_benchmark=y_pred_benchmark
- )
-
- is_num = is_numeric_dtype(function_loss)
- is_scalar = np.isscalar(function_loss)
- assert is_num and is_scalar, " ".join(
- ["Loss function with univariate input should return scalar number"]
- )
-
-
-@pytest.mark.parametrize("metric_func_name", LOSS_RESULTS.keys())
-def test_y_true_y_pred_inconsistent_n_outputs_raises_error(metric_func_name):
- """Test error for inconsistent number of outputs in y_true and y_pred."""
- metric_func = LOSS_RESULTS[metric_func_name]["func"]
- y = make_series(n_timepoints=75, random_state=RANDOM_STATES[0])
- y_train, y_true = y.iloc[:50], y.iloc[50:]
- y_true = y_true.values # Convert to flat NumPy array
- y_pred = y.shift(1).iloc[50:]
- y_pred = np.expand_dims(y_pred.values, 1) # convert to 1d NumPy array
- y_pred = np.hstack([y_pred, y_pred])
- y_pred_benchmark = y.rolling(2).mean().iloc[50:]
-
- # Test input types
- with pytest.raises(
- ValueError, match="y_true and y_pred have different number of output"
- ):
- metric_func(y_true, y_pred, y_train=y_train, y_pred_benchmark=y_pred_benchmark)
-
-
-@pytest.mark.parametrize("metric_func_name", LOSS_RESULTS.keys())
-def test_y_true_y_pred_inconsistent_n_timepoints_raises_error(metric_func_name):
- """Test error for inconsistent number of timepoints in y_true and y_pred."""
- metric_func = LOSS_RESULTS[metric_func_name]["func"]
- y = make_series(n_timepoints=75, random_state=RANDOM_STATES[0])
- y_train, y_true = y.iloc[:50], y.iloc[50:]
- y_pred = y.shift(1).iloc[40:] # y_pred has more obs
- y_pred_benchmark = y.rolling(2).mean().iloc[50:]
-
- # Test input types
- with pytest.raises(
- ValueError, match="Found input variables with inconsistent numbers of samples"
- ):
- metric_func(y_true, y_pred, y_train=y_train, y_pred_benchmark=y_pred_benchmark)
-
-
-@pytest.mark.parametrize("metric_func_name", LOSS_RESULTS.keys())
-def test_y_true_y_pred_inconsistent_n_variables_raises_error(metric_func_name):
- """Test error for inconsistent number of variables in y_true and y_pred."""
- metric_func = LOSS_RESULTS[metric_func_name]["func"]
- y = make_series(n_timepoints=75, random_state=RANDOM_STATES[0])
- y_train, y_true = y.iloc[:50], y.iloc[50:]
- y_true = y_true.values # will pass as NumPy array
- y_pred = y.shift(1).iloc[50:]
- y_pred = y_pred.to_frame()
- y_pred["Second Series"] = y.shift(1).iloc[50:]
- y_pred = y_pred.values
- y_pred_benchmark = y.rolling(2).mean().iloc[50:]
-
- # Test input types
- with pytest.raises(
- ValueError, match="y_true and y_pred have different number of output"
- ):
- metric_func(y_true, y_pred, y_train=y_train, y_pred_benchmark=y_pred_benchmark)
diff --git a/aeon/performance_metrics/stats.py b/aeon/performance_metrics/stats.py
index 800279d9da..98a7fef37f 100644
--- a/aeon/performance_metrics/stats.py
+++ b/aeon/performance_metrics/stats.py
@@ -311,5 +311,5 @@ def wilcoxon_test(results, labels, lower_better=False):
results[:, j],
zero_method="wilcox",
alternative="less" if lower_better else "greater",
- )[1]
+ ).pvalue
return p_values
diff --git a/aeon/performance_metrics/tests/test_numpy_metrics.py b/aeon/performance_metrics/tests/test_numpy_metrics.py
deleted file mode 100644
index ddd1742108..0000000000
--- a/aeon/performance_metrics/tests/test_numpy_metrics.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Tests for numpy metrics in _functions module."""
-
-from inspect import getmembers, isfunction
-
-import numpy as np
-import pandas as pd
-import pytest
-
-from aeon.performance_metrics.forecasting import _functions
-from aeon.testing.data_generation._legacy import make_series
-
-numpy_metrics = getmembers(_functions, isfunction)
-
-exclude_starts_with = ("_", "check", "gmean", "weighted_geometric_mean")
-numpy_metrics = [x for x in numpy_metrics if not x[0].startswith(exclude_starts_with)]
-
-names, metrics = zip(*numpy_metrics)
-
-
-@pytest.mark.parametrize("n_columns", [1, 2])
-@pytest.mark.parametrize("multioutput", ["uniform_average", "raw_values"])
-@pytest.mark.parametrize("metric", metrics, ids=names)
-def test_metric_output(metric, multioutput, n_columns):
- """Test output is correct class."""
- y_pred = make_series(n_columns=n_columns, n_timepoints=20, random_state=21)
- y_true = make_series(n_columns=n_columns, n_timepoints=20, random_state=42)
-
- # coerce to DataFrame since make_series does not return consisten output type
- y_pred = pd.DataFrame(y_pred)
- y_true = pd.DataFrame(y_true)
-
- res = metric(
- y_true=y_true,
- y_pred=y_pred,
- multioutput=multioutput,
- y_pred_benchmark=y_pred,
- y_train=y_true,
- )
-
- if multioutput == "uniform_average":
- assert isinstance(res, float)
- elif multioutput == "raw_values":
- assert isinstance(res, np.ndarray)
- assert res.ndim == 1
- assert len(res) == len(y_true.columns)
diff --git a/aeon/performance_metrics/tests/test_stats.py b/aeon/performance_metrics/tests/test_stats.py
index 6ee8bc6aaa..44560c7691 100644
--- a/aeon/performance_metrics/tests/test_stats.py
+++ b/aeon/performance_metrics/tests/test_stats.py
@@ -24,7 +24,7 @@ def test_nemenyi_test():
data_full = list(univariate_equal_length)
data_full.sort()
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data_full, path=data_path, include_missing=True
)
@@ -47,7 +47,7 @@ def test_nemenyi_test():
# to check the existence of a clique we select a subset of the datasets.
data = data_full[45:55]
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data, path=data_path, include_missing=True
)
@@ -72,13 +72,13 @@ def test_wilcoxon_test():
cls = ["HC2", "InceptionT", "WEASEL-D", "FreshPRINCE"]
data_full = list(univariate_equal_length)
data_full.sort()
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data_full, path=data_path, include_missing=True
)
p_vals = wilcoxon_test(res, cls)
assert_almost_equal(p_vals[0], np.array([1.0, 0.0, 0.0, 0.0]), decimal=2)
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data_full, path=data_path, include_missing=True
)
p_vals = wilcoxon_test(res, cls, lower_better=True)
@@ -89,7 +89,7 @@ def test__check_friedman():
"""Test Friedman test for overall difference in estimators."""
cls = ["HC2", "FreshPRINCE", "InceptionT", "WEASEL-D"]
data = univariate_equal_length
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data, path=data_path, include_missing=True
)
ranked_data = rankdata(-1 * res, axis=1)
@@ -97,7 +97,7 @@ def test__check_friedman():
# test that approaches are not significantly different.
cls = ["HC2", "HC2", "HC2"]
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls,
datasets=data,
path=data_path,
diff --git a/aeon/pipeline/_make_pipeline.py b/aeon/pipeline/_make_pipeline.py
index bbf8e12da1..6886cd7ae0 100644
--- a/aeon/pipeline/_make_pipeline.py
+++ b/aeon/pipeline/_make_pipeline.py
@@ -1,6 +1,7 @@
"""Pipeline making utility."""
__maintainer__ = ["MatthewMiddlehurst"]
+__all__ = ["make_pipeline"]
from sklearn.base import ClassifierMixin, ClusterMixin, RegressorMixin, TransformerMixin
@@ -19,7 +20,7 @@ def make_pipeline(*steps):
"""Create a pipeline from aeon and sklearn estimators.
Currently available for:
- forecasters, classifiers, regressors, clusterers, and transformers.
+ classifiers, regressors, clusterers, and collection transformers.
Parameters
----------
@@ -34,7 +35,7 @@ def make_pipeline(*steps):
Examples
--------
- Example 2: classifier pipeline
+ Example 1: classifier pipeline
>>> from aeon.classification.feature_based import Catch22Classifier
>>> from aeon.pipeline import make_pipeline
>>> from aeon.transformations.collection import PeriodogramTransformer
@@ -42,7 +43,7 @@ def make_pipeline(*steps):
>>> type(pipe).__name__
'ClassifierPipeline'
- Example 3: transformer pipeline
+ Example 2: transformer pipeline
>>> from aeon.pipeline import make_pipeline
>>> from aeon.transformations.collection import PeriodogramTransformer
>>> pipe = make_pipeline(PeriodogramTransformer(), PeriodogramTransformer())
@@ -85,8 +86,4 @@ def make_pipeline(*steps):
):
return CollectionTransformerPipeline(list(steps))
else:
- pipe = steps[0]
- for i in range(1, len(steps)):
- pipe = pipe * steps[i]
-
- return pipe
+ raise ValueError("Pipeline type not recognized")
diff --git a/aeon/pipeline/tests/test_make_pipeline.py b/aeon/pipeline/tests/test_make_pipeline.py
index ea8d5df6cc..2d569e00b8 100644
--- a/aeon/pipeline/tests/test_make_pipeline.py
+++ b/aeon/pipeline/tests/test_make_pipeline.py
@@ -13,19 +13,19 @@
from aeon.regression import DummyRegressor
from aeon.testing.data_generation import make_example_3d_numpy
from aeon.transformations.collection import Padder, Tabularizer
-from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer
+from aeon.transformations.collection.feature_based import SevenNumberSummary
@pytest.mark.parametrize(
"pipeline",
[
[Padder(pad_length=15), DummyClassifier()],
- [SevenNumberSummaryTransformer(), RandomForestClassifier(n_estimators=2)],
+ [SevenNumberSummary(), RandomForestClassifier(n_estimators=2)],
[Padder(pad_length=15), DummyRegressor()],
- [SevenNumberSummaryTransformer(), RandomForestRegressor(n_estimators=2)],
- [Padder(pad_length=15), TimeSeriesKMeans.create_test_instance()],
- [SevenNumberSummaryTransformer(), KMeans(n_clusters=2, max_iter=3)],
- [Padder(pad_length=15), SevenNumberSummaryTransformer()],
+ [SevenNumberSummary(), RandomForestRegressor(n_estimators=2)],
+ [Padder(pad_length=15), TimeSeriesKMeans._create_test_instance()],
+ [SevenNumberSummary(), KMeans(n_clusters=2, max_iter=3)],
+ [Padder(pad_length=15), SevenNumberSummary()],
[Padder(pad_length=15), Tabularizer(), StandardScaler()],
],
)
diff --git a/aeon/regression/compose/__init__.py b/aeon/regression/compose/__init__.py
index fc87eeff09..dcf2c29555 100644
--- a/aeon/regression/compose/__init__.py
+++ b/aeon/regression/compose/__init__.py
@@ -1,5 +1,6 @@
"""Implement composite time series regression estimators."""
-__all__ = ["RegressorPipeline"]
+__all__ = ["RegressorEnsemble", "RegressorPipeline"]
+from aeon.regression.compose._ensemble import RegressorEnsemble
from aeon.regression.compose._pipeline import RegressorPipeline
diff --git a/aeon/regression/compose/_ensemble.py b/aeon/regression/compose/_ensemble.py
index c0b7d53d42..14d3f837bb 100644
--- a/aeon/regression/compose/_ensemble.py
+++ b/aeon/regression/compose/_ensemble.py
@@ -6,9 +6,8 @@
import numpy as np
-from aeon.base.estimator.compose.collection_ensemble import BaseCollectionEnsemble
-from aeon.regression import BaseRegressor, DummyRegressor
-from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor
+from aeon.base.estimators.compose.collection_ensemble import BaseCollectionEnsemble
+from aeon.regression import BaseRegressor
from aeon.regression.sklearn._wrapper import SklearnRegressorWrapper
from aeon.utils.sklearn import is_sklearn_regressor
@@ -19,11 +18,11 @@ class RegressorEnsemble(BaseCollectionEnsemble, BaseRegressor):
Parameters
----------
regressors : list of aeon and/or sklearn regressors or list of tuples
- Estimators to be used in the ensemble. The str is used to name the estimator.
- List of tuples (str, estimator) of estimators can also be passed, where
- the str is used to name the estimator.
- The objects are cloned prior, as such the state of the input will not be
- modified by fitting the pipeline.
+ Estimators to be used in the ensemble.
+ A list of tuples (str, estimator) can also be passed, where the str is used to
+ name the estimator.
+ The objects are cloned prior. As such, the state of the input will not be
+ modified by fitting the ensemble.
weights : float, or iterable of float, default=None
If float, ensemble weight for estimator i will be train score to this power.
If iterable of float, must be equal length as _estimators. Ensemble weight for
@@ -49,14 +48,14 @@ class RegressorEnsemble(BaseCollectionEnsemble, BaseRegressor):
Attributes
----------
ensemble_ : list of tuples (str, estimator) of estimators
- Clones of estimators in _estimators which are fitted in the ensemble.
- Will always be in (str, estimator) format regardless of _estimators input.
+ Clones of estimators in regressors which are fitted in the ensemble.
+ Will always be in (str, estimator) format regardless of regressors input.
weights_ : dict
Weights of estimators using the str names as keys.
See Also
--------
- ClassifierEnsemble : A pipeline for classification tasks.
+ ClassifierEnsemble : An ensemble for classification tasks.
"""
_tags = {
@@ -76,17 +75,18 @@ def __init__(
wreg = [self._wrap_sklearn(clf) for clf in self.regressors]
super().__init__(
- _estimators=wreg,
+ _ensemble=wreg,
weights=weights,
cv=cv,
metric=metric,
metric_probas=False,
random_state=random_state,
+ _ensemble_input_name="regressors",
)
def _predict(self, X) -> np.ndarray:
"""Predicts labels for sequences in X."""
- preds = np.zeros(X.shape[0])
+ preds = np.zeros(len(X))
for reg_name, reg in self.ensemble_:
preds += reg.predict(X=X) * self.weights_[reg_name]
@@ -106,7 +106,7 @@ def _wrap_sklearn(reg):
return reg
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -121,12 +121,14 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
+ from aeon.regression import DummyRegressor
+ from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor
+
return {
"regressors": [
- KNeighborsTimeSeriesRegressor.create_test_instance(),
- DummyRegressor.create_test_instance(),
+ KNeighborsTimeSeriesRegressor._create_test_instance(),
+ DummyRegressor._create_test_instance(),
],
"weights": [2, 1],
}
diff --git a/aeon/regression/compose/_pipeline.py b/aeon/regression/compose/_pipeline.py
index 00bb1d4d11..3d161bf5df 100644
--- a/aeon/regression/compose/_pipeline.py
+++ b/aeon/regression/compose/_pipeline.py
@@ -3,7 +3,7 @@
__maintainer__ = ["MatthewMiddlehurst"]
__all__ = ["RegressorPipeline"]
-from aeon.base.estimator.compose.collection_pipeline import BaseCollectionPipeline
+from aeon.base.estimators.compose.collection_pipeline import BaseCollectionPipeline
from aeon.regression.base import BaseRegressor
@@ -82,7 +82,7 @@ def __init__(self, transformers, regressor, random_state=None):
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -97,18 +97,15 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor
from aeon.transformations.collection import Truncator
- from aeon.transformations.collection.feature_based import (
- SevenNumberSummaryTransformer,
- )
+ from aeon.transformations.collection.feature_based import SevenNumberSummary
return {
"transformers": [
Truncator(truncated_length=5),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
"regressor": KNeighborsTimeSeriesRegressor(distance="euclidean"),
}
diff --git a/aeon/regression/compose/tests/test_pipeline.py b/aeon/regression/compose/tests/test_pipeline.py
index c644e9f6ee..81f690ccb9 100644
--- a/aeon/regression/compose/tests/test_pipeline.py
+++ b/aeon/regression/compose/tests/test_pipeline.py
@@ -24,20 +24,20 @@
Tabularizer,
TimeSeriesScaler,
)
-from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer
+from aeon.transformations.collection.feature_based import SevenNumberSummary
@pytest.mark.parametrize(
"transformers",
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
[Padder(pad_length=15), Tabularizer(), StandardScaler()],
- [Padder(pad_length=15), SevenNumberSummaryTransformer()],
- [Tabularizer(), StandardScaler(), SevenNumberSummaryTransformer()],
+ [Padder(pad_length=15), SevenNumberSummary()],
+ [Tabularizer(), StandardScaler(), SevenNumberSummary()],
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
],
)
@@ -68,14 +68,14 @@ def test_regressor_pipeline(transformers):
"transformers",
[
[Padder(pad_length=15), Tabularizer()],
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
[Tabularizer(), StandardScaler()],
[Padder(pad_length=15), Tabularizer(), StandardScaler()],
- [Padder(pad_length=15), SevenNumberSummaryTransformer()],
- [Tabularizer(), StandardScaler(), SevenNumberSummaryTransformer()],
+ [Padder(pad_length=15), SevenNumberSummary()],
+ [Tabularizer(), StandardScaler(), SevenNumberSummary()],
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
],
)
@@ -108,7 +108,7 @@ def test_unequal_tag_inference():
n_cases=10, min_n_timepoints=8, max_n_timepoints=12, regression_target=True
)
- t1 = SevenNumberSummaryTransformer()
+ t1 = SevenNumberSummary()
t2 = Padder()
t3 = TimeSeriesScaler()
t4 = AutocorrelationFunctionTransformer(n_lags=5)
@@ -229,7 +229,7 @@ def test_multivariate_tag_inference():
n_cases=10, n_channels=2, n_timepoints=12, regression_target=True
)
- t1 = SevenNumberSummaryTransformer()
+ t1 = SevenNumberSummary()
t2 = TimeSeriesScaler()
t3 = HOG1DTransformer()
t4 = StandardScaler()
diff --git a/aeon/regression/convolution_based/_minirocket.py b/aeon/regression/convolution_based/_minirocket.py
index 2c723b5fbd..3e79965bba 100644
--- a/aeon/regression/convolution_based/_minirocket.py
+++ b/aeon/regression/convolution_based/_minirocket.py
@@ -146,7 +146,7 @@ def _predict(self, X) -> np.ndarray:
return self.pipeline_.predict(X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
diff --git a/aeon/regression/convolution_based/_multirocket.py b/aeon/regression/convolution_based/_multirocket.py
index 4218434a81..4cdf782cdb 100644
--- a/aeon/regression/convolution_based/_multirocket.py
+++ b/aeon/regression/convolution_based/_multirocket.py
@@ -152,7 +152,7 @@ def _predict(self, X) -> np.ndarray:
return self.pipeline_.predict(X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
diff --git a/aeon/regression/convolution_based/_rocket.py b/aeon/regression/convolution_based/_rocket.py
index 92c2ae7bf4..5bee6b5150 100644
--- a/aeon/regression/convolution_based/_rocket.py
+++ b/aeon/regression/convolution_based/_rocket.py
@@ -146,7 +146,7 @@ def _predict(self, X) -> np.ndarray:
return self.pipeline_.predict(X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
diff --git a/aeon/regression/deep_learning/_cnn.py b/aeon/regression/deep_learning/_cnn.py
index 58f0f4a5a8..c636c70087 100644
--- a/aeon/regression/deep_learning/_cnn.py
+++ b/aeon/regression/deep_learning/_cnn.py
@@ -312,7 +312,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -331,7 +331,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param = {
"n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_encoder.py b/aeon/regression/deep_learning/_encoder.py
index 4b73047c05..2183d5ee8a 100644
--- a/aeon/regression/deep_learning/_encoder.py
+++ b/aeon/regression/deep_learning/_encoder.py
@@ -298,7 +298,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -317,7 +317,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 8,
diff --git a/aeon/regression/deep_learning/_fcn.py b/aeon/regression/deep_learning/_fcn.py
index 374feb5b93..361bc2eef0 100644
--- a/aeon/regression/deep_learning/_fcn.py
+++ b/aeon/regression/deep_learning/_fcn.py
@@ -306,7 +306,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -325,7 +325,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param = {
"n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_inception_time.py b/aeon/regression/deep_learning/_inception_time.py
index 19493ac6db..5e6c6a56e9 100644
--- a/aeon/regression/deep_learning/_inception_time.py
+++ b/aeon/regression/deep_learning/_inception_time.py
@@ -136,6 +136,13 @@ class InceptionTimeRegressor(BaseRegressor):
Notes
-----
+ Adapted from the implementation from Fawaz et. al ..[1]
+
+ and Ismail-Fawaz et al.
+ https://github.com/MSD-IRIMAS/CF-4-TSC
+
+ References
+ ----------
..[1] Fawaz et al. InceptionTime: Finding AlexNet for Time Series
regression, Data Mining and Knowledge Discovery, 34, 2020
@@ -144,12 +151,6 @@ class InceptionTimeRegressor(BaseRegressor):
Hand-Crafted Convolution Filters, 2022 IEEE International
Conference on Big Data.
- Adapted from the implementation from Fawaz et. al
- https://github.com/hfawaz/InceptionTime/blob/master/regressors/inception.py
-
- and Ismail-Fawaz et al.
- https://github.com/MSD-IRIMAS/CF-4-TSC
-
Examples
--------
>>> from aeon.regression.deep_learning import InceptionTimeRegressor
@@ -327,7 +328,7 @@ def _predict(self, X) -> np.ndarray:
return ypreds
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -346,7 +347,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_regressors": 1,
@@ -463,18 +463,20 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
Notes
-----
- ..[1] Fawaz et al. InceptionTime: Finding AlexNet for Time Series
- regression, Data Mining and Knowledge Discovery, 34, 2020
-
- ..[2] Ismail-Fawaz et al. Deep Learning For Time Series regression Using New
- Hand-Crafted Convolution Filters, 2022 IEEE International Conference on Big Data.
-
Adapted from the implementation from Fawaz et. al
https://github.com/hfawaz/InceptionTime/blob/master/regressors/inception.py
and Ismail-Fawaz et al.
https://github.com/MSD-IRIMAS/CF-4-TSC
+ References
+ ----------
+ ..[1] Fawaz et al. InceptionTime: Finding AlexNet for Time Series
+ regression, Data Mining and Knowledge Discovery, 34, 2020
+
+ ..[2] Ismail-Fawaz et al. Deep Learning For Time Series regression Using New
+ Hand-Crafted Convolution Filters, 2022 IEEE International Conference on Big Data.
+
Examples
--------
>>> from aeon.regression.deep_learning import IndividualInceptionRegressor
@@ -699,7 +701,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -718,7 +720,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_lite_time.py b/aeon/regression/deep_learning/_lite_time.py
index 56e33849f6..88d88ffcca 100644
--- a/aeon/regression/deep_learning/_lite_time.py
+++ b/aeon/regression/deep_learning/_lite_time.py
@@ -16,16 +16,24 @@
class LITETimeRegressor(BaseRegressor):
- """LITETime ensemble Regressor.
+ """LITETime or LITEMVTime ensemble Regressor.
- Ensemble of IndividualLITETimeRegressor objects, as described in [1]_.
+ Ensemble of IndividualLITETimeRegressor objects, as described in [1]_
+ and [2]_. For using LITEMV, simply set the `use_litemv`
+ bool parameter to True.
Parameters
----------
n_regressors : int, default = 5,
- the number of LITE models used for the
+ the number of LITE or LITEMV models used for the
Ensemble in order to create
- LITETime.
+ LITETime or LITEMVTime.
+ use_litemv : bool, default = False
+ The boolean value to control which version of the
+ network to use. If set to `False`, then LITE is used,
+ if set to `True` then LITEMV is used. LITEMV is the
+ same architecture as LITE but specifically designed
+ to better handle multivariate time series.
n_filters : int or list of int32, default = 32
The number of filters used in one lite layer, if not a list, the same
number of filters is used in all lite layers.
@@ -90,12 +98,17 @@ class LITETimeRegressor(BaseRegressor):
Notes
-----
+ Adapted from the implementation from Ismail-Fawaz et. al
+ https://github.com/MSD-IRIMAS/LITE
+
+ References
+ ----------
..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing
tEchniques for Time Series Classification, IEEE International
Conference on Data Science and Advanced Analytics, 2023.
-
- Adapted from the implementation from Ismail-Fawaz et. al
- https://github.com/MSD-IRIMAS/LITE
+ ..[2] Ismail-Fawaz, Ali, et al. "Look Into the LITE
+ in Deep Learning for Time Series Classification."
+ arXiv preprint arXiv:2409.02869 (2024).
Examples
--------
@@ -119,6 +132,7 @@ class LITETimeRegressor(BaseRegressor):
def __init__(
self,
n_regressors=5,
+ use_litemv=False,
n_filters=32,
kernel_size=40,
strides=1,
@@ -143,6 +157,8 @@ def __init__(
):
self.n_regressors = n_regressors
+ self.use_litemv = use_litemv
+
self.strides = strides
self.activation = activation
self.output_activation = output_activation
@@ -191,6 +207,7 @@ def _fit(self, X, y):
for n in range(0, self.n_regressors):
rgs = IndividualLITERegressor(
+ use_litemv=self.use_litemv,
n_filters=self.n_filters,
kernel_size=self.kernel_size,
output_activation=self.output_activation,
@@ -240,7 +257,7 @@ def _predict(self, X) -> np.ndarray:
return vals
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -259,26 +276,43 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_regressors": 1,
- "n_epochs": 10,
+ "n_epochs": 2,
+ "batch_size": 4,
+ "kernel_size": 4,
+ }
+ param2 = {
+ "n_regressors": 1,
+ "use_litemv": True,
+ "n_epochs": 2,
"batch_size": 4,
"kernel_size": 4,
+ "metrics": ["mean_squared_error"],
+ "verbose": True,
+ "use_mini_batch_size": True,
}
- return [param1]
+ return [param1, param2]
class IndividualLITERegressor(BaseDeepRegressor):
- """Single LITE Regressor.
+ """Single LITE or LITEMV Regressor.
- One LITE deep model, as described in [1]_.
+ One LITE or LITEMV deep model, as described in [1]_
+ and [2]_. For using LITEMV, simply set the `use_litemv`
+ bool parameter to True.
Parameters
----------
- n_filters : int or list of int32, default = 32
+ use_litemv : bool, default = False
+ The boolean value to control which version of the
+ network to use. If set to `False`, then LITE is used,
+ if set to `True` then LITEMV is used. LITEMV is the
+ same architecture as LITE but specifically designed
+ to better handle multivariate time series.
+ n_filters : int or list of int32, default = 32
The number of filters used in one lite layer, if not a list, the same
number of filters is used in all lite layers.
kernel_size : int or list of int, default = 40
@@ -342,12 +376,17 @@ class IndividualLITERegressor(BaseDeepRegressor):
Notes
-----
+ Adapted from the implementation from Ismail-Fawaz et. al
+ https://github.com/MSD-IRIMAS/LITE
+
+ References
+ ----------
..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing
tEchniques for Time Series Classificaion, IEEE International
Conference on Data Science and Advanced Analytics, 2023.
-
- Adapted from the implementation from Ismail-Fawaz et. al
- https://github.com/MSD-IRIMAS/LITE
+ ..[2] Ismail-Fawaz, Ali, et al. "Look Into the LITE
+ in Deep Learning for Time Series Classification."
+ arXiv preprint arXiv:2409.02869 (2024).
Examples
--------
@@ -362,6 +401,7 @@ class IndividualLITERegressor(BaseDeepRegressor):
def __init__(
self,
+ use_litemv=False,
n_filters=32,
kernel_size=40,
strides=1,
@@ -384,7 +424,7 @@ def __init__(
metrics=None,
optimizer=None,
):
- # predefined
+ self.use_litemv = use_litemv
self.n_filters = n_filters
self.strides = strides
self.activation = activation
@@ -415,6 +455,7 @@ def __init__(
)
self._network = LITENetwork(
+ use_litemv=self.use_litemv,
n_filters=self.n_filters,
kernel_size=self.kernel_size,
strides=self.strides,
@@ -549,7 +590,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -568,12 +609,20 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
- "n_epochs": 10,
+ "n_epochs": 2,
+ "batch_size": 4,
+ "kernel_size": 4,
+ }
+ param2 = {
+ "use_litemv": True,
+ "n_epochs": 2,
"batch_size": 4,
"kernel_size": 4,
+ "metrics": ["mean_squared_error"],
+ "verbose": True,
+ "use_mini_batch_size": True,
}
- return [param1]
+ return [param1, param2]
diff --git a/aeon/regression/deep_learning/_mlp.py b/aeon/regression/deep_learning/_mlp.py
index cb9907fe7c..d593eb7b80 100644
--- a/aeon/regression/deep_learning/_mlp.py
+++ b/aeon/regression/deep_learning/_mlp.py
@@ -264,7 +264,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -283,7 +283,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param = {
"n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_resnet.py b/aeon/regression/deep_learning/_resnet.py
index 48f2d3c5f8..7592d4e683 100644
--- a/aeon/regression/deep_learning/_resnet.py
+++ b/aeon/regression/deep_learning/_resnet.py
@@ -328,7 +328,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -347,7 +347,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param = {
"n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_tapnet.py b/aeon/regression/deep_learning/_tapnet.py
index 4a03f02c66..09dd292180 100644
--- a/aeon/regression/deep_learning/_tapnet.py
+++ b/aeon/regression/deep_learning/_tapnet.py
@@ -250,7 +250,7 @@ def _fit(self, X, y):
return self
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -269,7 +269,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
param1 = {
"n_epochs": 10,
diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py
index 510fa30a3c..ed70031112 100644
--- a/aeon/regression/distance_based/_time_series_neighbors.py
+++ b/aeon/regression/distance_based/_time_series_neighbors.py
@@ -49,7 +49,7 @@ class KNeighborsTimeSeriesRegressor(BaseRegressor):
n_jobs : int, default = None
The number of parallel jobs to run for neighbors search.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
- ``-1`` means using all processors. See :term:`Glossary `
+ ``-1`` means using all processors.
for more details. Parameter for compatibility purposes, still unimplemented.
Examples
@@ -183,7 +183,9 @@ def _kneighbors(self, X):
return closest_idx, ws
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+ def _get_test_params(
+ cls, parameter_set: str = "default"
+ ) -> Union[dict, list[dict]]:
"""Return testing parameter settings for the estimator.
Parameters
@@ -198,7 +200,6 @@ def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dic
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
# non-default distance and algorithm
params1 = {"distance": "euclidean"}
diff --git a/aeon/regression/feature_based/_catch22.py b/aeon/regression/feature_based/_catch22.py
index 579ec874e7..87e158ca9b 100644
--- a/aeon/regression/feature_based/_catch22.py
+++ b/aeon/regression/feature_based/_catch22.py
@@ -195,7 +195,7 @@ def _predict(self, X) -> np.ndarray:
return self._estimator.predict(self._transformer.transform(X))
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -214,7 +214,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/regression/feature_based/_fresh_prince.py b/aeon/regression/feature_based/_fresh_prince.py
index 029e7535ec..4f5a4b1bcb 100644
--- a/aeon/regression/feature_based/_fresh_prince.py
+++ b/aeon/regression/feature_based/_fresh_prince.py
@@ -12,7 +12,7 @@
from aeon.regression.base import BaseRegressor
from aeon.regression.sklearn import RotationForestRegressor
-from aeon.transformations.collection.feature_based import TSFreshFeatureExtractor
+from aeon.transformations.collection.feature_based import TSFresh
class FreshPRINCERegressor(BaseRegressor):
@@ -52,7 +52,7 @@ class FreshPRINCERegressor(BaseRegressor):
See Also
--------
- TSFreshFeatureExtractor, TSFreshRegressor, RotationForestRegressor
+ TSFresh, TSFreshRegressor, RotationForestRegressor
References
----------
@@ -169,7 +169,7 @@ def _fit_fp_shared(self, X, y):
n_jobs=self._n_jobs,
random_state=self.random_state,
)
- self._tsfresh = TSFreshFeatureExtractor(
+ self._tsfresh = TSFresh(
default_fc_parameters=self.default_fc_parameters,
n_jobs=self._n_jobs,
chunksize=self.chunksize,
@@ -180,7 +180,7 @@ def _fit_fp_shared(self, X, y):
return self._tsfresh.fit_transform(X, y)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -202,7 +202,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/regression/feature_based/_summary.py b/aeon/regression/feature_based/_summary.py
index 4e2f89ee37..52f06ee8e2 100644
--- a/aeon/regression/feature_based/_summary.py
+++ b/aeon/regression/feature_based/_summary.py
@@ -11,7 +11,7 @@
from aeon.base._base import _clone_estimator
from aeon.regression.base import BaseRegressor
-from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer
+from aeon.transformations.collection.feature_based import SevenNumberSummary
class SummaryRegressor(BaseRegressor):
@@ -19,7 +19,7 @@ class SummaryRegressor(BaseRegressor):
Summary statistic regressor.
This regressor simply transforms the input data using the
- SevenNumberSummaryTransformer transformer and builds a provided estimator using the
+ SevenNumberSummary transformer and builds a provided estimator using the
transformed data.
Parameters
@@ -107,7 +107,7 @@ def _fit(self, X, y):
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
- self._transformer = SevenNumberSummaryTransformer(
+ self._transformer = SevenNumberSummary(
summary_stats=self.summary_stats,
)
@@ -145,7 +145,7 @@ def _predict(self, X) -> np.ndarray:
return self._estimator.predict(self._transformer.transform(X))
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -164,7 +164,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"estimator": RandomForestRegressor(n_estimators=10)}
diff --git a/aeon/regression/feature_based/_tsfresh.py b/aeon/regression/feature_based/_tsfresh.py
index 8ed45eff77..0d8cb5bc00 100644
--- a/aeon/regression/feature_based/_tsfresh.py
+++ b/aeon/regression/feature_based/_tsfresh.py
@@ -13,10 +13,7 @@
from aeon.base._base import _clone_estimator
from aeon.regression.base import BaseRegressor
-from aeon.transformations.collection.feature_based import (
- TSFreshFeatureExtractor,
- TSFreshRelevantFeatureExtractor,
-)
+from aeon.transformations.collection.feature_based import TSFresh, TSFreshRelevant
class TSFreshRegressor(BaseRegressor):
@@ -53,8 +50,8 @@ class TSFreshRegressor(BaseRegressor):
See Also
--------
- TSFreshFeatureExtractor
- TSFreshRelevantFeatureExtractor
+ TSFresh
+ TSFreshRelevant
TSFreshClassifier
References
@@ -119,13 +116,13 @@ def _fit(self, X, y):
ending in "_" and sets is_fitted flag to True.
"""
self._transformer = (
- TSFreshRelevantFeatureExtractor(
+ TSFreshRelevant(
default_fc_parameters=self.default_fc_parameters,
n_jobs=self._n_jobs,
chunksize=self.chunksize,
)
if self.relevant_feature_extractor
- else TSFreshFeatureExtractor(
+ else TSFresh(
default_fc_parameters=self.default_fc_parameters,
n_jobs=self._n_jobs,
chunksize=self.chunksize,
@@ -186,7 +183,7 @@ def _predict(self, X) -> np.ndarray:
return self._estimator.predict(self._transformer.transform(X))
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -205,7 +202,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/regression/hybrid/_rist.py b/aeon/regression/hybrid/_rist.py
index e96beb24b2..15e0f763fb 100644
--- a/aeon/regression/hybrid/_rist.py
+++ b/aeon/regression/hybrid/_rist.py
@@ -1,7 +1,7 @@
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.preprocessing import FunctionTransformer
-from aeon.base.estimator.hybrid import BaseRIST
+from aeon.base.estimators.hybrid import BaseRIST
from aeon.regression import BaseRegressor
from aeon.utils.numba.general import first_order_differences_3d
@@ -126,7 +126,7 @@ def __init__(
}
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return unit test parameter settings for the estimator.
Parameters
diff --git a/aeon/regression/interval_based/_cif.py b/aeon/regression/interval_based/_cif.py
index 34fede19d8..4899f39cab 100644
--- a/aeon/regression/interval_based/_cif.py
+++ b/aeon/regression/interval_based/_cif.py
@@ -5,7 +5,7 @@
import numpy as np
-from aeon.base.estimator.interval_based import BaseIntervalForest
+from aeon.base.estimators.interval_based import BaseIntervalForest
from aeon.regression import BaseRegressor
from aeon.transformations.collection.feature_based import Catch22
from aeon.utils.numba.stats import row_mean, row_slope, row_std
@@ -194,7 +194,7 @@ def __init__(
self.set_tags(**{"python_dependencies": "pycatch22"})
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -216,7 +216,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10, "n_intervals": 2, "att_subsample_size": 4}
diff --git a/aeon/regression/interval_based/_drcif.py b/aeon/regression/interval_based/_drcif.py
index f76f38711b..843bb3c7b4 100644
--- a/aeon/regression/interval_based/_drcif.py
+++ b/aeon/regression/interval_based/_drcif.py
@@ -6,7 +6,7 @@
from sklearn.preprocessing import FunctionTransformer
-from aeon.base.estimator.interval_based import BaseIntervalForest
+from aeon.base.estimators.interval_based import BaseIntervalForest
from aeon.regression import BaseRegressor
from aeon.transformations.collection import PeriodogramTransformer
from aeon.transformations.collection.feature_based import Catch22
@@ -220,7 +220,7 @@ def __init__(
self.set_tags(**{"python_dependencies": d})
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -242,7 +242,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10, "n_intervals": 2, "att_subsample_size": 4}
diff --git a/aeon/regression/interval_based/_interval_forest.py b/aeon/regression/interval_based/_interval_forest.py
index 746d2249d1..aa0195298f 100644
--- a/aeon/regression/interval_based/_interval_forest.py
+++ b/aeon/regression/interval_based/_interval_forest.py
@@ -5,7 +5,7 @@
import numpy as np
-from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
+from aeon.base.estimators.interval_based.base_interval_forest import BaseIntervalForest
from aeon.regression.base import BaseRegressor
@@ -201,7 +201,7 @@ def __init__(
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -223,7 +223,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10, "n_intervals": 2}
diff --git a/aeon/regression/interval_based/_interval_pipelines.py b/aeon/regression/interval_based/_interval_pipelines.py
index 04cce958a7..42ceb467b9 100644
--- a/aeon/regression/interval_based/_interval_pipelines.py
+++ b/aeon/regression/interval_based/_interval_pipelines.py
@@ -183,7 +183,7 @@ def _predict(self, X) -> np.ndarray:
return self._estimator.predict(self._transformer.transform(X))
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -202,7 +202,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.utils.numba.stats import row_mean, row_numba_min
diff --git a/aeon/regression/interval_based/_rise.py b/aeon/regression/interval_based/_rise.py
index 82d317e665..ef1d34d8bb 100644
--- a/aeon/regression/interval_based/_rise.py
+++ b/aeon/regression/interval_based/_rise.py
@@ -5,7 +5,7 @@
import numpy as np
-from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
+from aeon.base.estimators.interval_based.base_interval_forest import BaseIntervalForest
from aeon.regression import BaseRegressor
from aeon.transformations.collection import (
AutocorrelationFunctionTransformer,
@@ -162,7 +162,7 @@ def __init__(
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -185,7 +185,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
if parameter_set == "results_comparison":
return {"n_estimators": 10}
diff --git a/aeon/regression/interval_based/_tsf.py b/aeon/regression/interval_based/_tsf.py
index 1a82635c4b..c15da5a3ad 100644
--- a/aeon/regression/interval_based/_tsf.py
+++ b/aeon/regression/interval_based/_tsf.py
@@ -8,7 +8,7 @@
import numpy as np
-from aeon.base.estimator.interval_based.base_interval_forest import BaseIntervalForest
+from aeon.base.estimators.interval_based.base_interval_forest import BaseIntervalForest
from aeon.regression import BaseRegressor
@@ -162,7 +162,7 @@ def __init__(
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -181,7 +181,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"n_estimators": 2,
diff --git a/aeon/regression/interval_based/tests/test_cif.py b/aeon/regression/interval_based/tests/test_cif.py
index 83deef3732..db5d811ffa 100644
--- a/aeon/regression/interval_based/tests/test_cif.py
+++ b/aeon/regression/interval_based/tests/test_cif.py
@@ -8,7 +8,7 @@ def test_cif():
dr = CanonicalIntervalForestRegressor(use_pycatch22=True)
d = dr.get_tag("python_dependencies")
assert d == "pycatch22"
- paras = CanonicalIntervalForestRegressor.get_test_params(
+ paras = CanonicalIntervalForestRegressor._get_test_params(
parameter_set="contracting"
)
assert paras["time_limit_in_minutes"] == 5
diff --git a/aeon/regression/interval_based/tests/test_dr_cif.py b/aeon/regression/interval_based/tests/test_dr_cif.py
index 145c172eab..d6cf83d36f 100644
--- a/aeon/regression/interval_based/tests/test_dr_cif.py
+++ b/aeon/regression/interval_based/tests/test_dr_cif.py
@@ -8,6 +8,6 @@ def test_dr_cif():
dr = DrCIFRegressor(use_pycatch22=True)
d = dr.get_tag("python_dependencies")
assert d[0] == "pycatch22"
- paras = DrCIFRegressor.get_test_params(parameter_set="contracting")
+ paras = DrCIFRegressor._get_test_params(parameter_set="contracting")
assert paras["time_limit_in_minutes"] == 5
assert paras["att_subsample_size"] == 2
diff --git a/aeon/regression/interval_based/tests/test_interval_forest.py b/aeon/regression/interval_based/tests/test_interval_forest.py
index 55651660a1..173fe7dfa2 100644
--- a/aeon/regression/interval_based/tests/test_interval_forest.py
+++ b/aeon/regression/interval_based/tests/test_interval_forest.py
@@ -5,6 +5,6 @@
def test_cif():
"""Test with IntervalForestRegressor contracting."""
- paras = IntervalForestRegressor.get_test_params(parameter_set="contracting")
+ paras = IntervalForestRegressor._get_test_params(parameter_set="contracting")
assert paras["time_limit_in_minutes"] == 5
assert paras["n_intervals"] == 2
diff --git a/aeon/regression/shapelet_based/_rdst.py b/aeon/regression/shapelet_based/_rdst.py
index 6f0a0b9bc1..f8f27773ef 100644
--- a/aeon/regression/shapelet_based/_rdst.py
+++ b/aeon/regression/shapelet_based/_rdst.py
@@ -218,7 +218,7 @@ def _predict(self, X) -> np.ndarray:
return self._estimator.predict(X_t)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -237,6 +237,5 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {"max_shapelets": 20}
diff --git a/aeon/regression/sklearn/_wrapper.py b/aeon/regression/sklearn/_wrapper.py
index da8b37fa67..caf00f15b7 100644
--- a/aeon/regression/sklearn/_wrapper.py
+++ b/aeon/regression/sklearn/_wrapper.py
@@ -44,7 +44,7 @@ def _predict(self, X):
return self.regressor_.predict(X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -59,7 +59,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
return {
"regressor": RandomForestRegressor(n_estimators=5),
diff --git a/aeon/segmentation/_binseg.py b/aeon/segmentation/_binseg.py
index ad54b047e4..af64959000 100644
--- a/aeon/segmentation/_binseg.py
+++ b/aeon/segmentation/_binseg.py
@@ -124,7 +124,7 @@ def _get_interval_series(self, X, found_cps):
return pd.IntervalIndex.from_arrays(start, end)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -139,6 +139,5 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {"n_cps": 1}
diff --git a/aeon/segmentation/_clasp.py b/aeon/segmentation/_clasp.py
index 4aed2f866d..46dbc1900a 100644
--- a/aeon/segmentation/_clasp.py
+++ b/aeon/segmentation/_clasp.py
@@ -301,7 +301,7 @@ def _get_interval_series(self, X, found_cps):
return pd.IntervalIndex.from_arrays(start, end)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -316,6 +316,5 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {"period_length": 5, "n_cps": 1}
diff --git a/aeon/segmentation/_eagglo.py b/aeon/segmentation/_eagglo.py
index 8ffa22cc53..d482c47d1b 100644
--- a/aeon/segmentation/_eagglo.py
+++ b/aeon/segmentation/_eagglo.py
@@ -350,7 +350,7 @@ def _get_penalty_func(self) -> Callable: # sourcery skip: raise-specific-error
)
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> list[dict]:
+ def _get_test_params(cls, parameter_set: str = "default") -> list[dict]:
"""Test parameters."""
return [
{"alpha": 1.0, "penalty": None},
diff --git a/aeon/segmentation/_fluss.py b/aeon/segmentation/_fluss.py
index 261044f990..de32b9ed58 100644
--- a/aeon/segmentation/_fluss.py
+++ b/aeon/segmentation/_fluss.py
@@ -137,7 +137,7 @@ def _get_interval_series(self, X, found_cps):
return pd.IntervalIndex.from_arrays(start, end)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -152,6 +152,5 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {"period_length": 5, "n_regimes": 2}
diff --git a/aeon/segmentation/_ggs.py b/aeon/segmentation/_ggs.py
index c423dd2836..6fef577346 100644
--- a/aeon/segmentation/_ggs.py
+++ b/aeon/segmentation/_ggs.py
@@ -514,7 +514,7 @@ def _predict(self, X):
return labels
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""
Return testing parameter settings for the estimator.
diff --git a/aeon/segmentation/_hidalgo.py b/aeon/segmentation/_hidalgo.py
index ae34ac9949..c70010b7bf 100644
--- a/aeon/segmentation/_hidalgo.py
+++ b/aeon/segmentation/_hidalgo.py
@@ -653,7 +653,7 @@ def _predict(self, X, y=None):
return self._Z
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -671,7 +671,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"metric": "euclidean",
diff --git a/aeon/segmentation/_hmm.py b/aeon/segmentation/_hmm.py
index 49c8b277b5..6b82960303 100644
--- a/aeon/segmentation/_hmm.py
+++ b/aeon/segmentation/_hmm.py
@@ -386,7 +386,7 @@ def _predict(self, X):
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
diff --git a/aeon/segmentation/_igts.py b/aeon/segmentation/_igts.py
index ba3c2561e4..20d632435c 100644
--- a/aeon/segmentation/_igts.py
+++ b/aeon/segmentation/_igts.py
@@ -389,7 +389,7 @@ def __repr__(self) -> str:
return self._igts.__repr__()
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
diff --git a/aeon/segmentation/base.py b/aeon/segmentation/base.py
index d35f964b3b..6fbcc93100 100644
--- a/aeon/segmentation/base.py
+++ b/aeon/segmentation/base.py
@@ -105,10 +105,10 @@ def fit(self, X, y=None, axis=1):
self
Fitted estimator
"""
- if self.get_class_tag("fit_is_empty"):
+ if self.get_tag("fit_is_empty"):
self.is_fitted = True
return self
- if self.get_class_tag("requires_y"):
+ if self.get_tag("requires_y"):
if y is None:
raise ValueError("Tag requires_y is true, but fit called with y=None")
# reset estimator at the start of fit
@@ -149,7 +149,7 @@ def predict(self, X, axis=1):
self._check_is_fitted()
if axis is None:
axis = self.axis
- X = self._preprocess_series(X, axis, self.get_class_tag("fit_is_empty"))
+ X = self._preprocess_series(X, axis, self.get_tag("fit_is_empty"))
return self._predict(X)
def fit_predict(self, X, y=None, axis=1):
diff --git a/aeon/testing/data_generation/hierarchical.py b/aeon/testing/data_generation/hierarchical.py
index 178ee25f7b..34a94df4cc 100644
--- a/aeon/testing/data_generation/hierarchical.py
+++ b/aeon/testing/data_generation/hierarchical.py
@@ -139,7 +139,7 @@ def _bottom_hier_datagen(
rng = np.random.default_rng(random_seed)
- base_ts = load_airline()
+ base_ts = load_airline(return_array=False)
df = pd.DataFrame(base_ts, index=base_ts.index)
df.index.rename(None, inplace=True)
diff --git a/aeon/testing/estimator_checking/_estimator_checking.py b/aeon/testing/estimator_checking/_estimator_checking.py
index 4bca59e278..a6ba9dc130 100644
--- a/aeon/testing/estimator_checking/_estimator_checking.py
+++ b/aeon/testing/estimator_checking/_estimator_checking.py
@@ -43,9 +43,9 @@ def parametrize_with_checks(
----------
estimators : list of aeon BaseAeonEstimator instances or classes
Estimators to generate checks for. If an item is a class, an instance will
- be created using BaseAeonEstimator.create_test_instance().
+ be created using BaseAeonEstimator._create_test_instance().
use_first_parameter_set : bool, default=False
- If True, only the first parameter set from get_test_params will be used if a
+ If True, only the first parameter set from _get_test_params will be used if a
class is passed.
Returns
@@ -117,13 +117,13 @@ def check_estimator(
----------
estimator : aeon BaseAeonEstimator instance or class
Estimator to run checks on. If estimator is a class, an instance will
- be created using BaseAeonEstimator.create_test_instance().
+ be created using BaseAeonEstimator._create_test_instance().
raise_exceptions : bool, optional, default=False
Whether to return exceptions/failures in the results dict, or raise them
if False: returns exceptions in returned `results` dict
if True: raises exceptions as they occur
use_first_parameter_set : bool, default=False
- If True, only the first parameter set from get_test_params will be used if a
+ If True, only the first parameter set from _get_test_params will be used if a
class is passed.
checks_to_run : str or list of str, default=None
Name(s) of checks to run. This should include the function name of the check to
diff --git a/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py b/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py
index 3686f6e0b9..2763442df7 100644
--- a/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py
+++ b/aeon/testing/estimator_checking/_yield_anomaly_detection_checks.py
@@ -65,7 +65,7 @@ def check_anomaly_detector_univariate(estimator):
"""Test the anomaly detector on univariate data."""
estimator = _clone_estimator(estimator)
- if estimator.get_class_tag(tag_name="capability:univariate"):
+ if estimator.get_tag(tag_name="capability:univariate"):
pred = estimator.fit_predict(uv_series, labels)
assert isinstance(pred, np.ndarray)
assert pred.shape == (15,)
@@ -79,7 +79,7 @@ def check_anomaly_detector_multivariate(estimator):
"""Test the anomaly detector on multivariate data."""
estimator = _clone_estimator(estimator)
- if estimator.get_class_tag(tag_name="capability:multivariate"):
+ if estimator.get_tag(tag_name="capability:multivariate"):
pred = estimator.fit_predict(mv_series, labels)
assert isinstance(pred, np.ndarray)
assert pred.shape == (15,)
diff --git a/aeon/testing/estimator_checking/_yield_classification_checks.py b/aeon/testing/estimator_checking/_yield_classification_checks.py
index ba8ceed855..1a019b3d5c 100644
--- a/aeon/testing/estimator_checking/_yield_classification_checks.py
+++ b/aeon/testing/estimator_checking/_yield_classification_checks.py
@@ -100,7 +100,7 @@ def check_classifier_against_expected_results(estimator_class):
continue
# we only use the first estimator instance for testing
- estimator_instance = estimator_class.create_test_instance(
+ estimator_instance = estimator_class._create_test_instance(
parameter_set="results_comparison"
)
# set random seed if possible
@@ -141,7 +141,7 @@ def check_classifier_tags_consistent(estimator_class):
if multivariate:
X = np.random.random((10, 2, 20))
y = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1])
- inst = estimator_class.create_test_instance(parameter_set="default")
+ inst = estimator_class._create_test_instance(parameter_set="default")
inst.fit(X, y)
inst.predict(X)
inst.predict_proba(X)
@@ -166,7 +166,7 @@ def check_classifier_does_not_override_final_methods(estimator_class):
def check_contracted_classifier(estimator_class, datatype):
"""Test classifiers that can be contracted."""
- estimator_instance = estimator_class.create_test_instance(
+ estimator_instance = estimator_class._create_test_instance(
parameter_set="contracting"
)
diff --git a/aeon/testing/estimator_checking/_yield_clustering_checks.py b/aeon/testing/estimator_checking/_yield_clustering_checks.py
index 6f74139f13..4843f13056 100644
--- a/aeon/testing/estimator_checking/_yield_clustering_checks.py
+++ b/aeon/testing/estimator_checking/_yield_clustering_checks.py
@@ -43,7 +43,7 @@ def check_clusterer_tags_consistent(estimator_class):
multivariate = estimator_class.get_class_tag("capability:multivariate")
if multivariate:
X = np.random.random((10, 2, 10))
- inst = estimator_class.create_test_instance(parameter_set="default")
+ inst = estimator_class._create_test_instance(parameter_set="default")
inst.fit(X)
inst.predict(X)
inst.predict_proba(X)
diff --git a/aeon/testing/estimator_checking/_yield_collection_transformation_checks.py b/aeon/testing/estimator_checking/_yield_collection_transformation_checks.py
index ee7636d5b9..c62b9d5440 100644
--- a/aeon/testing/estimator_checking/_yield_collection_transformation_checks.py
+++ b/aeon/testing/estimator_checking/_yield_collection_transformation_checks.py
@@ -35,7 +35,7 @@ def check_channel_selectors(estimator_class):
"""
X, _ = make_example_3d_numpy(n_cases=20, n_channels=6, n_timepoints=30)
y = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
- cs = estimator_class.create_test_instance(return_first=True)
+ cs = estimator_class._create_test_instance(return_first=True)
assert not cs.get_tag("fit_is_empty")
cs.fit(X, y)
assert cs.channels_selected_ is not None
diff --git a/aeon/testing/estimator_checking/_yield_early_classification_checks.py b/aeon/testing/estimator_checking/_yield_early_classification_checks.py
index 1b071ee648..9459b39442 100644
--- a/aeon/testing/estimator_checking/_yield_early_classification_checks.py
+++ b/aeon/testing/estimator_checking/_yield_early_classification_checks.py
@@ -56,7 +56,7 @@ def check_early_classifier_against_expected_results(estimator_class):
continue
# we only use the first estimator instance for testing
- estimator_instance = estimator_class.create_test_instance(
+ estimator_instance = estimator_class._create_test_instance(
parameter_set="results_comparison"
)
# set random seed if possible
diff --git a/aeon/testing/estimator_checking/_yield_estimator_checks.py b/aeon/testing/estimator_checking/_yield_estimator_checks.py
index 34370855b1..20664bea73 100644
--- a/aeon/testing/estimator_checking/_yield_estimator_checks.py
+++ b/aeon/testing/estimator_checking/_yield_estimator_checks.py
@@ -91,7 +91,7 @@ def _yield_all_aeon_checks(
if has_dependencies:
if isclass(estimator) and issubclass(estimator, BaseAeonEstimator):
estimator_class = estimator
- estimator_instances = estimator.create_test_instance(
+ estimator_instances = estimator._create_test_instance(
return_first=use_first_parameter_set
)
elif isinstance(estimator, BaseAeonEstimator):
@@ -230,24 +230,23 @@ def _yield_estimator_checks(estimator_class, estimator_instances, datatypes):
def check_create_test_instance(estimator_class):
- """Check create_test_instance logic and basic constructor functionality.
+ """Check _create_test_instance logic and basic constructor functionality.
- create_test_instance and create_test_instances_and_names are the
- key methods used to create test instances in testing.
- If this test does not pass, validity of the other tests cannot be guaranteed.
+ _create_test_instance is the key method used to create test instances in testing.
+ If this test does not pass, the validity of the other tests cannot be guaranteed.
Also tests inheritance and super call logic in the constructor.
Tests that:
- * create_test_instance results in an instance of estimator_class
+ * _create_test_instance results in an instance of estimator_class
* __init__ calls super.__init__
* _tags_dynamic attribute for tag inspection is present after construction
"""
- estimator = estimator_class.create_test_instance()
+ estimator = estimator_class._create_test_instance()
# Check that method does not construct object of other class than itself
assert isinstance(estimator, estimator_class), (
- "object returned by create_test_instance must be an instance of the class, "
+ "object returned by _create_test_instance must be an instance of the class, "
f"found {type(estimator)}"
)
@@ -311,8 +310,8 @@ def check_set_params_sklearn(estimator_class):
we use the other test parameter settings (which are assumed valid).
This guarantees settings which play along with the __init__ content.
"""
- estimator = estimator_class.create_test_instance()
- test_params = estimator_class.get_test_params()
+ estimator = estimator_class._create_test_instance()
+ test_params = estimator_class._get_test_params()
if not isinstance(test_params, list):
test_params = [test_params]
@@ -342,8 +341,8 @@ def check_constructor(estimator_class):
"""Check that the constructor has sklearn compatible signature and behaviour.
Based on sklearn check_estimator testing of __init__ logic.
- Uses create_test_instance to create an instance.
- Assumes test_create_test_instance has passed and certified create_test_instance.
+ Uses _create_test_instance to create an instance.
+ Assumes test_create_test_instance has passed and certified _create_test_instance.
Tests that:
* constructor has no varargs
@@ -358,7 +357,7 @@ def check_constructor(estimator_class):
msg = "constructor __init__ should have no varargs"
assert getfullargspec(estimator_class.__init__).varkw is None, msg
- estimator = estimator_class.create_test_instance()
+ estimator = estimator_class._create_test_instance()
assert isinstance(estimator, estimator_class)
# Ensure that each parameter is set in init
@@ -382,7 +381,7 @@ def param_filter(p):
params = estimator.get_params()
- test_params = estimator_class.get_test_params()
+ test_params = estimator_class._get_test_params()
if isinstance(test_params, list):
test_params = test_params[0]
test_params = test_params.keys()
@@ -392,7 +391,7 @@ def param_filter(p):
for param in init_params:
assert param.default != param.empty, (
"parameter `%s` for %s has no default value and is not "
- "set in `get_test_params`" % (param.name, estimator.__class__.__name__)
+ "set in _get_test_params" % (param.name, estimator.__class__.__name__)
)
if type(param.default) is type:
assert param.default in [np.float64, np.int64]
diff --git a/aeon/testing/estimator_checking/_yield_regression_checks.py b/aeon/testing/estimator_checking/_yield_regression_checks.py
index 0c62c0f6ee..3a8e53882b 100644
--- a/aeon/testing/estimator_checking/_yield_regression_checks.py
+++ b/aeon/testing/estimator_checking/_yield_regression_checks.py
@@ -71,7 +71,7 @@ def check_regressor_against_expected_results(estimator_class):
continue
# we only use the first estimator instance for testing
- estimator_instance = estimator_class.create_test_instance(
+ estimator_instance = estimator_class._create_test_instance(
parameter_set="results_comparison"
)
# set random seed if possible
@@ -115,7 +115,7 @@ def check_regressor_tags_consistent(estimator_class):
if multivariate:
X = np.random.random((10, 2, 20))
y = np.random.random(10)
- inst = estimator_class.create_test_instance(parameter_set="default")
+ inst = estimator_class._create_test_instance(parameter_set="default")
inst.fit(X, y)
inst.predict(X)
diff --git a/aeon/testing/estimator_checking/_yield_segmentation_checks.py b/aeon/testing/estimator_checking/_yield_segmentation_checks.py
index 7f10d86d0f..898f034f05 100644
--- a/aeon/testing/estimator_checking/_yield_segmentation_checks.py
+++ b/aeon/testing/estimator_checking/_yield_segmentation_checks.py
@@ -56,12 +56,12 @@ def _assert_output(output, dense, length):
else: # Segment labels returned, must be same length sas series
assert len(output) == length
- multivariate = estimator.get_class_tag(tag_name="capability:multivariate")
+ multivariate = estimator.get_tag(tag_name="capability:multivariate")
X = np.random.random(size=(5, 20))
# Also tests does not fail if y is passed
y = np.array([0, 0, 0, 1, 1])
# Test that capability:multivariate is correctly set
- dense = estimator.get_class_tag(tag_name="returns_dense")
+ dense = estimator.get_tag(tag_name="returns_dense")
if multivariate:
output = estimator.fit_predict(X, y, axis=1)
_assert_output(output, dense, X.shape[1])
@@ -70,7 +70,7 @@ def _assert_output(output, dense, length):
estimator.fit_predict(X, y, axis=1)
# Test that output is correct type
X = np.random.random(size=(20))
- uni = estimator.get_class_tag(tag_name="capability:univariate")
+ uni = estimator.get_tag(tag_name="capability:univariate")
if uni:
output = estimator.fit_predict(X, y=X)
_assert_output(output, dense, len(X))
diff --git a/aeon/testing/estimator_checking/_yield_soft_dependency_checks.py b/aeon/testing/estimator_checking/_yield_soft_dependency_checks.py
index 4539fafd33..83c6f96b83 100644
--- a/aeon/testing/estimator_checking/_yield_soft_dependency_checks.py
+++ b/aeon/testing/estimator_checking/_yield_soft_dependency_checks.py
@@ -30,12 +30,12 @@ def check_python_version_softdep(estimator_class):
# should be compatible with python version and able to construct
if _check_python_version(estimator_class, severity="none"):
- estimator_class.create_test_instance()
+ estimator_class._create_test_instance()
# should raise a specific error if python version is incompatible
else:
pyspec = estimator_class.get_class_tag("python_version", None)
with pytest.raises(ModuleNotFoundError) as ex_info:
- estimator_class.create_test_instance()
+ estimator_class._create_test_instance()
assert "requires python version to be" in str(ex_info.value), (
f"Estimator {estimator_class.__name__} has python version bound "
f"{pyspec} according to tags, but does not raise an appropriate "
@@ -54,11 +54,11 @@ def check_python_dependency_softdep(estimator_class):
# should be compatible with installed dependencies and able to construct
if softdeps is None or _check_soft_dependencies(softdeps, severity="none"):
- estimator_class.create_test_instance()
+ estimator_class._create_test_instance()
# should raise a specific error if any soft dependencies are missing
else:
with pytest.raises(ModuleNotFoundError) as ex_info:
- estimator_class.create_test_instance()
+ estimator_class._create_test_instance()
assert (
"is a soft dependency and not included in the base aeon installation"
in str(ex_info.value)
diff --git a/aeon/testing/estimator_checking/_yield_transformation_checks.py b/aeon/testing/estimator_checking/_yield_transformation_checks.py
index 6bb5f0d991..6383c8797b 100644
--- a/aeon/testing/estimator_checking/_yield_transformation_checks.py
+++ b/aeon/testing/estimator_checking/_yield_transformation_checks.py
@@ -59,7 +59,7 @@ def check_transformer_against_expected_results(estimator_class):
continue
# we only use the first estimator instance for testing
- estimator_instance = estimator_class.create_test_instance(
+ estimator_instance = estimator_class._create_test_instance(
parameter_set="results_comparison"
)
# set random seed if possible
diff --git a/aeon/testing/estimator_checking/tests/test_check_estimator.py b/aeon/testing/estimator_checking/tests/test_check_estimator.py
index 1b2faa0e8c..dc3a33369a 100644
--- a/aeon/testing/estimator_checking/tests/test_check_estimator.py
+++ b/aeon/testing/estimator_checking/tests/test_check_estimator.py
@@ -9,7 +9,7 @@
from aeon.testing.estimator_checking._estimator_checking import _get_check_estimator_ids
from aeon.testing.mock_estimators import (
MockClassifier,
- MockClassifierMultiTestParams,
+ MockClassifierParams,
MockRegressor,
MockSegmenter,
)
@@ -25,7 +25,7 @@
MockAnomalyDetector,
# MockMultivariateSeriesTransformer,
TimeSeriesScaler,
- MockClassifierMultiTestParams,
+ MockClassifierParams,
]
test_classes = {c.__name__: c for c in test_classes}
@@ -44,7 +44,7 @@ def test_parametrize_with_checks_classes(check):
assert equal, msg
-test_instances = [c.create_test_instance() for c in list(test_classes.values())]
+test_instances = [c._create_test_instance() for c in list(test_classes.values())]
test_instances = {c.__class__.__name__: c for c in test_instances}
@@ -63,7 +63,7 @@ def test_parametrize_with_checks_instances(check):
@pytest.mark.parametrize("estimator_class", list(test_classes.values()))
def test_check_estimator_passed(estimator_class):
"""Test that check_estimator returns only passed tests for examples we know pass."""
- estimator = estimator_class.create_test_instance()
+ estimator = estimator_class._create_test_instance()
result_class = check_estimator(estimator_class, verbose=False)
assert all(x == "PASSED" for x in result_class.values())
diff --git a/aeon/testing/expected_results/expected_classifier_outputs.py b/aeon/testing/expected_results/expected_classifier_outputs.py
index c1ddedeaff..090896b79d 100644
--- a/aeon/testing/expected_results/expected_classifier_outputs.py
+++ b/aeon/testing/expected_results/expected_classifier_outputs.py
@@ -23,20 +23,6 @@
[1.0, 0.0],
]
)
-unit_test_proba["WeightedEnsembleClassifier"] = np.array(
- [
- [0.0116, 0.9884],
- [0.9884, 0.0116],
- [0.0116, 0.9884],
- [0.9884, 0.0116],
- [0.9884, 0.0116],
- [0.9884, 0.0116],
- [0.9884, 0.0116],
- [0.0116, 0.9884],
- [0.9884, 0.0116],
- [0.9884, 0.0116],
- ]
-)
unit_test_proba["MUSE"] = np.array(
[
[0.4451, 0.5549],
@@ -149,20 +135,6 @@
[1.0, 0.0],
]
)
-unit_test_proba["ShapeDTW"] = np.array(
- [
- [0.0, 1.0],
- [1.0, 0.0],
- [0.0, 1.0],
- [1.0, 0.0],
- [1.0, 0.0],
- [1.0, 0.0],
- [1.0, 0.0],
- [0.0, 1.0],
- [1.0, 0.0],
- [1.0, 0.0],
- ]
-)
unit_test_proba["KNeighborsTimeSeriesClassifier"] = np.array(
[
[0.0, 1.0],
@@ -513,19 +485,47 @@
[0.6, 0.4],
]
)
-
-basic_motions_proba["ChannelEnsembleClassifier"] = np.array(
+unit_test_proba["TEASER"] = np.array(
+ [
+ [0.2, 0.8],
+ [0.9, 0.1],
+ [0.0, 1.0],
+ [1.0, 0.0],
+ [1.0, 0.0],
+ [1.0, 0.0],
+ [1.0, 0.0],
+ [0.2, 0.8],
+ [0.8, 0.2],
+ [1.0, 0.0],
+ ]
+)
+unit_test_proba["ProbabilityThresholdEarlyClassifier"] = np.array(
[
- [0.0, 0.0825, 0.25, 0.6675],
- [0.0, 0.3325, 0.6675, 0.0],
- [0.0, 0.0825, 0.6675, 0.25],
- [0.0, 0.0825, 0.6675, 0.25],
- [0.0, 0.0825, 0.0, 0.9175],
- [0.0, 0.0825, 0.25, 0.6675],
- [0.0, 0.3325, 0.4175, 0.25],
- [0.25, 0.0825, 0.4175, 0.25],
- [0.0, 0.5825, 0.4175, 0.0],
- [0.25, 0.0825, 0.6675, 0.0],
+ [0.0, 1.0],
+ [1.0, 0.0],
+ [0.0, 1.0],
+ [1.0, 0.0],
+ [1.0, 0.0],
+ [1.0, 0.0],
+ [1.0, 0.0],
+ [0.0, 1.0],
+ [1.0, 0.0],
+ [1.0, 0.0],
+ ]
+)
+
+basic_motions_proba["ClassifierChannelEnsemble"] = np.array(
+ [
+ [0.0, 0.0, 0.25, 0.75],
+ [0.5, 0.25, 0.0, 0.25],
+ [0.0, 0.25, 0.25, 0.5],
+ [0.0, 0.75, 0.25, 0.0],
+ [0.0, 0.25, 0.0, 0.75],
+ [0.0, 0.0, 0.25, 0.75],
+ [0.0, 1.0, 0.0, 0.0],
+ [0.0, 0.5, 0.25, 0.25],
+ [0.0, 0.75, 0.0, 0.25],
+ [0.0, 0.75, 0.0, 0.25],
]
)
basic_motions_proba["ClassifierPipeline"] = np.array(
@@ -542,34 +542,6 @@
[0.0, 1.0, 0.0, 0.0],
]
)
-basic_motions_proba["WeightedEnsembleClassifier"] = np.array(
- [
- [0.0047, 0.007, 0.9814, 0.007],
- [0.0047, 0.007, 0.9814, 0.007],
- [0.0047, 0.007, 0.9814, 0.007],
- [0.0047, 0.007, 0.9814, 0.007],
- [0.0047, 0.007, 0.0047, 0.9837],
- [0.0047, 0.007, 0.9814, 0.007],
- [0.0047, 0.007, 0.9814, 0.007],
- [0.0047, 0.007, 0.9814, 0.007],
- [0.0047, 0.007, 0.0047, 0.9837],
- [0.0047, 0.007, 0.9814, 0.007],
- ]
-)
-basic_motions_proba["ColumnEnsembleClassifier"] = np.array(
- [
- [0.0, 0.08247423, 0.25, 0.66752577],
- [0.25, 0.08247423, 0.66752577, 0.0],
- [0.0, 0.08247423, 0.66752577, 0.25],
- [0.5, 0.08247423, 0.41752577, 0.0],
- [0.0, 0.08247423, 0.5, 0.41752577],
- [0.0, 0.08247423, 0.5, 0.41752577],
- [0.25, 0.33247423, 0.41752577, 0.0],
- [0.0, 0.08247423, 0.91752577, 0.0],
- [0.0, 0.58247423, 0.41752577, 0.0],
- [0.0, 0.33247423, 0.41752577, 0.25],
- ]
-)
basic_motions_proba["MUSE"] = np.array(
[
[3.67057592e-05, 1.12259557e-03, 6.67246229e-04, 9.98173452e-01],
@@ -949,31 +921,3 @@
[0.0, 0.8, 0.2, 0.0],
]
)
-unit_test_proba["TEASER"] = np.array(
- [
- [0.2, 0.8],
- [0.9, 0.1],
- [0.0, 1.0],
- [1.0, 0.0],
- [1.0, 0.0],
- [1.0, 0.0],
- [1.0, 0.0],
- [0.2, 0.8],
- [0.8, 0.2],
- [1.0, 0.0],
- ]
-)
-unit_test_proba["ProbabilityThresholdEarlyClassifier"] = np.array(
- [
- [0.0, 1.0],
- [1.0, 0.0],
- [0.0, 1.0],
- [1.0, 0.0],
- [1.0, 0.0],
- [1.0, 0.0],
- [1.0, 0.0],
- [0.0, 1.0],
- [1.0, 0.0],
- [1.0, 0.0],
- ]
-)
diff --git a/aeon/testing/expected_results/expected_regressor_outputs.py b/aeon/testing/expected_results/expected_regressor_outputs.py
index 6b435464da..2a630f7206 100644
--- a/aeon/testing/expected_results/expected_regressor_outputs.py
+++ b/aeon/testing/expected_results/expected_regressor_outputs.py
@@ -28,21 +28,6 @@
[0.0310, 0.0555, 0.0193, 0.0359, 0.0261, 0.0361, 0.0387, 0.0835, 0.0827, 0.0414]
)
-covid_3month_preds["RandomForestRegressor"] = np.array(
- [
- 0.0319,
- 0.0505,
- 0.0082,
- 0.0291,
- 0.028,
- 0.0266,
- 0.0239,
- 0.0946,
- 0.0946,
- 0.0251,
- ]
-)
-
covid_3month_preds["TSFreshRegressor"] = np.array(
[
0.0106,
diff --git a/aeon/testing/expected_results/results_reproduction/classifier_results_reproduction.py b/aeon/testing/expected_results/results_reproduction/classifier_results_reproduction.py
index 441a496442..54e4148a0c 100644
--- a/aeon/testing/expected_results/results_reproduction/classifier_results_reproduction.py
+++ b/aeon/testing/expected_results/results_reproduction/classifier_results_reproduction.py
@@ -5,11 +5,7 @@
from sklearn.utils._testing import set_random_state
from aeon.classification import BaseClassifier
-from aeon.classification.compose import (
- ChannelEnsembleClassifier,
- ClassifierPipeline,
- WeightedEnsembleClassifier,
-)
+from aeon.classification.compose import ClassifierChannelEnsemble, ClassifierPipeline
from aeon.classification.convolution_based import (
Arsenal,
HydraClassifier,
@@ -115,154 +111,156 @@ def _print_array(test_name, array):
def _print_results_for_classifier(classifier_name, dataset_name):
- if classifier_name == "ChannelEnsembleClassifier":
- classifier = ChannelEnsembleClassifier.create_test_instance(
- parameter_set="results_comparison"
- )
- elif classifier_name == "WeightedEnsembleClassifier":
- classifier = WeightedEnsembleClassifier.create_test_instance(
+ if classifier_name == "ClassifierChannelEnsemble":
+ classifier = ClassifierChannelEnsemble._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "ClassifierPipeline":
- classifier = ClassifierPipeline.create_test_instance(
+ classifier = ClassifierPipeline._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "BOSSEnsemble":
- classifier = BOSSEnsemble.create_test_instance(
+ classifier = BOSSEnsemble._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "ContractableBOSS":
- classifier = ContractableBOSS.create_test_instance(
+ classifier = ContractableBOSS._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "MUSE":
- classifier = MUSE.create_test_instance(parameter_set="results_comparison")
+ classifier = MUSE._create_test_instance(parameter_set="results_comparison")
elif classifier_name == "TemporalDictionaryEnsemble":
- classifier = TemporalDictionaryEnsemble.create_test_instance(
+ classifier = TemporalDictionaryEnsemble._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "WEASEL":
- classifier = WEASEL.create_test_instance(parameter_set="results_comparison")
+ classifier = WEASEL._create_test_instance(parameter_set="results_comparison")
elif classifier_name == "WEASEL_V2":
- classifier = WEASEL_V2.create_test_instance(parameter_set="results_comparison")
+ classifier = WEASEL_V2._create_test_instance(parameter_set="results_comparison")
elif classifier_name == "REDCOMETS":
- classifier = REDCOMETS.create_test_instance(parameter_set="results_comparison")
+ classifier = REDCOMETS._create_test_instance(parameter_set="results_comparison")
elif classifier_name == "ElasticEnsemble":
- classifier = ElasticEnsemble.create_test_instance(
+ classifier = ElasticEnsemble._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "KNeighborsTimeSeriesClassifier":
- classifier = KNeighborsTimeSeriesClassifier.create_test_instance(
+ classifier = KNeighborsTimeSeriesClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "Catch22Classifier":
- classifier = Catch22Classifier.create_test_instance(
+ classifier = Catch22Classifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "FreshPRINCEClassifier":
- classifier = FreshPRINCEClassifier.create_test_instance(
+ classifier = FreshPRINCEClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "RandomIntervalClassifier":
- classifier = RandomIntervalClassifier.create_test_instance(
+ classifier = RandomIntervalClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "QUANTClassifier":
- classifier = QUANTClassifier.create_test_instance(
+ classifier = QUANTClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "SignatureClassifier":
- classifier = SignatureClassifier.create_test_instance(
+ classifier = SignatureClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "SummaryClassifier":
- classifier = SummaryClassifier.create_test_instance(
+ classifier = SummaryClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "TSFreshClassifier":
- classifier = TSFreshClassifier.create_test_instance(
+ classifier = TSFreshClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "HIVECOTEV1":
- classifier = HIVECOTEV1.create_test_instance(parameter_set="results_comparison")
+ classifier = HIVECOTEV1._create_test_instance(
+ parameter_set="results_comparison"
+ )
elif classifier_name == "HIVECOTEV2":
- classifier = HIVECOTEV2.create_test_instance(parameter_set="results_comparison")
+ classifier = HIVECOTEV2._create_test_instance(
+ parameter_set="results_comparison"
+ )
elif classifier_name == "CanonicalIntervalForestClassifier":
- classifier = CanonicalIntervalForestClassifier.create_test_instance(
+ classifier = CanonicalIntervalForestClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "DrCIFClassifier":
- classifier = DrCIFClassifier.create_test_instance(
+ classifier = DrCIFClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "IntervalForestClassifier":
- classifier = IntervalForestClassifier.create_test_instance(
+ classifier = IntervalForestClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "RandomIntervalSpectralEnsembleClassifier":
- classifier = RandomIntervalSpectralEnsembleClassifier.create_test_instance(
+ classifier = RandomIntervalSpectralEnsembleClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "RSTSF":
- classifier = RSTSF.create_test_instance(parameter_set="results_comparison")
+ classifier = RSTSF._create_test_instance(parameter_set="results_comparison")
elif classifier_name == "SupervisedTimeSeriesForest":
- classifier = SupervisedTimeSeriesForest.create_test_instance(
+ classifier = SupervisedTimeSeriesForest._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "TimeSeriesForestClassifier":
- classifier = TimeSeriesForestClassifier.create_test_instance(
+ classifier = TimeSeriesForestClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "Arsenal":
- classifier = Arsenal.create_test_instance(parameter_set="results_comparison")
+ classifier = Arsenal._create_test_instance(parameter_set="results_comparison")
elif classifier_name == "RocketClassifier":
- classifier = RocketClassifier.create_test_instance(
+ classifier = RocketClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "HydraClassifier":
- classifier = HydraClassifier.create_test_instance(
+ classifier = HydraClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "MultiRocketHydraClassifier":
- classifier = MultiRocketHydraClassifier.create_test_instance(
+ classifier = MultiRocketHydraClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "OrdinalTDE":
- classifier = OrdinalTDE.create_test_instance(parameter_set="results_comparison")
+ classifier = OrdinalTDE._create_test_instance(
+ parameter_set="results_comparison"
+ )
elif classifier_name == "ShapeletTransformClassifier":
- classifier = ShapeletTransformClassifier.create_test_instance(
+ classifier = ShapeletTransformClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "LearningShapeletClassifier":
- classifier = LearningShapeletClassifier.create_test_instance(
+ classifier = LearningShapeletClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "MrSQMClassifier":
- classifier = MrSQMClassifier.create_test_instance(
+ classifier = MrSQMClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "SASTClassifier":
- classifier = SASTClassifier.create_test_instance(
+ classifier = SASTClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "ContinuousIntervalTree":
- classifier = ContinuousIntervalTree.create_test_instance(
+ classifier = ContinuousIntervalTree._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "RotationForestClassifier":
- classifier = RotationForestClassifier.create_test_instance(
+ classifier = RotationForestClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "ProbabilityThresholdEarlyClassifier":
- classifier = ProbabilityThresholdEarlyClassifier.create_test_instance(
+ classifier = ProbabilityThresholdEarlyClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "BaseEarlyClassifier":
- classifier = BaseEarlyClassifier.create_test_instance(
+ classifier = BaseEarlyClassifier._create_test_instance(
parameter_set="results_comparison"
)
elif classifier_name == "TEASER":
- classifier = TEASER.create_test_instance(parameter_set="results_comparison")
+ classifier = TEASER._create_test_instance(parameter_set="results_comparison")
elif classifier_name == "TEASER-IF":
classifier = TEASER(
classification_points=[6, 10, 16, 24],
diff --git a/aeon/testing/expected_results/results_reproduction/regressor_results_reproduction.py b/aeon/testing/expected_results/results_reproduction/regressor_results_reproduction.py
index 5c47340ef7..9d3007877a 100644
--- a/aeon/testing/expected_results/results_reproduction/regressor_results_reproduction.py
+++ b/aeon/testing/expected_results/results_reproduction/regressor_results_reproduction.py
@@ -60,67 +60,67 @@ def _print_array(test_name, array):
def _print_results_for_regressor(regressor_name, dataset_name):
if regressor_name == "FreshPRINCERegressor":
- regressor = FreshPRINCERegressor.create_test_instance(
+ regressor = FreshPRINCERegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "Catch22Regressor":
- regressor = Catch22Regressor.create_test_instance(
+ regressor = Catch22Regressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "SummaryRegressor":
- regressor = SummaryRegressor.create_test_instance(
+ regressor = SummaryRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "TSFreshRegressor":
- regressor = TSFreshRegressor.create_test_instance(
+ regressor = TSFreshRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "HydraRegressor":
- regressor = HydraRegressor.create_test_instance(
+ regressor = HydraRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "MultiRocketHydraRegressor":
- regressor = MultiRocketHydraRegressor.create_test_instance(
+ regressor = MultiRocketHydraRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "RocketRegressor":
- regressor = RocketRegressor.create_test_instance(
+ regressor = RocketRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "KNeighborsTimeSeriesRegressor":
- regressor = KNeighborsTimeSeriesRegressor.create_test_instance(
+ regressor = KNeighborsTimeSeriesRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "RISTRegressor":
- regressor = RISTRegressor.create_test_instance(
+ regressor = RISTRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "CanonicalIntervalForestRegressor":
- regressor = CanonicalIntervalForestRegressor.create_test_instance(
+ regressor = CanonicalIntervalForestRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "DrCIFRegressor":
- regressor = DrCIFRegressor.create_test_instance(
+ regressor = DrCIFRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "IntervalForestRegressor":
- regressor = IntervalForestRegressor.create_test_instance(
+ regressor = IntervalForestRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "RandomIntervalRegressor":
- regressor = RandomIntervalRegressor.create_test_instance(
+ regressor = RandomIntervalRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "RandomIntervalSpectralEnsembleRegressor":
- regressor = RandomIntervalSpectralEnsembleRegressor.create_test_instance(
+ regressor = RandomIntervalSpectralEnsembleRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "TimeSeriesForestRegressor":
- regressor = TimeSeriesForestRegressor.create_test_instance(
+ regressor = TimeSeriesForestRegressor._create_test_instance(
parameter_set="results_comparison"
)
elif regressor_name == "RDSTRegressor":
- regressor = RDSTRegressor.create_test_instance(
+ regressor = RDSTRegressor._create_test_instance(
parameter_set="results_comparison"
)
else:
diff --git a/aeon/testing/expected_results/results_reproduction/transform_results_reproduction.py b/aeon/testing/expected_results/results_reproduction/transform_results_reproduction.py
index 75d79f2063..3b8e8809c7 100644
--- a/aeon/testing/expected_results/results_reproduction/transform_results_reproduction.py
+++ b/aeon/testing/expected_results/results_reproduction/transform_results_reproduction.py
@@ -45,15 +45,15 @@ def _print_array(test_name, array):
def _print_results_for_transformer(transformer_name, dataset_name):
if transformer_name == "RandomIntervals":
- transformer = RandomIntervals.create_test_instance(
+ transformer = RandomIntervals._create_test_instance(
parameter_set="results_comparison"
)
elif transformer_name == "SupervisedIntervals":
- transformer = SupervisedIntervals.create_test_instance(
+ transformer = SupervisedIntervals._create_test_instance(
parameter_set="results_comparison"
)
elif transformer_name == "RandomShapeletTransform":
- transformer = RandomShapeletTransform.create_test_instance(
+ transformer = RandomShapeletTransform._create_test_instance(
parameter_set="results_comparison"
)
else:
diff --git a/aeon/testing/expected_results/tests/__init__.py b/aeon/testing/expected_results/tests/__init__.py
new file mode 100644
index 0000000000..6c64ca7f49
--- /dev/null
+++ b/aeon/testing/expected_results/tests/__init__.py
@@ -0,0 +1 @@
+"""Tests for expected estimator results."""
diff --git a/aeon/testing/expected_results/tests/test_expected_outputs.py b/aeon/testing/expected_results/tests/test_expected_outputs.py
new file mode 100644
index 0000000000..aea871e38c
--- /dev/null
+++ b/aeon/testing/expected_results/tests/test_expected_outputs.py
@@ -0,0 +1,73 @@
+"""Test expected outputs for estimators."""
+
+import numpy as np
+import pytest
+
+from aeon.testing.expected_results.expected_classifier_outputs import (
+ basic_motions_proba,
+ unit_test_proba,
+)
+from aeon.testing.expected_results.expected_regressor_outputs import (
+ cardano_sentiment_preds,
+ covid_3month_preds,
+)
+from aeon.testing.expected_results.expected_transform_outputs import (
+ basic_motions_result,
+ unit_test_result,
+)
+from aeon.testing.testing_config import PR_TESTING
+from aeon.utils.discovery import all_estimators
+
+
+@pytest.mark.skipif(
+ PR_TESTING,
+ reason="Don't want to run all_estimators multiple times every PR.",
+)
+def test_expected_classifier_outputs():
+ """Test estimators in the expected classifier outputs dict."""
+ classifiers = all_estimators(type_filter=["classifier", "early_classifier"])
+ classifier_names = [c[0] for c in classifiers]
+
+ for key, value in unit_test_proba.items():
+ assert key in classifier_names
+ assert isinstance(value, np.ndarray)
+
+ for key, value in basic_motions_proba.items():
+ assert key in classifier_names
+ assert isinstance(value, np.ndarray)
+
+
+@pytest.mark.skipif(
+ PR_TESTING,
+ reason="Don't want to run all_estimators multiple times every PR.",
+)
+def test_expected_regressor_outputs():
+ """Test estimators in the expected regressor outputs dict."""
+ regressors = all_estimators(type_filter="regressor")
+ regressor_names = [r[0] for r in regressors]
+
+ for key, value in covid_3month_preds.items():
+ assert key in regressor_names
+ assert isinstance(value, np.ndarray)
+
+ for key, value in cardano_sentiment_preds.items():
+ assert key in regressor_names
+ assert isinstance(value, np.ndarray)
+
+
+@pytest.mark.skipif(
+ PR_TESTING,
+ reason="Don't want to run all_estimators multiple times every PR.",
+)
+def test_expected_transformer_outputs():
+ """Test estimators in the expected transformer outputs dict."""
+ transformers = all_estimators(type_filter="transformer")
+ transformer_names = [r[0] for r in transformers]
+
+ for key, value in unit_test_result.items():
+ assert key in transformer_names
+ assert isinstance(value, np.ndarray)
+
+ for key, value in basic_motions_result.items():
+ assert key in transformer_names
+ assert isinstance(value, np.ndarray)
diff --git a/aeon/testing/mock_estimators/__init__.py b/aeon/testing/mock_estimators/__init__.py
index 624b566c61..32d947cb7d 100644
--- a/aeon/testing/mock_estimators/__init__.py
+++ b/aeon/testing/mock_estimators/__init__.py
@@ -5,7 +5,7 @@
"MockClassifier",
"MockClassifierPredictProba",
"MockClassifierFullTags",
- "MockClassifierMultiTestParams",
+ "MockClassifierParams",
"MockCluster",
"MockDeepClusterer",
"MockSegmenter",
@@ -22,7 +22,7 @@
from aeon.testing.mock_estimators._mock_classifiers import (
MockClassifier,
MockClassifierFullTags,
- MockClassifierMultiTestParams,
+ MockClassifierParams,
MockClassifierPredictProba,
)
from aeon.testing.mock_estimators._mock_clusterers import MockCluster, MockDeepClusterer
diff --git a/aeon/testing/mock_estimators/_mock_classifiers.py b/aeon/testing/mock_estimators/_mock_classifiers.py
index 6b4acd3c11..da766b8e16 100644
--- a/aeon/testing/mock_estimators/_mock_classifiers.py
+++ b/aeon/testing/mock_estimators/_mock_classifiers.py
@@ -5,14 +5,16 @@
import numpy as np
+from aeon.base._base import _clone_estimator
from aeon.classification import BaseClassifier
class MockClassifier(BaseClassifier):
- """Dummy classifier for testing base class fit/predict."""
+ """Mock classifier for testing fit/predict."""
def _fit(self, X, y):
"""Fit dummy."""
+ self.foo_ = "bar"
return self
def _predict(self, X):
@@ -21,7 +23,7 @@ def _predict(self, X):
class MockClassifierPredictProba(MockClassifier):
- """Dummy classifier for testing base class fit/predict/predict_proba."""
+ """Mock classifier for testing fit/predict/predict_proba."""
def _predict_proba(self, X):
"""Predict proba dummy."""
@@ -31,7 +33,7 @@ def _predict_proba(self, X):
class MockClassifierFullTags(MockClassifierPredictProba):
- """Dummy classifier able to handle all input types."""
+ """Mock classifier able to handle all input types."""
_tags = {
"capability:multivariate": True,
@@ -41,8 +43,8 @@ class MockClassifierFullTags(MockClassifierPredictProba):
}
-class MockClassifierMultiTestParams(BaseClassifier):
- """Dummy classifier for testing base class fit/predict with multiple test params.
+class MockClassifierParams(MockClassifier):
+ """Mock classifier for testing fit/predict with multiple parameters.
Parameters
----------
@@ -50,20 +52,21 @@ class MockClassifierMultiTestParams(BaseClassifier):
If True, predict ones, else zeros.
"""
- def __init__(self, return_ones=False):
+ def __init__(self, return_ones=False, value=50):
self.return_ones = return_ones
+ self.value = value
super().__init__()
- def _fit(self, X, y):
- """Fit dummy."""
- return self
-
def _predict(self, X):
"""Predict dummy."""
- return np.zeros(shape=(len(X),))
+ return (
+ np.zeros(shape=(len(X),))
+ if not self.return_ones
+ else np.ones(shape=(len(X),))
+ )
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -78,6 +81,27 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
- return [{"return_ones": False}, {"return_ones": True}]
+ return [{"return_ones": False, "value": 10}, {"return_ones": True}]
+
+
+class MockClassifierComposite(BaseClassifier):
+ """Mock classifier which contains another mock classfier."""
+
+ def __init__(self, mock=None):
+ self.mock = mock
+ super().__init__()
+
+ def _fit(self, X, y):
+ """Fit dummy."""
+ self.mock_ = (
+ MockClassifier().fit(X, y)
+ if self.mock is None
+ else _clone_estimator(self.mock).fit(X, y)
+ )
+ self.foo_ = "bar"
+ return self
+
+ def _predict(self, X):
+ """Predict dummy."""
+ return self.mock_.predict(X)
diff --git a/aeon/testing/mock_estimators/_mock_clusterers.py b/aeon/testing/mock_estimators/_mock_clusterers.py
index 0563129909..20f8ef39b2 100644
--- a/aeon/testing/mock_estimators/_mock_clusterers.py
+++ b/aeon/testing/mock_estimators/_mock_clusterers.py
@@ -38,7 +38,6 @@ def __init__(self, estimator=None, last_file_name="last_file"):
n_clusters=None,
estimator=estimator,
last_file_name=last_file_name,
- clustering_params={"n_init": 1, "averaging_method": "mean"},
)
def build_model(self, input_shape):
diff --git a/aeon/testing/mock_estimators/_mock_segmenters.py b/aeon/testing/mock_estimators/_mock_segmenters.py
index e8005a1c79..82ff6a81f6 100644
--- a/aeon/testing/mock_estimators/_mock_segmenters.py
+++ b/aeon/testing/mock_estimators/_mock_segmenters.py
@@ -26,7 +26,7 @@ def _predict(self, X):
return np.array([1])
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""
Return testing parameter settings for the estimator.
diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py
index 7ab34d2be3..55b9092443 100644
--- a/aeon/testing/testing_data.py
+++ b/aeon/testing/testing_data.py
@@ -788,16 +788,16 @@ def _get_capabilities_for_estimator(estimator):
Tuple of valid capabilities for the estimator.
"""
univariate = estimator.get_tag(
- "capability:univariate", tag_value_default=True, raise_error=False
+ "capability:univariate", raise_error=False, tag_value_default=True
)
multivariate = estimator.get_tag(
- "capability:multivariate", tag_value_default=False, raise_error=False
+ "capability:multivariate", raise_error=False, tag_value_default=False
)
unequal_length = estimator.get_tag(
- "capability:unequal_length", tag_value_default=False, raise_error=False
+ "capability:unequal_length", raise_error=False, tag_value_default=False
)
missing_values = estimator.get_tag(
- "capability:missing_values", tag_value_default=False, raise_error=False
+ "capability:missing_values", raise_error=False, tag_value_default=False
)
return univariate, multivariate, unequal_length, missing_values
diff --git a/aeon/testing/utils/estimator_checks.py b/aeon/testing/utils/estimator_checks.py
index ca774cbd59..1c9e8f8cb3 100644
--- a/aeon/testing/utils/estimator_checks.py
+++ b/aeon/testing/utils/estimator_checks.py
@@ -43,11 +43,11 @@ def _get_tag(estimator, tag_name, default=None, raise_error=False):
return None
elif isclass(estimator):
return estimator.get_class_tag(
- tag_name=tag_name, tag_value_default=default, raise_error=raise_error
+ tag_name=tag_name, raise_error=raise_error, tag_value_default=default
)
else:
return estimator.get_tag(
- tag_name=tag_name, tag_value_default=default, raise_error=raise_error
+ tag_name=tag_name, raise_error=raise_error, tag_value_default=default
)
diff --git a/aeon/transformations/collection/__init__.py b/aeon/transformations/collection/__init__.py
index c3f34c2bd5..0ffc7b7bce 100644
--- a/aeon/transformations/collection/__init__.py
+++ b/aeon/transformations/collection/__init__.py
@@ -11,7 +11,6 @@
"ElbowClassPairwise",
"DWTTransformer",
"HOG1DTransformer",
- "Resizer",
"MatrixProfile",
"Padder",
"PeriodogramTransformer",
diff --git a/aeon/transformations/collection/_acf.py b/aeon/transformations/collection/_acf.py
index 24c9767636..47809551e1 100644
--- a/aeon/transformations/collection/_acf.py
+++ b/aeon/transformations/collection/_acf.py
@@ -23,8 +23,9 @@ class AutocorrelationFunctionTransformer(BaseCollectionTransformer):
Parameters
----------
- n_lags : int or callable, default=100
- The maximum number of autocorrelation terms to use. If callable, the
+ n_lags : int, None or callable, default=None
+ The maximum number of autocorrelation terms to use. If None, set to
+ n_timepoints/4. If callable, the
function should take a 3D numpy array of shape (n_cases, n_channels,
n_timepoints) and return an integer.
min_values : int, default=0
@@ -54,7 +55,7 @@ class AutocorrelationFunctionTransformer(BaseCollectionTransformer):
def __init__(
self,
- n_lags=100,
+ n_lags=None,
min_values=0,
):
self.n_lags = n_lags
@@ -64,8 +65,10 @@ def __init__(
def _transform(self, X, y=None):
n_cases, n_channels, n_timepoints = X.shape
-
- lags = self.n_lags(X) if callable(self.n_lags) else self.n_lags
+ if self.n_lags is None:
+ lags = n_timepoints / 4
+ else:
+ lags = self.n_lags(X) if callable(self.n_lags) else self.n_lags
if lags > n_timepoints - self.min_values:
lags = n_timepoints - self.min_values
if lags < 0:
@@ -114,7 +117,7 @@ def _acf_2d(X, max_lag):
return X_t
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -129,7 +132,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_lags": 4,
diff --git a/aeon/transformations/collection/_ar_coefficient.py b/aeon/transformations/collection/_ar_coefficient.py
index 45b962571b..4a483d9a09 100644
--- a/aeon/transformations/collection/_ar_coefficient.py
+++ b/aeon/transformations/collection/_ar_coefficient.py
@@ -89,7 +89,7 @@ def _transform(self, X, y=None):
return Xt
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -104,7 +104,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"order": 4,
diff --git a/aeon/transformations/collection/_broadcaster.py b/aeon/transformations/collection/_broadcaster.py
index 500ca1d0fd..742e24d2c1 100644
--- a/aeon/transformations/collection/_broadcaster.py
+++ b/aeon/transformations/collection/_broadcaster.py
@@ -145,7 +145,7 @@ def _inverse_transform(self, X, y=None):
return Xt
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -160,7 +160,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.testing.mock_estimators._mock_series_transformers import (
MockUnivariateSeriesTransformer,
diff --git a/aeon/transformations/collection/_hog1d.py b/aeon/transformations/collection/_hog1d.py
index 347d9cabab..3deddf5931 100644
--- a/aeon/transformations/collection/_hog1d.py
+++ b/aeon/transformations/collection/_hog1d.py
@@ -25,8 +25,8 @@ class HOG1DTransformer(BaseCollectionTransformer):
scaling_factor : float
A constant that is multiplied to modify the distribution.
- Notes
- -----
+ References
+ ----------
[1] J. Zhao and L. Itti "Classifying time series using local descriptors with
hybrid sampling", IEEE Transactions on Knowledge and Data Engineering 28(3), 2015.
diff --git a/aeon/transformations/collection/_resize.py b/aeon/transformations/collection/_resize.py
index c81893009a..abfd696906 100644
--- a/aeon/transformations/collection/_resize.py
+++ b/aeon/transformations/collection/_resize.py
@@ -83,7 +83,7 @@ def _transform(self, X, y=None):
return np.array(Xt)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Returns
@@ -92,7 +92,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
params = {"length": 10}
return params
diff --git a/aeon/transformations/collection/_truncate.py b/aeon/transformations/collection/_truncate.py
index 8818b13878..2c20c1e010 100644
--- a/aeon/transformations/collection/_truncate.py
+++ b/aeon/transformations/collection/_truncate.py
@@ -105,7 +105,7 @@ def _transform(self, X, y=None):
return Xt
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -121,7 +121,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
params = {"truncated_length": 5}
return params
diff --git a/aeon/transformations/collection/channel_selection/_channel_scorer.py b/aeon/transformations/collection/channel_selection/_channel_scorer.py
index e4289f66af..4306cdfeb6 100644
--- a/aeon/transformations/collection/channel_selection/_channel_scorer.py
+++ b/aeon/transformations/collection/channel_selection/_channel_scorer.py
@@ -129,7 +129,7 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, TypingList]):
return self
@classmethod
- def get_test_params(cls, parameter_set: str = "default") -> TypingDict[str, any]:
+ def _get_test_params(cls, parameter_set: str = "default") -> TypingDict[str, any]:
"""Return testing parameter settings for the estimator.
Parameters
diff --git a/aeon/transformations/collection/compose/_pipeline.py b/aeon/transformations/collection/compose/_pipeline.py
index 3f66b2c4c1..d4c57b4957 100644
--- a/aeon/transformations/collection/compose/_pipeline.py
+++ b/aeon/transformations/collection/compose/_pipeline.py
@@ -4,7 +4,7 @@
__all__ = ["CollectionTransformerPipeline"]
-from aeon.base.estimator.compose.collection_pipeline import BaseCollectionPipeline
+from aeon.base.estimators.compose.collection_pipeline import BaseCollectionPipeline
from aeon.transformations.collection import BaseCollectionTransformer
from aeon.transformations.collection.compose import CollectionId
@@ -50,13 +50,13 @@ class CollectionTransformerPipeline(BaseCollectionPipeline, BaseCollectionTransf
--------
>>> from aeon.transformations.collection import Resizer
>>> from aeon.transformations.collection.feature_based import (
- ... SevenNumberSummaryTransformer)
+ ... SevenNumberSummary)
>>> from aeon.datasets import load_unit_test
>>> from aeon.transformations.collection.compose import (
... CollectionTransformerPipeline)
>>> X, y = load_unit_test(split="train")
>>> pipeline = CollectionTransformerPipeline(
- ... [Resizer(length=10), SevenNumberSummaryTransformer()]
+ ... [Resizer(length=10), SevenNumberSummary()]
... )
>>> pipeline.fit(X, y)
CollectionTransformerPipeline(...)
@@ -76,7 +76,7 @@ def __init__(self, transformers):
super().__init__(transformers=transformers, _estimator=None)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -91,16 +91,13 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`.
"""
from aeon.transformations.collection import Truncator
- from aeon.transformations.collection.feature_based import (
- SevenNumberSummaryTransformer,
- )
+ from aeon.transformations.collection.feature_based import SevenNumberSummary
return {
"transformers": [
Truncator(truncated_length=5),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
]
}
diff --git a/aeon/transformations/collection/compose/tests/test_pipeline.py b/aeon/transformations/collection/compose/tests/test_pipeline.py
index 21131233aa..635dc04c57 100644
--- a/aeon/transformations/collection/compose/tests/test_pipeline.py
+++ b/aeon/transformations/collection/compose/tests/test_pipeline.py
@@ -19,20 +19,20 @@
TimeSeriesScaler,
)
from aeon.transformations.collection.compose import CollectionTransformerPipeline
-from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer
+from aeon.transformations.collection.feature_based import SevenNumberSummary
@pytest.mark.parametrize(
"transformers",
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
[Padder(pad_length=15), Tabularizer(), StandardScaler()],
- [Padder(pad_length=15), SevenNumberSummaryTransformer()],
- [Tabularizer(), StandardScaler(), SevenNumberSummaryTransformer()],
+ [Padder(pad_length=15), SevenNumberSummary()],
+ [Tabularizer(), StandardScaler(), SevenNumberSummary()],
[
Padder(pad_length=15),
- SevenNumberSummaryTransformer(),
+ SevenNumberSummary(),
],
],
)
@@ -59,7 +59,7 @@ def test_unequal_tag_inference():
n_cases=10, min_n_timepoints=8, max_n_timepoints=12
)
- t1 = SevenNumberSummaryTransformer()
+ t1 = SevenNumberSummary()
t2 = Padder()
t3 = TimeSeriesScaler()
t4 = AutocorrelationFunctionTransformer(n_lags=5)
@@ -152,7 +152,7 @@ def test_multivariate_tag_inference():
"""Test that CollectionTransformerPipeline infers multivariate tag correctly."""
X, y = make_example_3d_numpy(n_cases=10, n_channels=2, n_timepoints=12)
- t1 = SevenNumberSummaryTransformer()
+ t1 = SevenNumberSummary()
t2 = TimeSeriesScaler()
t3 = HOG1DTransformer()
t4 = StandardScaler()
diff --git a/aeon/transformations/collection/convolution_based/rocketGPU/_rocket_gpu.py b/aeon/transformations/collection/convolution_based/rocketGPU/_rocket_gpu.py
index fbfd5d18e2..547c077aef 100644
--- a/aeon/transformations/collection/convolution_based/rocketGPU/_rocket_gpu.py
+++ b/aeon/transformations/collection/convolution_based/rocketGPU/_rocket_gpu.py
@@ -226,7 +226,7 @@ def _transform(self, X, y=None):
return output_rocket
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the transformer.
Parameters
@@ -242,7 +242,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
params = {
"n_filters": 5,
diff --git a/aeon/transformations/collection/dictionary_based/_borf.py b/aeon/transformations/collection/dictionary_based/_borf.py
index f1af5bf944..b4d7d3cc22 100644
--- a/aeon/transformations/collection/dictionary_based/_borf.py
+++ b/aeon/transformations/collection/dictionary_based/_borf.py
@@ -182,7 +182,7 @@ def _transform(self, X, y=None):
return self.pipe_.transform(X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -200,40 +200,7 @@ def get_test_params(cls, parameter_set="default"):
instance. `create_test_instance` uses the first (or only) dictionary in
`params`.
"""
- params = [
- {
- "window_size_min_window_size": 4,
- "window_size_max_window_size": None,
- "word_lengths_n_word_lengths": 4,
- "alphabets_min_symbols": 3,
- "alphabets_max_symbols": 4,
- "alphabets_step": 1,
- "dilations_min_dilation": 1,
- "dilations_max_dilation": None,
- "min_window_to_signal_std_ratio": 0.0,
- "n_jobs": 1,
- "n_jobs_numba": 1,
- "transformer_weights": None,
- "complexity": "quadratic",
- "densify": False,
- },
- {
- "window_size_min_window_size": 4,
- "window_size_max_window_size": None,
- "word_lengths_n_word_lengths": 4,
- "alphabets_min_symbols": 3,
- "alphabets_max_symbols": 4,
- "alphabets_step": 1,
- "dilations_min_dilation": 1,
- "dilations_max_dilation": None,
- "min_window_to_signal_std_ratio": 0.0,
- "n_jobs": 1,
- "n_jobs_numba": 1,
- "transformer_weights": None,
- "complexity": "quadratic",
- "densify": True,
- },
- ]
+ params = [{"densify": False}, {"densify": True}]
return params
@@ -581,12 +548,15 @@ def _ndindex_2d_array(idx, dim2_shape):
@nb.njit(cache=True)
def _get_norm_bins(alphabet_size: int, mu=0, std=1):
- return _ppf(np.linspace(0, 1, alphabet_size + 1)[1:-1], mu, std)
+ bins = []
+ for i in np.linspace(0, 1, alphabet_size + 1)[1:-1]:
+ bins.append(_ppf(i, mu, std))
+ return np.array(bins)
@nb.njit(fastmath=True, cache=True)
def _erfinv(x: float) -> float:
- w = -math.log((1 - x) * (1 + x))
+ w = -np.log((1 - x) * (1 + x))
if w < 5:
w = w - 2.5
p = 2.81022636e-08
@@ -599,7 +569,7 @@ def _erfinv(x: float) -> float:
p = 0.246640727 + p * w
p = 1.50140941 + p * w
else:
- w = math.sqrt(w) - 3
+ w = np.sqrt(w) - 3
p = -0.000200214257
p = 0.000100950558 + p * w
p = 0.00134934322 + p * w
@@ -612,9 +582,9 @@ def _erfinv(x: float) -> float:
return p * x
-@nb.vectorize(cache=True)
+@nb.njit(cache=True)
def _ppf(x, mu=0, std=1):
- return mu + math.sqrt(2) * _erfinv(2 * x - 1) * std
+ return mu + np.sqrt(2) * _erfinv(2 * x - 1) * std
@nb.njit(fastmath=True, cache=True)
@@ -796,17 +766,16 @@ def _length(a):
@nb.njit(cache=True)
def _hash_function(v):
-
byte_mask = np.uint64(255)
bs = np.uint64(v)
x1 = (bs) & byte_mask
- x2 = (bs >> 8) & byte_mask
- x3 = (bs >> 16) & byte_mask
- x4 = (bs >> 24) & byte_mask
- x5 = (bs >> 32) & byte_mask
- x6 = (bs >> 40) & byte_mask
- x7 = (bs >> 48) & byte_mask
- x8 = (bs >> 56) & byte_mask
+ x2 = (bs >> np.uint64(8)) & byte_mask
+ x3 = (bs >> np.uint64(16)) & byte_mask
+ x4 = (bs >> np.uint64(24)) & byte_mask
+ x5 = (bs >> np.uint64(32)) & byte_mask
+ x6 = (bs >> np.uint64(40)) & byte_mask
+ x7 = (bs >> np.uint64(48)) & byte_mask
+ x8 = (bs >> np.uint64(56)) & byte_mask
FNV_primer = np.uint64(1099511628211)
FNV_bias = np.uint64(14695981039346656037)
@@ -833,7 +802,7 @@ def _hash_function(v):
@nb.njit(cache=True)
def _make_hash_table(ar):
a = _length(len(ar))
- mask = a - 1
+ mask = np.uint64(a - 1)
uniques = np.empty(a, dtype=ar.dtype)
uniques_cnt = np.zeros(a, dtype=np.int_)
@@ -855,7 +824,7 @@ def _set_item(uniques, uniques_cnt, mask, h, v, total, miss_hits, weight):
break
else:
miss_hits += 1
- index += 1
+ index += np.uint64(1)
index = index & mask
return total, miss_hits
diff --git a/aeon/transformations/collection/dictionary_based/_paa.py b/aeon/transformations/collection/dictionary_based/_paa.py
index 2e8690e6b4..2cba574a1c 100644
--- a/aeon/transformations/collection/dictionary_based/_paa.py
+++ b/aeon/transformations/collection/dictionary_based/_paa.py
@@ -20,8 +20,8 @@ class PAA(BaseCollectionTransformer):
n_segments : int, default = 8
Dimension of the transformed data.
- Notes
- -----
+ References
+ ----------
[1] Eamonn Keogh, Kaushik Chakrabarti, Michael Pazzani, and Sharad Mehrotra.
Dimensionality reduction for fast similarity search in large time series
databases. Knowledge and information Systems, 3(3), 263-286, 2001.
@@ -124,7 +124,7 @@ def inverse_paa(self, X, original_length):
return X_inverse_paa
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -140,7 +140,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
params = {"n_segments": 10}
return params
diff --git a/aeon/transformations/collection/dictionary_based/_sax.py b/aeon/transformations/collection/dictionary_based/_sax.py
index b5f86c5d67..fe49423bda 100644
--- a/aeon/transformations/collection/dictionary_based/_sax.py
+++ b/aeon/transformations/collection/dictionary_based/_sax.py
@@ -229,7 +229,7 @@ def _generate_breakpoints(
return breakpoints, breakpoints_mid
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -245,7 +245,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
params = {"n_segments": 10, "alphabet_size": 8}
return params
diff --git a/aeon/transformations/collection/dictionary_based/_sfa.py b/aeon/transformations/collection/dictionary_based/_sfa.py
index 8a67793cbc..4947648376 100644
--- a/aeon/transformations/collection/dictionary_based/_sfa.py
+++ b/aeon/transformations/collection/dictionary_based/_sfa.py
@@ -1151,7 +1151,7 @@ def word_list_typed(self, word):
return letters
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -1167,7 +1167,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
# small window size for testing
params = {"window_size": 4}
diff --git a/aeon/transformations/collection/dictionary_based/_sfa_fast.py b/aeon/transformations/collection/dictionary_based/_sfa_fast.py
index ede46bdbf6..78e3a18b53 100644
--- a/aeon/transformations/collection/dictionary_based/_sfa_fast.py
+++ b/aeon/transformations/collection/dictionary_based/_sfa_fast.py
@@ -690,7 +690,7 @@ def transform_words(self, X):
)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -705,7 +705,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
# small window size for testing
params = {
diff --git a/aeon/transformations/collection/feature_based/__init__.py b/aeon/transformations/collection/feature_based/__init__.py
index f03108c342..f083c05476 100644
--- a/aeon/transformations/collection/feature_based/__init__.py
+++ b/aeon/transformations/collection/feature_based/__init__.py
@@ -2,16 +2,14 @@
__all__ = [
"Catch22",
- "TSFreshFeatureExtractor",
- "TSFreshRelevantFeatureExtractor",
- "SevenNumberSummaryTransformer",
+ "TSFresh",
+ "TSFreshRelevant",
+ "SevenNumberSummary",
]
from aeon.transformations.collection.feature_based._catch22 import Catch22
-from aeon.transformations.collection.feature_based._summary import (
- SevenNumberSummaryTransformer,
-)
+from aeon.transformations.collection.feature_based._summary import SevenNumberSummary
from aeon.transformations.collection.feature_based._tsfresh import (
- TSFreshFeatureExtractor,
- TSFreshRelevantFeatureExtractor,
+ TSFresh,
+ TSFreshRelevant,
)
diff --git a/aeon/transformations/collection/feature_based/_catch22.py b/aeon/transformations/collection/feature_based/_catch22.py
index 4db6ff1618..9727d0be31 100644
--- a/aeon/transformations/collection/feature_based/_catch22.py
+++ b/aeon/transformations/collection/feature_based/_catch22.py
@@ -7,6 +7,7 @@
__all__ = ["Catch22"]
import math
+import warnings
import numpy as np
from joblib import Parallel, delayed
@@ -16,6 +17,7 @@
from aeon.utils.numba.general import z_normalise_series, z_normalise_series_with_mean
from aeon.utils.numba.stats import mean, numba_max, numba_min
from aeon.utils.validation import check_n_jobs
+from aeon.utils.validation._dependencies import _check_soft_dependencies
feature_names = [
"DN_HistogramMode_5",
@@ -219,65 +221,75 @@ def _transform(self, X, y=None):
threads_to_use = check_n_jobs(self.n_jobs)
+ features = [
+ Catch22._DN_HistogramMode_5,
+ Catch22._DN_HistogramMode_10,
+ Catch22._CO_f1ecac,
+ Catch22._CO_FirstMin_ac,
+ Catch22._CO_HistogramAMI_even_2_5,
+ Catch22._CO_trev_1_num,
+ Catch22._MD_hrv_classic_pnn40,
+ Catch22._SB_BinaryStats_mean_longstretch1,
+ Catch22._SB_TransitionMatrix_3ac_sumdiagcov,
+ Catch22._PD_PeriodicityWang_th0_01,
+ Catch22._CO_Embed2_Dist_tau_d_expfit_meandiff,
+ Catch22._IN_AutoMutualInfoStats_40_gaussian_fmmi,
+ Catch22._FC_LocalSimple_mean1_tauresrat,
+ Catch22._DN_OutlierInclude_p_001_mdrmd,
+ Catch22._DN_OutlierInclude_n_001_mdrmd,
+ Catch22._SP_Summaries_welch_rect_area_5_1,
+ Catch22._SB_BinaryStats_diff_longstretch0,
+ Catch22._SB_MotifThree_quantile_hh,
+ Catch22._SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1,
+ Catch22._SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1,
+ Catch22._SP_Summaries_welch_rect_centroid,
+ Catch22._FC_LocalSimple_mean3_stderr,
+ ]
+
+ use_pycatch22_transform = False
if self.use_pycatch22:
- import pycatch22
-
- features = [
- pycatch22.DN_HistogramMode_5,
- pycatch22.DN_HistogramMode_10,
- pycatch22.CO_f1ecac,
- pycatch22.CO_FirstMin_ac,
- pycatch22.CO_HistogramAMI_even_2_5,
- pycatch22.CO_trev_1_num,
- pycatch22.MD_hrv_classic_pnn40,
- pycatch22.SB_BinaryStats_mean_longstretch1,
- pycatch22.SB_TransitionMatrix_3ac_sumdiagcov,
- pycatch22.PD_PeriodicityWang_th0_01,
- pycatch22.CO_Embed2_Dist_tau_d_expfit_meandiff,
- pycatch22.IN_AutoMutualInfoStats_40_gaussian_fmmi,
- pycatch22.FC_LocalSimple_mean1_tauresrat,
- pycatch22.DN_OutlierInclude_p_001_mdrmd,
- pycatch22.DN_OutlierInclude_n_001_mdrmd,
- pycatch22.SP_Summaries_welch_rect_area_5_1,
- pycatch22.SB_BinaryStats_diff_longstretch0,
- pycatch22.SB_MotifThree_quantile_hh,
- pycatch22.SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1,
- pycatch22.SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1,
- pycatch22.SP_Summaries_welch_rect_centroid,
- pycatch22.FC_LocalSimple_mean3_stderr,
- ]
- else:
- features = [
- Catch22._DN_HistogramMode_5,
- Catch22._DN_HistogramMode_10,
- Catch22._CO_f1ecac,
- Catch22._CO_FirstMin_ac,
- Catch22._CO_HistogramAMI_even_2_5,
- Catch22._CO_trev_1_num,
- Catch22._MD_hrv_classic_pnn40,
- Catch22._SB_BinaryStats_mean_longstretch1,
- Catch22._SB_TransitionMatrix_3ac_sumdiagcov,
- Catch22._PD_PeriodicityWang_th0_01,
- Catch22._CO_Embed2_Dist_tau_d_expfit_meandiff,
- Catch22._IN_AutoMutualInfoStats_40_gaussian_fmmi,
- Catch22._FC_LocalSimple_mean1_tauresrat,
- Catch22._DN_OutlierInclude_p_001_mdrmd,
- Catch22._DN_OutlierInclude_n_001_mdrmd,
- Catch22._SP_Summaries_welch_rect_area_5_1,
- Catch22._SB_BinaryStats_diff_longstretch0,
- Catch22._SB_MotifThree_quantile_hh,
- Catch22._SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1,
- Catch22._SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1,
- Catch22._SP_Summaries_welch_rect_centroid,
- Catch22._FC_LocalSimple_mean3_stderr,
- ]
+ if _check_soft_dependencies("pycatch22", severity="none"):
+ import pycatch22
+
+ features = [
+ pycatch22.DN_HistogramMode_5,
+ pycatch22.DN_HistogramMode_10,
+ pycatch22.CO_f1ecac,
+ pycatch22.CO_FirstMin_ac,
+ pycatch22.CO_HistogramAMI_even_2_5,
+ pycatch22.CO_trev_1_num,
+ pycatch22.MD_hrv_classic_pnn40,
+ pycatch22.SB_BinaryStats_mean_longstretch1,
+ pycatch22.SB_TransitionMatrix_3ac_sumdiagcov,
+ pycatch22.PD_PeriodicityWang_th0_01,
+ pycatch22.CO_Embed2_Dist_tau_d_expfit_meandiff,
+ pycatch22.IN_AutoMutualInfoStats_40_gaussian_fmmi,
+ pycatch22.FC_LocalSimple_mean1_tauresrat,
+ pycatch22.DN_OutlierInclude_p_001_mdrmd,
+ pycatch22.DN_OutlierInclude_n_001_mdrmd,
+ pycatch22.SP_Summaries_welch_rect_area_5_1,
+ pycatch22.SB_BinaryStats_diff_longstretch0,
+ pycatch22.SB_MotifThree_quantile_hh,
+ pycatch22.SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1,
+ pycatch22.SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1,
+ pycatch22.SP_Summaries_welch_rect_centroid,
+ pycatch22.FC_LocalSimple_mean3_stderr,
+ ]
+
+ use_pycatch22_transform = True
+ else:
+ warnings.warn(
+ "pycatch22 not installed, but 'self.use_pycatch22' is set to True."
+ "Please install pycatch22. Aeon catch22 will be used.",
+ stacklevel=2,
+ )
c22_list = Parallel(
n_jobs=threads_to_use, backend=self.parallel_backend, prefer="threads"
)(
delayed(
self._transform_case_pycatch22
- if self.use_pycatch22
+ if use_pycatch22_transform
else self._transform_case
)(
X[i],
diff --git a/aeon/transformations/collection/feature_based/_summary.py b/aeon/transformations/collection/feature_based/_summary.py
index 9228c6ef13..12dba4e756 100644
--- a/aeon/transformations/collection/feature_based/_summary.py
+++ b/aeon/transformations/collection/feature_based/_summary.py
@@ -1,7 +1,7 @@
"""Summary feature transformer."""
__maintainer__ = []
-__all__ = ["SevenNumberSummaryTransformer"]
+__all__ = ["SevenNumberSummary"]
import numpy as np
@@ -15,7 +15,7 @@
)
-class SevenNumberSummaryTransformer(BaseCollectionTransformer):
+class SevenNumberSummary(BaseCollectionTransformer):
"""Seven-number summary transformer.
Transforms a time series into seven basic summary statistics.
@@ -33,13 +33,13 @@ class SevenNumberSummaryTransformer(BaseCollectionTransformer):
Examples
--------
- >>> from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer # noqa
+ >>> from aeon.transformations.collection.feature_based import SevenNumberSummary # noqa
>>> from aeon.testing.data_generation import make_example_3d_numpy
>>> X = make_example_3d_numpy(n_cases=4, n_channels=1, n_timepoints=10,
... random_state=0, return_y=False)
- >>> tnf = SevenNumberSummaryTransformer()
+ >>> tnf = SevenNumberSummary()
>>> tnf.fit(X)
- SevenNumberSummaryTransformer(...)
+ SevenNumberSummary(...)
>>> print(tnf.transform(X)[0])
[1.12176987 0.52340259 0. 1.92732552 0.8542758 1.14764656
1.39573111]
diff --git a/aeon/transformations/collection/feature_based/_tsfresh.py b/aeon/transformations/collection/feature_based/_tsfresh.py
index c9bb815081..2420b226f6 100644
--- a/aeon/transformations/collection/feature_based/_tsfresh.py
+++ b/aeon/transformations/collection/feature_based/_tsfresh.py
@@ -1,7 +1,7 @@
"""tsfresh interface class."""
__maintainer__ = []
-__all__ = ["TSFreshFeatureExtractor", "TSFreshRelevantFeatureExtractor"]
+__all__ = ["TSFresh", "TSFreshRelevant"]
import numpy as np
import pandas as pd
@@ -29,7 +29,7 @@ def _from_3d_numpy_to_long(arr):
return df
-class _TSFreshFeatureExtractor(BaseCollectionTransformer):
+class _TSFresh(BaseCollectionTransformer):
"""Base adapter class for tsfresh transformations."""
_tags = {
@@ -147,7 +147,7 @@ def _get_extraction_params(self):
return extraction_params
-class TSFreshFeatureExtractor(_TSFreshFeatureExtractor):
+class TSFresh(_TSFresh):
"""Transformer for extracting time series features via `tsfresh.extract_features`.
Direct interface to `tsfresh.extract_features` [1] as an `aeon` transformer.
@@ -221,11 +221,11 @@ class TSFreshFeatureExtractor(_TSFreshFeatureExtractor):
>>> from sklearn.model_selection import train_test_split
>>> from aeon.datasets import load_arrow_head
>>> from aeon.transformations.collection.feature_based import (
- ... TSFreshFeatureExtractor
+ ... TSFresh
... )
>>> X, y = load_arrow_head()
>>> X_train, X_test, y_train, y_test = train_test_split(X, y)
- >>> ts_eff = TSFreshFeatureExtractor(
+ >>> ts_eff = TSFresh(
... default_fc_parameters="efficient", disable_progressbar=True
... ) # doctest: +SKIP
>>> X_transform1 = ts_eff.fit_transform(X_train) # doctest: +SKIP
@@ -234,7 +234,7 @@ class TSFreshFeatureExtractor(_TSFreshFeatureExtractor):
... "dim_0__longest_strike_above_mean",
... "dim_0__variance",
... ]
- >>> ts_custom = TSFreshFeatureExtractor(
+ >>> ts_custom = TSFresh(
... kind_to_fc_parameters=features_to_calc, disable_progressbar=True
... ) # doctest: +SKIP
>>> X_transform2 = ts_custom.fit_transform(X_train) # doctest: +SKIP
@@ -302,7 +302,7 @@ def _transform(self, X, y=None):
return Xt.to_numpy()
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -318,7 +318,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
features_to_calc = [
"dim_0__quantile__q_0.6",
@@ -357,7 +356,7 @@ def _get_names(self):
self.names = Xt.columns.tolist()
-class TSFreshRelevantFeatureExtractor(_TSFreshFeatureExtractor):
+class TSFreshRelevant(_TSFresh):
"""Transformer for extracting time series features via `tsfresh.extract_features`.
Direct interface to `tsfresh.extract_features` [1] followed by the tsfresh
@@ -453,11 +452,11 @@ class TSFreshRelevantFeatureExtractor(_TSFreshFeatureExtractor):
>>> from sklearn.model_selection import train_test_split
>>> from aeon.datasets import load_arrow_head
>>> from aeon.transformations.collection.feature_based import (
- ... TSFreshRelevantFeatureExtractor
+ ... TSFreshRelevant
... )
>>> X, y = load_arrow_head()
>>> X_train, X_test, y_train, y_test = train_test_split(X, y)
- >>> ts_eff = TSFreshRelevantFeatureExtractor(
+ >>> ts_eff = TSFreshRelevant(
... default_fc_parameters="efficient", disable_progressbar=True
... ) # doctest: +SKIP
>>> X_transform1 = ts_eff.fit_transform(X_train, y_train) # doctest: +SKIP
@@ -466,7 +465,7 @@ class TSFreshRelevantFeatureExtractor(_TSFreshFeatureExtractor):
... "dim_0__longest_strike_above_mean",
... "dim_0__variance",
... ]
- >>> ts_custom = TSFreshRelevantFeatureExtractor(
+ >>> ts_custom = TSFreshRelevant(
... kind_to_fc_parameters=features_to_calc, disable_progressbar=True
... ) # doctest: +SKIP
>>> X_transform2 = ts_custom.fit_transform(X_train, y_train) # doctest: +SKIP
@@ -581,7 +580,7 @@ def _fit_transform(self, X, y=None):
# lazy imports to avoid hard dependency
from tsfresh.transformers.feature_selector import FeatureSelector
- self.extractor_ = TSFreshFeatureExtractor(
+ self.extractor_ = TSFresh(
default_fc_parameters=self.default_fc_parameters,
kind_to_fc_parameters=self.kind_to_fc_parameters,
chunksize=self.chunksize,
@@ -622,7 +621,7 @@ def _fit(self, X, y=None):
# lazy imports to avoid hard dependency
from tsfresh.transformers.feature_selector import FeatureSelector
- self.extractor_ = TSFreshFeatureExtractor(
+ self.extractor_ = TSFresh(
default_fc_parameters=self.default_fc_parameters,
kind_to_fc_parameters=self.kind_to_fc_parameters,
chunksize=self.chunksize,
@@ -665,7 +664,7 @@ def _transform(self, X, y=None):
return Xt
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -681,7 +680,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
params = {
"default_fc_parameters": "efficient",
diff --git a/aeon/transformations/collection/feature_based/tests/test_summary.py b/aeon/transformations/collection/feature_based/tests/test_summary.py
index baf439d56b..d35e54f9ac 100644
--- a/aeon/transformations/collection/feature_based/tests/test_summary.py
+++ b/aeon/transformations/collection/feature_based/tests/test_summary.py
@@ -2,27 +2,27 @@
import pytest
-from aeon.transformations.collection.feature_based import SevenNumberSummaryTransformer
+from aeon.transformations.collection.feature_based import SevenNumberSummary
def test_summary_features():
"""Test get functions."""
- x = SevenNumberSummaryTransformer()
+ x = SevenNumberSummary()
f = x._get_functions()
assert len(f) == 7
assert callable(f[0])
- x = SevenNumberSummaryTransformer(summary_stats="percentiles")
+ x = SevenNumberSummary(summary_stats="percentiles")
f = x._get_functions()
assert len(f) == 7
assert isinstance(f[0], float)
assert f[1] == 0.887
- x = SevenNumberSummaryTransformer(summary_stats="bowley")
+ x = SevenNumberSummary(summary_stats="bowley")
f = x._get_functions()
assert len(f) == 7
assert callable(f[0])
assert f[6] == 0.9
- x = SevenNumberSummaryTransformer(summary_stats="tukey")
+ x = SevenNumberSummary(summary_stats="tukey")
assert len(x._get_functions()) == 7
with pytest.raises(ValueError, match="Summary function input invalid"):
- x = SevenNumberSummaryTransformer(summary_stats="invalid")
+ x = SevenNumberSummary(summary_stats="invalid")
x._get_functions()
diff --git a/aeon/transformations/collection/feature_based/tests/test_tsfresh.py b/aeon/transformations/collection/feature_based/tests/test_tsfresh.py
index 5cd858ebcc..2e23ba4baa 100644
--- a/aeon/transformations/collection/feature_based/tests/test_tsfresh.py
+++ b/aeon/transformations/collection/feature_based/tests/test_tsfresh.py
@@ -1,4 +1,4 @@
-"""Tests for TSFreshFeatureExtractor."""
+"""Tests for TSFresh."""
__maintainer__ = []
@@ -7,10 +7,7 @@
from aeon.datasets import load_unit_test
from aeon.testing.data_generation import make_example_3d_numpy
-from aeon.transformations.collection.feature_based import (
- TSFreshFeatureExtractor,
- TSFreshRelevantFeatureExtractor,
-)
+from aeon.transformations.collection.feature_based import TSFresh, TSFreshRelevant
from aeon.utils.validation._dependencies import _check_soft_dependencies
@@ -23,7 +20,7 @@ def test_tsfresh_extractor(default_fc_parameters):
"""Test that mean feature of TSFreshFeatureExtract is identical with sample mean."""
X = np.random.rand(10, 1, 30)
- transformer = TSFreshFeatureExtractor(
+ transformer = TSFresh(
default_fc_parameters=default_fc_parameters, disable_progressbar=True
)
@@ -47,7 +44,7 @@ def test_kind_tsfresh_extractor():
"dim_0__longest_strike_above_mean",
"dim_0__variance",
]
- ts_custom = TSFreshFeatureExtractor(
+ ts_custom = TSFresh(
kind_to_fc_parameters=features_to_calc, disable_progressbar=True
)
Xts_custom = ts_custom.fit_transform(X)
@@ -61,7 +58,7 @@ def test_kind_tsfresh_extractor():
def test_tsfresh_inputs():
"""Test incorrect input errors."""
with pytest.raises(ValueError, match="If `default_fc_parameters` is passed"):
- TSFreshFeatureExtractor(default_fc_parameters="wrong_input")
- ts = TSFreshRelevantFeatureExtractor()
+ TSFresh(default_fc_parameters="wrong_input")
+ ts = TSFreshRelevant()
X, y = make_example_3d_numpy()
ts.fit_transform(X, y)
diff --git a/aeon/transformations/collection/interval_based/_random_intervals.py b/aeon/transformations/collection/interval_based/_random_intervals.py
index c43b01f5a6..afcae013d9 100644
--- a/aeon/transformations/collection/interval_based/_random_intervals.py
+++ b/aeon/transformations/collection/interval_based/_random_intervals.py
@@ -475,7 +475,7 @@ def set_features_to_transform(self, arr, raise_error=True):
return True
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -490,7 +490,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
if parameter_set == "results_comparison":
return {"n_intervals": 3}
diff --git a/aeon/transformations/collection/interval_based/_supervised_intervals.py b/aeon/transformations/collection/interval_based/_supervised_intervals.py
index 5f787e6fb1..fcdc1faf86 100644
--- a/aeon/transformations/collection/interval_based/_supervised_intervals.py
+++ b/aeon/transformations/collection/interval_based/_supervised_intervals.py
@@ -545,7 +545,7 @@ def set_features_to_transform(self, arr, raise_error=True):
return True
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -560,7 +560,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
if parameter_set == "results_comparison":
return {
diff --git a/aeon/transformations/collection/interval_based/tests/test_intervals.py b/aeon/transformations/collection/interval_based/tests/test_intervals.py
index 731f630c31..f63edac3a7 100644
--- a/aeon/transformations/collection/interval_based/tests/test_intervals.py
+++ b/aeon/transformations/collection/interval_based/tests/test_intervals.py
@@ -1,10 +1,7 @@
"""Interval extraction test code."""
from aeon.testing.data_generation import make_example_3d_numpy
-from aeon.transformations.collection.feature_based import (
- Catch22,
- SevenNumberSummaryTransformer,
-)
+from aeon.transformations.collection.feature_based import Catch22, SevenNumberSummary
from aeon.transformations.collection.interval_based import (
RandomIntervals,
SupervisedIntervals,
@@ -32,7 +29,7 @@ def test_random_interval_transformer():
X, y = make_example_3d_numpy(random_state=0, n_channels=2, n_timepoints=20)
rit = RandomIntervals(
- features=SevenNumberSummaryTransformer(),
+ features=SevenNumberSummary(),
n_intervals=5,
random_state=0,
)
diff --git a/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py b/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py
index b18c5f25cc..2d47bc4211 100644
--- a/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py
+++ b/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py
@@ -346,7 +346,7 @@ def _check_input_params(self):
self.threshold_percentiles_ = np.asarray(self.threshold_percentiles_)
@classmethod
- def get_test_params(
+ def _get_test_params(
cls, parameter_set: str = "default"
) -> "Union[Dict, TypingList[Dict]]":
"""Return testing parameter settings for the estimator.
@@ -364,7 +364,6 @@ def get_test_params(
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
if parameter_set == "default":
params = {"max_shapelets": 10}
diff --git a/aeon/transformations/collection/shapelet_based/_rsast.py b/aeon/transformations/collection/shapelet_based/_rsast.py
index 32fb2f1605..b353eac3b2 100644
--- a/aeon/transformations/collection/shapelet_based/_rsast.py
+++ b/aeon/transformations/collection/shapelet_based/_rsast.py
@@ -56,7 +56,8 @@ class RSAST(BaseCollectionTransformer):
Parameters
----------
- n_random_points: int default = 10 the number of initial random points to extract
+ n_random_points: int default = 10
+ the number of initial random points to extract
len_method: string default="both" the type of statistical tool used to get
the length of shapelets. "both"=ACF&PACF, "ACF"=ACF, "PACF"=PACF,
"None"=Extract randomly any length from the TS
@@ -65,8 +66,6 @@ class RSAST(BaseCollectionTransformer):
the number of reference time series to select per class
seed : int, default = None
the seed of the random generator
- classifier : sklearn compatible classifier, default = None
- if None, a RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)) is used.
n_jobs : int, default -1
Number of threads to use for the transform.
@@ -114,6 +113,9 @@ def __init__(
self._kernels = None # z-normalized subsequences
self._cand_length_list = {}
self._kernel_orig = []
+ self._start_points = []
+ self._classes = []
+ self._source_series = [] # To store the index of the original time series
self._kernels_generators = {} # Reference time series
super().__init__()
@@ -156,7 +158,12 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "RSAST":
self.num_classes = classes.shape[0]
m_kernel = 0
- # 1--calculate ANOVA per each time t throught the lenght of the TS
+ # Initialize lists to store start positions, classes, and source series
+ self._start_points = []
+ self._classes = []
+ self._source_series = []
+
+ # 1--calculate ANOVA per each time t throughout the length of the TS
for i in range(X_.shape[1]):
statistic_per_class = {}
for c in classes:
@@ -187,11 +194,15 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "RSAST":
cnt = np.min([self.nb_inst_per_class, X_c.shape[0]]).astype(int)
- choosen = self._random_state.permutation(X_c.shape[0])[:cnt]
+ # Store the original indices of the sampled time series
+ original_indices = np.where(y == c)[0]
+
+ chosen_indices = self._random_state.permutation(X_c.shape[0])[:cnt]
self._kernels_generators[c] = []
- for rep, idx in enumerate(choosen):
+ for rep, idx in enumerate(chosen_indices):
+ original_idx = original_indices[idx] # Get the original index
# defining indices for length list
idx_len_list = c + "," + str(idx) + "," + str(rep)
@@ -292,6 +303,12 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "RSAST":
self._kernel_orig.append(np.squeeze(kernel))
self._kernels_generators[c].extend(X_c[idx].reshape(1, -1))
+ # Store the start position,
+ # class, and the original index in the training set
+ self._start_points.append(i)
+ self._classes.append(c)
+ self._source_series.append(original_idx)
+
# 3--save the calculated subsequences
n_kernels = len(self._kernel_orig)
diff --git a/aeon/transformations/collection/shapelet_based/_sast.py b/aeon/transformations/collection/shapelet_based/_sast.py
index 71669de963..c69d799c32 100644
--- a/aeon/transformations/collection/shapelet_based/_sast.py
+++ b/aeon/transformations/collection/shapelet_based/_sast.py
@@ -50,17 +50,18 @@ class SAST(BaseCollectionTransformer):
----------
lengths : int[], default = None
an array containing the lengths of the subsequences
- to be generated. If None, will be infered during fit
+ to be generated. If None, will be inferred during fit
as np.arange(3, X.shape[1])
stride : int, default = 1
- the stride used when generating subsquences
- nb_inst_per_class : int default = 1
+ the stride used when generating subsequences
+ nb_inst_per_class : int, default = 1
the number of reference time series to select per class
seed : int, default = None
the seed of the random generator
n_jobs : int, default -1
Number of threads to use for the transform.
- The available cpu count is used if this value is less than 1
+ The available CPU count is used if this value is less than 1
+
References
----------
@@ -104,6 +105,9 @@ def __init__(
self.nb_inst_per_class = nb_inst_per_class
self._kernels = None # z-normalized subsequences
self._kernel_orig = None # non z-normalized subsequences
+ self._start_points = [] # To store the start positions
+ self._classes = [] # To store the class of each shapelet
+ self._source_series = [] # To store the index of the original time series
self.kernels_generators_ = {} # Reference time series
self.n_jobs = n_jobs
self.seed = seed
@@ -137,8 +141,10 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":
classes = np.unique(y)
self._num_classes = classes.shape[0]
-
+ class_values_of_candidates = []
candidates_ts = []
+ source_series_indices = [] # List to store original indices
+
for c in classes:
X_c = X_[y == c]
@@ -148,6 +154,10 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":
choosen = self._random_state.permutation(X_c.shape[0])[:cnt]
candidates_ts.append(X_c[choosen])
self.kernels_generators_[c] = X_c[choosen]
+ class_values_of_candidates.extend([c] * cnt)
+ source_series_indices.extend(
+ np.where(y == c)[0][choosen]
+ ) # Record the original indices
candidates_ts = np.concatenate(candidates_ts, axis=0)
@@ -163,6 +173,9 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":
(n_kernels, max_shp_length), dtype=np.float32, fill_value=np.nan
)
self._kernel_orig = []
+ self._start_points = [] # Reset start positions
+ self._classes = [] # Reset class information
+ self._source_series = [] # Reset source series information
k = 0
for shp_length in self._length_list:
@@ -172,6 +185,13 @@ def _fit(self, X: np.ndarray, y: Union[np.ndarray, list]) -> "SAST":
can = np.squeeze(candidates_ts[i][j:end])
self._kernel_orig.append(can)
self._kernels[k, :shp_length] = z_normalise_series(can)
+ self._start_points.append(j) # Store the start position
+ self._classes.append(
+ class_values_of_candidates[i]
+ ) # Store the class of the shapelet
+ self._source_series.append(
+ source_series_indices[i]
+ ) # Store the original index of the time series
k += 1
return self
diff --git a/aeon/transformations/collection/shapelet_based/_shapelet_transform.py b/aeon/transformations/collection/shapelet_based/_shapelet_transform.py
index 34c478507e..bed9582d8a 100644
--- a/aeon/transformations/collection/shapelet_based/_shapelet_transform.py
+++ b/aeon/transformations/collection/shapelet_based/_shapelet_transform.py
@@ -395,7 +395,7 @@ def _transform(self, X, y=None):
return output
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -410,7 +410,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
if parameter_set == "results_comparison":
return {"max_shapelets": 10, "n_shapelet_samples": 500}
diff --git a/aeon/transformations/collection/signature_based/_signature_method.py b/aeon/transformations/collection/signature_based/_signature_method.py
index 92767c97ac..7255defe43 100644
--- a/aeon/transformations/collection/signature_based/_signature_method.py
+++ b/aeon/transformations/collection/signature_based/_signature_method.py
@@ -98,7 +98,7 @@ def _transform(self, X, y=None):
return self.signature_method.transform(X)
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -114,7 +114,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
params = {
"augmentation_list": ("basepoint", "addtime"),
diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py
index 2ddd4a65cd..031073b2e6 100644
--- a/aeon/transformations/series/__init__.py
+++ b/aeon/transformations/series/__init__.py
@@ -6,6 +6,7 @@
"ClaSPTransformer",
"DFTSeriesTransformer",
"Dobin",
+ "GaussSeriesTransformer",
"MatrixProfileSeriesTransformer",
"PLASeriesTransformer",
"SGSeriesTransformer",
@@ -30,6 +31,7 @@
from aeon.transformations.series._clasp import ClaSPTransformer
from aeon.transformations.series._dft import DFTSeriesTransformer
from aeon.transformations.series._dobin import Dobin
+from aeon.transformations.series._gauss import GaussSeriesTransformer
from aeon.transformations.series._matrix_profile import MatrixProfileSeriesTransformer
from aeon.transformations.series._pca import PCASeriesTransformer
from aeon.transformations.series._pla import PLASeriesTransformer
diff --git a/aeon/transformations/series/_acf.py b/aeon/transformations/series/_acf.py
index 56d4dab199..1e354cba02 100644
--- a/aeon/transformations/series/_acf.py
+++ b/aeon/transformations/series/_acf.py
@@ -111,7 +111,7 @@ def _acf(X, max_lag):
return X_t
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -127,7 +127,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return [{}, {"n_lags": 1}]
@@ -235,7 +234,7 @@ def _transform(self, X, y=None):
return Xt
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -251,7 +250,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return [{}, {"n_lags": 1}]
@@ -350,7 +348,7 @@ def _transform(self, X, y=None):
return Xt
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -366,6 +364,5 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
return [{}, {"n_lags": 1}]
diff --git a/aeon/transformations/series/_bkfilter.py b/aeon/transformations/series/_bkfilter.py
index d213e67dec..62440d1a2c 100644
--- a/aeon/transformations/series/_bkfilter.py
+++ b/aeon/transformations/series/_bkfilter.py
@@ -103,7 +103,7 @@ def _transform(self, X, y=None):
return XTr
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -119,7 +119,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
params = {"low": 6, "high": 24, "K": 12}
return params
diff --git a/aeon/transformations/series/_dobin.py b/aeon/transformations/series/_dobin.py
index 73a15a2fd3..98de8f36f0 100644
--- a/aeon/transformations/series/_dobin.py
+++ b/aeon/transformations/series/_dobin.py
@@ -64,18 +64,18 @@ class Dobin(BaseSeriesTransformer):
>>> import numpy as np
>>> import pandas as pd
>>> from aeon.datasets import load_uschange
- >>> _, X = load_uschange()
- >>> scaler = MinMaxScaler()
- >>> X = scaler.fit_transform(X)
+ >>> X = load_uschange()
+ >>> min = MinMaxScaler()
+ >>> Xt = min.fit_transform(X.T)
>>> model = Dobin()
- >>> X_outlier = model.fit_transform(X, axis=0)
- >>> X_outlier.head()
- DB0 DB1 DB2 DB3
- 0 1.151965 0.116488 0.286064 0.288140
- 1 1.191976 0.100772 0.050835 0.225985
- 2 1.221158 0.078031 0.034030 0.249676
- 3 1.042420 0.188494 0.218460 0.205251
- 4 1.224701 0.020028 -0.294705 0.199827
+ >>> X_outlier = model.fit_transform(X)
+ >>> X_outlier.T.head()
+ DB0 DB1 DB2 DB3 DB4
+ 0 4.786838 -1.332530 -1.891908 1.566322 0.753280
+ 1 7.290015 0.149297 -1.242303 0.558777 0.474924
+ 2 7.297553 0.419074 -1.688429 0.282187 0.573991
+ 3 0.954141 -1.639316 -0.423461 1.552961 0.434186
+ 4 3.702288 2.066720 -1.807646 -1.777854 0.422556
"""
_tags = {
diff --git a/aeon/transformations/series/_gauss.py b/aeon/transformations/series/_gauss.py
new file mode 100644
index 0000000000..863d8cf6b9
--- /dev/null
+++ b/aeon/transformations/series/_gauss.py
@@ -0,0 +1,75 @@
+"""Gaussian filter transformation."""
+
+__maintainer__ = ["Cyril-Meyer"]
+__all__ = ["GaussSeriesTransformer"]
+
+
+from scipy.ndimage import gaussian_filter1d
+
+from aeon.transformations.series.base import BaseSeriesTransformer
+
+
+class GaussSeriesTransformer(BaseSeriesTransformer):
+ """Filter a times series using Gaussian filter.
+
+ Parameters
+ ----------
+ sigma : float, default=1
+ Standard deviation for the Gaussian kernel.
+
+ order : int, default=0
+ An order of 0 corresponds to convolution with a Gaussian kernel.
+ A positive order corresponds to convolution with that derivative of a
+ Gaussian.
+
+
+ Notes
+ -----
+ More information of the SciPy gaussian_filter1d function used
+ https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter1d.html
+
+ References
+ ----------
+ .. [1] Rafael C. Gonzales and Paul Wintz. 1987.
+ Digital image processing.
+ Addison-Wesley Longman Publishing Co., Inc., USA.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from aeon.transformations.series._gauss import GaussSeriesTransformer
+ >>> X = np.random.random((2, 100)) # Random series length 100
+ >>> gauss = GaussSeriesTransformer(sigma=5)
+ >>> X_ = gauss.fit_transform(X)
+ >>> X_.shape
+ (2, 100)
+ """
+
+ _tags = {
+ "capability:multivariate": True,
+ "X_inner_type": "np.ndarray",
+ "fit_is_empty": True,
+ }
+
+ def __init__(self, sigma=1, order=0):
+ self.sigma = sigma
+ self.order = order
+ super().__init__(axis=1)
+
+ def _transform(self, X, y=None):
+ """Transform X and return a transformed version.
+
+ Parameters
+ ----------
+ X : np.ndarray
+ time series in shape (n_channels, n_timepoints)
+ y : ignored argument for interface compatibility
+
+ Returns
+ -------
+ transformed version of X
+ """
+ # Compute Gaussian filter
+ X_ = gaussian_filter1d(X, self.sigma, self.axis, self.order)
+
+ return X_
diff --git a/aeon/transformations/series/_pca.py b/aeon/transformations/series/_pca.py
index b1388bbb20..2c0d57967c 100644
--- a/aeon/transformations/series/_pca.py
+++ b/aeon/transformations/series/_pca.py
@@ -93,9 +93,9 @@ class PCASeriesTransformer(BaseSeriesTransformer):
>>>
>>> from aeon.transformations.series._pca import PCASeriesTransformer
>>> from aeon.datasets import load_longley
- >>> _, X = load_longley()
+ >>> data = load_longley(return_array=False)
>>> transformer = PCASeriesTransformer(n_components=2)
- >>> X_hat = transformer.fit_transform(X)
+ >>> X_hat = transformer.fit_transform(data)
References
----------
diff --git a/aeon/transformations/series/_scaled_logit.py b/aeon/transformations/series/_scaled_logit.py
index 7466b2f1a8..be483b9955 100644
--- a/aeon/transformations/series/_scaled_logit.py
+++ b/aeon/transformations/series/_scaled_logit.py
@@ -137,7 +137,7 @@ def _inverse_transform(self, X, y=None):
return X_inv_transformed
@classmethod
- def get_test_params(cls, parameter_set="default"):
+ def _get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
@@ -153,7 +153,6 @@ def get_test_params(cls, parameter_set="default"):
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
- `create_test_instance` uses the first (or only) dictionary in `params`
"""
test_params = [
{"lower_bound": None, "upper_bound": None},
diff --git a/aeon/transformations/series/tests/test_boxcox.py b/aeon/transformations/series/tests/test_boxcox.py
index fa98c9ab65..6274924ddb 100644
--- a/aeon/transformations/series/tests/test_boxcox.py
+++ b/aeon/transformations/series/tests/test_boxcox.py
@@ -12,12 +12,13 @@
def test_boxcox_against_scipy():
+ """Test BoxCoxTransformer against scipy implementation."""
y = load_airline()
t = BoxCoxTransformer()
actual = t.fit_transform(y)
- excepted, expected_lambda = boxcox(y.values)
+ excepted, expected_lambda = boxcox(y)
np.testing.assert_array_equal(actual, excepted)
assert t.lambda_ == expected_lambda
@@ -28,6 +29,7 @@ def test_boxcox_against_scipy():
"method, sp", [("mle", None), ("pearsonr", None), ("guerrero", 5)]
)
def test_lambda_bounds(bounds, method, sp):
+ """Test lambda bounds for BoxCox."""
y = load_airline()
t = BoxCoxTransformer(bounds=bounds, method=method, sp=sp)
t.fit(y)
diff --git a/aeon/transformations/series/tests/test_gauss.py b/aeon/transformations/series/tests/test_gauss.py
new file mode 100644
index 0000000000..6ab65ac107
--- /dev/null
+++ b/aeon/transformations/series/tests/test_gauss.py
@@ -0,0 +1,47 @@
+"""Tests for Gauss transformation."""
+
+__maintainer__ = []
+
+import numpy as np
+import pytest
+
+
+@pytest.mark.parametrize("sigma", [0.1, 0.5, 1, 2, 5, 10])
+@pytest.mark.parametrize("order", [0, 1, 2])
+def test_gauss(sigma, order):
+ """Test the functionality of Gauss transformation."""
+ n_samples = 100
+ t = np.linspace(0, 10, n_samples)
+ x1 = (
+ 0.5 * np.sin(2 * np.pi * 1 * t)
+ + 0.2 * np.sin(2 * np.pi * 5 * t)
+ + 0.1 * np.sin(2 * np.pi * 10 * t)
+ )
+ x2 = (
+ 0.4 * np.sin(2 * np.pi * 1.5 * t)
+ + 0.3 * np.sin(2 * np.pi * 4 * t)
+ + 0.1 * np.sin(2 * np.pi * 8 * t)
+ )
+ x12 = np.array([x1, x2])
+ x12r = x12 + np.random.random((2, n_samples)) * 0.25
+
+ from aeon.transformations.series._gauss import GaussSeriesTransformer
+
+ sg = GaussSeriesTransformer(sigma=sigma, order=order)
+ x_1 = sg.fit_transform(x1)
+ x_2 = sg.fit_transform(x2)
+ x_12 = sg.fit_transform(x12)
+ x_12_r = sg.fit_transform(x12r)
+
+ """
+ # Visualize smoothing
+ import matplotlib.pyplot as plt
+ plt.plot(x12r[0])
+ plt.plot(x_12_r[0])
+ plt.savefig(fname=f'Gauss_{sigma}_{order}.png')
+ plt.clf()
+ """
+
+ np.testing.assert_almost_equal(x_1[0], x_12[0], decimal=4)
+ np.testing.assert_almost_equal(x_2[0], x_12[1], decimal=4)
+ assert x_12.shape == x_12_r.shape
diff --git a/aeon/transformations/series/tests/test_yeojohnson.py b/aeon/transformations/series/tests/test_yeojohnson.py
index ddbf3e2ff7..37058346c4 100644
--- a/aeon/transformations/series/tests/test_yeojohnson.py
+++ b/aeon/transformations/series/tests/test_yeojohnson.py
@@ -11,11 +11,12 @@
def test_yeojohnson_against_scipy():
+ """Test YeoJohnsonTransformer against scipy implementation."""
y = load_airline()
t = YeoJohnsonTransformer()
actual = t.fit_transform(y)
- excepted, expected_lambda = yeojohnson(y.values)
+ excepted, expected_lambda = yeojohnson(y)
np.testing.assert_almost_equal(actual, excepted, decimal=12)
assert t._lambda == expected_lambda
diff --git a/aeon/utils/discovery.py b/aeon/utils/discovery.py
index 0819795516..8fd4a05efe 100644
--- a/aeon/utils/discovery.py
+++ b/aeon/utils/discovery.py
@@ -230,7 +230,7 @@ def _filter_tags(tags, estimators, name):
cond_sat = True
for key, value in tags.items():
- est_tag = est[1].get_class_tag(key)
+ est_tag = est[1].get_class_tag(key, raise_error=False)
est_tag = est_tag if isinstance(est_tag, list) else [est_tag]
if isinstance(value, list):
diff --git a/aeon/utils/networks/weight_norm.py b/aeon/utils/networks/weight_norm.py
new file mode 100644
index 0000000000..1a613f9b64
--- /dev/null
+++ b/aeon/utils/networks/weight_norm.py
@@ -0,0 +1,61 @@
+"""Weight Normalization Layer."""
+
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+if _check_soft_dependencies(["tensorflow"], severity="none"):
+ import tensorflow as tf
+
+ class WeightNormalization(tf.keras.layers.Wrapper):
+ """Apply weight normalization to a Keras layer."""
+
+ def __init__(self, layer, **kwargs):
+ """Initialize the WeightNormalization wrapper.
+
+ Args:
+ layer: tf.keras.layers.Layer
+ The Keras layer to apply weight normalization to.
+ """
+ if not isinstance(layer, tf.keras.layers.Layer):
+ raise ValueError("The `layer` argument should be a Keras layer.")
+
+ super().__init__(layer, **kwargs)
+
+ def build(self, input_shape):
+ """Build the weight normalization layer.
+
+ This method initializes weights `v` and `g` for weight normalization.
+ """
+ if not self.layer.built:
+ self.layer.build(input_shape)
+
+ self.w = self.layer.kernel
+ self.v = self.add_weight(
+ shape=self.w.shape,
+ initializer="random_normal",
+ trainable=True,
+ name="v",
+ )
+ self.g = self.add_weight(
+ shape=(self.w.shape[-1],), initializer="ones", trainable=True, name="g"
+ )
+ super().build(input_shape)
+
+ def call(self, inputs):
+ """Apply the normalized weights to the inputs."""
+ norm = tf.sqrt(tf.reduce_sum(tf.square(self.v), axis=0, keepdims=True))
+ normalized_kernel = self.g * self.v / norm
+ output = tf.nn.conv1d(
+ inputs, filters=normalized_kernel, stride=1, padding="SAME"
+ )
+ return output
+
+ def get_config(self):
+ """Return the config of the layer for serialization."""
+ base_config = super().get_config()
+ return {**base_config, "layer": tf.keras.layers.serialize(self.layer)}
+
+ @classmethod
+ def from_config(cls, config):
+ """Recreate the layer from its config."""
+ layer = tf.keras.layers.deserialize(config.pop("layer"))
+ return cls(layer, **config)
diff --git a/aeon/utils/tests/test_weightnorm.py b/aeon/utils/tests/test_weightnorm.py
new file mode 100644
index 0000000000..43b20293d5
--- /dev/null
+++ b/aeon/utils/tests/test_weightnorm.py
@@ -0,0 +1,55 @@
+"""Tests for the Weight Normalization layer."""
+
+import os
+
+import pytest
+
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+ not _check_soft_dependencies(["tensorflow"], severity="none"),
+ reason="soft dependency tensorflow not found in the system",
+)
+def test_weight_norm():
+ """Test the weight norm layer."""
+ import numpy as np
+ import tensorflow as tf
+
+ from aeon.utils.networks.weight_norm import WeightNormalization
+
+ X = np.random.random((10, 10, 5))
+ _input = tf.keras.layers.Input((10, 5))
+ l1 = WeightNormalization(
+ tf.keras.layers.Conv1D(filters=5, kernel_size=1, dilation_rate=4)
+ )(_input)
+ model = tf.keras.models.Model(inputs=_input, outputs=l1)
+ model.compile(
+ loss="mean_squared_error",
+ optimizer=tf.keras.optimizers.Adam(learning_rate=0.05),
+ )
+ assert model is not None
+ output = model.predict(X)
+
+ assert output.shape == (
+ 10,
+ 10,
+ 5,
+ ), f"Expected output shape (10, 10, 5), but got {output.shape}"
+ assert model.layers[1].weights is not None
+ assert len(model.layers[1].weights) == 4
+
+ model_path = "test_weight_norm_model.h5"
+ model.save(model_path)
+ loaded_model = tf.keras.models.load_model(
+ model_path, custom_objects={"WeightNormalization": WeightNormalization}
+ )
+ assert loaded_model is not None
+ loaded_output = loaded_model.predict(X)
+ np.testing.assert_allclose(
+ output,
+ loaded_output,
+ err_msg="Loaded model's output differs from original model's output",
+ )
+ if os.path.exists(model_path):
+ os.remove(model_path)
diff --git a/aeon/utils/validation/collection.py b/aeon/utils/validation/collection.py
index 2a321b372c..654a270b2a 100644
--- a/aeon/utils/validation/collection.py
+++ b/aeon/utils/validation/collection.py
@@ -82,7 +82,7 @@ def get_n_cases(X):
Parameters
----------
X : collection
- See aeon.utils.registry.COLLECTIONS_DATA_TYPES for details.
+ See aeon.utils.COLLECTIONS_DATA_TYPES for details.
Returns
-------
@@ -103,7 +103,7 @@ def get_n_timepoints(X):
Parameters
----------
X : collection
- See aeon.utils.registry.COLLECTIONS_DATA_TYPES for details.
+ See aeon.utils.COLLECTIONS_DATA_TYPES for details.
Returns
-------
@@ -129,7 +129,7 @@ def get_n_channels(X):
Parameters
----------
X : collection
- See aeon.utils.registry.COLLECTIONS_DATA_TYPES for details.
+ See aeon.utils.COLLECTIONS_DATA_TYPES for details.
Returns
-------
@@ -171,7 +171,7 @@ def get_type(X):
Parameters
----------
X : collection
- See aeon.utils.registry.COLLECTIONS_DATA_TYPES for details.
+ See aeon.utils.COLLECTIONS_DATA_TYPES for details.
Returns
-------
@@ -241,7 +241,7 @@ def is_equal_length(X):
Parameters
----------
X : collection
- See aeon.utils.registry.COLLECTIONS_DATA_TYPES for details.
+ See aeon.utils.COLLECTIONS_DATA_TYPES for details.
Returns
-------
@@ -370,14 +370,12 @@ def _equal_length(X, input_type):
if input_type == "pd-multiindex": # multiindex dataframe
X = X.reset_index(-1).drop(X.columns, axis=1)
return (
- X.groupby(level=0, group_keys=True, as_index=True).count().nunique()[0] == 1
+ X.groupby(level=0, group_keys=True, as_index=True).count().nunique().iloc[0]
+ == 1
)
raise ValueError(f" unknown input type {input_type}")
-# TODO: Test this function
-
-
def _is_numpy_list_multivariate(
x: Union[np.ndarray, list[np.ndarray]],
y: Optional[Union[np.ndarray, list[np.ndarray]]] = None,
diff --git a/aeon/visualisation/estimator/tests/test_shapelet_plotting.py b/aeon/visualisation/estimator/tests/test_shapelet_plotting.py
index c4f01f03a5..5b2708cba8 100644
--- a/aeon/visualisation/estimator/tests/test_shapelet_plotting.py
+++ b/aeon/visualisation/estimator/tests/test_shapelet_plotting.py
@@ -97,7 +97,9 @@ def test_ShapeletTransformerVisualizer(transformer_class):
import matplotlib.pyplot as plt
X, y = make_example_3d_numpy()
- shp_transformer = transformer_class(**transformer_class.get_test_params()).fit(X, y)
+ shp_transformer = transformer_class(**transformer_class._get_test_params()).fit(
+ X, y
+ )
shp_vis = ShapeletTransformerVisualizer(shp_transformer)
fig = shp_vis.plot(0)
@@ -126,7 +128,7 @@ def test_ShapeletClassifierVisualizer(classifier_class):
import matplotlib.pyplot as plt
X, y = make_example_3d_numpy()
- shp_transformer = classifier_class(**classifier_class.get_test_params()).fit(X, y)
+ shp_transformer = classifier_class(**classifier_class._get_test_params()).fit(X, y)
shp_vis = ShapeletClassifierVisualizer(shp_transformer)
fig = shp_vis.plot(0)
diff --git a/aeon/visualisation/results/tests/test_boxplot.py b/aeon/visualisation/results/tests/test_boxplot.py
index 22e952e0f8..cd35f423d2 100644
--- a/aeon/visualisation/results/tests/test_boxplot.py
+++ b/aeon/visualisation/results/tests/test_boxplot.py
@@ -30,7 +30,7 @@ def test_plot_boxplot():
cls = ["HC2", "FreshPRINCE", "InceptionT", "WEASEL-D"]
data = univariate_equal_length
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data, path=data_path, include_missing=True
)
diff --git a/aeon/visualisation/results/tests/test_critical_difference.py b/aeon/visualisation/results/tests/test_critical_difference.py
index f274e11bd8..bcd6645417 100644
--- a/aeon/visualisation/results/tests/test_critical_difference.py
+++ b/aeon/visualisation/results/tests/test_critical_difference.py
@@ -113,7 +113,7 @@ def test_plot_critical_difference(correction):
data_full = list(univariate_equal_length)
data_full.sort()
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data_full, path=data_path, include_missing=True
)
@@ -142,7 +142,7 @@ def test_plot_critical_difference_p_values():
data_full = list(univariate_equal_length)
data_full.sort()
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data_full, path=data_path, include_missing=True
)
diff --git a/aeon/visualisation/results/tests/test_scatter.py b/aeon/visualisation/results/tests/test_scatter.py
index c0f21c8c77..7d7a61616e 100644
--- a/aeon/visualisation/results/tests/test_scatter.py
+++ b/aeon/visualisation/results/tests/test_scatter.py
@@ -7,7 +7,7 @@
import pytest
import aeon
-from aeon.benchmarking import get_estimator_results_as_array
+from aeon.benchmarking.results_loaders import get_estimator_results_as_array
from aeon.datasets.tsc_datasets import univariate_equal_length
from aeon.utils.validation._dependencies import _check_soft_dependencies
from aeon.visualisation import (
@@ -36,7 +36,7 @@ def test_plot_pairwise_scatter():
cls = ["HC2", "FreshPRINCE"]
data = univariate_equal_length
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data, path=data_path, include_missing=True
)
fig, ax = plot_pairwise_scatter(
@@ -49,7 +49,7 @@ def test_plot_pairwise_scatter():
cls = ["InceptionTime", "WEASEL-D"]
data = univariate_equal_length
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data, path=data_path, include_missing=True
)
fig, ax = plot_pairwise_scatter(
@@ -62,7 +62,7 @@ def test_plot_pairwise_scatter():
cls = ["InceptionTime", "WEASEL-D"]
data = univariate_equal_length
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data, path=data_path, include_missing=True
)
fig, ax = plot_pairwise_scatter(
diff --git a/aeon/visualisation/results/tests/test_significance.py b/aeon/visualisation/results/tests/test_significance.py
index d1ac16e4bc..71b4a456c3 100644
--- a/aeon/visualisation/results/tests/test_significance.py
+++ b/aeon/visualisation/results/tests/test_significance.py
@@ -140,7 +140,7 @@ def test_plot_significance_corrections(correction):
data_full = list(univariate_equal_length)
data_full.sort()
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data_full, path=data_path, include_missing=True
)
@@ -172,7 +172,7 @@ def test_plot_significance():
data_full = list(univariate_equal_length)
data_full.sort()
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data_full, path=data_path, include_missing=True
)
@@ -206,7 +206,7 @@ def test_plot_significance_p_values():
data_full = list(univariate_equal_length)
data_full.sort()
- res = get_estimator_results_as_array(
+ res, _ = get_estimator_results_as_array(
estimators=cls, datasets=data_full, path=data_path, include_missing=True
)
diff --git a/aeon/visualisation/series/_series.py b/aeon/visualisation/series/_series.py
index 47b1203322..4bf4116b17 100644
--- a/aeon/visualisation/series/_series.py
+++ b/aeon/visualisation/series/_series.py
@@ -57,7 +57,7 @@ def plot_series(
--------
>>> from aeon.visualisation import plot_series
>>> from aeon.datasets import load_airline
- >>> y = load_airline()
+ >>> y = load_airline(return_array=False)
>>> fig, ax = plot_series(y) # doctest: +SKIP
"""
_check_soft_dependencies("matplotlib", "seaborn")
@@ -217,7 +217,7 @@ def plot_lags(series, lags=1, suptitle=None):
--------
>>> from aeon.visualisation import plot_lags
>>> from aeon.datasets import load_airline
- >>> y = load_airline()
+ >>> y = load_airline(return_array=False)
>>> fig, ax = plot_lags(y, lags=2) # plot of y(t) with y(t-2) # doctest: +SKIP
>>> fig, ax = plot_lags(y, lags=[1,2,3]) # y(t) & y(t-1), y(t-2).. # doctest: +SKIP
"""
@@ -317,7 +317,7 @@ def plot_correlations(
--------
>>> from aeon.visualisation import plot_correlations
>>> from aeon.datasets import load_airline
- >>> y = load_airline()
+ >>> y = load_airline(return_array=False)
>>> fig, ax = plot_correlations(y) # doctest: +SKIP
"""
_check_soft_dependencies("matplotlib", "statsmodels")
@@ -386,7 +386,7 @@ def plot_spectrogram(series, fs=1, return_onesided=True):
--------
>>> from aeon.visualisation import plot_spectrogram
>>> from aeon.datasets import load_airline
- >>> y = load_airline()
+ >>> y = load_airline(return_array=False)
>>> fig, ax = plot_spectrogram(y) # doctest: +SKIP
"""
_check_soft_dependencies("matplotlib")
diff --git a/aeon/visualisation/series/tests/test_series_plotting.py b/aeon/visualisation/series/tests/test_series_plotting.py
index c3c878ec19..b54c1f9fbc 100644
--- a/aeon/visualisation/series/tests/test_series_plotting.py
+++ b/aeon/visualisation/series/tests/test_series_plotting.py
@@ -17,7 +17,7 @@
plot_spectrogram,
)
-y_airline = load_airline()
+y_airline = load_airline(return_array=False)
y_airline_true = y_airline.iloc[y_airline.index < "1960-01"]
y_airline_test = y_airline.iloc[y_airline.index >= "1960-01"]
series_to_test = [y_airline, (y_airline_true, y_airline_test)]
diff --git a/conftest.py b/conftest.py
index ace2d0b708..0c1299b8cc 100644
--- a/conftest.py
+++ b/conftest.py
@@ -8,11 +8,24 @@
least once, but not necessarily on each operating system / python version combination.
"""
-__maintainer__ = []
+__maintainer__ = ["MatthewMiddlehurst"]
def pytest_addoption(parser):
"""Pytest command line parser options adder."""
+ parser.addoption(
+ "--nonumba",
+ default=False,
+ help=("Disable numba via the NUMBA_DISABLE_JIT environment variable."),
+ )
+ parser.addoption(
+ "--enablethreading",
+ default=False,
+ help=(
+ "Allow threading and skip setting number of threads to 1 for various "
+ "libraries and environment variables."
+ ),
+ )
parser.addoption(
"--prtesting",
default=False,
@@ -28,33 +41,38 @@ def pytest_configure(config):
"""Pytest configuration preamble."""
import os
- # Must be called before any numpy imports
- os.environ["MKL_NUM_THREADS"] = "1"
- os.environ["NUMEXPR_NUM_THREADS"] = "1"
- os.environ["OMP_NUM_THREADS"] = "1"
- os.environ["OPENBLAS_NUM_THREADS"] = "1"
- os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
+ if config.getoption("--nonumba") in [True, "True", "true"]:
+ os.environ["NUMBA_DISABLE_JIT"] = "1"
- import numba
+ if not config.getoption("--enablethreading") in [True, "True", "true"]:
+ # Must be called before any numpy imports
+ os.environ["MKL_NUM_THREADS"] = "1"
+ os.environ["NUMEXPR_NUM_THREADS"] = "1"
+ os.environ["OMP_NUM_THREADS"] = "1"
+ os.environ["OPENBLAS_NUM_THREADS"] = "1"
+ os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
- from aeon.testing import testing_config
- from aeon.utils.validation._dependencies import _check_soft_dependencies
+ import numba
- numba.set_num_threads(1)
+ numba.set_num_threads(1)
- if _check_soft_dependencies("tensorflow", severity="none"):
- from tensorflow.config.threading import (
- set_inter_op_parallelism_threads,
- set_intra_op_parallelism_threads,
- )
+ from aeon.utils.validation._dependencies import _check_soft_dependencies
- set_inter_op_parallelism_threads(1)
- set_intra_op_parallelism_threads(1)
+ if _check_soft_dependencies("tensorflow", severity="none"):
+ from tensorflow.config.threading import (
+ set_inter_op_parallelism_threads,
+ set_intra_op_parallelism_threads,
+ )
- if _check_soft_dependencies("torch", severity="none"):
- import torch
+ set_inter_op_parallelism_threads(1)
+ set_intra_op_parallelism_threads(1)
- torch.set_num_threads(1)
+ if _check_soft_dependencies("torch", severity="none"):
+ import torch
+
+ torch.set_num_threads(1)
if config.getoption("--prtesting") in [True, "True", "true"]:
+ from aeon.testing import testing_config
+
testing_config.PR_TESTING = True
diff --git a/docs/api_reference/anomaly_detection.rst b/docs/api_reference/anomaly_detection.rst
index c10487f3ad..e24fb50b67 100644
--- a/docs/api_reference/anomaly_detection.rst
+++ b/docs/api_reference/anomaly_detection.rst
@@ -69,6 +69,9 @@ Detectors
:toctree: auto_generated/
:template: class.rst
+
+ CBLOF
+ COPOD
DWT_MLEAD
IsolationForest
LOF
diff --git a/docs/api_reference/base.rst b/docs/api_reference/base.rst
index 5a7ba0d80a..3d06b37103 100644
--- a/docs/api_reference/base.rst
+++ b/docs/api_reference/base.rst
@@ -5,10 +5,6 @@ Base
The :mod:`aeon.base` module contains abstract base classes.
-.. automodule:: aeon.base
- :no-members:
- :no-inherited-members:
-
Base classes
------------
@@ -21,15 +17,3 @@ Base classes
BaseAeonEstimator
BaseCollectionEstimator
BaseSeriesEstimator
-
-Estimator base classes
-----------------------
-
-.. currentmodule:: aeon.base.estimator
-
-.. autosummary::
- :toctree: auto_generated/
- :template: class.rst
-
- hybrid.BaseRIST
- interval_based.BaseIntervalForest
diff --git a/docs/api_reference/classification.rst b/docs/api_reference/classification.rst
index b509093bc3..9f5482fec0 100644
--- a/docs/api_reference/classification.rst
+++ b/docs/api_reference/classification.rst
@@ -195,10 +195,9 @@ Composition
:toctree: auto_generated/
:template: class.rst
+ ClassifierChannelEnsemble
+ ClassifierEnsemble
ClassifierPipeline
- ChannelEnsembleClassifier
- WeightedEnsembleClassifier
-
Base
----
diff --git a/docs/api_reference/data_format.rst b/docs/api_reference/data_format.rst
index 28ed911f56..6cc20b2989 100644
--- a/docs/api_reference/data_format.rst
+++ b/docs/api_reference/data_format.rst
@@ -203,7 +203,7 @@ This section provides full set of instructions to create a format specification
for your dataset that is compatible with ``aeon``.
Remember that this begins with the assumption that you have the dataset readily available in
-expected `format `_.
+expected `format `_.
Few points to keep in mind while creating the dataset:
diff --git a/docs/api_reference/performance_metrics.rst b/docs/api_reference/performance_metrics.rst
index 71258ff710..408fdb7962 100644
--- a/docs/api_reference/performance_metrics.rst
+++ b/docs/api_reference/performance_metrics.rst
@@ -10,37 +10,6 @@ The :mod:`aeon.performance_metrics` module contains metrics for evaluating and t
:no-members:
:no-inherited-members:
-Forecasting
------------
-
-.. currentmodule:: aeon.performance_metrics.forecasting
-
-.. autosummary::
- :toctree: auto_generated/
- :template: function.rst
-
- make_forecasting_scorer
- mean_absolute_scaled_error
- median_absolute_scaled_error
- mean_squared_scaled_error
- median_squared_scaled_error
- mean_absolute_error
- mean_squared_error
- median_absolute_error
- median_squared_error
- geometric_mean_absolute_error
- geometric_mean_squared_error
- mean_absolute_percentage_error
- median_absolute_percentage_error
- mean_squared_percentage_error
- median_squared_percentage_error
- mean_relative_absolute_error
- median_relative_absolute_error
- geometric_mean_relative_absolute_error
- geometric_mean_relative_squared_error
- mean_asymmetric_error
- mean_linex_error
- relative_loss
Segmentation
------------
diff --git a/docs/api_reference/transformations.rst b/docs/api_reference/transformations.rst
index 06f641a3a9..a88889597f 100644
--- a/docs/api_reference/transformations.rst
+++ b/docs/api_reference/transformations.rst
@@ -113,10 +113,10 @@ Feature based
:toctree: auto_generated/
:template: class.rst
- TSFreshRelevantFeatureExtractor
- TSFreshFeatureExtractor
Catch22
- SevenNumberSummaryTransformer
+ TSFresh
+ TSFreshRelevant
+ SevenNumberSummary
Interval based
@@ -183,6 +183,7 @@ Series transforms
ClaSPTransformer
DFTSeriesTransformer
Dobin
+ GaussSeriesTransformer
MatrixProfileSeriesTransformer
PLASeriesTransformer
SGSeriesTransformer
diff --git a/docs/examples.md b/docs/examples.md
index 1d8508a0dd..7b4b269b2f 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -108,7 +108,7 @@ Shapelet based TSC
:::
:::{grid-item-card}
-:img-top: images/logo/aeon-logo-blue-2-transparent.png
+:img-top: examples/classification/img/early_classification.png
:class-img-top: aeon-card-image-m
:link: /examples/classification/early_classification.ipynb
:link-type: ref
@@ -155,7 +155,7 @@ Overview of Time Series Clustering (TSCL)
:::
:::{grid-item-card}
-:img-top: images/logo/aeon-logo-blue-2-transparent.png
+:img-top: examples/clustering/img/partitional.png
:class-img-top: aeon-card-image-m
:link: /examples/clustering/partitional_clustering.ipynb
:link-type: ref
@@ -186,7 +186,7 @@ Overview of Transformations
:::{grid-item-card}
:img-top: examples/transformations/img/tsfresh.png
:class-img-top: aeon-card-image-m
-:link: /examples/transformations/feature_extraction_with_tsfresh.ipynb
+:link: /examples/transformations/tsfresh.ipynb
:link-type: ref
:text-align: center
@@ -301,7 +301,7 @@ ClaSP segmentation
:::
:::{grid-item-card}
-:img-top: images/logo/aeon-logo-blue-2-transparent.png
+:img-top: examples/segmentation/img/hidalgo.png
:class-img-top: aeon-card-image-m
:link: /examples/segmentation/hidalgo_segmentation.ipynb
:link-type: ref
@@ -350,7 +350,7 @@ Using aeon distances with scikit-learn
:::{grid-item-card}
-:img-top: images/logo/aeon-logo-blue-2-transparent.png
+:img-top: examples/similarity_search/img/sim_search.png
:class-img-top: aeon-card-image-m
:link: /examples/similarity_search/similarity_search.ipynb
:link-type: ref
@@ -361,7 +361,7 @@ Intro to similarity search
:::
:::{grid-item-card}
-:img-top: images/logo/aeon-logo-blue-2-transparent.png
+:img-top: examples/similarity_search/img/distance_profile.png
:class-img-top: aeon-card-image-m
:link: /examples/similarity_search/distance_profiles.ipynb
:link-type: ref
@@ -372,7 +372,7 @@ Deep dive into distance profiles
:::
:::{grid-item-card}
-:img-top: images/logo/aeon-logo-blue-2-transparent.png
+:img-top: examples/similarity_search/img/code_speed.png
:class-img-top: aeon-card-image-m
:link: /examples/similarity_search/code_speed.ipynb
:link-type: ref
@@ -492,33 +492,33 @@ Benchmarking algorithms
:::{grid-item-card}
:img-top: images/logo/aeon-logo-blue-2-transparent.png
:class-img-top: aeon-card-image-m
-:link: /examples/benchmarking/regression.ipynb
+:link: /examples/benchmarking/published_results.ipynb
:link-type: ref
:text-align: center
-Benchmarking extrinsic regression algorithms
+Loading published results
:::
:::{grid-item-card}
:img-top: images/logo/aeon-logo-blue-2-transparent.png
:class-img-top: aeon-card-image-m
-:link: /examples/benchmarking/regression_results_per_dataset.ipynb
+:link: /examples/benchmarking/reference_results.ipynb
:link-type: ref
:text-align: center
-Compare regression algorithms on a single dataset
+Getting estimator reference results
:::
:::{grid-item-card}
:img-top: images/logo/aeon-logo-blue-2-transparent.png
:class-img-top: aeon-card-image-m
-:link: /examples/benchmarking/reference_results.ipynb
+:link: /examples/benchmarking/regression.ipynb
:link-type: ref
:text-align: center
-Getting estimator reference results
+Benchmarking extrinsic regression algorithms
:::
diff --git a/docs/getting_started.md b/docs/getting_started.md
index a3659adf15..ba347c79f4 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -12,74 +12,157 @@ package. If you want help with scikit-learn you may want to view
the very latest algorithms for time series machine learning, in addition to a range of
classical techniques for the following learning tasks:
-- {term}`Time series classification` where the time series data for a given instance
-are used to predict a categorical target class.
-- {term}`Time series extrinsic regression` where the time series data for a given
-instance are used to predict a continuous target value.
-- {term}`Time series clustering` where the goal is to discover groups consisting of
-instances with similar time series.
-- {term}`Time series similarity search` where the goal is to evaluate the similarity
-between a time series against a collection of other time series.
-
-Additionally, it provides numerous algorithms for {term}`time series transformation`,
-altering time series into different representations and domains or processing
-time series data into tabular data.
-
-The following provides introductory examples for each of these modules. The examples
-use the datatypes most commonly used for the task in question, but a variety of input
-types for
-data are available. For more information on the variety of
-estimators
-available for each task, see the [API](api_reference) and [examples](examples) pages.
-
-## Time Series Data
+- **Classification**, where a collection of time series labelled with
+ a discrete value is used to train a model to predict unseen cases ([more details](examples/classification/classification.ipynb)).
+- **Regression**, where a collection of time series labelled with
+ a continuous value is used to train a model to predict unseen cases ([more details](examples/regression/regression.ipynb)).
+- **Clustering**, where a collection of time series without any
+ labels are used to train a model to label cases ([more details](examples/clustering/clustering.ipynb)).
+- **Similarity search** where the goal is to evaluate the similarity
+between a query time series and a collection of other longer time series ([more details](examples/similarity_search/similarity_search.ipynb)).
+- **Anomaly detection** where the goal is to find values or areas of a
+ single time series that are not representative of the whole series.
+- **Segmentation** where the goal is to split a single time series into
+ regions where the series are sofind areas of a time series that are not
+ representative of the whole series ([more details](examples/segmentation/segmentation.ipynb)).
+- **Forecasting**, where the goal is to predict future values for a time
+ series (new module coming soon).
+
+`aeon` also provides core modules that are used by the modules above:
+
+- Transformations, where a either a single series or collection is
+ transformed into a different representation or domain. ([more details](examples/transformations/transformations.ipynb)).
+- Distances, which measure the dissimilarity between two time series or
+ collections of series and include functions to align series ([more details](examples/distances/distances.ipynb)).
+- Networks, provides core models for deep learning for all time series tasks ([more
+ details](examples/networks/deep_learning.ipynb)).
+
+There are dedicated notebooks going into more detail for each of these modules
+(linked above). This guide is meant to give you the briefest of
+introductions to the main concepts and
+code for each task to get started. For more information on the variety of
+estimators available for each task, see the links above, the [API](api_reference) and
+[examples](https://www.aeon-toolkit.org/en/latest/examples.html)
+pages.
+
+## A Single Time Series
A time series is a series of real valued data assumed to be ordered. A univariate
-time series is a singular series, where each observation is a single value. For example,
+time series has a single value at each time point. For example,
the heartbeat ECG reading from a single sensor or the number of passengers using an
-airline per month would form a univariate series.
+airline per month would form a univariate series. Single time series are stored
+by default in a numpy array (algorithms use numpy arrays internally whenever possible).
+We can also handle `pd.Series` and `pd.DataFrame` objects, but these are simply
+converted to `np.ndarray` internally. The airline series is a classic example of a
+univariate series from the forecasting domain. The series is the monthly totals of
+international airline passengers, 1949 to 1960, in thousands.
```{code-block} python
>>> from aeon.datasets import load_airline
->>> y = load_airline() # load an example univariate series with timestamps
->>> y.head()
-Period
-1960-08 606.0
-1960-09 508.0
-1960-10 461.0
-1960-11 390.0
-1960-12 432.0
-Freq: M, Name: Number of airline passengers, dtype: float64
+>>> y = load_airline() # load an example univariate series as an array
+>>> y[:5]
+606.0
+508.0
+461.0
+390.0
+432.0
```
-A multivariate time series is made up of multiple series, where each observation is a
-vector of related recordings in the same time index. An examples would be a motion trace
-of from a smartwatch with at least three dimensions (X,Y,Z co-ordinates), or multiple
-financial statistics recorded over time. Single multivariate series input typically
-follows the shape `(n_timepoints, n_channels)`.
+A multivariate time series is made up of multiple series or channels, where each
+observation is a vector of related recordings in the same time index. An examples
+would be a motion trace from a smartwatch with at least three dimensions (X,Y,Z
+co-ordinates), or multiple financial statistics recorded over time. Single
+multivariate series input typically
+follows the shape `(n_channels, n_timepoints)` when stored in numpy arrays
+(sometimes called wide format).
```{code-block} python
>>> from aeon.datasets import load_uschange
->>> y, X = load_uschange("Quarter") # load an example multivariate series
->>> X.set_index(y).head()
- Consumption Income Production Savings Unemployment
-Quarter
-1970 Q1 0.615986 0.972261 -2.452700 4.810312 0.9
-1970 Q2 0.460376 1.169085 -0.551525 7.287992 0.5
-1970 Q3 0.876791 1.553271 -0.358708 7.289013 0.5
-1970 Q4 -0.274245 -0.255272 -2.185455 0.985230 0.7
-1971 Q1 1.897371 1.987154 1.909734 3.657771 -0.1
+>>> data = load_uschange() # load an example multivariate series
+>>> data[:,:5]
+[[ 0.61598622 0.46037569 0.87679142 -0.27424514 1.89737076]
+ [ 0.97226104 1.16908472 1.55327055 -0.25527238 1.98715363]
+ [-2.45270031 -0.55152509 -0.35870786 -2.18545486 1.90973412]
+ [ 4.8103115 7.28799234 7.28901306 0.98522964 3.65777061]
+ [ 0.9 0.5 0.5 0.7 -0.1 ]]
+```
+
+We commonly refer to the number of observations for a time series as `n_timepoints`.
+If a series is multivariate, we refer to the dimensions as channels
+(to avoid confusion with the dimensions of array) and in code use `n_channels`. So
+the US Change data loaded above has five channels and 187 time points. For more
+details on our provided datasets and on how to load data into aeon compatible data
+structures, see our [datasets](examples/datasets/datasets.ipynb) notebooks.
+
+## Single series modules
+
+Different `aeon` module work with individual series or collections of series. Estimators
+in the `anomaly detection` and `segmentation` modules use single
+series input (they inherit from `BaseSeriesEstimator`). The functions in `distances`
+take two series as arguments.
+
+### Segmentation
+
+Time series segmentation (TSS) is the process of dividing a time series into
+segments or regions that are dissimilar to each other. This could, for
+example, be the problem of splitting the motion trace from a smartwatch into
+different activities such as walking, running, and sitting. It is closely related to
+the field of change point detection, which is a term used more in the statistics
+literature. Full information is available in the [segmentation notebooks](Segmentation.ipynb).
+
+The `aeon`
+```{code-block} python
+>>> from aeon.datasets import load_airline
+>>> from aeon.segmentation import ClaSPSegmenter
+>>> series = load_airline()
+>>> clasp = ClaSPSegmenter() # An example segmenter
+>>> clasp.fit(data) # fit the segmenter on the data
+>>> clasp.fit_predict(ts)
+[51]
+```
+
+### Distances
+Distances between time series is a primitive operation in very many time series
+tasks. We have an extensive set of distance functions in the `aeon.distances` module,
+all optimised using numba. They all work with multivariate and unequal length series.
+
+```{code-block} python
+>>> from aeon.datasets import load_japanese_vowels
+>>> from aeon.distances import dtw_distance
+>>> data = load_japanese_vowels() # load an example multivariate series
+>>> dtw_distance(data[0], data[1]) # calculate the dtw distance
+14.416269807978
```
-We commonly refer to the number of observations for a time series as `n_timepoints`. If a series is multivariate, we refer to the dimensions as channels
-(to avoid confusion with the dimensions of array) and in code use `n_channels`.
-Dimensions may also be referred to as variables.
+### Anomaly Detection
-Different parts of `aeon` work with single series or collections of series. The
-`anomaly detection` and `segmentation` modules will commonly use single series input, while
-`classification`, `regression` and `clustering` modules will use collections of time
-series. Collections of time series may also be referred to as Panels. Collections of
-time series will often be accompanied by an array of target variables.
+Anomaly detection (AD) is the process of identifying observations that are significantly
+different from the rest of the data. More details to follow soon, once we have
+written the notebook.
+
+```{code-block} python
+>>> from aeon.datasets import load_airline
+>>> from aeon.anomaly_detection import STOMP
+>>> stomp = STOMP(window_size=200)
+>>> scores = est.fit_predict(X) # Get the anomaly scores
+```
+
+
+
+
+### Forecasting
+
+A new module for time series forecasting (TSF) is coming soon, we are relaunching our
+forecasting module.
+
+
+## Collections of Time Series
+
+The estimators in the `classification`,
+`regression` and `clustering` modules learn from collections of time
+series (they inherit from the class `BaseCollectionEstimator`). Collections of
+time series will often be accompanied by an array of target variables for supervised
+learning. The module `similarity_search` also works with collections of time series.
```{code-block} python
>>> from aeon.datasets import load_italy_power_demand
@@ -96,17 +179,28 @@ time series will often be accompanied by an array of target variables.
['1' '1' '2' '2' '1']
```
-We use the terms case when referring to a single time series
+We use the terms case and instance interchangably when referring to a single time series
contained in a collection. The size of a collection of time series is referred to as
-`n_cases`. Collections of time typically follows the shape `
-(n_cases, n_channels, n_timepoints)` if the series are equal length, but `n_timepoints`
-may vary between cases.
-
-The datatypes used by modules also differ to match the use case. Module focusing
-on single series use cases will commonly use `pandas` `DataFrame` and `Series` objects
-to store time series data as shown in the first two examples. Modules focusing on
-collections on time series will commonly use `numpy` arrays or lists of arrays to
-store time series data.
+`n_cases` in code. Collections have the shape `
+(n_cases, n_channels, n_timepoints)` if the series are equal length. We
+recommend storing collections in 3D numpy arrays even if each time series is univariate (i.e.
+`n_channels == 1`). Collection estimators will work with 2D input of shape `(n_cases,
+n_timepoints)` as you would
+expect from `scikit-learn`, but it is possible to confuse a collection of
+univariate series of shape `(n_cases, n_timepoints)` with a single multivariate
+series of shape `(n_channels, n_timepoints)`. This potential confusion is one reason
+we make the distinction between series and collection estimators.
+
+If `n_timepoints` varies between cases, we store a collection in a `list` of 2D numpy
+arrays, each with the same number of channels. We do not have the capability to use
+collections of time series with varying numbers of channels. We also assume series
+length is always the same for all channels of a single series.
+
+Collection estimators closely follow the `scikit-learn` estimator interface, using
+`fit`, `predict`, `transform`, `predict_proba`, `fit_predict` and `fit_transform`
+where appropriate. They are also designed to work directly with `scikit-learn`
+functionality for e.g. model evaluation, parameter searching and pipelines where
+appropriate.
```{code-block} python
>>>from aeon.datasets import load_basic_motions, load_plaid, load_japanese_vowels
@@ -126,25 +220,21 @@ store time series data.
>>> X4[0].shape
(12, 20)
```
+## Collection based modules
-## Time Series Classification (TSC)
+### Classification
-Classification generally uses numpy arrays to store time series. We recommend storing
-time series for classification in 3D numpy arrays of shape `(n_cases, n_channels,
-n_timepoints)` even if each time series is univariate (i.e. `n_channels == 1`).
-Classifiers will work with 2D input of shape `(n_cases, n_timepoints)` as you would
-expect from `scikit-learn`, but other packages may treat 2D input as a single
-multivariate series. This is the case for non-collection transformers, and you may
-find unexpected outputs if you input a 2D array treating it as multiple time series.
-
-Note we assume series length is always the same for all channels of a single series
-regardless of input type. The target variable should be a `numpy` array of type `float`,
-`int` or `str`.
+Time series classification (TSC) involves training a model on a labelled collection
+of time series. The labels, referred to as `y` in code, should be a `numpy` array of
+type `float`, `int` or `str`. Internally the labels are converted to `int` for use
+in a training algorithm.
The classification estimator interface should be familiar if you have worked with
`scikit-learn`. In this example we fit a [KNeighborsTimeSeriesClassifier](classification.distance_based.KNeighborsTimeSeriesClassifier)
with dynamic time warping (dtw) on our example data.
+
+
```{code-block} python
>>> import numpy as np
>>> from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
@@ -164,25 +254,28 @@ KNeighborsTimeSeriesClassifier()
Once the classifier has been fit using the training data and class labels, we can
predict the labels for new cases. Like `scikit-learn`, `predict_proba` methods are
available to predict class probabilities and a `score` method is present to
-calculate accuracy on new data.
+calculate accuracy on new data. Explore the wide range of
+algorithms available in `aeon`, including the very latest state-of-the-art, in the
+[classification notebooks](examples/classification/classification.ipynb).
-All `aeon` classifiers can be used with `scikit-learn` functionality for e.g.
-model evaluation, parameter searching and pipelines. Explore the wide range of
-algorithm types available in `aeon` in the [classification notebooks](examples.md#classification).
+### Regression
-## Time Series Extrinsic Regression (TSER)
-
-Time series extrinsic regression assumes that the target variable is continuous rather
-than discrete, as for classification. The same input data considerations apply from the
+Time series regression assumes that the target variable is not a discrete label as
+with classification, but is instead a continuous variable, or target variable. The
+same input data considerations apply from the
classification section, and the modules function similarly. The target variable
should be a `numpy` array of type `float`.
-"Time series regression" is a term commonly used in forecasting. To avoid confusion,
-the term "time series extrinsic regression" is commonly used to refer to the traditional
-machine learning regression task but for time series data.
+Time series regression is a term commonly used in forecasting when used in
+conjunction with a sliding
+window. However, the term also includes "time series extrinsic regression" where the
+target variable is not future values but some external variable.
In the following example we use a [KNeighborsTimeSeriesRegressor](regression.distance_based.KNeighborsTimeSeriesRegressor)
-on an example time series extrinsic regression problem called [Covid3Month](https://zenodo.org/record/3902690).
+on an example time series regression problem called [Covid3Month](https://zenodo.org/record/3902690).
+More info in our [regression notebook](examples/regression/regression.ipynb)).
+
+
```{code-block} python
>>> from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor
@@ -200,9 +293,9 @@ KNeighborsTimeSeriesRegressor()
0.002921957478363366
```
-## Time Series Clustering (TSCL)
+### Clustering
-Like classification and regression, time series clustering aims to follow the
+Like classification and regression, time series clustering (TSCL) aims to follow the
`scikit-learn` interface where possible. The same input data format is used as in
the TSC and TSER modules. This example fits a [TimeSeriesKMeans](clustering._k_means.TimeSeriesKMeans)
clusterer on the
@@ -225,14 +318,51 @@ TimeSeriesKMeans(n_clusters=3)
After calling `fit`, the `labels_` attribute contains the cluster labels for
each time series. The `predict` method can be used to predict the cluster labels for
-new data.
+new data. See our clustering notebook for [more details](examples/clustering/clustering.ipynb).
-## Transformers for Single Time Series
+### Similarity Search
+
+The goal of time series similarity search is to find the best matches between a
+query time series and a database (collection) of time series which are usually
+longer than the query. See our notebook for [more details](examples/similarity_search/similarity_search.ipynb)
+ The following example shows how to use
+the [TopKSimilaritySearch](similarity_search.top_k_similarity.TopKSimilaritySearch)
+class to extract the best `k` matches, using the Euclidean distance as similarity
+function.
+
+```{code-block} python
+>>> import numpy as np
+>>> from aeon.similarity_search import TopKSimilaritySearch
+>>> X = [[[1, 2, 3, 4, 5, 6, 7]], # 3D array example (univariate)
+... [[4, 4, 4, 5, 6, 7, 3]]] # Two samples, one channel, seven series length
+>>> X = np.array(X) # X is of shape (2, 1, 7) : (n_cases, n_channels, n_timepoints)
+>>> topk = TopKSimilaritySearch(distance="euclidean",k=2)
+>>> topk.fit(X) # fit the estimator on train data
+...
+>>> q = np.array([[4, 5, 6]]) # q is of shape (1,3) :
+>>> topk.predict(q) # Identify the two (k=2) most similar subsequences of length 3 in X
+[(0, 3), (1, 2)]
+```
+
+The output of predict gives a list of size `k`, where each element is a set indicating
+the location of the best matches in X as `(id_sample, id_timestamp)`. This is equivalent
+to the subsequence `X[id_sample, :, id_timestamps:id_timestamp + q.shape[0]]`.
+
+Note that you can still use univariate time series as inputs, you will just have to
+convert them to multivariate time series with one feature prior to using the similarity
+search module.
+
+## Transformers
+
+We split transformers into two categories: those that transform single time series
+ and those that transform a collection.
+
+### Transformers for Single Time Series
Transformers inheriting from the [BaseSeriesTransformer](transformations.base.BaseSeriesTransformer)
-in the `aeon.transformations.series` transform a single (possibly multivariate) time
-series into a different time series or a feature vector.
+in the `aeon.transformations.series` package transform a single (possibly multivariate)
+time series into a different time series or a feature vector. More info to follow.
The following example shows how to use the
[AutoCorrelationSeriesTransformer](transformations.series.AutoCorrelationSeriesTransformer)
@@ -247,7 +377,9 @@ class to extract the autocorrelation terms of a time series.
>>> res[0][:5]
[0.96019465 0.89567531 0.83739477 0.7977347 0.78594315]
```
-## Transformers for Collections of Time Series
+
+
+### Transformers for Collections of Time Series
The `aeon.transformations.collections` module contains a range of transformers for
collections of time series. By default these do not allow for single series input,
@@ -367,48 +499,3 @@ the available `scikit-learn` functionality.
>>> gscv.best_params_
{'distance': 'euclidean', 'n_neighbors': 5}
```
-
-## Time series similarity search
-
-The similarity search module in `aeon` offers a set of functions and estimators to solve
-tasks related to time series similarity search. The estimators can be used standalone
-or as parts of pipelines, while the functions give you the tools to build your own
-estimators that would rely on similarity search at some point.
-
-The estimators are inheriting from the [BaseSimiliaritySearch](similarity_search.base.BaseSimiliaritySearch)
-class accepts as inputs 3D time series (n_cases, n_channels, n_timepoints) for the
-fit method. Univariate and single series can still be used, but will need to be reshaped
-to this format.
-
-This collection, asked for the fit method, is stored as a database. It will be used in
-the predict method, which expects a single 2D time series as input
-(n_channels, query_length), which will be used as a query to search for in the database.
-Note that the length of the time series in the 3D collection should be superior or
-equal to the length of the 2D time series given in the predict method.
-
-Given those two inputs, the predict method should return the set of most similar
-candidates to the 2D series in the 3D collection. The following example shows how to use
-the [TopKSimilaritySearch](similarity_search.top_k_similarity.TopKSimilaritySearch)
-class to extract the best `k` matches, using the Euclidean distance as similarity
-function.
-
-```{code-block} python
->>> import numpy as np
->>> from aeon.similarity_search import TopKSimilaritySearch
->>> X = [[[1, 2, 3, 4, 5, 6, 7]], # 3D array example (univariate)
-... [[4, 4, 4, 5, 6, 7, 3]]] # Two samples, one channel, seven series length
->>> X = np.array(X) # X is of shape (2, 1, 7) : (n_cases, n_channels, n_timepoints)
->>> topk = TopKSimilaritySearch(distance="euclidean",k=2)
->>> topk.fit(X) # fit the estimator on train data
-...
->>> q = np.array([[4, 5, 6]]) # q is of shape (1,3) :
->>> topk.predict(q) # Identify the two (k=2) most similar subsequences of length 3 in X
-[(0, 3), (1, 2)]
-```
-The output of predict gives a list of size `k`, where each element is a set indicating
-the location of the best matches in X as `(id_sample, id_timestamp)`. This is equivalent
-to the subsequence `X[id_sample, :, id_timestamps:id_timestamp + q.shape[0]]`.
-
-Note that you can still use univariate time series as inputs, you will just have to
-convert them to multivariate time series with one feature prior to using the similarity
-search module.
diff --git a/docs/glossary.md b/docs/glossary.md
deleted file mode 100644
index f660adadaf..0000000000
--- a/docs/glossary.md
+++ /dev/null
@@ -1,191 +0,0 @@
-# Glossary of Common Terms
-
-The glossary below defines common terms and API elements used throughout `aeon`.
-
-```{glossary}
-:sorted:
-
-Time series data
-Time series
-Series
- Data with multiple individual {term}`variable` measurements with accompanying
- {term}`timepoints` which are ordered over time or have an index indicating the
- position of an observation in the sequence of values.
-
-Timepoint
-Timepoints
- The point in time that an observation is made for a {term}`time series`. A time
- point may represent an exact point in time (a timestamp), a time period (e.g.
- minutes, hours or days), or simply an index indicating the position of an
- observation in the sequence of values.
-
-Variable
-Variables
- Refers to some measurement of interest. Variables may be singular values
- (e.g. time-invariant measurements like a patient's place of birth) or a sequence
- of multiple values as a {term}`time series`.
-
- For time series data, multiple variables may be referred to as {term}`channels`.
-
-Target variable
-Target variables
- The {term}`variable`(s) to be predicted in a learning task using
- {term}`Independent variables`, past {term}`timepoints` of the variable itself, or
- both. Also referred to as the dependent or endogenous variable(s).
-
-Independent variable
-Independent variables
- The {term}`variable`(s) that are used to predict the {term}`target variable`(s)
- in a learning task. Also referred to as exogenous variables Commonly also known as
- features and attributes in traditional machine learning settings.
-
-Channel
-Channels
- A channel is a singular {term}`time series` in a data set which contains multiple
- time series {term}`variables`. A dataset with multiple channels is
- {term}`multivariate`.
-
-Time series machine learning
- A general term for using machine learning algorithms to learn predictive models
- from {term}`time series` data. `aeon` is a library for time series machine learning
- algorithms.
-
-Forecasting
- A {term}`Time series machine learning` task focused on prediction future values of
- a {term}`time series`.
-
-Time series classification
- A learning task focused on using the patterns across {term}`instances` between the
- {term}`time series` and a categorical {term}`target variable`.
-
-Time series regression
- A learning task focused on using learning patterns from multiple {term}`time series`
- and a continuous {term}`target variable`. There are two related but distinct
- learning tasks that fall under this category: {term}`time series forecasting
- regression` and {term}`time series extrinsic regression`.
-
-Time series forecasting regression
- This learning relates to {term}`forecasting` {term}`reduced ` to
- regression through a sliding window. This is the more familiar type of regression
- in literature.
-
-Time series extrinsic regression
- A learning task focused on using the patterns across {term}`instances` between the
- {term}`time series` and a continuous {term}`target variable`. The `aeon`
- `regression` module is focused on this type of regression.
-
-Time series clustering
- A learning task focused on discovering groups consisting of {term}`instances` with
- similar {term}`time series`.
-
-Time series annotation
- A collection of learning tasks focused on labelling the {term}`variables` of a
- {term}`time series`. This includes the related tasks of anomaly detection, change
- point detection and segmentation.
-
-Time series transformation
-Time series transformers
- Transformers usually refers to classes in the `transformation` module of `aeon`.
- These classes are used to transform {term}`time series` data into a different
- format. This may be to reduce the dimensionality of the data, to extract features
- from the data, or to transform the data into a different format.
-
- See {term}`series-to-series transformation` and {term}`series-to-features
- transformation` for types of transformer.
-
-Time series similarity search
- A task focused on finding the most similar candidates to a given
- {term}`time series` of length `l`, called the query. The candidates are
- extracted from a collection of {term}`time series` of length equal or
- superior to `l`.
-
-Collection transformers
- {term}`Time series transformers` that take a {term}`time series collection` as
- input. While these transformers only accept collections, a wrapper is provided to
- allow them to be used with singular time series datatypes.
-
-Series-to-series transformation
- {term}`Time series transformers` that take a {term}`time series` as input and
- output a (different) time series. An example of this is the Discrete
- Fourier Transform (DFT).
-
-Series-to-features transformation
- {term}`Time series transformers` that take a {term}`time series` as input and
- output a set of features (in {term}`tabular` format for {term}`time series
- collections`. An example of this is the extraction of the mean and various other
- summary statistics from the series.
-
-Instances
-Instance
- A member of the set of entities being studied and which an machine learning
- practitioner wishes to generalize. For example, patients, chemical process runs,
- machines, countries, etc.
-
- May also be referred to as cases, samples, examples, observations or records
- depending on the discipline and context.
-
-Panel
-Time series panel
- Common alternative name for {term}`time series collection`.
-
-Time series collection
-Time series collections
- A datatype which contains multiple {term}`instances` of time series. These series
- may be {term}`univariate time series` or {term}`multivariate time series`. The time
- series contained within may be of different lengths, sampled at different
- frequencies, contain differing {term}`timepoints` etc.
-
- Also referred to as a {term}`panel time series` depending on context and discipline.
-
-Univariate
-Univariate time series
- A single {term}`time series`.
-
-Multivariate
-Multivariate time series
- A {term}`time series` with multiple {term}`channels`. Typically observed for the
- same observational unit. Multivariate time series is typically used to refer to
- cases where the series evolve together over time.
-
- An example of time series data with multiple channels is data extracted from a
- gyroscope sensor, which can produce different time series data for the x, y and
- z axes of the device.
-
-Reduction
- Reduction refers to decomposing a given learning task into simpler tasks that can
- be composed to create a solution to the original task. In `aeon` reduction is used
- to allow one learning task to be adapted as a solution for an alternative task.
-
-Trend
- When time series show a long-term increase or decrease, this is referred to as a
- trend. Trends can also be non-linear.
-
-Seasonality
- When a {term}`time series` is affected by seasonal characteristics such as the time
- of year or the day of the week, it is called a seasonal pattern.
- The duration of a season is always fixed and known.
-
-Tabular
- A 2 dimensional data structure where the rows of the matrix represent {
- term}`instances` and the columns represent {term}`variables`. This is the most
- common data structure used in `scikit-learn`.
-
- A {term}`univariate time series` can be formatted in this way, where each
- variable of being measured for each instance are treated as
- features and stored as a primitive data type in the 2d data structure. E.g., there
- are N instances of time series and each has T {term}`timepoint`, this would yield
- a matrix with shape (N, T): N rows, T columns.
-
-random_state
- A parameter for controlling random number generation in estimators and functions.
- Follows the conventions of [scikit-learn](https://scikit-learn.org/stable/glossary.html#term-random_state).
-
- If `int`, random_state is the seed used by the random number generator;
- If `RandomState` instance, random_state is the random number generator;
- If `None`, the random number generator is the `RandomState` instance used by
- `np.random`.
-
-n_jobs
- A parameter for controlling the number of threads used in estimators.
- Follows the conventions of [scikit-learn](https://scikit-learn.org/stable/glossary.html#term-n_jobs).
-```
diff --git a/docs/papers_using_aeon.md b/docs/papers_using_aeon.md
index c9c127edbe..5764664dbb 100644
--- a/docs/papers_using_aeon.md
+++ b/docs/papers_using_aeon.md
@@ -18,6 +18,10 @@ the paper and a link to the code in your personal GitHub or other repository.
and experimental evaluation of recent time series classification algorithms.
Data Mining and Knowledge Discovery, online first, open access.
[Paper](https://link.springer.com/article/10.1007/s10618-024-01022-1) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/tsc_bakeoff/tsc_bakeoff_2023.html)
+- Spinnato, F. and Guidotti, R. and Monreale, A. and Nanni, M. (2024). Fast, Interpretable,
+ and Deterministic Time Series Classification With a Bag-of-Receptive-Fields.
+ IEEE Access, vol. 12, (pp. 137893-137912).
+ [Paper](https://ieeexplore.ieee.org/document/10684604) [Code](https://github.com/fspinna/borf)
- SchΓ€fer, P, and Leser, U. (2023). WEASEL 2.0: a random dilated dictionary transform
for fast, accurate and memory constrained time series classification.
diff --git a/examples/benchmarking/bakeoff_results.ipynb b/examples/benchmarking/bakeoff_results.ipynb
deleted file mode 100644
index adba224a8d..0000000000
--- a/examples/benchmarking/bakeoff_results.ipynb
+++ /dev/null
@@ -1,390 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "# Benchmarking: retrieving and comparing against published results\n",
- "\n",
- "You can access all archived published results for time series classification (TSC)\n",
- "directly with aeon. These results are all stored on the website\n",
- "[timeseriesclassification.com](https://timeseriesclassification.com). Coming soon,\n",
- "equivalent results for clustering and classification. These are reference results and\n",
- " will not change. The mechanism for recovering these results is intentionally hard\n",
- " coded and not generalised, to remove any potential for confusion. To more flexibly\n",
- " load the latest results for classification, clustering and regression, see the\n",
- " notebook [Loading reference results](./reference_results.ipynb).\n",
- "\n",
- "These results were presented in three bake offs for classification: The first bake\n",
- "off [1] used 85 UCR univariate TSC datasets. The second bake off [2] introduced the\n",
- "multivariate TSC archive, and compared classifier performance. The third bake off [3],\n",
- "the bake off redux, compared univariate classifiers on 112 UCR datasets. Note the\n",
- "third bake off, or bake off redux as we call it, introduced 30 new datasets.\n",
- "These data and results for them will be available if the paper is accepted for\n",
- "publication.\n",
- "\n",
- "We provide dictionary of classifier/index in results used in each bake off in\n",
- "the file ``aeon.benchmarking.results_loaders``.\n",
- "\n",
- "We compare results with the critical difference graph described in the benchmarking\n",
- "documentation. Note that\n",
- "the way we group classifiers has slightly changed and hence there may be small\n",
- "variation in cliques from published results.\n",
- "\n",
- "The published results for two bake offs can be recovered from [time series\n",
- "repo](https://timeseriesclassification.com/results/PublishedResults/) directly or\n",
- "with aeon."
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## [The great time series classification bake off, 2017](https://link.springer.com/article/10.1007/s10618-016-0483-9)\n",
- "\n",
- "The first TSC bake off, conducted in 2015 and published in 2017 compared 25\n",
- "classifiers on the 85 UCR data that were released in 2015. The classifiers used are:"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T21:59:01.125666Z",
- "start_time": "2024-09-25T21:57:57.610795Z"
- }
- },
- "source": [
- "from aeon.benchmarking.results_loaders import uni_classifiers_2017\n",
- "\n",
- "print(uni_classifiers_2017.keys())"
- ],
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dict_keys(['ACF', 'BOSS', 'CID_DTW', 'CID_ED', 'DDTW_R1_1NN', 'DDTW_Rn_1NN', 'DTW_F', 'EE', 'ERP_1NN', 'Euclidean_1NN', 'FlatCOTE', 'FS', 'LCSS_1NN', 'LPS', 'LS', 'MSM_1NN', 'PS', 'RotF', 'SAXVSM', 'ST', 'TSBF', 'TSF', 'TWE_1NN', 'WDDTW_1NN', 'WDTW_1NN'])\n"
- ]
- }
- ],
- "execution_count": 1
- },
- {
- "cell_type": "markdown",
- "source": [
- "The dataset used for the first bake off [1] are described in [4] and listed as\n",
- "``uni_classifiers_2017``. They are listed as:"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "source": [
- "from aeon.datasets.tsc_datasets import univariate2015\n",
- "\n",
- "print(\n",
- " f\"The {len(univariate2015)} UCR univariate datasets described in [4] and used in \"\n",
- " f\"2017 bakeoff [1]:\\n{univariate2015}\"\n",
- ")"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T21:59:01.637328Z",
- "start_time": "2024-09-25T21:59:01.632313Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The 85 UCR univariate datasets described in [4] and used in 2017 bakeoff [1]:\n",
- "{'UWaveGestureLibraryX', 'TwoLeadECG', 'ProximalPhalanxTW', 'TwoPatterns', 'UWaveGestureLibraryZ', 'Lightning2', 'InlineSkate', 'OSULeaf', 'InsectWingbeatSound', 'MiddlePhalanxOutlineAgeGroup', 'DiatomSizeReduction', 'FacesUCR', 'Wafer', 'PhalangesOutlinesCorrect', 'ToeSegmentation2', 'ECG5000', 'DistalPhalanxOutlineAgeGroup', 'WormsTwoClass', 'CBF', 'MiddlePhalanxOutlineCorrect', 'RefrigerationDevices', 'FaceAll', 'SonyAIBORobotSurface1', 'ECGFiveDays', 'WordSynonyms', 'FaceFour', 'SyntheticControl', 'Haptics', 'DistalPhalanxOutlineCorrect', 'Phoneme', 'Plane', 'ItalyPowerDemand', 'Strawberry', 'Wine', 'SwedishLeaf', 'ShapesAll', 'UWaveGestureLibraryAll', 'Adiac', 'ChlorineConcentration', 'BirdChicken', 'UWaveGestureLibraryY', 'Worms', 'LargeKitchenAppliances', 'ProximalPhalanxOutlineAgeGroup', 'Lightning7', 'CinCECGTorso', 'Car', 'ElectricDevices', 'ECG200', 'Fish', 'FordA', 'ProximalPhalanxOutlineCorrect', 'SmallKitchenAppliances', 'DistalPhalanxTW', 'NonInvasiveFetalECGThorax1', 'Herring', 'OliveOil', 'CricketX', 'Yoga', 'ShapeletSim', 'Meat', 'Coffee', 'ArrowHead', 'Trace', 'ToeSegmentation1', 'NonInvasiveFetalECGThorax2', 'FordB', 'Mallat', 'GunPoint', 'MoteStrain', 'FiftyWords', 'Symbols', 'Ham', 'StarLightCurves', 'ScreenType', 'Earthquakes', 'MedicalImages', 'Computers', 'BeetleFly', 'CricketY', 'MiddlePhalanxTW', 'Beef', 'CricketZ', 'SonyAIBORobotSurface2', 'HandOutlines'}\n"
- ]
- }
- ],
- "execution_count": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "You can pull down results for the original bake off for either the default train/test\n",
- "split and for results averaged over 100 resamples."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T21:59:06.322426Z",
- "start_time": "2024-09-25T21:59:01.977374Z"
- }
- },
- "source": [
- "from aeon.benchmarking.results_loaders import get_bake_off_2017_results\n",
- "\n",
- "default = get_bake_off_2017_results()\n",
- "averaged = get_bake_off_2017_results(default_only=False)\n",
- "print(\n",
- " f\"{len(univariate2015)} datasets in rows, {len(uni_classifiers_2017)} classifiers \"\n",
- " f\"in columns\"\n",
- ")"
- ],
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "85 datasets in rows, 25 classifiers in columns\n"
- ]
- }
- ],
- "execution_count": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "Once you have the results you want, you can compare classifiers with built in aeon\n",
- " tools.\n",
- "\n",
- "\n",
- "Suppose we want to recreate the critical difference diagram\n",
- " published in [1]:\n",
- "\n",
- "\n",
- "\n",
- "This displays the critical difference diagram [6] for comparing classifiers. It shows\n",
- " the average rank of each estimator over all datasets. It then groups estimators for\n",
- " which there is no significant difference in rank into cliques, shown with a solid\n",
- " bar. The published results used the original method for finding cliques called the\n",
- " post hoc Nemenyi test. Our plotting tool offers this as an alternative. See the docs\n",
- " for ``aeon.visualisation.plot_critical_difference`` for more details. To recreate the\n",
- " above, we can do this (note slight difference in names, ``MSM_1NN`` is `MSM` and\n",
- " ``FlatCOTE`` is ``COTE``."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:01:28.707811Z",
- "start_time": "2024-09-25T21:59:08.753076Z"
- }
- },
- "source": [
- "from aeon.visualisation import plot_critical_difference\n",
- "\n",
- "classifiers = [\"MSM_1NN\", \"LPS\", \"TSBF\", \"TSF\", \"DTW_F\", \"EE\", \"BOSS\", \"ST\", \"FlatCOTE\"]\n",
- "# Get columm positions of classifiers in results\n",
- "indx = [uni_classifiers_2017[key] for key in classifiers if key in uni_classifiers_2017]\n",
- "plot, _ = plot_critical_difference(averaged[:, indx], classifiers, test=\"Nemenyi\")\n",
- "plot.show()"
- ],
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\Matthew Middlehurst\\AppData\\Local\\Temp\\ipykernel_14676\\1537479541.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
- " plot.show()\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "
"
- ],
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "execution_count": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "Note there are some small differences in averaged rank. This may be due to\n",
- "differences in how ties in rank were handled. The cliques are identical. Given that\n",
- "these results were generated in 2014/2015 and matlab was used to draw the diagrams, we think this\n",
- "is an acceptable reproduction. Subsequent to the 2015 bake off we switched to using\n",
- "pairwise Wilcoxon sign rank tests with the Holm correction. This creates slightly\n",
- "different cliques."
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## The great multivariate time series classification bake off [2], 2021\n",
- "[Link to paper](https://link.springer.com/article/10.1007/s10618-020-00727-3)\n",
- "\n",
- "The multivariate bake off [2] launched a new archive and compared 11 classifiers on 26\n",
- "multivariate TSC problems"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "source": [
- "from aeon.benchmarking.results_loaders import multi_classifiers_2021\n",
- "from aeon.datasets.tsc_datasets import multivariate_equal_length\n",
- "\n",
- "print(multi_classifiers_2021.keys())\n",
- "print(\n",
- " f\"The {len(multivariate_equal_length)} TSML multivariate datasets described in \"\n",
- " f\"and used in the 2021 multivariate bakeoff [1]:\\n{multivariate_equal_length}\"\n",
- ")"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:01:28.773322Z",
- "start_time": "2024-09-25T22:01:28.767339Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dict_keys(['CBOSS', 'CIF', 'DTW_D', 'DTW_I', 'gRSF', 'HIVE-COTEv1', 'ResNet', 'RISE', 'ROCKET', 'STC', 'TSF'])\n",
- "The 26 TSML multivariate datasets described in and used in the 2021 multivariate bakeoff [1]:\n",
- "{'FaceDetection', 'LSST', 'RacketSports', 'ArticularyWordRecognition', 'EthanolConcentration', 'StandWalkJump', 'Cricket', 'FingerMovements', 'PhonemeSpectra', 'Handwriting', 'MotorImagery', 'Epilepsy', 'Heartbeat', 'DuckDuckGeese', 'PenDigits', 'Libras', 'NATOPS', 'HandMovementDirection', 'EigenWorms', 'SelfRegulationSCP2', 'SelfRegulationSCP1', 'ERing', 'BasicMotions', 'PEMS-SF', 'AtrialFibrillation', 'UWaveGestureLibrary'}\n"
- ]
- }
- ],
- "execution_count": 5
- },
- {
- "cell_type": "markdown",
- "source": [
- "The results table below shows the performance figures for accuracy, balanced\n",
- "accuracy, AUROC and F1.\n",
- "\n",
- "\n",
- "\n",
- "We can recreate the accuracy graph by loading the results from tsc.com and plotting\n",
- "like so:"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "source": [
- "from aeon.benchmarking.results_loaders import get_bake_off_2021_results\n",
- "\n",
- "default = get_bake_off_2021_results()\n",
- "averaged = get_bake_off_2021_results(default_only=False)\n",
- "print(\"Shape of results = \", averaged.shape)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:01:29.757184Z",
- "start_time": "2024-09-25T22:01:28.805238Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Shape of results = (26, 11)\n"
- ]
- }
- ],
- "execution_count": 6
- },
- {
- "cell_type": "code",
- "source": [
- "plot, _ = plot_critical_difference(averaged, list(multi_classifiers_2021.keys()))"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:01:30.001964Z",
- "start_time": "2024-09-25T22:01:29.769151Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "D:\\CMP_Machine_Learning\\Repositories\\aeon\\.venv\\lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:600: UserWarning: Exact p-value calculation does not work if there are zeros. Switching to normal approximation.\n",
- " return result_to_tuple(hypotest_fun_out(*samples, **kwds))\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "
"
- ],
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "execution_count": 7
- },
- {
- "cell_type": "markdown",
- "source": [
- "Note there are some differences in cliques due to slightly different methodology.\n",
- "This will be explained in more detail in a technical document soon. We will also\n",
- "add more reference results in due course."
- ],
- "metadata": {
- "collapsed": false
- }
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/benchmarking/benchmarking.ipynb b/examples/benchmarking/benchmarking.ipynb
index 819818d720..6bc1f8517c 100644
--- a/examples/benchmarking/benchmarking.ipynb
+++ b/examples/benchmarking/benchmarking.ipynb
@@ -18,16 +18,13 @@
"`aeon`'s `benchmarking` module is designed to provide benchmarking functionality while enforcing best\n",
"practices and structure to help users avoid making mistakes (such as data leakage, etc.) which invalidate\n",
"their results. The `benchmarking` module is designed for easy usage in mind, as such it interfaces\n",
- "directly with `aeon` objects and classes. Previously developed estimator should be usable as they are without\n",
- "alterations.\n",
+ "directly with `aeon` objects and classes.\n",
"\n",
"We also include tools for comparing your results to published work and for testing\n",
"and visualising relative performance of algorithms. See\n",
"\n",
- "- [Loading results from timeseriesclassification.com](./reference_results.ipynb)\n",
- "- [Comparing outputs for regressors](./regression_results_per_dataset.ipynb)\n",
- "- [Bake off results](./bakeoff_results.ipynb)\n",
- "- [Reference results](./reference_results.ipynb)\n",
+ "- [Loading published results files](./published_results.ipynb)\n",
+ "- [Loading and using reference results](./reference_results.ipynb)\n",
"- [Regression bechmarking](./regression.ipynb)\n",
"\n",
"\n",
diff --git a/examples/benchmarking/published_results.ipynb b/examples/benchmarking/published_results.ipynb
new file mode 100644
index 0000000000..44870bffab
--- /dev/null
+++ b/examples/benchmarking/published_results.ipynb
@@ -0,0 +1,934 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": [
+ "# Benchmarking: retrieving and comparing against published results\n",
+ "\n",
+ "You can access all archived published results for time series classification (TSC)\n",
+ "directly with ``aeon``. These results are all stored on the website\n",
+ "[timeseriesclassification.com](https://timeseriesclassification.com). \n",
+ "\n",
+ "These are reference results tied to publications and will not change. The datasets and \n",
+ "estimators for recovering these results are intentionally hard coded and not generalised, \n",
+ "to remove any potential for confusion. To more flexibly load the latest results for \n",
+ "classification, clustering and regression, see the notebook on\n",
+ "[loading reference results](./reference_results.ipynb).\n",
+ "\n",
+ "We compare results with the critical difference graph described in \n",
+ "[this notebook](./plotting_results.ipynb). Note that the way we group classifiers \n",
+ "has slightly changed and hence there may be small variation in cliques from published \n",
+ "results.\n",
+ "\n",
+ "The published results can be recovered from the [time series classifcation\n",
+ "website](https://timeseriesclassification.com/results/PublishedResults/) directly or\n",
+ "with ``aeon``."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Classification\n",
+ "\n",
+ "These results were presented in three bake offs for classification: The first bake\n",
+ "off [[1]](#references) used 85 UCR univariate TSC datasets. The second bake off [[2]](#references) introduced the\n",
+ "multivariate TSC archive, and compared classifier performance. The third bake off [[3]](#references),\n",
+ "the bake off redux, compared univariate classifiers on 112 UCR datasets. \n",
+ "\n",
+ "### The great time series classification bake off, 2017\n",
+ "\n",
+ "The first TSC bake off [[1]](#references), conducted in 2015 and published in 2017 compared 25\n",
+ "classifiers on the 85 UCR data that were released in 2015. The publication is\n",
+ "available [here](https://link.springer.com/article/10.1007/s10618-016-0483-9).\n",
+ "\n",
+ "You can pull down results for the original bake off using the following function. \n",
+ "The default train/test split is returned as the first resample, and there are\n",
+ "100 resamples available for most experiments. The data resampling function used is \n",
+ "not the same as the one available in ``aeon``."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:19.629001Z",
+ "start_time": "2024-10-29T13:24:17.516939Z"
+ }
+ },
+ "source": [
+ "from aeon.benchmarking.published_results import (\n",
+ " load_classification_bake_off_2017_results,\n",
+ ")\n",
+ "\n",
+ "results_dict = load_classification_bake_off_2017_results(num_resamples=10)\n",
+ "results_dict[\"FlatCOTE\"][\"GunPoint\"]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([1. , 0.98666667, 0.99333333, 1. , 1. ,\n",
+ " 0.97333333, 0.98 , 0.99333333, 1. , 1. ])"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 1
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "We were unable to recover experiment resamples past a certain point for some \n",
+ "classifier/dataset combinations. Missing resamples will return a NaN"
+ ]
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:19.658921Z",
+ "start_time": "2024-10-29T13:24:19.653934Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "results_dict[\"DTW_F\"][\"FordB\"]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.74938272, 0.88888889, 0.78765432, 0.88148148, 0.87407407,\n",
+ " 0.87654321, nan, nan, nan, nan])"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 2
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:21.433202Z",
+ "start_time": "2024-10-29T13:24:19.859384Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "results_arr, datasets, classifiers = load_classification_bake_off_2017_results(\n",
+ " num_resamples=100, as_array=True, ignore_nan=True\n",
+ ")\n",
+ "results_arr.shape"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(85, 25)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 3
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "The dataset used for the first bake off are described in [[4]](#references):"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:21.449136Z",
+ "start_time": "2024-10-29T13:24:21.443151Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "datasets"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Adiac',\n",
+ " 'ArrowHead',\n",
+ " 'Beef',\n",
+ " 'BeetleFly',\n",
+ " 'BirdChicken',\n",
+ " 'Car',\n",
+ " 'CBF',\n",
+ " 'ChlorineConcentration',\n",
+ " 'CinCECGTorso',\n",
+ " 'Coffee',\n",
+ " 'Computers',\n",
+ " 'CricketX',\n",
+ " 'CricketY',\n",
+ " 'CricketZ',\n",
+ " 'DiatomSizeReduction',\n",
+ " 'DistalPhalanxOutlineCorrect',\n",
+ " 'DistalPhalanxOutlineAgeGroup',\n",
+ " 'DistalPhalanxTW',\n",
+ " 'Earthquakes',\n",
+ " 'ECG200',\n",
+ " 'ECG5000',\n",
+ " 'ECGFiveDays',\n",
+ " 'ElectricDevices',\n",
+ " 'FaceAll',\n",
+ " 'FaceFour',\n",
+ " 'FacesUCR',\n",
+ " 'FiftyWords',\n",
+ " 'Fish',\n",
+ " 'FordA',\n",
+ " 'FordB',\n",
+ " 'GunPoint',\n",
+ " 'Ham',\n",
+ " 'HandOutlines',\n",
+ " 'Haptics',\n",
+ " 'Herring',\n",
+ " 'InlineSkate',\n",
+ " 'InsectWingbeatSound',\n",
+ " 'ItalyPowerDemand',\n",
+ " 'LargeKitchenAppliances',\n",
+ " 'Lightning2',\n",
+ " 'Lightning7',\n",
+ " 'Mallat',\n",
+ " 'Meat',\n",
+ " 'MedicalImages',\n",
+ " 'MiddlePhalanxOutlineCorrect',\n",
+ " 'MiddlePhalanxOutlineAgeGroup',\n",
+ " 'MiddlePhalanxTW',\n",
+ " 'MoteStrain',\n",
+ " 'NonInvasiveFetalECGThorax1',\n",
+ " 'NonInvasiveFetalECGThorax2',\n",
+ " 'OliveOil',\n",
+ " 'OSULeaf',\n",
+ " 'PhalangesOutlinesCorrect',\n",
+ " 'Phoneme',\n",
+ " 'Plane',\n",
+ " 'ProximalPhalanxOutlineCorrect',\n",
+ " 'ProximalPhalanxOutlineAgeGroup',\n",
+ " 'ProximalPhalanxTW',\n",
+ " 'RefrigerationDevices',\n",
+ " 'ScreenType',\n",
+ " 'ShapeletSim',\n",
+ " 'ShapesAll',\n",
+ " 'SmallKitchenAppliances',\n",
+ " 'SonyAIBORobotSurface1',\n",
+ " 'SonyAIBORobotSurface2',\n",
+ " 'StarLightCurves',\n",
+ " 'Strawberry',\n",
+ " 'SwedishLeaf',\n",
+ " 'Symbols',\n",
+ " 'SyntheticControl',\n",
+ " 'ToeSegmentation1',\n",
+ " 'ToeSegmentation2',\n",
+ " 'Trace',\n",
+ " 'TwoLeadECG',\n",
+ " 'TwoPatterns',\n",
+ " 'UWaveGestureLibraryX',\n",
+ " 'UWaveGestureLibraryY',\n",
+ " 'UWaveGestureLibraryZ',\n",
+ " 'UWaveGestureLibraryAll',\n",
+ " 'Wafer',\n",
+ " 'Wine',\n",
+ " 'WordSynonyms',\n",
+ " 'Worms',\n",
+ " 'WormsTwoClass',\n",
+ " 'Yoga']"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 4
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "The classifiers used are as follows: "
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:21.484041Z",
+ "start_time": "2024-10-29T13:24:21.478059Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "classifiers"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['ACF',\n",
+ " 'BOSS',\n",
+ " 'CID_DTW',\n",
+ " 'CID_ED',\n",
+ " 'DDTW_R1_1NN',\n",
+ " 'DDTW_Rn_1NN',\n",
+ " 'DTW_F',\n",
+ " 'EE',\n",
+ " 'ERP_1NN',\n",
+ " 'Euclidean_1NN',\n",
+ " 'FlatCOTE',\n",
+ " 'FS',\n",
+ " 'LCSS_1NN',\n",
+ " 'LPS',\n",
+ " 'LS',\n",
+ " 'MSM_1NN',\n",
+ " 'PS',\n",
+ " 'RotF',\n",
+ " 'SAXVSM',\n",
+ " 'ST',\n",
+ " 'TSBF',\n",
+ " 'TSF',\n",
+ " 'TWE_1NN',\n",
+ " 'WDDTW_1NN',\n",
+ " 'WDTW_1NN']"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 5
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": [
+ "Once you have the results you want, you can compare classifiers with built in \n",
+ "``aeon`` tools.\n",
+ "\n",
+ "Suppose we want to recreate the critical difference diagram published in [1]:"
+ ]
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": ""
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "This displays the critical difference diagram [[6]](#references) for comparing classifiers. It shows\n",
+ " the average rank of each estimator over all datasets. It then groups estimators for\n",
+ " which there is no significant difference in rank into cliques, shown with a solid\n",
+ " bar. The published results used the original method for finding cliques called the\n",
+ " post hoc Nemenyi test. Our plotting tool offers this as an alternative. See the docs\n",
+ " for ``aeon.visualisation.plot_critical_difference`` for more details. To recreate the\n",
+ " above, we can do this (note slight difference in names, ``MSM_1NN`` is `MSM` and\n",
+ " ``FlatCOTE`` is ``COTE``."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:22.786587Z",
+ "start_time": "2024-10-29T13:24:21.510970Z"
+ }
+ },
+ "source": [
+ "from aeon.visualisation import plot_critical_difference\n",
+ "\n",
+ "subsample = [\"MSM_1NN\", \"LPS\", \"TSBF\", \"TSF\", \"DTW_F\", \"EE\", \"BOSS\", \"ST\", \"FlatCOTE\"]\n",
+ "idx = [classifiers.index(key) for key in subsample if key in classifiers]\n",
+ "\n",
+ "plot_critical_difference(results_arr[:, idx], subsample, test=\"Nemenyi\")"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(
"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "source": [
- "clst = get_available_estimators(task=\"clustering\")\n",
- "print(len(clst), \" clustering results available\\n\", clst)"
- ]
+ "execution_count": 3
},
{
"cell_type": "markdown",
@@ -161,458 +587,573 @@
"collapsed": false
},
"source": [
- "## Classification example\n",
+ "## Loading results (classification example)\n",
"\n",
"We will use the classification task as an example. We will recover the results for\n",
- "FreshPRINCE [4] is a pipeline of TSFresh transform followed by a rotation forest classifier.\n",
- "InceptionTimeClassifier [5] is a deep learning ensemble. HIVECOTEV2 [6] is a meta\n",
- "ensemble of four different ensembles built on different representations. WEASEL2 [7]\n",
- "overhauls original WEASEL using dilation and ensembling randomized hyper-parameter\n",
- "settings.\n",
+ "FreshPRINCE [[4]](#references) a pipeline of TSFresh transform followed by a rotation forest classifier.\n",
+ "InceptionTimeClassifier [[5]](#references) is a deep learning ensemble. HIVECOTEV2 [[6]](#references) is a meta\n",
+ "ensemble of four different ensembles built on different representations. RDST [[7]](#references)\n",
+ "extracts random shalepets with dilation to form a pipeline.\n",
+ "\n",
+ "See [[1]](#references) for an overview of recent advances in time series classification. We also store \n",
+ "results for other learning tasks, such as regression [[2]](#references) and clustering [[3]](#references).\n",
"\n",
- "See [1] for an overview of recent advances in time series classification."
+ "If you do not set `path`, results are loaded from https://timeseriesclassification.com/results/ReferenceResults.\n",
+ "You can download the files directly from there. To read locally, set the `path` variable.\n",
+ "While we don't show this here, the `task` parameter can be set to `regression` or \n",
+ "`clustering` to recover those results."
]
},
{
- "cell_type": "code",
- "execution_count": 4,
"metadata": {
"ExecuteTime": {
- "end_time": "2024-02-06T15:20:36.169679Z",
- "start_time": "2024-02-06T15:20:35.648074800Z"
- },
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Returns an array with each column an estimator, shape (data_names, classifiers)\n",
- "By default recovers the default test split results for 112 equal length UCR datasets.\n",
- "Or specify datasets for result recovery. For example, 4 datasets. HIVECOTEV2 accuracy ItalyPowerDemand = 0.9698736637512148\n"
- ]
+ "end_time": "2024-10-29T13:24:12.440063Z",
+ "start_time": "2024-10-29T13:24:12.436074Z"
}
- ],
+ },
+ "cell_type": "code",
"source": [
- "from aeon.benchmarking.results_loaders import (\n",
- " get_estimator_results,\n",
- " get_estimator_results_as_array,\n",
- ")\n",
- "from aeon.visualisation import (\n",
- " plot_boxplot,\n",
- " plot_critical_difference,\n",
- " plot_pairwise_scatter,\n",
- ")\n",
- "\n",
"classifiers = [\n",
" \"FreshPRINCEClassifier\",\n",
" \"HIVECOTEV2\",\n",
" \"InceptionTimeClassifier\",\n",
- " \"WEASEL-Dilation\",\n",
+ " \"RDSTClassifier\",\n",
"]\n",
- "datasets = [\"ACSF1\", \"ArrowHead\", \"GunPoint\", \"ItalyPowerDemand\"]\n",
- "# get results. To read locally, set the path variable.\n",
- "# If you do not set path, results are loaded from\n",
- "# https://timeseriesclassification.com/results/ReferenceResults.\n",
- "# You can download the files directly from there\n",
- "default_split_all, data_names = get_estimator_results_as_array(estimators=classifiers)\n",
- "print(\n",
- " \" Returns an array with each column an estimator, shape (data_names, classifiers)\"\n",
- ")\n",
- "print(\n",
- " f\"By default recovers the default test split results for {len(data_names)} \"\n",
- " f\"equal length UCR datasets.\"\n",
- ")\n",
- "default_split_some, names = get_estimator_results_as_array(\n",
- " estimators=classifiers, datasets=datasets\n",
- ")\n",
- "print(\n",
- " f\"Or specify datasets for result recovery. For example, {len(names)} datasets. \"\n",
- " f\"HIVECOTEV2 accuracy {names[3]} = {default_split_some[3][1]}\"\n",
- ")"
- ]
+ "datasets = [\"ACSF1\", \"ArrowHead\", \"GunPoint\", \"ItalyPowerDemand\"]"
+ ],
+ "outputs": [],
+ "execution_count": 4
},
{
+ "metadata": {},
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "If you have any questions about these results or the datasets, please raise an issue\n",
- "on the associated [repo](https://github.com/time-series-machine-learning/tsml-repo). You can also recover\n",
- "results in a dictionary, where each key is a classifier name, and the values is a\n",
- "dictionary of problems/results.\n"
- ]
+ "source": "The `get_estimator_results` function returns the resutls as a dictionary of dictionaries, where the first key is the classifier name and the second key is the dataset name."
},
{
- "cell_type": "code",
- "execution_count": 5,
"metadata": {
"ExecuteTime": {
- "end_time": "2024-02-06T15:20:36.415023200Z",
- "start_time": "2024-02-06T15:20:36.170677Z"
- },
- "collapsed": false
+ "end_time": "2024-10-29T13:24:12.649503Z",
+ "start_time": "2024-10-29T13:24:12.493919Z"
+ }
},
+ "cell_type": "code",
+ "source": [
+ "from aeon.benchmarking.results_loaders import get_estimator_results\n",
+ "\n",
+ "results_dict = get_estimator_results(estimators=classifiers, datasets=datasets)\n",
+ "results_dict[\"HIVECOTEV2\"][\"ItalyPowerDemand\"]"
+ ],
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Keys = dict_keys(['FreshPRINCEClassifier', 'HIVECOTEV2', 'InceptionTimeClassifier', 'WEASEL-Dilation'])\n",
- "Accuracy of HIVECOTEV2 on ItalyPowerDemand = 0.9698736637512148\n"
- ]
+ "data": {
+ "text/plain": [
+ "0.9698736637512148"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "source": [
- "hash_table = get_estimator_results(estimators=classifiers)\n",
- "print(\"Keys = \", hash_table.keys())\n",
- "print(\n",
- " \"Accuracy of HIVECOTEV2 on ItalyPowerDemand = \",\n",
- " hash_table[\"HIVECOTEV2\"][\"ItalyPowerDemand\"],\n",
- ")"
- ]
+ "execution_count": 5
},
{
+ "metadata": {},
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "The results recovered so far have all been on the default train/test split. If we\n",
- "merge train and test data and resample, you can get very different results. To allow\n",
- "for this, we average results over 30 resamples. You can recover these\n",
- "averages by setting the `default_only` parameter to `False`."
- ]
+ "source": "Most results files have multiple resamples. These can be returned as an array using the `num_resamples` parameter."
},
{
"cell_type": "code",
- "execution_count": 6,
"metadata": {
+ "collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-06T15:20:36.645407800Z",
- "start_time": "2024-02-06T15:20:36.416020800Z"
- },
- "collapsed": false
+ "end_time": "2024-10-29T13:24:12.797109Z",
+ "start_time": "2024-10-29T13:24:12.653493Z"
+ }
},
+ "source": [
+ "results_dict = get_estimator_results(\n",
+ " estimators=classifiers, datasets=datasets, num_resamples=30\n",
+ ")\n",
+ "results_dict[\"HIVECOTEV2\"][\"ItalyPowerDemand\"]"
+ ],
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Results are averaged over 30 stratified resamples.\n",
- " HIVECOTEV2 default train test partition of PigArtPressure = 1.0 and averaged over 30 resamples = 0.9823717948717949\n"
- ]
+ "data": {
+ "text/plain": [
+ "array([0.96987366, 0.96987366, 0.9494655 , 0.96793003, 0.96015549,\n",
+ " 0.96793003, 0.96793003, 0.95626822, 0.96695821, 0.96695821,\n",
+ " 0.96793003, 0.96695821, 0.95724004, 0.94557823, 0.96987366,\n",
+ " 0.96598639, 0.96501458, 0.96015549, 0.9718173 , 0.96793003,\n",
+ " 0.96598639, 0.95626822, 0.96112731, 0.96695821, 0.96209913,\n",
+ " 0.95918367, 0.96209913, 0.95918367, 0.95043732, 0.96598639])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "source": [
- "resamples_all, data_names = get_estimator_results_as_array(\n",
- " estimators=classifiers, default_only=False\n",
- ")\n",
- "print(\"Results are averaged over 30 stratified resamples.\")\n",
- "print(\n",
- " f\" HIVECOTEV2 default train test partition of {data_names[3]} = \"\n",
- " f\"{default_split_all[3][1]} and averaged over 30 resamples = \"\n",
- " f\"{resamples_all[3][1]}\"\n",
- ")"
- ]
+ "execution_count": 6
},
{
+ "metadata": {},
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
"source": [
- "So once you have the results you want, you can compare classifiers with built in aeon\n",
- " tools. For example, you can draw a critical difference diagram [7]. This displays\n",
- " the average rank of each estimator over all datasets. It then groups estimators for\n",
- " which there is no significant difference in rank into cliques, shown with a solid\n",
- " bar. So in the example below with the default train test splits,\n",
- " FreshPRINCEClassifier and WEASEL-Dilation are not significantly different in ranks to\n",
- " InceptionTimeClassifier, but HIVECOTEV2 is significantly better.\n",
- " The diagram below has been performed using pairwise Wilcoxon signed-rank tests and forms cliques using the Holm correction for multiple\n",
- "testing as described in [8, 9]. Alpha value is 0.05 (default value).\n"
+ "Different measures can be recovered, such as accuracy, F1, AUROC, and logloss \n",
+ "using the `measure` parameter. The default is accuracy."
]
},
{
- "cell_type": "code",
- "execution_count": 7,
"metadata": {
"ExecuteTime": {
- "end_time": "2024-02-06T15:20:36.730180100Z",
- "start_time": "2024-02-06T15:20:36.646403900Z"
- },
- "collapsed": false
+ "end_time": "2024-10-29T13:24:13.152370Z",
+ "start_time": "2024-10-29T13:24:12.998570Z"
+ }
},
+ "cell_type": "code",
+ "source": [
+ "results_dict = get_estimator_results(\n",
+ " estimators=classifiers, datasets=datasets, measure=\"logloss\"\n",
+ ")\n",
+ "results_dict[\"HIVECOTEV2\"][\"ItalyPowerDemand\"]"
+ ],
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
- "
"
+ "0.1217826955959029"
]
},
+ "execution_count": 7,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
+ "execution_count": 7
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
"source": [
- "plot = plot_critical_difference(\n",
- " default_split_all, classifiers, test=\"wilcoxon\", correction=\"holm\"\n",
- ")"
+ "Results can also be returned as an array using the `get_estimator_results_as_array` function. \n",
+ "This function shares the same parameters as `get_estimator_results`.\n",
+ "\n",
+ "This function returns the results as a numpy array, where the first dimension is the dataset and \n",
+ "the second dimension is the estimator. The datasets used in the array are returned as a list \n",
+ "alongside the results. \n",
+ "\n",
+ "Multiple resamples will be averaged instead of returned as separate arrays."
]
},
{
- "cell_type": "markdown",
"metadata": {
- "collapsed": false
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:13.457526Z",
+ "start_time": "2024-10-29T13:24:13.252075Z"
+ }
},
+ "cell_type": "code",
"source": [
- "If we use the data averaged over resamples, we can detect differences more clearly.\n",
- "Now we see WEASEL-Dilation and InceptionTimeClassifier are significantly better than the\n",
- "FreshPRINCEClassifier."
- ]
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
+ "\n",
+ "results_arr, datasets = get_estimator_results_as_array(\n",
+ " estimators=classifiers, datasets=datasets\n",
+ ")\n",
+ "results_arr"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.89 , 0.91 , 0.91 , 0.9 ],\n",
+ " [0.62857143, 0.86857143, 0.86285714, 0.85714286],\n",
+ " [0.94 , 1. , 1. , 1. ],\n",
+ " [0.89795918, 0.96987366, 0.96598639, 0.93974733]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 8
},
{
- "cell_type": "code",
- "execution_count": 8,
"metadata": {
"ExecuteTime": {
- "end_time": "2024-02-06T15:20:36.809967Z",
- "start_time": "2024-02-06T15:20:36.730180100Z"
+ "end_time": "2024-10-29T13:24:13.498441Z",
+ "start_time": "2024-10-29T13:24:13.493454Z"
}
},
+ "cell_type": "code",
+ "source": [
+ "datasets"
+ ],
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
- "
"
+ "['ACSF1', 'ArrowHead', 'GunPoint', 'ItalyPowerDemand']"
]
},
+ "execution_count": 9,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
- "source": [
- "plot = plot_critical_difference(\n",
- " resamples_all, classifiers, test=\"wilcoxon\", correction=\"holm\"\n",
- ")"
- ]
+ "execution_count": 9
},
{
- "cell_type": "markdown",
"metadata": {},
+ "cell_type": "markdown",
"source": [
- "If we want to highlight a specific classifier, we have the `highlight` parameter, which is a dict including the classifier that we would like to highlight and the colour selected, such as: `highlight={HIVECOTEV2: \"#8a9bf8\"}`"
+ "By default if a dataset is missing for any estimator, the dataset is removed\n",
+ "from the results and list of datasets. If you want to keep the dataset, use the\n",
+ "`include_missing` parameter. Missing results will be filled with a NaN value."
]
},
{
- "cell_type": "code",
- "execution_count": 9,
"metadata": {
"ExecuteTime": {
- "end_time": "2024-02-06T15:20:36.891747900Z",
- "start_time": "2024-02-06T15:20:36.809967Z"
+ "end_time": "2024-10-29T13:24:13.686913Z",
+ "start_time": "2024-10-29T13:24:13.550278Z"
}
},
+ "cell_type": "code",
+ "source": [
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
+ "\n",
+ "results_arr_miss, datasets = get_estimator_results_as_array(\n",
+ " estimators=classifiers, datasets=datasets + [\"invalid\"], include_missing=True\n",
+ ")\n",
+ "results_arr_miss"
+ ],
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
- "
"
+ "array([[0.89 , 0.91 , 0.91 , 0.9 ],\n",
+ " [0.62857143, 0.86857143, 0.86285714, 0.85714286],\n",
+ " [0.94 , 1. , 1. , 1. ],\n",
+ " [0.89795918, 0.96987366, 0.96598639, 0.93974733],\n",
+ " [ nan, nan, nan, nan]])"
]
},
+ "execution_count": 10,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
- "source": [
- "plot = plot_critical_difference(\n",
- " resamples_all,\n",
- " classifiers,\n",
- " test=\"wilcoxon\",\n",
- " correction=\"holm\",\n",
- " highlight={\"HIVECOTEV2\": \"#8a9bf8\"},\n",
- ")"
- ]
+ "execution_count": 10
},
{
- "cell_type": "markdown",
"metadata": {},
- "source": [
- "Besides plotting differences using the critical difference diagrams, different versions of boxplots can be plotted. Boxplots graphically demonstrates the locality, spread and skewness of the results. In this case, it plot a boxplot of distributions from the median. A value above 0.5 means the algorithm is better than the median accuracy for that particular problem."
- ]
+ "cell_type": "markdown",
+ "source": "For both methods, the default value for `datasets` will load all available datasets for the estimators. We will use this for our later examples."
},
{
- "cell_type": "code",
- "execution_count": 10,
"metadata": {
"ExecuteTime": {
- "end_time": "2024-02-06T15:20:37.111160200Z",
- "start_time": "2024-02-06T15:20:36.890750400Z"
+ "end_time": "2024-10-29T13:24:14.044956Z",
+ "start_time": "2024-10-29T13:24:13.887377Z"
}
},
+ "cell_type": "code",
+ "source": [
+ "results_arr, datasets = get_estimator_results_as_array(\n",
+ " estimators=classifiers, num_resamples=30\n",
+ ")\n",
+ "results_arr"
+ ],
"outputs": [
{
"data": {
- "image/png": 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",
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+ ],
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"
+ },
"metadata": {},
"output_type": "display_data"
}
],
- "source": [
- "plot = plot_boxplot(\n",
- " resamples_all,\n",
- " classifiers,\n",
- " relative=True,\n",
- " plot_type=\"boxplot\",\n",
- " outliers=True,\n",
- " y_min=0.4,\n",
- " y_max=0.6,\n",
- ")"
- ]
+ "execution_count": 12
},
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "Apart from well-known boxplots, different versions can be plotted, depending on the purpose of the user:\n",
- "- `violin` is a hybrid of a boxplot and a kernel density plot, showing peaks in the data.\n",
- "- `swarm` is a scatterplot with points adjusted to be non-overlapping.\n",
- "- `strip` is similar to `swarm` but uses jitter to reduce overplotting.\n",
- "\n",
- "Below, we show an example of the `violin` one, including a title."
- ]
+ "source": "Besides plotting differences using the critical difference diagrams, different versions of boxplots can be plotted. Boxplots graphically demonstrates the locality, spread and skewness of the results."
},
{
"cell_type": "code",
- "execution_count": 12,
"metadata": {
"ExecuteTime": {
- "end_time": "2024-02-06T15:20:37.594867200Z",
- "start_time": "2024-02-06T15:20:37.343539300Z"
+ "end_time": "2024-10-29T13:24:19.368696Z",
+ "start_time": "2024-10-29T13:24:18.313044Z"
}
},
+ "source": [
+ "from aeon.visualisation import plot_boxplot\n",
+ "\n",
+ "plot_boxplot(\n",
+ " results_arr,\n",
+ " classifiers,\n",
+ " plot_type=\"boxplot\",\n",
+ ")"
+ ],
"outputs": [
{
"data": {
- "image/png": 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",
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"
+ },
"metadata": {},
"output_type": "display_data"
}
],
- "source": [
- "plot = plot_boxplot(\n",
- " resamples_all,\n",
- " classifiers,\n",
- " relative=True,\n",
- " plot_type=\"violin\",\n",
- " title=\"Violin plot\",\n",
- ")"
- ]
+ "execution_count": 13
},
{
- "cell_type": "markdown",
"metadata": {},
- "source": [
- "From the critical difference diagram above, we showed that InceptionTimeClassifier is not significantly better than WEASEL-Dilation. Now, if we want to specifically compare the results of these two approaches, we can plot a scatter in which each point is a pair of accuracies of both approaches. The number of W, T, and L is also included per approach in the legend."
- ]
+ "cell_type": "markdown",
+ "source": "From the critical difference diagram above, we showed that InceptionTimeClassifier is not significantly better than RDSTClassifier. Now, if we want to specifically compare the results of these two approaches, we can plot a scatter in which each point is a pair of accuracies of both approaches. The number of W, T, and L is also included per approach in the legend."
},
{
- "cell_type": "code",
- "execution_count": 13,
"metadata": {
"ExecuteTime": {
- "end_time": "2024-02-06T15:20:38.076577900Z",
- "start_time": "2024-02-06T15:20:37.593869400Z"
+ "end_time": "2024-10-29T13:24:19.685876Z",
+ "start_time": "2024-10-29T13:24:19.398616Z"
}
},
+ "cell_type": "code",
+ "source": [
+ "from aeon.visualisation import plot_pairwise_scatter\n",
+ "\n",
+ "plot_pairwise_scatter(\n",
+ " results_arr[:, 2],\n",
+ " results_arr[:, 3],\n",
+ " classifiers[2],\n",
+ " classifiers[3],\n",
+ ")"
+ ],
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\Tony\\AppData\\Local\\Temp\\ipykernel_16436\\401140627.py:13: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
- " fig.show()\n"
- ]
+ "data": {
+ "text/plain": [
+ "(
"
- ]
+ ],
+ "image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "source": [
- "methods = [\"InceptionTimeClassifier\", \"WEASEL-Dilation\"]\n",
- "\n",
- "results, datasets = get_estimator_results_as_array(estimators=methods)\n",
- "results = results.T\n",
- "\n",
- "fig, ax = plot_pairwise_scatter(\n",
- " results[0],\n",
- " results[1],\n",
- " methods[0],\n",
- " methods[1],\n",
- " title=\"Comparison of IT and WEASEL2\",\n",
- ")\n",
- "fig.show()"
- ]
+ "execution_count": 14
},
{
"cell_type": "markdown",
@@ -620,56 +1161,28 @@
"collapsed": false
},
"source": [
- "[timeseriesclassification.com](https://timeseriesclassification.com) has results for classification, clustering and regression. We are constantly\n",
- "updating the results as we generate them. To find out which estimators have results\n",
- " `get_available_estimators`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "# References\n",
- "[1] Middlehurst et al. \"Bake off redux: a review and experimental evaluation of\n",
- "recent time series classification algorithms\", 2023, [arXiv](https://arxiv.org/abs/2304.13029)\n",
+ "## References\n",
+ "\n",
+ "[1] Middlehurst, M., SchΓ€fer, P. and Bagnall, A., 2024. Bake off redux: a review and experimental evaluation of recent time series classification algorithms. Data Mining and Knowledge Discovery, pp.1-74.\n",
"\n",
- "[2] Holder et al. \"A Review and Evaluation of Elastic Distance Functions for Time Series Clustering\", 2023, [arXiv](https://arxiv.org/abs/2205.15181) [KAIS](https://link.springer.com/article/10.1007/s10115-023-01952-0)\n",
+ "[2] Holder, C., Middlehurst, M. and Bagnall, A., 2024. A review and evaluation of elastic distance functions for time series clustering. Knowledge and Information Systems, 66(2), pp.765-809.\n",
"\n",
- "[3] Guijo-Rubio et al. \"Unsupervised Feature Based Algorithms for Time Series\n",
- "Extrinsic Regression\", 2023 [arXiv](https://arxiv.org/abs/2305.01429)\n",
+ "[3] Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D.F. and Bagnall, A., 2024. Unsupervised feature based algorithms for time series extrinsic regression. Data Mining and Knowledge Discovery, pp.1-45.\n",
"\n",
- "[4] Middlehurst and Bagnall, \"The FreshPRINCE: A Simple Transformation Based Pipeline\n",
- " Time Series Classifier\", 2022 [arXiv](https://arxiv.org/abs/2201.12048)\n",
+ "[4] Middlehurst, M. and Bagnall, A., 2022, May. The freshprince: A simple transformation based pipeline time series classifier. In International Conference on Pattern Recognition and Artificial Intelligence (pp. 150-161). Cham: Springer International Publishing.\n",
"\n",
- "[5] Fawaz et al. \"InceptionTime: Finding AlexNet for time series classification\", 2020\n",
- "[DAMI](https://link.springer.com/article/10.1007/s10618-020-00710-y)\n",
+ "[5] Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D.F., Weber, J., Webb, G.I., Idoumghar, L., Muller, P.A. and Petitjean, F., 2020. Inceptiontime: Finding alexnet for time series classification. Data Mining and Knowledge Discovery, 34(6), pp.1936-1962.\n",
"\n",
- "[6] Middlehurst et al. \"HIVE-COTE 2.0: a new meta ensemble for time series\n",
- "classification\", [MACH](https://link.springer.com/article/10.1007/s10994-021-06057-9)\n",
+ "[6] Middlehurst, M., Large, J., Flynn, M., Lines, J., Bostrom, A. and Bagnall, A., 2021. HIVE-COTE 2.0: a new meta ensemble for time series classification. Machine Learning, 110(11), pp.3211-3243.\n",
"\n",
- "[7] SchΓ€fer and Leser, \"WEASEL 2.0 - A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification\", 2023 [arXiv](https://arxiv.org/abs/2301.10194)\n",
+ "[7] Guillaume, A., Vrain, C. and Elloumi, W., 2022, June. Random dilated shapelet transform: A new approach for time series shapelets. In International Conference on Pattern Recognition and Artificial Intelligence (pp. 653-664). Cham: Springer International Publishing.\n",
"\n",
- "[8] GarcΓa and Herrera, \"An extension on 'statistical comparisons of classifiers over multiple data sets' for all pairwise comparisons\", 2008 [JMLR](https://www.jmlr.org/papers/volume9/garcia08a/garcia08a.pdf)\n",
+ "[8] Garcia, S. and Herrera, F., 2008. An Extension on\" Statistical Comparisons of Classifiers over Multiple Data Sets\" for all Pairwise Comparisons. Journal of machine learning research, 9(12).\n",
"\n",
- "[9] Benavoli et al. \"Should We Really Use Post-Hoc Tests Based on Mean-Ranks?\", 2016 [JMLR](https://jmlr.org/papers/v17/benavoli16a.html)\n",
+ "[9] Benavoli, A., Corani, G. and Mangili, F., 2016. Should we really use post-hoc tests based on mean-ranks?. The Journal of Machine Learning Research, 17(1), pp.152-161.\n",
"\n",
- "[10] Demsar, \"Statistical Comparisons of Classifiers\n",
- "over Multiple Data Sets\" [JMLR](https://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf)\n"
+ "[10] DemΕ‘ar, J., 2006. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine learning research, 7, pp.1-30.\n"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "pycharm": {
- "is_executing": true
- }
- },
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/examples/benchmarking/regression.ipynb b/examples/benchmarking/regression.ipynb
index 53ef906341..c0b0f979fd 100644
--- a/examples/benchmarking/regression.ipynb
+++ b/examples/benchmarking/regression.ipynb
@@ -8,13 +8,13 @@
"source": [
"# Benchmarking time series regression models\n",
"\n",
- "Time series extrinsic regression, first properly defined in [1] then recently\n",
- "extended in [2], involves predicting a continuous target variable based on a time\n",
+ "Time series extrinsic regression, first properly defined in [[1]](#references) then recently\n",
+ "extended in [[2]](#references), involves predicting a continuous target variable based on a time\n",
"series. It differs from time series forecasting regression in that the target is\n",
"not formed from a sliding window, but is some external variable.\n",
"\n",
- "This notebook shows you how to use aeon to get benchmarking datasets with aeon and how\n",
- " to compare results on these datasets with those published in [2]."
+ "This notebook shows you how to use `aeon` to get benchmarking datasets and how\n",
+ " to compare results on these datasets with those published in [[2]](#references)."
]
},
{
@@ -25,138 +25,195 @@
"source": [
"## Loading/Downloading data\n",
"\n",
- "aeon comes with two regression problems in the datasets module. You can load these\n",
- "with single problem loaders or the more general load_regression function."
+ "`aeon` comes with two regression problems built-in the datasets module.\n",
+ "`load_covid_3month` loads a univariate regression problem, while `load_cardano_sentiment`\n",
+ "loads a multivariate regression problem.\n"
]
},
{
- "cell_type": "code",
- "execution_count": 14,
"metadata": {
- "collapsed": false
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:23:55.892932Z",
+ "start_time": "2024-10-29T13:23:49.338194Z"
+ }
},
+ "cell_type": "code",
+ "source": [
+ "from aeon.datasets import load_covid_3month\n",
+ "\n",
+ "X, y = load_covid_3month()\n",
+ "X.shape"
+ ],
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(140, 1, 84) (61, 1, 84) (107, 2, 24)\n"
- ]
+ "data": {
+ "text/plain": [
+ "(201, 1, 84)"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "source": [
- "from aeon.datasets import load_cardano_sentiment, load_covid_3month, load_regression\n",
- "\n",
- "trainX, trainy = load_covid_3month(split=\"train\")\n",
- "testX, testy = load_regression(split=\"test\", name=\"Covid3Month\")\n",
- "X, y = load_cardano_sentiment() # Combines train and test splits\n",
- "print(trainX.shape, testX.shape, X.shape)"
- ]
+ "execution_count": 1
},
{
- "cell_type": "markdown",
"metadata": {
- "collapsed": false
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:23:55.912879Z",
+ "start_time": "2024-10-29T13:23:55.903874Z"
+ }
},
- "source": [
- "there are currently 63 problems in the TSER archive hosted on\n",
- "timeseriesclassification.com. These are listed in the file datasets.tser_datasets"
- ]
- },
- {
"cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "source": [
+ "from aeon.datasets import load_cardano_sentiment\n",
+ "\n",
+ "X, y = load_cardano_sentiment()\n",
+ "X.shape"
+ ],
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['AcousticContaminationMadrid', 'AluminiumConcentration', 'AppliancesEnergy', 'AustraliaRainfall', 'BIDMC32HR', 'BIDMC32RR', 'BIDMC32SpO2', 'BarCrawl6min', 'BeijingIntAirportPM25Quality', 'BeijingPM10Quality', 'BeijingPM25Quality', 'BenzeneConcentration', 'BinanceCoinSentiment', 'BitcoinSentiment', 'BoronConcentration', 'CalciumConcentration', 'CardanoSentiment', 'ChilledWaterPredictor', 'CopperConcentration', 'Covid19Andalusia', 'Covid3Month', 'DailyOilGasPrices', 'DailyTemperatureLatitude', 'DhakaHourlyAirQuality', 'ElectricMotorTemperature', 'ElectricityPredictor', 'EthereumSentiment', 'FloodModeling1', 'FloodModeling2', 'FloodModeling3', 'GasSensorArrayAcetone', 'GasSensorArrayEthanol', 'HotwaterPredictor', 'HouseholdPowerConsumption1', 'HouseholdPowerConsumption2', 'IEEEPPG', 'IronConcentration', 'LPGasMonitoringHomeActivity', 'LiveFuelMoistureContent', 'MadridPM10Quality', 'MagnesiumConcentration', 'ManganeseConcentration', 'MethaneMonitoringHomeActivity', 'MetroInterstateTrafficVolume', 'NaturalGasPricesSentiment', 'NewsHeadlineSentiment', 'NewsTitleSentiment', 'OccupancyDetectionLight', 'PPGDalia', 'ParkingBirmingham', 'PhosphorusConcentration', 'PotassiumConcentration', 'PrecipitationAndalusia', 'SierraNevadaMountainsSnow', 'SodiumConcentration', 'SolarRadiationAndalusia', 'SteamPredictor', 'SulphurConcentration', 'TetuanEnergyConsumption', 'VentilatorPressure', 'WaveDataTension', 'WindTurbinePower', 'ZincConcentration']\n"
- ]
+ "data": {
+ "text/plain": [
+ "(107, 2, 24)"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "source": [
- "from aeon.datasets.tser_datasets import tser_soton\n",
- "\n",
- "print(sorted(list(tser_soton)))"
- ]
+ "execution_count": 2
},
{
+ "metadata": {},
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "You can download these datasets directly with aeon load_regression function. By\n",
- "default it will store the data in a directory called \"local_data\" in the datasets\n",
- "module. Set ``extract_path`` to specify a different location.\n"
- ]
+ "source": "The datasets used in [[2]](#references) can be found in the `tser_soton` list in the datasets module. `tser_soton_clean` includes modifiers such as _eq and _nmv for datasets that were originally unequal length or had missing values. These are cleaned versions of the datasets."
},
{
- "cell_type": "code",
- "execution_count": 16,
"metadata": {
- "collapsed": false
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:23:56.479407Z",
+ "start_time": "2024-10-29T13:23:56.474411Z"
+ }
},
+ "cell_type": "code",
+ "source": [
+ "from aeon.datasets.tser_datasets import tser_soton\n",
+ "\n",
+ "tser_soton"
+ ],
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CardanoSentiment (107, 2, 24) (107,)\n",
- "Covid3Month (201, 1, 84) (201,)\n"
- ]
+ "data": {
+ "text/plain": [
+ "['AcousticContaminationMadrid',\n",
+ " 'AluminiumConcentration',\n",
+ " 'AppliancesEnergy',\n",
+ " 'AustraliaRainfall',\n",
+ " 'BarCrawl6min',\n",
+ " 'BeijingIntAirportPM25Quality',\n",
+ " 'BeijingPM10Quality',\n",
+ " 'BeijingPM25Quality',\n",
+ " 'BenzeneConcentration',\n",
+ " 'BIDMC32HR',\n",
+ " 'BIDMC32RR',\n",
+ " 'BIDMC32SpO2',\n",
+ " 'BinanceCoinSentiment',\n",
+ " 'BitcoinSentiment',\n",
+ " 'BoronConcentration',\n",
+ " 'CalciumConcentration',\n",
+ " 'CardanoSentiment',\n",
+ " 'ChilledWaterPredictor',\n",
+ " 'CopperConcentration',\n",
+ " 'Covid19Andalusia',\n",
+ " 'Covid3Month',\n",
+ " 'DailyOilGasPrices',\n",
+ " 'DailyTemperatureLatitude',\n",
+ " 'DhakaHourlyAirQuality',\n",
+ " 'ElectricityPredictor',\n",
+ " 'ElectricMotorTemperature',\n",
+ " 'EthereumSentiment',\n",
+ " 'FloodModeling1',\n",
+ " 'FloodModeling2',\n",
+ " 'FloodModeling3',\n",
+ " 'GasSensorArrayAcetone',\n",
+ " 'GasSensorArrayEthanol',\n",
+ " 'HotwaterPredictor',\n",
+ " 'HouseholdPowerConsumption1',\n",
+ " 'HouseholdPowerConsumption2',\n",
+ " 'IEEEPPG',\n",
+ " 'IronConcentration',\n",
+ " 'LiveFuelMoistureContent',\n",
+ " 'LPGasMonitoringHomeActivity',\n",
+ " 'MadridPM10Quality',\n",
+ " 'MagnesiumConcentration',\n",
+ " 'ManganeseConcentration',\n",
+ " 'MethaneMonitoringHomeActivity',\n",
+ " 'MetroInterstateTrafficVolume',\n",
+ " 'NaturalGasPricesSentiment',\n",
+ " 'NewsHeadlineSentiment',\n",
+ " 'NewsTitleSentiment',\n",
+ " 'OccupancyDetectionLight',\n",
+ " 'ParkingBirmingham',\n",
+ " 'PhosphorusConcentration',\n",
+ " 'PotassiumConcentration',\n",
+ " 'PPGDalia',\n",
+ " 'PrecipitationAndalusia',\n",
+ " 'SierraNevadaMountainsSnow',\n",
+ " 'SodiumConcentration',\n",
+ " 'SolarRadiationAndalusia',\n",
+ " 'SteamPredictor',\n",
+ " 'SulphurConcentration',\n",
+ " 'TetuanEnergyConsumption',\n",
+ " 'VentilatorPressure',\n",
+ " 'WaveDataTension',\n",
+ " 'WindTurbinePower',\n",
+ " 'ZincConcentration']"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "source": [
- "small_problems = [\n",
- " \"CardanoSentiment\",\n",
- " \"Covid3Month\",\n",
- "]\n",
- "\n",
- "for problem in small_problems:\n",
- " X, y = load_regression(name=problem)\n",
- " print(problem, X.shape, y.shape)"
- ]
+ "execution_count": 3
},
{
+ "metadata": {},
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "This stores the data in a format like this\n",
- "\n",
- "If you call the function again, it will load\n",
- "from disk rather than downloading\n",
- "again.\n",
- " You can specify train/test splits."
- ]
+ "source": "You can download and load these problems from `.ts` files with the `load_regression` function. `load_from_tsfile` is also available if you want to load from file."
},
{
- "cell_type": "code",
- "execution_count": 17,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:23:56.586140Z",
+ "start_time": "2024-10-29T13:23:56.488375Z"
+ }
},
+ "cell_type": "code",
+ "source": [
+ "from aeon.datasets import load_regression\n",
+ "\n",
+ "X_train_bc, y_train_bc = load_regression(\"BarCrawl6min\", split=\"train\")\n",
+ "X_test_bc, y_test_bc = load_regression(\"BarCrawl6min\", split=\"test\")\n",
+ "(X_train_bc.shape, y_train_bc.shape), (X_test_bc.shape, y_test_bc.shape)"
+ ],
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CardanoSentiment (201, 1, 84) (201,)\n",
- "Covid3Month (201, 1, 84) (201,)\n"
- ]
+ "data": {
+ "text/plain": [
+ "(((140, 3, 360), (140,)), ((61, 3, 360), (61,)))"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "source": [
- "for problem in small_problems:\n",
- " trainX, trainy = load_regression(name=problem, split=\"train\")\n",
- " print(problem, X.shape, y.shape)"
- ]
+ "execution_count": 4
},
{
"cell_type": "markdown",
@@ -167,42 +224,62 @@
"## Evaluating a regressor on benchmark data\n",
"\n",
"With the data, it is easy to assess an algorithm performance. We will use the\n",
- "DummyRegressor as a baseline, and the default scoring\n",
- "\n"
+ "`DummyRegressor` as a baseline which takes the mean of the target, and use R2 score\n",
+ " as our scoring metric.\n",
+ "\n",
+ "This should be familiar to anyone who has used `scikit-learn` before."
]
},
{
"cell_type": "code",
- "execution_count": 18,
"metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CardanoSentiment Dummy score = 0.09015657223327135\n",
- "Covid3Month Dummy score = 0.0019998715745554777\n"
- ]
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:10.310331Z",
+ "start_time": "2024-10-29T13:23:56.597111Z"
}
- ],
+ },
"source": [
- "from sklearn.metrics import mean_squared_error\n",
+ "from sklearn.metrics import r2_score\n",
"\n",
"from aeon.regression import DummyRegressor\n",
"\n",
+ "small_problems = [\n",
+ " \"BarCrawl6min\",\n",
+ " \"CardanoSentiment\",\n",
+ " \"Covid3Month\",\n",
+ " \"ParkingBirmingham\",\n",
+ " \"FloodModeling1\",\n",
+ "]\n",
+ "\n",
"dummy = DummyRegressor()\n",
"performance = []\n",
"for problem in small_problems:\n",
- " trainX, trainy = load_regression(name=problem, split=\"train\")\n",
- " dummy.fit(trainX, trainy)\n",
- " testX, testy = load_regression(name=problem, split=\"test\")\n",
- " predictions = dummy.predict(testX)\n",
- " mse = mean_squared_error(testy, predictions)\n",
+ " X_train, y_train = load_regression(problem, split=\"train\")\n",
+ " X_test, y_test = load_regression(problem, split=\"test\")\n",
+ "\n",
+ " dummy.fit(X_train, y_train)\n",
+ " pred = dummy.predict(X_test)\n",
+ "\n",
+ " mse = r2_score(y_test, pred)\n",
" performance.append(mse)\n",
- " print(problem, \" Dummy score = \", mse)"
- ]
+ "\n",
+ " print(f\"{problem}: {mse}\")"
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "BarCrawl6min: -0.012403529243674827\n",
+ "CardanoSentiment: -0.21576890538877036\n",
+ "Covid3Month: -0.004303695576216793\n",
+ "ParkingBirmingham: -0.0036438945855674643\n",
+ "FloodModeling1: -0.0016071827276951112\n"
+ ]
+ }
+ ],
+ "execution_count": 5
},
{
"cell_type": "markdown",
@@ -210,137 +287,300 @@
"collapsed": false
},
"source": [
- "## Comparing to published results\n",
+ "## Comparing to reference/published results\n",
+ "\n",
+ "How do the dummy results compare to the published results in [[2]](#references)? We can use the \n",
+ "`get_estimator_results` and `get_estimator_results_as_array` methods to load \n",
+ "published results.\n",
"\n",
- "How does the dummy compare to the published results in [2]? We can use the method\n",
- "get_estimator_results to obtain published results."
+ "`get_available_estimators` will show you the available estimators with stored\n",
+ " results the task."
]
},
{
- "cell_type": "code",
- "execution_count": 19,
"metadata": {
- "collapsed": false
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:10.457810Z",
+ "start_time": "2024-10-29T13:24:10.370922Z"
+ }
},
+ "cell_type": "code",
+ "source": [
+ "from aeon.benchmarking.results_loaders import get_available_estimators\n",
+ "\n",
+ "get_available_estimators(task=\"regression\")"
+ ],
"outputs": [
{
- "ename": "URLError",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[1;31mSSLCertVerificationError\u001B[0m Traceback (most recent call last)",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\urllib\\request.py:1346\u001B[0m, in \u001B[0;36mAbstractHTTPHandler.do_open\u001B[1;34m(self, http_class, req, **http_conn_args)\u001B[0m\n\u001B[0;32m 1345\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 1346\u001B[0m \u001B[43mh\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreq\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_method\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreq\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mselector\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreq\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1347\u001B[0m \u001B[43m \u001B[49m\u001B[43mencode_chunked\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreq\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mhas_header\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mTransfer-encoding\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1348\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mOSError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err: \u001B[38;5;66;03m# timeout error\u001B[39;00m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\http\\client.py:1285\u001B[0m, in \u001B[0;36mHTTPConnection.request\u001B[1;34m(self, method, url, body, headers, encode_chunked)\u001B[0m\n\u001B[0;32m 1284\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001B[39;00m\n\u001B[1;32m-> 1285\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_send_request\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mencode_chunked\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\http\\client.py:1331\u001B[0m, in \u001B[0;36mHTTPConnection._send_request\u001B[1;34m(self, method, url, body, headers, encode_chunked)\u001B[0m\n\u001B[0;32m 1330\u001B[0m body \u001B[38;5;241m=\u001B[39m _encode(body, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbody\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m-> 1331\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mendheaders\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mencode_chunked\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mencode_chunked\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\http\\client.py:1280\u001B[0m, in \u001B[0;36mHTTPConnection.endheaders\u001B[1;34m(self, message_body, encode_chunked)\u001B[0m\n\u001B[0;32m 1279\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m CannotSendHeader()\n\u001B[1;32m-> 1280\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_send_output\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmessage_body\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mencode_chunked\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mencode_chunked\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\http\\client.py:1040\u001B[0m, in \u001B[0;36mHTTPConnection._send_output\u001B[1;34m(self, message_body, encode_chunked)\u001B[0m\n\u001B[0;32m 1039\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_buffer[:]\n\u001B[1;32m-> 1040\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmsg\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1042\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m message_body \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 1043\u001B[0m \n\u001B[0;32m 1044\u001B[0m \u001B[38;5;66;03m# create a consistent interface to message_body\u001B[39;00m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\http\\client.py:980\u001B[0m, in \u001B[0;36mHTTPConnection.send\u001B[1;34m(self, data)\u001B[0m\n\u001B[0;32m 979\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mauto_open:\n\u001B[1;32m--> 980\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconnect\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 981\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\http\\client.py:1454\u001B[0m, in \u001B[0;36mHTTPSConnection.connect\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 1452\u001B[0m server_hostname \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhost\n\u001B[1;32m-> 1454\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msock \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_context\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwrap_socket\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msock\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1455\u001B[0m \u001B[43m \u001B[49m\u001B[43mserver_hostname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mserver_hostname\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\ssl.py:500\u001B[0m, in \u001B[0;36mSSLContext.wrap_socket\u001B[1;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001B[0m\n\u001B[0;32m 494\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mwrap_socket\u001B[39m(\u001B[38;5;28mself\u001B[39m, sock, server_side\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m 495\u001B[0m do_handshake_on_connect\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m 496\u001B[0m suppress_ragged_eofs\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m 497\u001B[0m server_hostname\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, session\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[0;32m 498\u001B[0m \u001B[38;5;66;03m# SSLSocket class handles server_hostname encoding before it calls\u001B[39;00m\n\u001B[0;32m 499\u001B[0m \u001B[38;5;66;03m# ctx._wrap_socket()\u001B[39;00m\n\u001B[1;32m--> 500\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msslsocket_class\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_create\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 501\u001B[0m \u001B[43m \u001B[49m\u001B[43msock\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msock\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 502\u001B[0m \u001B[43m \u001B[49m\u001B[43mserver_side\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mserver_side\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 503\u001B[0m \u001B[43m \u001B[49m\u001B[43mdo_handshake_on_connect\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdo_handshake_on_connect\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 504\u001B[0m \u001B[43m \u001B[49m\u001B[43msuppress_ragged_eofs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msuppress_ragged_eofs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 505\u001B[0m \u001B[43m \u001B[49m\u001B[43mserver_hostname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mserver_hostname\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 506\u001B[0m \u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m 507\u001B[0m \u001B[43m \u001B[49m\u001B[43msession\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msession\u001B[49m\n\u001B[0;32m 508\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\ssl.py:1040\u001B[0m, in \u001B[0;36mSSLSocket._create\u001B[1;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001B[0m\n\u001B[0;32m 1039\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdo_handshake_on_connect should not be specified for non-blocking sockets\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m-> 1040\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdo_handshake\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1041\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (\u001B[38;5;167;01mOSError\u001B[39;00m, \u001B[38;5;167;01mValueError\u001B[39;00m):\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\ssl.py:1309\u001B[0m, in \u001B[0;36mSSLSocket.do_handshake\u001B[1;34m(self, block)\u001B[0m\n\u001B[0;32m 1308\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msettimeout(\u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m-> 1309\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_sslobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdo_handshake\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1310\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n",
- "\u001B[1;31mSSLCertVerificationError\u001B[0m: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1129)",
- "\nDuring handling of the above exception, another exception occurred:\n",
- "\u001B[1;31mURLError\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[1;32mIn[19], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01maeon\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mbenchmarking\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m get_available_estimators, get_estimator_results\n\u001B[1;32m----> 3\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[43mget_available_estimators\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtask\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mregression\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m)\n\u001B[0;32m 4\u001B[0m results \u001B[38;5;241m=\u001B[39m get_estimator_results(\n\u001B[0;32m 5\u001B[0m estimators\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mDrCIF\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFreshPRINCE\u001B[39m\u001B[38;5;124m\"\u001B[39m],\n\u001B[0;32m 6\u001B[0m task\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mregression\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 7\u001B[0m datasets\u001B[38;5;241m=\u001B[39msmall_problems,\n\u001B[0;32m 8\u001B[0m measure\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmse\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 9\u001B[0m )\n\u001B[0;32m 10\u001B[0m \u001B[38;5;28mprint\u001B[39m(results)\n",
- "File \u001B[1;32mC:\\Code\\aeon\\aeon\\benchmarking\\results_loaders.py:240\u001B[0m, in \u001B[0;36mget_available_estimators\u001B[1;34m(task, return_dataframe)\u001B[0m\n\u001B[0;32m 233\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m 234\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m task \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mt\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m is not available on tsc.com, must be one of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mVALID_TASK_TYPES\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 235\u001B[0m )\n\u001B[0;32m 236\u001B[0m path \u001B[38;5;241m=\u001B[39m (\n\u001B[0;32m 237\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhttps://timeseriesclassification.com/results/ReferenceResults/\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 238\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mt\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/estimators.txt\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 239\u001B[0m )\n\u001B[1;32m--> 240\u001B[0m data \u001B[38;5;241m=\u001B[39m \u001B[43mpd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread_csv\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpath\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 241\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m return_dataframe:\n\u001B[0;32m 242\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m data\n",
- "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:912\u001B[0m, in \u001B[0;36mread_csv\u001B[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001B[0m\n\u001B[0;32m 899\u001B[0m kwds_defaults \u001B[38;5;241m=\u001B[39m _refine_defaults_read(\n\u001B[0;32m 900\u001B[0m dialect,\n\u001B[0;32m 901\u001B[0m delimiter,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 908\u001B[0m dtype_backend\u001B[38;5;241m=\u001B[39mdtype_backend,\n\u001B[0;32m 909\u001B[0m )\n\u001B[0;32m 910\u001B[0m kwds\u001B[38;5;241m.\u001B[39mupdate(kwds_defaults)\n\u001B[1;32m--> 912\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_read\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:577\u001B[0m, in \u001B[0;36m_read\u001B[1;34m(filepath_or_buffer, kwds)\u001B[0m\n\u001B[0;32m 574\u001B[0m _validate_names(kwds\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnames\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[0;32m 576\u001B[0m \u001B[38;5;66;03m# Create the parser.\u001B[39;00m\n\u001B[1;32m--> 577\u001B[0m parser \u001B[38;5;241m=\u001B[39m TextFileReader(filepath_or_buffer, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwds)\n\u001B[0;32m 579\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mor\u001B[39;00m iterator:\n\u001B[0;32m 580\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m parser\n",
- "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1407\u001B[0m, in \u001B[0;36mTextFileReader.__init__\u001B[1;34m(self, f, engine, **kwds)\u001B[0m\n\u001B[0;32m 1404\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moptions[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhas_index_names\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m kwds[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhas_index_names\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m 1406\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles: IOHandles \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m-> 1407\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_make_engine\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mengine\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1661\u001B[0m, in \u001B[0;36mTextFileReader._make_engine\u001B[1;34m(self, f, engine)\u001B[0m\n\u001B[0;32m 1659\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mode:\n\u001B[0;32m 1660\u001B[0m mode \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m-> 1661\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles \u001B[38;5;241m=\u001B[39m \u001B[43mget_handle\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 1662\u001B[0m \u001B[43m \u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1663\u001B[0m \u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1664\u001B[0m \u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mencoding\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1665\u001B[0m \u001B[43m \u001B[49m\u001B[43mcompression\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcompression\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1666\u001B[0m \u001B[43m \u001B[49m\u001B[43mmemory_map\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmemory_map\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1667\u001B[0m \u001B[43m \u001B[49m\u001B[43mis_text\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mis_text\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1668\u001B[0m \u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mencoding_errors\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mstrict\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1669\u001B[0m \u001B[43m \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mstorage_options\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1670\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1671\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 1672\u001B[0m f \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles\u001B[38;5;241m.\u001B[39mhandle\n",
- "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\pandas\\io\\common.py:716\u001B[0m, in \u001B[0;36mget_handle\u001B[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001B[0m\n\u001B[0;32m 713\u001B[0m codecs\u001B[38;5;241m.\u001B[39mlookup_error(errors)\n\u001B[0;32m 715\u001B[0m \u001B[38;5;66;03m# open URLs\u001B[39;00m\n\u001B[1;32m--> 716\u001B[0m ioargs \u001B[38;5;241m=\u001B[39m \u001B[43m_get_filepath_or_buffer\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 717\u001B[0m \u001B[43m \u001B[49m\u001B[43mpath_or_buf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 718\u001B[0m \u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 719\u001B[0m \u001B[43m \u001B[49m\u001B[43mcompression\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcompression\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 720\u001B[0m \u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 721\u001B[0m \u001B[43m \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstorage_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 722\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 724\u001B[0m handle \u001B[38;5;241m=\u001B[39m ioargs\u001B[38;5;241m.\u001B[39mfilepath_or_buffer\n\u001B[0;32m 725\u001B[0m handles: \u001B[38;5;28mlist\u001B[39m[BaseBuffer]\n",
- "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\pandas\\io\\common.py:368\u001B[0m, in \u001B[0;36m_get_filepath_or_buffer\u001B[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001B[0m\n\u001B[0;32m 366\u001B[0m \u001B[38;5;66;03m# assuming storage_options is to be interpreted as headers\u001B[39;00m\n\u001B[0;32m 367\u001B[0m req_info \u001B[38;5;241m=\u001B[39m urllib\u001B[38;5;241m.\u001B[39mrequest\u001B[38;5;241m.\u001B[39mRequest(filepath_or_buffer, headers\u001B[38;5;241m=\u001B[39mstorage_options)\n\u001B[1;32m--> 368\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[43murlopen\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreq_info\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mas\u001B[39;00m req:\n\u001B[0;32m 369\u001B[0m content_encoding \u001B[38;5;241m=\u001B[39m req\u001B[38;5;241m.\u001B[39mheaders\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mContent-Encoding\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m 370\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m content_encoding \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgzip\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m 371\u001B[0m \u001B[38;5;66;03m# Override compression based on Content-Encoding header\u001B[39;00m\n",
- "File \u001B[1;32mC:\\Code\\aeon\\venv\\lib\\site-packages\\pandas\\io\\common.py:270\u001B[0m, in \u001B[0;36murlopen\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 264\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 265\u001B[0m \u001B[38;5;124;03mLazy-import wrapper for stdlib urlopen, as that imports a big chunk of\u001B[39;00m\n\u001B[0;32m 266\u001B[0m \u001B[38;5;124;03mthe stdlib.\u001B[39;00m\n\u001B[0;32m 267\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 268\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01murllib\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mrequest\u001B[39;00m\n\u001B[1;32m--> 270\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m urllib\u001B[38;5;241m.\u001B[39mrequest\u001B[38;5;241m.\u001B[39murlopen(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\urllib\\request.py:214\u001B[0m, in \u001B[0;36murlopen\u001B[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001B[0m\n\u001B[0;32m 212\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 213\u001B[0m opener \u001B[38;5;241m=\u001B[39m _opener\n\u001B[1;32m--> 214\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mopener\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mopen\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\urllib\\request.py:517\u001B[0m, in \u001B[0;36mOpenerDirector.open\u001B[1;34m(self, fullurl, data, timeout)\u001B[0m\n\u001B[0;32m 514\u001B[0m req \u001B[38;5;241m=\u001B[39m meth(req)\n\u001B[0;32m 516\u001B[0m sys\u001B[38;5;241m.\u001B[39maudit(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124murllib.Request\u001B[39m\u001B[38;5;124m'\u001B[39m, req\u001B[38;5;241m.\u001B[39mfull_url, req\u001B[38;5;241m.\u001B[39mdata, req\u001B[38;5;241m.\u001B[39mheaders, req\u001B[38;5;241m.\u001B[39mget_method())\n\u001B[1;32m--> 517\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_open\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreq\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 519\u001B[0m \u001B[38;5;66;03m# post-process response\u001B[39;00m\n\u001B[0;32m 520\u001B[0m meth_name \u001B[38;5;241m=\u001B[39m protocol\u001B[38;5;241m+\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_response\u001B[39m\u001B[38;5;124m\"\u001B[39m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\urllib\\request.py:534\u001B[0m, in \u001B[0;36mOpenerDirector._open\u001B[1;34m(self, req, data)\u001B[0m\n\u001B[0;32m 531\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n\u001B[0;32m 533\u001B[0m protocol \u001B[38;5;241m=\u001B[39m req\u001B[38;5;241m.\u001B[39mtype\n\u001B[1;32m--> 534\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_chain\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mhandle_open\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mprotocol\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mprotocol\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m+\u001B[39;49m\n\u001B[0;32m 535\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_open\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreq\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 536\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m result:\n\u001B[0;32m 537\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\urllib\\request.py:494\u001B[0m, in \u001B[0;36mOpenerDirector._call_chain\u001B[1;34m(self, chain, kind, meth_name, *args)\u001B[0m\n\u001B[0;32m 492\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m handler \u001B[38;5;129;01min\u001B[39;00m handlers:\n\u001B[0;32m 493\u001B[0m func \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mgetattr\u001B[39m(handler, meth_name)\n\u001B[1;32m--> 494\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 495\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m result \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 496\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\urllib\\request.py:1389\u001B[0m, in \u001B[0;36mHTTPSHandler.https_open\u001B[1;34m(self, req)\u001B[0m\n\u001B[0;32m 1388\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mhttps_open\u001B[39m(\u001B[38;5;28mself\u001B[39m, req):\n\u001B[1;32m-> 1389\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdo_open\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhttp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mHTTPSConnection\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreq\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1390\u001B[0m \u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_context\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcheck_hostname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_check_hostname\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\urllib\\request.py:1349\u001B[0m, in \u001B[0;36mAbstractHTTPHandler.do_open\u001B[1;34m(self, http_class, req, **http_conn_args)\u001B[0m\n\u001B[0;32m 1346\u001B[0m h\u001B[38;5;241m.\u001B[39mrequest(req\u001B[38;5;241m.\u001B[39mget_method(), req\u001B[38;5;241m.\u001B[39mselector, req\u001B[38;5;241m.\u001B[39mdata, headers,\n\u001B[0;32m 1347\u001B[0m encode_chunked\u001B[38;5;241m=\u001B[39mreq\u001B[38;5;241m.\u001B[39mhas_header(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTransfer-encoding\u001B[39m\u001B[38;5;124m'\u001B[39m))\n\u001B[0;32m 1348\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mOSError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err: \u001B[38;5;66;03m# timeout error\u001B[39;00m\n\u001B[1;32m-> 1349\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m URLError(err)\n\u001B[0;32m 1350\u001B[0m r \u001B[38;5;241m=\u001B[39m h\u001B[38;5;241m.\u001B[39mgetresponse()\n\u001B[0;32m 1351\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m:\n",
- "\u001B[1;31mURLError\u001B[0m: "
- ]
+ "data": {
+ "text/plain": [
+ " regression\n",
+ "0 1NN-DTW\n",
+ "1 1NN-ED\n",
+ "2 5NN-DTW\n",
+ "3 5NN-ED\n",
+ "4 CNN\n",
+ "5 DrCIF\n",
+ "6 FCN\n",
+ "7 FPCR\n",
+ "8 FPCR-b-spline\n",
+ "9 FreshPRINCE\n",
+ "10 GridSVR\n",
+ "11 InceptionTime\n",
+ "12 RandF\n",
+ "13 ResNet\n",
+ "14 Ridge\n",
+ "15 ROCKET\n",
+ "16 RotF\n",
+ "17 SingleInceptionTime\n",
+ "18 XGBoost"
+ ],
+ "text/html": [
+ "
\n",
+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
regression
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+ "
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+ " \n",
+ " \n",
+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
XGBoost
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+ "
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+ " \n",
+ "
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+ "
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+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
+ "execution_count": 6
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "`get_estimator_results` will load the results to a dictionary of dictionaries with regressors and datasets as the keys. ParkingBirmingham was originally unequal length, so we remove the _eq from the end of the results key to match the dataset name using a parameter."
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:10.577517Z",
+ "start_time": "2024-10-29T13:24:10.510668Z"
+ }
+ },
"source": [
- "from aeon.benchmarking import get_available_estimators, get_estimator_results\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results\n",
"\n",
- "print(get_available_estimators(task=\"regression\"))\n",
- "results = get_estimator_results(\n",
- " estimators=[\"DrCIF\", \"FreshPRINCE\"],\n",
- " task=\"regression\",\n",
+ "regressors = [\"DrCIF\", \"FreshPRINCE\"]\n",
+ "results_dict = get_estimator_results(\n",
+ " estimators=regressors,\n",
" datasets=small_problems,\n",
- " measure=\"mse\",\n",
+ " num_resamples=1,\n",
+ " task=\"regression\",\n",
+ " measure=\"r2\",\n",
+ " remove_dataset_modifiers=True,\n",
")\n",
- "print(results)"
- ]
+ "results_dict"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'DrCIF': {'BarCrawl6min': 0.44579994072037,\n",
+ " 'CardanoSentiment': -0.3243974127240141,\n",
+ " 'Covid3Month': 0.0710586822956002,\n",
+ " 'ParkingBirmingham': 0.7139985894810016,\n",
+ " 'FloodModeling1': 0.8965591366052275},\n",
+ " 'FreshPRINCE': {'BarCrawl6min': 0.3936563948097531,\n",
+ " 'CardanoSentiment': -0.1300226564057678,\n",
+ " 'Covid3Month': 0.1888015511133555,\n",
+ " 'ParkingBirmingham': 0.7304688812606739,\n",
+ " 'FloodModeling1': 0.9296442893073922}}"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 7
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
- "source": [
- "this is organised as a dictionary of dictionaries. because we cannot be sure all\n",
- "results are present for all datasets."
- ]
+ "source": "`get_estimator_results` will instead load an array. Note that if multiple resamples are loaded, the results will be averaged using this function."
},
{
- "cell_type": "code",
- "execution_count": null,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:10.671430Z",
+ "start_time": "2024-10-29T13:24:10.605441Z"
+ }
},
- "outputs": [],
+ "cell_type": "code",
"source": [
- "from aeon.benchmarking import get_estimator_results_as_array\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
"\n",
- "results, names = get_estimator_results_as_array(\n",
- " estimators=[\"DrCIF\", \"FreshPRINCE\"],\n",
- " task=\"regression\",\n",
+ "results_arr, names = get_estimator_results_as_array(\n",
+ " estimators=regressors,\n",
" datasets=small_problems,\n",
- " measure=\"mse\",\n",
+ " num_resamples=1,\n",
+ " task=\"regression\",\n",
+ " measure=\"r2\",\n",
+ " remove_dataset_modifiers=True,\n",
")\n",
- "print(results)\n",
- "print(names)"
- ]
+ "results_arr"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.44579994, 0.39365639],\n",
+ " [-0.32439741, -0.13002266],\n",
+ " [ 0.07105868, 0.18880155],\n",
+ " [ 0.71399859, 0.73046888],\n",
+ " [ 0.89655914, 0.92964429]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 8
},
{
+ "metadata": {},
"cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "we just need to align our results from the website so they are aligned with the\n",
- "results from our dummy regressor"
- ]
+ "source": "The names of the datasets are also returned as missing values are removed from the results by default. Each row corresponds to a dataset and each column to a regressor."
},
{
+ "metadata": {},
"cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "source": [
+ "names"
+ ],
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "('CardanoSentiment', 'Covid3Month')\n",
- "[[0.09821203 0.08379797]\n",
- " [0.0018498 0.00161534]]\n"
- ]
+ "data": {
+ "text/plain": [
+ "['BarCrawl6min',\n",
+ " 'CardanoSentiment',\n",
+ " 'Covid3Month',\n",
+ " 'ParkingBirmingham',\n",
+ " 'FloodModeling1']"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
+ "execution_count": 9
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
"source": [
- "import numpy as np\n",
- "\n",
- "paired_sorted = sorted(zip(names, results))\n",
- "names, _ = zip(*paired_sorted)\n",
- "sorted_rows = [row for _, row in paired_sorted]\n",
- "sorted_results = np.array(sorted_rows)\n",
- "print(names)\n",
- "print(sorted_results)"
+ "we just need to align our results from the website so they are aligned with the\n",
+ "results from our dummy regressor"
]
},
{
@@ -354,31 +594,37 @@
},
{
"cell_type": "code",
- "execution_count": 21,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:10.748218Z",
+ "start_time": "2024-10-29T13:24:10.742209Z"
+ }
},
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "regressors = [\"DrCIF\", \"FreshPRINCE\", \"Dummy\"]\n",
+ "results = np.concatenate([results_arr, np.array(performance)[:, np.newaxis]], axis=1)\n",
+ "results"
+ ],
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "('CardanoSentiment', 'Covid3Month')\n",
- "(0.09015657223327135, 0.0019998715745554777)\n",
- "[[0.09821203 0.08379797 0.09015657]\n",
- " [0.0018498 0.00161534 0.00199987]]\n"
- ]
+ "data": {
+ "text/plain": [
+ "array([[ 0.44579994, 0.39365639, -0.01240353],\n",
+ " [-0.32439741, -0.13002266, -0.21576891],\n",
+ " [ 0.07105868, 0.18880155, -0.0043037 ],\n",
+ " [ 0.71399859, 0.73046888, -0.00364389],\n",
+ " [ 0.89655914, 0.92964429, -0.00160718]])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
- "source": [
- "paired = sorted(zip(small_problems, performance))\n",
- "small_problems, performance = zip(*paired)\n",
- "print(small_problems)\n",
- "print(performance)\n",
- "all_results = np.column_stack((sorted_results, performance))\n",
- "print(all_results)\n",
- "regressors = [\"DrCIF\", \"FreshPRINCE\", \"Dummy\"]"
- ]
+ "execution_count": 10
},
{
"cell_type": "markdown",
@@ -388,9 +634,7 @@
"source": [
"## Comparing Regressors\n",
"\n",
- "aeon provides visualisation tools to compare regressors.\n",
- "\n",
- "## Comparing two regressors\n",
+ "`aeon` provides visualisation tools to compare regressors.\n",
"\n",
"We can plot the results against each other. This also presents the wins and losses\n",
"and some summary statistics."
@@ -398,34 +642,48 @@
},
{
"cell_type": "code",
- "execution_count": 22,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:19.487406Z",
+ "start_time": "2024-10-29T13:24:10.776119Z"
+ }
},
+ "source": [
+ "from aeon.visualisation import plot_pairwise_scatter\n",
+ "\n",
+ "plot_pairwise_scatter(\n",
+ " results[:, 0],\n",
+ " results[:, 1],\n",
+ " \"DrCIF\",\n",
+ " \"FreshPRINCE\",\n",
+ " metric=\"r2\",\n",
+ ")"
+ ],
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
- ""
+ "(,\n",
+ " )"
]
},
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
"metadata": {},
"output_type": "display_data"
}
],
- "source": [
- "from aeon.visualisation import plot_pairwise_scatter\n",
- "\n",
- "fig, ax = plot_pairwise_scatter(\n",
- " all_results[:, 1],\n",
- " all_results[:, 2],\n",
- " \"FreshPRINCE\",\n",
- " \"Dummy\",\n",
- " metric=\"mse\",\n",
- " lower_better=True,\n",
- ")"
- ]
+ "execution_count": 11
},
{
"cell_type": "markdown",
@@ -434,67 +692,205 @@
},
"source": [
"\n",
- "### Comparing multiple regressors\n",
- "\n",
- "We can plot the results of multiple regressors on a critical difference diagram,\n",
+ "We can plot the results of multiple regressors on a critical difference diagram [[3]](#references),\n",
"which shows the average rank and groups estimators by whether they are significantly\n",
"different from each other."
]
},
{
"cell_type": "code",
- "execution_count": 23,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:19.575178Z",
+ "start_time": "2024-10-29T13:24:19.517299Z"
+ }
},
+ "source": [
+ "from aeon.visualisation import plot_critical_difference\n",
+ "\n",
+ "plot_critical_difference(results, regressors)"
+ ],
"outputs": [
{
"data": {
- "image/png": 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",
+ "text/plain": [
+ "(, )"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
"text/plain": [
""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 12
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "A function for plotting a boxplot is also available."
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:19.794582Z",
+ "start_time": "2024-10-29T13:24:19.603069Z"
+ }
+ },
+ "source": [
+ "from aeon.visualisation import plot_boxplot\n",
+ "\n",
+ "plot_boxplot(results, regressors, relative=True, plot_type=\"boxplot\")"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(, )"
]
},
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
"metadata": {},
"output_type": "display_data"
}
],
+ "execution_count": 13
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "Sometimes it is interesting to compare the performance of estimators on a single specific dataset. We use the BarCrawl6min dataset here."
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:21.899956Z",
+ "start_time": "2024-10-29T13:24:19.825476Z"
+ }
+ },
+ "cell_type": "code",
"source": [
- "from aeon.visualisation import plot_critical_difference\n",
+ "from aeon.regression import DummyRegressor\n",
+ "from aeon.regression.feature_based import FreshPRINCERegressor\n",
"\n",
- "res = plot_critical_difference(\n",
- " all_results,\n",
- " regressors,\n",
- " lower_better=True,\n",
- ")"
- ]
+ "fp = FreshPRINCERegressor(n_estimators=10, default_fc_parameters=\"minimal\")\n",
+ "fp.fit(X_train_bc, y_train_bc)\n",
+ "y_pred_fp = fp.predict(X_test_bc)\n",
+ "\n",
+ "d = DummyRegressor()\n",
+ "d.fit(X_train_bc, y_train_bc)\n",
+ "y_pred_d = d.predict(X_test_bc)"
+ ],
+ "outputs": [],
+ "execution_count": 14
},
{
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:22.126355Z",
+ "start_time": "2024-10-29T13:24:21.909947Z"
+ }
+ },
"cell_type": "code",
- "execution_count": 24,
+ "source": [
+ "from aeon.visualisation import plot_scatter_predictions\n",
+ "\n",
+ "plot_scatter_predictions(y_test_bc, y_pred_fp, title=\"FreshPRINCE - Covid3Month\")"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(,\n",
+ " )"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 15
+ },
+ {
"metadata": {
- "collapsed": false
+ "ExecuteTime": {
+ "end_time": "2024-10-29T13:24:22.395605Z",
+ "start_time": "2024-10-29T13:24:22.177188Z"
+ }
},
+ "cell_type": "code",
+ "source": [
+ "plot_scatter_predictions(y_test_bc, y_pred_d, title=\"Dummy - Covid3Month\")"
+ ],
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
- ""
+ "(,\n",
+ " )"
]
},
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
"metadata": {},
"output_type": "display_data"
}
],
+ "execution_count": 16
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
"source": [
- "from aeon.visualisation import plot_boxplot\n",
+ "## References \n",
"\n",
- "res = plot_boxplot(\n",
- " all_results,\n",
- " regressors,\n",
- " relative=True,\n",
- ")"
+ "[1] Tan, C.W., Bergmeir, C., Petitjean, F. and Webb, G.I., 2021. Time series extrinsic regression: Predicting numeric values from time series data. Data Mining and Knowledge Discovery, 35(3), pp.1032-1060.\n",
+ "\n",
+ "[2] Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D.F. and Bagnall, A., 2024. Unsupervised feature based algorithms for time series extrinsic regression. Data Mining and Knowledge Discovery, pp.1-45.\n",
+ "\n",
+ "[3] Garcia, S. and Herrera, F., 2008. An Extension on\" Statistical Comparisons of Classifiers over Multiple Data Sets\" for all Pairwise Comparisons. Journal of machine learning research, 9(12)."
]
}
],
diff --git a/examples/benchmarking/regression_results_per_dataset.ipynb b/examples/benchmarking/regression_results_per_dataset.ipynb
deleted file mode 100644
index f1a18ac01e..0000000000
--- a/examples/benchmarking/regression_results_per_dataset.ipynb
+++ /dev/null
@@ -1,140 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Benchmarking: comparing estimators on a specific datasets\n",
- "\n",
- "Sometimes it is interesting to compare the performance of estimators on a\n",
- "single specific dataset.\n",
- "\n",
- "We use two aeon classifiers for our examples: FreshPRINCERegressor [1], a pipeline of TSFresh transform followed by a rotation forest regressor, and DrCIFRegressor [1], an extension of the CIF algorithm using multiple representations.\n",
- "\n",
- "The Covid3Month dataset is used.\n",
- "\n",
- "We start by running both classifiers and getting their predictions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-06T12:40:53.243013300Z",
- "start_time": "2023-12-06T12:40:32.990683600Z"
- }
- },
- "outputs": [],
- "source": [
- "from aeon.datasets import load_covid_3month # univariate regression dataset\n",
- "from aeon.regression.feature_based import FreshPRINCERegressor\n",
- "from aeon.regression.interval_based import DrCIFRegressor\n",
- "from aeon.visualisation import plot_scatter_predictions\n",
- "\n",
- "X_train, y_train = load_covid_3month(split=\"train\")\n",
- "X_test, y_test = load_covid_3month(split=\"test\")\n",
- "\n",
- "# Running FP\n",
- "fp = FreshPRINCERegressor(n_estimators=10, default_fc_parameters=\"minimal\")\n",
- "fp.fit(X_train, y_train)\n",
- "y_pred_fp = fp.predict(X_test)\n",
- "\n",
- "# Running DrCIF\n",
- "drcif = DrCIFRegressor(n_estimators=10)\n",
- "drcif.fit(X_train, y_train)\n",
- "y_pred_drcif = drcif.predict(X_test)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we would like to compare the predictions made by both regressors, we can use scatterplots as follows:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-06T12:40:53.601055700Z",
- "start_time": "2023-12-06T12:40:53.247028700Z"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plot_scatter_predictions(y_test, y_pred_fp, title=\"FreshPRINCE - Covid3Month\")\n",
- "\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-06T12:40:53.953688200Z",
- "start_time": "2023-12-06T12:40:53.601055700Z"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plot_scatter_predictions(y_test, y_pred_drcif, title=\"DrCIF - Covid3Month\")\n",
- "\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "python3.11",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.5"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/classification/SastVsViz.ipynb b/examples/classification/SastVsViz.ipynb
new file mode 100644
index 0000000000..4f42de828b
--- /dev/null
+++ b/examples/classification/SastVsViz.ipynb
@@ -0,0 +1,160 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "shape of the array: (50, 1, 150)\n",
+ "n_samples = 50\n",
+ "n_channels = 1\n",
+ "n_timepoints = 150\n"
+ ]
+ }
+ ],
+ "source": [
+ "from aeon.datasets import load_classification\n",
+ "\n",
+ "X_train, y_train = load_classification(\"GunPoint\", split=\"train\")\n",
+ "X_test, y_test = load_classification(\"GunPoint\", split=\"test\")\n",
+ "\n",
+ "print(f\"shape of the array: {X_train.shape}\")\n",
+ "print(f\"n_samples = {X_train.shape[0]}\")\n",
+ "print(f\"n_channels = {X_train.shape[1]}\")\n",
+ "print(f\"n_timepoints = {X_train.shape[2]}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\vanya\\OneDrive - University of Southampton\\Documents\\Vanya's Directory\\aeon\\aeon\\base\\__init__.py:24: FutureWarning: The aeon package will soon be releasing v1.0.0 with the removal of legacy modules and interfaces such as BaseTransformer and BaseForecaster. This will contain breaking changes. See aeon-toolkit.org for more information. Set aeon.AEON_DEPRECATION_WARNING or the AEON_DEPRECATION_WARNING environmental variable to 'False' to disable this warning.\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "\n",
+ "from aeon.classification.shapelet_based import SASTClassifier\n",
+ "\n",
+ "stc = SASTClassifier(classifier=RandomForestClassifier(ccp_alpha=0.01)).fit(\n",
+ " X_train, y_train\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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vn3MAs9nMW2+9Rffu3Zk+fTqBgYG8++67LvsJ5Hf+r+Vzfmk8e6HPpZo3b87SpUvZsWMHN998s9vx1NRU9u3bR3BwMF988YXb8eze9tk/S7Zp04bq1auzcuVKbr31Vlq1akWbNm247bbbqFGjhtt4Ebl2KsCLiFxn/Pz8qFKlCo8++ihxcXFMnjyZqVOn8vTTT5OUlAT8dWN6uXLlygG4bEqaff6tt97K119/TWhoKDfddJPH8T4+PpQuXdotnl0wT0xMzDHv5ORkwsLCMJs9P1xVrlw5jh8/Tmpqap4L8Nn93fP6vvMiu9juiadNvnLL0w8hl64sEhEREZGru9I9ckJCAgC7d+9m9+7dOV4j+7yoqCjmzJnDxIkTWblyJfPnz2f+/PlYLBbat29PTEyMx3vi3LrW+8pNmzbxzjvvsHHjRiCrcFy9enVatmzJn3/+6bJqPyfx8fFERETk+r7T0z1rtqu9Xm5/Rrm8kH8t98l5/Zxnq1y5Mo0bN+a3336jfPnyLk/XZsvP/BfEzxLly5f3GL/az2LZ85+cnMy4ceNyvH72XPj7+zNr1iw+/fRTFi9ezE8//cRPP/0EZD39HBMTQ+PGjXOVs4jkjgrwIiLXsTZt2jB58mR27doF/HXzderUKY/nZ9+UZT/umG3dunV88803hIeHEx8fT0xMDB988IHbeJvNRmZmJj4+Pi7x7Ju1y1fFXCo4OJi4uDgyMjI83mRmX+Py3HLj0vddrVq1Ar22iIiIiNxYLr9HDgoKAmDAgAHExMTk6hq1a9fmnXfewW63s337dtasWcP8+fNZtmwZKSkpHlcSF4UTJ04wbNgwDMPghRde4JZbbqF69er4+/uTnp6e65aOQUFBpKSkYBiGW1E7PT0dq9WKxWIpkJxz+zPK5avdr0V+PucA3377Lb/99hvh4eEcOHCA//3vfzz33HPO4wU1//mR02Kiq/0slj0XtWrV4ocffsjVa4WHh/Pcc8/x3HPPcfjwYdasWcOSJUv47bffePjhh1mxYkWeF02JSM7UA15E5DqW3V8w++anfv36AGzevBmbzeZ2/rp164CsVT3ZkpOTefHFF7FarUyZMoXo6GgWL17Md9995zbeMAy2bt3qFv/jjz8AaNq0aY651q9fH4fD4Tz3UqdOneLQoUNUr17dWZzPyyrw7Pe9YcMGt2Pp6els2bKFoKAgKlWqlOtrioiIiMiN6fJ75Hr16gF4vI8F+P777/nf//7nLNjPnDmTN954A8MwsFgsNG7cmEcffZRvvvmGwMBAl3vOon5ycenSpaSmpjJs2DCGDh1K3bp1nS1L9u7dC1x9RTpAnTp1SE1NZc+ePW7H/u///o8mTZrw+++/F0jONWvWJCAggD179nhcpe3pZ5RrldfPOcDx48d58803CQsL45tvvqFGjRp88cUXLvNQUPOfH6dOnXK2zrnU1X4WCw4OpkqVKhw5csRjf/rt27fz9ttvO1e5//rrr4wePdrZ279atWoMGDCAyZMn06pVKxISEjx+3YhI/qkALyJynUpNTWXq1KkA3HXXXUDWY4lt27YlNjaWjz76yOX8Xbt2MWnSJHx9fbnnnnuc8dGjR3Ps2DFGjBhB3bp1GT16NP7+/rzxxhseV6m89957zpYvkHVT+/XXX1OhQgVuu+22HPO97777AHjnnXdcbvzS0tJ47bXXcDgc9O7d2xnP3hzI0y8Scrr2+PHjOXr0qDNus9l48803SUxMpGvXrtfUKkZERERErn+e7pErVqzIrbfeyvbt25kyZYrL+QcOHOD111/n008/dRbsN2zYwPTp090WpJw9e5b09HSXtiR5uWctCNnF3rNnz7rEExISGD16tMdcsp9evTTeq1cvAMaMGeOysvro0aPMnz+f4OBgmjRpUiA5+/j40L17d5KTk3nrrbdc8jh69Cjvv/8+JpOJnj17FsjrQd4/59kr2pOSknjhhReoXLkyo0ePdsZTUlKA/M1/QXrrrbfIzMx0frx8+XJWrlxJw4YNnb908KRPnz5kZmby+uuvu/SWT01N5bXXXuPzzz93/nLk+PHjTJs2jYkTJ7pcIz09nTNnzmA2m7WwSaSAqQWNiIiXrV+/3mXjIMMwOHv2LMuWLeP8+fN06NCBu+++23n83//+Nw888ADjx49n9erVNG3alFOnTrF8+XIcDgdvvPEGVapUAeDHH3/k22+/JSoqihEjRgBZKxz+/ve/M2bMGF588UUmTZrksrJn9+7d9OjRgzvvvJO4uDiWLl2KxWLhrbfeumJvyK5du/Lrr78yb948unXrxu23346Pjw+rVq0iNjaW2267jeHDhzvPz97gav78+QD06NEjx1UxzZo1Y+TIkYwfP56ePXvSvn17QkNDWbduHXv27KFBgwYuj46KiIiIyI0tr/fIb7zxBg888AD/+c9/WLJkCU2aNCE+Pp7FixeTmprKyy+/7Cysjxw5kl9++YXnn3+eRYsWUatWLeLj41myZAmGYfCPf/zDed283LMWhDvvvJPw8HBmzZrFyZMnqVOnDmfPnmXFihWkpaURHBxMUlISNpvN+cuBChUqcOjQIZ577jluvvlmhgwZQq9evVi+fDk//vgjPXr04LbbbiMzM5MffviBlJQUPv30U7e2k9fin//8J5s2bWLu3Lls27aNVq1akZiYyIoVK0hKSuLJJ5/0uMHotcjL53zKlCmsW7eOtm3bOhcFtWjRggEDBvDVV1/x1ltvMXr06HzNf0FavXo1vXv35pZbbuHYsWOsWLGCsLAw/vOf/1xx3PDhw1m7di2LFi1i586d3HrrrZjNZn788UdOnDhB586d6dq1KwDdu3dn1qxZzJkzh927d9OiRQsyMzP55ZdfOHz4MMOGDXP27ReRgqECvIiIl61fv57169c7P7ZYLISEhBAVFUXXrl3p06ePS4G8UqVKzJ07lwkTJrB8+XK+/PJLwsLCaN++PcOHD3dumHP+/HleffVVLBYLb775psvq8KFDh7Jo0SJWr17NV199xQMPPOA8Nn78eL766ivmzp2Lj48Pd9xxByNHjqRu3bpXfS///e9/iY6OZvbs2Xz//feYzWZq1arF8OHD6d+/v8sGrS1atGDIkCF8++23TJ8+nerVq1/xh5lRo0ZRv359pk6dyo8//ojD4aBq1ao8++yzPPTQQ1r9LiIiIlKM5Pce+ZNPPmH58uVMmzaNsLAwmjdvzrBhw7j11lud59asWZOZM2fy6aef8vvvv7Nq1SoCAwO5+eabeeSRR7j55pud5+b1nvValS1blqlTp/L++++zdetW1q9fT7ly5WjXrh0jRozgiy++4Ouvv2b16tXcfvvtAPzjH//gpZdeYsWKFezcuZMhQ4ZgMpkYO3YsX375JXPnzmXOnDlYLBaaNGnC448/TnR0dIHmHRISwsyZM5k0aRKLFi1i5syZBAUF0axZMx566CGX+S8ouf2c79+/n/fee4/AwED+/e9/u1zj2WefZeXKlcyZM4cOHTpw55135nn+C9L06dMZM2YMs2fPJigoiB49ejBy5EjnAquc+Pj4MHHiRL788kvmz5/PN998g4+PD9WqVeOxxx7jvvvuc/b8DwgI4PPPP+eLL77gxx9/ZObMmUBW26LHHnusQJ9UEJEsJqOwmleJiMgNZfDgwaxfv56lS5d63OhURERERERECl779u05duwY27dvL5SV9SLiXeoBLyIiIiIiIiIiIiJSCFSAFxEREREREREREREpBCrAi4iIiIiIiIiIiIgUAvWAFxEREREREREREREpBFoBLyIiIiIiIiIiIiJSCFSAFxEREREREREREREpBCrAi4iIiIiIiIiIiIgUAqu3ExBXZ84keTsFKWHMZhORkUGcP5+Cw6EtIUSkeNP3PPGmMmVCvJ2CeElR3+Pre13eac7yTnOWP5q3vNOc5Z3mLH80b3mnOcvdPb5WwIuUcGazCZPJhNls8nYqIiKFTt/zRKQk0Pe6vNOc5Z3mLH80b3mnOcs7zVn+aN7yTnOWO1oBf5lff/2Vjz/+mO3bt2M2m2nSpAlPPfUUTZs2veK4Xr16sWPHDrf43XffzdixYwspWxERERERERERERG5XqkAf4l169bxyCOPULt2bZ5++mlsNhtfffUVgwYN4ssvv6RJkyYexzkcDvbv30+nTp3o2LGjy7FKlSoVReoiIiIiIiIiIiIicp1RAf4Sb775JhUqVGD27NkEBAQA0LNnT+655x7ef/99Jk+e7HHc4cOHSU9Pp2PHjvTo0aMIMxYRERERkSvJ7xOusbGxvP3226xfvx6AO+64gxdeeIHIyMgiyFpEREREigv1gL8oISGBPXv20LlzZ2fxHaB06dK0bNmSzZs35zh27969ANSqVauw0xQRERERkVzKfsI1KSmJp59+mpEjR3LkyBEGDRrEli1bchwXFxfHkCFD2Lx5Mw8//DBDhw5lxYoVDB06lIyMjCJ8ByIiIiJyo9MK+IuCg4NZvHixS/E9W1xcHBaLJcexe/bswWQyUbNmTQzDIC0tjcDAwMJMV0REREREriK/T7hOnjyZkydP8t133zkX2TRp0oShQ4cyb948+vXrV1RvQURERERucCrAX2SxWKhevbpbfNeuXWzcuJHbbrstx7F79+4lJCSEN954g8WLF5OamkqVKlV4+umnuffee/OUh9msnYOlaFksZpe/RUSKM33PEyk5sp9wHTp0qMcnXFevXp3j2O+//57o6GiXJ1zbtGlDjRo1+P7771WAFxEREZFcUwH+ClJSUnj++ecBGDFiRI7n7d27l8TERNLT0xkzZgzx8fFMnTqVZ555hszMTHr27Jnr14yMDMJkUgFeil5oqPvTHyIixZW+54kUf/l9wjUhIYHY2Fjuvvtut2MNGjRg5cqVBZ2qiIiIiBRjKsDnIC0tjUcffZRdu3bx2GOP0aJFixzPHThwIBaLhQEDBjhjXbt2pWvXrowZM4Zu3bpdsYXNpc6fT9EKeClSFouZ0NAAEhPTsNsd3k5HRKRQ6XueeFNERJC3UyhR8vuE66lTpwAoV66c27EyZcqQnJxMUlISISEhuc6lqJ9y1dM+eac5yzvNWf5o3vJOc5Z3mrP80bzlneYsd1SA9yAhIYERI0awadMm+vTpw1NPPXXF8wcNGuQW8/f3p0ePHowbN459+/ZRp06dXL22w2HgcBj5SVvkmtjtDmw2FaNEpGTQ9zyRkik3T7impKQAeFw57+fnB0BqamqeCvDeespVT/vkneYs7zRn+aN5yzvNWd5pzvJH85Z3mrMrUwH+MufOnWPo0KHs3r2b/v378/rrr+f7WpGRkUDWDbqIiIiIiHhPbp9wdTiu/ss5szlvq7yK+ilXPe2Td5qzvNOc5Y/mLe80Z3mnOcsfzVveac5y95SrCvCXSE5OZtiwYezevZuHHnqIF1988apjYmNjGTFiBN26deOxxx5zOXbw4EEAKleuXCj5ioiIiIjI1eXlCdegoKwfotLT092OZceyz8ktbz3lqqd98k5zlneas/zRvOWd5izvNGf5o3nLO83ZlalBzyVef/11du3axYMPPpir4jtApUqViI+PZ86cOSQnJzvjx48fZ+7cubRq1YoyZcoUVsoiIiIiInIF586dY/DgwWzatIn+/fszevToK55fsWJFAM6cOeN27PTp04SGhhIYGFgouYqIiIhI8aMV8Bft2bOHBQsWEBISQr169Zg/f77bOT169CA2NpaNGzfSvHlzqlSpgtls5tVXX+XJJ59kwIAB9O3bl6SkJL766iusViuvvfaaF96NSO7Y7XbWrl1FcnI8wcHhtGx5S643DBYRERG53uXnCdfQ0FAqV67M9u3b3Y7t2LGDhg0bFkaqIiIiIlJMqQB/0YYNGwBISkrK8ca8R48ebNiwgRdffJG33nqLKlWqANC5c2fGjRvHJ598wpgxY/D39yc6OppnnnmGWrVqFdl7EMmLhQsXEBPzMkeOHHbGqlatRkzMm3Tt2t2LmYmIiIgUjPw84QrQqVMnpk6dyv79+53382vWrOHgwYMMHz68sNIVERERkWLIZBhG0TcjlBydOZPk7RSkBFi4cAHDhw+mU6fOPPPMP2nTpiVr1mzgvffGsHTpYiZNmqYivIgUS1armYiIIOLiUtSjUIpcmTIh3k6hRNmzZw/dunUjJCSEl156yeNTfp6ecAU4f/48Xbt2xWKxMGzYMNLT0/nss8+oUqUKs2bNwtfXN0+5FPU9vr7X5Z3mLO80Z/lz+byZMpIxJx/HknQUc9IxTOkJGD6BGL6hGL4hYPHBlJGE6UI85vREsKVhMhxg2MDhwLD64wgq5/xjWPww2S5gsqfDxb//+ncGYAAXN4U2mTGsARg+QRg+QZjsF7Cc34P1/B4s5/dgTk/AMFvB4odh8cHwCcYeUhlHaJWsvwNKgdkKZiuG2YrhF449uCL45m2fjLzOmVyd5ix/NG95pznL3T2+CvDXGRXgpbDZ7XZatWpKvXr1mTJlBr6+Vuc3y4wMG0OGDGDnzp2sW7dJ7WhEpNjRDaJ4kwrwRevLL7/k3//+9xXP2b17N3PnznU+4dq7d2/nsQMHDvDWW2/x+++/4+/vT7t27fjnP/9J6dKl85yLCvDXP81Z3mnO8id73pK2LsV34ydYT//p7ZQKnOEXhiOkIobPpftlmHD4BmP4hePwj8AIiMQWWQdbmcZg8bni9fS1lneas/zRvOWd5kwF+BuSCvBS2Fav/pVeve7lhx9+pEWLaLdvlhs2rOPee+/i22+/59Zbb/N2uiIiBUo3iOJNKsCXXCrAX/80Z3mnOcsfn/RzhG96H8euRagaA4ZPIJkVosmsdAsZVdphBJV1O0dfa3lXnObMbrfzww/fsWTJD+zfv48LF9KIiIikUaMm9OnTn0aNmhTYaxXmvLVt24KmTZszbtynBXrd3Nq48Xc++OBdYmMPExgYyGefTad8+fL5vl72+5kw4bMb4mvtp59+5F//eoE5cxZQoULFAr12bu7x1QNepIQ5deokAHXr1vd4vF69+i7niYiIiIiIyDUyDPx2zSZo43iwpXo7m+uGKTMV3yMr8T2ykqC1b2Er34L0ml3IqN4hqwWPlGgOh4OXX/4nq1f/yq233saQIcMICgrmxInjLFq0kOXLlzJq1DP06zfQ26le1xwOB6+++iJpaakMHz6CoKBgypZ1/2VXcbVt25+89dYbXs1BBXiREqZcuazfcO7atYMWLaLdju/cucPlPBEREREREbk2gb9/gP+2aZhMkPU/4sYA64nfsZ74naC1/+VC3b6kNn8crAXbU15uHCtXrmDVql8YMWIkgwcPdTk2aNAQhg8fzEcfjaVduzspX76Cl7K8/p0/f474+Dhuu+0OHnhgiLfTKTKGYTB//lzGjn2PjIx0r+aiArxICdO6dRuqVq3GBx+8y5QpMwCz85jD4WDs2PeoWrU6rVu38V6SIiIiIiIixYQp9Qz+O74soIsBJiuYLVkf2C4UzHUvYfgEYI+Iwh5WFQwHJntG1p/UM1iSjmJKTyzw13Rjz8R/+1dYzu8lrfMHgIrwl9p6fjM/Hf+RUxdOeTxuNoGPj5XMTBsOL7U6KudfjjsrdqRxZNN8X2PLlo0AtGnj3h43MDCIXr368uGH77F16xYV4K8gMzMTgODgYC9nUnTS09N5/PGH2b17J40aNSEwMIh169Z4LR8V4EVKGIvFQkzMmwwfPpghQwbw9NP/oE2blqxfv4H33/8/li5dzKRJ07QBq4iIiIiISAHwO7AIHJ57Ixu+waQ1GpJV8A6phBFYBmxpmDOSMGUkgz0Dwy8Uwy8Uh28YWP1dV9DbMzCnnMacchJz6mkwHGANwLD4YVj9wOqPYfHHsPqDxReDrLEmDHDYMdlSMWWmYspMAcOBPbQqjpBKYDJ7zBfAlJ6AOelY1jjDDg5bVh6pp7EkHcOcfAxLyilw2C95o/ascRfis87PJZ8TGzAvfQr6Tcz1mOJu6/nN/Gfz69gN+xXPM5tNOLxVfQf2Juzmt9OreblpDI0i89enPTAw6xcv3377NU899Q+sVtcy5n339eO++/q51S9+/vkn5s37mj17dpGcnExwcDANGjRm2LC/UbduPed5bdu2oHfvvjRq1IQvv5zKkSOHiIyMpHv33gwePJSVK5czdernHD58mDJlytCjx30MHDjYOf6JJ/7GmTOnef31txg79l127dpJSEgIbdu245FHHic8PPyK7y8pKYkpUybx888/cebMKcLCwmjVqg3Dho3IVX/29PR0ZsyYxtKlizhx4jj+/gE0atSEBx8cRsOGjQB4880YFi1aCMCiRQtZtGghQ4c+wvDhI3K8bmzsEaZMmcSGDetITk6iQoWKdO58L/37P4CPT86bJu/du5vp06ewdetm4uLO4+vrR82atejbdwAdOtzlPO/ChQtMnPgRv/22mpMnT+Lv70/Dho0YNOghGjdu6jzv2LGjfPLJeLZv/5Pz588RERFJixbRuZqfjIwMzp8/xz/+8SLdu/firbf+fdX5LEwqwIuUQF27dmfSpGnExLxM584dnPGqVaszadI0unbt7sXsREREREREig+//T94jGfU6kJKy2cwAiJdD/gGYw8sk7uLW3xxhFbGEVo5TzldS1nW8AvD7heWz8EGpswULOd24XvsN3yOrcFyfs8Vh1iPr4dvR8Ad74LJP3+vW4z8dPzHqxbfrxd2w86K48vyXYC/997ufP31TObN+5pff13JbbfdTtOmzWnSpBllypT1uHBw9uwZjB37Ls2a3czQoY9gtfqwe/dOFi1ayLZtW/n66+8IDAx0nr969a8sW7aEPn36U6pUbxYsmMunn37E9u1/snXrFvr06U+PHqX49ttv+OijDyhXrhwdOnRyjk9ISOCppx6jceNmjBz5JPv27WHBgm/ZuPF3Jk2a7vJal0pMTOSxx4Zx8uQJunXrSfXqNTl27Cjz5n3DmjW/MmHCF1SuXCXHublw4QKjRj3Kjh3buO22O7jvvv7ExZ1n/vy5jBz5MK+99ibt23ekR4/e1K4dxdix79GkSTO6d+9FrVq1c7zu3r17GDnyEQzDQc+efahUqTIbN/7OhAnj2L9/H6+9NtrjuG3b/uTxxx+hbNly9O7dj4iIcI4dO8aCBXN57bUXKVu2rHPD3JiYl1i/fh333dePatWqce7cOebOnc2oUY8yceJUateOIikpiVGjHsUwDHr2vI/IyEgOHNjPt99+zaZNfzB9+mz8/HL+fhAUFMScOQvcfmnjLddHFiJS5Lp27U6XLveyYcNvJCfHExwcTsuWt2jlu4iIiIiISAGxnN+L5fxet7itYjTJ7TwXsoo1kwnDNxhbhRbYKrSAFn/HlHIav4NL8DuwCMu53Z7HHVlH0NInSeg4Fix+RZuzeE3lylV4771xvPlmDEePxjJv3jfMm/cNANWr16Rz53vo128gvr6+ANjtdqZOnURUVB3+97+PXOobISEhfPXVNDZsWMvtt7d3xk+dOsmnn06mfv2GWK1mbrvtFnr06MGaNauYOHEKdevWB6BZsxY88EAf1qz51aUAn5ycRLduvXj++ZedsRo1ajF27LvMnDmdYcP+5vG9TZz4MUePxjJ+/EQaNmzsjHfp0pWHHx7M//43hv/7v7E5zs3MmdPZsWOb22r2Xr36MGTI/bzzzptER7emYcPGlCpVmrFj36NixUrcffc9V5zzDz74PzIzM5g4cSo33ZRVqO/Z8z7MZjPLli1m8OCh1KxZy23ctGmTARg3biKlS5d2xhs3bsI///kUy5cvpVGjJsTHx7Nq1S/07NmHkSOfdJ7XokUr3njjVXbt2kHt2lH8/vs6Tp06yeuvv+Wyer5cufL88MN3HDx40OVphsuZzWbM5pyf5Clq108mIlLkLBYLbdu2Y8CAAbRt207FdxERERERkQLkdyCH1e83XbkIVpIYQWW50HAwCd2/IrHrFAz/cI/nWU/8TuCmT4o2uevQnRU7YjHdGD+7W0wW2le86+onXkFWe5iv+eCDjxk4cDD16tXHYrFw6NABJkwYxyOPDCExMSHr9SwWvv12Ef/738cu9Y20tDQslqw1yKmpqS7Xr1ixEvXrN3R+XKtWVnG5UqUqzuJ71sdZT5mcOXPGLceHH3Zt59K7d1+Cg4P5+ecVHt+TYRgsX76U6tVrULlyVeLj451/IiNL0aBBIzZsWOeW66VWrFhGQEAAgwY95BIvVao0ffrcT3JyEuvW/ZbjeE/i4+PZsmUTrVu3cRbfs40a9QxTp87McVX+W2+N4ZtvFroU3202m7MNUvZ7CQwMJDg4mJ9+WsaCBd9y7txZABo0aMjMmXPp1q0nAGXLZrWYmTr1c1at+oW0tDQA+vd/gClTZl6x+H490gp4EREREREREZGC5rDju3+Re9zqR2b1jkWfzw3AVqYhiZ0/IXTxCEwX4t2O++/4igv1+uEIunp/7OKqcWRTXm4aw4rjy677TVjbV7wr3+1nLmWxWLj55pbcfHNLAJKTk/nll5/44ouJ7N+/ly+++Iwnn3wWAB8fH7Zu3cyKFcs4evQox48f49SpExhG1kRk/52tVKlSLh9n9zi/tJAMOFuZOC7bzyE8PJxSpdzPrVixEkeOHPb4fuLj40lMTCAxMYGuXXP+XnDmzGmqVavu8dixY0epXLkqfn7uT4TUrHkTACdOHMvx2p6cPJk1T9Wq1XA7FhlZisjIUh5GZTGbzSQmJjJjxnQOHTrA8ePHOX78qHMD2Ox59/X15aWXYvjPf17nnXfevJhvLaKjb6FTp85ERdUFsgryQ4YMZ/r0ybzwwjNYrVYaNGhE69Zt6NKlK6VL57JN13VCBXgRERERERERkQJmPfkH5lT31bLc1BF8g8DmeWPWks4ecROJnT8ldMkITGlxlx3MJGDTJ6S0fc07yV0nGkU2uWJh22o1ExERRFxcCrYb9OssLS2NqVM/p3z5CvTo0dvlWHBwMPfc043mzVvSv38PNm783XlszJj/MH/+XKpXr0mDBg255ZY21K5dhyNHDvPuu/91e53slfH55ePj6zFut9tz7DLguLhBcePGTRk69JEcr12mTNkcjxkGmC7dkNnD9bNb8+SWzZa1QXJO172SxYt/4N//fpWIiAiaNm1Ox46dqFnzJsqWLcvDDz/ocm67dncQHd2adevWsH79Wv7443dmzpzOrFlfMmrUs/Ttez8AjzzyGL1792XNmlVs2LCOTZv+YMuWTUyd+jnvv/+Rc6PZG4EK8CIiIiIiIiIiBSyn9jPU7160idyA7BG1SLxrHGHfDeLyLWP99n3HhYaDsYfX9E5yUiT8/PyYNesrIiIi6Nq1h8didvny5QkODsbfP2szzi1bNjN//lzuuqszr776hkshedu2rYWS57lzZ0lNTXXZbDUjI4Pjx4/nuHo9PDyCgIBAkpISadmyldvxDRvWYjZbrlhAr1SpEseOxZKenu62Cv7gwQNAVr/0vKhYsSIAR44ccju2b99epk37gh49etO8eQuXY+np6bz99ptUqlSZzz6bSlBQsPPY1q2bXc5NTU1h3769VKxYidtvb+/sx7937x6efPIxJk+eSN++93Pu3FkOHNhP06bN6datJ9269cQwDJYuXcQbb7zKzJnTGD36nTy9P29SD3gRERERERERkYJkS8P30I9uYcM/Aqq19UJCNx57qbqk39TV/YBhEPDH+KJPSIqU2WymS5d7OXXqJBMmjHNr/QLw449LSEhI4M47OwCQkBAPZLU0ubT4Hh8fz8KFC4CslekFyeFwMGvWly6x2bO/Ii0tlY4dO3kcY7FYaNfudg4ePMCPPy5xObZv317++c+n+N//xjjb3nhyxx0dSEtLY/r0yS7xuLjzfPPNLAIDg4iOviVP7yW7//zatWvc2ud8880sli9fSnBwsNu4CxcukJaWRoUKFV2K7zabjRkzpgN/zfv+/ft4/PGHmTz5M5dr1KhRk+DgYOcTCd9/v4Cnnx7JL7/85DzHZDLRqFHWkx832h6GWgEvIiIiIiIiIlKAfI/8gikzzS2eUaszARYrkF70Sd2A0po9it+BxWDYXOK+R1ZiPb0VW9nGXspMisLIkU9x4MB+ZsyYxm+/rebOOztQrlx50tLS2LhxA6tW/UKrVm3o0yerZUnjxk0JCwtj6tTPSU1NvbhK/Bg//LCA5ORkAJKSkgo8z+nTJ3P0aCwNGzZmx45tLFq0kPr1G3Lfff1zHPPYY6PYtGkj//73v1i/fi316zfk9OlTzJv3DRaLhWeffeGKrzlw4IOsXv3rxT74+7j55pbEx8exYMFckpKSeOWV1wkICMjze3n66ef4+99H8Le/DaFXr76UK1eejRt/Z8WKZfTs2cfZo/1SYWFhNGvWnPXr1/Kf/7xOo0ZNSExMYOnSxRw5cgiz2Uxycta8N2rUhOjoW5g37xuSkhJp1uxm7HY7P/20nOPHjzFy5FMAdOvWk3nzvuG//32D7dv/pGbNWsTHx7Ngwbf4+Pg4P+c3ChXgRUREREREREQKkN/+7z3GM2+6l7yXxEouR1B5MhrcT8C26W7HAv8YS2LniZCPftVyYwgMDGTcuE9ZtOg7Vqz4kQUL5pKQkEBAQCA1a9biuede5t57u2M2ZzX4CA8P5733xvPJJ+OYP38umZkZlClTljvu6MD99w9i4MD7WLfuNwYOHFygeY4f/xnvvvsWP/30I6VKlWbw4KE89NBw54aunpQuXYZJk6YxZcokVq/+lWXLFhMWFk6zZs0ZMmS4x0L3pQICAhg/fiLTp09m+fJl/PbbKoKCgmncuCkDBz6Y7/7odevWY+LEKUya9AkLFszlwoULVK5clWeffYHu3XvlOG706LcZP34s69ev5ccflxAZWYq6devxyiuv8957/2XLlk1cuHABf39/Ro9+mxkzprFixTLWrFkFmLjpptq8+upoOnXqDEBERCTjxn3KlCmT+PXXn5k37xsCAgJo3LgpMTFvUr9+w3y9P28xGZdv/ytedeZMwf8mTuRKisPmLCIiuaXveeJNZcqEeDsF8ZKivsfX97q805zlnebsCuwZRE6/DRyuq7YdYdVI7vstEZHBmrc88LElEv51dxwXkri8gpV011gyK9/qncSuY/rvM3/yOm9PPPE3Nm/eyKpVv1/13OJKX2u5u8dXD3iREsxut7Nq1S/MmDGDVat+KfBeaCIiIiIiIiWN9fwet+I7QHqNu7VaOx8M/3Bo+YjHY3675xZtMiIi+aACvEgJtXDhAlq1akr37vcwcOBAune/h1atmjo3JhEREREREZG8s5zd7jGeWa5ZEWdSjDR/EEdgabew77HVmDKSvZCQiEjuqQAvUgItXLiA4cMHU69efZYsWUFSUhJLlqygXr36DB8+WEV4ERERERGRfLLmUIC3l65XxJkUI76BZNTp7R63Z+JzZGWRpyMikhcqwIuUMHa7nZiYl+nUqTNTpsygZctogoODadkymilTZtCpU2diYl5ROxoREREREZF88FSAt4dVx/DVXiDXIrNmJ49xv0PLijgTkSzjxn1aovu/S+6pAC9Swqxdu4YjRw7z5JPPOncKz2Y2mxk16hmOHDnE2rVrvJShiIiIiIjIjcmUkYwl4ZBb3Fa6ftEnU8w4Imphj6jlFvc5thZTeqIXMhIRyR0V4EVKmFOnTgJQt67nG8B69eq7nCciIiIiIiK5Yzm3Ewz3uK1Mw6JPphjKqOFhFbzDhu+Rn4o+GRGRXFIBXqSEKVeuPAC7du3weHznzh0u54mIiIiIiEju5Nz/XSvgC0K6pwI84HtwaRFnIiKSeyrAi5QwrVu3oWrVanzwwbs4HA6XYw6Hg7Fj36Nq1eq0bt3GSxmKiIiIiIjcmDwW4M0WbJFRRZ9MMeQIrYq9VB23uM+J9ZguxHkhIxGRq1MBXqSEsVgsxMS8ydKlixkyZADr168jKSmJ9evXMWTIAJYuXUxMzGgsFou3UxUREREREbmhWM+6P2lsj6wNFj8vZFM8eVwF73Dge3h50ScjIpILKsCLlEBdu3Zn0qRp7Ny5g86dOxAaGkrnzh3YuXMnkyZNo2vX7t5OUURERERE5IZiSjuHOdl9Ly1b6QZeyKb4yqh+l8e4n9rQiMh1yurtBETEO7p27U6XLveyYcNvJCfHExwcTsuWt2jlu4iIiIiISD7k1P9dBfiC5QiphK1MA6xnXOfbevIPTKlnMALLeCkzERHPVIAXKcEsFgtt27YjIiKIuLgUbDbH1QeJiIiIiIiIG0/tZ0AF+MKQUb2TWwEeA3wP/0R6vX7eSUpEJAdqQSMiIiIiIiIico2sZ7Z5CPpjD6tR9MkUcxk1OnqM+5z8vYgzERG5Oq2AFxERERERERG5FobhcQW8rXQ9MKvNZ0FzBJXHXqoOlnO7XeI+J/8AwwCTyUuZSUGz2+388MN3LFnyA/v37+PChTQiIiJp1KgJffr0p1GjJt5OMVfatm1B06bNGTfuU6+8/saNv/PBB+8SG3uYwMBAPvtsOuXLl8/39bLfz4QJnxVglgXr5MmTTJz4Eb//vp6UlGRq1ryJBx8cStu2txd5LirAi4iIiIiIiIhcA3PSMUzpCW5xtZ8pPJnlmrsV4E0X4rEkHMQeXtNLWUlBcjgcvPzyP1m9+lduvfU2hgwZRlBQMCdOHGfRooUsX76UUaOeoV+/gd5O9brmcDh49dUXSUtLZfjwEQQFBVO2bFlvp1Wozp07yxNPPEJiYiJ9+vSnTJmyLFw4nxdeeJZXXx1Np06dizQfFeBFRERERERERK6B9Zz6vxc2m8PGqbTTxKWfJ8AaSM1yzfDfMcPtPOuJ31WALyZWrlzBqlW/MGLESAYPHupybNCgIQwfPpiPPhpLu3Z3Ur58BS9lef07f/4c8fFx3HbbHTzwwBBvp1MkvvhiIqdOneSjjz5zPiVxzz3dGDFiKGPH/h+33XY7AQEBRZaPCvAiIiIiIiIiItfAena7x/j1UIC3G3bSbGmk2lJIsaWQevFPYkYipy+c4vSF05xJO0VSZhIWkwWzyYzFZMHP4k+14GrUDLmJmqE3UTmwMhZz4ZSR7IadsxfOcDLtBCdTT3A+/RwJGQkkZMSTZEsgLvM8Z1PP4jAM55hSWJlkS8LHZMVsMmNggAEn9n/L0XJR1A9viLWQ8vU2n+Pr8Nu7AHPyMY/HTSbAx0pQpo1LpqxIOYIrkV67O5kVW+X7Glu2bASgTZvb3I4FBgbRq1dfPvzwPbZu3aIC/BVkZmYCEBwc7OVMiobdbmfJkkU0bNjIpUWRn58fffvez1tv/Zs1a36lQ4dORZZT8fxOJCIiIiIiIiJSRDxtwGr4heEIrlgor+cwHCRlJpHpyMBisuBj9gHgUPIh9ibsZk/CLg4nHyI5M4k0e1q+X2dnvOsvFqxmK75mX3zMPlhNPlkfW3zxMflgMplwGA4Mw8CBAwMj69+GA4BAayAhPqGE+ITgZ/EnMTOB+PQ44jPiOHvhLHbDnmMeZrPJpfgOcA4b+60WqmW4vj/ryQ28sfFfhPtFcmfFDrSveBflA4pPcdbn+DpClv0dHDnPl8mU9T9Ww/BaAZ7Tf+J7aBmJd43DVjE6X5cIDAwC4Ntvv+app/6B1epaxrzvvn7cd18/LBbXfRZ+/vkn5s37mj17dpGcnExwcDANGjRm2LC/UbduPed5bdu2oHfvvjRq1IQvv5zKkSOHiIyMpHv33gwePJSVK5czdernHD58mDJlytCjx30MHDjYOf6JJ/7GmTOnef31txg79l127dpJSEgIbdu245FHHic8PPyK7y8pKYkpUybx888/cebMKcLCwmjVqg3Dho3IVX/29PR0ZsyYxtKlizhx4jj+/gE0atSEBx8cRsOGjQB4880YFi1aCMCiRQtZtGghQ4c+wvDhI3K8bmzsEaZMmcSGDetITk6iQoWKdO58L/37P4CPj0+O4/bu3c306VPYunUzcXHn8fX1o2bNWvTtO4AOHe5ynnfhwgUmTvyI335bzcmTJ/H396dhw0YMGvQQjRs3dZ537NhRPvlkPNu3/8n58+eIiIikRYvoq87PwYMHSEtLpX79Rm7H6tdvCMCOHdtUgBcRERERERERuSEYBpa4fW5hW+n617QZaLo9neOpRzmeeozjKcc4nnacsxdOc+7COc6nn7tiwbqw2Bw2bA5bkb9uTrYHBFEt44JLLMxup1JmOsdMcXx76Gu+PfQ1jSObcG+VHjQt1RyzyeylbAuG394FVyy+X1ccdvz3zic5nwX4e+/tztdfz2TevK/59deV3Hbb7TRt2pwmTZpRpkxZt8I7wOzZMxg79l2aNbuZoUMfwWr1YffunSxatJBt27by9dffERgY6Dx/9epfWbZsCX369KdUqd4sWDCXTz/9iO3b/2Tr1i306dOfHj1K8e233/DRRx9Qrlw5l8JtQkICTz31GI0bN2PkyCfZt28PCxZ8y8aNvzNp0nSX17pUYmIijz02jJMnT9CtW0+qV6/JsWNHmTfvG9as+ZUJE76gcuUqOc7NhQsXGDXqUXbs2MZtt93Bfff1Jy7uPPPnz2XkyId57bU3ad++Iz169KZ27SjGjn2PJk2a0b17L2rVqp3jdffu3cPIkY9gGA569uxDpUqV2bjxdyZMGMf+/ft47bXRHsdt2/Ynjz/+CGXLlqN3735ERIRz7NgxFiyYy2uvvUjZsmWdq9FjYl5i/fp13HdfP6pVq8a5c+eYO3c2o0Y9ysSJU6ldO4qkpCRGjXoUwzDo2fM+IiMjOXBgP99++zWbNv3B9Omz8fPz95jLmTOnAChXzr1In937/vjx4znOQWFQAV5EREREREREJJ9MF+IwZSS7xe2RORe5LpduT+dYylEOJu1nb+Ie9iXuITbliHP1uHi2LSCIexLOucUbpKVwzPev4tzW81vYen4LlYIq061KT24rfwe+Ft+iTFXyoXLlKrz33jjefDOGo0djmTfvG+bN+waA6tVr0rnzPfTrNxBf36zPpd1uZ+rUSURF1eF///vIpUAfEhLCV19NY8OGtdx+e3tn/NSpk3z66WTq12+I1WrmtttuoUePHqxZs4qJE6dQt259AJo1a8EDD/Rxa12SnJxEt269eP75l52xGjVqMXbsu8ycOZ1hw/7m8b1NnPgxR4/GMn78RBo2bOyMd+nSlYcfHsz//jeG//u/sTnOzcyZ09mxY5vbavZevfowZMj9vPPOm0RHt6Zhw8aUKlWasWPfo2LFStx99z1XnPMPPvg/MjMzmDhxKjfdlPU9rGfP+zCbzSxbtpjBg4dSs2Ytt3HTpk0GYNy4iZQuXdoZb9y4Cf/851MsX76URo2aEB8fz6pVv9CzZx9GjnzSeV6LFq14441X2bVrB7VrR/H77+s4deokr7/+lsvq+XLlyvPDD99x8OBBl6cZLpWcnPX9ODDQvcd7dtH+woX8PxmUHyrAi4iIiIiIiIjkkyXhoMe4Pax6jmNSbSksi13Gn+e3cDTlCKfSThVSdsXbzoAgj/EGaSksDSvlFj+WcpQJu8bx7eE5PN/4X1QJrlrYKRa49Nrd8T207MZYBW+2cKF2j2u6RFZ7mK/ZvHkj69atYdOmP9izZzeHDh1gwoRxLFu2hA8/nEBoaBgWi4Vvv11EWlqaS/E96+OsEmhqaqrL9StWrORsSwJQq1ZWcblSpSrO4nvWx5UBOHPmjFuODz/s2s6ld+++fP75J/z88wqPBXjDMFi+fCnVq9egcuWqxMfHO49FRpaiQYNGbNiwjtTU1BxX0K9YsYyAgAAGDXrIJV6qVGn69Lmfzz6bwLp1v7kUr68mPj6eLVs20bZtO2fxPduoUc/w4INDc1yV/9ZbYzh37jwREZHOmM1mw+HI6oGUPe+BgYEEBwfz00/LiIqqw6233kapUqVp0KAhM2fOdY4tWzZr9frUqZ/j5+fHzTe3JCAggP79H6B//weu+D6y2y5dqf2S2ez+9ERhUgFeRERERERERCSfLAmHPMY9FeDTbGn8sHMeM3bMJDnTfdW8N/iafSkTUJZIv1KYALvhwG7YOJ12mvPp7qvLi4rFZCHMN5wwvzDKBpcmzBJJKd8yRPqV4mTqcXbEb2dv4m6O+PpRNSPdZWyDtJSs6lsOLYBOpZ3i35v+xbutPiTUN7Qo3k6ByazYisS7xuG/d/4VN2H19bFi8/ImrBdq98h3//dLWSwWbr65JTff3BLIWuH8yy8/8cUXE9m/fy9ffPEZTz75LAA+Pj5s3bqZFSuWcfToUY4fP8apUycwLk6EcdmElCrl+oua7B7nl67iBpz95x0O16dSwsPDKVXK/dyKFStx5Mhhj+8nPj6exMQEEhMT6Nq1Y47v+8yZ01SrVt3jsWPHjlK5clX8/PzcjtWseRMAJ054/vrIycmTWfNUrVoNt2ORkaWIjHT/pVY2s9lMYmIiM2ZM59ChAxw/fpzjx486N4DNnndfX19eeimG//zndd55582L+dYiOvoWOnXqTFRUXQAaNGjIkCHDmT59Mi+88AxWq5UGDRrRunUbunTpSunSZXLMJfuXFhcuXHA7lh0LDvb8y7vCogK8iIiIiIiIiEg+WRI8F9nsodWc/3YYDn6I/Y5vD88h2ZbkXBVaaDmZLNQMqUXFoEoEWoMIsgYTaA0kyBp08eOsv0v7lybUJwxTDoXq+PQ4DiTt50DSfhIzE7A5Msl02Mh0ZGBz2MhwZJDpyMRmZBXZTJgxm0yYMGMymTBhwmwyYxgGKbZkkjITScpM4oL9AiE+oYT7hhPuG0GEXyTlAspTIbAi5QMqUC6gPEHWIEwmE1armYiIIOLiUrDZXIufGfYMzJbXCN33w8XXz9oI1teRTmWbjaNX2DAyPiOOibs/4pmGz+f4/q9XtorRV+yrbrWa8Y0IIsXDnN0o0tLSmDr1c8qXr0CPHr1djgUHB3PPPd1o3rwl/fv3YOPG353Hxoz5D/Pnz6V69Zo0aNCQW25pQ+3adThy5DDvvvtft9fJXhmfXz4+nlsZ2e12jz3qARwXn15o3LgpQ4c+kuO1y5Qpm+OxrN8vef66zb5+dmue3LLZsvZ3yM9/D4sX/8C///0qERERNG3anI4dO1Gz5k2ULVuWhx9+0OXcdu3uIDq6NevWrWH9+rX88cfvzJw5nVmzvmTUqGfp2/d+AB555DF69+7LmjWr2LBhHZs2/cGWLZuYOvVz3n//I+dGs5erWDFr8+vsXvCXOn06K5a9wr6oqAAvIiIiIiIiIpJPnlbAG/7hGP7hQFbx/YPt77Lm1K8AmM35K/ZazVbKB1SglF9pIv0iKeVfhgBLAHbDjs2w4TAchPqEUiu0NjVCauJjzrn4nFvhfhE092tB89ItrvlahcHX4otv1Q5Y9i91xswmC1azD/9XZSDfBQXyw9HviEs/73H82tNr+PXUz7Qrf0cRZSy55efnx6xZXxEREUHXrj08FrPLly9PcHAw/v5Zfb23bNnM/Plzueuuzrz66hsuheRt27YWSp7nzp11axWTkZHB8ePHc1y9Hh4eQUBAIElJibRs2crt+IYNazGbLVcsoFeqVIljx2JJT093WwV/8OABwPMmpFeSXbg+cuSQ27F9+/YybdoX9OjRm+bNXb8fpKen8/bbb1KpUmU++2wqQUHBzmNbt252OTc1NYV9+/ZSsWIlbr+9vbMf/969e3jyyceYPHkiffvez7lzZzlwYD9NmzanW7eedOvWE8MwWLp0EW+88SozZ05j9Oh3PL6PqlWrExwczI4d292O7dixDcCl735RuLG3fhYRERERERER8SJPBfhL2898uX+Ks/ieW2UDytGmbFsG3zSUl5vGMK7Np0y/Yw7vtx7PK81e5/H6T9K/5kC6V+tFr+p96FvjfvrXHEiXKl2JCqtTIMX3G0VmueYe42Fnd9Gz+n181OYzRjV4lipBnvu9T9o9gbMX3Pt6i3eZzWa6dLmXU6dOMmHCOLfWLwA//riEhIQE7ryzAwAJCfFAVkuTS4vv8fHxLFy4AMhamV6QHA4Hs2Z96RKbPfsr0tJS6dixk8cxFouFdu1u5+DBA/z44xKXY/v27eWf/3yK//1vjLPtjSd33NGBtLQ0pk+f7BKPizvPN9/MIjAwiOjoW/L0XrL7z69du8atfc4338xi+fKlBAcHu427cOECaWlpVKhQ0aX4brPZmDFjOvDXvO/fv4/HH3+YyZM/c7lGjRo1CQ4Odj6R8P33C3j66ZH88stPznNMJhONGjUByPHpAshqAdS+/V1s2bKJP//c4oynp6czZ85MIiNL0bp1m1zNSUHRCngRERERERERkfywp2NOPu4evliAX3ZsMQsOf3vFS9QMqUX1kJpUCapC5aCq1AipSZhveCEkWzwZAZHYw2tgiXfdDNd68ncwDKxmK7eVv52GEY14Zt3fSc5Mcjkv1ZbK+B0f8K9m/8Zs0jrV68nIkU9x4MB+ZsyYxm+/rebOOztQrlx50tLS2LhxA6tW/UKrVm3o0yerZUnjxk0JCwtj6tTPSU1NvbhK/Bg//LCA5OSsPReSkpKu9JL5Mn36ZI4ejaVhw8bs2LGNRYsWUr9+Q+67r3+OYx57bBSbNm3k3//+F+vXr6V+/YacPn2KefO+wWKx8OyzL1zxNQcOfJDVq3+92Ad/Hzff3JL4+DgWLJhLUlISr7zyOgEBAXl+L08//Rx///sI/va3IfTq1Zdy5cqzcePvrFixjJ49+zh7tF8qLCyMZs2as379Wv7zn9dp1KgJiYkJLF26mCNHDmE2m0lOzpr3Ro2aEB19C/PmfUNSUiLNmt2M3W7np5+Wc/z4MUaOfAqAbt16Mm/eN/z3v2+wffuf1KxZi/j4eBYs+BYfHx/n5zwnw4ePYPXqX/jHP0bRv/8DREZGsnDhAg4c2EdMzH889s4vTCrAi4iIiIiIiIjkgyUxFk+7XNrDqrPp3B98tntCjmMbRDSif82B1AtvUJgplgi28i3cCvDm1LOYk47iCK0CQIRfJCPqPs67f77tNn5b3FaWHP2BLlW6Fkm+kjuBgYGMG/cpixZ9x4oVP7JgwVwSEhIICAikZs1aPPfcy9x7b3fM5qxfnISHh/Pee+P55JNxzJ8/l8zMDMqUKcsdd3Tg/vsHMXDgfaxb9xsDBw4u0DzHj/+Md999i59++pFSpUozePBQHnpouHNDV09Kly7DpEnTmDJlEqtX/8qyZYsJCwunWbPmDBky3GOh+1IBAQGMHz+R6dMns3z5Mn77bRVBQcE0btyUgQMfzLE/+tXUrVuPiROnMGnSJyxYMJcLFy5QuXJVnn32Bbp375XjuNGj32b8+LGsX7+WH39cQmRkKerWrccrr7zOe+/9ly1bNnHhwgX8/f0ZPfptZsyYxooVy1izZhVg4qabavPqq6Pp1KkzABERkYwb9ylTpkzi119/Zt68bwgICKBx46bExLxJ/foNr/g+SpUqzccff86ECeOYM2cmNpuNWrVu4p133ueWW9rma26uhcm4fPtf8aozZwr+N3EiV3KlDW1ERIobfc8TbypTJsTbKYiXFPU9vr7X5Z3mLO80Z1l8D/1I8E/Pu8X33/oCTx7/mjR7mvsYqw//aPQCzSJbFkWKN7zcfK35HlxK8MoX3eIpt/6L9KieLrEPt7/HLydXup0bZA3io1s/I9AaVBBpe5X++8yfvM7bE0/8jc2bN7Jq1e9XPbe40tda7u7x9WyNiIiIiIiIiEg+eOr/DgYTTy3zWHwH+GfLf9KyrPvGi5J/meVv9hi3nnbfeHNY1Agi/Uq5xVNsKfwQu7DAcxMRUQFeRERERERERCQfPBXgM3CwMf2Ex/Pvv2kgd1W7q5CzKnmMgFI4Qiq5xa1n3AvwQT5BPF5/lMfrfHfkW1IyUwo8PxEp2VSAFxERERERERHJB/cCvMFhixm7yeR27u0V2tOv5oAiyaskspVt7BazxB/ElJ7oFm8S2YxGkU3c4qm2VH44+l2h5CciJZcK8CIiIiIiIiIieWUYmC8rwGc6MtlvtrudGulXihF1R2LyUJiXgpFZ1r2gDmA986fHeL8ann8ZsvDIPK2Cl1wZN+7TEt3/XXJPBXgRERERERERkTwypZ3FlHlpn3eDC/YLHPP1czu3Z7X78DH7FF1yJZCtjPsKePDcBx6gbnh9GuewCv772PkFmpuIlGwqwIuIiIiIiIiI5JEl/qDLxzbDhs2wcczHtQAf4RdBh4qdijK1EskeUQvDJ8Atbj29Jccx/WoM9Bj/PnaBVsGLSIFRAV5EREREREREJI9c+78bpNmyVsNfvgK+R7X78LX4Fl1iJZXZiq10Q7ew9ew2cNg8DqkTXo/GkU3d4loFLyIFSQV4EREREREREZE8siQedv47e/U7wPFLVsCH+YbTseLdRZ5bSWXz0AfelJmGJW5/jmP61/S8Cn758WU4DEeB5SYiJZcK8CIiIiIiIiIieXTpCvgL9gsAxFmspFosznj3qr3ws7j3hJfCYSubQx/4M577wANEhdWlaanmbvHz6efYl7i3wHITkZJLBXgRERERESkxXnnlFQYPHpyrc3v16kWdOnXc/owaNaqQsxSRG8FfBXgDmyMTcG0/E+ITQqfKXYo+sRLMVqaRx7jPFfrAA3Sq1NljfN3pNdeck4iI1dsJiIiIiIiIFIU5c+YwZ84coqOjr3quw+Fg//79dOrUiY4dO7ocq1SpUmGlKCI3Clsa5uSTWf80bBgXw5e2n+lUqQv+Fn8vJFdyGX6h2MNruG2Qaz2d8wp4gCaRzfC3+DufZMj22+nVDLrpIUwmU4HnKiIlhwrwIiIiIiJSrNntdj7++GPGjRuX6zGHDx8mPT2djh070qNHj0LMTkRuRJaEI85/Z15c/Q6uK+A9tTWRwmcr09itAG9OOoYp7RxGQCmPY3wtfjQv3ZI1p351iZ+5cJoDSfupFXpToeUrIsWfWtCIiIiIiEixlZ6eTq9evfjwww/p0aMH5cqVy9W4vXuz+v7WqlWrMNMTkRvUpf3fbQ6b89/HLq6A97P4cVNoVFGnJYCtnPtGrHD1VfC3lG3jMb729OprzklESjYV4EVEREREpNhKT08nOTmZ999/n7fffhurNXcPAe/ZsweTyUTNmjUxDIPU1NRCzlREbiTZBXgDA5txSQH+4gr4+uENsJrVdMAbMst43ojV5wobsQI0K9UCX4uvW/y306sxDMPDCBGR3FEBXkREREREiq3g4GCWLl3KPffck6dxe/fuJSQkhDfeeIPmzZvTrFkzOnbsyPfff19ImYrIjSS7AG+7pP1MpsnEWasPAA0iPBeBpfA5wqph+IW4xa+2At7P4kfzUi3c4qfSTnI4+VBBpSciJZB+HSsiIiIiIsWW2WzGbM77uqO9e/eSmJhIeno6Y8aMIT4+nqlTp/LMM8+QmZlJz54985CDCbO56Dbws1jMLn/L1WnO8q6kz5k1ORaTCZfV78d9/DAubtbZrEwzrFb3uSnp85YfeZ8zM/ZyTfCJXeUStZ7djtVkB4tPjiNvrdCWtafXuMXXn13DTRE3TksyfZ3lj+Yt7zRnuaMCvIiIiIiIyGUGDhyIxWJhwIABzljXrl3p2rUrY8aMoVu3blgsllxdKzIyCJOp6Arw2UJDA4r8NW90mrO8K7FzlnIcTCaXFfAnff0wm02E+IbQrGpDzKacC1Ildt6uQZ7mrHpLOHpZ73bDRkTmYSjtuUc8QIfg2xm/4wMy7Bku8fXnfuPxliO88r38WujrLH80b3mnObsyFeBFREREREQuM2jQILeYv78/PXr0YNy4cezbt486derk6lrnz6cU+Qr40NAAEhPTsNsdRfa6NzLNWd6V6DlLTyQsLQGH4cBm2J3hk1YfHA6D+mENSYhP8zi0RM9bPuVnzizBdQj20Lc97cAfZPjfdMWxTSKase70WpfY4YQjbD26k6rB1XKfuBfp6yx/NG95pzmDiIigq56jAryIiIiIiEguRUZGAuRpU1aHw8DhKPoN/Ox2BzZbyfxhOL80Z3lXEufMEncEwwCbw+YSP+WTtYFn/fBGV52Tkjhv1yovc2aPqIunfVNNp3dgi7ryNaLLtHErwAOsOv4r/WpWydXrXy/0dZY/mre805xdmRr0iIiIiIiIXCI2NpZ77rmHjz/+2O3YwYMHAahcuXJRpyUi1wlL0lEAMi9pPwNw8mIBvlFEzi1OpGgYviE4Qt2/T1vP7bzq2JtLtcRicm8xtuncHwWSm4iUPCrAX+bXX39l4MCBNGnShGbNmvHQQw+xefPmq46LjY3liSeeIDo6mujoaJ577jnOnz9f+AmLiIiIiEiBqlSpEvHx8cyZM4fk5GRn/Pjx48ydO5dWrVpRpkwZL2YoIt5kTjoGgM1wLcCftvoS4RdBxcBK3khLLmMrVd8tZonfD7YLVxwX5BNE48imbvH9SftIyUwpqPREpARRAf4S69at45FHHiEpKYmnn36akSNHcuTIEQYNGsSWLVtyHBcXF8eQIUPYvHkzDz/8MEOHDmXFihUMHTqUjIyMHMeJiIiIiIj3xcbGMn/+fGJjYwEwm828+uqrHDt2jAEDBjB16lTGjx9P3759sVqtvPbaa17OWES8yZJ0FAcO7MZf7RYcwBkfXxpFNLnhNuosrmyl67kHHQ6scXuvOrZxpPtTDIZhsCN+W0GkJiIljArwl3jzzTepUKECs2fP5qGHHuLhhx9m9uzZBAYG8v777+c4bvLkyZw8eZIpU6bwt7/9jccee4yxY8eya9cu5s2bV3RvQERERERE8mzDhg0899xzbNiwwRnr3Lkz48aNw8/PjzFjxjB58mSaNm3KjBkzqFWrlhezFRFvMycdw3ZZ+5mzVh/sJhMNIxp7KSu5nK2UhwI8YDl79TY0jTysgAfYFrf1WlISkRJKm7BelJCQwJ49exg6dCgBAQHOeOnSpWnZsiWrV6/Ocez3339PdHS0y414mzZtqFGjBt9//z39+vUr1NxFRERERCR3VqxY4Rbr3bs3vXv3dovfdddd3HXXXUWRlojcQCxJR0m7rACfvQFrQw8rp8U77KXqeoxbz+0g/SpjqwRVJcQnhKTMJJf4n3E5d0cQEcmJVsBfFBwczOLFi3nooYfcjsXFxWGxuG/AAVmF+9jYWBo0aOB2rEGDBmzbpseTRERERERERIoFeybmlJPYDZtL+KSPL+UCylPGX/tDXC+yNmKt4ha35mIFvNlk9riZbmzyEeLT4wokPxEpObQC/iKLxUL16tXd4rt27WLjxo3cdtttHsedOnUKgHLlyrkdK1OmDMnJySQlJRESEpKrPMxmE2az+sVJ0bFYzC5/i4gUZ/qeJyIiItfCnHISw7C79H8HOOXjR91w900/xbtsperhmxjrErMkHMjaiNXqf8WxDSMbs+b0Krf49vg/ubVcuwLNU0SKNxXgryAlJYXnn38egBEjRuR4DuDStiabn58fAKmpqbkuwEdGBmnDFvGK0FD3r2ERkeJK3/NEREQkPyxJsdgNu1v8lI8v9UJu8kJGciW20vXxPbjUNehwYD2/B1vZK/fr97QCHmDr+S0qwItInqgAn4O0tDQeffRRdu3axWOPPUaLFi08nudwODzGL2U2536V3fnzKVoBL0XKYjETGhpAYmIadvvVv55FRG5k+p4n3hQREeTtFERE5BplbcBqc4uftPrSLbS2FzKSK8lxI9ZzO69agC8XUJ7S/qU5e+GsS3y7NmIVkTxSAd6DhIQERowYwaZNm+jTpw9PPfVUjucGBWX9IJWe7r6FR3Ys+5zccDgMHA4jbwmLFAC73YHNpmKUiJQM+p4nIiIi+WFJOurW/x3grG8A1UNqeiEjuRJ7qToe49ZzO6+6EavJZKJhRBNWnljuEj+VdorTaacoG+DeilhExBM1QL3MuXPnGDx4MJs2baJ///6MHj36iudXrFgRgDNnzrgdO336NKGhoQQGBhZKriIiIiIiIiJSdMxJR7E5XFvQJJstlAmrhY/Zx0tZSU4M3xAcYVXd4rnZiBWgUYTnVfLbtApeRPJAK+AvkZyczLBhw9i9ezcPPfQQL7744lXHhIaGUrlyZbZv3+52bMeOHTRs2LAwUhUpEHa7nbVrV5GcHE9wcDgtW96CxWLxdloiIiIiIiLXJSPhMA4u34DVl1qh6v9+vbKVqodvwhGXWO43YvXcB/7P81toX/GuAstRRIo3rYC/xOuvv86uXbt48MEHc1V8z9apUyd+++039u/f74ytWbOGgwcPcs899xRGqiLXbOHCBbRq1ZTu3e9h4MCBdO9+D61aNWXhwgXeTk1EREREROT6YxgYiYfdwlkFePV/v1557AN/cSPWq4n0i6RiYCW3+La4PzEMtQ8WkdxRAf6iPXv2sGDBAkJCQqhXrx7z5893+wMQGxvL/PnziY2NdY595JFHCAsL46GHHuKLL75gwoQJjBo1ivr169OjRw9vvSWRHC1cuIDhwwdTr159lixZQVJSEkuWrKBevfoMHz5YRXgREREREZHLmNLjITPZLX7Sx5ebQqOKPiHJlSttxJobjT2sgo/PiONoSqyHs0VE3KkFzUUbNmwAICkpKcfV7z169GDDhg28+OKLvPXWW1SpUgWAyMhIpk+fzltvvcXYsWPx9/enQ4cO/POf/8TX17fI3oNIbtjtdmJiXqZTp85MmTIDX18rwcFBtGwZzZQpMxgyZAAxMa/Qpcu9akcjIiIiIiJykSUxFpthd4uf8w2kcmBlL2QkuWEvVddjPDcbsQI0iGjM4qM/uMW3xW2lSrB7f3kRkcupAH/RAw88wAMPPHDV83r37k3v3r3d4jVr1mTixImFkZpIgVq7dg1HjhxmwoRJmM2uD8GYzWZGjXqGe++9i7Vr13Drrbd5KUsREREREZHriynpKHaHzS1uCauBxazyyvXK8A3GEVYV82V94HO7EWvDiEYe47vid9ClStdrzk9Eij+1oBEpYU6dOglA3br1PR6vV6++y3kiIiIiIiIC6XG7ceDe9zukVEMvZCN54akNTdZGrGlXHRvsE0K14Opu8T2JuwoiNREpAVSAFylhypUrD8CuXTs8Ht+5c4fLeSIiIiIiIgIp591XTNswUbZMMy9kI3lhK+VhAZrDgfX83lyNjwpzb2Nz9sJZzqefv9bURKQEUAFepIRp3boNVatW44MP3sXhcLgcczgcjB37HlWrVqd16zZeylBEREREROT640g45BY74+PDTWF1ij4ZyRNb6WvbiDUqh8/x3oTd+c5JREoONSkTKWEsFgsxMW8yfPhgHnzwfjp0uIvSpcM5ezae5cuXsWzZEiZNmqYNWEVERERERC7hl+LepvOcbyD1Aip4IRvJC3uk5wJ6bjdirR3qefyexF20KnvLNWQmIiWBCvAiJVDXrt15/PFRTJgwjqVLFzvjFouVxx8fRdeu3b2YnYiIiIiIyPXFYUsjMD3ZrQN8enB5zCY1F7jeZW3EWg1zwmGXuPWs59asl6sQWJEgaxApthSX+N6EPQWWo4gUXyrAi5RACxcu4KOPxnLXXXfTsWMn5wr4H39cykcfjeXmm1uqCC8iIiIiInLR+TObKeNhA1ZrWA0vZCP5YStVD9/LCvCW+IsbsVoDrjjWbDJTO6wOm89tdInvT9yL3WHDYlZ5TURypu8QIiWM3W4nJuZlOnXqzJQpM/D1tRIREURcXAoPPjiMIUMGEBPzCl263Ks2NCIiIiIiIsDZ0xsp4yEeVKphkeci+WMrVQ/fA4tdg4aB9fxebGUbX3V8VKh7AT7DkcGRlMPUCKlVkKmKSDGj56RESpi1a9dw5MhhnnzyWcxm128BZrOZUaOe4ciRQ6xdu8ZLGYqIiIiIiFxfUs5v9xgvXfbmIs5E8iunjVhz24YmKqyux/gebcQqIlehArxICXPqVNbGQXXr1vd4vF69+i7niYiIiIiIlHS2hANuMTMmQiO1Av5GYYusCyb3uOXczlyNvyk0ymN8T8Kua0lLREoAFeBFSphy5coDsGuX59/y79y5w+U8ERERERGRkszusOGX7L5A6YJvMCbfIC9kJPniG4QjtJpb2JrLAnyQTxCVgiq7xfckagW8iFyZCvAiJUzr1m2oWrUaH3zwLg6Hw+WYw+Fg7Nj3qFq1Oq1bt/FShiIiIiIiItePo6lHKZNxwS2eHlTOC9nItfDUhsa5EWsu1A6t4xY7mXqCpMzEa85NRIovFeBFShiLxUJMzJssXbqYIUMGsH79OpKSkli/fh1Dhgxg6dLFxMSM1gasIiIiIiIiwL6E3ZSzZbjFLWE1vJCNXAtbKQ994A0D6/k9uRofFeZegAfYm5C78SJSMqkAL1ICde3anUmTprFz5w46d+5AaGgonTt3YOfOnUyaNI2uXbt7O0UREREREZHrwsmzW/AxDLd4YKTnTTnl+uWxAA9Yz+auDU2OG7GqDY2IXIHV2wmIiHd07dqdTp06M2XKZ5w4cZQKFSozZMjD+Pr6ejs1ERERERGR60bSue1uMTNmrOG1SfdCPpJ/zo1YL/t9Sm43Yq0cVAU/ix/pdtfP/F5txCoiV6ACvEgJtXDhAmJiXubIkcPO2CeffExMzJtaAS8iIiIiIgJkOjIxEg+5xa1mC/aQSkWfkFwb3yDsodWxJBxyCVvP7cjVcIvJwk2hUWyP+9MlvjdxLw7DgdmkRhMi4k7fGURKoIULFzB8+GDq1avPkiUrSEpKYsmSFdSrV5/hwwezcOECb6coIiIiIiLidYeSDnjcgNVismIPqeyFjORa2Uu7t5GxxB+EzNxtxBrlYSPWNFsqx1KOXnNuIlI8qQAvUsLY7XZiYl6+2H5mBi1bRhMcHEzLltFMmTKDTp06ExPzCna73dupioiIiIiIeNW+xL2Uy3TfgNVsDcAILO2FjORa5bgRa1xuN2LNqQ+82tCIiGcqwIuUMGvXruHIkcM8+eSzGIbBqlW/MGPGDFat+gXDMBg16hmOHDnE2rVrvJ2qiIiIiIiIV+1P2kf5TPdO76bQqqB2IzckW6n6HuPWs7lrQ1M7LMpjfH/ivnznJCLFm3rAi5Qwp06dBODQoUM8+uhwlx7wVatW4/nnX3E5T0REREREpKQ6kLiPsrZMl5jFZIbQal7KSK6VrdS1bcQa5htOaf/SnL1w1iV+IGl/AWUoIsWNfl0rUsKUK1cegMcff9hjD/iRIx9xOU9ERERERKQkSrOlcTb5EOF2m0s8q/+7NmC9YfkEYg+t7ha25rIAD1Az5Ca32OHkg9gcNg9ni0hJpwK8SAnTsmUrLBYLZcqU5YsvvnTpAf/FF19SpkxZLBYrLVu28naqIiIiIiIiXnMwaT9lMtzbz1hNVhwqwN/Q7KXd+8DnZSPWmiG13GI2h43YlCPXnJuIFD8qwIuUMBs2rMNut3P27BmGDn2A9evXkZSUxPr16xg69AHOnj2D3W5jw4Z13k5VRERERETEa/Yl7qW8hw1YLWYL9pDKXshICkqOG7Ge352r8TVD3VfAQ9YvbURELqcCvEgJk93bffz4iezcuYPOnTsQGhpK584d2LlzJ+PHf+pynoiIiIiISEm0P3Ev5TwU4LNWwKsAfyPzWIAn921oPK2AB/WBFxHPtAmrSAmT3du9evXqrFu3mQ0bfiM5OZ7g4HBatryFjRt/dzlPRERERESkJNqftJd6NtcCvMVkAUzYgyt6JykpEDluxHp2R67Gh/mGE+lXivPp51ziBxL3FVCGIlKcaAW8SAnTunUbqlatxgcfvIvJZKJt23YMGDCAtm3bYTKZGDv2PapWrU7r1m28naqIiIiIiIhXJGUmcirtlFsLGqvJgiOwNFj9vZSZFAifQOxh1d3CeduI1X0V/KHkg9i1EauIXEYFeJESxmKxEBPzJkuXLmbIkAEuPeCHDBnA0qWLiYkZjcVi8XaqIiIiIiIiXnEgMauVyOUFeItZ7WeKC7uHNjSWhEO534jVQx/4TEcmR1OPXmtqIlLMqAAvUgJ17dqdSZOmeewBP2nSNLp27e7tFEVERERERLxmf9I+zIZBmcxMl7jVZNUGrMWErXR992AeNmKtFeJ5I1a1oRGRy6kHvEgJ1bVrd7p0udetB7xWvouIiIiISEl3OPkQkbZMrJc1CbeYLGSEVPJSVlKQct6IdQe2ck2vOj7njVj3cScdryU1ESlmVIAXKcEsFgtt27YjIiKIuLgUbDaHt1MSERERERHxuiPJh9zbz2RvwKoV8MWCLbJODhux5q4PfLhfBBF+kcSln3eJ70/aX0AZikhxoRY0IiIiIiIiIiIXZToyOZ56jLI2TwV41AO+uPAJxB5Wwy2cl41YPbWhOZykjVhFxJUK8CIiIiIiIiIiFx1NicVhOHJYAQ92taApNjy1obEkHITM1FyN99SGJsORoY1YRcSFCvAiIiIiIiIiIhcdST4E4LkAb/XH8I/0QlZSGOylPfSBN8j1Rqw1Q7URq4hcnXrAi4iISIlgt9tZu3aVNp4WERGRKzqSfBiAspcV4K1ma1b/d5PJG2lJIch5I9ad2Mo1u+p4bcQqIrmhAryIiIgUewsXLiAm5mWOHDnsjFWtWo2YmDfp2rW7FzMTERGR681hDyvgTRf/TxuwFi9ZG7GawHDdidV6dkeuxkf4RRLhF0FcepxLXBuxisil1IJGREREirWFCxcwfPhg6tWrz5IlK0hKSmLJkhXUq1ef4cMHs3DhAm+nKCIiIteRIymHCLTbCXbYnbGs/u8mHOr/Xrz4BGIPq+4WtuRhI9aa2ohVRK5CBXgREREptux2OzExL9OpU2emTJlBy5bRBAcH07JlNFOmzKBTp87ExLyC3W6/+sVERESk2EvMSCQuPY5yOW7AqhXwxY3njVgPXfNGrMe0EauIXKQCvIiIiBRba9eu4ciRwzz55LOYza63PWazmVGjnuHIkUOsXbvGSxmKiIjI9cS5Aavt8v7vWQV4rYAvfuyl67sHDbCe25Wr8Z5WwAPs10asInKRCvAiIiJSbJ06dRKAunU9/GAF1KtX3+U8ERERKdmOpHjegNViytpCTyvgi58rbcSaG7VCPRfg9ybuyXdOIlK8qAAvIiIixVa5cuUB2LXL80ZaO3fucDlPRERESrbDyQcB3FrQmE0WMIEjuII30pJC5NyI9TK5LcBH+EVS2r+0W3yfCvAicpEK8CIiIlJstW7dhqpVq/HBB+/icDhcjjkcDsaOfY+qVavTunUbL2UoIiIi15MjyVkr4Mtc0oLGjBkTJhyBZcHi663UpLD4BGAPr+EWtpzN/UasN4VGucWOJB8mw55+TamJSPGgAryIiIgUWxaLhZiYN1m6dDFDhgxg/fp1JCUlsX79OoYMGcDSpYuJiRmNxWLxdqoiIiLiZQ7DcUkLmkxn3Nn/PbiiV/KSwudxI9bEQ5CRkqvxngrwdsPOgaT915qaiBQDKsCLiIhIsda1a3cmTZrGzp076Ny5A6GhoXTu3IGdO3cyadI0unbt7u0URaQIvfLKKwwePDhX58bGxvLEE08QHR1NdHQ0zz33HOfPny/kDEXEW06nnSLDngGGQdlLVsBbTNkFeLWfKa7snvrAG2A9vztX46PC6niMqw2NiABYvZ2AiIiISGHr2rU7Xbrcy4YNv5GcHE9wcDgtW96ile8iJcycOXOYM2cO0dHRVz03Li6OIUOGkJGRwcMPP4zdbmfSpEns3r2bOXPm4OurNhQixc2hi/3fw+02fAzDGXduwBpcySt5SeGzla7vMW49txNb+eZXHV8jpBZmkxmH4drycG+CCvAiogK8iIiIlBAWi4W2bdsRERFEXFwKNpvj6oNEpFCcOXOGiIgIrNai+XHEbrfz8ccfM27cuFyPmTx5MidPnuS7776jVq1aADRp0oShQ4cyb948+vXrV1jpioiXHEk+BEBZW6ZL3LkCPkQtaIorW2RU1kasl/ziBcB6bkeuxvtb/KkSVJXDF7+Gsu1L2ltQKYrIDUwtaEREREREpFDs2bOH1157zbkJ8qFDh+jSpQvt2rWjdevWfPXVV4WeQ3p6Or169eLDDz+kR48elCtXLlfjvv/+e6Kjo53Fd4A2bdpQo0YNvv/++8JKV0S8KHsD1rKZf7WfMQFmU1bpxB6kFjTFljWnjVh35foSnvrAn047RUJGwjWlJiI3PhXgRURERESkwG3fvp2+ffsye/ZsTpw4AUBMTAwHDx6katWqWK1W3njjDX7++edCzSM9PZ3k5GTef/993n777Vytuk9ISCA2NpYGDRq4HWvQoAHbtm0rjFRFxMsOpxwCoEzm5f3fTQA4QtSCpjizlXJvQ5OXjVhrh7kX4EF94EVELWhERERERKQQfPLJJzgcDt5++23Kly/PyZMnWbt2LY0bN2bWrFnEx8fTvXt3pk6dyu23315oeQQHB7N06dI8tbs5deoUgMfV8mXKlCE5OZmkpCRCQkJydT2z2YTZbMr1618ri8Xs8rdcneYs74rbnF2wXeBU2kkAtw1YTSbAZMYcWh7zNb7f4jZvRaGo5swoWx/T/oVucb+EPdgr3HzV8XUj6nqMH0jeS6vyra45v7zQ11n+aN7yTnOWOyrAi4iIiIhIgdu4cSN333033bt3B+DXX38FoGvXrphMJiIiIrjrrrsKvZ2L2WzGbM7bD4UpKVmrHQMCAtyO+fn5AZCamprrAnxkZBAmU9EV4LOFhrrnL1emOcu74jJnu87HYjKByWSi3CU94K0WK2aTCULLE1E6vMBer7jMW1Eq9Dmr0RzWun+vDk3dDxHtrjo8NKwuQb6BpNnSXOKH0vYTERFUYGnmhb7O8kfzlneasytTAV5ERERERApcQkIClSr91a5h1apVmEwmbrnlFmcsICCAjIwMT8O9Krtn/ZXkpah//nxKka+ADw0NIDExDbtdG07nhuYs74rbnG05ug2HI2sDzktb0Jix4DAMbAEVSInLXSuSKylu81YUimzOfKsShgkM19fIOLKZtFq5+9zXCK7FtvN/usS2n9nB+fPJRfqLWH2d5Y/mLe80Z+TqF2wqwIuIiIiISIErX768s/e7zWZj7dq1lC5dmtq1azvP2bFjR643RS1KQUFZP0ilp6e7HcuOZZ+TGw6H4SzsFSW73YHNVjJ/GM4vzVneFZc52x2f1afbZBiUyfxrBbzFZMEwwBZUoUDfZ3GZt6JU+HPmhy2sBpa4/S5Ry5kduX7dmiE3uRXgUzJTOJp0jAqBFQss09zS11n+aN7yTnN2ZSrAi4iIiIhIgWvWrBlLly6ldevWbN68mcTERAYMGABAcnIyX375JWvXruWBBx7wcqbuKlbMKpKcOXPG7djp06cJDQ0lMDCwqNMSkUK0P3EvAOF2G1ayfmFmxoyJrKddHMFFXzyVomcrVc+tAG9OPJy1Eavv1X/xWju0jsf43oTdXinAi8j1QR3yRURERESkwD311FOUKlWKl19+mdmzZxMREcGIESMA+L//+z/ef/99KlasyCOPPOLlTN2FhoZSuXJltm/f7nZsx44dNGzY0AtZiUhhybCnE5tyBICyl7SfsZr/WrPoCK5Q5HlJ0bOXquceNMB6fleuxtcOjfIY35u4+1rSEpEbnFbAi4iIiIhIgatYsSJff/01P/zwA4Zh0LlzZ0qXLg1AdHQ0YWFhDBkyhMjISC9n6lmnTp2YOnUq+/fvp1atWgCsWbOGgwcPMnz4cC9nJyIF6VDyQRwX+36Xtf1VgLeYLM5/20MquY2T4sdWur7HuPXsTmzlb77q+FL+pYnwiyAuPc4lvu/iExYiUjKpAC8iIiIiIoUiIiLCY4uZe+65h3vuuccLGXkWGxvLxo0bad68OVWqVAHgkUceYf78+Tz00EMMGzaM9PR0PvvsM+rXr0+PHj28nLGIFKRLi6NlL+n/rhXwJY8tsjaYTGC47tthPbcz19eoHVqH9WfWusQOJh0g05GJj9mnQPIUkRuLWtCIiIiIiEihSUxMZObMmbz66qs8+eSTAPzxxx9s3LjRy5n9ZcOGDTz33HNs2LDBGYuMjGT69OnUrVuXsWPHMmXKFDp06MDEiRPx9fX1YrYiUtBcC/CXroC/WIA3m3EEli3qtMQbrAHYw2u6h/NQgL/JQxsau2HnYNKBa0pNRG5cWgEvIiIiIiKFYtmyZbz44oukpKRgGAYmkwmAn3/+mYkTJzJ06FCee+65Is1pxYoVbrHevXvTu3dvt3jNmjWZOHFiUaQlIl60/9IC/MUWNBaTGRNZ37McQeXBrPJJSWErXd99I9aEw5gykjF8g686/kp94KPCPG/SKiLFm1bAi4iIiIhIgdu6dStPP/00fn5+PPXUU9x7773OY61ataJixYp88cUXLFmyxItZikhJl2pL4XjqMefHZWxZLWicq98BR3DFIs9LvMfmaSNWwHI+dxup1gqt7fyF86X2xOduI1cRKX5UgBcRERERkQL30UcfERgYyDfffMOIESOoUaOG89itt97KrFmzCA8PZ/r06V7MUkRKugOJf610NhsGpS/2gLdeUoC3q/97iWLPoQBvPZu7NjQB1gCqBlVzi+9JzF0BX0SKHxXgRURERESkwG3atIm7776b8uXLezxeunRpOnXqxL59+4o4MxGRv+xP+qv9TIQtEytZm2+6bsBaqcjzEu+xRdYGs3u5LC994KPC6rrFzl44w/n089eUm4jcmFSAFxERERGRApeWlkZw8JV75fr5+ZGamlpEGYmIuHPZgPVi+xkAi8ni/LdWwJcw1gDsYZ42Yt2R60vk1Ot9b4JWwYuURCrAi4iIiIhIgatatSp//PFHjscNw2DDhg1UqVKlCLMSEXG1P/Gvp3DKZmZvwGoB/urhrR7wJY+ttHsbGnPCEUwZybka72kFPMCeRPWBFymJVIAXEREREZECd8899/Dnn38yduxYDMNwOWaz2XjnnXfYtWsXnTt39lKGIlLSJWQkcObCaefHZW1ZBfhL+78DOELUgqakudaNWCsEVCTYx/0psD0JKsCLlETWq58iIiIiIiKSN8OHD2fFihV8/PHHzJkzBx8fHwBGjBjBzp07OX36NHXq1GH48OFezlRESqr9l7SfASibvQGr+a/2M5itOAJKF2Vach3IeSPWHdjK33zV8SaTidqhddh0zvVJsP2J+7A5bC57DIhI8acV8CIiIiIiUuD8/PyYNm0agwcPJi0tjePHj2MYBj///DOJiYn069ePL7/8koCAAG+nKiIl1OUF+DK27BY0l27AWh4uLchLiWCLjLrmjVhre+gDn+nI5HDywWvKTURuPPqV2xW88sorHD58mGnTpl313F69erFjh/uGHHfffTdjx44tjPRERERERK5rAQEBvPTSS7zwwgscPHiQhIQEgoKCqFGjBr6+vt5OT0RKuP1J+1w+LpuZgYnLN2BV//cSyeqPPawmljjXr5G8FODr5NQHPmE3tUJrX1N6InJjUQE+B3PmzGHOnDlER0df9VyHw8H+/fvp1KkTHTt2dDlWqZJ6xYmIiIhIyWY2m6lVq5a30xARcTIMw2UDVothUNqWeXH1uzZglayNWC8vwGdtxJqE4Rty1fE35VBk35Owiy5VuhZIjiJyY1AB/jJ2u52PP/6YcePG5XrM4cOHSU9Pp2PHjvTo0aMQsxMRERERuTF88MEHuTrPZDIxatSoQs5GRATS09NZtepntm37k7iMcxytE4uP1Yqvrx+RtkzMuK5+B3AEV/BOsuJ1tlL18Nv7nVvccm43tgotrjo+0BpEleCqxCYfcYnvTczdRq4iUnyoAH+J9PR0+vbty+7du+nZsye//fZbrsbt3ZvVN06rekREREREsnz88ceYTCYMw3A7ZjJlrS41DEMFeBEpdIZhMHv2DL77bh7JySlcsKdhq2rDUd1GRrqJ1NRUauMADLfNMe0heqq9pMpxI9ZzO3NVgAeoHVrHrQB/Ku0UCRnxhPmGX2uKInKDUAH+Eunp6SQnJ/P+++9zzz330L59+1yN27NnDyaTiZo1a2IYBmlpaQQGBhZytiIiIiIi16+33nrLYzw1NZVDhw6xYMEC6taty0svvVTEmYlISWIYBu+/P4ZffllJQkY88Rnx2IxMAsMD8Tf8MWHChJmIC6kYDgOrybVM4gjSCviSyrkRq8PhEreedd//Lyd1wuqy4vgyt/iehN20LNPqmnMUkRuDCvCXCA4OZunSpViteZuWvXv3EhISwhtvvMHixYtJTU2lSpUqPP3009x7772FlK2IiIiIyPWrV69eVzw+ePBgevXqxfr166lTp04RZSUiJc3s2TP45ZeVnEw7QYothQrNKlDp5sqcCz9Dmj0Ne6Yde6adsvZMDAMy0tPx9/9rQZ1WwJdgBbARa+1Qz///bU/CLhXgRUoQFeAvYTabMZvNeR63d+9eEhMTSU9PZ8yYMcTHxzN16lSeeeYZMjMz6dmzZx5yMGE2m65+okgBsVjMLn+LiBRn+p4ncv2oWrUqd999NzNmzGDw4MHeTkdEiqH09HS++24eCRnxpNhSaDqoKWXqlcUwDE4kHMNkMWO1mDFbzVRItoEDLmRewN8/ADCBxQcjoJS334Z4kceNWBNjc70Ra6WgygRaA0m1pbrE1QdepGRRAb4ADBw4EIvFwoABA5yxrl270rVrV8aMGUO3bt2wWCxXuMJfIiODnD0xRYpSaGiAt1MQESky+p4ncn0ICwvj6NGj3k5DRIqpVat+Jjk5hfiMeCo0q0CZemUBuGBPw3HJ/hRmq4UKhgPDbmAYBhkZ6fj6+uMIrggm/dK+JLOVqp/DRqy7sFVoedXxZpOZm0Kj2Hp+s0t8b+IebA6b254DIlI86b/0AjBo0CC3mL+/Pz169GDcuHHs27cv14/Vnj+fohXwUmTsdjvr1v1GYuJ5QkMjadXqllz/skhE5EZksZgJDQ0gMTENu91x9QEiBSgiIsjbKVxXEhMTWbZsGaVLl/Z2KiJSTG3b9mfWhqtGJpVuruyMp9pT3c6t4LCDI6sAn5lpw9cX7MHq/17S2UtfaSPWqxfgIasP/OUF+Ax7BvsT91In3PP1RaR4UQG+EEVGRgJZG03llsNh4HAYVz9R5BotXLiAmJiXOXLksDNWtWo1YmLepGvX7l7MTESk8NntDmw2FeBFCtNzzz3nMe5wOEhJSWHjxo0kJiYyfPjwIs5MREqK1NQUHEbW/78PLPXX029pl7UDsRoGpe12516bxsXV8Q71fy/xbBG1c9iINfd94OuHN/QY33p+swrwIiWECvDXKDY2lhEjRtCtWzcee+wxl2MHDx4EoHLlyp6GinjNwoULGD58MJ06dWbixC9o06Yla9Zs4L33xjB8+GAmTZqmIryIiIhckwULFlzxuNVq5d577+Xvf/97EWUkIiWNr68fyUlJpNvS2b99H6FVwggKCiLV7FqAL2OzYTIAwwATzraw9mAV4Es8qz/28FpYzu91DedhI9aosLr4mH3IdGS6xLfGbaEvA3IYJSLFiQrw16hSpUrEx8czZ84cBg8eTHBwMADHjx9n7ty5tGrVijJlyng5S5G/2O12YmJeplOnzkyZMgNfXyvBwUG0bBnNlCkzGDJkADExr9Cly71qRyMiIiL5NnXqVI9xk8mEj48PVatWdT4xKiJSkAzDYPbsGSxdupjTx05jj7Bx7I9jZAbbOJ94HkcZO2azGbPZjCPTQdnUCzgyDQwHGGYDqzWrVOJQCxoBbKXquRXg87IRq6/Fl3rh9dl6fotLfG/CbtJsaQRYtTeRSHGnAnwexcbGsnHjRpo3b06VKlUwm828+uqrPPnkkwwYMIC+ffuSlJTEV199hdVq5bXXXvN2yiIu1q5dw5Ejh5kwYRJms+uGQmazmVGjnuHee+9i7do13HrrbV7KUkRERG500dHR3k5BREogwzB4//0x/PLLSi6Y0sgw0jFSDRL3JuJb1ZegqCAsWLCl2zBsWa1myqVnYtgcYBjY7Q7S0lIxDAf24IpefjdyPbCVqoffXvenunK7EStA48imbgV4u2FnZ/x2mpduUSB5isj1S9t559GGDRt47rnn2LBhgzPWuXNnxo0bh5+fH2PGjGHy5Mk0bdqUGTNmUKtWLS9mK+Lu1KmTANStW9/j8Xr16rucJyJSXNjtdlat+oUZM2awatUv2O12b6ckIiIiBWz27Bn88stKTqad4Hzmeco2LktQWDB+fn7Er4rnwskLOC44cGQ4MC5uulrOYcMwADOYLGB3OEhLS2PctLnOfvBSctlLe/7Z2Xp2R66v0Siiqcf45ZuzikjxpBXwV7BixQq3WO/evendu7db/K677uKuu+4qirRErkm5cuUB2LVrBy1auK9M27lzh8t5IiLFgTaeFil8d9xxR77GmUwmfvrpp4JNRkRKpPT0dL77bh4JGfGk2FJoOqgppeuWYdvsPzm55QSZhg1M4Mh0YDKbsvq9W0xUxIHFz4IJwA72TAepNjNLfltLqcoz6ddPfbpLMltkbTBbwOG6eMN6dnuur1E9pAbBPsEkZya7xP+M25LDCBEpTrQCXqSEad26DVWrVuODD97FcdlO7g6Hg7Fj36Nq1eq0bt3GSxmKiBSs7I2n69Wrz5IlK0hKSmLJkhXUq1ef4cMHs3DhlTeKFJHcOXnyZL7+nDhxwtupi0gxsWrVzyQnpxCfEU+FZhUoU68sJpOJhv0aUavjTfgH+ONb3jdrk1UTmH3NmH3NVLDZsVjMWH2tWAN88Am0cjTVICEjngULviU9Pd3bb028yeKHPeImt7D19NZcX8JsMtMooolb/EjyYeLT464pPRG5/mkFvEgJY7FYiIl5k+HDBzNkyACefvoftGnTkvXrN/D++//H0qWLmTRpmjZgFZFiQRtPixSdXbt2eTsFESnhtm37kwv2NGxGJpVuruyMm0wmaravhSnCxHn/cxgYmKwmTBYTABUdBuZL7gPMVgtxAX7EZ8QTlhzBqlU/06FDpyJ/P3L9sJVpjOXcbpeYOfUM5pSTOIJy9/R4o8gm/HZ6tVt8W9xW2pa/vUDyFJHrk1bAi5RAXbt2Z9KkaezcuYPOnTsQGhpK584d2LlzJ5MmTVM7BhEpNrI3nn7yyWdz3Hj6yJFDrF27xksZioiISEFJTU3BYWQ95RtYKsDteFJyEll9ZsDim1Vw9zcMwh3u+8KklInEZmSSbk9j27Y/Cy9puSFklm3kMZ6XVfCNIt1XwIP6wIuUBFoBL1JCde3anS5d7mXDht9ITo4nODicli1v0QpQESlWtPG0iPelpqYSHx/vsvGxYRhkZmYSHx/PypUrefbZZ72YoYgUF4GBQZhNWb9wTz2Xhl+ov8txR+AlhXYTYEB5uz27Ju8iITQEOIMdB6mpKYWWs9wYbGUae4xbT28lo0buno4oH1CBMv5lOXPhtEt86/ktGIaR1RpJRIolFeBFSjCLxULbtu2IiAgiLi4Fm81x9UEiIjcQbTwt4j3p6ek899xz/Pjjj277zlxOBXgRKQh16tTl22+/JiMzk+3L/6R65xoEBQURFhae9SRcxF8FTsNugBkqZNgxmdybAxxLzYpZMBMYGFRk70GuT46QyhgBEZjSXPu1W8/k7emIRpFNWHF8mUvsXPpZTqadoEJgxWvOU0SuT2pBIyIiIsXWpRtPZ2ZmsmrVL8yYMYNVq34hMzNTG0+LFKIJEyawZMkSAgICaNKkCVarlUqVKtG4cWNCQ0MxDINSpUrx9ttveztVEbnBGYbBrFlfMXXqF5w4cYKMhAzObDvDkc1HOHrsKLt27+Tk6ROYg/8qwDsyHYCJChkOzCYTGAb2TDu2tEwyUzP5ecE+bEk2fEx+NGzouf2IlCAmk8dV8NZzu8Ce+016G0c29RhXGxqR4k0r4EVERKTYyt54etiwQdx0U2XS0tKcxwICAkhLS+Pzz6er/ZZIIVi6dCkRERF8//33REZGMnz4cMLCwnjvvfew2Wz85z//YcaMGURERHg7VRG5gRmGwfvvj+GXX1aSkBEPwQYZ59OxWC2cWHacxFpBhNUNJz0zHVM5MBwGGGBggN2gqsmCPcOOLcP+/+zdd5yUd7n//9d931N2Z7YX2tIhdEIKIYgkREkPmOJJIjGamFjO8fhVk6NHPbaoP/VojifHGMsxYhJNORJLGunBhCAppNMDhLLAAtt3p+zM3OX3x8LCMrOErbO7vJ8+Isx1f+7PXnMz26753NcHPO/gnLB+cz3xuJ9t0XfZt2+fWoQIqSEn49/1Qvuga+Or3YQ9JHN/96PNKM7cymZt3dtcMPLi7qYoIv2UVsCLiIjIoNfRL8z6RVqk9+zZs4dzzz2XkpISAKZPn84bb7wBgM/n41vf+hZjxozh3nvvzWaaIjLALVv2ACtXPs++eBU1iRpGnzOGcQvGk1+YT9AXJP5enL2P7aFufS12i334RA/chEv43TrsFhvP89r+a0x6xMM+zBKDYHEODz74J2677Va8gwV6OTHZ5d3fiLUwUMjYvHFp8Xfq38Z27QxniMhgoAK8iIiIDFqO43DLLd/k/PMvZOvW3TzyyOPcf//9PPLI42zdupvzz7+QW275VrvNIUWkZ3ie11Z8Bxg9ejT79++nubkZANM0mT9/Plu2bMlWiiIywCUSCR599CEakw1E7SinXHsKM688mTM+N4eTLphEuCiPvFAeQSuIr8QPLhiegWlaWIYFNpS1JFoL656H4TMwgyY1BTmMXDSK8lPKSQWT7IvvZeXKF3jwwf/L9lOWLLLLpoGZftekr/r4C/CQuQ1N3I6xsWF9V1MTkX5OBXgREREZtF5+eTW7du3kS1/6N/x+P/Pnn82SJUuYP/9s/H4/X/zizezatYOXX16d7VRFBp2hQ4eyZ8+etsejR48GYOvWrW2xQCBAbW1tn+cmIoPDqlUvEIlEaUg2MPzU4ZRPHQK03uE2/sMTOPvrC5j20RmMmTeW8NgQGK1v/uXn5xMKh0nttRldaLTGgxZmwMKwTGr8QcbOGsu8G+dzyrWnErWjNCbreeSRv5FIHH+/bxlkfLk4JSelhw+809a+6HicVjY7Y/y1mle7nJqI9G8qwIuc4A7dZikiMhjt378PgClTpmU8PnXqtHbjRKTnzJ07l+eee47XX38dgMmTJ2NZFsuXLwda71B56aWXKCsry2aaIjKArVu3lhYnju2lqDh9ZNpxy29RcXoFk6+YQrA0iGEaGKaBbdtYpsnwljCFOQam38Lym5imiWVZ2OXDGTJkKAYG5VOHMPyU4TQkG4hEoqxa9UKGTOREkcqwEasZq8GMHv/PklMKpxL2hdPir9W8qt/NRQYpbcIq0kN27NhOU1NjttPoFM/z+PKXv4DPZ3LbbXfgugPzm31BQSFjx6b30RMRGTp0GACbNm1g9uw5acc3btzQbpyI9JxPf/rTLF++nGuvvZb//M//5NJLL+X888/nvvvuY/PmzTQ2NrJlyxaWLFmS7VRFZICKxaK4ngtAqDS3w3FxJ4ZhHt73xfM8UrbNiIYU5IIv4MO0Dq9PrAuG2p1fMXskVW/tJeHEWbduLQsXnt/Dz0QGCnvIybBxWVrcd+AdknnDj2sOy/RxatlsVu1r/2bOgfh+KqO7GJ03pkdyFZH+QwV4kR5QW1vL3Lmn4rputlPpsg9/+Kxsp9BllmWxbt1WSktLs52KiPQzc+fOY/ToMfz85z/jnnse4Mib/1zX5fbb/5vRo8cyd+687CUpMkiNHj2a++67j//5n/9hyJDWthDf+MY3eO+991izZg0Ap512Gl/84hezmaaIDGChUBjTaP3eHquNEyzIyTgu7sTxjlhsZBgGnucxNNa66eWRxXmA2kD7Avyh4r6DSywW7bH8ZeCxM6yAh9Y+8MnxFxz3PLPL5qQV4KF1FbwK8CKDjwrwIj2gtLSUl19+c8CtgI/H4yxe3PpDwuOPP0MgEMxyRl1TUFCo4ruIZGRZFrfc8kNuvPETXHfdEm666SvMm3cGr766httu+y+efvpJli79I5aVvqGWiHTflClT+M1vftP2uLy8nIceeohNmzaRk5PD2LFjs5eciAx4M2bM5O9/X4HP8LPn9d0UjyvOOC5mx3BSDsbB//l9PoyEyQhaf3/zXK9dEb4u0H41faw2DoCFSSiU3jpEThxu3gi83BKMeF27uO9A5zZiPaXkNCzDwvGcdvHXal7lirFXdjtPEelfVIAX6SEDsQVKNHp49cbMmScTDHZ826aIyEC1aNFHWLr0j9xyyze58MKFbfHRo8eydOkfWbToI1nMTmTw+s53vsPll1/OqaeemnZsypQpWchIRAab+fMXcNddv6MoWUTVm1UMnT60bSPWQzzPIxqP4KQcTMPCMAwCgSAjzApGhg4ABm7KwfQdvkuu9qgC/J7XduMz/AStXGbMmNkXT036K8MgVX4ygV3Ptwv76jaD3QK+zHdhHC3sDzOteAZr695uF9/SuJnGZAOFgaIeSlhE+gNtwioiIiKD3qJFH+GVV97ikUce5/777+eRRx7nlVfeVPFdpBctW7aMa665hgsuuIBf//rX7N27N9spicggEwwGWbz4MgoDRYR9Yd669y3WPbiW+u31JJpaqN9ez7on1pJsSWJgYmKQk5OLYRjMHf9BKvI8TMPESbm4dutK5GZfgJR5+M646o0HqHqriqJAEfn5ecyfvyBbT1f6CXtIhjY0roOvdmOn5pldlr4/EcAbNa91JS0R6ce0Al5EREROCJZlMX/+2RQXh6mvj2LbA3ffDpGB4K677uKRRx7hmWee4ec//zm/+MUvmDNnDpdddhkXXHABubm6805Euu+qq5awZ89uVq58gcZkPdVvV1P11uE3/ELTQoSMMCatK99zc1tXKE8tncbUigLMSD0eBqmYjeV32ZcbItHUQqw2zp7XdlP1VhVhX5jCQDGLF19GMDgw23ZKz7HLM98F4ateiz00/a6vjpxedgZ3vXtnWnxNzSt8aMS5Xc5PRPofFeBFRERERKTHfeADH+ADH/gA3/ve93j22Wd55JFHWLVqFa+88grf//73ueCCC7jssss488wzs52qiAxghmFw001fpaJiJI8++hCFkWISThwHFwsTr8LDNlLk5OQeLL639nqfmD+RYbk2sWSQZDKBi4eTctm4q5GVf2rdHNNn+CkLllEYKObssxdw5ZUfy+Izlf7CLpsGpgVu+/7tvup1nZpnaO4wRueNYVdkZ7v423VvknSSBKxAt3MVkf5BBXgREREREek1gUCAiy++mIsvvpiGhgYef/xxHn/8cR5++GEeeughRowYwXPPPZftNEVkADMMg6uvvobLLvsoq1a9wLp1a4nFooRCYd6a9DrN/mYM4/Amq0Nzh1Hguhh2C+FwGMsyaWlpwfQMmu0ihoeKsDAJWrnk5YX5yEcu58orP9ZuDjmB+XJwik/Cqt3UPtzJAjy0roI/ugCfdJKsq3+H08pmdytNEek/VIAXEREREZE+UVRUxMKFC1s3RYxG2bhxo3rDi0iPCQaDLFx4PgsXng9A3I7xyRc+hkH7wvlJBZMwmw997THIyQmRk5NLMplg6ITZfGjYaEKhMDNmzGT+/AVqOyNp7PIZaQV4M7ofI1aNFyo/7nlml53J33b8OS2+puYVFeBFBhEV4EVEREREpFc1Njby5JNP8thjj/H666/jeR7FxcV88pOf5Iorrsh2eiIySG1r3pYxPrFwElZkz1FRg0Agh/MWf5pzRszt/eRkQLPLZhAkvXDuq15HasyHjnueiQUnURgoojHZ0C6+pvplPj3pc1imynYig4E+k0VEREREpMe1tLTw3HPP8eijj/KPf/wD27axLIsPf/jDXH755SxYsACfT7+OiEjv2db0bsb4SQWTMHe9kvGYk1fRmynJIJEqn5Ex7u9kAd40TE4vO4MVe59pF29MNrK+YR0nl5zSnTRFpJ/QT7wiIiIiItLj5s2bRzwex/M8pk2bxuWXX86iRYsoLi7OdmoicoLY0phegLcMi7F547Eif00/wQA3PLQPMpOBzi0cgxfIw0hG2sV9NZ3vA//BoWelFeAB/rF/pQrwIoOECvAiIiJyQkgmk9x55++oqtrN8OEjue66TxMIBLKdlsiglZuby9VXX83ll1/OpEmTsp2OiJxgPM/j3aZNafExeWMJWAHM5qNb0IAbGgKWfjaQ42CY2GXT8e9tfyeFVbMeXAdM67inml40g8JAIY3Jxnbxlw+s5jOTP49PbWhEBjx9FouIiMig973vfZvf/OYOHMdpi33nO9/kn//5C3z3uz/IYmYig9fKlSuxrOMvQIiI9KTK6C7qE/Vp8ZMKJwNgRarSjrl5I3o9Lxk87PIZaQV4IxXHatyOUzzxuOexTB8fGPJBntz9eLt4zI7xVu0bzC6f0yP5ikj2mNlOQERERKQ3fe973+aXv/w5JSUl/Ou/fpFf/epX/Ou/fpGSkhJ++cuf873vfTvbKYoMSiq+i0g2vV33Zsb4jOKZ4LmYkb1px5x89X+X42d30AfeV935NjTzhp6dMb76wKpOzyUi/Y9WwIuIiMiglUwm+c1v7qCgoIBgMIdf/vL2tmMjR46ioKCA3/zml3zjG99WOxoREZFB5K3a19NipmEys/gUjHgtOKm041oBL51hl8/MGPdVryUx6bJOzTW5cAolwVLqErXt4muqXybpJAhYwa6mKSL9gFbAi4iIyKB111134jgOTU1NTJ8+g6eeWkFzczNPPbWC6dNn0NTUhOPY3HXXndlOVURERHpIwkmwoWF9WvykgkmE/WGsDKvfQQV46Rwvpxg3P/0105UV8KZhMm/o/LR4i9PCG7WvdSk/Eek/VIAXERGRQeu997YBsGDBh7jnngc444w55OXlccYZc7jnngdYsOBD7caJyOBUWVnJF77wBebMmcOcOXP493//d+rq6t73vMsvv5zJkyen/ffFL36xD7IWka5aX78W27XT4rNKTwPIuAErqAWNdJ5dlt6GxmrYBqlYp+f64NCzMsb/sf/FTs8lIv2LWtCIiIjIoGUYBgCzZp2CabZfd2CaJjNnzuKFF/7eNk5EBp/6+nquu+46kskkn/70p3Ech6VLl7J582YefPDBDttPua7Ltm3bOP/88zn33HPbHauoUJFOpD/rqP/7qQcL8B2vgB/eaznJ4GSXzyCw/en2Qc/DV7sRe9jpnZprQv5JDM0dyv74/nbx12vWELdj5PpC3U1XRLJEBXgREREZtE47bTa///2d3H//H/n617+Nz3e40GbbNv/3f/e2jRORnnXHHXdw5plncsYZZ3Q4ZsWKFTz77LP86Ec/6rU87r77bvbt28ejjz7KhAkTAJg1axaf+tSneOihh7jqqqsynrdz504SiQTnnnsul156aa/lJyJdl0gkWLXqBdatW0ssFiUUCjNjxkzeyElv2ZHvz2d8/kSggxXwpoUbGtrbKcsg03Ef+HWdLsAbhsG8oWfxtx1/bhdPuSnW1LzK2cPO6WqaIpJlakEjIiIig1ZFxUgAampqmDVrCnff/Xv27t3L3Xf/nlmzplBTU9NunIj0nDvuuINXX331mGNeeuklHnvssV7NY/ny5cyZM6et+A4wb948xo0bx/Llyzs8b8uWLQDtzhOR/sHzPP70p/u58cZP8Itf/Jwnnl3O3/+xgieeXc7Pf38bG6vWE4/HAK/tnJNLTsE0WksgZqQqbU43PAxMq6+eggwSdunkjK8bX/XaLs33waFnZ4y/tH9Vl+YTkf5BK+BFRERk0Jo7dx6jR4/BNE127drJzTd/kZtvbu3dbJoWY8eOw3U95s6dl+VMRQa++++/P62g/Ze//IXVq1dnHG/bNuvXr2fIkCG9llNjYyOVlZVccMEFacemT5/O888/3+G57777LoZhMH78eDzPIx6PEwrp9n+RbPM8j9tuu5WVK5+nMdlAQ7IB20u1HQ+NDRHywsTjcRzHJS8vDBicUnp4NbIVSV8B72gDVukKK4hTchJWzaZ24a5sxAowOjyGkeFR7I5Wtou/XfcmLU4LOVZOl1MVkexRAV5EREQGLcuyuOWWH3LDDdeSk5NLS0u87VggEGDHju38/vf3Ylla8SbSXRdeeCE/+9nPiEajQOut9Hv37mXv3sy9lgGCwSBf+tKXei2n/ftb++gOHZreVqK8vJxIJEJzczP5+flpx7ds2UJ+fj4/+MEPePLJJ4nFYowaNYqbbrqJSy65pNdyFpFjW7bsAVaufJ598SqidpThpw6n4vSRhEpzidXG2RXdge3ZeJgkkwnicYvc3FxmlZzSOoFrY0b3pc3rqgAvXWSXzUwrwJuxaozoAbxw595kNgyDM4fMY/f2P7WLp9wUb9W+wdwhWjQiMhCpAC8iIiKDXkebrGrzVZGeU1JSwjPPPEM8HsfzPM4991yuu+46PvnJT6aNNQwDn89HSUkJPl/v/Upy6M2A3NzctGPBYBCAWCzWYQG+qamJRCLBrbfeSkNDA3/4wx+4+eabSaVSXHbZZcedh2kamGbffb2xLLPdn/L+dM06LxvXLJFI8NhjD9OYaiDqRDnlE6cwZOrhAmewIMi+hgBGyiAVT+Fi0NISZ0r5FMrDZQAYzTUYngtHf0oWjsTn6/3notda5/X3a+YOm4mx+cG0eLBuPXbhsE7PN2/YPP5yVAEeYE3Ny8wfMf+45ujv16y/0nXrPF2z46MCvIiIiAxajuNwyy3fZNasU6ipqWH37sO385aVlVFWVsYtt3yLiy66RKvgRXpASUlJ29+/8IUvcOaZZ1JRUZG1fFzXfd8xppn5F8ZrrrkGy7JYsmRJW2zRokUsWrSIW2+9lcWLFx/3142SknBW3vArKEh/40GOTdes8/rymj3xxEoSiThNqUZGnl7BiJOHtzseSUZwcbH8Fq7t4qZcMEzKW8ooLg63Dmqugwyfj6Fh4wgdGtMH9FrrvH57zSbOgZXpr6n85s1Q/JFOT3da0UyGrx3G/tj+dvE3614jrzCA3/Qf91z99pr1c7punadrdmwqwIuIiMig9fLLq9m1aye7du3kggsuYunSe5g37wxWr17Df//3rTz11BNt4z74wbOynK3I4PKFL3yh7e+xWIzNmzfT2NjIOeecQ1NTEwUFBb2eQzjcWkxLJBJpxw7FDo052rXXXpsWy8nJ4dJLL+WOO+5g69atTJ48+bjyqKuL9vkK+IKCXJqaWntgy/vTNeu8bFyzl156leaWKEknyfBTR2CnnHbHG1oa8Q7uu2r5LZyUg+u5xDcnqK9vvSPGX7WNkOcdPTURswzn4JjepNda5/X/a1ZOgT+MkYy0i9q73iDaxdfU7NIzeTTycLtYcyLCqm0vc0rZae97fv+/Zv2Trlvn6Zpx+A3eY1ABXkRERAatqqrW3tMLF57HPfc8QCDgIy8vzBlnzOGeex7g4x+/kueee6ZtnIj0rIaGBn74wx/yxBNP4DgOhmGwYcMG7r//fh566CF++tOfcvLJJ/faxx8xorWnc3V1ddqxAwcOUFBQ0OmNVQ+t8o/FYsd9jut6uG56wa+3OY6LbZ+Yvwx3la5Z5/XlNYtEIjhua9E9tySXo+vokdThAqhx6E2vFPhrA205+hv3pJ0HkMwdhteH//Z6rXVef75mqbIZ+Pe83C5mVa/HTqbA7PxdlmeUzeXRnQ+nxVfvW82MolOOe57+fM36M123ztM1OzY16BEREZFBq7a2BoBLLlmc1mbCNE0uumhRu3Ei0nOamppYsmQJjz76KCNHjmT8+PF4B6teruuyY8cObrjhBrZv395rORQUFDBy5EjWr1+fdmzDhg3MmDEj43mVlZVcfPHF/PrXv047dijfkSNH9myyIvK+QqEwptH6/TxWG293zPEcWuzDMe/gm17mfpO80OF9HszmPekTWwG83LJeyFhOFHZZhu8ndgtWw3tdmm9S4RQK/Ol3iq2pfgXXU5FTZKBRAV5EREQGrdLS1l+mly9/NK0XtOu6PPHEY+3GiUjP+fWvf8327dv53ve+x5NPPsmFF17Yduzzn/88//3f/00sFuO3v/1tr+Zx/vnn89JLL7Ft27a22OrVq9m+fTsXX3xxxnMqKipoaGjgwQcfJBI5vKJ27969/PWvf+XMM8+kvLy8V/MWkXQzZswkx8rFZ/jZ8/rudsdidpQjF7Y7KQcDg8C+HGbMmNkWtyLpd725ecMz9oUXOV52eeY3dH3Va7s0n2VYzC4/My3ekKxnS+PmLs0pItmjAryIiIgMWsOHt7afWLHiWa67bgmvvvoKzc3NvPrqK1x33RJWrHi23TgR6TnPPPMMZ511FldffTVA2iakF198MQsWLGDNmjW9msdnPvMZCgsLuf7667nrrrv4zW9+wxe/+EWmTZvGpZdeCrSueH/44YeprGzdqNk0Tb7zne+wZ88elixZwh/+8Ad++ctfcuWVV+Lz+fjud7/bqzmLSGbz5y8gLy9MUaCIqjerqN54oO1Y1D7ca9u1ndYCvGFSEClg/vwFbcfMDAV4J08/B0j3dFyAX9flOc8s/0DG+KvVL2eMi0j/pQK8iIiIDFpz585j9OgxzJp1Chs2rOfCCxdSUFDAhRcuZOPGDcyadQqjR49l7tx52U5VZNDZv38/U6dOPeaYCRMmcODAgWOO6a6SkhLuvfdepkyZwu23384999zDwoULufPOOwkEAgCsWbOGf//3f2/3ZsCFF17IHXfcQTAY5NZbb+Xuu+/mlFNO4YEHHmDChAm9mrOIZBYMBlm8+DIKA0WEfWHeuvct1j24lvrt9TS3NOM5LnZLilQ8hYGJlTS5fME/EQwGWydwkpix9K85bn5FHz8TGWy8nOKMryNfTdcL8DNLZpFr5abFX6l+qa2lm4gMDNqEVURERAYty7K45ZYfcuONn+C88y7gC1/4EmVlRdTUNPDcc8/wzDNPsXTpH7Gszm+OJSLHVlhYyO7du485ZufOnRQUpPe47Wnjx4/nzjvv7PD4FVdcwRVXXJEWP++88zjvvPN6MzUR6aSrrlrCnj27WbnyBRqT9VS/Xc2+9VUUf6oYDDAwMA0LE4MRxkiuunJJ27lmdB9kqFtqBbz0BLt8BoGj9hiwGrZBMgqBcKfn85t+Ti2bzer9L7aL74/vY1d0J2PyxnYnXRHpQyrAi4iIyKC2aNFHWLr0j9xyyzd5+ukn2+KjR49l6dI/smjRR7KYncjgdeaZZ/L000+zadMmpkyZknb8nXfeYcWKFe16w4uIvB/DMLjppq9SUTGSRx99iMJIMfFhEVJmqvU4BoZhkJOTy1VnLmnX/ipT/3cAVwV46QF2+QwC7z3VPuiBr3YD9vAzujTnnPK5aQV4gFcPvKQCvMgAogK8iIiIDHqLFn2Eiy66hDVrXiISaSAvr4gzzviAVr6L9KLPf/7zrFixgmuuuYZrrrmG9957D4CnnnqKt956i/vvvx+fz8fnPve5LGcqIgONYRhcffU1XHbZR1m16gX+smcZ7wW2YRgGfp+PQCCIYRhML57Z7jzzqNXJhzhqQSM9wC7ruA98Vwvwp5Wejs/0Ybt2u/gbta9x5fglHZwlIv2NesCLiIiIiEiPmzBhAr/+9a8JhUL87ne/Y8WKFXiex5e//GXuuusucnJyuP3225k0aVK2UxWRASoYDLJw4fkUTC0kPy+fvHAewWAOhmEQ9oXTVghbzR2tgB/eB9nKYGeXTgYzfZ1rdzZizfWFmFl8clp8a9MWGhL1XZ5XRPqWVsCLiIjIoPfYY49wyy3fZNeunW2x0aPHcMstP1QLGpFeNHfuXFasWMFzzz3HunXraGpqIhwOM3XqVM4991zC4c73xBUROVLMjvJe89a0+NSi6ZhG+zWHZiR9Bbznz8ULFvVWenIisYLYJZPw1WxoF+7ORqwAp5bO5s3aN9Lib9a+zodGnNutuUWkb6gALyIiIoPaY489knET1meffZobb/yE+sCL9LJAIMBFF13ERRddlO1URGQQ2tSwEc9L31n16PYzAGakKi3m5lXAEX3iRbrDLp+RVoA3YzWY0f244aFdmvP0sjP4/bu/TYu/XrtGBXiRAUIFeBERERm0HMfhllu+yaxZp7Bx44Z2m7COGjWaWbNO4ZZbvsVFF12ifvAi3VRZWdnlc0eNGtWDmYjIiWR9w9qM8UwFeCvDCni1n5GeZJfPgI3L0uK+6nUku1iAH5I7lFHh0VRGd7WLv137JrZr48vQ9kZE+hd9loqIiMig9fLLq9m1aye7du3kggsu4ne/u5t5885g9eo1/Pd/38pTTz3RNu6DHzwry9mKDGznnXceRhdWkRqGwYYNG95/oIhIBhvq09t7ZOr/jh3HiKf3zNYGrNKT7PL0N36gtQ1NcuzCLs97WtnstAJ8i9PChoZ1nFxySpfnFZG+oQK8iIiIDFpVVa2brS1ceB733PMAgYCPvLwwZ5wxh3vueYCPf/xKnnvumbZxItJ1Z5xxRrZTEJETTGv/921p8cz93/dlnEMr4KUnufmj8IIFGImmdnFfdeY7NY7X6WVn8PDOv6bF36h5TQV4kQFABXgREREZtGprawC45JLFmOZRv4ibJhddtIjnnnumbZyIdN0f//jHbKcgIieYTQ0bcT03LT69eEZazIp2UIAPqwAvPcgwsMum49/zUruwr2YjuDZ0sV3MpILJhH1hona0Xfy1mle57qQbu3QHmoj0HfP9h4iIiIgMTKWlZQAsX/4ortv+F3TXdXniicfajROR3hWLxbKdgogMIp3p/25G92cc64SH9WhOIhnb0NgtWA3vdXlOy/RxaunpafH98X3sjaXvbSAi/YsK8CIiIjJoDR8+AoAVK57luuuW8Oqrr9Dc3Myrr77CddctYcWKZ9uNE5Ge9+CDD3L11Vczc+ZMZs+eDcB9993Ht7/9berq6rKcnYgMZJWRnWmxkC/E6KP7vwNmhyvgu7YxpkhH7PL0OzCg+21oTivL3OrtjdrXujWviPQ+taARERGRQWvu3HmMHj2GkpISNmxYz4UXHt78avToMcyadQp1dfXMnTsvi1mKDE6e5/HlL3+Zp59+Gs/zyMnJwbZtAHbs2MGDDz7Im2++yf33309BQUGWsxWRgag2UZsWGxUejWVYafGMBXjTh5db0hupyQnMLpueMe6rXkdi8ke7PO+ppadhGAae57WLv16zhsWjL+vyvCLS+7QCXkRERAYty7K45ZYf8vbbbzF16jR+8pOfsXTpUn7yk58xZcpU3n77LW655f/DstJ/UReR7rnvvvt46qmnuOSSS1i5ciU33nhj27Evf/nLLFmyhK1bt3LXXXdlMUsRGcjqE+l30ZQESzOOzbQJq5s3DAyVRaRneTlFuAUj0+K+mnXdmjfPn8/kwilp8U0NG4imohnOEJH+Qt9pREREZFBbtOgjLF36RzZt2sjXvvZv3HjjjXzta//Gpk2bWLr0jyxa9JFspygyKP35z39m8uTJ/Nd//RdDhgxpt0FcOBzmu9/9LjNnzuTpp5/OYpYiMlCl3BTNqea0eHEw84p2K5beA17tZ6S3ZGpDYzW8h5GMdGve00vnpMUcz+Gdure6Na+I9C4V4EVERGTQW7ToI7zyyls88sjj3H///TzyyOO88sqbKr6L9KLt27czf/78Y44544wz2Lt3bx9lJCKDSUOiPmO8JFMB3vMyroDXBqzSW+yyDH3gPbBqNnRr3tPK0jdiBVhb/3a35hWR3qUe8CIiInJCsCyL+fPPprg4TH19FNt2s52SyKAWCARoamo65pj6+nr8fn8fZSQig0l9MvMmzsWB9AK8kWgAJ5kW1wp46S0dbsRasw57RPoq9uM1KjyG4mAx9Ue9AbW+vnsbvIpI79IKeBERERER6XHTp09nxYoVHRbha2pqWLFiBTNmZC5SiIgcS12GDVghcwuajBuwAq5WwEsvsUsmg5m+5tVf3b0+8IZhML345LT43tge6jLsiSAi/YMK8CIiIiIi0uM+9alPUVtbyyc/+UlWrlxJY2Mj0Lrq/fnnn+e6666jqamJT3ziE1nOVEQGoqNXAB+SaRPWTO1nQAV46UVWALt0clrYV70OPK9bU8/MUIAHWFf/TrfmFZHeoxY0IiIiIiLS4xYsWMC//du/cdttt/G5z32uLT5v3jwAPM/j85//PB/60IeylaKIDGAdr4AvTotZ0fQNWEEFeOlddtkMfNXr28WMeC1mdB9u3vAuzzujowJ83ducPeycLs8rIr1HBXgREREREekVn/nMZ5g/fz7Lli1j3bp1NDU1EQ6HmTp1Kv/0T//Eqaeemu0URWSAytQDPmgFybVCafGOW9CoB7z0Hrt8Bmz8U1rcV72OZDcK8ENyh1KeM4TqlgPt4uvUB16k31IBXrLO8zxisVi20zghHXndYzFtSJgtoVAIwzCynYaIiEivmDp1Kt/97neznYaIDDKZ+l0XB0sy/lydqQDvBfLwAnm9kpsIgF0+M2PcV7OO5LjzujX3jOKZ/L3quXax6pYD7I/vY2iu7uwQ6W9UgJesi8VijBvX9Xd/pWdMnjw+2ymcsLZvryIcDmc7DRERkV7z5ptvsmHDBpqamigpKWHWrFlMmTIl22mJyABWn6EAXxJI7/8OmQvwWv0uvc3NH4kXLMRINLaL+7q5ESu0tqE5ugAPsL5+rQrwIv2QCvDH8K1vfYudO3fyxz/+8X3HVlZW8pOf/IRXX30VgHPOOYevf/3rlJSk78AuIiIiInIieO211/j2t7/Njh07gNY7HwEMw2DmzJn86Ec/YuLEiVnMUEQGqkw94Isy9H8HMDP0gFf/d+l1hoFdPh3/7tXtwr7ajeCkwPJ3eeoZJR1txLqWD4/o3up6Eel5KsB34MEHH+TBBx9kzpw57zu2vr6e6667jmQyyac//Wkcx2Hp0qVs3ryZBx98kEAg0AcZDw5Tbp+CGTSzncYJ5chfhKXvuAmXTV/clO00REREes3GjRv5zGc+QyKR4LzzzuO0004jHA7T1NTEa6+9xgsvvMB1113HsmXLqKioyHa6IjKAJJwEMTu9jWlJMMMCONfGjFWnh7UCXvqAXTYjrQCPncBq2IZT2vU7wUqCpYwIVbA3tqddfF39222/44tI/6EC/FEcx+HXv/41d9xxx3Gfc/fdd7Nv3z4effRRJkyYAMCsWbP41Kc+xUMPPcRVV13VW+kOOmbQVAFeREREZBD4xS9+QSqV4n//938566yz2h278cYbee655/h//+//8ctf/pIf/ehHWcpSRAaiTO1nAIoD6QV4M1YNGQqSWgEvfcEun5Ex7qte160CPMD04plpBfj6RD17Y3sYUzi6W3OLSM9SpfMIiUSCyy+/nF/84hdceumlDB16fO+IL1++nDlz5rQV3wHmzZvHuHHjWL58eW+lKyIiIiLSb73xxhuce+65acX3QxYuXMiHPvQhVq5c2ceZichAV5/MXIDPtAI+U/93ACdPBXjpfR0W4Gt6pg98Juvr13Z7bhHpWSrAHyGRSBCJRLjtttv4yU9+gs/3/jcINDY2UllZyfTp09OOTZ8+nXXruv9FVURERLrPcRxWrVrJAw88wKpVK3EcJ9spiQxqqVTqfVvLjBw5kmg02kcZichgUdfRCviMBfj0/u+gFfDSN7xgIW7BqLS4r7r7RfLpxTMzxtfVv9PtuUWkZ6kFzRHy8vJ4+umnj6vwfsj+/a3fzDOtli8vLycSidDc3Ex+fv5xzWeaBqZ5YvXi9vn0PpCc2Hw+U58HIr3s0Ucf5tvf/g927drZFhs9egw/+MGPWLz40ixmJjJ4zZkzh2effZYvfelLGfdEsm2b1atXM3v27CxkJyIDWX2GDVihtS/20TpaAa8CvPQVu3wGgabKdjGrcQdGshkvcHy1okwKA4WMzhvDrsjOdvF19e/gem6X5xWRnqcC/BFM08Q0O1cEO7RiJzc3N+1YMBgEIBaLHXcBvqQkfMJthqk9auVEV1wcJhwOZzsNkUHrr3/9K9dffy2LFi3iT3/6P2bMmMG6dev40Y9+xPXXX8uf//xnrrjiimynKTLofPOb3+Taa6/luuuu4+tf/zqzZs1qO7Z//37+8z//k/379/Ozn/0M121fKOjsz+QicmKpT9RnjBcFitNiViRDAd4ANzSkp9MSyShVPpPAtifaBz3w1WwgNeLMbs09o/jktAJ8c6qZXZGdlJZkbn8jIn1PBfhuOvqXhUw68wtEXV30hFsBf+Rtx26ig+tpgBkw339cb46FdhvEdmps0oVjbETeH8YaAaPtzR835cKxLltnxvoNjIOvadd24RgdH/pirGd7eE7HF8LwGRhWF8Y6Hp59jLGWgeE7PPbI1099fZRksuP8RaTrHMfhpptu5oILLuKuu+7D7/eRl5fLtGmzuOuu+7j22o9x883/xtlnn4tlWdlOVwa54uIT683Wz372sziOw5tvvsnHPvYxcnJyGDp0KC0tLW13kQJcemn7u1AMw2DDhg19na6IDCB1GVbA5/pC5PrSF8ZlWgHv5paB5e+V3ESOZpd1tBHr2h4owM/k8cpH0+JbGt/lVFSAF+kvVIDvpkOrVhOJRNqxQ7HOrGx1XQ/XPUaVdBCy7cOFyE1f3JRxTN7JeYy9eWzb443/byNeMvN1Ck0OMf4b49seb/7KZpzmzNXZ3HG5TPju4c1zt/zHFlK1qYxjgyOCnPSjk9oeb/veNhJ70//dAfylfib/bHLb4+0/3k58ezzjWCvfYuovprY93vGzHcQ2xzKONQIG0397eL+BXXfsIvJOJONYgBl3H/6Gu/u3u2l6ranDsdP+dxpGsLVAvPfuvTT8o6HDsVNun4KvoPXLx74H9lG3InMPRoBJt04iUN56m8OBPx+g5smaDsdO/OFEcipyAKh+tJrqh6s7HDv+O+MJjQ8BUPt0LfuXZe7tCDD2a2PJm5oHQN3zdVTdW9Xh2DFfHkP+Ka13rDS81MCepXs6HDvq86MonFMIQNPrTVT+qrLDsRU3VlB8VuuKnMjaCDv/5/AqBdt2230eiEjP+cc/VrFr105+85uluC44TuvnmuO4uC78v/93E5dcch6rVq3igx/MvFGkiHRNS0sLfr+fESNGtMWSySSmaTJ8+PAsZiYiA12mTViLM6x+hw4K8OH0FrIivcUpmdT6ho/Tvtbgq+7+noGTCqdmjG9tfLfbc4tIz1EBvpsO/UJRXZ1eKDxw4AAFBQWEQqG+TktERESA/ftbf+meMmVaxuNTp05rN05Ees6KFSuynYKIDFKZNmHN1P8dMm/Cqv7v0qcsP3bpFHwH2m+86qtZB54H3WhDXBgopCynnJqW9jWprU1buzyniPQ8FeC7qaCggJEjR7J+/fq0Yxs2bGDGDN3y0xlTbp/Srm1Km6O+Hx25Yvz9xk7+r8mZx2UYe+QK9/dz5Mr59zPuG+OO2f7lSGP/bexxjx39hdHHPXbkZ0fCZzo+bgQOX4wR149gxCdHHNfYYUuGMezqjn+ANfyHxw75pyEMubzjXotHji1fXE75xeXHNbb0/FJKF2b+gfvosSXnlLStRM841nd4bNEHitpWuL/f2ILTC5j2v5kLfEBbqxqAvJl5TLl9Sod3fIhIzxk6tPXr06ZNG5g9e07a8Y0bN7QbJyIiIv1ffYYCfHGwJH1gKo6RSL8LWCvgpa/ZZdPTCvBGvB4zUoWb3/Hv3sdjYsFJaQX4nZEdJB31ORXpL1SA7wHnn38+f/jDH9i2bRsTJrQWZVevXs327du58cYbs5zdwGIGzcwF+AzjOjNn1scGBthYfy+N9ZnH/VWnt8YaPqNd4bzHxlpGuyL7+43tzOtHRLpu7tx5jB49hp///Gfcc88DwBF7Y7gut9/+34wePZa5c+dlL0mRQW7btm3U1dXhOA6ed3jlQCqVoqGhgeeff57//u//zmKGIjKQxO0YLU5LWrw4mL7IJlP7GdAKeOl7dvlM4P/S4r6adSS7WYCfkH8SLx9Y3S7muA7bGrYxzBzdrblFpGeoAN9JlZWVvPHGG5x22mmMGjUKgM985jM8/PDDXH/99dxwww0kEgl+97vfMW3atLRNpURERKTvWJbFLbf8kBtv/ATXXbeEm276CvPmncGrr67httv+i6effpKlS/+oDVhFekFDQwOf/vSnM94pejQV4EXkeGVqPwOZW9BYKsBLP3GsjViT487v1twTCiZmjG+q28SwMhXgRfoDLcHspDVr1vDv//7vrFmzpi1WUlLCvffey5QpU7j99tu55557WLhwIXfeeSeBQCCL2YqIiMiiRR9h6dI/snHjBi68cCEFBQVceOFCNm7cyNKlf2TRoo9kO0WRQemXv/wl69ato6KigosvvpicnBwmT57MRRddxMSJE/E8j9LSUu67775spyoiA0im9jMAxYH0FjSZ+r8DOCrASx9z8yvwcorS4j2xEev4/MwF+HfrtRGrSH+hFfDHkGnjqCuuuIIrrrgiLT5+/HjuvPPOvkhLREREOmnRoo9w0UWXsGbNS0QiDeTlFXHGGR/QyneRXvTCCy8wfPhwHn/8cQKBAP/8z/+MaZptq91/+9vfctttt1FVVZXlTEVkIKlLdrQCPlMBvoMV8HkqwEsfMwzs8hn4K1e1C/tqN4GTAsvf5anD/jDDQyOoiu1tF99cv7nLc4pIz9IKeBERETkhWJbF/Plns2TJEubPP1vFd5Fetm/fPs4555y2O0KnTZvG22+/3Xb8s5/9LFOnTmXZsmXZSlFEBqD6RG3GeKZNWDMW4E0fXk56v3iR3paxDY2TxGrY2u25JxSclBbb2bSTuB3v9twi0n0qwIuIiIiISI+zLIv8/Py2x6NHj6auro66usOrV+fMmcPOnTuzkZ6IDFD1ifqM8YwtaCLpBXg3bxgYKoVI37PLO+oD3/02NBMytKHxPI/tzdu6PbeIdJ++64iIiIiISI8bMWIEO3bsaHs8enTrRnBbt7Zf6dfQ0NCHWYnIQJepB3yeP4+Alb7/WqZNWN3w0F7JS+T92GXTM8Z7ogA/McMKeIAtjVu6PbeIdJ8K8CIiIiIi0uPOOussVqxYwSOPPALA5MmTCQaD/OlPfwIgGo3y97//naFDVQwTkeNXl0xvQZOp/Qyuk7EFjTZglWzxggW4haPT4j1RgB+bPx7DMNLi25pUgBfpD1SAFxERERGRHnfjjTdSVFTE1772NZYtW0Y4HObSSy9l+fLlLFy4kPPPP59du3Zx4YUXZjtVERlAMq2Az9h+JrqvdXPLo7h5Fb2Sl8jxSGXoA2817sBINndr3hwrh1Hh9OK+VsCL9A8qwIuIiIiISI8rLy/nL3/5Cx//+MeZNGkSAF/96lf58Ic/zN69e2loaOCSSy7hX/7lX7KcqYgMFJ7nUZehAF8SLE2LWY07Ms7hFI7t4axEjl+HfeBr1nd77kx94PfFqoikIt2eW0S6x5ftBEREREREZHAaOnQo3/rWt9oe5+Xl8atf/Yrm5mYCgQDBYDCL2YnIQFMfqyMSbyZl23ieh2EY+H0+CqyCtLFWU+YNnlWAl2yyy2dmjPuq15EaMbdbc08oOIm/Vz2XFn+veSsnl5zSrblFpHu0Al5ERERERPpUfn6+iu8ictw8z+NPf7qfz3/l00SjURKJFpLJBIlEC9FolL/d92f+9Kf78Tyv7ZwOV8AXpLfpEOkrTvFJYPnT4r25Eeu2pq0Z4yLSd7QCXkREREREesWmTZv485//zK5du4jFYu2KY4cYhsG9996bhexEZCDwPI/bbruVlSufp7GsAc9z8Tj8tcTAoP5APQ+8cx979uzmppu+imEYWI3pK+DdvGHgz+3L9EXas/zYpVPwHVjbLuw78A54LhhdXyc7Om8slmHheE67uDZiFck+FeClX3ETbrZTEOkTeq2LiMhg9+qrr3LDDTfgOE7GwvshhmH0YVYiMtAsW/YAK1c+z754FbY/RYG/AMtvYZgGnuvhpBzqG+uJx+OsXPkCI0eO4qqrlmBmWAHvFIzp+ycgchS7fGZaAd5INGLVb8MpybyK/Xj4TT9j88elrXjf1qwCvEi2qQAv/cqmL27KdgoiIiIi0gPuuOMObNvmc5/7HOeeey4lJSXZTklEBphEIsGjjz5EY7KBqB1l4oKJRHOibccNy8BnmUy9cBpr/7CWxmQ9jzzyNy676HzMWE3afE6hCvCSfalhs8lZf39a3L9vTbcK8AAT8k9KK8DXtNTQmGygMFDUrblFpOtUgBcRERERkR63fv16Fi5cyE033ZTtVERkgFq16gUikSgNyQaGnzqcnCFBoolo2rihk4ZRc0oN1W9XUxgpZu3KvzEsw3zagFX6A3vYaWAYcNTdYf6qNbRMu6Zbc08omAh70uM7mrczq/TUbs0tIl2nArz0K1Nun4IZ1N7AMvi5CVd3fIhkged5x2yFISI9a/z48dlOQUQGsHXr1tLixLG9FBWnjyTiNaWN8ZkWhmFQMXskVW/tJeHEqdv2GuSnz+eqAC/9gBfIxy6diq9mQ7u4b9/r4Npgdr1UNzYv8/fdHZH3VIAXySIV4KVfMYOmCvAiIgPAjh3baWpqzHYaneJ5Hl/+8hfw+Uxuu+0OXHdgFuILCgoZO3ZcttMQeV+zZs3inXfeyXYaIjKAxWJRXK9176RQaS71bl3aGJ/hbzsO4OCSnzqQcT6tgJf+wh5+RloB3khGsWo345RP7/K8I8OjMm7EuqN5e5fnFJHuUwFeREREOqW2tpa5c0/FdQfuZsIf/vBZ2U6hyyzLYt26rZSWlmY7FZFj+uIXv8i1117L73//ez71qU9ps1UR6bRQKIxptC7QitXGcUrttDG+g6uFY7VxACxMhgVi6ZP5cnBDQ3ovWZFOSA0/g5y196TF/fvWdKsAH7ACjAhXUBnZ1S6+M7Kjy3OKSPepAC8iIiKdUlpayssvvzngVsDH43EWL74AgMcff4ZAIJjljLqmoKBQxXfplz7+8Y+nxcLhMLfeeiu/+c1vGDVqFDk5OWljDMPg3nvv7dXcKisr+clPfsKrr74KwDnnnMPXv/71990YtqvniUjPmDFjJn//+wp8hp89r+/GOjf9bmmf0VrW2PPabnyGn6CVy+i8ZNo4p3BMa99tkX4gNeSU1lYzbvs3lfxVa2iZeX235h6TNy6tAL8ntpukkyRgBbo1t4h0jQrwIiIi0mkDsQVKNHp407aZM08mGMzNYjYig8/rr7/e4bGmpibWr1+f8Vhvr4yvr6/nuuuuI5lM8ulPfxrHcVi6dCmbN2/mwQcfJBDIXIzo6nki0nPmz1/AXXf9jqJkEVVrqxi6YAimz2o3xmf6qN54gKq3qigLllGYH6bUrIGjbtRT+xnpV/y52OUz8O1/q314/5vgpMDyd3nqsXljWcUL7WKu57I7Wsn4ggldnldEuk4FeBERERER6bbnnnsu2ylkdPfdd7Nv3z4effRRJkxoLTzMmjWLT33qUzz00ENcddVVPXqeiPScYDDI4sWX8cAD99LiayEVT2H5XSy/hWEaeK7HgTf3s3/5AcK+MIWBYq6++ByMlqVpczkFY7LwDEQ6lho+J60Aj53AV70We9hpXZ53bH7HG7GqAC+SHSrAi4iIiIhIt1VUVGQ7hYyWL1/OnDlz2oroAPPmzWPcuHEsX768w0J6V88TkZ511VVL2LNnN3/f8BwRoxk35eKkDm8wGX+3pXXle6CYs89ewEfOmgXPpM/jFA28u/dkcEsNn03uW79Ni/ur1nSvAJ+X+bW+I6KNWEWyJb2BmoiIiIiISA+prq5u9/ixxx7jO9/5Dr/73e9obm7u1Y/d2NhIZWUl06enb2g3ffp01q1b16PniUjPMwyDm276KmdfuADLMLEMq91/Q7xhVJSM5JprruWmm76Kr3FHxnm0Al76G7t8JvjS9yTy71vTrXkLA0UUB4vT4jubd3RrXhHpOq2AFxERERGRHpdKpfj617/O448/zpo1a8jLy2Pp0qX813/9F57nAfDnP/+ZP/3pTxQWFvZKDvv37wdg6NChacfKy8uJRCI0NzeTn5/fI+d1xDQNTLPvNn+0LLPdn/L+dM06r6+v2SnzTuGVjS+RTCSx7RSe52EYBtdd9SmuOOejBIOthUx/866Me60apWPx+bL/76vXWucN2mvmy8EZdgq+Pa+0D1evxUcCfF3fr2hcwTjeqn0TDKD1Wy47o9uxLKPX914ZyAbta60X6ZodHxXgRURERESkx911110sX76ciRMnEo/HycnJ4c477yQ3N5fvfOc7VFZW8qtf/Yrf/OY3fO1rX+uVHA5tvpybm17EOFSsi8ViaYX0rp7XkZKScFYKHgUF2my6s3TNOq+vrlliTwzLNMnNzQFy2uIfv/QqCoNHvIkXqyStAl8wnOLysj7J83jptdZ5g/KaTTgL9r7aPuY5FEc3w9gPdnnaKeWTeav2TUzDaC3CA3EnRjIYYVh4WDcSPjEMytdaL9M1OzYV4EVEREREpMctX76ck046ib/+9a/4/X5efvllGhoaWLJkCZdddhkAa9eu5bnnnuu1Arzruu87xjTTV2x19byO1NVF+3wFfEFBLk1NcRzn/Z+L6Jp1RV9fs70N+3Fdr13MZ/qwoyb1sWhbLL9mG6bXfpydN4pofZT+QK+1zhvM18wqmkneUa9XgMTmlbQUntLleUcERgHgel7bCniAt3dvIDjk+N48PhEN5tdab9E1g+Li8PuOUQFeRERERER63K5du/jYxz6G3+8H4B//+AeGYbBgwYK2MZMnT+bll1/utRzC4dZfiBKJRNqxQ7FDY3rivI64rpdWOOwLjuNi2yfmL8NdpWvWeX11zepa6tJiBf5CXAdcWj++kYxgRGs4+rMtlT+23/276rXWeYPxmtlFUwj5QhipWLu4ueeVbj3X0eGxrX856pNhW+M2Tis5o8vznigG42utt+maHZsK8NKvuAl9sva1Qz1Y1Qeub+m1LiIig92hwvsh//jHP7Asi9mzZ7fFGhsbKSgo6LUcRowYAaRvBAtw4MABCgoKCIVCPXaeiPSehmRDWuzojSbNxp0Zz3UKtQGr9FOmD3vY6fgrX2wX9tVuwEg04gW7tkfK8NAI/KafhJtsF98Z2d7lVEWk61SAl35l0xc3ZTsFEREREekB48ePZ/Xq1biuy7vvvsvGjRuZPXs2eXl5ANTV1fHss88yYcKEXsuhoKCAkSNHsn79+rRjGzZsYMaMGT16noj0noZEfVqsKNC+AG817ch4rlM4rjdSEukRqRFz0wrweODf+yrJced1aU6f6WNc0Tg21WxuF9/RrAK8SDZoi1oREREREelxH/3oR9m8eTMXXnghn/jEJwC46qqrAPjb3/7G5ZdfTn19PR//+Md7NY/zzz+fl156iW3btrXFVq9ezfbt27n44ot7/DwR6Xme59GQTC/AFwaK2j22GndkPN/VCnjpx5IjzswY9+/tXou2CYXpb3Dvj+8jbscyjBaR3qQV8JJ1oVCI7dursp3GCSkWizF9eus35c2b3yMQ0K7V2aBb2EVEZDC68soraW5u5re//S2mafIv//IvLF68GIDKykoaGhr4yle+woUXXtireXzmM5/h4Ycf5vrrr+eGG24gkUjwu9/9jmnTpnHppZe25fPGG29w2mmnMWrUqOM+T0T6RtSO4nhOWrworQCfoQWNLwc3NKSXMhPpPrdwLG54KGZ0f7u4f+/L4HnQxXaxE4smZozviuxkctHULs0pIl2jArxknWEYndrESnpHKBQmGFQBXkRERHrODTfcwA033JAWv/rqq7nhhhva2tH0ppKSEu69915+/OMfc/vtt5OTk8PChQv56le/SiAQAGDNmjV84xvf4Mc//nFbAf54zhORvpFp9TtkaEGTYQW8Uzi2ywVMkT5hGKRGzCW45eF2YTOyD7NpJ27h2C5NO75ofMb4jsh2FeBF+pgK8CIiIiIi0qeGDh3apx9v/Pjx3HnnnR0ev+KKK7jiiis6fZ6I9I1M/d/hqAK8k8q4At7pYvFSpC+lKtIL8ACBPS/T0sXXcKYWNNBagBeRvqUe8CIiIiIiIiLSb3W4Aj54uABvNVeCa6eNsYt7b6NnkZ6SGj4HMtyo0Z0+8HmBPIbkprdf2qmNWEX6nArwIiIiIiIiItJvdVSALz5iBbxVvy3jGKdIBXjp/7ycIuzSaWlxf9UacFJdnnds/ri02M7ojox7KohI71EBXkRERERERET6rfoOCvCFR2zCajWoAC8DW6pibnrQbsFX/U6X5xyXn94HPukk2Rer6vKcItJ5KsCLiIiIiIiISL/VmGxIiwWtILm+3LbHGQvwvhzc/BG9mJlIz0mNyFCAB/x7XurynOMKMm/Eur058xtWItI7VIAXERERERERkX6rPsMmrO02YAWs+vfSxjhFY8FQ2UMGBrv8ZDx/blo80I0+8OPyM98Bsj2S/vkiIr1H34lEREREREREpN/K1AO+6Ij2MzhJrOZdaWPsoom9mJVID7P82MNmp4drN2K0NHRpyvKccsK+cFp8e7MK8CJ9SQV4EREREREREem3MrWgKQoesQFr405w3bQxTlHm9hsi/VWy4gPpQQ/8Va92aT7DMDKugt/e/B6e53VpThHpPBXgRURERERERKRfclybxmRjWrz4iBY0VsPWzOcWawW8DCwd94Ff3eU5M23EGkk1U5uo6fKcItI5KsCLiIiIiIiISL/UmGrKGC88sgBfn3lDSa2Al4HGLRiNmzcsLR6ofBFcp0tzjs1QgAfY0by9S/OJSOepAC8iIiIiIiIi/VKm9jPQfgW8ryG9AO/5Q7jh9EKmSL9mGKRGnpUebmnAV722S1OOzRuXMb69OfMbVyLS81SAFxEREREREZF+KdMGrACFR2zCajWkbyjpFE8Aw+ittER6TXL02RnjgcoXujRfRagCv+lPi2+PaCNWkb6iAryIiIiIiIiI9EsNicwF+KJgUetf7Dhm8+60406h2s/IwJQaNhvPH0qLB3Y936X5LNPHmLyxafHtzSrAi/QVFeBFREREREREpF9q6KAFTdHBFjRWw3bw0o87xRN6MSuRXmQFSI38YFrYbNyF2bijS1Nm2oi1pqWaSKq5S/OJSOeoAC8iIiIiIiIi/dL7taDJ1H4GVICXgS05ekHGeGBX19rQdLQRq1bBi/QNFeBFREREREREpF/K1IImz5/X1tM60wasAHaRCvAycKUqPghmesmuq21oxuWpAC+STSrAi4iIiIiIiEi/lKkFzaH2MwBWfXoB3gvm4+WW9WZaIr3KCxaQGnp6WtxX/Q5GvK7T843JG4tppJcAtRGrSN9QAV5ERERERERE+qVMLWjaFeAzrIB3iiaAYfRqXiK9LZWpDY0Hgd0vdnqugBWkIjQyLb5DK+BF+oQK8CIiIiIiIiLSL2UuwBe1/iUZxYzsSzuu/u8yGCRHddQH/vkuzZepD/ye2G6STqJL84nI8VMBXkRERERERET6naSTIGbH0uJFwdYV8L7GzKt31f9dBgM3fwRO8cS0uH/vK2DHOz3fuAwFeM/z2BnZ0ZX0RKQTVIAXERERERERkX4nU/93OLwCPlP/dzjYgkZkEEiOPic9aCdai/CdNDZvXMa4+sCL9D4V4EVERERERESk38nUfgYO94DP1P8d1IJGBo9kpj7wQHD7M52eK9MKeFAfeJG+oAK8iIiIiIiIiPQ7jR2tgD/UgqZmQ9oxL6cIL6c4LS4yEDmlU3HDQ9LigV1/h2S0U3Pl+fMpyylPi29XAV6k16kALyIiIiIiIiL9Tn3iGCvg7Ti+6nVpx+zSKb2dlkjfMQyS4y9Mj9uJ1iJ8J2VaBb8ruhPXc7uSnYgcJxXgRURERERERKTf2R/flzFeHCjBd2AtuHbasdSw2b2dlkifSoy/OGM8uO3xTs81JkMf+KST5EB8f6fnEpHjpwK8iIiIiIiIiPQ7e2N70mJhX5h8fz7+/W9kPMcednpvpyXSp5ySk3CKJ6bF/VWvYsSqOzXX6PCYjPGdkR1dSU1EjpMK8CIiIiIiIiLS7+yLV6XFhodGYBgG/n2vpZ/gy8EundoHmYn0rcSEDKvgPY/ge092ap7ReZkL8LtUgBfpVSrAi4iIiIiIiEi/4nhOxgL8sNAIcBIZ+7+nhswCy98X6Yn0qeT4C8FIj3e2Dc2w0HD8ZvrnyK7ozq6mJiLHQQV4EREREREREelXaltqsDP0eB+eO7y1+O6k0o6p/YwMVm54KKlhZ6TFrbp3seq3Hvc8lmExMjwqLb4rogK8SG9SAV5ERERERERE+pWq+N6M8RGhCvxVGdrPACkV4GUQS2ZqQwMEtz3RqXlG541Ni1XF95J0El1JS0SOgwrwIiIiIiIiItKvVMUyF+CHh0Zk3oDVF8Qum97LWYlkT3LMh8EKpMUD7z0Bnnvc82TaiNXzPHZHd3crPxHpmArwIiIiIiIiItKvdFSAHxYow3fgnbR4qvxk9X+XQc0L5JEcvSAtbkb349/78nHPMybDCniAXdEdXcxMRN6PCvAiIiIiIiIi0q9kKsAX+AsobNgOTjLtmPq/y4kg0UEbmty3l4LnHdcco/LSV8CD+sCL9CYV4EVERERERESkX6mK78XzPBKJFiLRCM2RZuL74mx/8X4gvdCo/u9yIkiN+ABebmla3Lf/LXxVa45rjuJAMXn+/LT4zsiO7qYnIh1QAV5ERERERERE+o2Uk2Jn7XYaGuqJRqMkEi0kkwkObDtA/TtP0NBQT0tLjLZCvBXALp+R1ZxF+oTlJz7jExkPhd763+NaBW8YBmMyrIKvjGoFvEhvUQH+KJWVlXzhC19gzpw5zJkzh3//93+nrq7ufc+7/PLLmTx5ctp/X/ziF/sgaxEREREREZGBz/M8fvKr/49oPIrjuTie0/ZfrLaBiflNOK5LPB4nGo0CHvaQkzNuTikyGLVM/ie8nKK0uG//W/j2vXZcc4zKsBFrfaKeSKq5u+mJSAa+bCfQn9TX13PdddeRTCb59Kc/jeM4LF26lM2bN/Pggw8SCGT+hu66Ltu2beP888/n3HPPbXesoqKiL1IXERERERERGfCWLXuAlzeuxhnq4uFi+S0sv4VhGiw4rYBQdRNOysXDIJlMYFkW3tDTsp22SN/x5xKf8UlCr92edij01m9pGn7G+07R0UasOyM7mF48s7sZishRVIA/wt13382+fft49NFHmTBhAgCzZs3iU5/6FA899BBXXXVVxvN27txJIpHg3HPP5dJLL+3LlEVEREREREQGhUQiwaOPPkR0WBQPF3+uH9NntR0/K8fGl+vH9DukYjYuHi0tcZzSkzGymLdIX2uZchW56/6A0dLQLu7b9wa+fa/DyGMX4UcfYyNWFeBFep5a0Bxh+fLlzJkzp634DjBv3jzGjRvH8uXLOzxvy5YtAO3OExEREREREZHjt2rVC0QiUVrCLVh+q13xPcd1WVBXBYDps7D8Jq7nknTg+c3v3zZWZFDx53arF/yo8OiM8V3qAy/SK1SAP6ixsZHKykqmT5+edmz69OmsW7euw3PfffddDMNg/PjxeJ5HLBbrzVRFREREREREBp1169bS4sQxCsHyW+2OXRSLE3Lttsem3wI8Xq7J5Z0Nm/o4U5Hsa5lyFV6wMC3uq3od/9bHjnluri9Eec6QtPjOyI6eSk9EjqAWNAft378fgKFDh6YdKy8vJxKJ0NzcTH5+ftrxLVu2kJ+fzw9+8AOefPJJYrEYo0aN4qabbuKSSy7pVB6maWCaunlO+obPd/g9OMsy2z0Wkd6hN2qzJ5mMt/09kYgfY6T0plAohGHoZx0REUkXi0VxPRersLXn+yGm5/HRpqZ2Yw8df7CygGGF0T7NU6Rf8IeIz7iW0Ou/TDuUu/onMOVsIL1Af8jovDFUtxxoF9sd2YXnefpZTaSHqQB/UOvu6ZCbm5t2LBgMAhCLxToswDc1NZFIJLj11ltpaGjgD3/4AzfffDOpVIrLLrvsuPMoKQnrC530mSP3FS4oyCUcDmcvGZETRDQapbQ0/c1e6VsTJ47NdgonrEgkou83IiKSUSgUxrAMzDwTz/UwrNbfjT8YizHUToFxeFW853q802DyblOI8SF9X5ETU8uUq8ndcD9GvL5d3EhF4YmvwXm/6vDc0XljeL1mTbtY3IlT3XKAIbn6fUWkJ6kAf5Druu87xjQzrw6+5pprsCyLJUuWtMUWLVrEokWLuPXWW1m8eDGWZWU892h1dVGtgJc+c+iNJ4CmpjjJZBaTETlBHPl5J3Iiqq+PnrDfb4qLVSASETmWGTNm8uwbz5Aw4jgpB59lgufxT81NaZusuimHe7cHCVq5zJihTSPlBBUIE/3Af5C34qvpxypfJbDuPuypH8946pi8sRnjuyI7B3YB3kli1W3BV7sBX927YFjYJZNIjvkQXk5xtrOTE5QK8AcdWomVSCTSjh2KdbRa69prr02L5eTkcOmll3LHHXewdetWJk+efFx5uK6H6x57swyRnmLbh994chy33WMR6R1Hfp7t/0oeYb/edO1L3sENqXS3Wd+KpjyG/lcEaP0c0PcbERHJZP78Bfz6yTuIGCZOysH0mcywU0xNJDCOWP3u2g47Gj3eqi9jRGke8+cvyGLWItmVHPNhEictJrjl0bRjOWt+QWLYmTjFE9OOjQqPyTjfrugOZpfP6fE8e5t/z2py196Db/9bcMR+EQBBIPzyj0kNm01y7Lkkxl0IAS2MkL6jAvxBI0aMAKC6ujrt2IEDBygoKCAUCnVqzpKSEgD1+hURkYzCfoNwQIXgvqXrLSIi0l8Fg0GmfWA6/3BW4mGSiqf4p0gDHoAHruPiphyclMuDu/LID5SwePFlbW1jRU5UsTlfwV+1BjOyr13ccFPk//2rNF78+7TV3yNCFViGheM57eI7Izt7Pd+eZMRrCb/yXwS2P33sga6Lf++r+Pe+SuiNXxGd828kx18EWpgjfUAF+IMKCgoYOXIk69evTzu2YcMGZsyYkfG8yspKPve5z7F48WL+5V/+pd2x7du3AzBy5MieT1j6nR07ttPU1JjtNDolHj+8CeHate8QCAzMH1wLCgoZO3ZcttMQEREREZFuGjG1gsC7QZLJBDNTSeYd3Dg9GUvSWok3iNg+1tSN5eyzF3DllR/Lar4i/YEXyCNy1vcpePKzBz9PDjMbd1HwzBdouuB/8QJ5bXGf6aMiPJJdRxXcKwdKAd5zCb77EKHXfo6RjHTqVKOlgbyV3ya19TGiH/gGbsGoXkpSpJUK8Ec4//zz+cMf/sC2bduYMGECAKtXr2b79u3ceOONGc+pqKigoaGBBx98kE984hPk5bV+Mdu7dy9//etfOfPMMykvL++z5yDZUVtby9y5px7XXgL91cUXn5ftFLrMsizWrdtKaWlptlMREREREZFu2BffS15emKJmm+/X1mFggAcWFhitbeT+XjeCf/rYJ7nyyo+prZzIQfaw02mZfi056+5NO2bVbCJ/xb/RdN7tYB1eeDc6b2xaAX5vbA+2a+Mz+3HJMBUjf+W38O96oVvT+Pe+QtFDVxGdczOJKVf2UHIi6frxZ1Pf+8xnPsPDDz/M9ddfzw033EAikeB3v/sd06ZN49JLLwVaV7y/8cYbnHbaaYwaNQrTNPnOd77Dl770JZYsWcKVV15Jc3Mz999/Pz6fj+9+97tZflbSF0pLS3n55TcH3Ap4ANM0KCjIpbm5BccZmG8gFBQUqvguIiIiIjII7I3tJeS4fK/uAIV4eIaBiYEv4McwDCx/Dpf8x4P4i0ZkO1WRfid22ufxV72Gr25T2jFf1WvkPf8fRD70EzhYXB8THssq2hexHc9hT2x3h5u0ZpsRq6bg2S9h1W7umQmdJOGX/hPPl4s75SM9M6fIUVSAP0JJSQn33nsvP/7xj7n99tvJyclh4cKFfPWrXyUQCACwZs0avvGNb/DjH/+YUaNab1G58MILueOOO/jf//1fbr31VnJycpgzZw4333xz20p6GfwGagsUn8+kuDhMfX1Um+KJiIiIiEjWtDgt2LED3Ly/kopUAgwDAwhYQUI5rRsmxk/7FxXfRTpiBWk69+cUPXkjZvOetMOBXc+T//QXiCz4IV5uKaPzOtiINbKzXxbgrbp3yX/2S5jRAx2O8QJ5tEz/OKkhp+CUTsFINBLYuYLgjqexatLfmDgk/PJPiFTMhuKTeiN1OcEZnud57z9M+kp1dXO2U5ATjArwIn0rGo0ybtxwAPb/W17GTVgtE3J8h+PRZMffqk0Dcv1dGxtLeXT0U4BhQKiLY+MpD/cYP10c+Zw7M7bF9jjWjTqdGRvy03bLesL2ONaXv86MzfWDeXBs0vFIOT0zNscHltn5sSnHI3mMsUEf+Low1nY9EnbHYwMW+K3DY+tiHkN/1tqbc/v2KsLhcMcnD2Ll5fnZTkGypK9/xtfPd52na9Z5PXXNjFgNwW3L8Ve9ilOzgXjzjrQxIStE0MohOfbDRM75CRhmNzLPLr3WOk/XrPP8sb0UPXEjbvOBjD/Du6EyIgt+zL6iUfzLP9JbLl825qN8fOJ1fZDp8QvseJbwqlswUvEOxyTHnUd0zlfwQmXpBz2PwM4VhF75KWasJuP5zojZBK65l/rGuF5rx0mfn8f3M75WwIuIiGTJoYLk0S4+ycfya0Jtj4f8VzOxVOY5FoyxeP76w8XMsT+PUBPLXNGePcJkzWcOb7w07ZcRdjZmHjut3GT95w+PPePOKBuqM/9ANabQYMeXD//QcfbdUV7bm3lsWcig+quHx150X4wXdmau+ob8EP2PgrbHH10W5/EtHVd9ve8eHvuJv8X584aOx0a+kU+49eY2PvdYC/e83cEFBg58JY/ycGsx+eanWvjVax2P3f6lPMYWtY795nMJ/uulZIdj1/1LmOlDLAB+9GKC773Q8dhXPx3mjIrWsT9/Ocm/P5vocOzfrwtxztjWH/F++3qKLzzR0uHYx5bkcskkPwD3rU3xqYc7Hrvsn3K5cnrr2L9ttLnqzx3/8nPXpTlcf0rrBX5qq82iBzoeKyIiJyDXwb/3ZYLv/o1A5UpwW38W8NzM399Mw8QpnUJk/vcHdPFdpK94BSPho7/Hu38JJNLfBDZjNRQ89TnC+SP5bcO7WK5NxPTxTijMS3mFVEa2ZyHrzIxkM6GXf0pw2+MdjvFyiojM/x6pUfOPMZFBcuxCUiPOJPeNX5KzcVnaEF/Va/DG3TDh6h7IXOQwFeBFREREREREpE9YtZvJe/HbWPXb0o45XuY35b1QOU0LbwN/bm+nJzJ4lE8iesEdhB//HNgZFlm4LlbjLobYDrbnUOg4VDQmuKixjub9VYSbHLxgIZ4/jBcI4+SPwh5+Bl4gL32uXuKreo28F7+DGd3f4RincCzN5/4ct2Dkcc3pBfKIzf0ahh0nuOXR9AEv3oZZcjrkj+9q2iJp1IKmn1ELGulrul1IpG+pBY1a0HRlrFrQDA5qQZMd999/P3/4wx/Yu3cvY8aM4Z//+Z+55JJL3ve85557js9//vMZjz366KNMmjTpuHNQC5r+T9es87pyzQLbnyZv1S1gZ17pHkk1k/La32lmGyaByx/BKZ/e3ZT7Bb3WOk/XrPOOvGZe1Vry//7VDovYMTtKIsPdJ0WBIgyOuuPEtLCHnkpy5HySo8/BLRjVG+ljJBoJvXY7wXcfOuY4e/hsmj90K16w4JjjMn6MZITChz+GGak6HDNafz5PlUyi4eJ7wPJ3et4TjT4/1YJGRESkXwsHjIwF+EzjOjPn8TqyaN6TY3N7aeyRb0r05NigzyDYC2MDlkHAyu5Yv2Xg74WxPtPAFzj+seHjHCvS05YuXcpPf/pTLrzwQq6//nqeeeYZbr75ZgzD4OKLLz7muVu2bME0TX784x+3vQl3yPDhw3szbZHBx3PJfeNX5L5z1zGHOV77d3cbLYuHJsznk4Ok+C6SDU75dBo/cj95L34b/+7VacctI/MPgI7n4Du65ZPr4Kt6DV/Va4TW/A/JcecTO/0LuPkVPZOs5xHc+hih1/4Ho6XhmEMTJy0m+oFvdrlI7gXyiJz1PQqe/CwctSDIqt1Mzob7aZnZv/rgy8ClAryIiIiIiAw6TU1N3HHHHSxatIif/exnAFx11VV84hOf4Kc//SkXXHABltXxu05btmxh5MiRXHbZZX2UscggZbeQ//zX8Ve+eMxhHh4bcnLZHsxlRyCHXcEc3gvmct6IuX2UqMjg5eUU0Xzuz8l95/fkvvkbjry11TItyHAXpOM6+N6nuB3Y/jSBnStomXo18Vk34gULu5igh69qDaE3f4PvwNvHHhrIIzr3ayTHX9S6ZL0b7GGn0zL9WnLW3Zt2LPTWb0mOvxA3PLRbH0MEVIAXEREREZFBaMWKFcRiMZYsWdIWM02Ta665hptvvpk333yT2bNnd3j+u+++y4QJE/oiVZF+J5FIsGrVC6xbt5ZYLEooFGbGjJnMn7+AYPB47wUDPI+8f/zgmMV3N1RG4qRLebt8Et969/a042PyxnXlKYjI0QyT+KxPkxo+h5yN/4cZO4BnBUkZJq/uX8n0eISwe7iFSEd7MqRxbXLW30fOpgdJDT2F1PA5pEaciVMyCcz3KTt6Lv49L5H79p34Dqx93w+VGn4G0bNuwQ0PO77cjkPstM/j37Maq/699gfsFkJrbiNyzn/22MeSE5cK8CIiIiIiMuisW7cOgOnT27eumDZtWtvxjgrwtm2zfft2zj77bKC1GGlZFj6ffn2SwelQwX3t2nd4/fU1bNu2jUDAT1E4AK5DQ3OMe/9oY1omM2aczJIl1/KhD30IOPaeHsGtjxJ478kOj7dMvYrYGTeD5efdXQ9lHDMmb2zXn5iIpLGHnExkyMntYr9b9SkaW2qYGYvwgUgjM+JRyjEIdWZiJ4l/76v4974Kr98Bpg+naBxO8UScwrF4RxTjrUgVVt1mfHVbMm8QezRfDrHTPk/LtCVwdFuc7rKCROd9i4LlN6QdCmx/Bt+kK7BHzOnZjyknHP0EKSIiIiIig86BAwcoLCwkNze3Xby8vByAvXv3dnjujh07SKVS7Nixg0WLFrF161b8fj/nnXce3/rWtygpKenV3EV6W6aCu9/vI9JUT1NTE0UBl1DAYW+VS2OLh4tBjs/AsixeefEAL7+8mqKiQqZMmcKsWacxfXr66nizcQfhl3+SOQHTR3Tu10hMvqIttCOyI22YYRiMVgFepNeNyRvDm4la3grn81a4dUPJkC/E3fPvxXTimJF9+Pf8g0Dli/iq3wH3ODbbdG2sui1YdVu6lVtq9NlEz/wqbt6Ibs1zLPaQWSROWkzO1kfTjoVf/gmNl/6fNmSVblEBXkREREREBozdu3cf83h+fj6FhYVEo1FycnLSjh+KxePxDud49913AXjnnXf47Gc/y/Dhw3n99df5wx/+wJYtW3jwwQczzt0R0zQwze71qe0MyzLb/Snv70S5Zp7n8ac/PcDDD/+NSKSZyp3v0djYSFHQ5eQhDjMmGpxUYjIsz2BI2I9lGlQ1u6zc6bJyV4qdDTbReAI7FsWLN/B63T42vvMGw0aO5+67l3LppZdz9dVLMNwUeSv/A8NpgaNe+l4gj+j5t+MMO7VdQWJXZHtaviNCIwgHc9PiA9mJ8lrrSbpmndfZazamYCxv1r7RLhazYzR5zZSGSiFUiD1kMvapN2BEq8l5838JbP4beMdRiO8iNzyM+LyvYY85BxPo7X/95JlfIrjreUhFgMPt5X1NOwhveoDErOt7OYOBSZ+fx0cFeBERERERGTAWLlx4zOOf+cxn+MpXvoLruhgZNmc7FMt07JDx48fz+c9/nssvv5zRo0cDcO655zJmzBi++93v8uc//5lrr732uHMuKQkf8+P1loKCwVW47AuD+Zp5nscPf/hDnnvuOYhWU7t3N4mmKJ892eTqaX5GFliYRmvRyW8ZJB0PAxgaNjmzwuILc/w8u93m4U022+pcwMEw4uAksPdFSZRUsGzZ/dTW7uebHzQx6t7NvEHiRf9JwaT57UIpN8We+O60N6oml02iuPjYbW4GqsH8Wustumadd7zXbPqwKTyyM/3ztY59TCwe3T5YHIaRP4Z5n4aVt8K2v/dEqoeFSmH2pzBP+Tj5gU41weme4jCcfRM89wPMo752hd6+k9DsqyCvvO/yGWD0+XlsKsCLiIiIiMiA8dOf/vSYxydNmgRAOBympSW9r+yhle/hcMdFvSlTpjBlypS0+Ec/+lF+8IMf8Morr3SqAF9XF+3zFfAFBbk0NcVxnN5bnTiYDPZrlkgk+MEPbmH58kcp9BrINRJcNt7gM6fmUhCEoA9a7NaCe8AySB0svgPkBwxsF0zD4/zxPs4fb7GjwSPuGKQcqG7xs3q3w9s1eylwm5m6fScxN5XW/gkgOeWfiJd/EOqj7eI7mreTtFNp44cHRlJ/1NiBbrC/1nqDrlnndfaalRnDcV0vLb6uaiMn5UzPcAZgDoNzfoY58138u17At+cVrANvY7h2l3J2Q2UkTr6e5JQrwJcLUQ+iffv5b427jIIhD+Lu39j+QDJGYvVSWs74f32az0Cgz0+O641iFeBFRERERGTAuPTSS49r3PDhw2lsbCSZTBIIBNriBw4cAGDo0KGd/th+v5+CggJisVinznNdL2Nho7c5jottn5i/DHfVYLtmnuexbNkDPPzwX3n99dcoMOMEcmy+fU6QOcMNDMMjaBmAcbDg7mEacOjVGrQM/JZBJHnomnjkBwxOG97aaqCuxaA27nLKiBwS5FBEA6GASUuLdbBN0+E3npzi8TTPvgkyXN/3GtPbzwCMCo0dVP8eRxpsr7W+oGvWecd7zYYGR2AaJu5RLWV2NO14//MLJ5KcORFm3gipOP4Db+Kr2YhVvwWrfitW086MPePdvGE4JZOwSyZjl00jNWIuWAe/X2fr39lnwsLvwv0fwzvq27Z/wzIi06+HwOC8K6e79Pl5bCrAi4iIiIjIoDN9+nQ8z2Pjxo3MmjWrLb5xY+uqtpkzZ3Z47q233spTTz3FI488Qih0+Pb3hoYG6urqqKio6L3ERXqI53ncdtutvLjyeRr27cRNNFNU4HL1dD/zR7a2mIHDq98POfK9ohwfJB2vXUE+4DPaClMlOR6FAfCMFkwjSdw2SdgunmeSTCYIBA7ulWAFiCz4Mfgy752wM0P/d4CxeeO6dQ1E5Pj4TT8jQhXsjla2i++K7OzkRLmkKuaRqph3OOakMJJNrX8/9MXDl4MXyOtGxr2o4jRSYxfi2/5cu7CRjJDz7t9omXH8d8CJHKIO+SIiIiIiMugsWLCAYDDIH//4x7aY67rcf//9VFRUcMopp3R47ogRI6isrOQvf/lLu/gvf/lLABYvXtwrOYv0pGXLHuDFlc9jNlcSa6phaJ5Faa7F52YHwTjcnt002u+T2mJ77GnyWLvfxQNSzuFjOT4OL4/PIOgzW497HqnU4TYU0TO/glM8scPzdjSnF+DDvjAlwdLjeq4i0n2jw2PSYrtjlThdbCnTxvLj5Za2/hcqa/2vvxbfD+pow9WcDfeBk94uS+T9aAW8iIiIiIgMOsXFxXz2s5/lF7/4BZ7nMXfuXJ566ilee+01brvtNizLahv77LPPAq0brQJceeWV/OUvf+E///M/2b59OxMnTmT16tU888wzXH311ZxxxhlZeU4ixyuRSPDoow9hxmuxks188KRiKmsiXDXZJuSDWOpQyd3D9Vq7Lqyvdlm+xeb1vQ6RZGsrmtsuzGXlDpviXPjQWB/FmRewt7GO2OvA8zw8f4jYnH8jMemyY563M7IjLTY2f1xWNi8WOVGNyhsDB1a1i9muTVW8ipHhUVnKKjuc8hnYw07Dt++NdnEzeoDA9qdITlyUpcxkoFIBXkREREREBqV//dd/JTc3l/vuu49nnnmGsWPHctttt3HxxRe3G/ejH/0IOFyADwQCLF26lNtuu42nnnqKZcuWMWrUKL7xjW/wyU9+ss+fh0hnrVr1AtFIFKuljnOnleLR2j7hsikWrufhNyFxcGzCbl0N/70XEsRtg7qYi2lAUY7BE1tS2C78ZaPNXzak+O3iEOVHtD8+3K7GwDBNnCP612xiIhOuuAcvVH7MXBsS9TQmG9LiY9R+RqRPjclLXwEPrW+QnWgFeID4jOvIP6oAD5C77h6SEy45fBuRyHFQAV5ERERERAYlwzC48cYbufHGG485bsWKFWmx4uJivv/97/P973+/t9IT6TVvvfUmTXVVhOLNzCrNoTrqsPBUD8uAFhvyAgbRlIfnGWyscWlOeFw2NcD976TIDxpsqnGwXZMnt9qcN96H40F9C/zm9SRfmRckaLXWnjyvteOMAZiGQcx2qIyY/G7nWM76xJcZ/z7Fd2i/+t3zPJLJBCnb5q3n3uDnT/yMGTNmMn9+a0spEek9ozoowFdGdsLQs/o4m+xLjfwgTvEErPpt7eJW/Xv4d/+D1Kj5WcpMBiIV4EVEREREREQGAc/zWLbsAZYte4CWxgOMyE0R8qIsGOZS4Gstlidsj4AFuT6D5qTHL9ck2dHgcs5YHxNKTN6scskLGOxqdEm5Jg9tSlIbh5Jck+feszl9hI8p5RYJG3L9Jp7lp6CwhN0xP7c+vYfd3lBCQ4bxjfkLjivnHZH3AI94PE5LS0tr6xo8Nry0ns31m/n731dw112/Y/Hiy7jqqiVqSyPSS4bkDCVoBUk4iXbxbc1bs5RRlhkG8RmfJO/F76Ydyl3/BxXgpVNUgBcREREREREZ4DzP47bbbuXFlc/jtTTjphIYOS5N0TgjhlmknNYNVjGgOQFBH7y0x+PdutY2NMvftfE8j4YWaGjxSLgG2xugLGRiGR5b61yG5xt87/kW5o7yM39sLkMKg8QJ8cY2l+c27sUJFODml7N48WXHvWJ9R/N2IpEoyWQCFw/Pc/E8j+p91eCAz/BTlCzigQfuY8+e3dx001dVhBfpBaZhMi5/ApsaNrSLb2ncjOu5mIaZpcyyJznuAtw3fokZPdAu7qt6HV/1OuzyGVnKTAYaFeBFREREREREBrhlyx7gxZXPYzZXkm/GMYI+cvwuw/JMApaBzwTHg5TTWoTfXu/xy1eSVEcdaqIetge5QT9+f4CyIfkEwkXU1dVhey6+nFxCgQj7WhIkcHmx0uXlPTECgRR+XwLPCuCFhuLmlnHW2Qu48sqPHXfea957hWQygeO5eLhYfouglcOMr8wkVhtnz+u7qXqzirgTZ+XKFxg5chRXXbWkF6+kyIlrUuHktAJ81I5SFdtLRXhklrLKIstPy7SPE1pzW9qhnHV/IPKhn2YhKRmIVIAXERHJkmjSe/9BIoOAXusiIr0rkUjw6KMPYcZrsZLN/OxjU/ifZ3YyLBBnYjGkXI+AZVAQhGgSEg78z6sp6lJ+4rZLMOjDcX0UDhnGxIkncfrpZzBz5snMP9hGZtWqF1i79h1ef30N27ZtIxXwkxcO4vcZ2K6BZ+USzgvzkY9czpVXfuy4V6hH4hH2J/a1rnzHxZ/rx/RZ5AfyCIZzCBbkUDyumKHTh/LWvW/RmKznkUf+xqWXXqGe8CK9YHLhVOBvafF3GzedmAV4oGXS5eS+fSdGMtIuHtj5HGZTJW7BibdBrXSeCvAiIiJZMvRnkfcfJCIiIvI+Vq16gWgkitVSx7nTSlkwuZTKujhTrErAoznhEfRBjg9yfAZVUY/qmEltNIUVDOHPLeGjlyzmP/7jOxkL2wsXns/ChecDrcX+VateYMOGdbhuCtP0M23ajC5tlPqXVf+Hi4vnta58N30WADlWbrtx5VOHMPyU4VS/XU1hpJhVq15oy0dEes5JBZMyxjc3buJDI87t42z6iUCYxOR/Imft3e3jHuSu/yPRD/xHVtI6Ftu1iaSaaXFaiDtxWpwWbDd18Outh+u5+EwfQTNI0MohaAUJWkECZuufPsOnVl89TAV4ERERERERkQHsrbfepKmuilC8mVmlOeyurOTyiS6+qEki5QIeCdsjYUPS8fjSEy1URU3C+UUUV0zmrLMXHHdv9WAwyMKF53PBBRdSXBymvj6KbbtdyvvlfS/h5bRuumr5rbZ47lEFeICK2SOpemsvCSfOunVrVYAX6QXFwRLKc4ZQ3dK+5/m7TZuylFH/EJ/2MXLW3wuu3S4e3PIIsVM+h5db2qf5HIjvZ29sD02pJiKpZppSTdS21HAgvp/98X3UJmq6Nb9pmIR8YcK+MPn+fPL8+ZTllDMkZyhDcocyNHcoQ3KGke/P76FnNPipAC8iIpIl+/8tj3BAKwtk8IsmPd3xISLSCzzPY9myB1i27AFaGg8wIjdFyIvSVB9nWGESv+VhYJCwwTvYDewvm+HN/dCCn5KyPD5/zbWdahvTk7nvy9nb9tgwWz++dbDwc7RQaWtR3sElFov2TZIiJ6BJhZPTCvCVkV1EU1HC/vTPzROBFyonMXERwXcfan/ASZGz8U/ET/t8r378pJNgU+NG3qh5jTdrX2dvbE+vfjzXc4mkmomkmtkf39fhuBwrh6GhYVQUDMfnBsm1QuRauYR8YXJ9rX+GrBDFwWIqwqPIsXJ6Ne/+TAV4ERGRLAkHDBXgRUREpEs8z+O2227lxZXP47U046YSGDku1U1xphdb+I3WonvAMgj6TBzPoLbFYF1zLqFgitKS4Sy+/OqsbWi6K7qTlmACEgefj+thWAb5/oKMbwbEauMAWJiEQidmEVCkL0wunMo/9r+YFt/WvIWTS07p+4T6ifiMTxDc8hActbVRzqZlxGdeD/5Qtz9G0klSGd3Jjubt7IzuYE90N3tje6hpqe723L2hxWlhZ/MOKqM7cd333/OpPGcIo/JGMzI8ilHhMYwKt/49aA3+PT1UgBcREREREREZQBKJBD/60fd4/PFHKfQaCTpxPL9B0O/DNEzKQwau54HXWqj3AL9pMGTkREZtrWdUuY1TPJxTTjk1a8/h1eqX8ft8JBMGBgZOysFnmeT7CzKO3/PabnyGn6CVy4wZM/s4W5ETx6TCyRnjmxs3ndAFeLdwLKlRC/DveqFd3Eg0k7PlYVqmde7NzEgqwo7m99gReY/tze+xI7Kd3dFKXK9rLb0GguqWA1S3HOCNmtfaxfP8+YR9YfL8eYR9YUqCZQzJGUJ57lDKc8oJ+ULt+tO7eHiei4uHZViEfWECZqBf961XAV5ERERERESkHzu08enate/w+utr2Lp1C/v376M06BDIsfnXeWHufSvO0JDHvAqPlOsRsAw8DxzXw/M8mlMGb+52eW5DLV5oKHn5ecyfvyBrz+m16lcIBILEYjFwwUk6YEBTtBE37FBYWIRpmgBUbzxA1VtVlAXLyM9y3iKD3Zi8cfhNPyk31S6+pfHE7gMPEJ95XVoBHiBn3R9pmfxRsAIZz6uK7eWt2jeojO5ib2w3e6J7aEjW93a6A8bhdjddn8MyLPL8eYwMj+LCkYuYO2RezyXYA1SAFxERERERkRPSocL2hg3rcN0Urmvgui5gkEwmCASCtPYbMIjFohw40NoXeciQoYRCobZjR4/tqcee57J+/To2bFiP49gkYhHiLQlyjBReMkVh2GBuhY8zhrnUTrQ4b5xB0AfNCY+gD3IO/safcDz+55UUz+7cjBkqwc0tY/HiywgG+/62/0QiwRMvPso7sbdJJZOk7BSe64EJkU0RqtfVUDi5kJziHMJGmNh7Mfa9tY+wL0xhoDhreYucKHymjwkFE9nUsLFd/N3Gzbiei2mYWcos++whs7CHzMJ34O12cTO6n5zNf6Zl2jVAaw/1bU1bWFPzCmuqX2F3tLLXc7MMi7KccobmDmNo7jBKg6WEfGFyrBxyrFz8ph/TMDENE8MwsF2bFqeFpJMg4SZIOAmSToIWt4WEkyCaihCxI0RSzTQk66lN1OJ5799mJlscz6Ex2UhjspENDev55qxbmFWavbu8jqYCvIiIiIiIiJxQDm1e+uijDxGNRMGOUltXx/6aehwXQn4Py/IRSzrYjofppXBcD9PykWNByrZJuQamz0/IT9vYo8/tzuNowibeksBzbYaELQzPIRZ3GZ5vEE16DAkZ5AUMrp4RZEyRxRfOMHBcl5TjgQEJ2yNhtz7fn/wjwTPbIRUoojh/NGedvYArr/xY1q553Yha4qfFDrdaMFp7wDevaya2KUbTlkYMTAwMAlaA4UUjKAoUc3YW8hY5EU0qnJJWgI/aUapie6kIj8xSVv1DfOZ15D9381FRD/+bv+GpcJg3mjbzTv3bRFLNvfLxw74wp5SexqmlpzM8VEGBv4A8fz4hX6hX3xyxXZualmoOtOznQHw/++P7OBDfT3XiAFG3maaWZmJ2rF+00PE8j9UHXlQBXkRERERERCQbjty81IzXYsZrqaxppiGWoigIBbkeNTGPpoRHUQ6kvNaCd0muQWEwSW0c4m2PE9TGoSnhUZxrUJRrtJ3b3cd+C/wejCgwyPOnqGuBiaUmeX4YPsyistGlLBdmlENBwMEwwPMMoklIOK0bsBqGwZPvefxxrUsLQUrK8vj8Nddy5ZUf67VeuYfuKli3bi2xWLRtJf9LL61m+/b3sAM2udNzCLpBMAEDDKu1D3zqgI2FhZeAVDyF6Zk4roOZY3DN9b2bt4gcNqlgSsb4u42bTvgCfGrUWSRLJmHUrMP2bBzXxvYcvGQ91a/8iNWlQ3vsYxUGiqgIjWREuIIRoQomFUxmYuEkLMPqsY9xvHymj2Gh4QwLDW8f95kUF4epr4+SSjkk3AQxO0bcjhGzo0TsCFWxvVRGd1EZ2UlldBcxO9br+frNzO2AskUFeBERERERERlYnBS4KTAMMHxgWnCcK/+WLXugtfjeXImVbKY0z0+sxc/oAgNcmzw/2K7D6EKD2pgLNkwpM8nxQVGugWl6TCwxcF3IC4LjeYwuNLFMKAia2K7L6EKj3eOJJQbhAJSHLOriLqW5rY9Lc02aEx4FQfBbBuGAQUPcw2d67Gx0CfkNcn0Gpw+3WFftkOMzmD3CoixkELBgWJ5ByN/6vA4V3HP9EPRB0oGquMWLB0KEgilKS4az+PKrueqqzm0UeLyOXOEeiUSJ2zFqa2poqGsg2ZLEdlL4y/yYfpPC0QVwsH5k5VgYloGbcBl+6TCSO1JYzRZug8OB9QcoKihi2LDhXHrpFSq+i/SRjjZifbdpMx8acW4fZ5N9MTtKVayKTY0beKPmNUyrmm/ZkbRxH2mo4cnCEhp9/k7NbxgGI0IVjMsbz9j8cYzLn8DYvPEUBDJvSt1fGYZxsOVNDgRL2uKnlp7e9nfP86hP1lMZ3UllZBf741U0p5oPFuujNCUbqGmpwfGcLueR6wtx7ogLuvVcepoK8CIiIiIiItI5roNhxzBSUYzUEX8mIxjJZoxkE0aiqTXmOeDa4DkYTurg2NbxeA6eFQArgGcGAA/DSYJrH/wzieGkwEliuClwEhhOAtwMt7gbtBbjDRPPtMCwwPSBYeEZJpgWLibnvLeVeacm8OwUY8py2V7TQtL28Bkmk8pyqY44+Eww8PAwWqc1oDxkEE15mAdrwHkBg4YWjxYbUo7H0DyThpbW/riWASW5BtEUtPZ1bx0fTYJ3HI/dg212TQOCVusH/PD41op1QdDAcSGaah2UsMF3xEI/0zDwPA+fZRDML2dUmceochuneDinnNL92/Ez9c13HKfdCnc7YBOvi5GMJjFyDWxsrHwLL+hROK8Qw9f6nAy/geEzMAwDvz+AbcUInRQi6A8yZfJUNvxlPdVvVxOLxVm16gUWLjy/2/mLyPsrDpZQnjOE6pYD7eLvDtKNWF3PZV+8it2xncSrm9ldV0V1vJraRA1VsSoakw3tT8gN8U5umJPj0XbhoOfyT/UHWFpe0eHHKsspY0RoJBXhkYwMjWJs/njG5I0laJ0Ye1sYhkFJsISSYAmzSjJ/T3I8h4ZEPQdaDtCQqCd5sEd9wk1guzamYWBgYhoGKdcmakeI2VGidowhOUM4e9iHGJU3uo+f2bGpAC8iIiIiIjIIGfE6/NWbMSN7DxbFm1sL5HbL4dXihnnw7waeYQAGZrIJM1aNGTuAEavBcO3DBW3DwnBawE5k86ll5gFea9Nz46iFc4fWTdvJFoYF4+C3CfpM/GaCcQWtxfzWx62tZcDDMgycg4XwoGVgGrQV34OWgeeBz4S8AAQtE59J22r0Q0XzQ8X3Q+O943xsGYfObN1INW4ffi6WYeDzQSzVOjrheASc1hX0eB6GYeB6UBn18WZ9C89tiOCFhpKXn8f8+Qu6fnmPWuHe4sSpq6uhvrqexJEr3DHx+Xx4AY+cohzsmI3neviKfeTPyCd3VG7bnKbPbJs7GAzg5jskI0l8jo/GxgYqZo+k6q29JJw469atVQFepA+dVDg5rQC/K7KTmB0l5AtnKauekXSSrG9Yy9q6t9nWtJXtkfeIH2yLYpoGrvv+m43eWzqMn+7elhY/r7Gex4rK2O9vLagHrACnlp7OGWVncnrZGeT583v2yQxClmFRmlNGaU5ZtlPpMSrAi4iIiIiIDAZOiuB7TxDc+Sw0bqGg+QDe+9cQjsvRBe2BKpWyOVjpJugzSdiHV9Ln+KDliEK3aRg4By/g0ceOfhz00bbhaU88tkwD+2AByDQOv4EA4HoePsMgx2cQt1vHNCc9ghYEfQYJ12TlziRPbHN5fV8cf345bm4ZixdfRjDYWhA6uk97KBRmxoyZzJ+/oG3MIYlEghdffJ7//d9fsW3b1nYr3FPRFOTSfoX7WYU0vtwIQQPXcAlNCJE8kMT1XPJPzcdfFqDthXnwjQbDa30QyA+SaE7gei7RaJTy0nIAHFxisfYrTUWkd00unMLq/S+mxbc2beHkklP6PqFuak418dKBf/BGzWusrXubpJvs1nzv5YRYnVfIvEhju7gPj881Rnn91Cs5ueQUZhTPJHCCrG6XjqkALyIiIiIiMgjk/eN7BLY9gXGoZ4qk8Y54R8IyjXZvUFiGgedlaG1DaxHcO8ZjywC33WMD94gRnX1sHvHP53rgt1o3VoXWwn9eEHL94HgGScfD8+C57TZ/3uCwrcHFdiDhmsSNEC22y4QSHyUlJbS0tPDww39tt4rd9Vzw4K9//TOpVJIJEyYya9apgMf69evYsGE9jU2NxOMxzOLWFeuHVrgHi4JpK9ytXAvP9sCE8NQ8vITXdt2Dw4N4rnfoRoWDF9GDg21sLL/ZdsBxHWK18dbrg0koNLBX3IoMNB31gd/cuGnAFOA9z2NT4wae3vMkL+1f1a2+4pncXzKUudFmApj4TB+WYeE3/SyIxTktOIFU2ewe/XgDnmtjNlViJptaW9Elo4fb2R3991TrY+yWY89p+PD8uXj+MJ4/hFM0nuS483HDw/rmOR0nFeBFREREREQGOLN5L4FtT2Q7jX7vyE08Hddr9z6Fc7B9S/vSeivXO3oVevvHTmu9ud1c3Xl8ZAG+xYZwwDjYk7615YzfBp9pEPTBPypd7noryXsNBg4W+5scWmxoTrmEwi5FpSYN0Qa+//3vcuDA/tZ2L0UBnBwXTEg0ttDS2AKugeW32L2vkudfWAEeOK6Dle/Djqcwc00sv0Xp2aU0vNSQcYV73sw8IuuirT3eDcib0frY8zyCI4MYVmvcO7hy37VdTMsEF1zHOeLSG1imxZ5XduMz/AStXGbMmJn27yIivWds3nj8pp+Um2oX3zIA+sA7nsOL+57nkZ1/ozK6q0fnNgyDkwomc1rp6ZxWNpvw2j+Rs/kvaePyXvoxDcNOwwsW9ujHH1A8D6thG/6qV/FXrcG37w2MZPrmtT0td90faLz4LtyCUb3+sY6XCvAiIiIiIiIDnJGK4uISs6M47sHlxYbBof+18o74//Z/O3hC2v+3e3ywFYqBgWGYmJgYhz7GkR/L4PDHNEyMQxuhmr6DK9VCB1eqhcHytW6w6iTBSbR+HCvQujGr6WvdmNUK4Fl+MP14VhDPlwNWsDXmeQc3eXXAc8BzMVy77bHhHfq7C65D7d5KtmzegC/VzPACi6JQkO31USw8SkMGoYDB21U2Sdsj1w9NidbnPKbIpDBo8NJuG9uFCcUmQ/NM3qiyCVoGY4tNinLgzarWdewV+QYluQav7nFwPYMxha2Pn9xqk3BhconJiHyD+9emSLpw+nCL8cUGv38zhYfBvFEmG6pdHLe1P/wNp/l5r97hrxtsbM+gIe5y1hgfZ431E/QZfGRqiGXrk7xVZZNwDSKOQU5RkOCQIA1N9ezbXIWTcPFcF3+Jn4TXQv6oApxmByNhkpsbImUnSdYncW0XwzLwbA9fqQ/XdTCCRtsKdzPH7HCFe6A0gJtoOthXpvVxcGSK6EYoPru4teAeMCHZ+vLzUh6pSIpAfgDP80g2JzAMA9MwSexNUPVWFWXBMvK72b9eRDrPZ/qYUDCRTQ0b28XfbdyM1/aGZf/iei4vH1jNn967j72xPd2aK9cXojhQzNDcoQwPVTAsdzgjQhWML5hAvr+gbVz81M8R3P5UWmHZiNcSevVnRM/6frfyGGjMpt1tBXf/vjUY8fo+z8GI15Oz6UFic27u84/dERXgRUREREREBji7aAIbLKhIHrFSMUMDeA9IHNyAtbWY7mG2teN2MYGkYVDn81Pr81Nn+WkxTSzPw8LD8lqPx02TuGnRYpoH/37wsWHSbFlETYuI1fr40DJzn+kj18plWGgEI0MjqQiPYljucHKsHAJWkIAZoMBfQGlOGaZhpuXeE/yJBD+78RPEq3dixfbzH4vG8D+v7iQaa8F0E3zrw/n8n0kkdwABAABJREFU8rUETS0OnudgOwYBX2s/+K/O8/PAOptYymt7/Md3bCJJD9OAr30wyNI3baIpDwOPr30wyAPrnHaPn9/pEk15PPeezdc+GGRHI0RTHlvrUnztg0GakwbRlMdDm2zmVli8tMfFwOMrT7dwzhiLolyTt/d7JB147F2bx961W6+vYeKZPkxfgJjtECj2Mf68CbTUtVC/vQ4jaBCpimDlWBghg+IPF2PX28S2xDB8YFgGOcU5rSvRS1yS+5P4S/xYeRaBoQGS+5OtK9pn5hFZF2lbyX7kCncMsJsczODhfzu7ySE8OUyqJoW/xI+X8vAsD9Nv4iZd3KTL3nv3Eh4bJn9qfus8EYPqrQdw9jiEfXkUBorb9a8Xkb4zqWBKWgE+akfZG9tDRXhklrJKl3QSrD7wD5ZXPsyO5u2dOjfsCzOxYBLjCyZwUtEkZo6YgpXIxc/xfc3xckuJzfkK4VW3pB0Lbl1Ocux5pEad1amcBhw7iX/r44TW/x++A2uznQ0AZrwm2ym0owK8iIhIlkRTPbQznhy3Qyv0+uOKncFMr3WR3rczupMfDBvOJ2tgTKKFhGWxPRBkezCXXYEc6i0fMcsifkRBvK/Zrk2z20xz42a2NG7ucFzADDAiVEFFeCRlOeWEfXmEfCFCvjAFgQKKA8WUBEsJ+/I6/fU8GAyyePFl/N8D94Id40ePvUdJnp/9tkHYtPjes02E/VAbcykIGtTGXVpsg2F58J+rkhTlGNS3QNjv8aMXk+QHoS7uURA0+NGqBPkBk9qYR0GQbj9eVWljYFLf4hEOwNPvubiex4GoS30cbA9yA378gQC54XzqIy2kXJtAkY+5X5hLZF+E/W/tI2XYJJNJfGEfVpFFeFqYvOl57P7dbjA9MA3KLy6n9rlazJCJ0+QQKAlghSzyZuThxBySB1IYJgSHBGlKNLUucD9yhfsGwIDI/8/efYdHVW19HP/OTDLpCb0jTRJK6L0ICAhIkSJViiioqNgQFHyVa0dUxCsoiiIWkGujiCC9g1RB6SXU0EN6nXbeP2JGhhQCEkL5fZ4nD8w+bc/KZOacNfusvTMBnzI+JO1JnzA1cWcChe4pRMG7CmL8PamsK9WFycuEyWIibn0cjhgH8bHxxO+I//tOCjN+Vl+KB5UgxFqQFi1a0qtX36t92YnIv1A5mzrwB+L23RAJ+OOJx1h6chFrzqwk2ZGc6+0qBFWkbpEG1C1cj0rBlbGYLAB4eZkpGBxATEwSDkfWc4JkJe3OzliPLsE7ckOmZQEb3iKu+48Y1qBc7+9mYUqNwWff/+DgXPyToq7ZxO/Xgq1cm/zuggcl4EVERPJJ8ffzvv6diIjcHkKsIcRbvJhcPL3eqdlswuW6ga6Er4DNZeNo4hGOJuY8itFisuBr8cXbbMXb7I232RsvsxdWsxWvvx+n/98Lb5M3mEzYnTbSwtLw6+dH1PlCmFMDSEhOJSHWi/M2J74+JqxWsBkmTptM+FhN+HqZiDKZsFpNxFhN2E0mUkzgZTeIszmxJ7s4leTCxwviLGCzw0mbgY/DRbzDRXKck6hUF0EBZuL9zaQYJmKd4G+BeDOkpLqITjMI8Ep/nJxmEI2JgCAz/oEWHBYzZ2xGekkYkwmTy0QBlwkDK35WX4oWKU6AbwDHjx4nmSRCKobgVd6LU3+ewquqN2aTmeCCQdgT7GCG4DrBOFIchDQKwWQ24V3UG0uQF4G1AjMqx+CypyeeQuqHkHo8FbO/Ob0mvM2FX0U/sKSXGXLZXQRWCyQtMg3DaeBMcxJQOIDA8EDwAv8q/mAGs78ZV6rLXfvdcBjYo+1Er4vGmeDCZICPnw8WixfBAUGUK1yBwMBA7ruvO7169dUX5yL5JDSkSpbtB+L3c3eptte5N/9wGk6+i/iGX47NyfU2gd5BtC7Zlral21PSv9S17ZDJRFLTlwmZ2wuTLcljkTn5PIFrXiGh9ftgvkXSsIaB9fBCAja9j9kWf22/2Dd7uSdUTf/XD89ZVzyZXDZM9mSwJ2OyJ+MKLEVq1d7YyisBLyIiIiIiItdQQZ9CtCt9L0tO3j4TsToNJ0mOJCDpsutmUsTAGuBNaqoTwwjAB3+8XS5cLgMH4I2B1WTCMMDAwIv0ij4OU/pkq+aLHnsB3n+ve+m2dsAPA/+LHvti4JfD40vX9wKCzX8XDPq71Iu3lzd+fn5YrT6YTCaiks5jqWzC3/DD5A+RiSfwa+aLH77pE6Ia6ROeAlgCLJj9zXiHeKc/9rPgsrsIbpBe09hkMWE4Dfe6AcEB+N3pl77M20RQ7SACqgS4H5t9zBS5twgu2z+jRQu2LuieYuDiEe/w9wSsLoiaF4WXwwsfXy+C/YLx9/enUqU7qVevATVq1KR585YqOyOSzwr5FKKIb1GiUs97tB/Ix4lYU52p/HfX+2yN2pyr9Uv5l6ZH+V40KdYcq8WaZ/1yBRQnucFzBKx/M9My7xNrCdg4nqQmL+XbXWjXijnpDAEb3sY7cn16Q26fjsmEo3BVHCUb4CgUlp5ct6bPB2N4+bv/Tx7+jvKTEvAiIiLXkb+/P0eOnM7vbtyWkpOTqV69EgD79x/GavXL5x7dnvz9/fO7CyK3rIfDHqV24TrsjP0TmymF2OR4kuxJpDpTsZgsmDFjNpkxmyyYTWYsf/8fwGU4cRounIYTl+H6+7HT/TjjX7vLToI9HrvLfpne3OhM+Pn54+vrh82WhsPhwMgY/238M1ntxXkFwzBwudKTzGaz2WNUdqZ18+KxyYS3l5c76X4x46L7/k1mE067091u8ba4R7S7d3axS8vtX7xrF5i9zLhMLvekqRn13zMeGxYDkzV9ZH7GCHeM9G0zJmo07MY/yzBhO5yGz1lfgvyDqVTpTh577EnuuksJd5EbUWhIlUwJ+OOJx0hxJOPndX3P62LSohn/15tExB+67LpFfYvRq0JfWpRoheU6jTxPq9wN65EleJ/K/OWAz/7ZuAJKkFJryHXpyzXntOO7/yf8/vgkfcR5bjYpWAl7yQbYSzbAUaLeLVmGJ7eUgBcREbmOTCYTAQEB+d2N256/fwA+PkrAi8itxWKy0KBoY5qUbErBgldewza3DMMgxZlMnC2OOFssqc5UUp0ppDpTSXGk4DQc2F0OHC47DsOBw+XAbthxuOxEp0UTmXSCc6lnPZLG+cVkMuHj44uPz81dtsfjywCX4ZlkN6ePaufv70w8kvGQKSFvMpn+/jIifdS8xWJxT5oK6SPaMxLwmLIf4Q7pZWoMw8BkmNLru5tNWGwWCuwuxJ3Vg1RiRuQmEBZShQ1n12ZqPxR/kBqFal23fhxPPMq4P9/I9GXApe4ILEf7Mh1pXfIevK53yReTiaRmYwmZPwBTamymxX5/fILLvxhplbtc3379G4aBd+RaArZ8iDnu2GVXdxaoQGqV9BIwhl/h69DBm4MS8CIiIiIiIpJrJpMJf68A/L0CrrqOrs1p43TKKRLsCdicadhcaaQ6UzmbcpZTSZFEJp/gVPJJHC7HNe79rcnbywtbmgkTf49+z2YUu2EY6aPYvf9ZwWV3pZedyajP/neZm4wR7i6LK+sR7kZ6sj+rEe4mc/pyk8mExWLBarXi5fKmWExxwpKrUG9IA5WYEblJVA4OzbJ9f9y+65aAX3N6JZ/t/xib05blcovJQosSrWhbugOVg0Pz9Us9V2BJEtp+SPCix8CRlml5wLpX8Y5cR3K94biCy+ZDD3PP69xf+G2fkuWI/kvZy95FcrX+OErUv+nL7OQFJeBFRERERETkurJarJQLLJ/jOoZhYHPZSHIkkeJIJsmRSExaDDG2aGLTYoizx2F32bA77dhcNhyGA7vLhs2VPtre/vePzWXD4bJjYGA1+2C1WLGarRgYpDiSSXYkk+pMAcDb7IWX2Yq3yQuL2Qur2Tt9QldT+gSvGZO7mjGT4kwhxZFMoiORNGdq+pjxv0f121w2nIbzmsTKZDJd9m4Bq9WH5ORkTJhx2p1YvC3ubV12FxZfC2arGWfa36Vp7IZ7BLthNzD5XDSC3mFg8von2Z4xwt3sbcYwGekj4f+ujZ/VCHez2YyJ9Me+vn5ULVqNDmU70ax4C3wtvtckJiJy/VQIqoS32TtT6bHrUQfe5rTx1cHPWXpycbbrBHkH8WLNlwkrUDXP+5NbjqI1SGg5jqAVz7s/Fy5mPboM6/GVpIbdj61iBxyFq944tc8NA6/TW/D760u8T2+57Oouv8KY271GctFmeXLX3a1CCXgRERERERG54ZhMJnwsPvhYfMCnUJ4ey2IxUbBgALGxydckgZDx5UGiPYFERyJOlxMfiw9Wiw8+Zh/MJjNOw5Fed9/lcNfbz2izmq34e/kT4BWAt9mKyWRKX+5KL+/jNJw4DAdOl+PvLx7sLPjtFxYumk+ULYrktDjSYlMxW804TA6K31MC78JeRC2OIuVICiZMOBId2GPsWELSJ2X18vfCmeTE4usFXum13p0pTiw+lvSJXP8e1Y4JsIE3VoL8AilVsjQ1atTCZDJht9vx8/HjzjsrU7dOfYoFlaBQHv/uRCRveZm9qBhUif2XJNwPxu93z/OQF6LTLjD+zzc5nBCR7Tol/Evyf7VepYR/yTzpw79hv6MlSY1HE/D7uKxXcDnx3fsDvnt/AIs3jiLVcYaUx7AGuScodQaWxlmoMq6AknkzqtwwMCefxRJ9EEvMQbyiD+IVvQ9z3PFcbZ5WuQu2Js9ToEQpiLmKCdFvI0rAi4iIiIiIyG3NZDJd0yTSxV8eFKbINdmnxWRJL+diybpsy7Cew0k5kcKaNauJs8Vw6sJJ4qLjsIRYOD7zGAGVAggOC8HH6kPinsT0WvBOcJx3YAn0wpTmxJnkxJ5qxxJoweRrwpXiIi0pDTDh5WPBYvbCgoWCgQWpWrUq/fs/QKdO3XE6b87a+SKSO2EhVTMl4BPtiZxKPknpgDLX/Hhxtlhe/eP/OJ18Ktt1qhSoygs1/48g7+BrfvxrJa1KT8xJZ/D7a3rOKzrteJ3dgdfZHVkuNqyBOAtVxlG4Ko6iNXAUCccVmIukvOHCZE/GZIvHnByFOelM+k/iabxiDmKJPojJlnjFz8tZ8E6SGj6Po1RDvLwunclbsqIEvIiIiIiIiMhNzmQy8dxzoyhdugzz588l2LsAx4zDxMXFY3JA6sFUUg+n4u3tjY+PD17+XgR4BeJIcWAymSgeVAKfkj5EX7hAXHQsRhqYfUwElwrB28uKPcWOxWImPLwm/foN4O6776ZEiULExCSRaSZXEbmlVA4Jy7L9YPz+a56AT7In8eb2/+SYfO9QpiMPVh56/SdZvQopdZ/E8CuE3x9TMNmTr2ofJlsiXme243Vmu7vN8AnG8PK9qMTNP+/DJsMAZxome+I1fXs2/AqRXPcJ0u68D8yWa7fj28CN/0oVERERERERkcsymUz06fMA3brdz7p1q9m58y+2bdtCREQEVqs3PoG+GCYDC2Z8QvwIDAzgvvu6c9993Vm/fg27du0kOTkJq9WHjHozNlsa/v4BhIfX8Jg4VaMeRW4foSFVsmw/ELePViXbXLPjpDnTGPfnaxxNPJLlch+LD8OqDKd5iZbX7Jh5zmQitdoDpFXogN+fn+O7/2dw/fs5Qkxp8ZjS4q9BBy/PsAaSWq0fKdUHgjXguhzzVqMEvIiIiIiIiMgtxMfHhzZt2tGmTTsA0tLSWLdutTvBnlVC/eL1RUQuVsinEEV8ixCVGuXRfmlZmn/D4XLw3l9vZ7vPUv6lGVljDGUD77hmx7yeDL9CJDd+kdRq/fD/YwrWYyvA5cjvbuXI8C1ASvUBpFXphWENzO/u3NSUgBcRERERERG5hV2akBcRuVKhwVWISl3n0XYi6TgpjmT8vPz/9f6/2P8pf0Zvz3JZCf+SvFb3bQr4FPzXx8lvruA7SGw1DhwpeEXtwfvsDrzO7cASfQBzWjw4bfnXObMXzpDyOAtVxl6iPmkV24OXX/715xaiBLyIiIiIiIiIiIhkK7RAFTac80zAG4bB/rh91C5c91/te82ZVSw/tSTLZYV9ijC2zpu3RPLdg5cfjhL1cJSo59nutGOyJ2FOuYAl5iBe0QewRB/A68JeTKmx1+zwLv8iOAuF4ixYGUehyjgLVsYZXA4s3tfsGPIPJeBFREREREREREQkW6HBWU/E+vu5df8qAX8yKZKp+z7OclmwdzCv1Hmdor5Fr3r/Nx2LN4alAE7fAjgLVsJWsUN6u2FgTjyF1/ldeEXtwpx09qKNTBf99+//my24rMEY1qD0H58COAOK4wosiSugGFh8rttTEiXgRUREREREREREJAcVgyoR5B1Egj3Bo/33cxsYEjoMq8V6xfu0OdP4YNd40pxpmZb5Wnx5uc5rlA4oc9V9vqWYTLiCSmMLKo2tYvv87o1cIU1bLiIiIiIiIiIiItmymL1oVvyuTO0pjmS2RW25qn1+eeBzjicey3LZsKrDqRBU6ar2K3KjUQJeREREREREREREcnRXiVZZtq89u+qK95VT3fd7SnegWfEWV7xPkRuVEvAiIiIiIiIiIiKSo8rBYRT3K5Gp/Y+orSReUpomJznVfS8XWJ7BlYdedR9FbkRKwIuIiIiIiIiIiEiOTCZTlqPgnYaT38+tz9U+Llf3/fkao6+qnrzIjUwJeBEREREREREREbmsu0q0zLJ9zZlVudr+cnXfS/qXutquidywlIAXERERERERERGRyyrlX5pKwZUzte+L3cP5lHM5bptz3ff2qvsutywl4EVERERERERERCRXWmQ7GevqbLc5GLdfdd/ltqUEvIiIiIiIiIiIiORK02LNMZsypxRXnFpKkj0pU/uu6L94ffsr2dZ9H1HjRawWnzzpq8iNQAl4ERERERERERERyZUCPgWpWah2pvazKWf4zx9jiE6LdrdtPb+Zt/58lVRnapb7eqzKk5TyL51XXRW5IXjldwdERERERERERETk5nFXiVbsuPBHpvZjiUd5eesLdCzbmaOJR1h3Zg1Ow5nlPtqWbk/zbCZ1FbmVaAT8JU6cOMHw4cNp2LAhDRs25IUXXiA6OjrPthMREREREREREbmZNC7alNIBZbJcdj71HF8f/JLVp1dmm3yvX6QhQ0Ify8suitwwNAL+IjExMTz44IPYbDaGDh2K0+lk2rRp7N+/nx9//BGr1XpNtxMREREREREREbnZWC1Wng8fzRs7xhKTdmUDUJuXaMmTVZ/By6y0pNwe9Eq/yFdffcWZM2eYP38+lSpVAqBWrVo89NBDzJ07l969e1/T7URERG5WR48eIT4+Lr+7cUVSUlLc/9+58y+s1ptzoqfg4BDKl6+Q390QERERkdtc2cA7eKv+e7y5fSynkk/mapt7SndgaNiwLCdxFblVKQF/kQULFtCwYUN3Eh2gadOmVKhQgQULFmSbSL/a7URERG5GFy5coHHjOrhcrvzuylXr2PGe/O7CVbNYLOzadYjChQvnd1dERERE5DZX1Lcob9Yfz7g/3+Bg3P4c1+1argf9Kz2IyWS6Tr0TuTEoAf+3uLg4Tpw4Qfv27TMtq169OqtWrbqm24mIiNysChcuzMaN22+6EfAAZrOJ4GA/EhJScTpvzi8QgoNDlHwXERERkRtGkHcwY+u8wcd7PmTjuQ0ey0r5l6ZScGU6lOlEaEhYPvVQJH8pAf+3s2fPAlC8ePFMy4oWLUpiYiIJCQkEBQVdk+2yYzabMJv1TaBcPxaL2eNfEZHcuPPOSpdf6QZksZgJDvYjPj7lpk3Ai8jVOX78OJ07d+bzzz+nUaNGudrmu+++45tvvuHUqVOUK1eOYcOG0alTpzzuqYiIyM3H1+LL8zVGcyBuH+dTz1HIpwjlA8vj5+Wf310TyXdKwP8tKSkJAD8/v0zLfHzSa8QmJydnSqRf7XbZKVQoQLfiSL4IDs78GhYRuVXpPU/k9hIfH8+TTz5JWlparreZNm0a7777Lh06dGDw4MEsXbqUESNGYDKZ6NixYx72VkRE5OYVGlKF0JAq+d0NkRuKEvB/y00dW7M58wjhq90uO9HRSRoBL9eVRoOKyO1E73mSnwoWDMjvLtyWIiIiGD58OIcPH871NvHx8UyePJnOnTszYcIEAHr37s3AgQN59913ad++PRaLJa+6LCIiIiK3ECXg/xYQkH5BlNWomIy2jHWuxXbZcbkMXC4j1+uLXCtOpwuHQ8koEbk96D1P5PYwZ84cXnnlFYKCgujVqxc//vhjrrZbsWIFycnJ9OvXz91mNpt54IEHGDFiBNu3b6d+/fp51W0RERERuYWo6PPfSpUqBcD58+czLTt37hzBwcH4+2euW3W124mIiIiISN7av38/nTp1Yv78+dStWzfX2+3atQuA6tWre7RXq1bNY7mIiIiIyOVoBPzfgoODKVOmDLt37860bM+ePYSHh1/T7UREREREJG+NGDECq9V6xdudO3eOkJCQTPM8FS1aFIBTp05d0f7MZtN1LTNpsZg9/pXLU8yunGJ2dRS3K6eYXTnF7OoobldOMcsdJeAv0q5dO7755hsiIiKoVKkSABs2bODIkSMMGTLkmm8nIiIiIiJXJjIyMsflQUFBhISEAFxV8h0gKSkJX1/fTO0ZbSkpKVe0v0KFAjCZrv88T5pw+sopZldOMbs6ituVU8yunGJ2dRS3K6eY5UwJ+Is88sgjzJs3j8GDB/Pwww+TlpbGF198QbVq1ejatSsAJ06c4I8//qBu3bqULVs219uJiIiIiMi/16ZNmxyXP/LII4wcOfJfHcPlcmWZMM9ou9JkenR00nUfAa8Jp6+MYnblFLOro7hdOcXsyilmV0dxu3KKGRQsePm5P5WAv0ihQoWYMWMG48aN46OPPsLX15c2bdowatQo9+iZLVu2MGbMGMaNG+dOwOdmOxERERER+ffefffdHJeHhob+62MEBASQmpqaqT1j5HtAwOUvtC7mchm4XMa/7teV0oTTV04xu3KK2dVR3K6cYnblFLOro7hdOcUsZ0rAX6JixYp8/vnn2S7v0aMHPXr0uOLtRERERETk37sed5iWLFmSuLg4bDabx4Cac+fOAVC8ePE874OIiIiI3BpUIV9EREREROQi1atXxzAM9u7d69Ge8bhGjRr50S0RERERuQkpAS8iIiIiInKRli1b4uPjw7fffutuc7lcfPfdd5QuXZratWvnX+dERERE5KaiEjQiIiIiInJbW7ZsGQBt27YFoGDBgjz66KNMmjQJwzBo3LgxixcvZuvWrUycOBGLxZKf3RURERGRm4gS8CIiIiIiclt7++23gX8S8ABPPvkkfn5+zJw5k6VLl1K+fHkmTpxIx44d86ubIiIiInITUgJeRERERERueT169KBHjx5ZLluxYkWmNpPJxJAhQxgyZEhed01EREREbmGqAS8iIiIiIiIiIiIikgeUgBcRERERERERERERyQMmwzCM/O6EiIiIiIiIiIiIiMitRiPgRURERERERERERETygBLwIiIiIiIiIiIiIiJ5QAl4EREREREREREREZE8oAS8iIiIiIiIiIiIiEgeUAJeRERERERERERERCQPKAEvIiIiIiIiIiIiIpIHlIAXEREREREREREREckDSsCLiIiIiIiIiIiIiOQBJeBFRERERERERERERPKAEvAiVyEtLY0vv/yS+++/n7p161KvXj26d+/OF198QUJCQpbbXLhwgeTkZPfj0aNHExYWdr26fMUmTZpEWFgYkZGR+d0VEbkNJCUl8dVXX3H//fdTv359ateuzf3338+sWbNwuVwe67Zu3ZqBAwfmU0+zl5SUxNtvv03Lli2pXbs2gwYNYvfu3fndLRG5jZw4cYLhw4fTsGFDGjZsyAsvvEB0dHR+d+uGsXbtWh544AFq1apFnTp1GDx4MDt27PBYRzHM2b59+wgPD2fSpEke7Yqbp+joaF5++WWaNm1K3bp1GTBgAH/88YfHOopZZrt27eKhhx6idu3a1K1bl2HDhnH48GGPdRS3dC+//HKW58O5jc/tGsfs4pabzwe4PeOWXcwult1nA9yeMcuKV353QORmc/r0aYYOHUpERARt27alR48eGIbB9u3bmThxIt9//z2fffYZFStWdG+zevVqRo4cyZw5c/D398/H3ouI3HiOHDnC448/TmRkJF26dKFHjx7YbDZWrFjBq6++yubNm5kwYQJm8409bmDEiBGsX7+egQMHUrp0aWbMmMHAgQOZM2cO5cqVy+/uicgtLiYmhgcffBCbzcbQoUNxOp1MmzaN/fv38+OPP2K1WvO7i/lq06ZNPPLII1SuXJnnnnsOh8PBd999x4ABA5g5cya1atVSDC/D4XAwZswY7Ha7R7vi5ikxMZH+/ftz7tw5Bg8eTHBwMDNnzmTw4MH8+OOPhIWFKWZZOHz4MAMHDsTPz48nnngCgOnTp/PAAw8wb948ihcvrrj97ccff+THH3+kYcOGHu25jc/tGsfs4pabzwe4PeOWXcwult1nA9yeMcuWISK5lpaWZnTr1s2oXbu2sWHDhkzLd+zYYTRs2NBo3bq1kZyc7G7/6KOPjNDQUOPEiRPuthdffNEIDQ29Lv2+Gln1WUTkWktNTTXuvfdeo2HDhsbevXszLX/77beN0NBQ44svvnC33X333caAAQOuZzcva926dUZoaKjx/fffu9uioqKMBg0aGCNGjMjHnonI7eKDDz4wqlatahw6dMjdtn79+kzvTberLl26GK1atfI4Rz9//rzRoEED48EHHzQMQzG8nMmTJxvVq1c3QkNDjY8++sjdrrh5+uCDD4ywsDBj8+bN7rZz584ZNWvWNEaOHOleRzHzNHbsWCM0NNTYvXu3u+3PP/80QkNDjXfeeccwDMXN4XAYkyZNMsLCwozQ0NBM58O5jc/tFsfLxS03nw+GcXvF7XIxu1h2nw2GcXvF7HJu7KFkIjeY2bNns2fPHl588UWaNGmSaXmtWrV46aWXiIyMZNq0afnQQxGRm8t3331HREQEY8aMoUqVKpmWP//88xQuXJgffvgBwzDyoYe5s2DBAnx9fenWrZu7rXDhwnTo0IHly5eTlpaWf50TkdvCggULaNiwIZUqVXK3NW3alAoVKrBgwYJ87Fn+i4uL48CBA3To0AE/Pz93e5EiRWjQoIG7zIBimL39+/czZcoU98jkiylu/zAMgzlz5tCqVSsaNGjgbi9atCgvvPCCu00xyywyMpKCBQtSrVo1d1vNmjUpUKAABw4cAG7vuKWlpdG9e3cmTZpE165dKV68eKZ1chuf2ymOl4tbbj8f4PaJW25eaxly+myA2ydmuaEEvMgVmDdvHv7+/nTv3j3bde677z6KFi3K/PnzgfRa75MnTwagTZs2mWpn7dy5k4EDB1KzZk2aNm3K22+/TWpqqsc6p0+fZtSoUTRu3JgaNWrQrVs3fvnlF491Ro8eTYcOHZg5cyYNGjSgQYMGrF69Ott+nj9/nv/7v/+jefPm1KlThx49erBo0aIcn//u3bt56qmnaNq0KdWrV6dJkyY8//zznDlzxmO9WbNm0aVLF2rVqkWjRo144okn3CdNGRYvXsz9999PnTp1qFevHg899BBbt271WMflcvHFF1/QoUMHwsPDueuuu3jzzTdJTEz0WG/z5s3079+fBg0aUKdOHfr27cuyZctyfC4icmNYsGAB/v7+dOrUKcvlVquVWbNmMX/+fEwmU5brGIbBrFmz6NmzJ3Xq1KFGjRp06NCBqVOneiTt4+LiGD16NK1atSI8PJy2bdvy/vvveyTHbTYbb731Fm3atCE8PJyWLVvy6quvEhsbm+Pz2LVrF6GhoZluo6xevTopKSlERETkMiIiIlcuLi6OEydOUL169UzLqlevzq5du/KhVzeOwMBAFi1axODBgzMti4mJwWKxKIY5yCgv0LRpU+677z6PZYqbp8jISM6ePUvTpk2B9HOUpKQkAPr370/v3r0Vs2yUK1eOuLg4j9rQsbGxJCQkULRo0ds+bmlpaSQmJjJx4kTGjx+Pl5dnRencxud2i+Pl4pabzwe4veJ2uZhlyOmzAW6vmOWGasCL5JLT6WTXrl3UrFkTHx+fbNczmUw0atSIX3/9lfPnz9OnTx8SExNZunQpY8aMoXLlyh7rP/jgg3Tu3JlOnTqxatUqvv76a1wuFy+//DIAZ8+epVevXgAMHDiQkJAQli9fzqhRozh37hxDhw517+v06dNMmTKFJ598kqioKGrXrp1lH2NjY+nZsyexsbH079+fsmXL8ttvv/HMM88wceJEOnbsmGmb/fv388ADD1CuXDkeffRR/Pz82L59O3PnzuXcuXN8++23AMydO5dXX32Vbt26MXDgQGJiYvjmm28YOHAgy5YtIygoiE2bNvHcc8/RokULevXqRWpqKjNnzuShhx5iwYIF3HHHHUD6lwrz58+ne/fuDB48mIiICGbNmsUff/zBrFmz8PHxISIigscee4yqVavy7LPPAvDTTz8xfPhwvv32W4+RJyJyYzEMg71791K3bl28vb2zXe9y9dM//PBDPv30U7p3707v3r1JTk5m7ty5TJgwgaJFi7q/NH366afZt28fgwYNolixYvz55598/vnnxMTE8NZbbwHw6quvsnDhQgYNGkTZsmWJiIjg22+/5ejRo3z11VfZ9uHs2bNZvt8ULVoUgFOnTnmM6BIRuZbOnj0LkOUotaJFi5KYmEhCQgJBQUHXu2s3BIvFQvny5TO179u3jz/++IO77rpLMczB559/zrFjx/jkk09wOBweyxQ3T8eOHQPSR8++9957fP/99yQkJHDHHXcwZswYWrdurZhlY+jQoaxatYoRI0YwevRoTCYT7777Ll5eXgwYMOC2j1tgYCBLlizJNhma2/jcbnG8XNxy8/kAt9d73eViliGnzwa4vWKWG0rAi+RSXFwcNpvNnUzJSbFixQA4d+4cderUISwsjKVLl9K2bVvKlCnjse4TTzzhTqL37t2bDh06sGzZMncC/oMPPsButzN//nz3fgcMGMDzzz/Pf//7X7p3707hwoUBSE1N5e233852JGmGzz//nDNnzvDVV1+5S+n07NmTrl27MnXq1CwT8N999x0mk4lvvvmGAgUKANCnTx9sNhsLFiwgJiaGggULsmDBAkJDQxk/frx72ypVqvDuu+9y4MAB6tWrx2+//Yavry9Tpkxxj2ht2rQpTz31FHv27OGOO+5g48aNzJs3j9dee42+ffu699WyZUuGDBnC//73Px588EFWrFhBcnIyH3/8MQULFgSgU6dO9OnTh3379ikBL3IDi4mJweFw5Op9NTt2u50ZM2bQqVMn3nnnHXd7z549adKkCYsXL6Z79+5cuHCBjRs38uKLL/Lwww8D0KtXL1wuFydPnnRvt2DBAnr27MmIESPcbX5+fqxZs4akpCQCAgKy7EdSUhK+vr6Z2jPaUlJSrvo5iohcTsYI24tvn8+QMXAkOTn5trnIzY2kpCRefPFFAB577DHFMBsHDx7k448/ZuzYsZQoUYLIyEiP5Yqbp/j4eAD++9//YrFYeOmllzCbzUybNo0nn3ySadOmuWOlmHkqVaoUjz76KG+88QZdu3YF0pOjH374IeHh4Wzfvh24feNmNpsxm7MvYpHbv8Xb7W/2cnHLyqWfDxltcHvELTcxu9xnA9xeMcsNlaARyaWMMgYZtyDlJOObwtzUK+7SpYv7/2azmWrVqhEVFQWkl2BZtmwZ9evXx8vLi+joaPdPu3btsNlsrF+/3mN/uUk4r1q1itDQUI869t7e3kyZMsVdLudSr776KitWrHAn3wESExPdb5wZyaUSJUoQERHB5MmTOXHiBJCeNF+wYAH16tVzr5OUlMRbb73FwYMHAQgNDWXx4sV06NABgKVLl2IymWjZsqXH865WrRpFixZl1apV7n0BvPnmm/z1118YhkGBAgVYvHhxpnI/InJjyTixy2rERG55e3uzYcMGXn/9dY/2mJgYAgMDSU5OBiAoKAh/f39mzZrF4sWL3SeEb7/9tsfI9hIlSvDbb78xe/Zsd9mZZ555hp9//jnb5HuG7ErkAFd84i8iciVcLtdl19H70D9SUlIYNmwY+/btY9iwYdSvX18xzILT6WTMmDHUq1eP3r17Z7mO4ubJZrMB6YO3Zs2aRY8ePejWrRszZ84kODiYCRMmKGbZ+O9//8t//vMf6taty/vvv8/48eMJDw9nxIgRLFu2THG7jNzGR3HMWVafD6D3uovl5rMBFLNLaQS8SC4VKlQIb29vLly4cNl1z507B/wzEj4nRYoU8Xjs6+uL3W4HIDo6msTERJYtW5ZtTfPTp097PM4YDZ+TkydP0rx580ztOZV5MJlMxMTE8Nlnn7F//36OHz/OqVOn3F8yZLy5Pvnkk+zYsYNJkyYxadIkKlasSOvWrendu7d7/wMGDGDdunV8++23fPvtt5QqVYrWrVtz//33u0s0HDt2DMMwaNWqVZb9yUiEdejQgaVLl/Lrr7/y66+/UrhwYVq1akX37t01+l3kBhcSEpLr99WceHt7s2rVKpYvX86RI0c4duwYcXFxwD9fhFqtVl5//XVeeeUVnn76aby9vWnQoAHt27enW7du7pHqr776Ks8++yxjxozBbDZTq1Yt2rdvz/33309wcHC2ffD3989yotWMOT0ul7wXEfk3Mt5jsnofymjT+1C6uLg4HnvsMbZv307Pnj3dJQwVw8ymTZvGvn37+O6779x1uTNGeKekpBAdHa24XcLf3x+Adu3aERIS4m4PDg6mdevWzJkzRzHLQnx8PF988QXVq1fnq6++cg9669SpE/fffz9jx47lyy+/BBS37OT2daXXX/ay+3wAfUZcLDefDUFBQYrZJZSAF8klk8lEnTp12LlzJ2lpadnWgTcMg23btlG2bNlcJeBzGlGfkdRu3769RxmWi5UtWzbX+8vgdDpzrGOflVWrVvHEE09QrFgxGjduTIsWLahRowZr167ls88+c69XokQJ5s2bx6ZNm1i+fDlr167liy++4Ouvv+aLL76gcePGBAYGMmPGDHbs2MGyZctYu3YtM2bMYObMmbzzzjt069YNwzAICAjIdkR+Rv+9vb356KOP2L9/P0uXLmXt2rXMmTOHn3/+mWeeeSbb2bhFJP9d/L5qs9kyTWCaYfLkyRw6dIgxY8ZkqiFoGAajRo3i119/pV69etSuXZu+ffvSoEEDHnzwQY91u3Tpwl133cWyZctYs2YNGzZsYMOGDcycOZOffvoJHx8fmjRpwsqVK1m5ciWrVq1i3bp1vPPOO0yfPp3Zs2dn+tI0Q8mSJd1fvl4soy2r2ociItdKqVKlADh//nymZefOnSM4ONidGLydXbhwgYceeoj9+/fTp08fXnvtNfcyxTCztWvXYrfb3fNRXWzatGlMmzaNjz/+GFDcMmR83hcqVCjTskKFCmEYhnvAlGL2j6NHj2Kz2ejcubPH9ay3tzf33Xcf7733nntQheKWtdy+h+m9Lms5fT6APiMulpvPhm+++YaqVasCilkGJeBFrkDXrl3ZvHkz33//PYMGDcpyneXLl3PixIlrkvgtVKgQfn5+OBwOmjZt6rHs1KlT7NmzJ8t6WpdTqlQpjh8/nqk9I3H+yiuvZFr2xhtvUK5cOX7++WePN8n58+d7rLd//34AmjRp4i5xs23bNh588EFmzJhB48aNOXLkCAkJCdSuXZvatWszcuRIDh06RP/+/fn666/p1q0bpUuXZt26dYSHh2cadbp48WJ3KZyTJ09y+vRp6tevT1hYGMOHD+fMmTM8+OCDfPXVV0rAi9zg7rnnHjZv3syCBQvck6VeLC0tjR9++IGUlBT3PA8X27p1K7/++itPPPEEzzzzjLvd6XQSGxvrnncjMTGRffv2UblyZXr27EnPnj2x2Wy89957fPPNN6xbt47mzZuzd+9eSpYsSadOnejUqRMul4vp06fz7rvvuidnzUq1atVYtGgRDofDY8KiPXv24OPjk2kCbhGRayk4OJgyZcqwe/fuTMv27NlDeHh4PvTqxpKYmMjDDz/M/v37GTx4MGPGjPFYrhhm9uKLL7pHNWaIiopi1KhRdO3alW7dulG9enXF7SKVK1fGarVy6NChTMsiIyPx8fGhUKFCitklMgZhZFXCNWNQmsvlUtxykNv3ML3XZXa5zwdQ3C6Wm8+GKlWqKGaXuH2K7YhcAz169KBOnTpMmDCBdevWZVq+d+9eXnnlFcqUKeOeWBX+qWuVm5rwF/Py8qJFixasXr2affv2eSx75513ePLJJ4mJibni59GqVSt27tzJrl273G0Oh4Np06axY8eOLJP6sbGxlCpVyiP5fvbsWZYuXQqkJ7sAnn76aV544QX3Y0hPTHl7e7tHM7z++us88cQT7hrMABUrViQ4ONi9TuvWrQGYMmWKRz9WrFjB008/7U78T5kyhcGDB7tn2Ib0UfjFixfP1d0AIpK/+vbtS+nSpXnvvfc4cOCAxzKXy8Xrr7/O2bNnGTJkSJYj5DPqtN95550e7T/99BPJycnu+vL79++nf//+/PTTT+51rFaru+yVl5cXMTEx9O3b1+OuHrPZTI0aNYCc7zBq3749ycnJzJkzx90WHR3NokWLaN++vUdSXkQkL7Rr147ff/+diIgId9uGDRs4cuQIHTt2zMee3Rhee+019u3bx6BBg7JMroBieKnw8HCaNm3q8VO3bl0g/S7cpk2bEhISorhdxN/fn9atW7Nq1Sr3XFcAJ06cYMWKFbRq1QqLxaKYXaJy5coUK1aMOXPmeJSrsNlszJs3j4IFCxIaGqq4XUZu46M4esrN5wMobhly+9kAitnFdDUocgXMZjOTJ0/m8ccfZ+jQobRr145GjRphsVj4888/mT9/PiVLluSTTz7xqGWVcQviF198QYsWLWjTpk2ujzly5Eg2bdpE//796d+/P6VKlWLVqlWsXLmSPn36XNWoyscee4xFixYxaNAgBg4cSPHixVm4cCEHDhxg6tSpWW7TokULFi5cyNixY6lRowaRkZH8+OOP7iR6xr9Dhw7l5ZdfZvDgwXTo0AHDMJg3bx5paWk88MADAAwZMoRHHnmE/v37061bN3x8fFi2bBnHjx9n/PjxQPrErW3atOHLL78kMjKSpk2bcvLkSWbOnEmpUqUYMmQIkF5Pfv78+fTv358+ffoQEhLCxo0b2bRpE08//fQVx0ZEri+r1crHH3/MkCFD6NmzJ126dCE8PJz4+HgWLVrEnj17uOeeezy+1LxYnTp1CAwMZNy4cZw8eZKQkBD3iHofHx/3e1PdunWpV68eEydO5PTp04SFhXH69GlmzJhBxYoVadKkCVarlc6dO/Pdd9+RkpJCnTp1iI2NZcaMGRQpUoR777032+fRqlUrGjVqxGuvvcaJEycoXrw4M2bMwOVy8eSTT+ZJ7ERELvbII48wb948Bg8ezMMPP0xaWhpffPEF1apVo2vXrvndvXx14MABfvnlF4KCgqhatSrz5s3LtE7Xrl0Vw6ukuHkaNWoUmzdvZtCgQQwaNAhvb2+++eYbfHx8GDFiBKCYXcpisTB27Fiefvpp952KLpeL2bNnExERwbvvvou3t7fidhm5jY/i+I/cfj6A4nY1FLN/mIwrHZIrIthsNubOncvs2bM5cuQIDoeDO+64g44dO9K3b1+CgoI81o+Pj+eZZ55h69atlClTht9++43Ro0czZ84cd8mWDFm1Hzt2jI8++oj169eTnJxM2bJl6dWrFwMHDnSPyMxuf9k5c+YMH3zwAatXr8ZmsxEWFsZTTz1Fs2bNAJg0aRKTJ09m+fLllClThri4ON59911Wr15NQkICJUqUoHXr1txzzz3069ePUaNGuRNkc+fO5ZtvvuHYsWO4XC7Cw8MZNmyYe98AK1euZOrUqURERJCWlkblypV56KGH6NSpk3sdu93OF198wdy5czl58iSFChWiSZMmPPPMM+4abAB//PEHH3/8MXv27CExMZHy5cvTp08f+vfvj8lkylU8RCR/nT17lm+++YbVq1dz6tQpXC4XoaGh9OrVi549e3r8Lbdu3ZrSpUvz7bffAullrt5//3327duH1WqlQoUKDBo0iL/++su9z6JFixITE8PHH3/MypUrOXfuHCEhIbRq1YpnnnmGokWLAumTpk6dOpUFCxZw+vRp/Pz8aNKkCc8991yOE1UDJCQk8P7777No0SLsdjs1atTghRdeoHr16nkXOBGRixw+fJhx48axdetWfH19adGiBaNGjcp2/orbxcyZM3n99ddzXCfjHFoxzFlkZCRt2rRh+PDhPPXUU+52xc3TiRMneO+999iwYQOGYVCvXj1GjRrlMXhKMcvs999/55NPPmHnzp1A+p3Uw4YNo0WLFu51FLd0l54PZ8htfG7XOF4atyv5fIDbM27ZvdYult1nA9yeMcuKEvAiIiIiIiIiIiIiInlANeBFRERERERERERERPKAEvAiIiIiIiIiIiIiInlACXgRERERERERERERkTygBLyIiIiIiIiIiIiISB5QAl5EREREREREREREJA8oAS8iIiIiIiIiIiIikgeUgBcRERERERERERERyQNKwIuIiIiIiIiIiIiI5AEl4EVERERERERERERE8oAS8CIiIiIiIiIiN4kTJ04wZ84c9+PWrVsTFhaGw+HIx17lncjISMLCwujXr9+/2s+SJUvYt2/fNeqViEjuKQEvIiIiIiIiInIT2LdvHx07dmT9+vXutkGDBjF8+HDM5lszxRMcHMzw4cO5//77r3of77//Pk899RTR0dHXsGciIrnjld8dEBERERERERGRy4uLi8Nms3m0DR48OH86c50EBwfz1FNP/at9REVFXaPeiIhcuVvz61ERERERERERERERkXymBLyIiIiIiIiIyA1u9OjRDBo0CID58+cTFhbG7NmzM9WA37RpE2FhYXz11Vf89ttv9OjRg5o1a9K8eXPGjx+PzWbj9OnTPPfcczRo0IBGjRrx2GOPcezYsUzHPHPmDGPHjqVly5aEh4fTokULXnnlFc6ePeux3uzZswkLC2PBggXMmDGDdu3aUbNmTTp06MDUqVOzrE8fGxvL+PHjadu2LeHh4TRq1IjHH3+cHTt2eKyXVQ34SZMmERYWxs6dO5k6dSrt27cnPDycVq1a8c4775CUlOReNywszF0z/6GHHiIsLOzqfgEiIldJJWhERERERERERG5wbdu2BWDOnDmEhobSrl07qlatmu368+bN4+DBg7Rv354GDRqwaNEivvzyS6Kjo9mwYQMlSpSgZ8+e7N69m1WrVnH8+HHmz5+Pl1d6qujgwYMMGjSImJgYWrVqRaVKlTh+/Dg//fQTK1asYMaMGVSoUMHjmF9++SV79uyhQ4cOtGzZkjVr1jBhwgT++OMPpkyZgslkAuD06dM88MADnDp1ilq1atGmTRvOnDnD8uXLWb16NW+//TbdunW7bExee+01IiIi6NChA61bt2bx4sVMnz6ds2fPMnHiRACGDx/OsmXL2LdvH127dqVs2bJXE34RkaumBLyIiIiIiIiIyA2ubdu2BAUFMWfOHMLCwi5bF33Pnj1MmTKF1q1bA9C7d286duzI3Llz6dKlC++99x4mkwnDMOjfvz/btm1j586d1KlTB4AXXniB2NhYpk6dSosWLdz7Xb16NY8++ihjxozhf//7n8cxd+3axeTJk7nnnnsAGDFiBI8//jgrV65k/vz53HfffQC88sornDp1imeeeYYnnnjCvf3u3bsZMGAAr7zyCo0aNaJkyZI5PscTJ04wf/58ypQpA8CwYcNo164dixcv5vz58xQtWpSnnnqKkydPsm/fPrp160bTpk1zE24RkWtGJWhERERERERERG4xlSpVciffMx4XKFAAgKFDh7pHo5tMJnfS/eTJkwD89ddf7Nmzh3bt2nkk3wFatmxJs2bN2L59OxERER7LmjVr5k6+A/j5+TFmzBgAdxmYs2fPsnbtWipWrOiRfAeoXr06Q4YMwWazMXv27Ms+xy5duriT7wAhISHUrVsXp9NJZGTkZbcXEbkeNAJeREREREREROQWU758+UxtAQEBxMbGcscdd3i0+/r6AmCz2QDYuXMnABcuXGDSpEmZ9pOcnAykj1ivVKmSu71x48aZ1g0LCyMgIIA9e/YAuP9t0KBBlv2uX78+AHv37s3+yf0tq+cYHBwMgN1uv+z2IiLXgxLwIiIiIiIiIiK3GH9//2yXWa3WHLeNj48HYMuWLWzZsiXb9eLi4jweZ1cyJjAwkKioKAASEhIACAoKynLd4sWLA5CSkpJjHwF8fHwytWWM7DcM47Lbi4hcD0rAi4iIiIiIiIiIW0BAAAAjR47kkUceyfV2qampmdoMwyAhIYGCBQsC6cl4SC9Fk5WM5H9GuRwRkZudasCLiIiIiIiIiNwEMkZ357Vq1aoB6bXgszJz5kwmT56cqc76n3/+mWndvXv3kpycTK1atTz2vWPHDhwOR6b1N23aBEBoaOjVP4FLXK+4iYhkRQl4EREREREREZGbgJdXeiGDrBLX11LdunWpWLEiS5cuZenSpR7Ltm3bxrhx4/jqq6/co9oz/PLLL+zYscP9OCkpiXHjxgHQu3dvAEqUKEHz5s05ceIEn3zyicf2+/btY9q0aVitVjp27HjNnk9G3FQXXkTyg0rQiIiIiIiIiIjcBDJqrK9Zs4bx48fTpk2bPDmO2Wzmvffe46GHHmL48OE0b96csLAwTp8+zdKlSzEMg3HjxrlL1WSwWq0MGDCA9u3bU6BAAVatWkVkZCR9+vShVatW7vVef/11+vfvz8cff8z69eupXbs2Z8+eZfny5bhcLt544w3Kli17zZ5PRtw++ugjtm7dypNPPumeeFZEJK8pAS8iIiIiIiIichMoWbIkzz//PF999RUzZszAz88vz44VHh7O7Nmz+eyzz1i7di2bNm2iUKFCtGrVikcffZSaNWtm2uaBBx4gMDCQWbNmER0dTaVKlXj88cfp2bOnx3qlS5dm9uzZfPrppyxfvpyZM2cSEhJC69atGTJkSJb7/jceeOABtm/fzubNmzl27Bhdu3blzjvvvKbHEBHJjsnQtNAiIiIiIiIiInKVZs+ezZgxYxg2bBjPPfdcfndHROSGohrwIiIiIiIiIiIiIiJ5QAl4EREREREREREREZE8oAS8iIiIiIiIiIiIiEgeUA14EREREREREREREZE8oBHwIiIiIiIiIiIiIiJ5QAl4EREREREREREREZE8oAS8iIiIiIiIiIiIiEgeUAJeRERERERERERERCQPKAEvIiIiIiIiIiIiIpIHlIAXEREREREREREREckDSsCLiIiIiIiIiIiIiOQBJeBFJEt//vknI0eO5O6776ZGjRo0aNCAHj168OGHHxITE5Np/YEDBxIWFsaxY8fyobdXb/bs2YSFhTFx4sSr3ofL5eKHH37g7Nmz17BnIhAZGUlYWBj9+vVzt12L12xemDRpEmFhYbn+mT17dn53WURERP6FjHOSS3/Cw8Np3LgxAwYM4H//+x9OpzPTthnnDT/++ONVHTsxMZEvv/zy3z4FycGtFmOHw8H48eNp1qwZ4eHhdOzYMb+7dEMKCwujRYsW+d0NkVuOV353QERuPDNnzuSNN97Az8+Pu+++m5IlS5KUlMTOnTuZMmUKs2bN4uuvv6ZKlSr53dUbwvPPP8/ChQtZsmRJfndFbgNVq1Zl+PDh1K9fP7+74qFhw4YMHz7co23ZsmXs27ePNm3aULVqVY9llz4WERGRm1OVKlVo27at+3FKSgoXLlxgw4YN/Oc//+HXX3/l888/x8/Pz71OxnlDtWrVruqY7du3x2Kx8PDDD//r/kvWbrUY//jjj3z55ZeULl2awYMHU6hQofzukojcRpSAFxEPkZGRvP3225QrV45Zs2ZlOjGZMWMGb7zxBiNHjmT+/PmYTKZ86umNIyoqKr+7ILeRqlWr3pDJ60aNGtGoUSOPtpMnT7Jv3z7atm1Ljx498qlnIiIikpeqVq3KU089lak9KSmJMWPGsHjxYl588UU++ugj97KszhuuRFRUFMWLF7/q7eXybrUY7927F4AXXniBDh065HNvROR2oxI0IuJh9erVOBwOevXqleWogAEDBlCtWjUOHjzIwYMH86GHIiIiIiJyowsICGD8+PGUL1+exYsXs2PHjvzuktzGbDYbAAULFsznnojI7UgJeBHxYLfbAThw4EC26/znP//h008/pUSJEpmWxcXF8eabb9KiRQtq1KhB+/bt+eyzzzLVfnS5XPz4448MHDiQRo0aUb16dRo3bsxjjz3G1q1bPdbNqBG5detWJk+eTMuWLalVqxZdu3bl+++/z7KPZ86cYezYsbRs2ZLw8HBatGjBK6+8kus67ampqXzyySd06tSJGjVq0LBhQ4YNG8aff/7pXiejPvfmzZsBaNeuHa1bt85xv6NHjyYsLIwTJ07w5ptv0rRpU+rUqUOfPn1YvHhxltssWbKEIUOG0KRJE6pXr06DBg0YNGgQy5cv91gvow7n7NmzefbZZ6lZsyZNmjRh6dKlACQkJDBp0iS6detGnTp1CA8Pp1WrVowZM4aTJ0967GvgwIHUr1+f6Oho/u///o8mTZpQu3Zt+vbtyx9//AGk38bZqVMnatasSfv27fn6668xDMNjP1u2bGHo0KE0b96cGjVq0KZNG1577TXOnTuXZd8HDhyYY/wANm3aRFhYGF999RVLly6lV69e1KpVi4YNG/Lss89y/PjxTNvExsYyfvx42rZtS3h4OI0aNeLxxx/PdCGY8Tt9/fXXmTx5MvXr16du3bq8//777mVvvfUWv//+O/3796d27do0btyY//u//yMxMZHY2FjGjh1LkyZNqFevHgMHDmTnzp2Z+rN7925GjhxJq1atCA8Pp06dOnTr1o0vv/wyyzqpWcUqowZ8dvVXL/6ZNGmSxz5Wr17N4MGDqV+/PjVr1qRr1658++23uFwuj/VyU8v9cq/5nGT8PWzbto2ePXsSHh5O69atOXXqlHvZhg0bMm2X3ZwTERERPP/88+7anm3atGH8+PHExcVddR9FRETk6vn5+TF48GAA5s+f727PqgZ8amoqEyZMoEuXLtSuXZv69eszcOBAFi5c6F4n47wH4OzZs4SFhTF69Gj38mPHjjF27FjuueceatasSa1atejYsSMffvghqampHn0LCwvjiSeeYP/+/QwbNoz69etTu3ZtBgwYwO+//57puRiGwQ8//EDv3r2pW7cujRs35sEHH2T9+vWZ1v035yRPPfUUYWFhbNq0KdOypKQkateuTbt27Tzaf/nlF/r27UudOnXc1xa//PJLlvs/f/48b731Fm3atKFmzZq0adOGsWPHuq+TLhfj1NRUJk+eTMeOHalRowb169dn8ODBrF69OtOxwsLCeOyxx/j+++9p2rQptWvXZuTIkQDExMTw2muv0aFDB2rWrEmjRo0YOnRolud+2cnNOX7GtcOcOXMAGDRoULbxvZjT6eTbb7+le/fu1KpVi3r16vHggw+yZs0aj/Vef/11wsLCMt0FkpiYSOvWralatSobN250t1/Na3Tv3r0MGTKEunXr0qBBA55++mnOnTtHamoq7733Hi1atKBOnTr07NmTdevWeezjaq4/s5Lb6weXy8XUqVPp3r07derUoW7duvTq1Yvvvvsu07WiyO1EJWhExEPz5s0xmUzMmzeP5ORk7r//fho1aoS/v797ndq1a2e7/eOPP46Xlxft2rXD6XSycOFCPvjgA2JiYjxO3MaMGcPcuXMJCwvjvvvuw8vLi127drFq1SrWrVvHjz/+mKkm5Lhx4zhw4ACdO3fG19eX5cuXM3bsWPbt28d//vMf93oHDx5k0KBBxMTE0KpVKypVqsTx48f56aefWLFiBTNmzKBChQrZPofk5GQGDRrEzp07qVGjBv379ychIYHFixfzwAMP8MEHH9C+fXuCg4MZPnw4c+bM4eTJkwwcOJAyZcrkKs7PPvssx44do3PnzjgcDpYsWcLTTz/NqFGjGDp0qHu9jz76iI8//pg77riDjh074uvry6FDh1i9ejWbNm1iypQpmRKgEyZMICAggAEDBnDgwAFq165NcnIyffv2JSIigmbNmtGsWTNSU1PZsGEDs2fP5vfff+e3337zqM3pcDh44IEHMJlMdOvWjePHj7Ns2TKGDBlC9+7dmTNnDvfeey9NmjRh3rx5vP322xQoUICuXbsCsG3bNh5++GGCg4O55557CAwMZPfu3Xz33XesW7eO+fPn4+vrC/xT17x06dK5ih/Ar7/+yq5du7j77rtp2LAh27Zt47fffmPHjh0sXboUb29vAE6fPs0DDzzAqVOnqFWrFm3atOHMmTMsX76c1atX8/bbb9OtWzePfS9evJi0tDS6detGbGysx2t+06ZNfPfdd7Ro0YJ+/fqxatUqfvrpJy5cuMDJkydxuVzcd999REZGsmzZMh555BGWLFlCcHAwAOvWrWPYsGH4+fnRtm1bihQpwpkzZ1i2bBnjx4/n/PnzvPjii7mOQ0bsLpWUlMTXX38NQJ06ddztn3/+Oe+//z6FChVyv47XrVvHm2++yZYtW/jvf//rLi2V1X4vFRQUlOu+Zufpp5+mYsWKDBw4kFOnTlGqVKkr3sfGjRt5/PHHsdvttG3bljJlyrB3716+/PJLVqxYkWVJLREREcl7DRs2BHAPWsnOs88+y8qVK2nevDktWrQgMTGRxYsX89xzz5GQkECfPn3c5z2TJ08mICCAhx56yF2ab9++ffTv3x+Hw0Hbtm0pVaoU0dHRLFu2jClTpnD48GGPMjgAR48epW/fvtx555306tWLkydPsmTJEoYOHcqcOXMIDQ316N+iRYsoVaoUnTt3xmKxsGDBAoYMGcKECRPo1KkT8O/PSbp3786SJUv49ddfM5XpWbp0KSkpKR7nrq+++iqzZs2idOnS3Hffffj4+LBixQpGjRrF7t27GTNmjHvdY8eO0b9/f86fP0/Dhg1p164dR44c4fvvv2fdunX88MMPOcY4ISGBgQMHsnfvXkJDQ+nTpw9xcXGsWLGCRx99lOeee45hw4Z59Hnnzp38/vvvdOvWDcMwqFatGjabjaFDh7J3717atGnDPffcQ1RUFIsWLWL9+vV89tlnl50INLfn+KVLl2b48OHuuYm6d+9O6dKlc7zucDqdPPnkk6xcudL92nA6nSxdupRHHnmEl156iQcffBCAUaNGsX79epYsWcKSJUvcX4689tprnDx5kscee4zGjRsDV/caPXz4MP369aNWrVr07duXzZs3s3jxYk6dOkVAQAAnTpygXbt2xMXF8euvvzJs2DAWLlzIHXfc4bGf3F5/ZuVKrh/eeustZsyYQd26denbty92u53ly5fz2muvcebMGUaMGJHjsURuWYaIyCW++uoro2rVqkZoaKgRGhpqVKtWzejZs6cxfvx4Y/PmzYbL5cq0zYABA4zQ0FCjZ8+eRmJiorv90KFDRrVq1Yx69eoZDofDMAzD2LlzpxEaGmoMHjzYcDqdHvt57733jNDQUOOdd95xt3300Ufufvz555/u9gsXLhidO3c2QkNDjS1btrjbu3XrZlSpUsVYvXq1x75XrVplhIaGGn369HG3/fzzz0ZoaKjxwQcfuNveeuutTG2GYRgnT540mjRpYtSpU8eIiYnJ9NyPHj2aY1wNwzBefPFFIzQ01GjQoIFx7Ngxd/vx48eN5s2bG9WrVzeOHz9uGIZhnD9/3qhWrZrRqVMnIzk52WM/3333nREaGmo89dRTmZ5LnTp1jAsXLnisP23aNCM0NNSYNGmSR7vT6TT69OljhIaGGitXrsz0nPr06WOkpaW525999lkjNDTUqF69urF//353+/r1643Q0FDj4Ycfdrc99dRTRmhoqMfzNAzDeOmll4zQ0FBj/vz5l41XVjZu3Oh+bS5fvtzd7nK5jAcffNAIDQ01Fi5c6G4fMmSIERoaanz88cce+9m1a5dRu3ZtIzw83Dh16pRhGIZx4sQJ977Xrl3rsf7Fy7755ht3e3R0tFGrVi0jNDTUGDBggGGz2dzLnn/++UzPtXPnzkZ4eLhx+PBhj/0fOnTICAsLMxo1apTpmH379nW3ZfWavZTD4TAeeeQRIzQ01Pj888/d7bt37zaqVKlidO7c2YiOjna3O51OY8SIEUZoaKjx/fffZ7vfK5Xxev/5559zXN63b99M7wUZy9avX59pu0v/5lJTU41mzZoZtWvXNnbv3u2xbsbfyogRI67RsxIRERHD+Oec5MUXX8xxveTkZCM0NNRo2LChuy3j/P6HH34wDMMw9u/fb4SGhhojR4702Pb48eNG9erVjXbt2nm0h4aGGnfddZdH22OPPWaEhoYamzdv9miPiooy6tata1SpUsVISEjw2Mel1x2GYRiTJk0yQkNDjVdffdXdNn/+fCM0NNR48MEHPfZx7Ngxo27dukbTpk0Nu91+Tc5J7Ha70bRpU6NBgwYe5+GGYRgPPfSQERYWZkRGRhqGYRhLly51X1ddfL2QmprqPl9at26du/3hhx82QkNDjRkzZnjs94svvsgUi6xiPHbsWCM0NNR45ZVXDLvd7m4/fvy40aJFCyMsLMzYsWOHxz5CQ0ON//3vfx77WbFihREaGmpMnDjRo33Lli2ZrimycyXn+Ibxz7nlxo0bL7vvr776yggNDTVeeOEFj+cZGxtr3HvvvUbVqlWNQ4cOudt37NhhVK1a1WjWrJkRHx9vLFiwwAgNDTV69erlsf3VvkbHjRvnbrPZbEaLFi2M0NBQo127dh7rf/DBB0ZoaKjx6aefZnreubn+zDjmxb/3K7l+SEhIMKpUqWI88MADHs8vJibGaNy4sVG7dm2PayWR24lK0IhIJg8++CA//fQT3bp1IyQkBIfDwV9//cW0adMYMGAA3bt3Z/fu3VluO3ToUAICAtyPK1WqRPny5UlISCAmJgaAokWLMn78eF566SXMZs+3oYzRARnrXqxHjx7UrFnT/bhQoUI899xzAMydOxeAv/76iz179tCuXbtMoyZatmxJs2bN2L59OxEREVn23+l08vPPP1O0aFGefvppj2WlSpVi0KBBJCUledwKezUefvhhj1EJZcuW5dFHH8Vut/Prr78C4OXlxbvvvssbb7zhMTIdco5T48aNM42qadq0Ka+//rr7FuAMZrPZPSopOjo6074GDRqE1Wp1P65Xrx4ArVq18hgRVLduXSC9hEsG4+9bDC8tKfTiiy+ydu1a9yihqxUaGuox+t9kMtGqVSsAdxmas2fPsnbtWipWrMgTTzzhsX316tUZMmQINpuN2bNneywLDg6mWbNmWR43ICCABx54wP24YMGCVKxYEYDBgwe7R97DP3HJKPFjGAbPPPMMEyZMyHQXRqVKlShSpEiWv9Mr9fbbb7N69Wq6d+/uMaLlxx9/xOVyMXLkSI/6l2azmRdeeMG9zvXWvn37TO8FV2LFihWcP3+efv36Zbpzpl+/fpQvX55FixaRmJj4b7sqIiIiVyjj3Cinz+GM88bDhw97nJOWLVuW3377jXnz5l32OAMHDmT8+PE0aNDAo71w4cJUrlwZl8tFbGxspu0effRRj8cZ55cXlzXMuNYYPXo0gYGB7vY77riDMWPG8PDDD5OcnHxNzkm8vLzo3LkzcXFxrF271t1+/vx5Nm7cSMOGDd2jt3/44QcA/u///s/jesHHx8d9nZRxbnfu3DnWr19P1apV6d+/v8cxBwwYwNChQ6lVq1a2/bLZbPzyyy8EBwfzf//3f3h5/VNQoWzZsjz77LMYhpFlidBLJz3N+H3v3buX5ORkd3v9+vVZsmQJU6ZMybYfcPXn+Ln1ww8/YLFYeOWVVzyeZ0hICI8//jhOp9Nj37Vq1eLRRx/l/PnzvPrqq7z22msEBAQwYcIEj+2v9jX6yCOPuP/v7e3t/j3179/f4/V46bXHxXJz/ZmVK7l+MAwDwzA4ffq0x3VhgQIF+Pnnn1m7dq3HtZLI7UQlaEQkS9WqVWP8+PE4nU727NnD5s2b2bBhAxs3bmTv3r08+OCDzJ49O9OtbeXLl8+0r4xkcMbJVfHixenWrRsul4uDBw9y+PBhIiMjOXTokPvW1KzqYDdp0iRTW8ZJxp49ewDc9bYvXLiQqe71xX3YvXs3lSpVyrT8yJEjJCYmEhwczCeffJJp+dGjRz2Od7Vy81wKFCjgTlIfPXqUiIgIIiMjiYiIYNu2bUDWcSpbtmymtipVqlClShXS0tL466+/OHr0KCdOnGD//v3umoRZ7evSJHFGKaJLj5FRSiZjDgGAvn37smzZMsaMGcPkyZNp3rw5TZs2pVmzZhQrViyrsFyRrMoIZZRDyehHRiwvPcnNUL9+fSD95P9iZcqUcd9GeamyZctisVg82jK+dLr07yEjLhmTPplMJtq2bQukX0QdOHCAEydOcPToUXbu3MmFCxeA9N/FpcfIrRkzZrhv+3z99dc9lmX8faxdu5a//vor07a+vr7s3bsXwzAwmUxZ/g1dKigoKNMXO1cqq9fslch4XkeOHMmyzxaLBYfDwf79+91fIomIiMj1kZSUBOAxSOdSYWFhNGjQgC1bttCyZUvq1atH06ZNadGiBVWqVMnVcTIGT8TGxrJ//36OHz/O8ePH2b17N7t27QLIVK+6QIECmSblzDifzDh/g/RzRX9/f3dt9Iv17NnT/f9rdU7SvXt3vvrqK3799VfatGkDpJdfdDqd9OjRI9PxFixYkGkww6Xnw/v27cMwjCzLifr4+DBq1Khs+wPp1yPJyck0b94cHx+fTMuzO68OCQkhJCTEo61p06aUL1+eVatW0axZMxo1akTTpk256667ciwVmuFqz/FzIzk5mUOHDhEYGMj06dMzLc84X7/0evDJJ59kzZo17mT2u+++m+kc92peo0FBQRQuXNijLeOa7HLXHhfLzfVnVq7k+iEoKIj77ruPefPm0a5dO2rWrEmzZs1o3rw5tWrV+lcDbkRudkrAi0iOLBYLNWrUoEaNGgwZMoTIyEieeeYZdu3axTfffMPLL7/ssX7Gh35WjIsmXZkzZw4ff/wxJ06cAMBqtVKlShWqV69OZGRklhO0ZDXpa8Y3/vHx8R7/btmyhS1btmTbl+wmQMpoP3XqFJMnT77i7XMrN88FYNWqVXzwwQfs378fSB8Rc+edd1KjRg0OHTqUZZyy+h3YbDYmT57Md999R0JCApA+yrtGjRpUrlw50yj1DBfX/r/YxaPis9OsWTNmzpzJ9OnTWbduHd9//z3ff/893t7e3Hfffbz88svZ7j83supDRtI8Iy4ZzzW7OuXFixcHICUlxaM9p9dxTn3OTVwiIiIYP348a9ascfezbNmy1KtXj4MHDxIXF3fVExRl1LssXbo0kydPztSfjNfWt99+m+N+kpKSCAwMzPFvIEPp0qX/dQL+0js8rlTG81qxYgUrVqzIdj1NxioiInL9ZYyEvdwX7p9//jnTp0/n119/5ffff+f3339nwoQJlC9fnpdeeomWLVvmuP25c+d45513WLx4MQ6HA0g/16tbty7FixfP8hojq0RyVoMwYmNjKViwYLYDNDJcq3OSKlWqUK1aNVauXElSUhIBAQH88ssv+Pv7e0zAmnG8rAYOXXqsjJHVVzt/T27Pqy8e0Q5Zn1f7+vry/fffM3XqVBYtWsTKlStZuXIlkD6C/dVXX/W48/lq+3LpOX5uZOw7MTHxiq4Hvb29adu2Lbt378bb2zvLLzqu5jX6b689MuT2+vNSV3r98Pbbb1OzZk1mz57Njh072L59O5MnT6Z48eKMGDEi09xbIrcLJeBFxM3pdNKlSxeAbEuslClThv/7v/+jX79+HD58+KqOs2zZMkaPHk2ZMmX44IMPqF69untU8Zo1a7Kdjf3SWeHhnxOCjFH2GSNrRo4c6XGrXm5lbN+iRQs+//zzK94+t7J6LhkncRnPZefOnTzxxBMEBwfz5ptvUrt2bcqVK4fVaiUiIoI5c+bk+njvvfce33zzDU2bNuWhhx4iLCzMfWL6/vvvZ5uA/7fq1q1L3bp1sdls/PXXX6xbt465c+fy888/YzabefPNN/PkuBkyTirPnj2b5fKM10+BAgXytB8ZkpOTGTx4MBcuXODxxx/n7rvvplKlSu7XXXZlb3LjwIEDPPfcc/j4+PDJJ59kGikD/7y+N2zYkOXyS2V88ZMfMi5wLx0FBJkv7DKe18SJE+nYsWPed05ERERyLeMO14xRydnx8/PjiSee4IknnuDMmTNs3LiRZcuWsXTpUp588kkWLVpEmTJlstzWMAweffRR9u7dS//+/enUqRN33nmne+R17969PUpiXKmAgACSkpLcdwleLC0tDS8vLywWyzU9J+nWrRtvv/02y5cvp3r16uzZs4fu3bt7JGQDAgKw2Wxs3779sl8OZPQtI8F8qeTk5ByTvbk9r770joLsFChQgBdeeIEXXniBY8eOsWHDBhYvXszvv//O0KFDWbFihUd5lavpy9Wc42fEqVKlSldUdvTw4cN89tlnFChQgNjYWF588UVmzpzpvqs1r1+jl5Ob68+sXOn1g5eXFwMGDGDAgAFER0ezceNGVq1axcKFC3nxxRcpU6bMZd8LRG5Fuv9DRNwsFguGYRAREcGOHTsuu35GAvdKZSSO33rrLTp16kT58uXdJyaHDh0CyHIE8J9//pmpLaMUS0YdvIxai1ndHgcwc+ZMJk+enO3JTcWKFd230WV169769euZMGHCv05YZ/Vc/vjjD+Cf5zJ//nycTiejRo2iV69eVK5c2T3KIac4ZWXu3Ln4+PgwZcoUWrRo4fG7u9J95YbL5WLq1KlMnDgRSB+dUb9+fZ599lm+++47gBzvULhWMl4PO3bscI8yudimTZsAPOrZ56UNGzZw7tw5unTpwjPPPEPNmjXdJ7XR0dHumqdX+ruIiopi2LBhpKSk8P7772d7q3bVqlWBrF9/iYmJvPXWW8yYMeOKjp1XMupDZty2nsHlcrnvnMmQ8byy+7v/5JNPmDJlSpY1NUVERCTv2Gw2Zs2aBUDXrl2zXW/Hjh2888477muQEiVK0K1bNyZPnkyPHj2w2+1s37492+3379/P3r17adCgAWPHjqVevXruxKbdbneXkbza892wsDCSk5M5cOBApmXvv/8+tWrVYuvWrdf0nKRLly54e3uzbNkyd1mTS0cPV61alZSUlCz7derUKd5++213/fyM88Os+uZyubj77ru59957s+1PxYoV8fPz48CBA1mOmL6S8+q1a9fy5ptvcuzYMQDKlStHv379+Oqrr2jUqBFxcXFZPqcMeXmOHxgYSNmyZTl+/HiWczPt3r2b8ePHu0fsAzgcDl544QVSU1N5//336d69O9u3b/cY0JXXr9HLyc31Z1au5PohIiKCCRMmuGNTqFAhOnbsyLvvvsuwYcOAzPODidwulIAXEQ8PP/wwACNGjMiyZl58fDwTJkwAPOsdXomM2xAz6udlOHDgAFOnTgXI8kTqm2++cZ+YQHrS8b///S8Wi4X7778fSB9xXbFiRZYuXcrSpUs9tt+2bRvjxo3jq6++ynZkhtVq5b777uP8+fN88MEHHqNvo6KiGDt2rLuPGTIShVn1OTuffPIJUVFR7sfHjh3jiy++ICAgwF33Pbs4nT59mg8++OCKjunj44PD4ch0sv/rr7+yatUqwLN++79lNptZsWIFn332WaaTrIzkaXYjmK6lEiVK0Lx5c06cOJHp1tx9+/Yxbdo0rFbrdRs1nfE7vfh3D+kjp1555RX36+1KXkupqak8/vjjnDx5kpEjR7rrhGYl42/2vffe4/z58x7LJkyYwDfffHNVtTLzQsbEthdf3AB8/fXXmW75bdu2LQUKFGDmzJmZLg4WLlzIf//7X+bOnUtwcHDedlpERETc0tLSGDNmDMeOHaNLly6ZJiW9WGJiItOnT2fSpEke59+GYbgnlLy4hI23t7fH+VJGKZm4uDiPdqfTybhx49znDldyjnWx7t27A+nnUBeXNYmMjGTevHkEBgZSq1ata3pOUqhQIVq2bMnatWtZuHAhpUuXplGjRh7rZJzbvfHGGx4TuzqdTl5//XW+/vprd/xKly5Nw4YN2b17Nz///LPHfmbMmEFsbCzNmzd3t10a44wykomJiYwbN85jWWRkJBMnTsRkMuWqxMipU6f49ttvM91xnJaWxvnz5zGbze6JZrOS1+f4PXv2xG6389prr3kMykpOTuY///kPX375pceXEFOmTGHnzp10796du+66i9GjR1OkSBEmT57sPrfO69fo5eTm+jMrV3L9YDab3YOwLh1xn9tSVCK3KpWgEREPvXr1IiIigunTp9OjRw/q1atHtWrV8PX1JTIykjVr1pCUlMSoUaOuejLD7t27s2DBAl566SXWrFlDsWLFOHLkCKtXr3afjGY1KsRut3P//ffTrl07vL29WbFiBefPn2fEiBHuER1ms5n33nuPhx56iOHDh9O8eXPCwsI4ffo0S5cuxTAMxo0bl+MkUC+88AI7duxg+vTpbNy4kQYNGpCWlsaSJUuIiYnhoYce8rhtrmTJkkD6iW/9+vUZPnz4ZWMQFRVF165dadOmDXa7nSVLlpCSksK4ceMoWrQoAJ06dWL69Ol8+OGH7N69mzvuuINTp06xYsUKvL298fb2zvWI3vvvv59PP/2Unj170qFDB7y9vfnrr7/YunUrRYoUISoq6pqPDh45ciSDBw9m8ODB3HPPPZQpU4YzZ86wZMkSfH19eeaZZ9zr7t27l2XLllG6dGmPiaWuhddff53+/fvz8ccfs379emrXrs3Zs2dZvnw5LpeLN95447qdCNarV4/y5cuzbt06+vfvT506dYiPj2f16tWcP3+eggULEhMTQ2xsbK7ror/99tv89ddflCtXDrPZzCeffJJpQt2MiVLr1q3LE088wSeffEKnTp1o3bo1BQsWZMuWLezcuZOKFSsyYsSIvHjqV6xr165MmjSJOXPmEBUVRZUqVdi9ezdbt26lTp06HqPgAgMDeffddxk+fDj9+vWjdevWlCtXjoiICFavXo2/vz/vvPOOJn4SERHJA3v37vWYcDQtLY0zZ86wYcMGLly4QKNGjTJNDH+ppk2b0qpVK1atWkWXLl1o2rQpFouFjRs3snfvXtq3b+9RU7tkyZIcP36cl156iYYNG9K1a1fq1q3LH3/8Qc+ePWnSpAl2u521a9dy9OhRChcuzIULF676fLd79+4sX76cZcuW0bVrV+666y7sdjsLFy4kKSmJqVOnus/Pr+U5Sffu3Vm2bBlHjx7liSeeyFRmpkuXLu4Sjx07dqRly5YEBASwZs0aIiIiqF+/Pg899JB7/ddee43+/fvz0ksvsXDhQkJDQzl06BBr1qyhUqVKHufnl8a4W7dujBo1iu3btzN79mx27dpFo0aNiI+PZ8WKFSQkJPDMM8+4J/bMyX333cf333/Pjz/+yP79+6lfvz52u501a9Zw7NgxHn744cvebZ2X5/hDhgxh48aN/Pbbb+zdu5dmzZphNptZtmwZp0+fpkOHDnTu3BlILxv66aefUrRoUcaMGQOkl755+eWXefbZZ3nhhRf4+eefKV++fJ6+Ri8nN9efWbmS64cKFSrQr18/Zs2axb333svdd9+Nr68vf/75J1u3bqV27doecxiI3E6UgBeRTEaPHk27du346aef2LZtGz/99BM2m42iRYvSunVrBgwYkOOkOJfTvHlzPv74Y6ZOncry5csBKFWqFAMHDuSxxx6jZ8+e/PHHH8THx3uMDnn22Wc5deoU8+bNIzk5mSpVqvDqq6/Stm1bj/2Hh4cze/ZsPvvsM9auXcumTZsoVKgQrVq14tFHH71s34OCgvjf//7Hl19+yW+//cb//vc//P39ufPOO+nfv3+mkRTDhg0jIiKCbdu2sXfvXgYPHpxtvcIMb7zxBuvXr2fx4sU4nU5q167NsGHDaNCggXudsLAwpk+fzkcffcTGjRtZs2YNJUuW5L777uPxxx9n9OjR/P7770RERFCpUqUcj/fUU08RFBTE7Nmz+fHHH923Vr7yyiu0atWKNm3asGrVKp588skc93Ml6tevz3fffcfUqVPZsWMHS5cuJSQkhDZt2vD4449TuXJl97p79+5l8uTJNGzY8Jon4EuXLs3s2bP59NNPWb58OTNnziQkJITWrVszZMiQf/VavlJ+fn58+eWXfPDBB+6T1mLFilGjRg2GDh3Khg0b+O9//8vKlSt54IEHcrXPI0eOAOmjWN55550s17l4otRnnnmG8PBwvv32W5YtW4bdbqdUqVIMGzaMhx566LrVw7+cggULMnPmTCZOnMiWLVvYtm0btWvXZsaMGSxatCjTbegtW7bkxx9/ZOrUqWzatIlVq1ZRrFgxunTpwmOPPXbZvxERERG5Ovv27WPfvn3ux97e3hQoUIBq1arRuXNnunTp4i43mR2z2cyHH37IjBkzmD9/PnPmzMHhcFChQgXGjBlD//79PdYfO3Ysb7zxBr/88gsnT56kW7dufPzxx3z00UesWbOGGTNmUKRIESpVqsRLL71EbGwsL7zwAitXrryq+tMmk4mPPvqImTNnus+nLRYLtWrV4oknnqBhw4buda/lOUnLli0pVKgQ0dHR2Y4sf+edd2jUqBE//PCDu1RN2bJlGTlyJAMGDPAY1FGxYkVmz57NlClTWLVqFRs3bqRgwYL069ePp59+2uMaJqsYZ1wnTZs2zX2dFBAQQJ06dRg8eHCu5zPKOCeePn06y5Yt43//+x+Qfv3z+OOP52oUfV6e43t7e/P5558zc+ZM5s2bx88//4y3tzflypXj8ccf5/7778disZCWlsaLL76Iw+HglVdecZeUAbj33nv59ddfWbZsGRMnTuTFF1/M09fo5eTm+jM7V3L98PLLL1OlShV++uknFixYQEpKCmXKlOGpp57i4Ycfdt89LnK7MRl5VWBKROQamTRpEpMnT+bNN9+kV69e+d2df2X06NHMmTOH6dOn07Rp0/zujoiIiIiIiNyidP0pcmPQvdgiIiIiIiIiIiIiInlACXgRERERERERERERkTygBLyIiIiIiIiIiIiISB5QDXgRERERERERERERkTygEfAiIiIiIiIiIiIiInlACXgRERERERERERERkTygBLyIiIiIiIiIiIiISB7wyu8OiKfz5xOu6/HMZhOFCgUQHZ2Ey6XpAC6l+ORM8cmZ4pMzxSdnik/OFJ+cKT45y6/4FC0adN2OJTcWnePfehTjvKcYXx+Kc95TjPOeYnx9KM5Zy805vkbA3+bMZhMmkwmz2ZTfXbkhKT45U3xypvjkTPHJmeKTM8UnZ4pPzhQfudXpNZ73FOO8pxhfH4pz3lOM855ifH0ozldPCXgRERERERERERERkTygBLyIiIiIiIiIiIiISB5QAl5EREREREREREREJA8oAS8iIiIiIiIiIiIikgeUgBcRERERERERERERyQNKwIuIiIiIiIiIiIiI5AEl4EVERERERERERERE8oAS8CIiIiIiIiIiIiIieUAJeBERERERERERERGRPKAEvIiIiIiIiIiIiIhIHvDK7w6IiIiISP5KS0tj3brV7Nq1k+TkJPz9AwgPr0Hz5i3x8fHJ7+6JiIiIiIjctJSAFxEREblNGYbBDz/MYv78uSQmJmGzO3EZBmaTiZUrVzB9+hd06dKN3r37YTKZ8ru7IiIiIiIiNx0l4EVERERuQ4ZhMHHie6xZs5qEZBsJKXacTpd7ucViJijZxqxZMzl5MpLnnhulJLyIiIiIiMgVUgJeRERE5Db0ww+zWLNmNVFxKaTYnJQPq0/Fao0JDClCYlwUh/ds5Oj+raTZnaxZs5oyZcrSu3e//O62iIiIiIjITUUJeBEREZHbTFpaGvPnzyUh2UaKzUnzjkMoXSHcvdwvIISipSpRplIt1i2cRkKyjV9+mUPXrj1UE15ERERuC7FpMUSlncdluHAaTpwuJwYGpfxLU9i3SH53T0RuIkrAi4iIiNxm1q1bTWJiEgkpdsqH1ad0hXAMwyA5zUGqzUmgnzc+3hZKVwinfGh9Ig9tIzExiXXrVtOmTbv87r6IiIjc7mxJWJJOY048hTnpLGDC8PbH8A5I/9cnBGfwHeDle0W7PZtyho3nNrDp/O8cjNuf7XpFfYtRtUA1qhaoTvWCNSjpX+pfPiG5VZlsCVgPL8ISexjDJwR76aY4ilQHsyW/uybXkRLwIiIiIreZXbt2YrM7cTpdVKjamKQUO9EJaaTZnQDEJKQR5G+lcLAPFas35uj+LdgcTnbt2qkEvIiIiFx3JlsC1mMrsR5ZjFfUHkxp8bnYyIQzpBzOgpVxFArFUSQcR/FaYPG8m89luFh9egWLIhdwOCEiV/05n3qO82fOsebMKgAqh4TRvvS9NCnWHKvFeqVPT241hoHX2T/wOTAXn6PLwGlzL/Lb8TmGTxD2Uo2xlW2Brfw9YPHOx87K9aAEvIiIiMhtJjk5CZdh4F+oLMnmwsRHJ2daJyHZRmKynQCfwpgtVlwug+TkpHzorYiIiNyWnHasJ1ZjPfwb1sj14LRf2faGgSX2KJbYo1iPLE1vs1ixF6+DvVQj7KUacdTqx9QDn7Avdu+/6urBuP0cjNvPVwen0bpUW9qVvpfifiX+1T7l5mS5sI/AtWOxxGT/ZY4pLQHrkaVYjyzFuetbElq/jyuo9HXspVxvSsCLiIiI3Gb8/QMwm0x4efuRZndhsaTfAmv1thDg40Vckg2XYWBgEJfsolC5uphj/8LfPyCfey4iIiK3PMPAO3ItAZs/wBx/4tru22nD+9QmvE9tJMWZQqDJSbOAILwDQtjjF4DTZPJY3WKyYDVbMZvMWEwWbC4bqc7UbHefaE/gl2Nz+PX4PO4q0ZLu5XpRLuSOa/sc5IZljj9B8OLHc3eHxt8s0QcImT+AxJZvYy/dJA97J/lJCXgRERGR24DLMEhNc+Dv6014eA1WrlxBVNQh7Mkx+BcqTqEgH/x9vTCZTBQIshIdn0Zckg1HWiLxkdspXsBKeHiN/H4aInIZdpedU4lnCAypnN9dERG5YpbYw/hvnoD3yY25Wt/wK4wzsCSYLJjsSZgcyZjsyZjSYsHIehun4SDRnogLF4WAe+OiuTcumkSzha0BQfwVUgLfcu1pULIF4QVr4mX+J3XmMlycSDrO3tjd7I3Zze7YncTZ4jIdI72szUpWn15J8xJ38XCdwRSk+FVERG4WJlsCQcuezTr5brZgL9UYc9JpLDGHM2+bFk/Q0qdIrvskqTUGwyVfBMnNTwl4ERERkVuYy2XwZ0QUS7ecoFCwL0M7V6N585ZMn/4FQck29q/9kkZt+xFQLNy9jcVspmgBP1JjjrJv6yL8rRAUFEjz5i0B2Lz3LCUK+XNH8aD8eloikoXotGjGbhvN2ZQzlA4qxXPhL1DOv2J+d0tE5PIcqfhvm4Tvvh/A5cp6HbMX9jJNSavQHkfhKrgCSmQ/yao9Ga+YQ1hiDuAVfQhL1G68LuzF4bSR6EjIMjcf7DLonOKge9pZiP4R27lIbOVOYy/bEsMamN4Fk5lygeUpF1ieDmU64XA52Bq1icWRv7Er5q8su7LuzFo2LF5H3cIN6FK2G1ULVMekBOutxeUgcOWLWOKOejYHlyE17H7SKnXG8CsEgDnxNN4nf8dv9wzMccf+Wdkw8N82Ga8Le0hs8WamuQrk5qYEvIiIiMgtyOky2Lb/HL9tPM7ZmPQa76ejkzlxLpGyxQLp0qUbs2bNJC0uhfULp1E+tD4VqzcmMKQIiXFRHN69kaMHtuJntRAU4keXLt3w8fEhNjGNn1ZFYHe6qFu5KJ2alKNQcDYXvyJyXa07s5qzKWcAOJ10mv9s/T9eqf0GlYLvzOeeiYhkz5QaQ9Dy5/A6tzPL5c4iVUkN64mtXGsMn+Dc7dTbH0exmjiK1STt76Z1x39l844JhCd5UyslkRL2fybG9LX44mfxA/5OjDtS0yd9PbYSLN7YyjTDVqE9tjJ3gbefezsvsxeNizWjcbFmnEyKZPHJhSw/tQTbRZNuZth6fjNbz2+mUnBl7rujO42KNsZiVlruVuC/eQLepzZ5tDkL3klcxy/B6lnC0RVYkrSwHtgqtCNg3X+wHlvlsdx6dAWBvExiq/FgMud11+U60V+6iIiIyC3E5TLYsu8cK/44yclzniO8yhcPwjDSW3r37sfJk5GsWbOahGQbJw5t4+j+Le51vSxmCgRYCfK30qJFS3r16gvA2j9PYXemj0z74+B5/oqI4q6apWhbvyz+vjq1FMlPwVbPxFSSPYnXt7/MK3Ve587g0HzqlYhI9syxRwhe9gzmhJOZlrn8i5Bc/2lsFe/9V4lIwzCYe+xnvov4BgKC+D0gCAyDsrY0Ojl9uNfhjV/ssex34LRjPbYqPVHq5YutbAvSKrbHXropWKzu1UoHlOHh0Ee5v3wfFhyfx6LIBaQ4UzLtLiL+IBN3vUsBa0EaF2tCk2LNCStQFYvJctXPMTcMwyDZkUyiIwFvszcBXoFYzVaNxv+XfPZ+j+/eHzzaDL+CJLSdmCn57rGONZDEu9/Hd+d0/P/42KNkkvXoCvw3f0Byo5F51W25zkxGxlWY3BDOn0+4rsfz8jJTsGAAMTFJOBzZ3OZ1G1N8cqb45EzxyZnikzPFJ2eKT2ZOl4vtB6JYsvUEUbEpmM0mXC4DA6hQIpj2DcsSWraAx0WWYRj88MMs5s+fS2JiEjaHE5fLwGw2YfWyEBgYwH33dadXr77u7RxOF7/vPsOSzSdITLW79+Xv48U9DcrSvEZJvCw39mid/Hr9FC2qkj23q+t1ju90OXhzx6vsivnL/R4A4O/lz8u1X6NySNh16cftQJ9DeU8xvj7yM85eZ7YRtOJ5TGmXvEdavEkJH0RKjcHg7f+vjmEYBtMPfs5vJ37NtKxZ8bt4stqzeJu9MSeewnp8FdZjK/E6ux1ykSozrIHY7miFrUI77KUawSWj2RPtCSw8MZ+FkfNJcSa735OzUtCnIPWLNKJycCiVgu+ktH+ZKxod73Q5iE6L5nzqub9/znM+9SznU88TkxZNgj2eRHsiTsPpsZ3FZCHQO4gC1gJUL1iDekUaUK1AuEfd+5tBfr2OLRf2E/LrAM+ySRZv4jtMxVGsZq73433ydwJXvYjJluTRntzwOVKrD7hW3f3XbuT35b+idzDz0NdYTBYGhw4lNKTKdTt2bs7xlYC/wSgBf2NRfHKm+ORM8cmZ4pMzxSdnt3p80tLSWLduNbt27SQ5OQl//wDCw2vQvHlLfHyyrgf5zaJ9bD8UBaTfPG02m6hQMph76pelcpmQHEc3Xc3xUtIcrPgjktU7/hkRD1A42JdOTcpR+84iN+yIKiXg5Xq7nuf4Nmca7+58i50xf3oke/z+TsKHKgl/Tdzqn0M3AsX4+sivOFsPLyJw7X/A5fBodwWWIKHNhzgLXZuJpL879A1zjv2Uqb3LHd0YcOdgzFmMrDelxmA9sQbr0eXpZUUu6WNWDL9CpFXsQFqlzjgLhXpMounAxsbYNcza8z/OpZzLVb+tFivlAitQwFqAAK9AArwC8Pfyx4VBiiOZZEcyKc5k4m3xnE89R3TaBVzGtfn9+Vn8qFm4Nk2L3UXjYk2zjNGNJr9ex4HLn8d6fJVHW2LLN9Pv2rhCXqc2E7z0Kc/XmwkSW43HVr7tv+zptXGjvi8n2ON5csOjpDjSy26W9C/FR00+vW7HVwL+JqQE/I1F8cmZ4pMzxSdnik/OFJ+c3arxyTQi3e7EZRiYTSas3ukj0rt06Ubv3v0yJbd3H4nmiwV7AKhcJoRebcMoEeKD05m3p3oxCWn8tvEYW/ef8yh307d1ZRpVK56nx75aSsDL9Xa9z/GdJjsf7H6Hzae3eLQHewczvuFEivgWva79uRXdqp9DNxLF+PrIjzh7n1hD0PIRmUaZO4tUIb7Nhxj+1+Y9asGJX/jqwBeZ2gdVfpgud3TL1T5MaXHpteCPLMb79JZcjYx3FryTtDs7k3ZnZwzfgu4Yn78Qx/rT6/n1+FwOxR+80qeTLyqHhPFo2BOUD6qQ313JUX68ji0xhwiZ28ejLbXGIJLrP3PV+7RGLCBwzdhLDuRNfPspOIrXuer9Xis36vvyvGM/M+PQ1+7Hwd7BTGsx47odPzfn+DfXPSUiIiIityjDMJg48T13TfaEFDvOi0aWWyxmgpJtzPrfLHYfT+KhAX2oUPKfes/VyhekeY2S1LqzCFXKFXSfHHsUlMwDBYN8eOCeUFrULsUv649yMDKWgoE+1A0tkuN2VzPqXkRyx8fiwxvN32D0ypfYHvWHuz3eHs/7O9/h9brjsF5Ut1hE5HrxOvcXQatGZ0pk2+9oSUKLtzwmOP031pxZlSn5bjKZeLraCJqXaJnr/Rg+IaSFdiMttBumlAtYjy7H58hivM7uyHYbS8wh/Ld8iP+2ydjuaIWj2v0QcjdeZi+aFb+LZsXv4mRSJL+fW8/v59ZxPDGH+vP57GDcfl7Y8iwdy3Shd8V++HtlX9P8duP315cejw2fIFJqDvlX+7RV6kRy0ln8t338T6PTTtCKkcR2/d81+3LqVuI0nCw5ucijrWah2vnTmRxoBPwNRiPgbyyKT84Un5wpPjlTfHKm+OTsVozP999/x6xZM4mKSyHF5qR8WH0qVmtMYEgREuOiOLxnE7GJqRSr2Agf/xBKFfTi7ac6Zrmv/IqPYRjsOxaDC6hevpDHsj8PRVGpdAgBvl5XPcr/WtEIeLne8usc/2xUDG/98Rp/Rf/psfzukm14vOrTN2yZqJvBrfg5dKNRjK+P6xlnc+wRQhY+jCkt3qM9tWpvkhuOBPO1mYR0+4VtjP/zzUz1zodVHU6bUu2uyTHMSWexHl2K9fBivKL25LiuyQTmAmVJrtyD5Er3YfgW8Fh+MimSLec3cSj+ABEJB4lKjbrqflnNVor6FaOobzGK+haliG8xivgWJcQ7hCDvYIKswQR6BeIw7CTZk0hyJJJoT+Rg/AG2RW3OcWR+QZ+CPFZlOPWKNLjq/uWV6/1+YY47RoE593t8kZRS+1FS6jz273duGAT8/jY++2d7NDtK1ie+3SfX7O/katyI78vborbwzp9veLS9WW88YQWqXrc+aAS8iIiIyE0gLS2N+fPnkpBsI8XmpHnHIZSuEA6kJ7Vt+FE8vBgF0mzY0pJxGQanY50cPRVD+VIF87n3/zCZTFS9JPEOcD42hW+X7MfbYsYcv5/dG+aQkJSc/Sj/WTM5eTKS554bpQShyL9gtVgZET6a0VtHcCb5tLt95enl3BkcSrsyV16jVkTkapiSzxO8dHim5Hta5S4kN3rBo2b6v3Eo/gDv7xyXKfner9LAa5Z8B3AFFCe1+gBSqw/AHHsEn4gF+Bz+DXPimaw3iIvEd8t/8dk2hbSK95JarW96rXigdEAZSgeU+WdVWxwR8Qc5k3KaJHsiSY4kkhxJJDuSsJgs+Hn54Wfxx98r/aeIb1GK+hajiG8xgr2Dc3nu5EeQ9z93UtYqXIeeFfoQmxbDtgtbmHP0J86meD6XmLQYxv/1Jk9VG8FdV3AXwa3Ib+d0j+S74e1HarW+12bnJhNJjV/EnHQW78j17mav01vx+/OLa5Pkv4Usjlzg8bh8YIXrOgFrbikBLyIiIpLP1q1bTWJiEgkpdsqH1ad0hXAMwyA+yU5MYhqOv5PUFi8vLE5v4s8exBT9JxF7fSh/DS8m88qizcdxugySkhJJSSmEf9XeJBxcT9kyfpeM8t/I0f1bSbM7WbNmNWXKlKV373753X2Rm1qAdwCjarzES1tHkuZMc7d/eWAq5YNuzItUEbm1mGyJBC8Znik5bS/TlKQm/3fNku+xaTG8+9fb2Jw2j/aOZbvQvVzPa3KMrLgKVCCl3nBS6j6B15lt+B6ch/XocrikHwA4bfgcnIfPwXk4StQlJXwg9jLN4aKJTkOsIdQtUj/P+puTAj4FaVOqHXcVb8W8Yz8z+9iPOC6aFNQwDCbt+QCn4aRVydb50sf8Zk48hU/EQo+2tCq9MXxCruFBvEhs8QYh8/phTjrrbvb7cyr2EnVxlLzx7kLID2eST7P9wh8ebe3LdLwhB/Dc+FMZi4iIiNzidu3aic3uxOl0UaFqY5JS7Bw/m8j5uBR38h3A38eLgj6pHNv2Mylxp9i1a2c+9jr3ujarQKMqRUlNTcFlGFisgZSv252SNboSUKgcfgEhFC1ViUZt+9O84xBSbE4Skm388ssc0tLSLn8AEcnRHYHleKKq56RwTsPJ+zvHEZsWk0+9EpHbgmEQsPY/WGIOeTQ7ilQjodW7YPG+Jodxuhx8uPs9YtKiPdqbF2/Bg5WHXJ+EnMmMo2QDElu8SUyfxSQ1fgFnwTuzXd3rzB8ELXuOkLm98Tn4S9YJ+3xitVjpVbEfExt9TJ3CdT2WGYbBx3s+ZMWppfnUu/zlt/MbcF10h4WXDynVB1zz4xg+ISS2Ggfmi1K3BgSueRlTSnT2G95Gll5S+93fy/+K5ni4npSAFxEREclnyclJuP6+jTUwpDDRCWnYL0m8lykaQKkiARQokF5yxuUySE5Oypf+XqngACtFTceI2/0DCecPY/HyxuLlhc3h5NSFJE5FJZFmT7+QKV0hnPKh9UlMsZOYmMS6davzufcit4amxZtzX7nuHm0xaTF8um8ymhZMRPKK766vsR5f5dHmCi5Dwj0fXbMJVwFmRnzD7phdHm3VC9bgyWrPYjZd/9SX4RNMWtU+xHX9H4ldvoaqnTHMWRehsMQeIWDdaxT4qQs+e7+/oRLxJfxLMqbWf+hU9r5My6bsncSSk7/lQ6/yjyn5PD4H53q0pYZ2x/DLXILxWnAUq0Vy3Sc92szJUQSufQWMG6MGe36xOdNYcdrzS6C7S7bF1+KbTz3KmRLwIiIiIvnM3z8A898js5LiL1AkJP3E0dfqRZmigZQqEoCvNf2iLTEufWIus9mEv39A/nT4KuzatZPUhCiO/zGHAj5p+Hj/M4FUcpqDE+cSSU5Lv8W5YvXGOJwubA7nTTPKX+Rm8EDFgdQoVMujbVvUFladXp5PPRKRW5nXqc34b5vs0Wb4hBB/z8cYvtduDpv1Z9cy//hcj7ZCPoV5LnwUXtkkva8bkwln8ZrQaQIJfX8jpfajGH6Fs1zVnBxFwMZ3KTC7Oz4H5sJFpV/yk8lk4sHKQzJ9iQvw+b4prD+7Jh96lT/8ds8Ap/2fBrMXqeGD8vSYqeGDsJdu4tHmfXIjvrtn5Olxb3Trz64l0Z7o0XYjz22jBLyIiIhIPnG5DH7ffYZSFWpg9bZgsZg5vGcjfn+PeC9dxB9fq8Vjm8O7N+JlMWP1shAeXiOfen7lLh7lX7BAAcoUDaB4QT+8LOmno95eZvz+fq6BIUWAm2uUv8jNwGL24pnqIwmxetapnX7gc86nnMunXonIrcicdJag1WM8JqrEBIkt38IVXCb7Da/Q8cRjTNn7kUebxWRhZI3RhFgLXLPjXAuGfxFS6jxGTK8FJDX/D84CFbJcz5x4hoD1b1Bgzv1YjyzxjGE+MZlMDKg0mO7lM9fS/3TvZE4ln8yHXl1nLkd6qaCLpFXugiugeN4e12QmscUbuPyLeDT7b/sYS/TBvD32DWzxSc86/DUL1aKUf+l86s3lKQEvIiIikg8iTsUx8Ycd/LDyEMeTixIYGECQnzdH92/l5JFd+Fq9MtUrPXlkF0cPbCXQz5ugoECaN78xaxxm5eJR/olxUZhMJoL8rdxRPJBCQb4UCfF1P9+MUf7efiE31Sh/kZtBiDWEx6oM92hLcabw8d7/4rrNb2cXkWvEaSdw1YuYUmM9mlPqPJ5pJO+/keJI5v2d4zwmmAZ4OOxRKoeEXbPjXHMWb9Iq30dctx9IaPshjhJ1s1zNHB9J4KoxBC9+LFMN/fxgMpnoV3EgvSr09WhPdaYycee7mSa/vdV4nfkDU1q8R1tK+IPX5diGb0ESW7wFF18auBwErnn5hipZdL0cjNtPRLzn30SHMp3yqTe5c9Mn4L/77js6dOhAzZo16dKlCwsWLMjVdsuXLycsLCzLnwMHDgAwevTobNcJCwtj4MCBV7Q/ERERkZiENL5ZvJ/Js3cSGZU+ujsyKonm9/QiyN+Kn9XCuoXT2LR0JudPRZCSFMf5UxFsWjqTdQun4We1EORvpUuXbvj4+OTzs8m98HDPUf4ZzCYThYJ9CPD9ZxK2w7s34h9SjEI1+5MUWIvo+NT86LLILatB0Ua0KtnGo213zE4WRebuWkpEJCf+Wybidc6zhJy97F2k1Hz4mh3DMAw+2/cxp5NPebS3KtmGe0p1uGbHyVMmM/aydxF/7+fE3zsVR/HaWa7mdXobIb/0w3/T+5hsCde3j5cwmUz0rvgAd1/yGXI08QjfHvoyn3p1fViPr/R47CxcBVdw2et2fEfJ+pnK3VhiDuG//dPr1ocbxbpLyh4V8S1C3SIN8qk3uZPPxbD+nWnTpvHuu+/SoUMHBg8ezNKlSxkxYgQmk4mOHTvmuO3Bgwcxm82MGzcu0+iykiVLAtCnTx+aNMn87ezixYtZvnw5d9999xXtT0RERG5fdoeTFX+cZPm2SI8JVksXCaD7XRWpWKoZyReOsGbNahKSbZw4tI2j+7e41/OymCkQYCXI30qLFi3p1atvVoe5YTVv3pLp078gKNnG0f1bKVOpFqUrhGdaL2OU/50Ne2M2mzmf7MO4GX/Qum5pWtcr41E7XkSu3uDKQ9kV8ydRqVHuthmHvqJWoTqUDrh25SFE5PZiPbwI373fe7S5gkqTeNfrcA0nQ11+agnrz671aKsQVJFHwoZlysncDBwl6hF/7xd4n9qI3x+f4BW1x3MFlwvfPbPwObKYpMYvYivfNn86+rchYcM4GH+AyKQT7rZFkQupXrAGjYs1y8ee5RHDyDSZsK3c3Vmvm4eS6zyOd+QGjzsifHd9ja1M82zvpLgV7Yv1/PtoVbItFtONfY1w0ybg4+PjmTx5Mp07d2bChAkA9O7dm4EDB/Luu+/Svn17LJbsg3/w4EHKlClDt27dsl2nTp061KlTx6Pt1KlTvP766zRv3pyHHnroivYnIiIitx/DMPgz4gLz1x8hOuGfW6QDfb3p2LgcjaoVx2xOv1B87rlRlC5dhvnz5xKUmITN4cTlMjCbTVi9LAQGBnDffd3p1avvTXdx6ePjQ5cu3Zg1ayZpdifrFk6jfGh9KlZvTGBIERLjoji8eyNHD2zFz2rBiI8guOydYDLhcLlYsvUEm/aepUvTCtQNLXLTPX+RG02AdwBPVH2G17e/4m6zu+xM2vMBb9Z7N/8nLhSRm44l5hCB69+4pNFKwt3vYfgEX7PjHEs8ypcHpnq0+Xv583yN0VgtN8/dgZmYTNhLN8FeqjHWYyvw3/IB5sQznqukRBO48kVsFdqR1PhFDN8C+dJVH4sPz4W/wJj/Z+/O46Oo7/+Bv2Zm791kNyF3INwQTk8UFcEiCKIoSvHWelRq1Wq16u+rtVq1akUt9eil0ioWtN63VZQK4oHciNz3kZD72nt3Zn5/RDaZ2WRJNrvZHK/n49FHmc/O7LyZrCF5z3ve71W/QVBpaoHyty3PYEDaIORZe1YhqlS5GaJHOysl2H9yCgIxwT3xD3C+f0XTkF4VcHx5H+rOfxWqydH5MXWygBzAPvdezdooV3RRT1fTbX+qWrp0KbxeLy699NLImiiKuOyyy3D77bdj3bp1OPHEE1s9fvv27Rg8eHC7z/voo48iEAjg/vvv1/ziF+/7ERERUc/23ld78cX6psFUoiBgwph8TDupCDaL9kcxQRBw8cWXYdas2VixYhk2bfoeXq8HNpsdo0ePwYQJk7pV2xm9iy66FIcOHWxTlf/4UXm44cbJ+GzNQXy5oQRhRUWdJ4h/L9mGr74vxazTB6IoNy2Ffxui7m9M5jGY3vccTeuZXfU78crul3HlkGtiHElEpCUE3Uj7351AWNs2znPK3ZD7JK4fuy/sw5++fwwhJaRZ/+WIW5BrzUvYeVJKEBAccCaCfU+FdeOLsG56CZC1f1/Tnk9hPLwG7tPuRajfxJSEWeToj58PvwF/bTYE1xv24qlNT+APJ87r8hXJ7WHet1SzrTiLIDtbHqKbbHLmUHiPvxG21U3XXXSXwvbdk/BMuD8lMXWmXfU7IKtyZFsQBAxOH5rCiNqm2ybgN23aBAAYNWqUZn3kyJGR11tLwIfDYezZswcTJzZ+kwoEApAkCQZD7MuxceNGfPrpp7jmmmtQVFTU4fcjIiKinu/4YdlYtv4QVADD+7kw6/RByMu0xTzGbDbjzDPPwplnntU5QXYSQRDaXeV/3mkDMX5UHt5bsQc/7K0GAOw5XI8/v74BJ43IxazTB8Ji4s9cRPG6YsjV2FC9TtNH+b19b2NsxrE4ps9xMY4kIvqRqsK+4vcQ6/ZrlgPDL0Bg6HkJPI2K57f9DSXeQ5r16X3PwficUxN2ni7DYIXv+F8iMORc2L97EsYD2pY7gq8KaZ/dhsDQ8+A5+S7AaO30EM/IPxPf12zEl4e/iKztrN+BJYf+2+WHYraHvv97sOgnQAqfxvSPugKmA8thKFsfWTPveA/BfhMRSkFrnM60vW6bZru/fQCshs7/7LdXt/1tpby8HE6nE1ar9iJnZ2cDaGwV05q9e/ciFAph7969OPfcc7Fz504YjUZMnToV9957LzIzM1s87q9//SvMZjPmzp2bkPcjIiKinkVWFNR7QshIa6pS75fjwFnjitA3x45RAzJ7feuUeKr8c1xW/PzckdiyrwbvfrkbZbU+qAAOlLthMvSc6iqiVGhsI3An7l51h6ai7JnN8/HESU/BZc5IYXRE1B1YNi2EaZ9uQGXWCHhOvjOh51l2eKkm0QsAg9IG46qhiRvu2hUp6f3QcOZ8mPb8F/ZvH4MQ0A5iNe94D4aK7+H+yTzIrkGdGpsgCLh++A3YWb9dcyP3lV0v45Sc0+A0uTo1nmSQandDrNunWUtF/3cNUYL79IfgfPdiCCFvZNnx9R9QmzMWqrVPCoNLrm11WzTbw5zFKYqkfbpcAv7gwYMxX09LS4PT6YTH44HFYol6/ciaz+dr9T22b98OoLGife7cucjPz8eaNWuwcOFC7NixA6+//nrUe5eUlGD58uW48MILoxLq8bxfa0RRiPSB7QySJGr+n7R4fWLj9YmN1yc2Xp/YeH1i68zrEwgEsHz5MmzatLFZongsJk7UJoq37a/BW8t2AwDuuvw4SGJTbOeeNiDpcTbXHT4/BoMV06ZNx7Rp09t8zJjBfTBiQAa+3FCK/67ch5+eMRgmU/sT8N3h+hB1poFpg3HFkKvx0o4FkbW6YC2e3fxn3HPs/RATODiRiHoWQ+kq2NY8o1lTzelo+Mk8IIH92Ct85fjnNm3fd6vBhttG3wWjaEzYebosQUBw0NkI5Z0Ix1cPwXjwK83LUu0eON+/Eu5T70FwcOdWnlsNNswtvgkPrP1tZM0b9uLfO1/CTSNv7dRYkkF/c0mxZSGcNaqVvTuPklYA78l3wr7igcia4K+F46uH0HDm/JRW6CeLqqrYUa+tgB/mTFyLq2Tqcgn4M888M+br119/Pe644w4oitJiBdmRtVjVZYMGDcKNN96ICy64INJKZsqUKejfvz/uv/9+vPHGG7jiiis0x7z11luQZTlqPd73a01mpj0llXHp6V3/cY1U4vWJjdcnNl6f2Hh9YuP1iS2Z10dVVbz88st4/fXX4Xa74Q82tUr58ssv8PLL/8ScOXMw7ZzZeHXJdqzdVhY5dv2uakw5qX/SYmurnvr5ufDMNEw/bSBsFu0v3LsP1eGt/+3EZdOGoyA7eghVIBDA0qVLsX79eng8Htjtdhx77LGYPHlyt+6tT5QI5/Q7D9/XbMDaytWRtQ3V6/DB/ndxXv8LUhgZEXVVorsEaV/8H6CqTYsC4J70MBRHQcLOo6gK/rrlafhkbaHlDcU3I8/Ws4Z9Ho1qy0bDlKdg3vkebCuf0FQ/I+yHY/l9CBxe2/j0gaFthaCJMDpjDCbkTcKKw8sia1+Ufo4zC6ai2DWy0+JIBn37mVDRT4AucmM6MGQmjPuXwbT/i8ia8cCXMO94B4FhPe/f7jL/YdQF6zRrw50jUhRN+3S5BPy8efNivj5s2DAAgN1uh9/vj3r9SOW73W5v9T2Ki4tRXBz9iMLs2bPx0EMPYeXKlVEJ86VLl2LAgAEtHhfP+7WmutrT6RXw6elW1Nf7IMtKp523u+D1iY3XJzZen9h4fWLj9Ykt2ddHVVU8+eQ8LFv+BRo8QTR4Qwg3O49BEpHuCGPhBxvxzgYL7I6mYaD989LQJ82EmhpPwuNqq97y+Qn4gpE/q6qKf72/CXtL6/H9rgpMPKYAZ4/vD6vZAFVV8Z//vIJ3330bHo8bgVBjIYeqqvjoo4/x1FPP4PzzL8DFF1+a1EKIjIzWfz4lSjVBEHDjiFtx53e3oCZQE1lfvGshRrhGYmg3qTAjok4S9iNt6Z0Q/LWaZd+xv0CoMLH92D899DE21WzUrJ2RfyZOzZ2Q0PN0G4KAwNDzEco9Dmn/uwtS9Q7Ny+bt78BQsQkNP5kHxdl5BSFXDrkGaypXwRduuinw/La/Yd64+ZDELpd+bBPRcxhSpbblScrbzzQnCPCcdi+MFRsg+Jr+7bavfAKhvBOhpPdLYXCJp+//nmZM6zbDl7vcfwHnn39+m/bLz89HXV0dgsEgTCZTZL28vBwAkJub2+5zG41GpKenw+v1atarqqqwefNmXH/99Ql5v1gURYWiqEffMcFkWUE43HN/Qe8oXp/YeH1i4/WJjdcnNl6f2JJ1ff7zn8X44osvUFnngy8oY8DwEzFo5Hg4nFloqK3EgQN7IdoLYDQ7EAiGIPp8yOnjxLmnDsAJw7MhCkKX+Lr1ps9PTUMANQ0BqABkRcX/1h3Cqi3lOHt8Eb755GV8+eUyNHiDaPCFIMtNCXhJEpHmDuLf/34Z+/fvx2233dnr+/RT7+U0OfGrkbfjwXW/i6zJqow/bZqHx0/6MxzGtBhHE1GvoapwfP0HSFVbNcuhfqfDd8zPE3qqw95SvLzzX5q1PuYsXD00sefpjpT0ItSd8yLs3z0B87a3Na9JNTvhfP9yeE79HYKDpnVKPJnmTFw86DK8uP2FyNp+9z7899BHOKdf4obxdibTvi8026o5DaHc41MTTCtUSwbcp92HtM9ua1oM++H48j7Un/0CIPaceUk76rTfc4Y5i7vNz+1d45mJOIwaNQqqqmLLFu2dqCPbY8aMafXYxx9/HFOmTIlKjNfW1qK6uhqFhYWa9XXr1kFVVZxyyikJeT8iIiLqugKBAN5//x00eIPwBWVMmHEdTp5yObILBkM0ORAw5sLZ70TY0nOgAlAUGZ6SNbh9zmiMK86B2E1+COxpMtLMuPvy4zFtXBGMP/Z2d/tDePnjTVhfkQ03MlHrCaLf0BMw+cJbcMHP/4DJF96CfkNPQK0niMo6H5YvX4bXX381xX8TotQak3kMLhjwU81apb8Cz27+MxS1d9zQI6LYLFtegWnXx5o1xdkf7okPJbQ1h6zK+MuWPyMoBzXrvxz5K9iNfKoMAGCwwHPqvY3XXtdyRgj54Fh2D+zfPArIgU4JZ3rhDBQ5tFX3/9m9GNWB6k45f6Lp288E+54OSF1v5kCo30QEhs3SrBnKN8Lyw8upCShJtrWQgO8uum0CftKkxsFnL7/c9GFSFAWLFy9GYWEhjj322FaPLSgowIEDB/Dmm29q1v/yl78AAGbOnKlZ37q18QvcUpuZeN6PiIiIuq4VK5bB7fagwRfCgOEnonDg6MhrAoBASAYASAYD1EAtdn/9Iur3fInV361IUcR0hMkoYfrJRbj7ihNw3JAsQFXh9/shWDORf+xPceI5v8HxZ1yKnMLBsDmcyCkcjJOnXI4JM66DLyijwRvEe++9jUCgc35JJeqqLh54WVTP3jWVq/D+/rdbOYKIegtDyXewrZqvWVONNtRPfhKqKbFPyXx04H1srdUWXZ5VeDaOyTwuoefpCYKDZ6B25r8hZwyOes289Q04P7wWYv3BpMchiQb8fPgvNWu+sBf/2b0o6edONMFfA8PhNZq1YP/JKYrm6Dwn/QZKmrYA2Lb2b1Etirorv+zHPvdezRoT8J0gIyMDc+fOxfvvv4/f/OY3eP311zF37lysXr0ad9xxBySp6RGLzz77DJ999llke86cORg1ahT++Mc/4sEHH8TixYtx8803Y+HChbj44osxbtw4zbn27dsHq9WKzMzMFmNp7/sRERFR17Vp0/cIhmTIsoJBI8drXjMZJTjtJhgNIgr62JHfxwafuwbBsIxNm75PUcSkl5FmxlXTizG+yIewpwKqCkgGI1RjGsprfFH7Fw4cjQHDToTbF4Lb7cGKFctaeFei3kMSDbht9F1IN6Zr1hfvehlbajenKCoiSjXTzg+Q/tktgKJ9GsY98SEoroEJPddhXyle2aWt3s215uKKIVcn9Dw9ieIaiLpzX0JgaHQRqFS1Fc73L4fxwPKkxzHCNRKT8rWJ6i9KP8chT/JvACSSsWSldsCwwYxQ4fjWD0g1o+3Hp1CaPY2rhOFYcT8gh1IXV4Lsqt+heRJPFEQMSR+awojap9sm4AHgpptuwl133YV169bhoYceQnl5OebPn48ZM2Zo9nvkkUfwyCOPRLZNJhMWLFiA2bNn45NPPsEjjzyCXbt24e6778bvf//7qPPU1tbC4XC0Gkd734+IiIi6Lq/XA+XHH7Ydzqyo1/ukW1CU44DNYoi8rigqvN7UDV2llh3e+z2qvn8dJT98CqOhcfRRH6e5xX0HjRqPsKzwZgrRjzLNmbh19B2aNUVVMH/TY6gL1qUoKiJKCUWGbdV8OL6MTuT5jr0eoaIzEn7KF7e/gJCiPdeNI26F1WBN+Ll6FIMVngm/h2fC7wGD9mceIehG2me3wbb6GUAJJzWMywdfBZPYNK9RUZVuVwVvqNik2Q7ljwO6+OcvnHMM/KOv1KxJVdtg3bggRREljn4Aa5GjPyySpZW9u54uN4S1PQRBwHXXXYfrrrsu5n5Lly6NWsvIyMCDDz6IBx988Kjnef7554+6T3vej4iIiLoum80e6ePeUFsJwWiHxdT0I5MoNlWVuOsqI2s2G3uRdjWNN1MU1B76HgVTZkORrJqvZXO8mUIUbWzmsZgz8BK8vqdpNkJNoAaPbXgIvz32AfZgJuoFhGADHF/cDeOhb6JeC/Y/A75j5yb8nKsrvsOaylWatel9z8HIjNGtHEF6gaEzEc4aCcf/7oJUt1fzmuX7F2Go/B4NEx+GastOyvkzzJk4p995eHvfG5G1b8q/wu76XRiUHt0mpysyVP6g2Q5ndY/Pn/e4G2A8uAJSze7ImnXjAgT7TYScNTLGkV3bdl3/9+HOESmKJD7dugKeiIiIKNFGjx4Dk1GCJIk4VFKKgxUelNV4EZajhw/u/uFbGCQRJoOE0aNbHwBPqdH8Zoq3oQppNlOr+/JmClHLZg+8GGMyj9Gs7ajfjoc3/B6eEG9WEfVIqgJD2XrYvn0MrjdntZh894+8BO4zHkvo0FUACMpBvLhDWwTpNLlwyaArEnqe3kDOGIy6cxciOHBq1GuG0jVwvXsJjAe/Str5z+t/IWwGm2Zt8a6FSTtfQilhGKq1Fdfh7pK8lsxwT3gAEJtac0NR4Pjyd502jDfRVFWNSsAPSx+eomjiwwQ8ERERUTMTJkyCw2GHy+mC6CiAHA6jwRtCWFY1+x3aswl7t6+Gw2pEWpoDEyZMSlHE1JrmN1N2b/425r68mULUMkmQcOuo3yDDrJ2HtaNuG5PwRD2NHIR17d/gev1cpH90HSxbXoPgr9XuIxrgOe138J58JyAmvqnCu/vfQpmvTLN2xZCr+cRNvEx2uCc9Cs/4u6K+XoK/FmlLboFt9VNJ6RHuMDowq/9PNWsbqtfhh5qu3+pPqt0NhLXJ6m6TgAcgZ42Eb6y2W4hUuxe2dX9PUUQdU+Y7jPpQvWatOw1gBZiAJyIiItIwm82YOXMWsoZOgiQZEQx44S7fgYbKvfB56lBRsgsrlyzCio8WwGqSkGYzYebMWTCbW+4tTqlz5GZKmtWIvdtW49CeTS3ux5spRLE5TS7cf9wf4DS5NOtMwhP1IEoY6UtuhnXDCxA9ZS3uolozUD/9HwgMm5WUEMp9ZXh77+uateHOYkzK+0lSztdrCAICIy5G/dnPQ7HnRr1s+X4h0j++DmJ94oekzug3Ey5ThmZt0a6FUFW1lSO6Bn37GSWtEKolo5W9uybf2Gsh99EmqS2bFsJQtj41AXWAvvo93ZiOXGteiqKJDxPwRERERDrHnnI2HLkjIIoC1LAfu1e/iaVvPYP3/nU/lr71DA7uXAOX3YQspxUTJ07CnDmXpDpkasGRmylpNhOsJgkrPlqAlUsWofzQLnjddSg/xJspRG1VaO+LB45/pOUk/Pr74Q41pCYwIkoI8/p/wlC6ptXXQ/njUHfuywjnHpu0GF7asUAzeFUQBFw3/AYIghDjKGqrcM5Y1J23GKGiiVGvGSp+gPO9S2He8R6QwOS4WTLjpwMv1qztqNuG1ZXfJewcyaAfwNqdqt8jJCPcpz+offJBBRwr7gdCvtTFFYft9dp2QMOcxd3u+wIT8ERERETNhGUFby/fDbvDAavVCrF6PfKcBuRm2pDtsiI304b8Pnbk52TgssuuwG233dntfgDsTS666FJMnDgJWU4rXHYTDuxcg6VvPY23X7gXS996mjdTiNqh1SR8/Xbcu+b/odJfkZrAiKhjStbBsu4fUcuyayB8x/8StbPfRsP0v0Nx5CcthA1V6/BdhbZd3LTCszEwbVDSztkbqRYXGib/Cd6T74huSRPywr7iATj+dycEf03CznlmwVnItWor71/Z/TIUNXq+UldhqNys2Q5njUpRJB0jZwyG9/gbNWti/UHY1jydoojis61ui2a7uw1gBYDEN+wiIiIi6saWbyhBWW1jVUjxgBzM/dX9+Pqr5di06Xt4vR7YbHaMHj0GEyZMYqV0NyAIAm677U4UFvbF+++/gzS3B6GwDEEUoSoKjAYJDocd5513AebMuYQ3U4iO4kgS/v6196AuWBtZP+Q5iHtW34l7j30ARY7+qQuQiNon6AE+ugtongwVRTRMfRah/JOATvh3UVVVvLr735q1NGMaLubg1eQQBPhHXopQ7rFI++JuiPUHNC+b9v0PxvKNcE+4H6G+p3X4dAbRgIsGXY5nfvhTZO2Aez++q/gG43M6/v4JF/ZDqt2pXeqOFfA/8o+6Aqb9X8BQvjGyZtnyGkJFZyBUcHIKI2uboBzAfvc+zdowZ/cawAowAU9EREQUUesO4NPvGn8JEQUBsycNhtViwZlnnoUzzzwrxdFRvARBwMUXX4ZZs2ZjxYpl2Lx5ExQlBFE0YuTI0byZQtROR5LwD677HaoDVZH1mkA1frfm/+H/jrkPI1zdN1lB1JtYv5kH1O7XrPmOub5TE3M/1HyPnfU7NGuXD/4ZHEZHp8XQG8l9RqB25iLYv3uisfVMM4KvCmlLbkFgyDnwjrutw/3PJ+ROxDv73sABd9Nn7fU9r+Kk7FMgCl2rOYehejugNLshJQgI9+l+FdcRogT36Q/A9e4lmsGy9hUPoG7Wf6Ca0lIY3NEd8h6KelpiUPqQFEUTPybgiYiIiH703oo9CIRlAMCpo/PQN4e/+PUkZrMZZ555FqZNm46MDDtqajwIh7vu489EXVmhvS8ePnEe/rD+fhzyNA3u84a9eGjd7/CzoddhauH0pCVWvq/egG/KV0AUJKQb0+E0uZBudMJlzkC2JRsZpgxIIn/dJYrFtPczmHa8p6lyD+eMhW/stZ0axzv73tRs51hzcUb+5E6Nodcy2eGZcD9C/U6H/as/QAjUaV427/wQpgMr4B33awSGzIz7iQhREDF7wMX486bHI2v73fuwpvI7jMse36G/QqLpB7DKrkGA0ZaiaBJDSS+C58RbYf92XmRN9JTB9t2T8Ez4feoCa4NDHu0TGlmWbFgkS4qiiR9/IiEiIiICEArLqPMEAQAOqxFnn8wWCkREsWRZsvGHEx7Doxsewva6rZH1kBLCC9v+jq/KvsQNI25Gga0woef9tvxrPPn9H2PuIwoiMs19kGPJxficUzG97zlsMUXUjOCvhf3rP2jWVKMN7ol/iOoNnkx7GnZhQ/U6zdrMolm8gdbJgv0nI5Q9Fo6vHoDx4Nea14RAHewrHoB51wdwn3IPFOeAuM4xPudUFNr7am7avr7nVZyYdXKX+v5sqOwBA1hbECie09heqHRVZM28430EB56FUOGpKYwstoO6BHw/e78URdIxXes5DyIiIqIUMRok3HzhGFw+ZRgunDgINgt/8SMiOhqHMQ33HfcQTsgaF/XaltofcMfKW/DuvjcRVsIJOd8B9378ZfOfj7qfoiqo9Fdgc+0m/HP7c1hy6L8JOT9RT2Ha/z8IgQbNmmf8/4OSltgbZkfzzl5t9Xu6MR2T86d0agzUSLVloWHK0/CcejdUkz3qdUPpGrjeuRjWdf8A5GC7318SJMwecLFmbU/DbqytWh13zMnQUwawRhFEeCbcB1VXzW//9jFADrRyUOod9GoT8IVMwBMRERF1b4Ig4MTiHBw3NDvVoRBRF7Fp0yZcc801OPbYY3H88cfjhhtuwO7du1MdVpdilsy4c8zdmN53RtRrISWEf+98CdevuArP/PAnfFv+FXxhb1zn8YQ8ePz7R+CX/e0+9o29ryKkhOI6L1FPJLpLNdvh/HEIDj6nU2M47C3FNxVfadZm9JsJk8S5LCkjCAgM/ylqL3gTwQEt3AhRwrCufw6udy+B4fCadr/9qbkTkG8r0Ky9sedVqKoab8QJJQQbINZpZyL0mAQ8AMVRAO+42zRrYv1BWDe+mJqA2qD5ExMA0NfGBDwREREREVGPsXv3blx55ZXYtm0bbrzxRtxwww3YsGEDLrvsMpSVlaU6vC5FEg24bvgN+N1xDyLbkhP1ujvkxvLDX+DJ7x/DNcsvx8Prf49PDn6EKn9lZJ+AHMCqipX4y+ancNu3N2HexoextnI1FFWBoip4dvN8lHpLNO873FmME7NOwlDncORac2EUjS3GVxOowf9KP0vsX5qoGxOCbs22kl4Yd3/veL23/21N4tUiWTCtb+feBKCWqbZsuH/yGBqm/BmKIy/qdbFuH9I/ngv7igeiPkuxNFbBX6RZ21m/I6oNUapIlVu0C6IBcsbQ1ASTJIFhsxDOGaNZs37/L4j1B1o5InVkJRz1736hvW+KoukYPltNREREvdoHX+/FsH4uDOvnSnUoRNTFvPTSS/B6vVi0aBFGjmzsATt+/HjMmTMHL774Iv7f//t/KY6w6xmbeSz+NP5ZvLrr3/jwwHst7iOrMtZXrcX6qrV4YdvfMShtMDLMmfi+egOCSlNbg4OeA1hVsRK51jwMSBuI1ZXfad5nYNog3HfcQ5pqWVVVUR+qR6W/HP/a/jy2NetN/+6+t3Bm/lT2liZCY6Vvc6oprVPPXxOojropdlbh2XAYHZ0aB8UW6nc6avNOhG39P2DZvAhQtMPrzTveg/HwGrgnPYJw9ug2veeE3Il4fc+rKPMdjqy9tucVHJN5XMp7wUf1f+8zHJBavrHbbQkiPKfcA+d7lwFHboDJIdi//SMapj7b6TfiYjnsOwxZlTVrfdmChoiIiKh72X6gFp+vPYi/vbsJby7blepwiKiLOXjwIDIyMiLJdwAYO3YsXC4Xtm/fnsLIujaLZMHVw36Oh0+ch6HO4Ufdf3fDLqypXKVJvjdX5juMleXfaNbSjGm4c8w9Ua0qBEGA0+TE4PShuHjQ5ZrXyn1lWFG2vJ1/G6KeSQxpq5bVTk58f3Tgfc1sCEmQcE7ReZ0aA7WR0QrvuF+j7tx/tziQVGw4hPSProXl+xcBVYk+XkcSDVFV8DvqtuH7mg2JijhuhsofNNs9qf1Mc3LmMPhHXKJZMx76FqZ9S1MUUcsOebXtZ5wmJxzGzr1ZmChMwBMREVGvFJYVvNUs6d43mxVXRKTVv39/1NXVobq6OrJWW1uLhoYGZGdzVsTRDHMW45ETH8dfT1uAa4fNxdjMYyAJUoffVxAE3Db6LmRbo1vdNDc6YyyGpg/TrL2193UobUgQEfV0+rYhqrnzkloBOYBPDn2sWZuU/xNkmvt0WgzUfnKf4ag/50V4xt8VNcgTigzb6meQ9smNELwVR32v0/POiGpX9vbeNxIZblz0A1jlPtE3HHoK33E3QLFladZsKx8Hgp4URRTtoEc3gLWb9n8H2IKGiIiIeqnlG0pQVusDAAzITcO4EbETOUTU+/z85z/HF198gdtvvx3/93//B0EQMG/ePBgMBlxxxRVtfh9RFCCKnfdItySJmv9PtXxHLmY6zsPMgefBE/JgXdUarCr/DmsqV8ET0v6iLwoCRmSMwkjXKHxXsRL7GvZGvd9VQ6/GcTnHtenccwZfjEfWPRTZLvEewuqqb3Fq3oQO/Z262jXuiXiNk0sMNWg6TQiWNBgMnXOtv6/drBnGLAgCLhz00047f2frWZ9lEfKYS+EeMAm2L+6BoUxbtW46vAquj66Fe+Y/odpzW30XA0z46aA5+Nvmv0TWNtVsxF7PLgxxtr/neiKuseCpgOQtB5r9d6Hmje6xn0sY0hE45Q7Ylv5fZEnyVcCxaQH8J/26xUM6+7Nc6tNWwPdL69dtvx5MwBMREVGvU+sO4NPvGisqREHA7EmDIXahfodE1DUUFBRg7ty5eOihh3D++ecDACRJwp///GeMHt22XrcAkJlpT0lf2/R0a6ef82gyYEffnLMxc8TZCCkhbKrchO9Kv0NADmB45nCMzx8Pp9kJALhR/QU2VW7COzvfwfJDy6GoCi4ZfgmuHnNlm6/nVNdP8Nq+xdhduzuy9u6BNzGj+KyEfE264jXuaXiNk0TxaXo921xZsGXYO+XUuw9u19yUHNlnJEb3PXq7qu6uR32WM4YCl78CfPtX4Nu/aVrPiJ5SuD69CbhkEWDLbPUtLkg/D6/vfRXV/qanzD4seQe/H/D7uMPq0DWu+lbb/9xkh3PAKEDs+JNbXdbxFwC73wf2fR1Zsm55FdbTrgccrRcnddZn+XCwRPO9YnjOEGR00vepRGMCnoiIiHqd91bsQSDcONDn1NF56JvD9jNEFO2pp57CX//6V5x00km46KKLIMsyFi9ejNtvvx1//vOfMWXKlDa9T3W1p9Mr4NPTraiv90GWu3a7lQHGYRhQ1NQmRvECNd6mqvi+hkG4ufh23DDsFgSVAGwGO2prvS29VavO7zcbT1bPi2xvr96Bz3cswwnZ4+KOuztd4+6K1zi50r11EH4cwCgKAjxhE4I1ndN6YtWh1VAUNbI9PG0kajrp3KnQoz/LI6+DlHEMbP+7B2Lz1jNVuyC/+jO4ZzwPxGhvdHbfmXh5+4uR7WUHluOHgztQYC9oVxiJuMbmPWtgUZs+l+HMYnjq/HG9V3cijrsTaftmA0eGnYYCCCx7Fv5TowfNd+ZnWVVV7K3dp/lekSHkdMnvFW25KcAEPBEREfUq2w/UYt3OSgCAw2LE2Sf3T3FERNQV1dfX44UXXsCoUaPw4osvQpIaK+DOOecczJ49G/fddx8mTpwIk8l01PdSFFXzC2RnkWUF4XBPSfaIMMEa199nXJ9TUGArRIn3UGTtlZ2LMNp5DCSxY78S96xr3DXxGieBqgJBN1S1qeA3bLB3ynX2y37srNupWStOH9UrvsY99bMczj4ewZmL4fz45xDr9kXWxcptsH1yC+rPehYwtFwxfWbeNLyx+7VISyJVVfH2njfxi+Kb4oqlI9fYWr4JzfLvCPUZ2SO/XlHs/eAfej7M296KLJm2vAHvqKugtNJGqDM+y5X+CvjD2hsg+ebCbvs16Z6Nc4iIiIjiEJYVvNls8OrM0wbAZmE9AhFF27t3L4LBIM4999xI8h0AjEYjzjvvPFRVVWHXrl0x3oG6CkmQMKv/bM3arvqdWLz75aSdc2X5N3hw3e/wzA9/Ql2wLmnnIYpLyAtNphGAauqcpwG3122FfKTSFoAoiBjuHNEp56bkUa2ZqJ/2VyiOPM26oWw90pbeAcihFo+zG+2YWjhNs/a/ks9QHahucf9kkpq1KgOAcJ/e87n0jb0WaH5DWgnDuvGfqQsIwCGPtv+7VbJ260HNTMATERFRr7F8QwnKmw1ePbGYg1eJqGVHKttVNbpyXVEUzf9T13d63hnItWoTQ+/texsry79J6HlkJYwXt7+AJ75/FN9Xb8Dyw1/gua1/OfqBRJ1IDLmj1lRT621CEumHmu8124PThsDaSnU0dS+KPQ/10/4O1apNkhoPfQvr9/9q9bhz+p0PSWi60S2rMj468F7S4myRHIDoLdcuOQd0bgwppDjy4R9+oWbNvP0diO6SFEUEHPQe0GwX2PumZJ5OojABT0RERL3GqAGZGNrXxcGrRHRUQ4cORU5ODt5++20EAoHIejAYxLvvvouMjAwMGzYsxjtQV2IQDbhl1G80SR4A+MuWp1DqTUyCoSFUj4c3PIAPdYmjrbWbE/L+RIkiBBuiF42dM9hwc+0mzfbIjLYPtKauT0nvh/qz/gJV1/fdsmkhBH9Ni8dkmjNxRv6ZmrVPD/0XnlDn9foWG0oA3f122VHYaefvCnxjrgEkY9NCiqvgD3m0Cfi+9r4piiQxmIAnIiKiXiM304Zfnj8Kt805hoNXiSgmSZJw3333YdeuXfjpT3+Kl156Cf/6178we/Zs7Nq1C/fccw+MRuPR34i6jGHO4bh62M81a76wF098/ygCcqCVo9pmn3sv/m/Vb/B99Yao17xy+4bGEiWbENRVwJvsgCi1vHMCBeQAdtRt16yNdDEB39PImUPRMOVpoFmdixDyxUzmntf/As22L+zFZyX/TVaIUaQGbbsT1eJq/O+iF1HtOfAP17ZrM+94D2LDoVaOSK6DuhY0hbZ+KYkjUZiAJyIiol5FEAQm34moTaZOnYp//vOfcLlcmD9/Pp566imkpaXhueeew3nnnZfq8CgO0wpn4PS8MzRr+9378NzWv7TYbqgtvir7Er9dfSfKfWUtvh5WwggpLfc/JkqFqAS8OTX93wVBQLGr9/TZ7k3COWMRGHyOZs2y5TWI7tIW9y+wFeLknFM0ax/sfw9hJZy0GJvTJ+Dl9O6d7I2Xb8zVgNRsuLwiw7rhhZTEwgp4IiIiom4kLCsIhdmnmYjic8opp+Dll1/G+vXrsX79eixevBgTJ05MdVgUJ0EQMLf4RvRzFGnWlx/+AvM2PoyGUH2b3yukhPD8lr/jz5seP2oFvS/siyteomQQQroWNOb0Tjnv5tofNNuD0obAZuhdVca9ie/YG6IHe67/R6v764dl1wZr8HXZl8kKT0PUJeCVXtZ+5gjVlg1/8RzNmnnXBxDrD7ZyRHLUB+tRr/v3uK+9qJW9uwcm4ImIiKhHW76hBPNeWYste6tTHQoREXUBFsmCO8bcDaukHfy4uvI73LHyVmyuaexRragK9jTsxrv73sQL2/6Od/a+iQ1V61AXrEOlvwK//t+v8dH+D6Lef2DaoKg1r9x5vYyJjia6Ar5zBrAe+W/riFFsP9OjKWkF8Bf/VLNm3vkBpNrdLe4/JH1YVEuiDw68G/fTSe0h6dqsyGndu9q6I3xjfgYYLE0LigLzznc7NYZDugGskiAhx5LTqTEkmuHouxARERF1T7XuAD797gACYRkvfLgFd19+PLJc1qMfSEREPVqBrRA3jfw1nvj+Uc16daAKv1/3WxzX5wTsqt+JumBti8dLogQV0U9XTS6YguuG3YArls3RJI38YX9C4yfqiKghrJ2QgA/KQWyv36pZ4wDWns839jpYtr8DHPkeqKqwrv0r3JOfaHH/c4rO0wzq3dOwG1vrNmOEa1RS44yqgE/rnRXwAKBa+8A/bBYsm1+NrJl3ftD4REMn1XEf1LWfKbAXQhK7dwqbFfBERETUY723Yg8C4cZeo6eOzmPynYiIIk7OOQX3HHs/0o3a9huqqmJt5epWk+8AICuyZtsoGnHjiFvwyxG3wCSZoqrrfRzESl2IGDWENfk94HfUb9P08xYEAcXOkUk/L6WWas2Eb9QVmjXTvv/BULGpxf1PyBqHHGuuZu3D/e8lLT4AgKqwAl4nMPR8zbboKYexdGWnnf+QbgBr324+gBVgAp6IiIh6qO0HarFuZyUAwGEx4uyT+6c4IiIi6mqO63MCHj/5aYzOGBv3e+Ra8/DwiY/jJwVTIms2g02zjzfMBDx1HUJIl4C3JL8HvL79zADHQNiN7P/eG/hHXwFVN2fAuubZFveVBAln9z1Xs/Zd5bco8x1OWnyCtxKQg5q13jqE9Qg5cxjkPsM1a+YdSb4R0oy+Ar7Q3v2/HkzAExERUY8TlhW8tWxXZHvmaQNgs3TvxxaJiCg5Ms2Z+N1xD+LSwVdCFKJ/Rc615uG03NMxNH0YTKJJ89rJOePx2Lj5UX3frZI2Ae9jAp66ECGga0HTCRXwP9Tq+r9njEn6OalrUE1p8I29VrNmLF0Fsf5Ai/tPLpiieYpIVVX89+CHSYtP0rWfgWSCau2TtPN1F4Eh52m2Tfu/AAJtH1TeEfoe8H3t3f+JBP4mSkRERD3O8g0lKKv1AQAG5KbhxOLuPbSHiIiSSxREXDhgDsZkjMUHB94DVBWjMsZgbOaxyLPlR/aTlTBKvCUo9R9Ev6x89JUGQZajBwRaDNoWNF62oKEuJKoC3pzcCviQEsL2Ol3/dw5g7VX8Iy6C9fsXIfhrI2umA8vhH3V51L42gx2TC6biwwNNFdeflyzBRQMvhVX3dFEi6BPwcloh0MLN2N4mMGg6bKvmA0daR8khmHZ9DORdG/vADvKFfaj0V2rWCtmChoiIiKhrOTJ4FQBEQcDsSYMhCkKKoyIiou5gqHM4bht9J24bcxfO6nu2JvkOAJJoQD9HEU7Nm4Cx2WMhtPLviy2qAt6XtJiJ2kvQ94BP8hDWnfXbEVJCmrVkD9WkLkYyI9hvombJdGB5q7tP73eOZtsX9uKL0qVJCU3U9X9Xenn/9yNUiwvBojM0a6btyW9DU+I9FLVWYCtI+nmTjQl4IiIi6lH0g1f75iT/sWoiIqLm9D3gfTIT8NR1CEFdC5okJ+B/aKH/u8PIn896G30C3lC2FkIrLU3yrPkYl32yZu3DA+9BUZWEx9ViBTwBAAJDtW1opMrNQMW2pJ7zkK7/e641FybJnNRzdgYm4ImIiKjHqK734/vd1QA4eJWIiFLHqmtBwx7w1JVEV8AnNxm+z71Hsz0ig9XvvVGoYDwgGZsWFAXGQ1+3uv85/bTJ3zLfYaytXJ3wuERdAl5J6/7tThIlVDAeii1Lu7jpraSeU18B3xMGsAJMwBMREVEPkpluwf+77DiM7J/BwatERJQy+iGsXibgqQvp7B7w1YFqzXa+tfu3k6A4GK0I5Z+kWYrVhmakazQGOAZq1paU/DfhYbECPgZRQnDwudq1Le8Bcqjl/ROgzF+m2e4p3y+YgCciIqIeJctlxc/PHYlxHLxKREQpoh8U6GcLGuoqlDCEkO7zmOQWNDW6BHymuU9Sz0ddV7BI24bGePCrVpO5giDg7H7a5O+6qjWo9FckMCCPZjAsAMjsAa/h17WhgbcahgMrkna+Cp82AZ9jzU3auToTE/BERETU4wiC0OpgPCIiomSzStoWNKyAp65CCHmiF5OYgFdVFdWBKs1ahjkzaeejri3YV5uAF4JuGMrXt7r/qbmna76fqqqKpSVLEhaP5NYN/BQAJa1nVFwniuLsj3DOWM2aaUfyhrGW+8s129mW7KSdqzMxAU9ERETd3trtFQiG5FSHQUREBIBDWKnriur/DiQ1Ae8ON0BWtT+jZTIB32up9hzIWcWaNdP+Za3ub5EsmJA3SbP2ecmSqM9UvKR67cBPxZYD9ICBn4mmH8ZqKFkFKOGEnyekhKKemMm2sAK+S1i8eDGmT5+OsWPHYubMmfjwww/bfOzHH3+MCy+8EGPHjsXkyZPxxBNPwO/3x32OjsRCRERE8dl+oBYvf7oNf1y0Fpv3Vh/9ACIioiTjEFbqqoRgg25BBIy2lndOAH3/dwBwmTKSdj7q+vRV8KaDXwKq2ur+UwunabarA1VYX7U2IbFED2Bl//eWBPvpnlwIeWCo2pLw81T5K6PWsi09o61ot07AL1iwAA888ACGDx+Oe+65Bzk5Obj99tvx0UcfHfXY1157Db/+9a/hdDpx991347TTTsMLL7yA3/72t3GdoyOxEBERUdsEAgF8/vmnmD//Cdx7773405+ewIJ31wCqihp3AG5f8gYCERERtRWHsFJXpa+AV00OIIlt+/TVrE6TCwbRkLTzUdcXLNJWtIv1ByHV7Wl1/4FpgzEobbBm7bNDnyQkFn0LGvZ/b5lq7QM5Y5BmzVC6KuHnKdcNYLUZbLAb7Qk/Typ02+969fX1ePbZZ3HuuefiySefBABcdNFFuPLKKzFv3jxMmzYNkiS1eGxNTQ3++Mc/YsKECXjuueci+9lsNrz44ou49dZbUVRU1OZzdCQWIiIiOjpVVfHaa6/g/fffgdvtQSgsQxBFWHLGwlHkgSB4kZMu4YThPaNHIBERdW8cwkpdlRDSJ+CTO4A1uv87q997OzlzOBR7DkRPU69v44HlkF2DWj1mSuE0PLf1r5HttVWrUR2o6vBAX7FeXwHPBHxrQnnjYKjdHdk2lq6Cf+y1CT1HhU/b/z2nh7SfAbpxBfzSpUvh9Xpx6aWXRtZEUcRll12G0tJSrFu3rtVjlyxZAo/Hg9tuu02TGL/kkktw4403Qpbldp2jI7EQERFRbKqqYv78x/HKK4tQWl6DkioPDld7UekWYM4/EbKiQpZl7Pz2VTz15yegxniElYiIqDPoe8B7w17++0RdghjQtqBRk1xdqm9B09GEKfUAgoCQrqVJrD7wADAhdyLMzXqzK6qCpSWfdTgUVsC3XSh/nGbbWLYekAMJPYe+Aj7b2jPazwDdOAG/adMmAMCoUaM06yNHjtS83pI1a9bA5XJFjvX7/QiHwxg4cCBuvfVWDBw4sF3n6EgsREREFNtrr72C5cuXobLOh1pPEP2GnoDJF96C8effCYvdBclgRNWBDSg7fBDLly/D66+/muqQiYiol7NK1qg1vxw9b4yos0VVwJvTk3o+fQuaDBMHsFJ0T3FDxUYI/ppW97cabDgtV3vM5yWfQlGV+INQwhDdpdolJuBbFc47AUCzdlVyEIby7xN6jnJ/z62A77YtaMrLy+F0OmG1an+wyc5ufPS8pKSk1WP37duHvLw8bNiwAQ8//DA2btwIo9GIGTNm4L777oPD4WjXOToSi54oChDF5PVf05MkUfP/pMXrExuvT2y8PrHx+sTG69MoEAjggw/ehdsXhD8o4/Rzfo7CgaPhDYRwqMILSZJgMtowZPBgfLV1Kdy+IN5//x3Mnv1TmM3mo5+gh+LnJzZeHyJKNv0QVgDwyb4W14k6U1QP+KRXwGtb0GSamYAnIJR/ImCwAOEfb0yqgOnAlwgMPa/VY6YWTsPSkiWR7Up/BTZWr8exfY6PKwbRfRhQZM2azCGsrVLN6ZCziiFWbY2sGUtXIZx/YsLOUeHruRXwXS4Bf/DgwZivp6Wlwel0wuPxwGKxRL1+ZM3na73HXn19PdxuN6699lrMnj0bc+fOxdq1a/HSSy+htLQUCxcuhCAIbT5HR2LRy8y0Q0jiAJTWpKfzB8FYeH1i4/WJjdcnNl6f2Hr79fn44+UIBHxw+8MYNPIk9B96DFRVRUXtkSpCATmZdrgcY1Ey4iQc2LEagYAP69atxNlnn53S2LuC3v75ORpeHyJKFv0QVgDwhb0Ak4+UYkJQ14LGnNwe8DVBbVVzBv8bIACQzAgWjodp3xeRJUPZupgJ+MFpQ9HfMQD73Hsja58d+iTuBLy+/YxqskM1u+J6r94iXHASjLoEvA+/TNj7R1fAMwGfNGeeeWbM16+//nrccccdUBSlxUT1kbVYSexgMIiysjLccsstuOmmmwAAU6dOhcPhwNNPP41ly5bhjDPOaPM5OhKLXnW1p9Mr4NPTraiv90GWO/DoTg/F6xMbr09svD6x8frExuvT6JtvvoPHG0QoJKP/8JMRDsuo8wQRDMkABFhMEuxmCeGwjAHFJ2P35pXweIP45pvvMH78xKO+f0/Fz09sqbo+GRnJrTIkoq7DIBpgFI0IKaHImk/2pjAiokZRFfCdPYSVLWjoR+Gc4zQJeKnZgM+WCIKAKYXTsGDbPyJrqyu/gzvkhsvQ/lZKYsMBzbaS1hdIQUFsdxLOHwd8vzCybajcBIS8gDH6pnN7hZRQVMuqbLagSZ558+bFfH3YsGEAALvdDr8/uofekWpzu731X3COtIqZM2eOZn3WrFl4+umnsXLlSpxxxhltPkdHYtFTFBWK0vnDeWRZQTjMX9Bbw+sTG69PbLw+sfH6xNbbr4/b7Yb847+LDmcfqKr6Y/K9UZaz8WkzVVXhcDYO9ZIVFW63u1dftyN6++fnaHh9iCiZbAYb6oJ1kW1fuO1PRhMlS1QPeJMjaeeSVRl1wVrNGoew0hFyxmDNtqFmJ6AqgNB6i8DTc8/ASzsWIKyEG99DlbGq4ltMLTqr3eeXGvQDWNl+5mjCeccBogGQf7y5rMgwlq1DqO9pHX7vKn9l1Fo2K+CT5/zzz2/Tfvn5+airq0MwGITJZIqsl5c3Pq6Qm9v6XZLc3Fxs374dmZnaO699+jT+Q+DxeNp1jo7EQkRERK2z2ewQf6xEcddVwmp3IstpQWa6BYGQAoup6Qd0d13jD22iKMBmY6UxERGllkWyog5NCXhvmBXwlHpRLWiMyUvA1wfrooZkZpgzknY+6l7CGUN0C36I7lIoMRLhdqMdx2Yej9WV30XWvilfEVcCXmzQtsDmANY2MNqA/GOAg6ublg6vTkgCvtyv7f9uM9hgT/KMis7UbSdPjRo1CqqqYsuWLZr1I9tjxoyJeSwA7Ny5U7N+pP98fn5+u87RkViIiIiodaNHj4HJKEGSROze/G1kXRIFpNtNmjZvu3/4FgZJhMkgYfRo/ttLRESppR+4yhY01BVEtaBJYg/4moC2/7soiEg3OZN2PupeVGtW1OdPqt111ONOzT1ds72xegMagvXtPr9Ur03AswK+jYrGazaNpd+1smP7VPj0/d97VjFzt03AT5o0CWazGS+//HJkTVEULF68GIWFhTj22GNbPfacc86BKIp4/vnnNesLFzb2MTrSh76t5+hILERERNS6CRMmweGwI81qxN5tq3Foz6YW9zu0ZxP2bl8Nh9WItDQHJkyY1MmREhERadkkbeUeW9BQVyDqWtAgiRXw+v7vTpMLkiAl7XzUzQgCZJe2DY1Us7OVnZucmDUORtEY2ZZVGSvLv41xRAtUFaJuCKuS1q9979Fb6RLwUtVWCIH23wDR01fAZ1t7TvsZoAu2oGmrjIwMzJ07F8888wxUVcX48ePxySefYPXq1Zg/fz4kqemb+meffQYAmDJlCoDGPvLXXHMNFixYAJ/Ph4kTJ2LVqlX44IMPcOmll0b6zLf1HO2JhYiIiNrObDZj5sxZeOWVRQiEZKz4aAEGDDsRg0adAlefXNRWlWH3D99g7/bVsJokpNlMmDlzFsxmc6pDJyKiXo4V8NQVRbWgSWYFfFA7UJH930lPzhgCQ9n6yLah5ugV8FaDDcf3ORErK76JrH1V9iXmjLmgzecVArUQgh5tLA5WwLdJ/rFQJRMQDjZuq4Dh8BqE+v+kQ29b7u/ZFfDdNgEPADfddBOsVisWLVqEJUuWYMCAAZg/fz5mzJih2e+RRx4B0JSAB4A777wThYWFWLRoEb788kvk5ubijjvuwHXXXRfXOdq6HxEREbXPRRddikOHDuLLb1bBNuwCNFTsxcov3oG35gBUVYVBEuGym5BmM2HixEmYM+eSVIdMREQEq8Gm2WYPeOoKolrQJHEIa01Am4DPMLH/O2mFXYPRvGymLRXwAHBK7gRNAn5j1QbUBerQ1jSnfgArRAmKI69Nx/Z6BhPk3GMhHWpqPWMsXdXhBHyFjxXwXZYgCLjuuuuikuZ6S5cubfHYyy+/HJdffnlCztHW/YiIiKh9BEHAbbfdCdH5NjaUGJHZdzTMgh8+sRqqosBokOBw2HHeeRdgzpxLNH3hiYiIUsUmaRPwbEFDKScHADmkWVJNyauAr9K1oGEFPOnJGboWNHV7Gz+jkrHlA350QtY4mEQTgkpjFbaiKlh+cDkmZE5u03lFz2HNtmLPBcRunSLtVOGCk7QJ+MOrOvye0RXwTMATERERdSpBEJBZOBIubwWCwSCKR+TDOnISRNGIkSNHY8KESWw7Q0REXYrFYNFse9mChlJMX/0OAGoSe8DrK+AzzZlJOxd1T3LGEO2CEobUcACya1DM4yySBSdkjcM35V9F1r448EWbE/CCT/vZVKxZbTqOGoULxumeXNgNwVcF1RrfTbawEo76fpHNFjREREREnUtRVGw7UAsIApxpdtx9wy+R1ScNNTUehMNKqsMjIiKKoq+A97MCnlKsxQR8J7agYQU86almJxRbFkRvZWRNqtl11AQ8AJySM0GTgF9fsR61gVo4pPSjHiv6a7RxWNgeqT3krJFQjTYIoaYby4byjXG3oan0V0StZfewCngx1QEQERERHc3+8gZ4A2EAwLB+Lkgif4QhIqKuzWawa7Y5hJVSLSoBLxkBQ/KeINQPYc1gBTy1QF8F39Y+8MdnnQiz1PT5VVUV35Z/3aZj9Ql4hQn49hENkPsM1yxJdXvifrsKXfsZm8EGu9Heyt7dE397JSIioi5v677ayJ+L+7tSFgcREVFbWQ1WzTaHsFKqiaEGzXYyq9/DShh1wTrNGhPw1BLZpesDX9u2BLxZMuOErJM0a18d/rJNxwpMwHeY7Byo2Zbq9sb9XuV+7QDWnB7WfgZgAp6IiIi6ga37m35ILi7iD8hERNT1WSQm4Klr0VfAJ3MAa22wJmqNPeCpJfoKeEPNrjYfe2rOBM32DzWboloftUT0a/dhC5r2k126BHxtByrgfdoK+CxLdtzv1VUxAU9ERERdmscfwv6yxoqt/EwbXA4OWyUioq7PZtD1gJfZA55SS5+AV5I4gLValwSVBAkOQ/IS/tR9hXUV8GLDASDsb9Oxx/U5HhapaeC1qqpYVbHyqMdFVcBbeXOovWTnAM22VLcXUNW43qtc14Imx8oKeCIiIqJOtX1/LY78KFfcn9UpRETUPVh1FfA+DmGlFBOC+hY0yUuItzSAVRCEpJ2Pui/ZNQho/tFQ295P3CSZcULWOM3axpr1Rz2OQ1g7Tt+CRgh5IXrLW9k7tgpdC5qeNoAVYAKeiIiIuridh5r6h7L9DBERdRf6IaxBJYiwEk5RNEQttaBJZgV8lWab/d+pVUYrlLS+miWpHW1ojsk8TrP9Q833kFW59QMUGUKgVrtk5u8Y7aXY8wCDRbMWbx/4ch8r4ImIiIhSavakwbj1p2MxbVwRBhWkpzocIiKiNrHqWtAAbENDqRVVAZ/EFjQ1QX0FPBPw1Dr9IFZDbdsT8GMyj9Fsu0Nu7G1ovYJeCNYDuk4pbEETB0GA7BqgWYqnD3xYCUfdsGMFPBEREVEnE0UBA/LSMf3kIhgk/uhCRETdg1WyRK1xECulkhDSVcCbk9mCRtvigxXwFEs4Q5uAl2p2tvnYLEs2CuwFmrXvqze0ur++/QwAqGZXm89HTfRtaNraOqi5Sn9F1FqOhRXwRERERERERHQU5hYS8D5WwFMKifoWNEkdwqqtaM009Unauaj7k11DNNvtaUEDAMf00bahidUHXvBpn85QTQ5AMrbrfNQoehBr+xPwFboBrDaDDXajvZW9uy8m4ImIiIiIiIgSTBTEqDY0PlbAUwpFD2HtvBY0GeyxTTHIugp40VMW9XmNZayuDc3W2s0IysEW9+UA1sSJqoCPowVNeS8YwAowAU9ERERdlC8Qxj/e+wFfrDuE8lpWDBIRUfdjlayabbagoVTSD2FVTMlrQVPt11XAm1kBT62T0/sDoqRZa08V/OjMsRAEIbIdUkLYVrelxX0FXQJeYQI+brJLm4AXfNUQAvXteo8K3QBWJuCJiIiIOtHOQ3XYur8G7361B19uKEl1OERERO1m01fAswUNpVBUD/gkVcAH5QA8YY9mjT3gKSbJCNnZX7tU2/Y+8A6jA8MyhmnWWusDr6+AVyz8bMZLTusXfeOkbm+73qNc14Imx9rz+r8DTMATERFRF7V1X9MPx8X9WZlCRETdj74Cni1oKJWiWtAkqQd8ta79DABkmJjkpNhkl34Qa/v6wJ+Qe4Jme2NN2xLwqsXVrvNQM5KxMQnffKmdfeAr2IKGiIiIKDVUVcXW/Y0/HBtEAUMKnSmOiIiIqP2sBl0CnhXwlCqqCiGkrUpXk9SCpiagTXCaJXPU0yBEenKGdhCrobZ9Cfjjc47XbO9u2Am37qkPABD82htEbEHTMVGDWNvZB77cxwp4IiIiopQor/WhuiEAABhU4ITZKB3lCCIioq6HQ1ipywj7AEXRLCWrBU11QNv/PcOcqenPTdSSsC4BL1XvBFS1zcePzhoNo2SMbKuqih9qvo/aL6oC3sqnMzpC3we+PS1owko46vtFtiU7EWF1OUzAExERUZfD9jNERNQTWCVtAp5DWClVRF37GSB5CfiagLbCONPEAax0dPoWNEKgLqptUiwmyYQRrpGate9baEPDIayJFVUB344WNJX+iqi1HEteR0PqkpiAJyIioi5n6/7ayJ+Li1wpi4OIiKgjooewMgFPqSEEo1txqEZ7Us5Vo+sBn2FmgpOOTrFHJ15Fb3kLe7bumD7HabY3Vq+Pfs+oHvD8fHaE7Bqk2RYbDgFyoE3HlvkOa7ZtBhvsSfq+lGpMwBMREVGXEgrL2HWoDgDgspuRl8meoURE1D3ph7D6w/4URUK9naDrha0arYBoSMq5qvUV8GZWwFMbSMaogaiip6zlfVtxTOYxmu1Sb4m2ylqRIQRqNfsoFrag6Qh9BTxUFVLd/jYdW+bXJuB7av93gAl4IiIi6mJ2HqpHSG7sUVrc38WeoURE1G3ph7B6WQFPKSIEtK08VGNy2s8A0S1oMsxMcFLbKHZtAlb0RrcoiWVA+iDYDdoK6u+rm9rQCMF6QNdWni1oOshog2LP0Sy1tQ1NuU97gyW3h7afAZiAJyIioi5G0/+9iD8QExFR96Ufwsoe8JQqURXw5rSknSuqBzwT8NRGik2byG1vCxpJkDA6c6xmbWOzPvCir1p/CFSzq13noGiyUzeItbZtCfgKn/brywp4IiIiok4y+fhCXDx5CI4ZnIVh/VypDoeIiChuNok94Klr0PeA79QKeBMT8NQ2ii1bs93eFjQAMDbjWM32DzXfR/6sH8CqmhyAZGz3OUhLdukS8G2sgC/za7++2ZacVvbs/pLT8IuIiIgoTk6HGeNH5mH8yJ77CCIREfUOFl0PeB8r4ClFhKCuBY0pOQl4X9gLn+zTrLEHPLVVR1vQAMDIjNGa7ZpANeqCtXCaXBD92ptDHMCaGPFWwJfrhrDmWnvu73+sgCciIiIiIiJKApuuBY0v7GtlT6LkEvUV8KbktKCpC9ZFrTlNrqSci3qejragAYB8WwFMkkmztqdhN4DoCngOYE2MqAr4+n2AIsc8xhf2oSGkvTHIFjRERERERERE1C76HvA+2QdVVVvZmyh5onrAJ6kC3h3WJtQMogEWyZKUc1HPE92Cpv0JeEmQ0N+hTQgfScCL/lrt+VgBnxCyc4BuIQTRXRrzmHJ/dHuhnB7cgoYJeCIiIuoSwrKC91bswdZ9NQiFY1dMEBERdQdWg7YFjaIqCCrBFEVDvVlUC5ok9YB36xL9acY0CIKQlHNRz6OvgBcCdYAcaPf7DHQM0mzvjSTg9S1oXO1+b4qmWjKjBjsfrQ98uU+bgM8wZ8AkmRMeW1fBBDwRERF1CXtLG/C/9Yfwj/d/wOv/25XqcIiIiDrMJtmj1rxhTwoiod5OP4RVSVILGreupYTDkJzzUM+k2KMroEVP+/vAD0jTVcC7W2lBY2ULmoQQhOg+8EdLwEcNYO257WcAJuCJiIioi9iyv+kH4uFFrtQFQkRElCD6CngAUQMqiTqDPgGfrBY0+p7O9iRV2lPPpBodgEHbsiiePvAD07QV8KXeEvjCPoi6BDyHsCZOewex6ivge3L/d4AJeCIiIuoitu5r/IFYADC8iD8MExFR92cUjZAESbPGQayUCoIuMZ6sIazRFfBMwFM7CEJUFXw8Cfgi+wCIgjbluc+9B4KuBQ2HsCZO9CDW/TH31yfgc1kBT0RERJRcdZ4gSqoaH8nvm+OAw2pMcURERESJYdMPYg17UxQJ9WZRFfDJ6gEf1p7HYWQLGmof2aZNxMaTgDdJJhTa+2rW9jTsjhrCygr4xFHsuq+bryrm/voWNKyAJyIiIkqybc3az4xg9TsREfUgUQl4mQl46mSKDNGnGz5pTk/KqfQV8GlMwFM7KbZszbboaX8CHmhhEGv9DgiBWu25mIBPGMXaR7Otf9qgOVVVo1vQsAKeiIiIKLmOtJ8BgOL+/EGYiIh6DqukTcB7WQFPnUz0HAaUsGZNTu+XlHNFV8CzBQ21j2rreAsaILoPfHntNkDV7sMEfOJEJeCDHiDsb3HfhlAD/LL2NVbAExERESWRoqjYdqAWAGA1GVCUy1/UiIio57DoBrFyCCt1Nql+n2ZbNTmgml1JOZd+CKvDwAp4ah85AT3gAWBA2mDNdkP9Xugz8Mn676A3UnUJeABRT94cUe4/rN1PENHHHH18T5LwBHxFRQXC4fDRdyQiIiICsL+8Ad5A488Ow/q5IImsDyAiop4jugc8E/DUucT6A5ptOb0IEISknMsT0lbA21kBT+2kRFXAV8T1PgMc2qGgaeEgZFWObKsmByBx7lSiqMbo6yn6Klvct0zXfibLkg1JNCQttq4g7t9wt2/fjvvvvx+KogAA9u7di7PPPhsTJ07E+PHjsXjx4oQFSURERD3X1n21kT+PYPsZIiLqYaySrgKeLWiok0l12gp4xVmUtHOxBzx1VIsJeFVp9/s4jA5kW5reK10OaxPw1sz4g6RogtDmPvAVPu1TDT29/zsQZwL+hx9+wJw5c/Daa6+htLQUAPD73/8ee/bsQVFREQwGAx566CEsW7YsocESERFRzzOifwYmHVOA3AwbiotcqQ6HiIgooTiElVJNqt+v2ZbTkpOAV1U1ugc8W9BQOym6FjRQZAittDI5muZ94NNlGeFmsxAUMwt/Ek2fgBd9VS3uV6ZrQZNjzWlxv54krgT8P/7xDyiKgsceewx5eXk4fPgwvv32W4wdOxb//e9/8fHHHyMrKwsLFy5MdLxRFi9ejOnTp2Ps2LGYOXMmPvzwwzYf+/HHH+PCCy/E2LFjMXnyZDzxxBPw+7VDANxuN/7whz9g4sSJGD16NCZPnoz58+cjGAxq9vv8888xfPjwFv+3ffv2hPxdiYiIeqL+eWmYdfog/N/lx8PpMKc6HCIiooSyGjiElVIrKgHv7J+U8/hkLxRdpTKHsFJ7qZZMQNeSMv4+8E0JeKeuAl6xMgGfaKpF+1RBawn4cp8uAd8LKuDjarCzdu1aTJs2Deeddx4A4MsvvwQAnHvuuRAEARkZGZg6dWq7kuHxWLBgAebNm4fp06fj6quvxpIlS3D77bdDEATMmDEj5rGvvfYafve73+HUU0/F3Xffjc2bN+OFF15AaWkpnnzySQCNd29vvvlmfPfdd7joooswfPhwrF+/Hv/4xz+wY8cO/PWvf428344dOyCKIh599FEIul5q+fn5if/LExERERERUZdn0beg4RBW6kxyCKK7RLuUnpwKeLeu/zvABDzFQZSgWLMgepqS7qK3AnKMQ1oz0NG8Aj4MWQ2jcRCrANXCBHyiRVfAt/zkgr4HfI41L2kxdRVxJeDr6upQWFgY2V6xYgUEQcApp5wSWbNarVFV4olUX1+PZ599Fueee24kYX7RRRfhyiuvxLx58zBt2jRIktTisTU1NfjjH/+ICRMm4LnnnovsZ7PZ8OKLL+LWW29FUVERPvvsM3zzzTe47777cPnllwMALr30UuTl5eHvf/87Vq9ejRNPPBFAYwK+b9++mDVrVtL+zkRERERERNS9cAgrpZLYcBBQVc2akt4vKefSJ+BFQYRVsrWyN1HrFFuONgHvKYuxd+sGpQ+O/Nkph6ECUFQFoiBBYQI+4aJ6wLcwhFVWZVT6tYN1c609vwI+rhY0eXl5kd7v4XAY3377LbKysjB06NDIPps3b0ZubvIu4NKlS+H1enHppZdG1kRRxGWXXYbS0lKsW7eu1WOXLFkCj8eD2267TZOkv+SSS3DjjTdClhvvq3333XcAEJVUP/vsswEA69evj6xt374dgwcPBhEREbWNqqr4bksZahoCqQ6FiIgoaWwSe8BT6kgNBzTbqjUDqik5fdndYe0AVofBEdUhgKgt9H3g421Bk2HKRLoxHUBjBTyASBsafbsU6ri2VMDXBKo1rYAAaIbl9lRxVcAfd9xx+PTTTzF+/HisX78e9fX1kUS42+3GokWL8O2330aqxpNh06ZNAIBRo0Zp1keOHBl5/Uh1ut6aNWvgcrkix/r9fhgMBgwcOBC33nprZL+bbroJF154Iex2u+b4mpoaAIDB0Hj5wuEw9uzZg4kTJwIAAoEAJEmKvE5ERETRSio9eOXzHQCA00bn46dn8EY2ERH1PFaDtgUNe8BTZ5Lq9mm25fTk9H8HgIaQLgFv5ABWio9i0yfgK1rZMzZBEDAgbRA2Vq9H+o/FtmE1DCNMrIBPAlWfgPdH94Av17WfMYkmuEw9/2sRV4b417/+NdasWYPf/va3UFUVmZmZ+MUvfgEAeOKJJ/Dqq6+isLAQ119/fUKDba68vBxOpxNWq/aHmezsbABASUlJS4cBAPbt24e8vDxs2LABDz/8MDZu3Aij0YgZM2bgvvvug8PR2KPM5XLB5XJFHf/KK68AAI4//ngAwN69exEKhbB3716ce+652LlzJ4xGI6ZOnYp7770XmZltv6smigJEsfPuEEuSqPl/0uL1iY3XJzZen9h4fWLrDddn+8E6HPkXryDLDoOh7X/X3nB9OoLXJzZeHyLqTPoWHGxBQ50pagBrktrPAIBH14LGzv7vFKeoBHycLWgAYEDaQGysXg/nkQp4RQYksAd8Eii6pwqEFoawlvu1X8tsa06veFImrgR8QUEB3njjDXz00UdQVRXTp09HVlYWAOCkk06C0+nEz372s3Ylno84ePBgzNfT0tLgdDrh8XhgsViiXj+y5vO1/kNNfX093G43rr32WsyePRtz587F2rVr8dJLL6G0tBQLFy5s9Yv/zjvv4JNPPsGpp56KsWPHAmhsPwMAGzduxNy5c5Gfn481a9Zg4cKF2LFjB15//fUWY21JZqY9JR+89HTr0XfqxXh9YuP1iY3XJzZen9h68vXZWVIfuek8/phCZGS0v0doT74+icDrExuvDxF1Bn0FPFvQUGeKTsAnrwJe34LmSOsPovaKbkETXwU8AAxKGwxRVeFQGivgGwexRieLqeMUW5ZmWwj5gJAPMDb9O6ivgM+x9Pz+70CcCXgAyMjIaLHFzIwZMzBjxoy4AzrzzDNjvn799dfjjjvugKIoLSaqj6zFSmIHg0GUlZXhlltuwU033QQAmDp1KhwOB55++mksW7YMZ5xxRtRxn332Ge69915kZ2fj0UcfjawPGjQIN954Iy644AIUFTVOE58yZQr69++P+++/H2+88QauuOKKo/7dAaC62tPpFfDp6VbU1/sgy0qnnbe74PWJjdcnNl6f2Hh9Yuvp18cXCGPbvmooiooslxUmQUVNjafNx/f069NRvD6xper6ZGTYj74TEfU4+gr4gByArMqQBKmVI4gSR9Ql4JX0oqSdSz+E1cEKeIpTdAua+HrAA8AAxyCkyXLkyVsFKlQoUCyu+AOkFrXUV1/0VUEx9o1s6yvge8MAVqADCXigsZL8o48+wubNm1FXV4ennnoKa9asgSAIkfYs7TVv3ryYrw8bNgwAYLfb4ff7o14/Uvmu79ve3JG2NXPmzNGsz5o1C08//TRWrlwZlYB/9913cc8998DhcOCFF15AXl5e5LXi4mIUFxdHnWf27Nl46KGHsHLlyjYn4BVFhaKoR98xwWRZQTjMX9Bbw+sTG69PbLw+sfH6xNZTr8/WfTWQf/z3bng/V9x/x556fRKF1yc2Xh8i6gw2Q/QTXv6wH3Yjb8pRkoV9ED3axKWcxAR8VA94A3vAU3z0CXgh5IUQdEM1tf+mTr6tANnQth0MK2GoZldHQqQWqEY7YDAD4UBkTfRXQUlvSsCX6Srgs1kBH9uSJUtw9913w+PxQFXVSMX5smXL8Pzzz+Oaa67BXXfd1e73Pf/889u0X35+Purq6hAMBmEymSLr5eWN/7jk5rb+BczNzcX27dujWuT06dM4LMDj0VbgLV68GA8++CBcLhdefPHFFpPtLTEajUhPT4fXy0cMiYiImtu6ryby5+L+7L9IRF1XdXU1/vSnP2Hp0qXw+/0YOXIkbr/99rgLjqj30begAQCv7GUCnpJOqo9u8Ss3S4QlmluXgGcPeIqXYsuOWhO95ZDjSMCLgoghJm1rFL/BBEjGuOOjVggCFEsmRHdpZEnU9YGPakHTSyrg45o8tXHjRtx2220wm8349a9/jXPOOSfy2sknn4yCggL861//wieffJKwQPVGjRoFVVWxZcsWzfqR7TFjxsQ8FgB27typWT/Sfz4/Pz+y9s477+CBBx5AdnY2Fi1a1GLy/fHHH8eUKVOiEu21tbWorq5GYWFhO/5mREREPZuqqti6vzEBbxAFDCl0pjgiIqKWud1uXH755fj4449x6aWX4tZbb0V5eTmuvvpqbNu2LdXhUTdhkaIT8L4wi7Qo+aT6fZptxZ4LtHBDKFHcYV0LGgMT8BQngwWqWTtDoCN94PuK2ieR3AZTK3tSR0X1gW+WgA8pIVQHtAn53tKCJq4E/F//+lfYbDa8+eab+MUvfoGBAwdGXjvttNPwn//8By6XC//+978TFqjepEmTYDab8fLLL0fWFEXB4sWLUVhYiGOPPbbVY8855xyIoojnn39es75w4UIATX3od+7cid/97nfIzMzEyy+/jMGDB7f4fgUFBThw4ADefPNNzfpf/vIXAMDMmTPb/fcjIiLqqcprfahuaHwscVCBE2Yje+ASUdf0/PPPY8+ePfj73/+OX/3qV/jZz36GRYsWQRAEvPDCC6kOj7oJURBhkSyaNb/sS1E01JtIddoEfDLbzwCAR9cDPs3IFjQUv6g+8J74+8DnQZtwrxX5+0ey6PvAi77qyJ8r/dE3UXIseVFrPVFcLWjWrVuHadOmafqgN5eVlYWzzjoLn376aYeCiyUjIwNz587FM888A1VVMX78eHzyySdYvXo15s+fD0lq+o/ps88+A9A4GBVo7CN/zTXXYMGCBfD5fJg4cSJWrVqFDz74AJdeemmkz/zTTz+NYDCI008/HRs2bMCGDRs0MQwfPhzFxcWYM2cO3nzzTfzxj3/Enj17MGTIEHz99ddYsmQJLr74YowbNy5p14GIiKi7YfsZIuoOVFXF22+/jTPOOEPz83x2djbuuusuGI18dJ3azmqwwi83zTDzsgKeOoHYcECzrTiTm4B3h3U94JmApw5QbNmQapo6V4jeshh7x9ZHV39cJaroHXXXnU+x9tFsi77KyJ/17WfsBnuvaccWVwLe5/PB4Yj9KJHZbE567/ObbroJVqsVixYtwpIlSzBgwADMnz8fM2bM0Oz3yCOPAGhKwAPAnXfeicLCQixatAhffvklcnNzcccdd+C6666L7LNq1SoAjQNY33333ajz33zzzSguLobJZMKCBQswf/58fPLJJ3jttdfQr18/3H333bjqqquS8VcnIiLqtjLSLBjez4Vdh+pRXORKdThERC06ePAgysrK8POf/xxAY0Le6/XCbrfj8ssvT3F01N1YJRtq0HQDmgl46gydWQGvqmr0EFb2gKcOUOzaFHlHWtBkyopmuwLhuN+LYtMn4IVmFfBl/sOa13pL/3cgzgR8UVER1qxZ0+rrqqpi1apV6NevX9yBtYUgCLjuuus0SfOWLF26tMVjL7/88pg/PH/zzTdtjiUjIwMPPvggHnzwwTYfQ0RE1BuNHdwHYwf3QTAkw2iIqxseEVHS7dvXmLjKysrC448/jv/85z9oaGhAUVER7r77bkyePLnN7yWKAkRRSFaoUSRJ1Pw/JV57r3GayQE0y7l75HoY+G9gTPwcd5yh4QCE5t96XP2jPneJus4B2Y+wok1qOi3p/JyDn+W4peVqPr+Sr6LVz9PRrnG6EobcbLtSUOFXvbxJ1E5t+SwL9izt1y1QFfm6Vfi1FfB5trxe8z0irgT8jBkz8Mwzz+Dpp5/Gr371K81r4XAYTz75JLZu3YqbbropIUESERFRz2Ni73ci6sLq6+sBAE899RQkScI999wDURSxYMEC3HTTTViwYAFOPfXUNr1XZqYdgtB5Cfgj0tOTN2yRGrX1Guen52FH/fbItkeoR0ZG73jsvqP4OY6Tvx4I1KJ5JiytaATQyueuo9e5wuuNutHYLzsPaSZ+zo/gZ7mdsvtpPr/mYCXMR/m+2do1VoQQqpttNxiM8Bvr0S+j91RgJ1LMz3J2oebrZgrWwvTj160yXKb5PjGoz4Be829hXAn46667DkuXLsXf/vY3vP7665H+h7/4xS+wZcsWlJeXY/jw4UetTCciIiIiIuqKgsEgAKCurg6ffPIJnE4nAGDy5MmYOnUqnnzyyTYn4KurPZ1eAZ+ebkV9vQ+y7rF7Soz2XuM0wQVFUSPbB2pKUFPjSWaI3R4/xx0jVWyBQ236zEEQUadkALrPXaKu88GGMs1nXBAEBN1AjcDPOT/L8TGo6bA3+wwrdYfR0Mr3zaNdY4e7FiJEKGh8rV4UsbNsL7JQkJzge6i2fJYlxaH53qO6K1Bf7QYEAftrD2i+T2SI2T3i38K23ESIKwFvNpvx8ssvY/78+XjrrbdQUdHYh2nZsmWwWCy46KKLcNddd8Fq5d09IiIiarK/rAF9sx2dmogiIoqHzWYDAJx11lmR5DsApKenY/LkyXj77bfhdruPOhsLABRF1fzC2VlkWUE4zGRPMrX1GmeatD1xK3wV/Nq0ET/H8RGr96J5/l1JK0BYlYBWrmVHr3Otv16zbTfYociIJDyJn+X2Ui05ms+w4KtCOBAApNaHoLd6jf11EAURitr4mkeUcNhzmF+POMX6LCtGl+brhpAfYZ8bitGKUm+pZt8cc16v+RrElYAHAKvVinvuuQf/93//hz179qCurg52ux0DBw6EyWRKZIxERETUA1TV+TH/9Q2wmQ04fWwBpp+cvEFgREQdlZvb+Fh6ZmZm1GuZmZlQVRU+n69NCXiiLEu2ZrvSH/8wQaK2kOoPaLaTOYAVANy6Aax2A783UscoNu33TaiA6KuE4shv93uJwQaIggj8mBh2SwaU+8sTECXp6YewAoDor0aFYkFICWnW86zt/1p2V3En4I8QRRGDBw9ORCxERETUg23dXwMA8AbCkFgBT0Rd3NChQ2EymbBz586o1w4ePAiz2dxicp6oJX3MWZrt6kAVFFVpTAgRJYFUv0+znewEvCfk1mynGdOSej7q+VSzs7HaXW5K2ore8vYn4OUQEPZrvt96RBHlvrIYB1HcjDbAYAHC/siS6KvE4bD23zuLZIHLlNHZ0aVMXAn4p556qk37CYKAW265JZ5TEBERUQ9zJAEPAMX9e88PW0TUPdlsNkyePBmff/45duzYgaFDhwIADhw4gKVLl+InP/kJJInDpKlt+ugq4GVVRm2wFplm3sSh5JDq92u2k14BH9ZWwDuYgKeOEgQo9lyI9QcjS6Kn/VXrQrDxsykJTf9me0QJFayATxrF2gdiw6HItuirRmkoqNknz5qfkgH1qRJXAv5vf/sbBEGAqkb3MTxy8VRVZQKeiIiIAABhWcGOg3UAAIfViMLs3jHtnoi6tzvvvBPfffcdrrrqKlx11VUwGo1YuHAhzGYzbr/99lSHR92Iy+SCJEiQVTmyVuWvYAKekkNVIeoS8Iqzf1JP2aBrQeNgCxpKAMWWrU3Ae+NJwDfOJ2heAe+WJAR85ZHcJSWWPgEv+CpRatA+JZNv610DcONKwD/66KMtrnu9XuzduxfvvfceiouLcc8993QoOCIiIuoZ9pY2IBBqTDoU98uAyB90iagb6Nu3L1577TU8/vjjWLBgAVRVxQknnIA777wTAwYMSHV41I2IgohMcx9NxWWlvxJDncNTGBX1VIK/BkLQo1mT05LdA16bXHMYmYCnjlNsOZpt0VfZ7vcQAz8m4NGYgA8KAsKCiLDsgyfs5tMaSaBYtTeXRV81SiXt1y7P1nv6vwNxJuAvuOCCmK9feeWVuOCCC/Ddd99h+HD+QEFERNTbbdG0n3GlLhAionbq168fnn766VSHQT1AliVbk4CvCnAQKyWHvv87JCMUe25SzxndgiY9qeej3kGxaNtWCv6aVvZs3ZEWNEcq4D1iUyuacl85E/BJoFq1c09EXyVKxRLNWr61d1XAJ2XiS1FREaZNm4ZXXnklGW9PRERE3czWfY0/LAsAhhex/zsREfU+fSzahESlv/2VnERtYaj4QbMtp/UFxOTOrGAFPCWDqkvAi/7adr/HkQQ8IECEqE3A+w93IDpqjWLRVsALviqU+bTXure1oEnayHWn04mDBw8efUciIiLq0eo8QZRUNT4G3S8nDQ6rMcURERERdb4sszYBXxVgAp6SQFVh3vm+ZimcNSrpp3WzBzwlgWJxabbjqoD/sQUN0FgF75a0FfCUeIqtj2Y77CnVzEABmIBPiPr6eixZsgRZWVlH35mIiIh6tG1sP0NERNRCBTxb0FDiSVVbINXs1KwFBp+T9PNGV8CzrQd1nGrWV8C3PwEvBnUJ+GYV8BX+sviDo1YpFm0CXtENz7UabEg3OjszpJSLqwf8XXfd1eK6oijweDxYu3Yt6uvrcd1113UoOCIiIur+ZFmF025CnSeIYrafISKiXirLnK3ZZgU8JYO++l1x5COcf2LSz9sQrtdspzEBTwkQVQEfqG33ewjBpptDkiDCq0nA80ZoMqhWbQJe8lUBqgMQBABAvjUfwo9/7i3iSsC/9957sd/UYMA555yDX/3qV3EFRURERD3HKaPzMH5ULkqrvMjNtKY6HCIiopTIsmgT8DWBGoSVMAxiXL+WE0WTAzDv/lizFBgyExCS1n0YABCUgwjKQc2anS1oKAH0PeCFoAeQg4BkavN7aFvQSPA0b0HDCvikUHQJeMhB2BQF3h+vfV4vaz8DxJmAX7hwYYvrgiDAaDSiqKgImZmZLe5DREREvY8gCCjIsqc6DCIiopTJskS3aK0KVCLXmpeCaKgnMu1fBiGg7cUeGHJu0s/rDruj1tiChhJBsUQ/PSv4a6Hac9r8HkKMFjTlvjKoqtrrqrGTTbFqc8KyqsAlhyMJ+N7W/x2IMwF/0kknJToOIiIiIiIioh7LbnDAJJk0lcJVfibgKXHMO97VbIfyx0FJK0z6efUDWAHAYWDhBXWcanY2ti1R1cia6K+B3I4EvBhs+nyK0A5hDcgBNIQakG5KT0zA1MhghWq0QQh5AQAyZLjkMEpgBtDYgqa3Se5zSERERNSr+YPhVIdARETUJQiCENUHvpJ94ClBRM9hGEu+1awFhs7slHN7dBXwVoMNElsrUSIIYmMSvhkx0L5BrELzBLwgwidpP5scxJocTX3gVSiqAmc4FHmNFfCtOOOMM+J6c0EQ8L///S+uY4mIiKh7c/tC+P2/vkO/bAdOHpmL8aNY4UdERL1bliULJd5Dke0qPxPwlBjmnR8ATUXCUE12BPtP7pRz6yvgHez/TgmkWDIg+Wsj24K/nQn4QPMBwQJEcwaa/8dS7ivH4PShHQuSoijWPhDrD0BRFQCAS24qzGICvhWHDx9OdhxERETUw2w/UAtZUbG3rAGDCvhYJxERUR+ztg98pb8iRZFQj6KqMO98X7MUHHgWYLB2yundIW0FvMPIBDwljmpxabbF9ibgg9obRGZbDhBuqnrnINbkODKIVVZlAE0JeLvBjjRj7/vdsE0J+K1btyY7DiIiIuphtu5r+uF4eFH0ACUiIqLeJsuibUFTxRY0lACGsnUQ6w9q1gJDzuu08zfoK+A5gJUSSD+IVWhWDX/0g2UIQV2LJFs+UN+UdK/wl3ckPGrFkQR8pAI+3JiA743V7wB7wBMREVESKKqKrftrAQBmg8QKeCIiIkQn4FkBT4lg3v62Zlt2DkA4e0ynnd8d1ibg0wxMwFPiqLoEfHsq4IWQJ2rNYdcmgMt9rIBPBtWSCaCpAt75YwV8Xi9NwHdoKobX60VtbS1kWY6sqaqKUCiE2tpafPHFF/jNb37T4SCJiIioeymp9KDBFwQADOnrhEHiPX8iIqI+Fm0LGlbAU0dJ1Ttg3v2xZi0w9DxAEDotBn0LGjtb0FACKWZ9BXw7EvCa/u+N0tP6A6VN26yATw59BXzGjwn4fGt+ymJKpbgS8IFAAHfddRc+++wzKIoSc18m4ImIiHqf5u1nitl+hoiICACQZdZWwLtDbvhlPyySJUURUbemqrB99ySgNpu+arAgMOTcTg3Dwx7wlEQdqoAP6hLwooQ+9r6apXJ/GVRVhdCJN616g9Z6wOfbClMWUyrFVY7297//HZ988gmsViuOOeYYGAwGFBYWYuzYsUhPT4eqqujTpw8ee+yxRMdLRERE3cCR9jMAUFzkSlkcREREXYm+Ah4Aqvysgqf4GA9+CWPpKs2ab/RVUH9MfHWWBl0LGgdb0FACdaQHvKgbwKqa0pBty9OsBeUg6kPRlfLUMY3fh1QoaCzcdobDgKoi39Y7K+DjSsB/+umnyMjIwKeffopXX30VJ510EsaOHYv//Oc/+Oqrr3DZZZehqqoKGRmseCMiIuptfIEw9h5u/CE2y2lBlsua4oiIiIi6Botkgd1g16xVBtgHnuIgh2BfNV+zpNhz4BtzVaeH4tYNYU3jEFZKINXi0my3rwI+OgGfac6EJEia9XLf4bjjo5Yp1sxI+xkAMECFXVGQZ+2dPeDjSsAfOnQIU6ZMQWZmY0P9UaNGYe3atQAAg8GAe++9F/3798e///3vxEVKRERE3cKOg3WQlcZHodl+hoiISEs/iJUV8BQPy9bXINbt16x5T7gZMHR+4YO+Bzxb0FAiRVXAB+sARW5lby19D3jFnA5JkJClexqpnH3gE06x9om0nzmiUDD12u8PcSXgVVWNJN8BoKioCGVlZWhoaLyzJIoiJkyYgB07diQmSiIiIuo2ymu8ONJBsbg/E/BERETN6RPwlX5WwFP7CP5aWNc/p1kLZ49CcNDZKYnHHdYn4FkBT4mjT8BDbaG3eytaqoAHgGxLrma9kgn4xJPMCEhGzdJAgys1sXQBcQ1hzc3NxaFDhyLbRUVFAICdO3fiuOOOAwCYTCZUVVUlIEQiIiLqTqac2A+njM7D9gO1GFLoTHU4REREXUofs7bykgl4ai/r+n9ACGqT3t6TfgMIcdVYdkhYCcMX9mrW2AOeEkk1u6LWRH8NZH1ivgWtJ+C1N0LLfUzAJ4NHMsDUbLvAkJ6yWFItru/O48ePx+eff441a9YAAIYPHw5JkvDhhx8CAGRZxjfffIOsrOgBM0RERNTz2S1GHDc0G2ajdPSdiYiIehH9INaqAFvQUNtJNTth2faGZi048CyEc45JSTyesCdqrbe2mKAkkYxQTdrPlNDGPvCirlJeNTcmgHOs2kGs5f6yDgRIrWkQVM12rmRLUSSpF1cC/uc//zlEUcQVV1yBd999F06nE2eddRYWLVqEK6+8EhdccAG2bt2KSZMmJTpeIiIiIiIiom4rugUNE/DURqoC+9ePAErTYENIJnhP/FXKQtIPYAXYgoYSL95BrFE94E0/JuAtOZr1ch8T8MlQA0WznSN2/oyKriKuBHxRUREWLVqESZMmISen8UN79913Y/jw4Vi1ahW2b9+O4447DrfccktCgyUiIiIiIiLqzrL0LWgCFVBVtZW9iZqYd7wHQ/kGzZpv9FVQHAUpigjw6Pq/myUzjKKxlb2J4hM1iLWtCfhWWtBk6p5Eqg227f2o7XxhH2rVkGatD3rv94a4esADQHFxMf7+979HtrOzs/HOO+9g69atsFgsGDBgQCLiIyIiom4iGJLxxKvrMbgwHccMzuIAViIiohboW9AE5SA8YTerhikmwV8D2+qnNGtKWiF8Y69JUUSNGnQV8Oz/Tsmg7wPf5gr4VhLwGSbt7ynesBdBOQiTZAIlxgHPfrglbTvSDMGIQIriSbW4KuDvu+8+rFu3rsXXiouLmXwnIiLqhXYdqkNFnQ/fbi7Dxl0cxE5ERNQS/RBWgG1o6Ohsq5+KaqfhGf//AIMlal9VVeEONaDUWwJ3yB31eiLpW9Cw/zslQ3QFfG2bjhMDLfeAd5miC4VYBZ9YB9z74BWbEvCSIMIQ8sY4omeLqwL+tddew+uvv46ioiLMmjUL559/PgoKUvfIExEREaXelv21kT8X93elLA4iIqKuzCAakGHOQE2gKdlT6a/AgLSBKYyKujLD4TUw73hfsxYcOBWhvqcBAKr8lXhn3xvYWrsFdaFa1AfrIatyZN80YxrybYUosBViUNpg/KRgCixSdOI+Hm5dCxo+yUHJoOoS8PFWwB/pAW8z2GAUjQgpTS1SaoM1yLHmdjBSOmKvew+cmgS8AUILMyN6i7gq4P/1r39h1qxZqKqqwlNPPYUpU6bg6quvxjvvvAOfz5foGImIiKgb2Lq/8QdhURAwtK8rtcEQERF1Yfoq+MpARYoioS5PDsH+zSOaJdVog+ek30BVVfyv5DPcvvJm/PfgR9jr3oOaQI0m+Q40tonZXrcVX5R+jn9ufw63fXsTynyHExKevsKeFfCUDHH1gFdVCEF9BXzjDSJBEKKq4GuDtR2KkbQOePbBIzVPwEsQAkzAt8spp5yCRx99FF9//TX+9Kc/YeLEiVi9ejXuvvtunHbaabj77ruxcuXKRMdKREREXVRlnQ8VtY034QfkpcFqjnvMDBERUY+n7wNfxRY01ArLDy9Dqt2rWfMdfxOqJAmPbfwD/rrlaXjD7WvrUOmvwANr70WFv+M3fuqDdZptu4EJeEo8fQK+TRXwYR+gKJoltdkTGi6zLgEfYAuaRFFVFfvce+EVm9LOkiBB7MUV8B367dhkMmHGjBmYMWMGamtr8dFHH+Gjjz7Cu+++i3feeQcFBQX4/PPPExUrERERdSGBQAArVizDpk3f47DXDo8wEEajAUMK+egxERFRLFnmbM12ZYAJeIom1h+Ebf3zmjU5qxhf9OmLv397EzxhT9zvXeEvxwNrf4uHT3oMGbDH/T6lvhLNdpYlu5U9ieKnH8IqBGqPeoyoq35vfJ/0yJ9dJu171rAHfMLUBGvgDrnhEbUV8AgmdyZFV5aw8jSXy4UzzzwTqqrC4/Fgy5YtKCkpOfqBHbR48WIsXLgQJSUl6N+/P2644Qacc845bTr2448/xvPPP4+dO3ciKysLM2bMwM033wyLpakX2ueff44bb7yxxePff/99DBs2LCGxEBERdReqquK1117B+++/A7fbg2BIRvrQs2HOCCAYDGDx84+hft9EXHTRpRAEIdXhEhERdTn6CvjKBFQiUw+jqrCvfAyQg01rArB8yFQ8uWlei4ecnncGJuROhNPkhNPkgsOYhppANUq8h1DiPYT/lXyGA579kf3LfIdx3+q78fSUpyDBGleYhzwHNduFtr5xvQ9RLNEV8LWAqgIxfteIanciAKqp6QmNDFOm5mUOYU2cfe49ABAZwioAEAURKhPw8aurq8N///tffPDBB1izZg1UVUVGRgauuuoqXHjhhYmIsVULFizAvHnzMH36dFx99dVYsmQJbr/9dgiCgBkzZsQ89rXXXsPvfvc7nHrqqbj77ruxefNmvPDCCygtLcWTTz4Z2W/Hjh0QRRGPPvpoVBIhPz8/IbEQERF1F6qqYv78x7F8+TI0eINo8IUgK0BGWiEUVYUc8KLswC688soBHDp0ELfddieT8ERERDrZuirhA+59UFQFohBXl1jqgUz7lsJ48GvN2u5+p+KR0nei9nWanPhF8U0Ylz0+6rV8WwHybQU4AeMwMe8n+P3ae3DQcyDyeomnBHcsuwMPHvcorGL72sf4wj5UB6o0a4V2JuAp8fRDWKGEIYTcUE2tP3kb1f/d6ACafY/VV8CzBU3i7HfvBQC4f0zAS4IBgNA4FPcoN056qrgS8H6/H59//jnef/99fPXVVwiHw5AkCZMnT8YFF1yASZMmwWBIbu/X+vp6PPvsszj33HMjCfOLLroIV155JebNm4dp06ZBatbsv7mamhr88Y9/xIQJE/Dcc89F9rPZbHjxxRdx6623oqioCEBjAr5v376YNWtWUmIhIiLqTl577RUsX74MlXU++IIyBgw/Ef1GTIBHcUJRFfh9laj1BBEIyVi+fBn69u2Hiy66NNVhExERdSmD04dptj1hDw569qPIMSA1AVHXEvTAtlJb5e4xWXG7sg+K7h7NqTkTcN3wG5BuSsfROE1O3HfcQ/j92t+ixHsosr6/fj9e3P5P/LL4lnaFWeqN7nqQby1o13sQtYW+Ah4ABH/tURLw2gp4/b76HvBsQZM4+9z7AAC+H3vAS8KPOVFFBsJ+wBjfEzfdWVy310899VTccccd+OKLLzB06FDcc889+PLLL/Hss8/izDPPTHryHQCWLl0Kr9eLSy9t+qVeFEVcdtllKC0txbp161o9dsmSJfB4PLjttts0ifFLLrkEN954I2S5aWL49u3bMXjw4KTFQkRE1F0EAgG8//47aPAG4QvKmDDjOpw85XKYHbkQRAGSJGHQ0FGYMOM6+IIyGrxBvPfe2wgEAqkOnYiIqEvJtmQjS9eGZkvt5hRFQ12Nbd3fIHqb5gKE1RCeSDOhQZfBObfofPx69J1tSr4fkWHOxP3HP4xca55mfVnp/1DuK2tXnIe82vYzOdZcmCRTu96DqE2MVsBg1iwdbRCrGNBVwJu1/524TBzCmiyRCnjpSAV8U+5VCPXONjRxJeCtViuuvvpqvPfee3jrrbdw5ZVXIiMj+m5UMm3atAkAMGrUKM36yJEjNa+3ZM2aNXC5XJFj/X4/wuEwBg4ciFtvvRUDBw4EAITDYezZsyeSgA8EAgiHwwmNhYiIqLtYsWIZ3G4PGnwhDBh+IgoHjgYAZKabUZhlR0aaGTazAYUDR2PAsBPh9oXgdnuwYsWyFEdORETU9RS7tL8/bqn9IUWRUFciVW2FZcurkW1ZDeNro4DlNu2g1KmF03HVkGvjavWX+WMS3iQ2JctlRca7+95q1/voE/CFtsJ2x0LUVvoqeOEoCXh9CxpFd6MqQ1cBXxusgaqqHYiQACCshCNtrnxHWtA0G8Yq6p5M6C3iKlVfvnx5yluqlJeXw+l0wmrVPraQnd3YSy/WANh9+/YhLy8PGzZswMMPP4yNGzfCaDRixowZuO++++BwNPY927t3L0KhEPbu3Ytzzz0XO3fuhNFoxNSpU3HvvfciMzOzw7HoiaIAUey8XkiSJGr+n7R4fWLj9YmN1yc2Xp/YuuL12bx5E0JhGbKsYNDIUyK/8AmCAJtFhM1ijOw7aNQp2LttFUJhGZs3b8K0adMTGktXvD5dCa9PbLw+RNQVjHCNxIrDTTept9ZuSWE01CUoMuxfP9zYIxmAosqoCbvxj/whmp7JE/POwM+H39ChOTvZlmxMKZyGjw68H1lbWroEswdejExzZowjm5ToBrAWcAArJZFqcQHuw5Hto1XA64ewNh/ACkRXwMuqDE/YDYex9bY2dHQl3kOQ1cbOIrIgwC+IcDWvgGcCvu2SmXw/ePBgzNfT0tLgdDrh8XhgsViiXj+y5vP5Wn2P+vp6uN1uXHvttZg9ezbmzp2LtWvX4qWXXkJpaSkWLlwIQRCwfft2AMDGjRsxd+5c5OfnY82aNVi4cCF27NiB119/HRaLpUOx6GVm2lMyrC49vff1X2oPXp/YeH1i4/WJjdcntq50fRQlBEEUIQgCXH1yYTC0/vOAq08uBEGAIIpQlBAyMuyt7tsRXen6dEW8PrHx+hBRKo1waivgqwKVqPCVI9uak6KIKNXM29+CobKxFZEKBQ2hBryemYXDpqbWG+NzTsWNI25JyMDe84ouwKeHPoaCxmRZWAnjg/3v4Kqh17bp+KgKeA5gpSRSzC40/+3j6BXw+gS8tgLeqRvCCgDVgWom4DvoSPuZI4IGE4RmDVj0N0Z6i+Q3a2+nM888M+br119/Pe644w4oitJiorp5NV5rgsEgysrKcMstt+Cmm24CAEydOhUOhwNPP/00li1bhjPOOAODBg3CjTfeiAsuuCAylHXKlCno378/7r//frzxxhu44oorOhSLXnW1p9Mr4NPTraiv90GWlU47b3fB6xMbr09svD6x8frE1hWvjygaoSoKVFVFbVUZTBZHq/vWVpVBVVWoigJRNKKmxpPQWLri9elKeH1iS9X1SdaNKCLqngrtfeEwOuBu1g93S+0PTMD3UoK3ErY1z/64pcIdcuOQ0YB3MrIj+xyTeRxuHXUHJDExqZw+lixMLjgTn5V8Gln75NBHmNX/p0ftKy+rsmaQKwAUsgKekkjVtaA5ag/4YOwe8EbRGPU9uC5YC6B/h+Ls7fb/OID1CNnoAMJNP2/31h7wXS4BP2/evJivDxvWOC3ebrfD7/dHvX6k2txub/0XnCOtYubMmaNZnzVrFp5++mmsXLkSZ5xxBoqLi1FcXBx1/OzZs/HQQw9h5cqVuOKKKzoUi56iqFCUzu85JcsKwmH+gt4aXp/YeH1i4/WJjdcntq50fUaOHI3PP/8ckiRi9+ZvkJU/EGU1PlhMEmxmA0zGppqU3T98A4MkwmiQMHLk6KT9HbrS9emKeH1i4/UholQSBRHFzpFYXfldZG1L3WZMzP9JCqOiVLGvmg8h6AagwhN2I6yG8R01BhsAAN2ZSURBVFz2QIR/rHQf4BiI34z5fzAkKPl+xAUDf4qlpZ9FquCDchAfHXgPlwy+IuZxFf5yhBXtjDxWwFMytb8HvL4CPrqyPcOUqUnA1wQ5iLWj9nn2arZFsxMIN11XtqDpIs4///w27Zefn4+6ujoEg0GYTE2DQ8rLywEAubm5rR6bm5uL7du3R3q4H9GnTx8AgMcTu0rPaDQiPT0dXq+3w7EQERF1FxMmTMK//vUC0rxB7N22GvmDT0TQlAe3LwS7xYj8PjYAwKE9m7B3+2q47CakpTkwYcKkFEdORETUNRW7dAl4DmLtlYwlK2Ha/V8AgC/sRVAJYbnDhe9tjU8b9jFn4e5j74fVYEv4ufNs+Tiz6Ex8sqepCv7jgx9gZtEFsBtbLyYs8Wir3+0GO9KNzoTHR3SEPgEvBmpj7q9vdaK0kIB3mlw44Nkf2a4NMAHfUfoWNAZrFuBhAr7bTp4aNWoUVFXFli3aQTVHtseMGRPzWADYuXOnZv1I//n8/HwAwOOPP44pU6ZEEu1H1NbWorq6GoWFhR2OhYiIqLswm82YOXMW0mwmWE0SdmxZh2DAB1mWYRCCqCjZhZVLFmHFRwtgNUlIs5kwc+YsmM3mo785ERFRLzTCpe0Df8hzEPW6tgnUw8kB2L95FAAQUoLwKwF4RREvZeUBAGwGG+459v42D0aNx2UjLtO0zvWGvfjk0Ecxj9H3fy+w903JPDvqPdrbgkY4SgsaAHCZte9Zywr4DvGEPKj0V2rWrLq2aiIT8G337LPPYtWqVTH3Wbp0Ke655564gmqLSZMmwWw24+WXX46sKYqCxYsXo7CwEMcee2yrx55zzjkQRRHPP/+8Zn3hwoUAmvrQFxQU4MCBA3jzzTc1+/3lL38BAMycObPDsRAREXUnF110KSZOnIQspxUZOQMhh0MI+j348u35WPrWMzi4cw1cdhOynFZMnDgJc+ZckuqQiYiIuqxBaYNhkkyatW11m1MUDaWCdeOLEOsPQIEMT7jxafyX++ShzmCEJEi4c8w9KHIktyd1//T+OCX3VM3aO/vexLbaLa0cAfZ/p06XnBY02vdkC5qO2e/R9n+XBAk2W55mrbHVVu8TVwuaZ599FoIgYNy4ca3u88033+CDDz7AI488EndwsWRkZGDu3Ll45plnoKoqxo8fj08++QSrV6/G/PnzIUlNfWg/++wzAI0DVIHGPvLXXHMNFixYAJ/Ph4kTJ2LVqlX44IMPcOmll0b6zM+ZMwdvvvkm/vjHP2LPnj0YMmQIvv76ayxZsgQXX3xx5O/fnliIiIi6M0EQcNttdyI3vx8+22GDCgFhfy1c5iBEqw0mgwSHw47zzrsAc+ZcwkooIiKiGAyiAcPSi7GpZmNkbUvtZozLHp/CqKiziPX7Yd34TwAqPCEPVKjYbrbis/TGave5xTdidObYToll9sCL8PXhryLbvrAXD67/He4ccw+O7XN81P6HPNoKeCbgKdlUs0uzLfprY+4vBnQV8C0k4F26BDxb0HTMPvcezXahvS9g0D550Ftb0LQpAb948WJ8+OGHmrU333wTX3/9dYv7h8Nh/PDDD8jJSe709ptuuglWqxWLFi3CkiVLMGDAAMyfPx8zZszQ7HfkJsCRBDwA3HnnnSgsLMSiRYvw5ZdfIjc3F3fccQeuu+66yD4mkwkLFizA/Pnz8cknn+C1115Dv379cPfdd+Oqq66KKxYiIqLuThAEHHPyWVhduRnBYBBpNjdGnX4abDY7Ro8egwkTJrHtDBERURuNcI3UJeDZB75XUFXYv30MUMLwy36E1TAUAP/IKYQqCDgt93T8JH/KUd8mUQalD8Zpuafjq7IvI2tBOYg/bngIvxp1O07LPV2zf4muBU2hvbBT4qTeS18Bj7AfCPsAgzV6ZzkAyEHt8W1oQVMXrO1omL2avv97kb0/VFV744MJ+BimT5+OJ598MjKcVBAElJSUoKSkpNVjzGYzbr311sRE2QpBEHDddddpkuYtWbp0aYvHXn755bj88stjHpuRkYEHH3wQDz74YEJiISIi6gm2HagFBAEmsxlXnjMdowYmry8pERFRT6bvA7+nYTf8sh8WyZKiiKgzGA9+CeOhbyGrYfhkHwDgQ1cW9pmtyLbk4PrhN3b6k4S/HHELfLIPaytXR9ZkVcafNz0Ob9iDqYXTAQDuUAPqgnWaYwtYAU9Jpu8BDzT2gVcc0Ql4/QBWAFBN0Qn46BY01R2IkPa5tS1o+jsGQg1ov4+xBU0MmZmZWLJkCXw+H1RVxZQpU/Czn/0sqgocaExEGwwGZGZmwmCIq8MNERERdXFb9zc+nmkQBQwpdKY4GiIiou5rqHM4JEGCrMoAGhOeO+q2YUzmMSmOjJJGDsC+8gmoUOAONSajaiQDXsvMgSiI+PXoO2A32js9LLNkxp1j7sFfNv8ZK8qWa157butf0cecheOzTozq/y4JEnKt2j7PRImmmtIAUQIUObIm+GsBR0HUvmILw6xbakHjNLk02+6QGyElBKNo7HC8vY2qqjigS8AXOfpDVbU3NXrrENY2Z8gzM5sq226++WacfPLJKCzkI0ZERES9TWWtD5V1fgDAgLx0mE2cdUJERBQvi2TBwLRB2Fm/I7K2pfYHJuB7MOumf0NsOARv2AsFCgBgYVYefKKEiwZegmHO4pTFZhAN+NWo2+EwOvDfgx9pXntjz6s4PuvEqP7vudY8GEQWYFKSCQJUswuCryqyJPprILe0qz7Ja7AAUnRSPdMc/RRvXbAWWZbsjkbb61T4yyNP8xzR3zEAaiCkWeutFfBiPAfdfPPNkQGkXq8X69atwxdffAEAqK+PvstEREREPce2A7WRPw8vcqUsDiIiop6i2DVSs72ldnOKIqFkEz1lsG78J8JKCAGlsUf1NosNXzpcKHaNxIUD5qQ4QkAURFw77Be4YMBPNes76rdjd/0uHPQe0KwX2tl+hjqHvg+84G95aKq+BY3SQvU7ANgNDkiCtpioNshBrPHYp+v/bjPYkGnuE/XkQW/tAR9XAh4Aamtrceedd+Kkk07CZZddhhtvvBFA48DW6dOnY+PGjUd5ByIiIuqORg/MxEVnDMExg/tgRP/oXoxERETUPvo+8NvrtyKshFMUDSWTbfVTUMM+eMKNM/ZUAC9kFcBqtOOWUb+JSgamiiAIuHjgZcg099Gsf3LoI5R4tC1oCmzsjkCdQ7W4NNuiv7bF/QRdCxq1hQGsQOPn3KXvAx9gAj4eexp2abaLHAMgCEL0zQ852Dgkt5eJ6xmh+vp6XHrppdizZw8GDBgASZKwe/duAICiKNi7dy+uvfZavP766xg4cGBCAyYiIqLUcjrMOGV0Hk4ZzV6fREQ9legugbF0FUT3YYieUoieMojecsBggZzWD3J6PyhpRQi7BkHOGgl08rDInqbYqa2AD8pB7G7YhWHO4SmKiJLBcHgtTLs/0bSe+Sw9A3ssVswdcjWyu1jbC0k0YErBWXhtzyuRtRVly+AwaBNqhRzASp1EXwEvtlYBr6uybqn/+xEucwaqApWRbVbAx2dn/XbN9pD0oQAA1eSI2lcIuqFazZ0SV1cRVwX83/72N+zZswcPPPAA/vvf/2L69OmR12688Ub86U9/gtfrxXPPPZewQImIiIiIiCj5jCUr4XrzAthXPAjr+udg3vE+jCXfQardC6lyK0x7lsC64Z+wr/g9nB9chfSPfw7IoaO/MbUq3ZSOvvZ+mrX1VWtSFA0lhSLDvnIewmoIAaWx+tMrinilTx5GZYzGmQVnpTjAlp1ZOA2i0JQ6CspBVAeqNPuwBQ11lra2oBEDugr4GAn4DF0FfC0r4NtNVVXsaDbHBACGpA9rfK2VBHxvE1cCfsmSJTj99NNx8cUXA2h8ZKO5GTNmYNKkSVi1alXHIyQiIiIiIqLOEfTAvuL3QDvanxjK1sO88/3kxdRLHJN5nGZ7LRPwPYp5+9uQqrfD+2PrGQBYnJkHn9GKG4p/pUlydyWZ5kyclD0+5j4FrICnTqKaXZptsZVkub4CXmmlBQ3QWAHfXG2wNq7YerNyfxncIe01P1IBD8kMSCbNa2Iv7AMf13f4srIyjBgxIuY+gwcPRnl5eVxBERERUde0bP0hbNtfg1BYSXUoRESUBLZ1f4Poaf/vceY9nyQhmt7lhKxxmu1d9TtYidlDCIE62Nb+BT7ZB1lt/Blqv8mMJc5MXDzocuTZ8lMcYWxnFZ7d6mtOkwsOY3SFK1EyRFfA17a4X1QP+FgtaPQ94IPV8QXXi+3UVb87jGnIseRGtjmINc4e8E6nEwcPHoy5z759+5Ce3vodJiIiIupePP4Q3l2xByqAopw03HbRMakOiYiIEkiq3AzLllc1a7JrAEL5J0Gx50Kx5UAIeSDVH4CheisMpU0V2obDayB4K6Hasjo77B5jhGsUrJIVPtkXWVtXtQY/KZiSwqgoEazr/g7FXw2/7I+s/TOrAAOcQ3Fuv/NSGFnbjM4YiwJbIUq8h6JeY/936kxqW3vAB/Q94GNUwJtcmm1WwLefvv/70PShmm4pqskBwdfUuqo3JuDjqoA/+eSTsWTJEmzdurXF1zdu3IilS5fi5JNP7lBwRERE1HVsP1AL9cc/Dy7kTXYioh5FCcPx9cOAqjatSSY0nPlneMf/P/jHXI3g4BkIFM+B96TbUT/laahGa9O+qgrTvs87P+4exCAaMDbzWM3a2qrVqQmGEkaq3g7L1tfhDXsja187nNhid+KXI26BJMZVF9mpBEHA1MLpLb5WaC/s5GioN1MsLs12qz3gde1QVHOMHvDmTM02nzxqv+gBrMM020pUBTx7wLfJjTfeCIPBgMsuuwxPPPEENm/eDAD45JNP8Nhjj+HKK6+EwWDAL37xi4QGS0RERKmzdV9t5M/FRRmt70hERN2OZetrkKq0BVa+Y66Dkt6v5QMMFoSKJmmWzHs+TVZ4vcbxWSdqtjdUr0e4Hf34qYtRVdhXzkNQ9iOsNn4dg4KAl/vk4bz+F6C/Y0Bq42uHM/LPhEk0Ra0X2JiAp86jWvtotoWgGwj7ovYTdENYlXZVwNdAbX4zmmKSlTB2N+zSrOkT8PpBrKyAb6PBgwfjb3/7G2w2G1544QUsXboUqqri17/+Nf71r3/BYrHg6aefxrBhw47+ZkRERNTlqaqKbQcaq0GMkoiB+ayAJyLqKUTPYVjX/lWzJrsGwDf6ZzGPCwycptk2lK2H6ClLeHy9ybF9TtBs+8JebK3bkqJoqKNMe5dAOrwGvmbV729nZANpfTF7wMUpjKz9HEYHTss9PWqdA1ipM8n26HkJkrs0ak2f4FVjDWHV9YAPKSHNEysU20HvQQTloGZt8JEBrD9iD/g4e8ADwPjx47F06VJ8/vnn2LRpE+rr62G32zFixAhMmTIFdrs9kXESERFRCh2u9qLO0/iD1ZBCJ4yGuO7hExFRF2T79nEIIW0FoeeU3wKSMeZxoYLxjX1dmz1Kbtq7BP5RVyQlzt4g05yJgWmDsKdhd2RtXeVqjM4Yk8KoKC4hH2yr5sMX9kH5sYlfhcGId13ZuG3YXJglc4oDbL9pfWfgf6XaVlP97EUpioZ6JaMVqsWlGb4qukuhZg3R7KavgFdjDArWJ+CBxip4u5F5zbbYWadtP5NjzYXT5NSs6RPwYi9sQdOhZmMmkwlnn302zj679YnYRERE1P1t218b+fOwIlfK4iAiosQylG+Aaf8XmrXAsFkI5x1/9IMlE4L9fwLzjvcjS6Y9TMB31PFZ4zQJ+DVVq3Dl0GtSGBHFw7ppIVR3CQJKILL2YlY+xuaMx4nZJ6UwsvgNTh+KU3Mm4OvyFQAaWyZlW3NSHBX1NoojH5ImAV8CWbNDGEJIW8EeqwLeJJlgM9g0Ve81wRoU2vl0R1vs0Pd/TxsatQ9b0LQxAX/gwIG4T9CvXys9A4mIiKjb2HagNvJn9n8nIuo5rOuf12yrFhe8J97S5uODA6ZqEvCGik0QG0qgpBUkLMbe5vg+J+LNPf+JbB/yHESZ7zByrXkpjIraQ/DXwPLDv+EOeyJr31vtWJeWhT8Pm5vCyDruppG/xkk54xGUgzgtd2Kqw6FeSHYUQKpsas0l6RLwLQ34jNUDHmisgm+egK8LchBrW+2q36HZ1refAVoYwhpiBXyLpk6dCkEQ2v3mgiBEBrQSERFR9xQKy9h1qA4A4LSbkJthTXFERESUCIaKTTAe+kaz5ht7LVSzs5UjooXyT4JqdkII1EXWTHuXwD8mdv94at2Q9KFIN6ajPtTUQmFt5Wqc3e/cFEZF7WHdtBDBQDXCamNaUAXwr6wC/HTQpd2+YtwkmZh4p5RSHNobvKKuB3xL1dWqOS1qrbkMcwZKvIci2zWB6g5E2HsE5AD2e/Zp1oY6o+eBRvWAD7ACvkXjxo1LdhxERETURe0uqUdIVgA0Vr/Hc1OeiIi6HuuGFzTbqjUD/uEXtu9NJGNjG5rt70SWzHs+ZQK+A0RBxHFZJ2BZ6f8ia2urmIDvLgRvBcybX0F9uGmuwgqHC+GMwZhZNCt1gRH1ELJDO4g1KgGv6/8O0QBIlpjvqe8DX8MK+DbZ07ALiqpEtgVBwMC0wVH7RSXgQ0zAt+jll19OdhxERETURaXbTTh9bD627a/FcPZ/JyLqEaSqrTAe+FKz5ht9FWBo/1NOgYHTNAl4qWorxPr9UNI5nDFex/U5UZOA31SzEQE50C0Hd/Y21o3/RCBYFxm8qgD4T2YO5g6/AQaxQ2P4iAiNPeCbk9wlmm3RW67ZVs1O4CgFRPoEfF2wNv4Ae5Gduv7v/exFsLRws0M/BJcV8B3g9Xphs9kS9XZERETUReT3sePCiY2VDKqqpjgaIiJKBOsGXe93sxP+4T+N673CecdDtWZA8DVVDJr3fArfMT/vUIy92bGZx+P/s3ffcVKVVx/Af/feKTtle68svTfpSFHBRhEUAVFQkYgFUzQaYxI14U00r8bXxJYoIgYEIzaCitKrCNKRXpftbG8zs1Puve8fy87uM21nZmdnZnfP9/PJJ9xnbnnmYd1hzj33HJ7j7ZmFNsmGnyqOtdvmnZ0FX1cI1dnPUCPW28e2RcUiLe16DIwbHMKZEdJxOJag4UwVgK0egA4AINSwJVHE6JZvBseqHTLgzZQB743zDvXfe0Q5l58BnEsAdcYa8HxrDv70008xd+5cDBw4EMOHDwcArF69Gs8//zwqKqheEiGEENLRUPkZQghp/4SK81Bd2cGMmQbMB5R+JlTxCli6TGaGVFe2+Tk7AgA6pQ69o/syY4fLD4ZoNsRbmqPvo95Si8Z0BRs4fB6XjPu63x/SeRHSkYi6VKcxvq7Y/mehOofdP6pLi+eMdsiAr6ISNF5xbMDa010A3qkEjQmQbG02r3DkVwBelmX88pe/xAsvvIBjx45BEAR7RlxOTg4+/fRT3H///aipqWnhTIQQQgghhBBCgsmp9rs6EuY+c1p1TnP2JGZbqDx/LSOR+GtYwnBm+1jFkRDNhHiDr8mF4vw6mCWzfWxTdBwGZt2OLH126CZGSEej0jk1C+eblaFxCsBHd23xlI4Z8FWUAd+iWmsNrpqKmbHuUT1d7iupnJvgcpbOlQXvVwB+9erV2LhxI6ZOnYpdu3Zh0aJF9td+9atfYd68ebhw4QJWrFgRsIkSQgghJPguFFSj2mAJ9TQIIYQEiFB1CaorW5ix+n73QVbp3RzhHVtCf6D5Q1KSBEXFObf7k5YNiR/GbJeYrqLYWORmbxJq2iPvwmRrCiiZOQ5fx6djbrf7QjgrQjomKdKhEWvttQC8LLsIwGe3eD7HGvA11hrYOlmGtq8uOGS/q3gVMnWuy/041oAHAM7SuerA+xWA/+yzz9C7d2/87W9/Q1JSEvM4uk6nw4svvoiBAwdi06ZNAZsoIYQQQoJLkmX8+9sz+OOKH/HGZ8ep/jshhHQAEaf/AzT7dS6r9Kjvd0/rT6zUOmUZCuWnW3/eTixL1wXRKjbL83jF0dBMhnjEV18Bf/FrWCWrfezb6Hhc320W4tTxIZwZIR2TYxmaxgA8V1/p1OBTjG65BI1jAB5oCMIT9y5UszfZu0Z2c99oWhEB8AIz1NnqwPsVgL98+TLGjRvncZ8RI0agsLDQ4z6EEEIICV/5JXWoq2/4IqnXKqn+OyGEtHeS6FSbvb7vXKfarP6yJfRjthVlpwJyXm9wpgpoD/4D2n3/C95Q3PIB7QDHcRgYyzbuPF55NDSTIR5FnFgFk81o3zbyPLYkdcOMrFkhnBUhHZdjI9bGEjS8Q/Y7eIXTvq5EKiMhcGyAuNJMvS09uVh7gdl214AVAMBxznXgzZQB3yKVStVifffKykoolUq/JkUIIYSQ0DubV2X/c+/MmJDNgxBCSGAoSo6CM7F1bc09pgfs/GK8QwC+PHgBeP3u5xHx00pEnF6LqK8fAFffMer3DoobwmyfqDgOURZDMxniEmcsA3/uc9jkpnIVG6PiMaXHAuiUuhDOjJCOyzGozjUG4KtymHExOssp89oVnuMR5fDEEQXg3ZNlGeerzzJjPdzUf7cf41DqjqcSNC3r378/tm3b5jYIX1ZWhm3btmHAgAGtmhwhhBBCQudsbpX9z32ynB/LJIQQ0r44Zr+LsT0gRWUG7PyOGfBC1WXAanSzd+Bw9ZVQFuyzb/PGMuj2/hnoAKXTBscPZbYNNgMu1VxwszcJhYjT/4HZ2hRIsoHDgZQ+uDn9thDOipCOTYx0zIBv6I/hT/33RvEO5aLKzKV+za0zKDYVOZXo6Rnd2+MxThnwVIKmZQsXLkR5eTnuv/9+7Nq1C9XV1QAast537NiBBx54ADU1NViwYEFAJ0sIIYSQ4Ki32JBT3PCPqsRoDeKjI0I8I0IIIa0iS04BeEv2pIBewhbXE+CbfcWUZSgqzro/IEAU5WecxlRXdkB18Zs2v3Zbi1PHI12XwYxRHfgwYjEAJ1fB1uyphJ1RMbi51wPuayETQlpNcqwBbywDbGbw1VeYcV8C8EkRycx2qanE7/l1dGer2M/daFW00/o5khwasVIJGi9MnDgRv/71r3Hu3Dk88sgj+OijjwAAY8eOxWOPPYaLFy/isccew4033hjQyRJCCCEkOC4UVEOUGjIHe2fFhHYyhBBCWk1RdhK8gQ0mWLoENgAPhQZidDeH67Z9GRrBTZBft//VDlEP3rEO/E+Vx0I0E+JIfe4L2OrL7dsygO9T+mJ88sTQTYqQTkDSpzoP1hRCqLrEDIlRLTdgbZSoSWK2r9a3/8+PtuJYfqZnVO8W+4XJ6s6dAe/3LdmHH34Y48aNw9q1a3HixAnU1NRAp9Ohb9++uPvuuzF06NCWT0IIIYSQsETlZwghpGNxzH6XortAjOnmZm//2RL6QahsKpESjDrwrjLgAYCz1EG3+4+ovfUdgPMr9ywsDI4biu/ym7L5z1SdRr1YjwiBnk4LKdEK+fgyJvv9gC4K4/ssgkDZ74S0KVmlh6yOZLOoKy+Dry1E8+JjYnRXr8+ZpGEzuEtMV1s5y47rjEMGfK/oPi0eIysdAvCdrAZ8qz4V+vbtixdffDFQcyGEEEJImDiT29C8TsFz6JEe3cLehBBCwposQ5WzlRkyd7kJaCFbzR+2hH5Qn19v3xbKXAfHA8ldAB4AlEUHoD7zKcx957b5PNpK/9gBEDjB3nxVlEWcqjyB6xKGh3hmnZvq0neQrjV+bLQ7tS9+mTwhRDMipHOR9GkQzM0ysXP3AbLE7hPtfQZ8ckQKs11aTyVoXKm31SOn9jIz1quF+u+Aiwx4CsD75siRIzh16hRqamoQFxeHwYMHo0+flu98EEIIISQ8lVWbUFZdDwDITomCWiWEeEaEEEJaQ6g4B762gBmzdLmpTa4lxvdlr12TA85SB1mld3NE63CWOvA1eeygIgKw1ds3dQf/AWv6GEhRWW0yh7amUWjRM7oXzlSdto8drzhKAfhQkmXYjrwFuVn2+5kILYb3e4Sy3wkJElGfCqG8WQD+8m7mdUmb4NNnj2MJmjprHYw2A7QKXavm2dGcrTwLqdmNDp7j0T2qZ4vHyQ414PlOFoD3+zm8gwcP4vbbb8e9996LP//5z/jHP/6BF198EXfeeSfmzJmDCxeoMzshhBDSHpktInpmxEDBc1T/nRBCrjlz5gwGDBiAN998M9RT8ZnqCpv9LulTnQLlgdLQiLXZjVvZfY32QBAqzrEDvIDaG/7qMCkzIk593GZzCAaqAx9eFPl7wFddZMZ2pfTB9cnjQzQjQjofSZ/GDlSw9d99yX4HgMSIJKexEmrE6uR0+WlmO0vfxauSaLLKMQOeasC36PTp03j44YdhNptx880347rrroNOp0NNTQ0OHjyInTt34oEHHsDatWuRnp4e6DkTQgghpA2lJ+rx+MwBMFtFSJLc8gGEENLB2Ww2PPfcc7BaraGeil8cA/CWNio/AwAQ1BBjezBZiYqyU7ClDGuTyykcgvtidFdYM8fD3OduqM98Zh9XFv7YJtcPlsFxQ/Hp5f/Yt3PrrqDSXIFYdVwIZ9V5GY++DXWz7Pd8pRq9By2h7HdCgshlI9ZmxOhsn86n5JWIVceh0lxhHyupv4rsSO/ryHcGJ8tPMtu9oryrgiI5PI1AJWi88Oabb8JqteLdd9/F+PHsHd5FixZh69at+PnPf463334bL730UkAmSgghhJDgUiup9AwhhADAu+++i/Pnz4d6Gn4Rqi5BqMphxizZk9r0mrb4vk4B+LYiONR/t8U3BALqe9zBBOCF6hxwxlLI2sQ2m0tb6hHVExqFFiab0T72U8UxTEi9MYSz6pzE6svQFB1A80rTe5J74u6UiSGbEyGdkeiYAe/4uo8BeABI1iSzAXhTsc/n6MhkWcYph+bq3tR/BwBZHcVsd7YAvF8laA4fPozJkyc7Bd8bTZo0CTfeeCN27drVqskRQgghhBBCSCidPXsW//znP/H444+Heip+UV3ZxmxL2gTYEge26TVtCf2YbUV52wXgHRuwitcC8GJ8b8gqtm6vsvhQm82jrQm8Av1jBjBjx6kMTUhc+vElpv6xkeeRNfjnEDhKXCAkmJxK0DgQo3wrQQMAiRHJzHYJNWJllNSXoLK+khnrFe1dBrysZD+TO1sJGr8C8FartcXSMhkZGTAYDH5NihBCCCGhUWOwQJKp7AwhhABNpWfGjh2LO+64I9TT8Z0sQ5WzhRlqKD/jdyswrzgG4PmafHDmmsBfSDRDqL7MXjvuWiYer4AteSjzmrL4YODnEESD4oYw28crjkCmz+ygKjbkIenKTmbsZEIPjE6/OUQzIqTzarkEje+lY5I0DgF401Wfz9GRnatib3rrlZFI0Xj+e2gkqxwy4K11gCy52bvj8etfXiNHjsSWLVtgsVhcvm6z2bB3714MH05d2QkhhJD2ZPk3p/HC8h+xcuNZ2MTO8w8iQghxZdmyZbhy5QqWLl0a6qn4RXXqPxAq2NI5li43tfl1xZgegEMtbMdSMYEgVF4EJJEZswfgAVhT2O+jyqKOFYCvNFciz3AlNJPppPb9uBQxItsLInPEc+DaqqcCIcQtWRUJ2aGuuJ2ggqRP8fmcyU4Z8BSAb+5sNftZ3iu6t9e//5z+rmSAs3aexG2/asD//ve/x/z58/HAAw/gt7/9LQYPburIfvXqVfz1r3/F1atX8dprr0GS2C/vPB/YbIs1a9Zg5cqVKCwsRJcuXfDoo49i6tSpXh377bffYtmyZbhw4QISEhIwZcoUPPHEE4iIaOje+9vf/hZffvml2+NHjhyJVatWAQC2bt3q9rHUr776Cr169fLxnRFCCCHBVWeyIq+kFjKAqxVGKIS2zZAkhJBwdv78ebz99tt44YUXkJKSgvz8fL/PxfMceD54ATpB4IHiE9Ds/z+g2WUlXTKQPgyKAH8nc6JQQ4rvDaGsqVGbuvIMkDU6oJdRVZ1jeslKUZlQaCObtjNGgmsWcxdq86A0l0HWJbX62sK1z0ghiJ+VWVGZSNQkotRUah87WL4f3WK6BW0OwRSKNfbkYOmP6Ja3jxmriu2K9Oy2v6nVlsJtnTsiWuO2I0emgne40cxxgBjdBQql7yHPFIegfampBILA0U22a85VX+vvwgGQgT6xfaFQePlzrY126v+uEA2QFdEBnWO48isAv3jxYoiiiCNHjuCee+5BREQEkpOTUV9fj6tXm+4OzZgxgzmO4zicOhW4+n/Lly/HK6+8gttuuw0PPvggNm/ejKeeegocx2HKlCkej127di2ef/55jB07Fs899xxOnTqF999/H0VFRXjttdcAAHPnzsWYMWOcjt24cSO2bt2KG29sanhz/vx58DyPl19+2ek/zNRU7x7HIIQQQkLpfH4VGh9k750VE8qpEEJISImiiOeeew7Dhg3DnDlzWn2+uDhdcL+8m2uBtU+Cl0U0/7bL3/Y/iI0P0hfdjEFAs9rv2tpz0MbqPBzgh7oL7PtLG4jY5teIHgpERDWsxzUxNT8BGYErJxQVpQnYubwxPnMc1l1YZ98+ULEPjwz/WVDnEGzBXmNXLKIFX+94A8+bmmoW8xyHjOufRESgf65DJBzWuaOjNW4D8V2Aygv2Tf7aZwKf3JP9PPBSL1VX5oa5RTZD0ImIVneOILEnZtGMnLpLAK6tMwcMzxji/TpLGoAXmLIzMREi0EF+h7bErwB8fX09lEol0tKaGh5YLBbwPB+0YHNNTQ3eeustTJs2zR4wnzNnDhYsWIBXXnkFt956KwTBdROUyspK/PWvf8W4cePw3nvv2ffTarX48MMP8ctf/hJZWVkYOnQohg5l6wYWFhZi6dKlGDduHBYuXGgfP3/+PDIyMjBz5sy2ecOEEEJIGztzpcr+596ZMSGbByGEhNry5ctx5swZrFmzBhUVFQAavn8AgMlkQkVFBSIjI6FUKr06X0WFIXgZ8LIM3Y7fQVWVy/T0MA96APWxI4DK4DzurYzsCW2z60sFx1Eb4GvrCk5A0ewa9ZE9YHa4hjZpKJS5TTW7Lef3wJQ6qdXXFgQeUVEa1NSYIAaxZNvQ6JH4Qmp6SvtCxUWcKjiPVK3nZoTtUajW2JW1Fz/GgAK29IIyIh6m9FthCtJ/U20lnNa5o6I1bjsRqkSor30O8Bxn/9yr16Q7fR54Q5A0gMwxjZbPFl1Cz2iqanG68hSsos2+zhw4JPOZqPRhnaMUGqb5al1ZCURlZltMN6i8uQnhVwB+27Zt/hwWUNu2bYPRaMS8efPsYzzP495778VTTz2FI0eOuK1Bv3nzZhgMBjz55JNMkP6ee+6BVquFKIoujwOAl19+GWazGS+++CKTxXLu3Dl07949AO+MEEIICT5ZlnE2r6GjvVLg0S2NsjwIIZ3X7t27YbVaMXv2bKfXli9fjuXLl2PlypUYNWqUV+eTJBmSFJxmmeqzn0FxcaM9M1yWAVvSQNQNfgywBS/wI8f2habZW+ZqCiDWVUCOiAnMBSQbhPKzaN6D1BLbGzaH92hJHgZFs6aZQtFBp31aQxSlgJ6vJT0j+yBKGYUaa1NT271F32NGl1lBm0OwBXuNHV2py8HnF9bgn7UV9jEFpwDXew5sUAX1v6u2FOp17gxojQPPpk2FSmYehoIsA9bILn6uNY+EiESm+WphXRG66nq0frLt3KmKU4AMe/mZTH0WVIjwaZ0lZSR4c1MAXjLVdJr/JvwKwIeDEydOAAD69+/PjPfr18/+ursA/KFDhxATE2M/tr6+HgqFAl27dsUvf/lLt9c8fvw4Nm3ahIULFyIrK8s+brPZcPnyZUyYMAEAYDabIQgCFIp2u7yEEEI6meIKI6oNDc3Ve6RHQ+ltLT9CCOmAnn32WXvGe6OysjI888wzmDFjBmbOnIk+ffqEaHbu8TV50O3/GzMmqyNRN/ElQPAuWz9QxOiugKACRIt9TFF+GtZ05xKf/hCqrzDnBtgGrI0cG7HyNfngDcWQdL435wsHAidgROJobC3cZB/bX/JDhw7Ah5JFtOCNk69hWG0lopsl6mkVWtT1pjUnJNREveunf8SoLn6fMykimQnAl5pK/D5XR3K+5iyz3Sva938HyapIAEX2bc5S637nDqZVEeKLFy+ioqICoihCbpZ6YLVaUVVVhR07duD//u//Wj1JV0pKShAdHQ2Nhq2hlZiYCKChVIw7V65cQUpKCo4dO4a//OUvOH78OJRKJaZMmYIXXngBer3rLsrvvPMO1Go1Fi9ezIzn5OTAarUiJycH06ZNw4ULF6BUKnHzzTfjD3/4A+Li4rx+XyFp0ARqBuIOrY9ntD6e0fp4RuvjWbDX50JBtb1PX9/sWO+b6YQI/fx4RuvjGa0PacmAAQOcxhqbsGZmZmLs2LHBnpJXVDmbAdHKNF6tG/dHSG4CFG1KUMIW3xuKkp/sQ4qyU4ELwFew5UAkXRLkiFin/cS4npDVUeDMTTdUFEUHYekxLSDzCIXRSWOZAPz5mnMory9DfERCCGfVMa25uBK5dVewsKYp+z1CiICcMhxiB21+S0h7Iuldl8EWo7P9PmeSJgmobNouqb/qfudOQpZlnKtmP3d7RTnf9G7xPOpIZpunALxnVVVV+NnPfoaTJ0+2uK+vAfjGf9i6ExkZiejoaBgMBkRERDi93jhmMpncnqOmpgZ1dXV46KGHMGvWLCxevBiHDx/Gv//9bxQVFWHlypVOTZIKCwuxa9cu3HXXXU4B9XPnzgFoyJBfvHgxUlNTcejQIaxcuRLnz5/Hp59+6nKurgS9QdM11AzEM1ofz2h9PKP18YzWx7Ngrc/Folr7DeBRg9L9aloUCvTz4xmtj2e0PqSjkfTpzLa5/zxYs24IzWQA2OL7MgF4oVlT1tZSlLOBADHeTSYex8Oach1UV3bYh5TF7TsAPyB2ELQKLYw2o33sx9J9uD2z/b6ncHS84ii+yVuPBKsFA00NNY4FToBG0FD2OyFhwtUNZkmXBCi1fp8zMSKZ2W6eDd9ZlZvLUGmuZMb8yoBXsgnPlAHfgrfffhsnTpxARkYGBg8ejG3btqFLly7o1q0bzp8/j/PnzyMhIQFvvPGGz+eeNMlzQ5yHH34YTz/9NCRJchmobhzzFMS2WCy4evUqfvGLX2DJkiUAgJtvvhl6vR5vvPEGdu7ciRtuuIE55osvvoAoipg/f77T+bp164bHH38cd955p700zeTJk9GlSxe8+OKL+Oyzz1we50pQGzSBmoG0hNbHM1ofz2h9PKP18SyY62OxiTh1qRySJCNGr4ZGgE/NdEKBfn48o/XxLFTr015ubJH2y5I9GabaPKiK9kPVfRzqey8AQvgrQIzvx2wryk4H7NyKCvZReFuc+0CALWU4G4AvOhiweYSCgldgeMJI7CreYR/bV7KXAvABVGetxVunXgcAXF9XbR/XKXSQVZGwZLe+kS8hpPVkVSRklQ6ctem7S2vKzwBAksYhAE8Z8DjrkP2uU+r8av4tqdgM+OYNWTs6vwLwO3fuRGpqKjZs2ACVSoVHH30UPM/bs93fe+89vP766ygqKmrhTM5eeeUVj6/36tXQeVin06G+vt7p9cbMd53O/RecxrI1jk2VZs6ciTfeeAP79+93CsBv27YN2dnZLms99unTx+X4rFmz8D//8z/Yv3+/1wH4YDZoao6agXhG6+MZrY9ntD6e0fp4Foz1uVpuhEatQI3Rgl6ZMRBFGQ0ddsIf/fx4RuvjGa0P8UVGRgbOnj3b8o6hxAswDf4ZrMMWQxWrAyoNgBS6n3FbAhuA5w1XwZkqIGu8L9HpkixDKHcIwLvLgIeLOvB1ReBrCyFFhqA0T4CMThrLBOBPV59EtaUK0aqYkM2po5BlGe+eedue7TmutgoAoBW0EDgFzF0mAYI6hDMkhNhxHCRdKviqC/YhMaZrq06Z7CIDXpIl8FznLV14uoqtgNI7uo9f6yGrKAPeJ8XFxZg1axZUKhWAhsann3zyif31xYsX47vvvsPatWsxdepUn849Y8YMr/ZLTU1FdXU1LBaLfR5AQ214AEhOTnZ3KJKTk3Hu3DmnUjLx8fEAAIOBzforLy/HqVOn8PDDD3s1t0ZKpRJRUVEwGo0t70wIIYSESGq8Dn9cOAJF5UYIQvDLoBFCCOmYxOhsQBEB2JoSpxTlp2HNuL5V5+VrC5yy5sR497VoxdjukCNiwNVX2ceUxQdhjryjVfMIpcFxQ6EW1DCLZgANQeMDpfsxOf3WEM+s/dtbshv7SvYCANIsZnS11EPJKaC+FnQ3d7stlNMjhDgQY3tA0TwA76Ihty8SHTLgRVlElaUScer4Vp23PXMMwPeLc+6V4w1ZFcVsd6YAvF+3bwRBQGRk02MDWVlZqKioQEVFU2OSkSNH4sqVK62foRv9+/eHLMs4fZp9jLFxe+DAgR6PBYALFy4w443151NT2SYOR44cgSzLGDPGdcOgV199FZMnT3YKtFdVVaGiogLp6ekujyOEEELCBcdxSEvQITnW/3qJhBBCCIMXYHMIhCjKWl8HXuHQgFWOiIGkdZ+A1VgHnjlHcfsuQ6MS1BgWP4IZawwaE/8ZbQb8+/xy+/a42ipw4KBT6gFwkDVxsKUMC90ECSFOTAPmQ46IAQCIcb1g7ta6G5GxqlgoeSUz1pnrwNdZa5Fbx8Z3+8f6G4Bnq5V0phI0fgXg09LSkJOTY99urHvuGNCuqqrye2ItmThxItRqNVatWmUfkyQJa9asQXp6OoYMGeL22KlTp4LneSxbtowZX7lyJQDnOvRnzjT8A89VmRmgYT3y8vLw+eefM+Nvv/02AGD69OnevSlCCCGEEEII6UBsCX2ZbUUAGrEKVZfZa8T1Ajz0AAOcy9Aoiw4Ccvsot+bOyCQ2QexE5XEYrOHdwyXcfXr5P02NBmUZ19dVQ6fQgbsWOjFn3wzwQghnSAhxJMb3Rc3s/wIPrEfdjI8AhaZV5+M4DokRScxYawPwddY6nK46hTpr+ws4n65iP7dVggo9onr6dS7ZoQY83w7Xw19+laAZP348PvroI6xfvx533HEHevfuDbVajU8++QQjR46EwWDA9u3bPZaBaa3Y2FgsXrwYb775JmRZxujRo7Fx40YcPHgQr7/+OgSh6UNxy5YtABoaowINdeQXLlyI5cuXw2QyYcKECThw4AC+/vprzJs3z15nvtGVK1eg0WicStY0mj17Nj7//HP89a9/xeXLl9GjRw/s3bsXmzdvxty5czFixAiXxxFCCCGhJsuyx8blhBBCSGs41oEXApABz5vKmG0psuUnjm2p7Hcy3nAVnLEUsi7JzRHh77r4YVDwCtgkG4CGMgkHy/ZjYupNIZ5Z+5Rbl4MNeV/Zt7Mt9ci2SVAqmzJhLVR+hpDwpI4CYlMbep8EoL9PkiYZhcYC+/ZVPxqx1lhqcKB0H34o+R4nKo9DlEWoBBXu7/EQbkm/vd18B3MqPxPf79pnj+/rLCupBrxPFi1ahK+//hrPPvss6uvrMWfOHMyYMQOffPIJjh49ivr6elRUVGDx4sWBni9jyZIl0Gg0WL16NTZv3ozs7Gy8/vrrmDJlCrPfSy+9BKApAA8AzzzzDNLT07F69Wrs3r0bycnJePrpp7Fo0SKn61RVVUGv1zuNN1KpVFi+fDlef/11bNy4EWvXrkVmZiaee+453H///QF6t4QQQkjgrdlyHhU19eidGYOJQ9KhVlFWFyGEkMCxxTs0YjWWNQS+tYl+n5MzOgTgNQktHiNGd3WuR199GdZ2HIDXKLQYEncdDpb9aB87QAF4v8iyjPfPvgtJbgooTairgVahBdAQJJP0KbAlui91SwjpOJJcNGL1VoW5AsvOvIPD5QeZ3ykAYBEteP/sv3Ck/BAe6/vzdtE4+5RDAH5QwiC/z+XchJUy4D1KTEzE559/jmXLltmzxZ955hmUlpZi+/bt4HkeU6dOxWOPPRbQyTriOA6LFi1yGTRvbtu2bS6Pve+++3Dfffe1eB3HUjWuxMbGYunSpVi6dGmL+xJCCCHhQJJlnL5SCUO9FYVlBtw0LCPUUyKEENLBSNFdICs14Kwm+5ii/DSsrQjA8/Xl7DW8CMCD4yDGdIVQ1tRDTKi6DGvaKL/nEQ5GJo5mAvCnKk/Q021+2H11J5vlKcuYauHAc02JCZaut7ZY6ogQ0jEkadibs6X1JV4dZ7IZ8cKh3+KqqdjjfofKDuDX+3+Ox/v+EtclDPe4byiZbCZcrr3IjA1K9D8ALzllwFMAvkXJycn4wx/+YN/W6/V45513UFtbC5VKBbVaHZAJEkIIIaRt5JfUwVBvBQD0zIiBQvCrNQwhhBDiHsdDjO8LRfFh+5Ci7BSsmRP8PiXvlAEf79VxtmiHAHz1Jb/nEC76x7IZ2bXWWuQZcpGl7xKiGbU/RpsBq86vYMZGyRFIsFrQmP0OAGYqP0NIp5HoZwb8+2f/1WLwvVG1pRovH1uKed0X4K7s2T7PMRjO1ZxhsvgFXkDf+L6or/WvzI9jBjwkGyCaAaHjx5AD/k07MjKSgu+EEEJIO3A2r8r+595ZMSGbByGEkI7NsQ68ovy0mz29IMtONeBlLwPwYkw3Zluoav8B+CRNMhIi2KcJTlb+FKLZtE9rL32MKkslM/awkIbmwXcxOhtirH9NBwkh7U+yhg3Al5vLIF7rt+HOrqLt2FW8gxlT8SqMThqLXw14Bs8M+h2iVdFOx318cRXOV59t9ZzbwulKtvxMj6ge0LSiya1TAB6dJwve7wz4M2fO4LPPPkNubi6MRiNkFx3kOY7DRx991KoJEkIIIaRtnM2tsv+ZAvCEEELaii2+L7OtKDsFyLJf5Tw4ax0gWpkxSetFCRq4CsBf9vn64ah/7EDsLGoqu3qq6gRuz5wWwhm1H1dNxfgu/xtmbFjcUHQ5u5cZs3Sj8jOEdCZJDgF4SZZQZi5DsibF5f7FxiIsO/tPZkyj0OLVkX9njukd3Qf/PP0mDpUdYPZdfu49vDT8VfBceD2R7Fj/vW9s/1adz10A3tsb6e2ZXwH4H3/8EQ899BBEUXQZeG9EdecIIYSQ8FRvsSGnuAYAkBAdgYRo/zMZCCGEEE8cM+A5UwV4YwkkXbKbI9zjjaVOY96WoBFjurLzqK8CV18JOSLW53mEk/4xA9gAPNWB99pnlz+BKIv2bYET8Hj0KPCmr5n9zF1vDfbUCCEhpFdEQiNoYBKb+peUmK66DMDbJBv+fvJvqBfrmfFH+ixx2j9aFYNnB/0BH19ahS9zPrOPX6w5j+1FWzAp7ZYAvxP/WUQLztewmfn9Ywe07qSCGuAVDaVnrqEMeA/eeust2Gw2PPLII5g8eTLi4uICPS9CCCGEtKELBdUQpYab6H2y2nfggRBCSHiTIjMgq/TMl2yh7JRfAXjOxDZgldWRXteOlfRpgKBkMuiFqkuwpQzzeR7hpJ9DQKTGWoN8Qx4y9VkhmlH7UGDIx87ibczYbRlTkXr1KDMmxveBFE019QnpTDiOQ6ImCbl1V+xj7hqxfnxpFS7WnGfGbkydhOuTx7s995yu92J/yQ8oNBbYx9dcWInRiddDp9QF4B203sXa87A5lN3pG9O6DHigIQueq6+yb/PWOojud+8w/Hq24eTJk5g0aRKefPJJDBw4EOnp6W7/RwghhJDwQ+VnCCGEBA3HO5ehKT/l16kc6797m/3ecLACYhQbSBWq238ZmqSIZMSr2TI8p6pOhGg27cfaS2uYJ/pVggp3dpkFZe4uZj9z9uRgT40QEgaSvGjEeq76DNZf+ZIZS9Om46Hej3g8t4JX4KFei5mxGmsNPrm82s/ZBt5ph/IzXfTZAbk54FiGprNkwPtdXKhbt24t70QIIYSQsHQmt6HZmMBz6JHu3AyIEEIICSQxwUUdeD/wRocMeI139d/t83AoQ9MR6sBzHOdUFuBUJQXgPcmpvYy9JXuYsakZdyCurtipzJE1a2Iwp0YICROOdeCv1rMBeFmWsfL8B8yYwAn41YCnESFEtHj+wfFDMSJxFDO2MX8Dcuty/JtwgJ1yaMAaiOx3wEUA3koBeLcGDx6M48ePB3ouhBBCCAmC6jozyqsbahR2TYlChMrvnuyEEEKIV2zxbB14RdnphkasPuLr2QC85HMA3rER6yWf5xCOHMvQnKw64bFfW2f3ySU2y1Sr0OKOLndB5ZD9LkVlQIxmb9oQQjoHxwD88fIjqLXW2LcPlv2Is9VnmH3mdZ+PrpHdvb7GAz0XQckr7duSLOGDc++F/Pe3KNlwtvo0M9YvppX136+RlJHMNmepDch5w51fAfhf/OIXOHToED744IOQ/1AQQgghxDfRejWWLhqFBbf0xg1DqVwcIYSQtufUiNVcDb6uyOfzOGYn+1SCBq4C8O0/Ax4A+scMZLarLVVMbWHS5Fz1WRws+5EZm551J/RKPVR5bADekjkBoGa2hHRKvaP7MNs11hp8eO59AA0B6jUXVzKvJ2mSMSXzDp+ukaxJwYwus5ixk5UnsK9krx8zDpycustOTWX7xvRzs7dvnEvQGAJy3nDnVcrbfffd5zSm0+nw6quv4l//+hcyMzMREeH8eAXHcfjoo49aP0tCCCGEBJReo8R1vRJDPQ1CCCGdhKRPg6yOAmduyh5UlJ+CJTLNp/M4NmGVtD5mwDtkM/PGUnCWWsiqSDdHtA/JmhTEqeNRYW5an5NVPyFdlxHCWYWnTy6xMYpIZSSmZk4Hb7gKoZzNZrVkTgjm1AghYaR7ZE8MTxjJ3LDbVbwD1ydPQJWlEvmGPGb/e7rNZ7LZvTWzyyzsKNqKsvqmG8yf5nyMUUljwHN+Vw5vlVMO9d/TtOmIUccG5NzOAfjOkQHvVQD+0KFDbl+rqanByZMnXb7G0Z1iQgghhBBCCCEcB1t8XygL99uHFGWnYfGxwWWrmrACEKOyAJ4HJMk+JlRdhi1pkE/nCTccx6Ff7ADsKd5pHztZeQK3pN8ewlmFn9NVJ3G84hgzNrPL3dAotFBe2MCMyyo9bElDgjg7Qkg44TgOD/d5HKf2nYDRZrSPv3fmHUiQmH2z9V1xffJ4v66jFtRY0GMhXj/xin0sry4XP5buw+iksf5NvpUc+4j0CVD2OwDIys5ZA96rAPzWrVvbeh6EEEIIIYQQQjowW0I/JgAvlLlO5PKktU1YISghRmZBqM5pGqrOafcBeAAYEDOQCcCfvlYHnhLjmvz3yhfMdqw6FrdlTAEAp/Iz1ozrAcH3bFZCSMcRp47D/T0fwr9Ov2UfKzeXOe03v8eDrcpWH500Fpn6LOTV5drHPs/5BKMSxwT9d7hFtOCnSvZGZb8ANWAF4PTEGWehALxdejrVhyWEEEI6gt3HClFcYUTvrBj0yYqFSimEekqEEEI6CTHBsRHrSUASAd7LzyLRCs5czQz52oQVAMSYbDYA30EbsVaaK1FkKkSalr7PA0ChsQCHyg4wY3d2mQ2VoAasJiiL2LrwVH6GEAIAN6XejD3Fu3Ci8rjL1wfEDsKguCGtugbP8ZiVPRd/P/GqfSyn9jIOlR3A8MSRrTq3r05UHodZNDNjg+OGBuz8skrHbHeWAHyrigmVlrINcL7++mu88MILeP/991Fb2zlq+BBCCCHtycGzpdh7shgrvj0Dk0UM9XQIIYR0ItZEtlEoZzX6FPzm68udxnwtQQO4asTaMQLwKZpUxDrU6D1Z+VOIZhN+vsldz2zrFDrcmNZQAklZuA8QrU0v8jys6aEp/UAICS8cx+GRvkug4lUuX5/f44GAZKmPThrrdMP085xPIMtyq8/tC8cm1T2jewes/jvgnAHPd5ISNH4F4K1WK379619jwoQJqKtrWKjly5fjmWeewdq1a/G3v/0Ns2fPRnV1dQtnIoQQQkiwGOqtyCtpuEGeFq9DtM71PyIJIYSQtiBrEyHpU5gxRan3AWLOofwMeAVkdbTP83AKwFdf9vkc4YjjOPSLYbPgHev4dla11hrsKGJL696cfhsihAgAzuVnbMnXQVZHBW1+hJDwlqJJxbzu853GxyaNQ/eongG5hsAJuCt7NjN2oeY8jlUcCcj5vSHJEg6W7WfGRiaMDug1nGrAd5ImrH4F4FesWIFvvvkG3bt3h8lkgs1mw7Jly6DRaPDXv/4VS5YswZUrV/Cvf/0r0PMlhBBCiJ/O5VWhMX+id2ZMKKdCCCGkk7I5ZMErSl0/0u+KywasfmQditFd2fPWFQJWk8/nCUf9Y9n1PVn1U9CzJ8PRpvxvYZEs9m2BE3B75rSGDVmCKn83s78l079mioSQjuv2zOnoGd3bvq3iVZjXfUFArzEueQKSNeyN6s8u/ydov8cv115EpbmSGQt0CRxZ5RiApwx4t7755hv07NkTX375JRITE3Hw4EFUVVVhxowZmDlzJn7+859j/Pjx1LyVEEIICSNnc6vsf+6dFROyeRBCCOm8HJudKkq8z4B3GYD3gxjdBWget5cBoSbHr3OFG8cM+EpzJa7WF4doNuHBKlnxXf4GZmxs8njEqRt+fhSlJ8CZ2ICTJXNi0OZHCGkfBE7A7wa/gElpt2BYwgg8N+RFpGhTA3sNXuGUBX+2+kzQyokdKGWz31O0qUjXZgT0GpJTBjwF4N3Kzc3FuHHjoFQ2dAT//vvvwXEcJk5s+pDq3bs3ios79wc9IYQQEi5kWcaZ3IYvl0qBR7c0eqyaEEJI8NkS2QC8UJ3j1FjVHd7ElqCRtb43YAUAKDSQ9GnsPKo6RhmaNG06IpVsfd28uishmk14+P7qLlRZ2AD7tMwZ9j8rHcrPiDHZkKIygzI3Qkj7oldG4tG+T+C3g5/HAIcnjgJlQsqNSIhIZMY+y/lPm1zL0QGH8jMjEkYFpL59c44Z8JBsgEPT147IrwB8Y+C90ffffw9BEDB8+HD7WHV1NaKi6Ms9IYQQEg6uVppQbWh49Lp7ejSUCiHEMyKEENIZ2eJ6AwLbg0RR6l2d8kBlwAMdtxErx3HI0mczY7mdOAAvyzK+yl3HjPWPHYBuUd3t2471362ZE4IxNUIIcUnBK3Bnl7uZsZOVJ3CiwvuSbf64aip2+rwYkTgq4NdxCsCjc2TB+xWA79atG/bu3QtJknDmzBmcPn0aQ4cOhV7fsIgVFRXYsmULunbt2sKZCCGEEBIMjdnvAJWfIYQQEkKCErb4vsyQosS7oIJjE1ZJk+hmz5Y5B+A7RgY8AGTqspjtXENuiGYSeicqjzsFlKZlzrT/mTOUQKi8yLxuoQA8ISTEbkybbC+T1eg/lz5q01rwh8oOMNt6ZSR6RfcJ+HUoAO+DWbNm4ezZs7jtttuwYEFDw4E5c+YAAL788kvceeedqKysxH333Re4mRJCCCHEb+ea13+nBqyEEEJCyKkOvJeNWAOaAe/QiFVR3XEC8Fn6Lsx2nqHzZsA7Zr+natNwXULTk/vKoh+Z12WV3qlRMCGEBJuSV2JW9hxm7Gz1GRytONxm13Ss/z4sYQQErg2emhbUAK9ghigA78bs2bPxm9/8BjU1NeB5Ho899himT58OAMjLy0NVVRWefvpp3HbbbQGdLCGEEEJ8Z7VJuFDQUF83WqdCSpw2xDMihBDSmTkGOBWlJwBJbPG4wJagYQPwfG0eIFr8Pl84ydKxAfhCQwFski1EswmdfEMejpQfYsamZc4AzzWFQRwD8NaU4U6BIUIICYWb0m5GYkQSM/bJpdVtkgVfZ63DqSq2HNyIhMCXn2nkmAXPWzt+AN7vT5aHHnoIDz30kNP43Llz8dBDD9nL0RBCCCEktHgeeOSO/jiTWwmVUgh4Ix1CCCHEF1aHDHjOaoRQfRlibA/3B8kyeCMbgPe7CSucM+AhSRBqcj3PoZ3IcChBI8oiCo0FTpnxHd3Xuf9ltvVKPSam3tQ0IMtQFrIZn9a0kcGYGiGEtEjBKzC76z145/Qb9rGLNRdwsGw/RiSODui1jpQfgiRLzLUHxQ0J6DWak1V6cPVV9m3KgPdDcnIyBd8JIYSQMCLwPLqnR2PqmGzcPDwz1NMhhBDSycnaREj6FGaspTrwnKUWcMjibk0GvKzSQ9KyNeQ7Sh14nVLnVDs4r5PVga+2VGNn8TZm7Jb026EW1PZtofqy000dayoF4Akh4WNCyg1I0aYyY/+5tJoJlgfCQYfyM4PihkCj0AT0Gs3Jqkhmm+sEGfABD8ATQgghhBBCCCGe2BJ9qwPvWH4GACSN/xnwgKtGrJdadb5w4lQHvq5z1YHflL+BKbsjcAJuzZjK7KMoYhsOStpESNHZwZgeIYR4ReAVmNP1XmYst+4Kfij5PmDXsEk2HHGoLT88oW1vRkpKNnGbs9S26fXCAQXgCSGEEEIIIYQElS3JoQ58yU8e9+ccy8+oIwFB1ao5iA7BVqE2r1XnazOiFfCx5q9jHfjcTtSI1SJasLFgAzM2LmUi4tRxzJjKqfzMKIDK9BFCwszY5HHIdCgttvbSGohyy71TvPFj6T6YbEZmbFgbB+Ada8BzFkObXi8cUACeEEII6cDO5VVh97FClFQa26RhDyGEEOIPxwx4oToHnLna7f58fTmz3ZryM/ZzRGWw16jNb/U5A4mvzkHkll8h7qNxiP7vPVAUH2r5oGsy9WywJrcTZcDvuboT1Rb2Z2la5gx2J8kGRfFBZsiaOqKtp0YIIT4TOAFzus1jxgqNBdhauKnV55ZlGeuufMaM9Yzu7XTDMtCcA/CUAU8IIYSQdmz/qav4YvclvLz6MK5c7fj/sCGEENI+2OJ6O2WwK0pPuN3fsVa3pEl0s6f3xEg2AC/UFrT6nAFhNUJ78B+IWTcXyrzdgGSDUHkBURsfRcSpNV5lw2c6ZMBfNRXDLJrbasZhQ5Zlp+arA2IHITuSbbqrKD/tlHFpTRvV5vMjhBB/jEwc4/R77KPzH6KsvrRV5z1eeRSXa9nya043LNuA7FiChmrAE0IIIaS9kmQZ5/KqAAARSgEZidQknRBCSJgQlLDF92WGFKXuy9DwJjYDXg5EBrxDAJ4zVQBWo5u9g0N1eRNivrgTET+tdGo6C0mCdv9r0O/+A2AzeTxPhs656Xq+IUxL7ATQ8YqjTg1np2fNdNpP6VB+RozJhqxt/U0dQghpCzzHY36PB5kxk2jCe2feadVTzutyPme2kzUpGJU0xu/zecupCauFAvCEEEIIaacKSg2oq7cCAHpmxEAh0Mc+IYSQ8GFLcmjEWuK+EatjE1ZJ27oGrAAg6tOcxkKZBa8++zn0O55zyvZ3pLr4HaK/eQi8h7mqBTWSNSnMWF4nqAO/PvdLZjtdl4Eh8dc57ad0aMBqTaXsd0JIeBscNxQ3pE5ixo6UH8Ku4u1+ne989VmcqGQ/d2d2mQWBE/yeo7dklY7ZpgA8IYQQQtqtM7mV9j/3zooJ3UQIIYQQF2yJDo1Yy04AsuRyX8cmrIGoAQ9FhFMgP2R14K0maA++4TzOK2DNHO80LFScQ9SGRQ1Z+25k6dkyNHl1uW727Bhy63JwvOIoMzYtcwZ4ziHsYTNBcZXdj8rPEELagwd6LkKMKpYZW3FuGSrM7j8L3PnvlS+Y7Vh1LCam3tSq+XnLMQOepxI0hBBCCGmvzuVW2f9MAXhCCCHhxuqQAc9ZDBCqLrncty2asALOZWhClQGvvvStUwagNX00qmZ+gtrJf0ftTX+DrNQyr/PGUuh3P+/2pkWWQx343A6eAe9Y+z1SGYkJKTc47ae8epQt78NxsKU4Z8kTQki40Sv1WNznMWbMYDPg/bP/9KkUTYEhH/tLf2DGpmbeASWvDMg8W+JUA56asBJCCCGkPaq32HC5uAYAkBAdgYRoTYhnRAghhLBkbSIkPVsmRag453LftmjCCjg3YuXrQhCAl2VEnP6EGbKmj0HtzW9Bis5u2O5yI6qnrYR4bbuRsmAfNMdXuDxtZvMMeFlGdeU5CBXnobh6BMq83VBd+hbK/O8BSQzkuwmJKnMldhXvYMZuzZgKlaB22ldZ9COzbUsc4JSNSQgh4WpE4miMTWafjDpQuh97S/Z4fY7/XmFrv2sVWtycfltA5ucNWeUYgO/4GfCKUE+AEEIIIYF3saAGotSQBdEnK7aFvQkhhJDQEGN7gK8rtm8rKi/C4rSTBZy5hhkKRBNWAJAi05ltoSb4jUoVJUchVF5gxur73gNwHDMmxXRFze3LEL1+HnNDQnPkn7AmD3XK4s7UZQEAYmxWPFOci971RkTlzAHnkIdnSxqIuhv+CknH3gxpTzYWfAtRbrqRIHACbk2/3eW+ykI2AG9NHdmmcyOEkEB7qNdinKg4hhpr02fje2feRvfIHkjRpno8try+zMUNyynQKnSuD2gDklMGfMcPwFMGPCGEENIBUf13Qggh7YEY053ZFqouOu3D1zvXtg1EE1bAOQNeCEEGfMTptcy2FJkOa8ZYl/vKmjjUTfgLG5yXZeh3/g5cfSWzb5o2Hak2G/5ccAm9640AwASpGylKfkL0+vugLNzXyncSGhbRjI353zBjE1JuQIzaOQGBq6+CUHGGGaMAPCGkvYlWReOh3o8wY0abEa/+9BLqxXq3x1WZK/F/J15hPguUvBJTMqa32VxdccyAh2QDRHNQ5xBsFIAnhBBCOqCz1+q/CzyHHunRoZ0MIYQQ4oYtphuz7aoGvGP5GfAKyKqogFzfsQY8X1sY1JIsnLEMqivbmLH6PrMBx8ahzdhSh8M0hA28NNSDfwGwGOxj6ppcvFxwBSnWpmcKXAXggYbAdOSmJdAcfc9tTflwtat4B2qtbP3gaVkzXO6rLD4INC+TrFDD5tCLgBBC2oOxSeMwNmkcM5ZbdwX/Ov2my3rwObWX8dzBX+NcNXsT8qa0m13esGxLTgF4dPwseArAE0IIIR2MJMuYOCQNA7rGoU9WLCJUVHGOEEJIeBJj2Qx4vrYAsJqYMc7kogGrQ3kWv6/vEICHZANvLAnIub0Rce5LtiGooIK55x0tHmca9BCsqSOYMWX+XsR9cjP0O38H9bl1iNrwM8SJNmYfURIhq3SQ9CmQ1Q436GVAc+RdRG59EhCtfr+nYJJkCV/lrmPGBsUNQZY+2+X+ysL9zLY1eSggqNpodoQQ0nY4jsOjfX9uLzfW6Puru/FN3npmbH/JD/j9oWdQVs/e0NYr9ZjZZVabz9VRZwzAt/tv5GvWrMHKlStRWFiILl264NFHH8XUqVM9HvPb3/4WX375pdvXR44ciVWrVvl8DX/mQgghhASC2WzGnj07ceLETzAaDdBqdRgwYCDGjZsY6qkRQgghbonRXQEOTFayUH0ZYkI/+zZvKmWOCVT5GQCQI2IBRQRga3pkn6/Nh6T3XEM3IEQr1Gc/Y4bM3W5zDoy7wguom/gXxPz3HnCmZiV6bGaoLm2E6tJGAA210BtdUUVgbb/b8fSo/wMAcJZa6He/CGXuTubUyrw90JxYCdPgRX6+seA5Wn4YhUa2bND0rJlu93dswErlZwgh7ZlGocEzg36HZw88BZPNaB9fdWEFKi0VqKgvR74xDzm1l52OTYhIwLODnkdCRGCamvtEUAO8grkBTQH4MLZ8+XK88soruO222/Dggw9i8+bNeOqpp8BxHKZMmeL2uLlz52LMmDFO4xs3bsTWrVtx4403+nwNf+dCCCGEtIYsy1i79mN89dU61NUZYLGKkGQZPMdh+/ZtWLHifUyfPhNz5swDF6BsQUIIISRgFBGQIjPA1+Q3DVVeYAPwRjYDPlANWAEAHAcxMoNpgirUFsDmkF3eFlR5O53K69T3neP18bImHrUT/oKozT9ns+ibaQzAn1Nr8Je0rhDMTdn9sioStTe9hogTK6E9/BYgNZWeUZ9ZC9OA+wFB6ctbCjrH7PdMXRYGxw11uS9fW8j8nAGANW1UW02NEEKCIlWbhl/0ewr/e/zP9jFJlrD+ivvE497RffDMoN8hWhUThBm6JqsjwZmaepfw1joErwBc8LXbAHxNTQ3eeustTJs2Da+99hoAYM6cOViwYAFeeeUV3HrrrRAEweWxQ4cOxdCh7IdyYWEhli5dinHjxmHhwoU+XaM1cyGEEEL8JcsyXn/9VezatRO1RgtqTVaIYtOXZ0HgEWm04OOPV6OgIB9PPvkMBeEJIYSEHTGmOxMYdawDz5vYILWkCVwGPACnADxfm+9h78CJOMM2X7UlDYQY39enc9jSRqJ66gpoj74HZcFep/r1AifguEaP/03tAjPPA9YaVFuqmoIuHIf6gQ9AjO2ByM2/sB/HG8ugyt0OS9db/HpvwZBTexknKo8zY9OyZrj9t45j9rusjoYY16vN5kcIIcEyPHEk7u56Dz67/J8W970hdRIW93kcSj60N1hlpZ4JwHf0DPh2WwN+27ZtMBqNmDdvnn2M53nce++9KCoqwpEjR3w638svvwyz2YwXX3zR/oHt7TUCPRdCCCHEG2vXfoxdu3airNqEKoMFmT2HYeKdv8Qt8/+IG+/8OTJ7DkOVwYKyahN27dqJTz9t+R9khBBCSLDZHOrAC1UXmW3eqQZ8YAPwUmQ6e/0gBOD56hwoig4xY/V9vM9+b05M6IfayX9H5dyNMIz+DWyJA+yvWXpMw98yejYE36/JrbvidA5rxvWwJQ1mxiJOf+LXfILl67x1zHa0Khrjkt2X3nMuPzPCY7NbQghpT2Z3vQdD44e5fV0tqPFAz0V4vO8vQh58B5zrwHPWjh2Ab7cZ8CdOnAAA9O/fnxnv16+f/fXhw4d7da7jx49j06ZNWLhwIbKympoXeHuNQM6FEEII8YbZbMZXX61DrdECk0XEuCmLkN51AMpr6lFeawbHJWLguLnI6D4YezYsR63RgvXrv8SMGXdBrVaHevqEEEKInRjjEICvZAPwLpuwBvL6Do1YgxGAVxY6ZGNHxMCSPblV55QjYmHuOxfmvnPBGcvASRZI+jSk/PgkLtU2rWmeIRcD4wY7HV/fdy70Jcfs24qrRyGUn4UY37tV82oLpaYS7CnexYzdmj4FKncNVWXJuQFrGtV/J4R0HDzH48kBz+DD88txrvoMYlSxyNBlIE2bgQxdJrpF9oBOqQv1NO0kpR7Na4VwltqQzSUY2m0AvqSkBNHR0dBoNMx4YmJD84DCwkKvz/XOO+9ArVZj8eLFfl0jkHMhhBBCvLFnz07U1RlQa7Iiu/dwpHdtyHYz1jfUgJVlGSolj/SuA5DdazjyLxxCXZ0Be/bsxKRJ4fs4OSGEkM7HMQDPG66Cs9TZs+OcStAEsAkr4JwBz9cWuNkzcBRlJ5lta/oYwF3w2A+yNsHe1zZT34UJwLvKgAcAS5ebIGkTmLr0EWfWwnD98wGbV6Csz/0CotxUbkfFq3BLhvvea0LlRXD1VcwYNWAlhHQ0GoUWj/X9eain4RWnDHiLIUQzCY6wC8Dn53vONoiMjER0dDQMBgMiIiKcXm8cM5lMXl2vsLAQu3btwl133YW4uDjmNW+vEai5AADPc+D54NXnFQSe+X/CovXxjNbHM1ofz2h9PGtpfU6dOgGrTYQoSujWbww4joPVJsFsbfgyqlIIUCoacgq69R+DnLMHYLWJOHXqBG699bbgvIk2RD8/ntH6eEbrQ0h4EaO7ADzPNAEVqi7BljQInKkCvKGY2V/SJAb2+lGZzDZnrgFnqYWsigzodZpTlJ1gtm0JA9zs2XqZOvb9FRjdfOcWlDD3uguao+/Zh9SXvoVx+C8gq6PbbH6+qjJXYmvhZmZsUvotiFa5n6OyiM1+l/SpkByefCCEEBI8zgF4yoAPqkmTJnl8/eGHH8bTTz8NSZJcNldpHPO2ydwXX3wBURQxf/58p9e8vUag5gIAcXG6kDTIi4rStLxTJ0br4xmtj2e0Pp7R+njmbn0kyQqO58FxHGLik6FQCCivqQfQ8BkSpVdBcS0AHxOfDI7jwPE8JMmK2NjwefSwtejnxzNaH89ofQgJE4IKYmQWhOqcpqGqi7AlDYIq/3vYU7kBQBEBMa5nQC8v6VIBjgPkpgvxtfk+N0T1FmepY94rANgS+rXJtQAgXcsG4AsN7pPe6nvfBc3x5U3NXG1mqM+vR/2ABW02P199k7ceVslq3xY4AXdk3enxGMeSP9a0UQ1/54QQQkJCVlIN+JB65ZVXPL7eq1dDl3KdTof6+nqn1xuzzXU674IL27ZtQ3Z2Nvr06eP0mrfXCNRcAKCiwhD0DPioKA1qakwQRanlAzoZWh/PaH08o/XxjNbHs5bWh+eVkCUJsiyjqvwqBKUWlbVmADI4jkOkRgmbreHLc1X5VciyDFmSwPNKVFa2/8f76OfHM1ofz0K1Ph3p5hchgSbGdmMD8NfqwCvz9zD7WdJGB7RUS8PFlJB0yeDrmjLthZq2C8AL5afZmwq8AFsb1llP07EldmqsNaiz1kKvdM7wl7WJsHSZBNXlTfaxiDOfor7fvQAvOO0fbAarARvzNzBjE1JuQEKEh6ciRCuUxWzDWyo/QwghoeX4lBlnoQB8UM2YMcOr/VJTU1FdXQ2LxQKVqukfYCUlJQCA5OTkFs9RXl6OU6dO4eGHH27VNQIxl0aSJEOS5JZ3DDBRlGCz0Rd0d2h9PKP18YzWxzNaH8/crU+/fgOwdetWCAKPS6d+AK9Pg3wtcy9Kq4TAc/btSyd/gELgoVQI6NdvQIdab/r58YzWxzNaH0LChxjTA8A2+7ZQdakhcFr4A7OfNXNcm1xfisxgAvB8XdvVgVeUnWK2xbiegNB2DdKTIpIhcAJTM73QWIBe0c5JaEBDM9bmAXi+tgDKgr2wZo5vszl667v8r2ES2RKvM7Pv9niMouwEYGMT5qypIwI+N0IIId6TVWxiSkcPwLfbwpf9+/eHLMs4ffo0M964PXDgwBbPceTIEciyjDFjxrTqGoGYCyGEEOKLceMmQq/XIVKjRO6FYyivbKqZF6Nv+hJfcPkEcs4dhF6jRGSkHuPGTQzFdAkhhBCPxJhuzLZQdRGKkmNOTdksGW0TgBcd6oELNXltch3AVf33/m12LQBQ8AqkaFOZsQIPZWhsSYMhOmTkR5z+T5vMzRf1Yj2+zlvPjI1NGoc0bbqbIxo4lp8RY3tA1sS52ZsQQkgwOGbA8x28BE27DcBPnDgRarUaq1atso9JkoQ1a9YgPT0dQ4YMafEcZ86cAQCX5Wd8uUYg5kIIIYT4Qq1WY/r0mYjUqpDUZQjM5npYzCYoJCNs5lqUFl7E/s2rsWfDcmhUAiK1KkyfPhNqddtl2BFCCCH+EmO7M9u8sQzqi9+w+8T3gawNbANW+7kj2SAuXxu8DPi2bMDayDFInW/0cIOB41Dfdy4zpCzYB85Y1hZT89qWgo2os7JN+lrKfgcAZZGL+u+EEEJCyqkGfAfPgA+7EjTeio2NxeLFi/Hmm29ClmWMHj0aGzduxMGDB/H6669DEJrq023ZsgUAMHnyZOYcV65cgUajQVyc67vf3l7Dl7kQQgghgTJnzjzk5+fjaEUaOA4QbVYc2fkhLIZyAIBC4BGjUyFSq8KECRMxe/Y9IZ4xIYQQ4poYmQnwCkCy2cfUF9la322V/Q40lKBpTqh1nyHeGpyxjCl1A7RtA9ZG6doMHMB++3ahwfMNBnPXW6Hd/zdwVqN9TJW3C+bed7XZHD2xSlasz/2SGRsaPwxdI7u5OeIaiwGK0p/Yc1EAnhBCQk5WOQbga93s6SWbCeqLGyALali63hL4fjGt1G4D8ACwZMkSaDQarF69Gps3b0Z2djZef/11TJkyhdnvpZdeAuAcgK+qqoJez/6F+3sNb/cjhBBCAoXjODz11DNY9fEn2HLgFERJiVi1CZJSA57noFII0Ot1uOOOOzF79j3guOA1+SaEEEJ8IighRnexN18FwATjgbar/w44l6DhDcWAaAUEZUCvoyg/5TAQATG6a0Cv4Uq6jn1/hcYWMvwVEbCmj4UqZ4t9KJQB+C0FG1FprmDG7sqe3eJxyquHAamp9j14AdbkoYGeHiGEEB9JgcyAl2VEbn3KXnLMUvAD6ib+pTXTC7h2HYDnOA6LFi3CokWLPO63bds2l+PLli0L2DW83Y8QQggJJI7jcP+992DuLDP27NmJEyc4GI0GaLU6DBgwEOPGTaSyM4QQQtoFMaY7G4BvRo6IadNa6ZJDCRrIMnhDMaSozIBeR1F6ktm2JfQD+LZ/YtqxBM1VUzFskg0K3n1IwJI1kQnAKwv3A1YToNS02TxdMdlM+DznE2asb0x/9Ilp+ckBx/IztsSBgFIb0PkRQgjxnWMGPCQbIJr9akrOV+cw/T6UeTsBWQbCKAGtXQfgCSGEENJArVZj0qRbMGnSLaGeCiGEEOIXMbY7cNn1a5aM6wGu7VqYyepoyCo9k4En1OYHPgAf5AasjdK0bAa8KIu4aip2yoxvzpoxDuB5QJKuHWSBqvAHWLrc1JZTdfLVlXWotlQzY/O6z/fqWMcGrFR+hhBCwoNTAB4NWfCyxvcAvKLiLHtupS6sgu9AO27CSgghhHRmsizDbBVb3pEQQghpJ8QY9/W8rRnj2/z6jnXgA96IVZZdNGANTgBer9QjWhXDjBUaPde5l9VRsCYPY8aUebsCPTWPquqrsC7nC2ZsaPww9I1ped04UzmEygvMmDV1ZEDnRwghxD/uAvD+cAzAi3G9/TpPW6IAPCGEENIOncurwtIPD+C7/bmoM1lDPR1CCCGk1WyxPVy/wAuwpo9u8+s71oEPdCNWvjYfnLmGGQtWAB5wLkNT0EIjVgCwZk1gtlV5u9ma6m1szZk1MNlMzNh9PR7w6lhl0QFmW1ZqgrrehBBCPBDUTn1W/A3ACxXnmG1bXC+/p9VWKABPCCGEtENbDuXDaLZh44FcXMivbvkAQgghJMxJ+nRAUDmN25KHQFZFtv31HerA8wEOwDtmv8sRMZD0qQG9hieO5WYKWsiABwBL5g3MNldfBUXJsUBOy60SUwnWXVjHjI1PuQFd9NleHa8s3M9s21KGBbypLiGEEP85ZsHzVj8C8LIMRfkZZogy4AkhhBDSajnFNbhQ0BB0T4zRYFD3+BDPiBBCCAkAXoAYne00bMkYF5TLt3UGvMv670GsUZvumAHvRQBeikyD6PBkgipIZWg+ubgGNslm3xY4AXO73evdwbLs1ICVys8QQkh4kZVsAN6fDHjOVAauvooZs8VTAJ4QQgghrbT1UNMX5puuSwfPh1eDGUIIIcRfYmx3pzFriALwfG0+IMsBO7+i9CSzHexyKI6NWAsN+ZC9eH+WrInMtip3R0DXxZXcuivYUbiVGbs5/TYka1K8Op6vzQdfV8yMUQNWQggJL44Z8JwfGfAKh/IzUEQ49XQJBxSAJ4QQQtqRonIDTlyuAABE61QY3jspxDMihBBCAscWwwbgpch0iNFdg3JtxxI0nNUEzlLjZm9fT26DooJ9RN6WOCAw5/ZSmo59fwabATXWlsvYWbJuYLb5mjzw1TkBnJmz1Rf/DalZkF8tqHF317leH+9YfkbWxEGMcb65QwghJHQkpwz4Wp/PITg0YLXF9QK48At3h9+MCCGEEOLW9sNNDdNuGJIOhUAf5YQQQjoOS7dbmTrd9f3mBa1Mi6RNAhwuxRuuBuTcQtUlwGZmxmwJ/QJybm8lRiRBwSuYsUJjy41Yxfi+kLSJzJgqb2dA59bc0fLDOFx2kBmbnnUnolUxXp/DZfmZIJb7IYQQ0jKnDHiLwedzOGbAh2P9d4AC8IQQQki7UVFTj8PnSwEAWrUCY/p79xg2IYQQ0l5I+jTU3PYuzH3uhmHs71Hf1/us51YTlJA0CcwQbyh2s7NvFGVs+RkpMg1yRGxAzu0tgROQqk1jxgoMXtS55zhYXZWhaQM2yYYPz7/PjEWrojE9a4b3J5FEKIsOMENUfoYQQsKPcwDe9wx4hasM+DBEAXhCCCGkndhxtACi1PA49vhBaVCrhBDPiBBCCAk8W9JgGMY8B3Pvu4L+GLmkS2a2HeuI+0tR6qIBawikO9SB96YRKwBYMicw24rSn8CZygM2r0abCr51uimwoNeD0Cp0Xp9DqDgLzsyWDrKmjgjI/AghhASOUxNWX2vAW43ga3KZIVt8n9ZOq01QAJ4QQghpB+pMVuw72fAYvErBY/zg1BDPiBBCCOl4HAPwQoBK0Dhl6IVLAN6bDHg0BLBlpbZpQAZUebsDOTXUWGrwyaXVzFiv2F64MW2ST+dxzH6XojIh6enfTYQQEm5kVSSzzVl8C8ArKs8DzXuC8zzEmG4BmFngUQCeEEIIaQcEnsNN12XYS8/oIpQtH0QIIYQQn0hahwx4YwAy4GUZfE0eMyTG9mj9ef2QpmUbsXpTAx4AIKhgTR/LDCnz9wRqWgCATy6thtFmZMaeGPoEeB+fgnBswGpNG9nquRFCCAk8WcU+3eRrAF4oZ29ui9HZgCKitdNqE4qWdyGEEEJIqGnUCtw2Kgs3Dk2HKEmhng4hhBDSIUk6tr9KIJqwcuZqp6CCGJnhZu+2la5jr1tSfxUW0QKVoGrxWGvGOKhytti3lYX7AdHKNM3115W6HGwu/I4ZG5cyHgMSBqCy0oemfKIZypKjzJA1leq/E0JIOHLMgOd9LEHj+HSZGKb13wHKgCeEEELaFbVKgJay3wkhhJA2IekDH4AXah3KvPC8U6A/WBwz4GVZRrGp0KtjLRlsBjxnNUJRcqzVc5JlGSvOLYMsN9URUPEq3N9roc/nUpQcB2zmZpMErKnDWz1HQgghgedUA97XDPiKc8y2La53q+fUVigATwghhBBCCCGEwEUTVkMJILfuyTPeIQAv6VIDkjXuD41Ci1h1LDPmbRkaWRMP0aG5narg+1bP6cfSfThZ+RMzNqPLXUjUJPl8LsfyM2J8H8jq6FbNjxBCSNuQVY4B+FrvD5ZsUFReYIZECsATQgghxB8HTl/F13tzUGO0hHoqhBBCSIcnOmamSzZwpvJWndMxAz5U5WcapWszmW1vG7ECgCXjemZbmb+3VXOxiBasvPABMxavTsCMLrP8Op9jA1YqP0MIIeFLakUGvFCTC4jsd2QblaAhhBBCiK9ESca3+3Ox9XA+/vzvg6iqM7d8ECGEEEL8JkfEAbzAjAmtLEPjlAEfFeIAvEMd+AJvG7ECsDoE4IXKC+AN/jeq/SZvPUpM7Pou6Pkg1ILa53Nxllooyk4yY9Y0CsATQki4csyAh2QDRO++8woO9d8lbSLkiFg3e4ceBeAJIYSQMHXwVDHKqkwAgOzUKMToff8ySgghxH+7d+/Gvffei8GDB2Po0KF48MEHcfTo0VBPi7QlXoCkTWSHWhmAF2rymO1QZ8A71oH3tgQNANgSBkBWs03z/M2CrzBX4POcT5ix3tF9MDZpvF/nUxQdBJrVkYeghDVpsF/nIoQQ0vacAvDwPgte4VD/3bFEWrihADwhhBAShmRZxld7Ltm3Jw8L7Zd1QgjpbPbv34+HH34YtbW1ePLJJ7FkyRLk5uZi/vz5OHas9Y0nSfhybJDamgxvwEUGfMhL0LDXLzTkMw1QPeIFWNNGM0NKP+vAr7m4EmaHTMeFvRaD4zi/zqcqYG8E2JIGA4oIv85FCCGk7bkOwHtXB14oZzPgw7n8DEABeEIIISQsnb5SibyrDf/4yEqKRM8MaiBGCCHB9Je//AWpqalYu3YtHnzwQfzsZz/D2rVrodVq8frrr4d6eqQNBTQAb6sHbyxjhsTITDc7B0eaQwkak2hCpaXC6+Ot6Q514Av3A6LVpzmcrz6LnUXbmLGb0iaje1QPn85jJ8tQ5rM3AizpY/07FyGEkOAQ1JBVOmbI8TPTJVmGwqEETTg3YAUoAE8IIYSEpc0Hmh5XnzQs3e9sMEIIIb6rrq7GuXPncNttt0Gj0djHExISMGLECCpD08FJumRmuzUlaIRa5/IuYmS6iz2DJ14dD5WgYsbyDXlu9nZmyWAD25zVBEXJUa+Pl2UZK86/z4xpBA3mdVvg9TkcCVWXnP6eHOvVE0IICT+SNonZ5o0tf+ZyxlJw9VXMGGXAE0IIIcQnl4tqcLGgGgCQFKvFgG7xIZ4RIYR0Lnq9Ht999x0efPBBp9cqKyshCILzQaTDEAMYgHcsPyNr4gCl1u/zBQLP8U5laAoM+W72diZr4iEmsLV2Hcu/eLLn6i6cr2YzF2d1nYMYtf/N85T5e5htSZcMMaa73+cjhBASHM43vUtaPMYx+11WaiGF+OZ2SxShngAhhBBCWFsPNX0Jnjw8AzxlvxNCSFAJgoDs7Gyn8TNnzuDw4cMYP963JpE8z4Hng/e7XBB45v+Jb7ioVDT/6BWMxVAo2LX0do2VhgLmXGJ0ptO5QiErMguXa5t6zRSa8n2aly1zHBTlZ+zbqoK9sIx+ssXjJFnCZzkfM2Mp2lTc0XUmFLx/awwA6sIf2HXOuh4KJd0o8wb9vmh7tMZtj9Y4ONpknfXJzO9vRX1pi59Hqqrz7O/8+F5QKMM7xB3esyOEEEI6maJyA07mVIADEBsVgeF9kgAv+6IRQghpOwaDAc8++ywA4JFHHvHp2Lg4XUhKiUVFaVreiTizdEXzb/Z8fQVio1SAoHTatcU1thQz51IldoMqVufhgODoldgDu4t32revWgoR68u8+k0Gji23b/JVFxEr1ABRqR4P252/G8WmIuaG1M+HLUFSfIzbY1pcY3MtUHKUWeeIvpMREQbr3J7Q74u2R2vc9miNgyOg65yQCZxv+v2tsZZD09Lvb8Ml9rM1fUBYfLZ6QgF4QgghJIyU19RDH6GEod6K28d0hULgYbNJoZ4WIYR0aiaTCY8++ijOnDmDxx57DMOHD/fp+IoKQ9Az4KOiNKipMUEU6TPEV5wUjSi52d1vWUZNwWXIzR5v93aNdSWXoGh2rnpVMsyVhjaZty/i+WRIUtO8LlVeRqUv84rogShVJDhzjX3IdHIzLH1muT1ElmWsOrGauW52ZDb6aga7vLa3a6y4vB26Zk1gZV6BmqjBQBisc3tAvy/aHq1x26M1Do62WGclHwtts89JsSIfdS38/o4sPAm+2TFGbVdYQ/g735sb2BSAJ4QQQsLIgK7x6PVADA6dK8XE69JhMphDPSVCCOnUqqur8cgjj+DIkSO4++678atf/crnc0iSzAQdg0UUJbqJ6w8hErKgBmxNn8FydRFsGufs7pbWmKvJQ/NYvlWXHhZ/J6kRbA34aks1Kk1ViFRGeXkGDpbU0VBd3mQf4a/sga3HnW6POF11Eueq2Lq9d2TNgijK8PS4X0trrM7dw65x0lDYuAggDNa5PaHfF22P1rjt0RoHRyDXmYtIZH6Hc4arns9tM4GryWV/70f3CPu/dyqORAghhIQZlVLA+MFpiFDRfXJCCAml8vJyLFiwAEeOHMHcuXPx5z//OdRTIsHAcc5N4Yx+NGKVbODritihyAw3OwdXckQyBI6tkZ7vQyNWALBkXM9sK4v2A6LF7f7/vfIFs50QkYixSde72dtLsgxl/vfMkDVjXOvOSQghJGgcP285U6XHzxJF5UX2ni3HwRYb/k23KQBPCCGEEEIIIQ7q6urw0EMP4ezZs3jwwQexdOnSkNRxJ6Ehah0C8AbfA/C84Sogiex5ozJbNa9AEXgF0nTpzFi+Idenc1jTxwLN/pPgrCYoiw+53De3LgeHyg4wY3dk3QmBb12ygVB5AbyxlJ1XxthWnZMQQkjwOAbgATj9Xm9OqDzPHh+VBSjCv/Y/BeAJIYSQMHA6pwLVdVRuhhBCwsWf/vQnnDlzBvfffz+ee+65UE+HBJmkT2G2/QnAC7VsRrms1EJWx7RmWgGVoWVvBhT4mAEva+JgSxjAjCnzdrvcd33uOmZbr4zEjWmTfbqeK8r8Pcy2pE+FGN211eclhBASHLJSDygimDHeUOx2f6HiHLNti+3ZJvMKNHq2nRBCCAkxk9mGVZvOwWoTMbp/Cu6a0C3UUyKEkE7t3LlzWL9+PSIjI9G3b1/897//ddpnxowZIZgZCRZJxwbgBQ/BAHf42jz2nJEZQBg9RZGhYwPweT5mwAOANXM8FKUn7NuqvF0wjnqGeZ/l9WXYU7yTOe72jKmIENiAiz9UBS7Kz4TRGhNCCGkBx0HUpUCozrEP8cYSt7srHDLgxTgKwBNCCCHEC3tPFMNksQEArDaJShwQQkiIHTjQUCqjtrbWbfY7BeA7Nqca8HW+B+CF2gJmW4wKj/rvjdJ1rcuABwBL5gRoDv/Tvs3XFUGougSxWT3er/P+C1FuKsWj4lW4NWOqHzNmcZZaKEqOsfOh8jOEENLuSLokNgBvcBOAl2UIFWwA3hbXqw1nFjgUgCeEEEJCyGoTsfNoIYCGMqo3DQuvL+eEENIZ3XfffbjvvvtCPQ0SQk4BeH9qwNe4yIAPI44Z8OXmMphsRmgUWq/PIcb2hKRLZtZHmbfLHoA3WA3YUrCROeamtMmIVkW3YubXrlO4H5CkpgFBCWvKiFaflxBCSHB5+5nLG4rBWeqYMbGdlKChGvCEEEJICP14ugS1poYu74N7JCApJvwbyBBCCCEdnWMJGs5cDdhMPp3DsQa8GGYB+DRtutNTdwVGH7PgOQ7WzPHMkKpZHfgfS39AvVjfbHcO07Jm+jxXVxzrv1tThgFK+ncUIYS0N5I2idnmja4D8I4NWGWV3unzOlxRAJ4QQggJEVGSsP1I0+Ppk64Lry/mhBBCSGclapOdxtw+Eu+KLIN3CsBnutk5NJS8EskaNnCRb8hzs7d7lswJzLai9Di4+koAwPdX2aasw+JHOF3TL5KNCfQDgDXj+taflxBCSNBJOjYAL7jJgFc4NGAV43q2m74fFIAnhBBCQuTo+XKU1zRkhfXOjEFGkj7EMyKEEEIIAEClg6xiP5d9KUPD1VeAs7IZ8+FWggYAMrTsTQF/AvDW1OGAollDVVmGMv97VFuq8VMlW6N9XMoEBIKy6Edw9VXMmCVjvOudCSGEhDVJy96YdXfD26n+ezspPwNQAJ4QQggJCVmWse1wU2bc5GHhlRVHCCGEdHaOj7ULBu8bsTqWnwGvcKpxGw4c68D7E4CHoIY1bRQzpMrbjf2leyHJTTXaVYIKwxJG+jVPR6pLbF15MaEvpCj6txQhhLRHjp+PXH05IFqd9lNUOmbAt48GrAAF4AkhhJCQOHWlEoXlBgBAl+RIdE+PCvGMCCGEENKcc1M47wPwzg1Y0wBeCMi8Aik9EAF4ABaHOvDKwh+wr2gnMzY8fiQihAi0mmiGKnc7M2Tuelvrz0sIISQkHEvQQAZ4Uyk7ZjOBr8llhtpLA1YAUIR6Aq21Zs0arFy5EoWFhejSpQseffRRTJ061eMxv/3tb/Hll1+6fX3kyJFYtWoVAKCurg5///vfsWnTJlRUVCApKQnTp0/HkiVLoFKp7Mds3boVjz/+uMvzffXVV+jVq/3clSGEENL29p9seox98rAMpyZohBBCCAkt5wC89yVohNoCZjvcGrA2ynQIwJfUX4VFtEAlqNwc4ZolYxx0zEAtUPQjoG0q4zM2OTAlYlR5e8BZDE0DHGDuektAzk0IIST4ZFUUoFADNrN9jDeUQNKn2bcVlRcBudlBHAdbbPcgzrJ12nUAfvny5XjllVdw22234cEHH8TmzZvx1FNPgeM4TJkyxe1xc+fOxZgxY5zGN27ciK1bt+LGG28E0FAe4IknnsCPP/6IOXPmoHfv3jh69CjeffddnD9/Hu+884792PPnz4Pnebz88stOQZTU1NQAvWNCCCEdxYJbe+PgmRKczKlAv65xoZ4OIYQQQhy0JgNeqGUzycM1AJ+mZeclyzKKTIXoos/26TyyNhFiQl8IZacBABbRgmGGGvx0LQCvETQYGj8sIHNWXWbLz9hShkF2zJ4khBDSfnAcJG0S8/SY401voZKt/y5FZQEKTVCmFwjtNgBfU1ODt956C9OmTcNrr70GAJgzZw4WLFiAV155BbfeeisEwfUjfkOHDsXQoUOZscLCQixduhTjxo3DwoULAQBbtmzBDz/8gBdeeAH33XcfAGDevHlISUnBv/71Lxw8eBDDhw8H0BCAz8jIwMyZM9voHRNCCOlIlAoeYwakYMyAlJZ3JoQQQkjQiTrHpnDeZ8DzDjXgw7EBKwBoFBokRCSgrL7MPpZvyPU5AA8AlswJ0DQG4CULhhtt+FCWAY7DyKTRPmfVu8JZ6qDK28WMmbve2urzEkIICS1Rl8IG4I0OAfgKtv57e2rACrTjGvDbtm2D0WjEvHnz7GM8z+Pee+9FUVERjhw54tP5Xn75ZZjNZrz44ov2DPYff/wRAJyC6rfffjsA4OjRo/axc+fOoXv39vPoAyGEEEIIIYQQ9xybsPKGYkCW3ezNcmzCKoZxg9B0rWMd+Hw3e3rWWAdekkXYZBtSrBakWxvKCYxNCkz5GWXuDrYxHy/A0mVSQM5NCCEkdBzrwDve9FY4ZMCLcRSAD4oTJ04AAPr378+M9+vXj3ndG8ePH8emTZswf/58ZGVl2ceXLFmCdevWQadjqtmhsrISAKBQNDxAYLPZcPnyZXsA3mw2w2az+fiOCCGEdAbGeudu7oQQQggJP44laDirCZy1ruUDLQZwpkr2XGGaAQ8AGU6NWHPd7OmZGNcHkjYRFsliHxtTVw29Uo9BcUNaM0U79aXvmG1r+ljIETEBOTchhJDQkbQOAXhjSdOGLEOoYAPwtrj21Wuz3QbgS0pKEB0dDY2GrfeTmJgIoKGkjLfeeecdqNVqLF68mBmPiYlB3759nfb/+OOPAQDXXXcdACAnJwdWqxU5OTmYNm0aBg8ejKFDh+Kpp55CRUWFT++LEEJIx1VWZcKLHxzA6s3nUFxhDPV0CCGEEOKBYwAeAPi6luvAC3UFTmNis0Zy4cYxAF/gZwY8OA7WrIlMAP6mmkqMShgDBd/66rdcfSWURfuZMXM3Kj9DCCEdgafG57yhGJyFvQEutrMSNGFXAz4/3/OHfWRkJKKjo2EwGBAREeH0euOYyWTy6nqFhYXYtWsX7rrrLsTFtdwEb926ddi4cSPGjh2LQYMGAWgoPwM0ZNIvXrwYqampOHToEFauXInz58/j008/dTlXV3ieA89zLe8YIILAM/9PWLQ+ntH6eEbr41lnXJ+dxwshShIOnS1BSrwWGUl6t/t2xvXxBa2PZ7Q+ntH6EEK8Iqgga2KZbHbecLXFx975OjYZTNImAArvvg+GgmMAvtBYAFGyQfAjaJ6fMQpxx9+zbyfZrJgiJLR6jgCgytkCSFLTgEINS+bEgJybEEJIaHkKwDs2YJVVeqcyceEu7ALwkyZ5rt/28MMP4+mnn4YkSfZa7c01jrl6zZUvvvgCoihi/vz5Le67ZcsW/OEPf0BiYiJefvll+3i3bt3w+OOP484777SXsJk8eTK6dOmCF198EZ999plX5weAuDid13MPpKio9tM5OBRofTyj9fGM1sezzrI+VbVmHDhdAp7nEKFS4I6JPaCNULZ4XGdZH3/R+nhG6+MZrQ8hpCWSLhlC8wC8seUMeMe6tZI+NeDzCiTHALwoiyg2FSNd53vZnC3WqxitUiPL0lD7nQeHPsU/wdh7XgtHtsyx/IwlcyKg1Lb6vISEi+XL38WKFcuYMZ7noVZHICUlBWPHjse99y5AdHQMs88TTyzG0aOHsWfPQZ+vKYoirl4tRlpaemumTq7Jz89DRkb49fyoq6vDK6/8Bfv27YUsS1i4cDHuvXeB3+dbuvRFbNjwFT79dD1SUwPzhJekdQjAm8oaen4ISigcGrCKcT0BH2KntbW1uP/+uZg2bQYWLXokIPP1VdgF4F955RWPr/fq1VDjR6fTob6+3un1xsx3x7rt7mzbtg3Z2dno06ePx/3++9//4ne/+x30ej3ef/99pKQ03Wnp06ePy+NnzZqF//mf/8H+/fu9DsBXVBiCngEfFaVBTY0Joii1fEAnQ+vjGa2PZ7Q+nnW29fnv7kuwWEUAwJj+yTCbLDCbLG7372zr4ytaH89ofTwL1frExnr371NCSPgQtSkQcMa+7Rhcd0UwsEF6x6BCuNErIxGtikG1pco+lm/I8ysAf7j8IIxRcXiwrAgAoORViLiyHSZLLWRVpN9z5OuKoLh6lBkzd7vN7/MREs7uuONODB48FAAgSRJqa2tx8uRP+PjjVfjuu6/x1lvLkJnZ1L/wgQcewvTpM32+TnFxMZ599leYMOHGkAUlO5LVq/+N99//F7Zv/yHUU3Hy4YfvY9u2zZg06RaMGDES/foNDPWUnDg2YYUM8PXlkHQpzvXffSg/U19fj+ee+zVKS0ta3rkNhV0AfsaMGV7tl5qaiurqalgsFqhUKvt4SUnDgiYnt/yPnPLycpw6dQoPP/ywx/3WrFmDpUuXIiYmBh9++GGLwfpGSqUSUVFRMBq9r/MrSTIkSfZ6/0ARRQk2G31Bd4fWxzNaH89ofTzriOtjNpuxZ89OnDjxE4xGA9SaSFyRB4FXqKAQeIwblOb1e+6I6xNItD6e0fp4RutDCGmJY0CAN5a2eIxTBryLWvLhJkOX4RCAz8UojPHpHHXWWlyoOY+SyBjMLyuGAjJUggoQLVBd+g7mPrP9nl/EyY+YbVmlhzXdt/kR0l4MGDAIt946xWF0HqZMmY7f/OZX+M1vfoVVq9ZCoWgI6Y0YMdqv6xQW5uPixQuYMOHGVs6YAMAPP3wPq9Ua6mm4dPFiQwD7mWcaEovDkayOAQRlQ9b7NbyhBJIuBQqHEjSilw1YL126gD/+8fe4dOliIKfql3Zb+LJ///6QZRmnT59mxhu3Bw5s+W7OkSNHIMsyxoxx/8G9bt06/OlPf0JiYiJWr17tMvj+6quvYvLkyU6B9qqqKlRUVCA9nR7lIYSQzkCWZXzyyRosWrQAb775D2zctBk7d3+PvSdLUV1rQFVVFSK5ckRpWy49QwghhJDQk7QOAXhDyxl07TMAn8Vs59Zd8fkcxyuOAQBqBAUO6iLBAVBwDQHCiPP/9XtunKEUEWe/YMYs3W4HBJWbIwjpmEaNGoO5c+9FXl4uNm36NtTTIe1I442BcA2+AwA4zkUd+GJw9ZXga3KZcW8asK5atQILF96H0tJSzJ17X0Cn6o+wy4D31sSJE6FWq7Fq1SoMHjwYQMOjOWvWrEF6ejqGDBnS4jnOnGl4lNBdRvuFCxfw/PPPIy4uDqtWrUJ2drbL/dLS0pCXl4fPP/8cCxY01VB6++23AQDTp0/34Z0RQghpj2RZxuuvv4pdu3ai1mhBrckKUZTA8QrEDhkIUZLBQcLRHZ/gddMZPPnkMyHp+UEIIYQQ70naRGabN/oTgA//RnHZ+q7M9sXaCz6f41jFEfuft0bFYbzRBKDh3zpC2WkIFedbbGDrivqnfwNis7J9vAKmgff7fB7S/omyiDprXain4ZZeqYfACW16jWnTZmDNmlXYvXsnpkxpiDW5qgG/detmfPbZx8jJyYHVakFGRhZuvXUK5s69FzzPM/XmV6xYhhUrltnreRuNhmvX2IGCgnyIoojExCSMH38DFi16BFptQ++FDRu+wksv/Qlvvvku9u7dg61bN6GysgKpqWmYOfNuzJnD9n4wGg1YuXIFduzYipKSEsTFxWH06LFYuPBhxMc3NWsuLS3BBx8sw75936OysgLx8QkYP34iFi582Kn+fXNbtmzBkiVL8MgjS7BgwULmtTNnTuNnP1uAuXPvxc9//pTP1zlz5hRWrVqBY8eOwmyuR3p6Ju68827ccced4DgO48YNt+87btxw3H77NPz+938EAOTkXMaKFctw+PBB1NbWIDExGRMm3IAHHliEqKgo+3F33z0d3bp1R//+A/Hxx6sgSTIeffQJ3HWX+6eHfvjhe/znPx/h9OlTsNlsyMrqgqlT78CsWXPA8zwOHz6IX/ziUWZuKSmp+Oyzr9ye02az4eOPP8KmTRtQWFiAyMgoDB06DIsWPYLs7C5uj/P25wZw//P5cHQiVDX5AABRApavXY8th15DYYkMBQ/0jQceGKTA4NgebufR6Ny5s5gyZTp+9rNHceVKDj75ZHWLx7SldhuAj42NxeLFi/Hmm29ClmWMHj0aGzduxMGDB/H6669DEJp+6W3ZsgVAQ2PU5q5cuQKNRoO4uDiX13jjjTdgsVgwfvx4HDt2DMeOHWNe7927N/r06YPZs2fj888/x1//+ldcvnwZPXr0wN69e7F582bMnTsXI0aMCPC7J4QQEm7Wrv0Yu3btRFm1CSaLiOzew9Gt32hAHY8KgwTRZkFF/glUlFzFrl07kZGR6fSPUkIIIYSEF6cM+JZK0MiS0z5OdW3DULdINphx1VQMg9UAndK73hWyLON4swD8Ma0eNk0cYGsqJaA+vw7GUc/4NrG6UqhOf8oMmXtMg6QPTNM/0n7svboHH5x7F9WW6lBPxa1oVTQe6vUIxiaPa7NrZGZ2gVqtxtmzp93us3PnNvzxj7/DiBGj8fDDj4HnOWzfvhVvv/13VFZW4PHHf4GJE2+CzWbDqlUrMGHCjZg48UbExMTCZrPhl798DBcunMeMGXfh7rvvgdFowLZtW/DJJ6thNBrx7LO/Z6730ktLodFEYPbseVAoFPjyy0/xxhuvQafTYerUOwA09GtcvPhBXLmSg1tuuQ333DMY+fn5+OKLtTh69DDefXcFdDo9CgsL8NhjD8FisWLGjLuQkpKKCxfOY926z7Fv3178618rEBMT4/J933DDDYiLi8fGjRucAvDfffcNANjn48t19u//Ab/97VPQ6XSYOfNuJCQkYOfO7Xj11ZdQXFyERx5ZguefX4qVKz/AlSs5eP75pUhPb+ihcezYETz11BMQBAVmzpyF1NQ0nDhxHGvXrsH33+/CP//5AWJjY+3zPHLkMM6ePYNFix5FdXUVhg93H0/8+OOP8Pbbf0dGRibuu+9+aDRa7Nq1Hf/4x99w+PBB/OUvryA7u6vT3DQa982rJUnCU089gcOHD+L668dj5sy7UVVViU8//RiHDx/ABx+sQmxsd6fjfPm58fTzaZjQFb+5dp/2f/cDa07vx92DdHigG1BrAT45AzyxScQrkw9izBjP/509//xSe8nyK1dyPO4bDO02AA8AS5YsgUajwerVq7F582ZkZ2fj9ddfx5QpbK2sl156CYBzAL6qqsrj4xcHDhwA0NCA9b//dX5k7oknnkCfPn2gUqmwfPlyvP7669i4cSPWrl2LzMxMPPfcc7j/frozTwghHZ3ZbMZXX61DrdECk0XEuCmLkN51AGRZxpWrdRAEDoKgQVZmBgp+ElFrtGD9+i8xY8ZdUKvVoZ4+IYQQQtxwDJ5zljrAagIUrgPTnKkckGzMmNgOMuAz9VkQOAGiLNrHcuouoX+sd436Co0FKKsvs29LHAdTj2mIOfOlfUx9cQOMw3/pW+mYg8vBiRbYu6TxAkyDFnl/POkw3j3zFow27/vrhUK1pRrvnnmrTQPwHMchKioa1dVVbvf55pv1iIjQ4G9/+wd4vqHy9PTpd+KXv3wMOTmXAQA9evRETU01Vq1age7de9hrzu/ZsxOnT5/CE0/8CvfcM99+zlmz5uLuu6dj69ZNTgF4nU6HZcv+DaWyoczmhAk34O67p+Prr9fZA95r1qxETs5lPP30c5g5c5b92MzMLLz66kv47rsNmDVrDl5//RXU19fjgw9W24PYADBx4o148skleP/9f+Hpp3/r8n0rFArcdtsUrFmzCmfOnEafPn0BNASHt2zZiL59+6Fbt4abjd5eR5Zl/O1vL0Or1WLFijVITGz4TLjjjrvwxBOL8cknqzFv3nzceusUfPXVOly5kmNfS0mS8PLLSyHLMt5770NkZzc8aXTnnXdjyJDr8L//+2f8859v4He/e9F+fZPJiKVLX2oxuFxQkI9//etNZGd3w7Jl/4ZGowEAzJ59D/785xewceO32LhxA26/fZrLubnz7bdf4/Dhg5g//0E8+ugT9vGBAwfjqaeewNq1H+OFF/7gdNy+fd97/XPj6efzYnkJcC0A//k54PosJf44vNZ+vtu7AfdvT8SZM6dbXKPm/ULDQbsOwHMch0WLFmHRIs8fwNu2bXM5vmzZMo/H/fCD952LY2NjsXTpUixdutTrYwghhHQMe/bsRF2dAbUmK7J7D0d61wH21+Ii1aisM0Mp8EhL74OiXsORf+EQ6uoM2LNnJyZNuiWEMyeEEEKIJ44Z8MC1MjSari72dlEjnhcgR7h+4jqcKHklMvVZyKm9bB+7XHvR6wD88YqjzHasOhYR/e4HmgXgOXMNVLk7Yel6s1fn5IzlwNE1zJi5+zRIkZT9Tjq3lhp9JiUlw2Qy4v/+739xxx13omfP3hAEAW+99V6L5x43biI2bNiKiAgNM15eXoaoqCiUl5c5HXPTTZPtwXcASE5OQVxcHMrLy+1ju3ZtR1RUNKZPn8kce/vt09CrV29kZXVBbW0t9u//AaNHj4VOp0dVVZV9v549eyMtLR27dm13G4AHgOnTG0r0bNy4wR6A37dvL6qqKvGznz0CAD5d59y5sygqKsTs2fPswXcA4HkeL7zwP7BYzNBqXd+QPXfuLPLz8zBt2gx78L3RtGkzsHr1v7FjxzY8++wf7FU8VCqVV011d+3aAVEUsWDBg/bgO9AQJ33kkSewceO32Lp1E26/fVqL52LPux0cx+Hee9lk4pEjR+O99z5Ely5ZLo/z5efG08+n+vQnwL5XAACpOuBAvhkf/ATcmg2kRwJJqRn4z2frgHZYyrVdB+AJIYSQcHDixE+wWEWIotRQduYajuMQpVMhUquEJDXkbnXrPxo5Zw/AYhNx4sRPFIAnhBBCwplSC1mlb8h8v4Y3XAXiXQfgBUMxsy1pEwG+bWtCB0q3yO5MAP5i7UWvj21e/x0ABsddBzk6C7aU66AoPmwfjzj1MSzZkwCOb/Gc6p9WAjZz0wDPwzToIa/nRDqWR/o80W5K0LQlm82GurpaJCQkut3noYcW49y5s1i37nOsW/c5YmJiMWzYcIwffwNuuGESFArPoUCFQomvvlqH48ePorCwAIWF+aiurgbHcZBl2Wn/uLh4pzGlUgVJkuzbhYWF6NatO1MuGmgIOPft2x8AcPr0SUiShL1792DaNLaCRXNmcz3U6giXr3Xt2g39+g3Ali0bsWTJL6FQKPDdd99ArVZj8uTbAAD5+bleX6eoqAAA0KVLttPrKSmpbo8FGrLUG+fkiOM4dO3aDfn5eaiurrKvYXR0TIt/Py2dOykpGXq9HkVFhS2ex1FRUSHi4uKY2vSN+vUbAIXC/e9ub39uPP183tw9AY23M/5nPPDUNuBvP3L4249AZqSMUUNicEP3Ixgy5Dqf31uoUQCeEEIIaSWj0QDp2j8qlLp4GOtt0EY0fcRyHAdBaLhLr49uaDAkSTKMRkPwJ0sIIYQQn0jaRAjNA/DGEkhu9uWN7a8Ba6NukT2wDVvs25e9DMDbJBtOVB5nxgbHDQEA1PecAX2zALyi5BgiTq5B/YD58IQzVUB1ei0zZu4+FVJUhpsjSEc3NnkcRiWN6fRNWC9cOAebzWbP7nYlLi4e7733Ic6cOYW9e/fg8OGD2LVrB7Zu3Yz+/f+Dt99e5jbIW1pagscffxilpVcxZMh1GDRoCGbMuAv9+w/E3/72Mo4dO+J0TGMZEU9E0QauhaxlUWz4zXrDDTdhxoxZbvcTBM+hzKlT78Crr76EAwf2YcCAwdi7dzduuGGSvQS1L9ex2RpKirU0d9dkj8c2zkOpbCqV4niDwt9zS5LMnNdbDe/X9/fqy8+Np5/Pz3p3x+qxgJIHhiYDG+cA+4tk7MkHfizi8PmeU/hs92LMnXsffv7zJ32eZyhRAJ4QQghpJa1WB0EZgdR+43C1WoJaZUJWkh487/yPl7rqhsfveJ5z+7giIYQQQsKHpE2CUNWUGc4bS90H4A0OAXgXJWzCVddItrFegSEf9WI9IgTXmaaNzlafhlk0M2MDrwXgLdmTIR1+h1kX7aE3YU0bCTGul+sTyjK0h94AZ6tvKjPA81T7nUDgBESrokM9jZDauPFbAMANN0xy+bosy7h8+SLMZjP69u2PPn364aGHFsNgqMOf//xH7N69A/v3/4Drrx/v8vjly99FUVEBXnvtTYwaNYZ5zVX5GW+lpqbZM8+bB+xtNhv+9Kc/YNiwEZgw4QYAgMViwYgRo5zOsXv3DkRFRbeYIT558i14883/w9atm1FWVgaLxWKvRQ8AaWlpXl8nNTUdAJCbe8VpvwMH9uG7777Bvfc+gO7dezi93lhb/tIl55uZsizjypXL0Ol0iIyM9Ph+XElLazp3z569mdeKi4thNBqQnJzs83lTU9Nx4MA+1NXVOfXM/N///TPS0tLw1FO/dDrO258bb34+v88CxqQBZyuBaDUwPqPhf9bMcTjb7xk8+eQSfPrpx3jooYeh07nv6xluWr5NRQghhBC3JFmGPnUQ4gffh9iMQRBtFthECTVGi8v9L53cB4XAQ6UQMGCAd3VVCSGEEBI6jo1YeWOJmz1dBOD17ScDPlufDd6hNMyVZiVp3HEsP9M1sltTkFQRgbrxf2ITKiUb9Dt/B9jqnU8my9Ae/DvU579ihs3dpkCKyvTqfRDSUR05cghffvkpsrO7uQ3AcxyH3//+N3j22adQV9f0tIBOp0ePHg3dLRuzrBv/v3l5kMbmrt2792TOu2PHVuTn5wGAPSvcF+PH34Cqqips2vQtM75t22Zs374FZnM94uLiMWjQEOzbtxfHjx9l9tu3by+ee+5pfPTRhy1eS6fTY+LEm/D997uxadO3SE1Nw3XXDbe/7st1evfug6SkZGze/B0qKyvt+8myjDVrVmHLlk2Ij294wrlxPRtL7zTWk9+06Vt789tG33yzHgUF+Zg48aYW348rEyfeCEEQsGrVCphMJmZe77//TwDub9J4Mn78REiShM8++w8zfvz4UXz11TpmDZrz9ufG25/PSjNw71fAX5q15qzvdSfS0tKRmJgEjuPAt5Pybo0oA54QQgjxU0GZAZ9tv4CcqxEQlBqIkgRrvQEa3oRoXbbz/pdPIOfcQcToVIiM1GPcuInBnzQhhBBCfCJp2VrLPgXgtb5nIIaKSlAjXZeBvLpc+9il2ovoHeO+1AXg3IB1cNxQZtuWOgL1A+5HxE8r7WNC1WVoD/4DxtHPMvtqjr6LiBMfMWOyoIJp8M98eSuEtGsnThxngrm1tbU4efI4duzYhtjYOLz00ises8Afemgx/vSnP+DRRxdiypTpiIyMwoUL57B+/Zfo2bMXhg8fCQCIjW1oEL1nzy6kpKRgwoQbMW7cROzevRO//vXPMX36DCgUShw9ehjbtm2GWq2G2WxGXV0dYmJifHpP8+c/iN27d+Cll/6EY8eOoHfvvsjLu4Ivv/wcvXr1wcyZDaVgfv3r32LJkofxq189jjvuuBNdu3ZHbm4O1q37HNHR0Viy5FdeXW/q1DuwceMGHDlyCIsWPeJUqsXb6ygUCjz99HN47rlfY+HCezFz5ixER8dg167tOHBgPx577Of2tWhcz+XL38WQIddhxIhR+O1vn8fTT/8Sixc/iJkzZyEtLQ2nTp3Et99+jdTUNDz22M99WsdG6ekZ+NnPHsO7776FhQvvxZQp06HRaLFnz04cOnQAY8eOx623TvH5vNOmzcCWLRvx/vv/wsWLF3DddcNRWlqCL774FCkpqVi40PXvYl9+blr6+RzVqwYqQxFm9QI+Pcth8XcyJvbQoz66GD/+89c4evQwZs2awzSfbQ8oAE8IIYT4yGS24bv9udjzU1FD7XeOQ0REBKoLf0LBqe2oq6tBSa/h6NZ/NPTRCairLsOlk/uQc+4gNCoBkVoVpk+fCbVaHeq3QgghhJAWOJaR4Y2lbvd1DMCLuvYTgAca6sA7BuA9qbHU4GLNBWbMMQAPAMahj0NZ+COE8jP2sYjTayHG94U1bRQkTQIiTn4EzdFl7IEcD+MNf6Hsd9KprF//Jdav/xJAQ0a7RqNFRkYm5s9/EHPmzEN0dIzH42+++TZoNBr85z+rsWbNKhgMdUhKSsbdd9+D++9/yB68z8rqgrlz78M33/wXf//735CSkoapU++A2WzGF1+sxTvvvAmtVov09Az85je/gyhKePXVl7B//16fg7t6vR7//OcH+PDDZdi1awe+++4bJCUl4667ZuOBBxbZm6p2794Dy5evwr//vRzbt2/FunWfIz4+ATfeOBkPPvgzZGR497tg6NBhSE/PQFFRIW6/fbrT675cZ+zYcXj77ffx73+/j//85yOIooTs7Gz88Y9/weTJt9r3u+++B3Dp0gWsXv1vnDz5E0aMGIXrrhuO9977EB9++D42bFgPg8GA5OQUzJs3HwsWPORX+ZlGCxY8iOzsbHzyyRqsWvUhABlZWdl48snf4M477/aqNr8jhUKB1157Ex999CE2b96IPXt2Ii4uHjfeOAmLFj2C2NhYl8f58nPT0s8nv/tXgKEIz48FusXIWHce+Md+C2z737z2/p7BnXfO9nvdQoWTXbUwJiFTWlob1OspFDxiY3WorDTAZnNXybDzovXxjNbHM1ofz9rr+lwsqMaqjWdR3azETFKMBrMmdsPXny7Drl07UWu0oNZktTfWAQCFwEOvUSJSq8KECRPx5JPPeGzm017XJ1hofTyj9fEsVOuTmOj/lyzSvtG/8ds3Ze5ORG59yr4taRNRd98m5zWWRMStGg1ITWtePX0VxIR+wZ6y3zbkfYUV55qC4Fn6Lnht1Jtu9//+6m78/cSr9m2VoMKHEz6Gklc67ctXXUbMV/cBNrPTa+AFQBKZIY7jwE99BZWpk+nnuA3R74u2R2vc9miNgyMY66zf+TuoLm1kxqpmfQkpKqtNrhcI3vwbnzLgCSGEEB/ERqphsjTUPVQKPG4ZkYkbhqZDIfB48slnkJ6ega++WofIOgMsNhGSJIPnOagUAvR6He64407Mnn2Px+A7IYQQQsKHUwma+nKnYDEA8KYyJvgOAJKu/dSAB5wbseYb8mARLVAJKpf7Hys/zGz3jxnoMvgOAFJMVxhGPAXdDy+7eNF5PU3jfg9dvxlApcHL2RNCCGnvxMgMZtuWcl1YB9+9RQF4QgghxAdxURG4ZUQWcoprcOf4boiLirC/xnEc5s5tqA24Z89OnDjxE4xGA7RaHQYMGIhx4yZS2RlCCCGknXEsQQNJAmeqABDFDDuWnwGvgBwR06ZzC7RsfTazLckScg056BHVy2lfWZZxzLH+e7xz+ZnmzL1nQZW/B8q83R73M458ErY+s6DzataEEEI6CnPPGdD89G9AsgG8AsbrloR6SgFBAXhCCCHEjROXy7HtUAEW39EPEaqmj8wbr0sHz2W4PU6tVmPSpFswadItwZgmIYQQQtqQrIlzKpHCG68CyGb2c2rAqksGON9r8IaSRqFFmjYdhcYC+9ilmosuA/B5hiuoMJczY67qvzM4DrUTX4Lu4N+hLPwRvKEYEK3NXgeM1z2B+v7zKVhBCCGdkBSZjqq7/wtl/h7YEgdDjOsZ6ikFBH2mEUIIIQ7Kq+vx5e5LOJlTAQDY+GMeZozran+dp/IxhBBCSOfB8ZC0ieDripuGDCVOu7kMwLdD3SK7swF4N41Y95f8wGzHqxOQrnWfoGCn1MIw5ncNf5ZlcPUV4OuKwZvKIEVlQozp5vfcCSGEtH+SLgXm3neHehoBRQF4Qggh5BqbKGHb4QJsOZgHa7MGqlcrjPZa7oQQQgjpfBwD8LzLAHwxs91eA/BdI7tjz9Vd9u3LbgLwP5buY7ZHJo32vccNx0HWxEPUxMO5CjwhhBDSMVAAnhBCSIdnNptbrMl+NrcSn++8hNJqk/24aK0KM8Z3xZAeCdQ0lRBCCOnEHOvAuwzAG9kMeLGdNWBt1M2hEeuVuhzYJBsUfFP4oNhUhJy6y8x+oxLHBGV+hBBCSHtDAXhCCCEdlizLWLv2Y3z11TrU1RlgsYqQZBk8x2H79m1YseJ93HL7XZDjh+DohTL7cTzHYcLgNNw6MpOp/U4IIYSQzknSJjLbnNFVBjw75tS8tZ3o6hCAF2UReYYrzPiPJWz2e5QyCn1i+gVlfoQQQkh7Q1EFQgghQdWYjX7q1AlIkhU8r0S/fgOYbPRAkGUZr7/+Knbt2olaowW1JivEZmVlBIFHpMmGLWeACF0OdHo9AKBrShTuvqE70hJ0AZsLIYQQQto3pwx4Y6nTPk4laPTtMwNep9QhWZOCq6am93Op9iITgN9fytZ/H544CgInBG2OhBBCSHtCAXhCCCFB4ZiNbrWJ4HgesiRh69atWLHifUyfPhNz5swLSLmXtWs/xq5dO1FWbYLJIiK793B06zca+ugE1FWX4dKpfcg5exAROQeQ2HMiIsxGzJ8yGMP7JFGTVUIIIYQwJB0bgOccGq5CtII3lTFDkrZ91oAHGsrQNA/AX669ZP9zhbkC56rPMPtT+RlCCCHEPQrAE0IIaXPustE5joMsyw3Z6EYLPv54NQoK8vHkk8+0KghvNpvx1VfrUGu0wGQRMW7KIqR3HdCQAc9xSNRFIzGtOzK6D8aebz+AUqWGzZqLwU8sp+A7IYQQQpw414BnM+B5UykgOxzTTpuwAg1laH4o+d6+falZI9aDpfuZfTWCBgPjBgdtboQQQkh7QwF4Qgghbc51NvoYxMQno6r8Ki6d+gE5Zw/CbBWxa9dOZGRkYs6ceT5do8ZgQV5pHUz1Nhw6egy2yL7QRPBIScgCF9kVeSV1MFtFROtUSIzRAADSuw5Ads9hyD+3F6nxOuzZsxOTJt3SFktACCGEkHbMMQDP2YyAuRYADwDgHTPiFWrI6uggzS7wHBuxXqq5gCt1Oeiiz8b+0r3Ma9clDIeSVwZzeoQQQki7QgF4QggJE4210U+c+AlGowFarQ4DBgwMeG30YF/TXTY6x3FQKASoIvRITOvWkI2+4QOYFQn4attBpPUZD6vIwVhvhdFsg6neBkO9DUazDcZ6Gx64rTeykiPt17lUVIN/f9fwOLShToI6ZRiUsgxVhA5Gs82+X7XBgkitChGqhjql3fqPRs7ZA7DYRJw48RMF4AkhhBDixLEJKwCgrgTgG+q8OzVg1SUD7fipuu5RPSBwAkRZBNDQiPXV4y/h+aFLcbLyBLPvqMSxoZgiIYQQ0m5QAJ4QQkLMsTa6xSpCkmXwHIft27cFvDZ6sK+5e/dOGMyALSIZPfsOhTquB4rKjbCJEqL0asToVACuZaP3Gg5tl/EQeA4fbz4LlYebAAaTldnWqps+0mRZQuNz4DzH28d5noMuQgmF0PSe9NEJAABJkmE0Glr1XgkhhBDSQSkiIKujwJlrmsbqrgJRjQF4tgGr2I7rvwOAXhmJWzJux7d5X9vHrpqK8fyhZ+1BeQBQ8koMib8uFFMkhBBC2g0KwBNCiAvBykZ3Vxu9UaBrowfrmruPFeJcXhXKqutxOV9E1ID50F/LRq+qM9v3s1ol5rhu/UehsMoCXqmG1WpzGYAXeA5atQKizBZaTYrV4PaRWdBEKLB9ywYc/mk/yiqqMXLSXCSmdgHPcS7fR111Q8M0nueg1ep8ep+EEEII6TwkbRKE5gH42mIgqqH2uWMJmvZc/73Rgh4Lcb76LC7UnLePVZormX0Gxw2FRqEJ9tQI6ZCWL38XK1YsY8Z4nodaHYGUlBSMHTse9967ANHRMcw+TzyxGEePHsaePQd9vqYoirh6tRhpaemtmTq5Jj8/DxkZmaGehpO6ujq88spfsG/fXsiyhIULF+Peexf4fb6lS1/Ehg1f4dNP1yM1NS2AMw0cURTx2Wf/wfr1X6KoqAhxcXG4+ebb8OCDi6BWRwR9PhSAJ4S0C8EMiAczG911bfTR0EcnoK66DJdO7Wt1bfRAXdMmSiivqUdZVT3Ka+pRWmVCeXU9rKKEJXcOZK6Re7UOJ3IqAAANsX3nbPQGbBBdH52AsiMboNcokNY9C3NvnQ1thKLhf+qG/1crBZdrH6NX45aRWQAAS2kWDu/6EhaDAVdO70VKele363Hp5D4oBB4qhYABAwa63Y8QQgghnZukTYRQeaFpoK4p6N4RA/BKXolfD/wtnv3xSdRYa1zuMyppTJBnRUjHd8cdd2Lw4KEAAEmSUFtbi5Mnf8LHH6/Cd999jbfeWobMzCz7/g888BCmT5/p83WKi4vx7LO/woQJN2LRokcCNf1Oa/Xqf+P99/+F7dt/CPVUnHz44fvYtm0zJk26BSNGjES/fh3/e+9rr/0V69d/iRtuuAmzZ9+Ds2fP4qOPPsTZs2fw2mtvBKy6gLcoAE8ICWvBDIgHOxvdXW30RhpdNBLTul+rjb4ctUYL1q//EjNm3OX3TQdfrvnjzi8hpI7AN0fqcNrwI6oNFodweQMOgE2UoBCaguvx0Q13lBU8hwjBgrqqK6iuKkP3vsMRF58MpaIh4K1UKmCzNT3GXFddhrLL+yHEaZEyMAFDeib49T7HjZuIFSveR6TRgpyzB5HRfTDzPhsVXD6BnHMHEaNTITJSj3HjJvp1PUIIIYR0fI6NWJsH4AUjW4KmIwTgASAhIhG/GvAM/ufoC5Adnj7kOR7DE0aGaGaEdFwDBgzCrbdOcRidhylTpuM3v/kVfvObX2HVqrVQKBpCeiNGjPbrOoWF+bh48QImTLixlTMmAPDDD9/DarW2vGMIXLzY8CTTM8/8Dnq9PsSzaXunTp2wx06eeeZ39vG0tDS8++7b2LZtCyZNujmoc6IAPCEkbAU7IB7sbPQ9e3airs6AWpMV2b2HuwwQA4210UegMOcnGMwyvtu6EyNHXQ+bKMMmSrDaJFhFCTabBL1Gie7p0czx2w/no9ZohVWUcPHSZUgJoxAZJSM1JgmI7Ir80jrIMhAfFQFthMJ+zYy8y4hI7g+O51BSUeu2HrtKIaC6zmIPugPA2IEpGNUvGdE6FbZv34w339yIsnIDNJwBmZPvAwCXf1eBykZXq9WYPn0mPv54NcxWEXs2LEd2r+Ho1r/Z3+fJfcg5dxAalYBIrQrTp89ss2a3hBBCCGn/JJ1DAL62eQa8YxPWlGBMKSgGxg3Gfd3vx0cX/s2M948dCL0yMkSzIp2SJIKzuH4aIxzIqiiAF9rs/KNGjcHcufdizZpV2LTpW0yZMr3NrkU6lsYbA50h+A4AGzY09C+55575zPicOfOwYsX72LDhKwrAE0JIo2AGxIORjW4y21BUboTJbIOh3oqdRwuhTB6GxDgeSb1HorDMAFGSIQPISmI/GFP63ABtl/HgOQ7fHJex49IRl9fo1yXOKQD/w8mrKK02AQAMdSKUsT0Rea0eu8lss+9nsYnQNvtYyOreHyUGQJYByWZGVmYC4qMjkBgd0fD/MRrER0UgUqt0CqZHaVX2P4cqG33OnHkoKMi338DJu3AIOWcP2F9XCHzDtbQqTJgwEbNn39Oq6xFCCCGkY5O0iexAYwa8aAFnqnDY1yFY387dkXUXzlefw/7SptIKE1Moa5YEj+ryZuj2/y84U2XLO4eIrImFYdSzsHRtu8DetGkzsGbNKuzevdMegHdVA37r1s347LOPkZOTA6vVgoyMLNx66xTMnXsveJ5n6s2vWLEMK1Yss9fzNhoN166xAwUF+RBFEYmJSRg//gYsWvQItFotAGDDhq/w0kt/wptvvou9e/dg69ZNqKysQGpqGmbOvNvpu7nRaMDKlSuwY8dWlJSUIC4uDqNHj8XChQ8jPr7pyefS0hJ88MEy7Nv3/f+3d+dhUdbrH8ffMww7gmCogDsKLrhk4pYpuWXmWi6lWFpmmno6WaeyU51fy6lTtpyyPU3LrTKX8lipuZdm5pK54K6JuCPKvs3z+4OYHIEBgYGAz+u6vIpnf27veWTu+c795eLFeGrUuI6bburGmDH35+l/f6Xvv/+eiRMn8sADExk1aozdupiYfYwdO4rhw0cwefKUaz5PTMxe5syZxa+/7iQ9PY2QkLoMHjyEAQMGYzKZ6NKlnW3bLl3aceut/fjnP/8PgGPHjjJr1kds3/4LiYmXCQysRdeuUdxzz334+vra9hsypD+NGoXSokVLFiyYg9VqMH78JG6/fWiB97x584989tlc9u3bS1ZWFvXq1ee22wZwxx3DMJvNbN/+C3/723i7a6tdO4gvv1xW4DGzsrJYsGAuK1d+Q1zcSapV8+X662/gvvseoEGD+gXuV9S8gcLzE3J6uH/66cesXfs9cXEnsVgsNGkSzvDhI+nSpWuB1wGwd+9v+Pr62bVqAnB39yA0NJS9e3c73N8ZVICXSquseoZXRbmx3bt3N1ZrJmazK82bR5RqbMu6PUtRR6MHN2hBo4gozp86QrqlOnOWbqBh42akpGWSkp5FSlqW7b939myCv/+fk3oeibvMjOV7bT8nZdTCK9gfDwOy8CD7imK4YRh2RW03Dy9ITQQMDMN+4tIrZWbnXedq+bM1TM6++fdjz8q2/1pxNb8Atqx+n2quaXTt3I6Hh139NciiKXg0eieq16hFwoUzHNmzudRHo5tMJh5++B+EhNRh2bKlVEtKJiMrG6vVwGw24WZxwcfHmwEDBjN06J1l3gNOREREKpa8LWhyRr1fPfodwOpTeUbAQ87vVZNaPEy1g77svbib9oEd6aoCvJQh700vYMpIKu/LcMiUehHvTS84tQBft2593N3d2b9/X4HbrF+/hv/7vyeJjOzI/fdPwGw2sXbtat55579cvBjPgw/+jW7dupOVlcWcObPo2vVmunW7merV/cnKyuKhhyZw6NBBBg68nSFD7iQlJZk1a77n88/nkZKSwuOP/9PufC+++Byenh4MHXoXFouFJUsW8tZbr+Ht7c1ttw0AIDU1lXHjRnP8+DF69+7DnXe2JjY2lsWLv2Dnzu188MEsvL19iIs7yYQJ95KRkcnAgbdTu3YQhw4dZOnSRfz00ybef38W1atXz/e+o6KiCAiowYoV3+QpwH/33XIA2/Vcy3m2bNnME09Mwdvbm0GDhnDdddexfv1apk17kdOnT/HAAxN5+unn+PTTjzl+/BhPP/0cISF1APj11x1MmTIJFxcLgwbdQVBQMLt37+KLL+bz448beO+9j/H397dd544d29m/P4b77hvPpUsJtGsXWeDf84IFc3nnnf9Sp05dRo68G09PLzZsWMubb77K9u2/8O9/v0KDBg3zXJunp1eBx7RarUyZMont23/hxhtvYtCgISQkXGThwgVs376Vjz+eg79/aJ79riVvipKfANOnv8HixV/88X79LpKSkvjqq0VMnfoIr7zyBp06dSnwPs6ePUutWvm3ggsMrMW+fXtJSkoq028EqABfRZVFAbW8lPUkmlcqj6J/WZ7z6thmZmVjMpsxrFZWr15dqrEtSkHcMAxq129Bw2adOXV8DylZrixbuZ4b2nXMacvyxx+zGVqF2vcS3xpzlhNnk8j6Y5sdvybg2fAW6tQ1cV2dJvx+NgnDMPDxcLVrrQLg17ALHkFtMZtM/HzUyp5zx/K9h8vJGXY/57Z3yWUymcnpoG5gNay4kPN1RbPJhNUAlytCmJ2eSOK5I3i4mqgZEsQNzRrjajFjcTFjcTHh6mLGYjET4Jt3Nu9hNzcm22pgcTGxYP4n/LB1A2cvJtGl770EBjXEZMq/HUzy5QukXj6Nb4AXXl7eedZfi4JGo5tMJgzDcNpodJPJxPDhIxg06A59ICciIiIlkqcAn3IesjMxp9hPwGq4emK4Vr6v+Xu4ePBA04nlfRkiVZrJZMLX149LlxIK3Gb58q/x8PDk1VfftI0m7t9/MA89NIFjx44C0LhxEy5fvsScObMIDW1s6zn/ww/r2bdvL5Mm/d2ufccddwxnyJD+rF69Mk8B3tvbm48++gRXV1cAunaNYsiQ/vzvf0ttBe/58z/l2LGjPProVAYNusO2b9269Zg27UW+++4b7rhjGG+88QppaWl8/PE8WxEboFu3m3n44YnMmPE+jz76RL73bbFY6NOnL/PnzyEmZh9NmzYDcorD33+/gmbNmtOoUWOAIp/HMAxeffUlvLy8mDVrPoGBOf8ODBhwO5MmjePzz+dx113R3HJLX5YtW8rx48dssbRarbz00nMYhsGHH86mQYOGAAwePIQ2bdry8ssv8N57b/Hkk/+ynT81NYXnnnvRYXEZ4OTJWN5/fzoNGjTio48+wdPTE4ChQ+/khReeYcWKb1mx4htuvbVfvtdWkG+//R/bt/9CdPRoxo+fZFvesmVrpkyZxBdfLOCZZ57Ks99PP/1Y5LwpSn7mbPcV7dt35NFHp9qW9ejRi8mTHyAmZp/DGCUnJ1GvXv6j9T08cmomaWmpKsCL85RlAbU8lHXP8CvPW9ZF/7I+Z0GxzS2gllZsDcMgMTWTrbsOYarWgOqe7tQOv5lTF1Ko6e+Bi/nPUduJKZmcTUjFr3EPPOt0xGwysXKPwY/Hd9kd08/LLU8Bft+xeHYcOm/7OSmrGm7V3bEYYMWNjMyciUGzrhpRbjKZcHHJ7etX8Gh0i9lERpb9ugBfD6LahODlbsHT3cKhA7v531fLOXshgZp1mnBD18GYCyiGn9y3gVOHthFUw5s+/R+iR48mjgN5hfq1/+zN2a5VGJvXf4uRlcKxfZupFdKowP1Kqx875D8a/crnj6uTR6O7u7vTo0dvevToXarHFRERkaojTw94w8CUegFT8pmrtqsFFfC9lMhfWXLnpypMCxpnK2yiz5o1a5GamsLrr7/MgAGDadIkHBcXF95++8NCj92lSze++WY1Hh6edssvXDiPr68vFy6cz7NP9+49bcV3gFq1ahMQEMCFCxdsyzZsWIuvrx/9+w+y2/fWW/sRFhZOvXr1SUxMZMuWzXTs2Blvbx8SEhJs2zVpEk5wcAgbNqwtsAAP0L9/ToueFSu+sRXgf/ppEwkJFxk79gGAazrPgQP7OXUqjqFD77IV3wHMZjPPPPM8GRnpBQ5WO3BgP7GxJ+jXb6Ct+J6rX7+BzJv3CevWreHxx5+y1Rjc3NyKNKnuhg3ryM7OZtSo0bbiO+S8737ggUmsWPEtq1ev5NZb+xV6LPvjrsVkMjFixN12y9u378iHH86mfv16+e53LXlT1PysWbMWO3ZsY/78Odx8cw+CgoKpWbMWn3++tND7MAwjz8ThV64DbMX/sqICfBVSVgXU8lTWk2hC+RT9y+Oc+cf2ihYiezcXK7YpaZl8/eMxLiamczEpnYTEdDKzrSSlNcS3cQg+BmTgRWZaJplZ7rj82Vrc9r7mz1Yq+RfEry6EA7ha7CfHyW80ujlnaHjefY1kzh/ZgpeblZbNmnD7rYPw8rDg5W7B2yOnuO5qMePqan8OP283Bnb58x/eyPAAvl/6Ea5ZFzm6ZyMh9cOc3hu9vPqxQ97R6JXxGzgiIiJSeRnu1cHFFbL/LH6Zk8/mU4CvXO1nRP4KMhr2IqN+9yo9CSvkjOZOSkrkuusCC9zm3nvHceDAfpYuXcTSpYuoXt2fG25ox003RREV1QOLxXEp0GJxZdmypezatZO4uJPExcVy6dIlW+3oagEBNfIsc3V1w2r98314XFwcjRqFXjGYLYebmxvNmrUAYN++PVitVjZt+oF+/XoWeH3p6Wm4u+f95jdAw4aNaN48gu+/X8HEiQ9hsVj47rvluLu707NnHwBiY38v8nlOnToJQP36DfKsr107qMB9IWeUeu41Xc1kMtGwYSNiY09w6VKCLYZ+ftUL/fsp7Ng1a9bCx8eHU6fiCj3O1U6diiMgIMCuN32u5s0jsFgKLloXNW+Kmp9PPPE0zzwzlXfffZN3332TkJA6REZ2pGfP3rRp09bhfXh5eZOenpbvurS0nOXe3mX7TTUV4KsQZxVQi6Is2qSUdc/wXOVR9C/rcxYUW5PJhMXigpuHD4HBjagT2ppNKz4lFR+Wrf6Zmo27kJSWnVNc/+NP55a16dWuru3YFhczW/adyXPO/AriOaPRXez29XS3kJGayKXT+/F0haD6dejYOjynAG5xwdXFhLtb3l+CerarQ+eI2n9sZ2bL5o3MnPEJcecvUSe0NR16jiwwHqf2rSP+6Dbca3hzU5vutArN+wtHURTcG/2Kv8s9P5Vqb/TyOGd+19CjR29uuaUP/v7eXLyYTFY+H5KIiIiI/KWYTFi9AjEn/lnUMKWcxeXqArxX/n1nRaSEzC4YHv6Fb1eJHTp0gKysLNvo7vwEBNTgww9nExOzl02bfmD79l/YsGEdq1evokWLz3jnnY8KLPKeO3eWBx+8n3PnztCmTVtatWrDwIG306JFS1599SV+/XVHnn2KMpI4Ozur0EGBuYMKo6K6M3DgHQVu5+LiuJR5220DmDbtRbZu/YmIiNZs2rSRqKgetnYj13KerKycedqKN6DRcLhv7nW4uv45yvDqDyiKe2yr1bA7blHl3O+13+u15E1R87Nly9Z88cVXbNu2lS1bNrN9+y989dUili79kuHDRzJ58sMFXk9QUDBnzpwu8FqrV69e5oP/VICvIq6lgFqaxemybJNSWM/wpNRMrIaBX+1wQtvcwtkT+0g1LCxftZ6uXW6yFWzdXHP6ahdFeRT9y+qchmGQlW0lNSObtevWk5rtQZZ7LRqHtcCvdnie7c9eTCXdvR4tek8hOysTF7OJhWsP4HbVOS9csv8U0s3VBR9PV5JSM3G3uOBfzR1/X3cuX0hj6+afOX/hHDUCQ2jdqS8WF/sc8XS3EOJuYcuqpZz5oz1Lr0EP0eOmgtuq5Aqsbv/VqF7du/HZvBn4uJvKdGR4Qb3RczmjN3p5nFNERESkMrB61bQrwLue/AnLsdX223irAC8izrFixbcAREX1yHe9YRgcPXqY9PR0mjVrQdOmzbn33nEkJyfxwgv/x8aN69iyZTM33nhTvvvPnPkBp06d5LXXptOhQye7dfm1nymqoKBg28jzKwv2WVlZPPvsU9xwQyRdu0YBkJGRQWRkhzzH2LhxHb6+foWOEO/ZszfTp7/O6tWrOH/+PBkZGbZe9ADBwcFFPk9QUAgAv/9+PM92W7f+xHffLWfEiHsIDW2cZ31ub/kjRw7nWWcYBsePH8Xb25tq1arlWV+Y4OA/j92kiX195vTp06SkJBc4CakjQUEhbN36U74TlL788gsEBwczZcpDefYrat4UNT/btWvP4cMH8fX1o2PHznTs2BnImTz34YcnsnDhAu699/4CR7E3bx7B/v37iIs7SXBwiG15WloaR44con37wtv8lDYV4KuIgorTWdnWnPYchoHZbCK4QQsahLUj9tA2kpKS+eGH9cXumezMNim5fcLjL6cRfzmd+MtprNpxAc/Q22gY5k2NGtfl2efC5TQy/xhlW61ue9wDW2A2mfhut8HGY9ts2w3s0pCoNn++QC8mpvHvT37BYjHj6mLGzdVsK9afjjtBtn8bPFxTCQuoRe16ze3OmZGVTUamFb/a4TRu04dzJw+QZnJj8bfriYzsgIvZhMkEbhYXgq+z7xt2OTmDbKuR0xPcbMJsMuFiNrFh/XqSktNITMvO94MGwzDIthoE1mlGo5Y3cy7uMOku1Zj/9QaahEeQnplNemY2aRlZdGsdYjfB6J5j8Sxad5i0jJxtrH98TSg5ycCn6RC8DAM3D2/OXkzFx9PV/rwYWA0DF4sb2VmZGAZkZmbZFeCrebrims9Xlibf3hIfT1c83S22HEhPb8x9338Ily9y+FQMQXUaOLUgXl4jw/PrjZ6RlY3VmvOadHNCb/TyOKeIiIhIZWD1sm/74BaziKs7MmTVLNncOSIi+dmxYxtLliykQYNGBRbgTSYT//znYyQnJzN//iJbEdXb24fGjZuwceM62yjr3P9e2R4kd3LX0FD7OcfWrVtNbOwJIKdoXpQ2KVe66aYo5s6dzcqV39Knz2225WvWrGLt2u+JiGhJQEANWrVqw08/bWLXrp20atXGtt1PP21i6tRH6dTpRlq3vt7huby9fejWrTs//riRM2dOExQUTNu27Wzrr+U84eFNqVmzFqtWfUd09Gj8/f1tMZs/fw7bt//C5MmPAH/GM/dDhtx+8itXfsudd0bb9YFfvvxrTp6MpW/f/tcUx1zdut3MBx+8zZw5s+ja9WZbH3jDMJgx4z2g4A9pHLnppm5s2bKJL7/8jNGjx9qW79q1k2XLlhbYWeFa8qYo+XnpUgLjx99LZGRHXnvtLdvxgoNDCAysyenTpzA7aPfUu3cflixZyLx5n/CPfzxpW75w4QIyMjK49dbixb0kVICvInbv/o2MzGyys600av7nJz0p6VmciU8l9+srAAHht2DyD8eVdFZsjyfF8xjVfdy4qVWw3TENw3BYmCtpm5TU9Cw83e1TdN3Ok2zZc4b4xLQ8fb2TMqvj5uuF1QCzq/3o5pzr/fP/HfUMd71q9HtaejZnLqaQ3/QNyUnZuF0XQUCNnMJ0tmFw5SMgJS2L83+M+PapG4lbYPOc0f8xBj/H/mbbLrC6J09G32B37M/XHGTv8bwTzCQnGfi1GUu1P4rh5xJS7UZzGwYcO50IgG+DLnjUvh6zycTmw1Z2nbH/5LVloxp2BXgMuJiUnuecOTH6Y6IKkxnrHxNaXPn37+qS880BNxeDs0f24k4KwQ2DGT3gzpxR7dXc8/Rdz1XT3yvPsvIoiJfXyPCre6M7s1VTeZ5TREREpKKzetV0uD4zKJLM4E4OtxERcWT37l12xdzExET27NnFunVr8PcP4MUXX3FYAL/33nE8++xTjB8/hr59+1Otmi+HDh3g66+X0KRJGO3atQfA3z8AgB9+2EDt2rXp2vVmunTpxsaN63nkkcn07z8Qi8WVnTu3s2bNKtzd3UlPTycpKYnq1atf0z1FR49m48Z1vPjis/z66w7Cw5tx4sRxlixZRFhYUwYNymkF88gjTzBx4v38/e8PMmDAYBo2DOX334+xdOki/Pz8mDjx70U63223DWDFim/YsWMb9933QJ7aVVHPY7FYePTRqUyd+ghjxuS8f/bzq86GDWvZunULEyZMtsUiN54zZ35AmzZtiYzswBNPPM2jjz7EuHGjGTToDoKDg9m7dw/ffvs/goKCmTBh8jXFMVdISB3Gjp3ABx+8zZgxI+jbtz+enl788MN6tm3bSufON3HLLX2v+bj9+g3k++9XMGPG+xw+fIi2bdtx7txZFi9eSO3aQYwZMzbf/a4lb4qSnxaLhX79BvL110uYMmUyN954E2azmZ9/3szOndu5445hdpPPXq1ly9b07dufr75azOXLl+nQoSN79+5h2bKl3HjjTbZvW5QlFeCriJSUZNtoZh+/P0eHZ2fnU1Y2WfD0rYXZBBez3FizPRZ/H/c8Bfi5Kw9wMDaBal5u+Hi64uPlSjVPV6p5ueHhCv9bs5U0kx8ZRjZd+o7I0yalRu1GBIfewK6fV2H1C+B/m3/nsuduEpIzib+cTmpGFq+M72w3YjotPZvTF1PyvcfcnuHWrHSyMvMWkWv4emC15ozSTrx0ntijP1PN25PajUJpGdaIjCwrWVlWavjaT+aRlW3N6TOemU2W1T5eVxemzQ4+kHBU9M9vv2xrQTM225/zalceqrDJSdMzs+1+9nS34OvlhrurCx5uLri7ueDu6sL+fSc5HnuYxKQUQlt0wNcv7zcMAnw9CPD14FzcYeJ2f0utAC9qt+hFeL3i9+gr64J4eY8Mz+2NXtxvnVSUc4qIiIhUVI4K8Fk1W5LY/TX7X8hFRK7R118v4euvlwA571E9Pb2oU6cu0dGjGTbsLvz8qjvcv1evPnh6evLZZ/OYP38OyclJ1KxZiyFD7uTuu++1Fe/r1avP8OEjWb78K/7731epXTuY224bQHp6OosXf8G7707Hy8uLkJA6PPbYk2RnW5k27UW2bNl0zcVdHx8f3nvvY2bP/ogNG9bx3XfLqVmzFrffPpR77rnPNqlqaGhjZs6cwyefzGTt2tUsXbqIGjWu4+abezJ69Fjq1KlbyJlyXH/9DYSE1OHUqbh8Rztfy3k6d+7CO+/M4JNPZvDZZ3PJzrbSoEED/u///k3PnrfYths58h6OHDnEvHmfsGfPb0RGdqBt23Z8+OFsZs+ewTfffE1ycjK1atXmrruiGTXq3mK1n8k1atRoGjRowOefz2fOnNmAQb16DXj44ccYPHhIkXrzX81isfDaa9OZO3c2q1at4Icf1hMQUIObb+7Bffc9YPsGwNWuJW+Kmp9TpjxO/foN+Pbb5Xz44TtkZ2f/cX//YPDgoYXey2OP/ZOQkDosX/41P/ywnsDAmtxzz32MGjW6XL7lbzLym8JYys25c4lOOe6bb77GipWrOHMxhe63TyYwOBSA1IxsUtOzyczKJivbSna2QXp6GqmpKbiYzbi7u+Pt403dmj5MGdbG7pjvLP6NQ3GX8j1fRno6ycnJZFsNspNP0+b6SNu69Mxs4s4n2wrMGemptp7h3t7edi1Lpo5sazc6+ud9Z1i49jABvu7U8PXA/4//BlTz4FDMdubNfp+TZ+Op2+QGh5Noblk1j9g/eoZPnvxQgcVHi8VsN0lkVraVzCwrmdlWMjOtzPh4Bj9u/pmLyVnc0G0oQSH17F7IaRnZpKbnTNqReOk8B3f9QDVvd8KahNGp040YRk6h3cfL1W5iUoBVv5zg1B9xshoGVitYDYOY/TGcOXOW9Ewr1wWHUsPfj+o+9iOVT8enYALSkhM4sncz3m4GLZo24ba+ffFwc8HDzYK7qws1/T3zfMsgP6tXr2T69DeJu5BsF9vcOQSysrJtX1sramyLKs88AmVUEC+NiYOvzh+xp/g4pvg4pvg4pvg4Vl7xCQws/pssqdic9Tt+QfQMcB63oyvxWTcVkylnEE3ON0Mhu0ZTLvd5H8NNr/PSojwuG4qz8ynGzqcYlw3FOX9F+R1fI+CriIiIlqxduwYXFzNH9v5kK8B7uVvw9Xa/qoD6FbGHthFcqwajRj9AROsIzOa8hU0fL1eq+7iTlJKRZ2R4ZmaWreVLQKD9yHkXs8ludPfVPcM9PDzwr+ZOgK9HnlHgbcMCade0Zr4jxpvV68bSz2dQzdPVaZNoWv5os5L7RZd2rcLYvP5b0i8ncyLmB4Lr2Bf9c4rdOV8dO/DTOhKOb8Ozhjc9hnWhx40NceTqgnyu1atPMn36x8RdSIbEGwjN54OG2gE5H1ps2baE+CPbcK/hTdc7o2jfrHgTQnXp0o1Zs2ZQLSWjTCcohfJrlaKR4SIiIiJytfxGwGf7h3K59zsqvouIiEi+VICvIopTQPXxtHBbr4KLm/f0aQrkjFBOy8gmMTWTpJRMElMy+GzhlxyIjSXd6kadG7rY7ediNuHqYsbFxYTFxQxWg19/+Q5vSzrtWofxzwmP4VLAV2UsLgV/haY8eoaXR2G6PM5ZcGw7Ub1GLRIunOHIns2lPkHp1deggriIiIiIlKes61pgeAZgSosHwOpbj8u3vIfhUb18L0xERET+sip8AX7+/Pl8+umnxMXFUb9+fcaPH89tt93mcJ8nnniCJUuWFLi+ffv2zJkzB4DVq1fz4IMP5rvdsmXLCAsLK9G1lBVnFlBNJhOe7hY83S3U/GMy0HVeyfwa9wtnLqbQrFkzuGKCDpPJRP3af44OORd3ioS4PbgHeOHn1aLA4ntRlHXP8PIo+pfHOaHg2JpMJgzDcNoEpSIiIiIifxkurly6dQbee+fg4XcdSWEjMCy+5X1VIiIi8hdWoQvwM2fO5JVXXqFPnz6MHj2aVatWMWXKFEwmE337FjwhxPDhw+nUKe/M9CtWrGD16tXcfPPNtmUHDx7EbDbz0ksv5ekvHRQUVOJrKUtlWUAtqOVNfo7s+QmLixk3iwsRES2LfU4on0k0y7roX17nzC+2mVnZmMxmDKsVVydPUCoiIiIi8ldg9atP6k3P4OHvjXExGdQHV0RERByosJOwXr58mW7dutG9e3dee+01AKxWK6NGjeLkyZOsXr0aFxeXIh8vLi6O/v3706ZNG2bMmGErHj7yyCPs2rWLVatWlcm1OHuCpqsntHRWATU9PZ377hvFqbMXSUjOoEvf+wpsk/LDNzOp7u1GcK0AZsz4tNTalpTlJJrlMVFoeU1OCn/Gdu/e3VitmZjNrjRvHuG0fuwVlSYocUzxcUzxcUzxcUzxcUyTsEpZ0ySslY9i7HyKcdlQnJ1PMXY+xbhsKM75q9STsK5Zs4aUlBTuuusu2zKz2cyIESOYMmUKO3bsoF27dkU+3ksvvUR6ejr/+te/7IqVBw4cIDS04NHbzrgWZ7p6QktnFVDLq03K1ddQVj3Dy2Oi0PKanBT+jO0tt/TRw1dERERERERERKQAFbYAv3v3bgBatGhht7x58+a29UUteu/atYuVK1cyZswY6tWrZ1uelZXF0aNH6dq1K5Az6tfFxQWLxT5spXktZaUsCqjl0SalvJXHRKGanFREREREREREROSvqcIW4M+ePYufnx+enp52ywMDA4GcljJF9e677+Lu7s64cePslh87dozMzEyOHTtGv379OHToEK6urvTq1YunnnqKgICAUr+WyqQ8+rGLiIiIiIiIiIiI/FX85QrwsbGxDtdXq1YNPz8/kpOT8fDwyLM+d1lqamqRzhcXF8eGDRu4/fbbbQX1XAcOHAByRsiPGzeOoKAgtm3bxqeffsrBgwdZuHAhHh4epXYtAGazCbO57ArRLi5mu/86w8iR0QwZMpQNG9aze/euK9qktKJr1792z/CyiE9Fpvg4pvg4pvg4pvg4pvg4pvg4pviIiIiIiEhZ+csV4Hv06OFw/f3338+jjz6K1WrNd8R07rKijqZevHgx2dnZREdH51nXqFEjHnzwQQYPHmxrTdOzZ0/q16/Pv/71L7788kuio6NL7VoAAgK8y2UkuK+vZ+EblYg3w4YNZtiwwU4+j3M4Pz4Vm+LjmOLjmOLjmOLjmOLjmOLjmOIjIiIiIiLO9pcrwL/yyisO14eFhQHg7e1NWlpanvW5o829vb2LdL41a9bQoEEDmjZtmmdd06ZN811+xx138Pzzz7Nlyxaio6NL7VoA4uOTy3wEvK+vJ5cvp5KdrUk0r6b4OKb4OKb4OKb4OKb4OKb4OKb4OFZe8fH3L/rvhCIiIiIiUjn85QrwAwcOLNJ2QUFBXLp0iYyMDNzc3GzLz549C0CtWrUKPcaFCxfYu3cv999//zVdo6urK76+vqSkpJTateSyWg2sVuOarqc0ZGdbS30S1spE8XFM8XFM8XFM8XFM8XFM8XFM8XFM8REREREREWersI0vW7RogWEY7Nu3z2557s8tW7Ys9Bg7duzAMAw6deqU7/pp06bRs2dPW6E9V0JCAvHx8YSEhJTatYiIiIiIiIiIiIhI5VJhC/DduuVM3jlnzhzbMqvVyvz58wkJCaFNmzaFHiMmJgYg3zYzAMHBwZw4cYJFixbZLX/nnXcA6N+/f6ldi4iIiIiIiIiIiIhULn+5FjRF5e/vz7hx45g+fTqGYdCxY0dWrFjBL7/8whtvvIGLi4tt2++//x7ImUD1SsePH8fT05OAgIB8zzF06FAWLVrEf/7zH44ePUrjxo3ZtGkTq1atYvjw4URGRl7ztYiIiIiIiIiIiIhI1VBhC/AAEydOxNPTk3nz5rFq1SoaNGjAG2+8Qd++fe22e/HFF4G8BfiEhAR8fHwKPL6bmxszZ87kjTfeYMWKFXzxxRfUrVuXqVOncvfddxfrWkRERERERERERESkajAZhlH2M35Kgc6dSyzT81ksZvz9vbl4MVmTkOVD8XFM8XFM8XFM8XFM8XFM8XFM8XGsvOITGFitzM4lfy36Hb/yUYydTzEuG4qz8ynGzqcYlw3FOX9F+R2/wvaAFxERERERERERERH5K1MBXkRERERERERERETECVSAFxERERERERERERFxAvWAFxERERERERERERFxAo2AFxERERERERERERFxAhXgRUREREREREREREScQAV4EREREREREREREREnUAFeRERERERERERERMQJVIAXEREREREREREREXECFeBFRERERERERERERJxABXgRERERERERERERESdQAV5ERERERERERERExAlUgBcRERERERERERERcQIV4CuxEydOMGnSJNq3b0/79u157LHHiI+Pd9p+Fc3GjRsZMWIErVu35vrrr2f06NHs3Lmz0P0GDx5MeHh4nj9/+9vfnH/RZai491nZ8yc2NjbfuFz5Z8uWLQXuX9nz56mnnmLUqFF5lpckLypTThUUn+I+j6By5VRB8SnJPVbm/Cnp8wgqfv4U5bWj549UJcrZ0uXsZ4zkFRMTQ0REBNOnT7dbrjiXTHx8PE899RSdO3embdu2REdHs337drttFOOS2717N2PGjKFNmza0bduW8ePHc+TIEbttFOfiKen7TMW9aEr6flVxLpilvC9AnOPixYvcc889ZGRkMHbsWLKzs5k5cyb79+9n4cKFuLm5lep+Fc2WLVu4//77adKkCQ8//DBZWVnMnz+f6Oho5s2bR+vWrfPdz2q1cvjwYXr37k3Pnj3t1oWEhJTFpZeJ4t5nVcifgIAAXnnllTzL09PTef7556lRowZNmzbNd9/Knj8LFy5k4cKFtG/f3m55SfKiMuVUQfEp7vMIKldOFRSfktxjZc+fkjyPoOLnT1FeO3r+SFWinC1dzn7GSF5ZWVlMnTqVzMxMu+WKc8kkJSUxcuRIzp49y+jRo/H19WXevHmMHj2ahQsXEh4erhiXgiNHjjBq1Cg8PT158MEHAZg1axYjRozgq6++olatWopzMZX0fabiXjQlfb+qOBfCkErp9ddfN5o1a2YcOnTItuzHH380wsLCjM8//7zU96to+vfvb0RFRRkpKSm2ZefOnTMiIyONe+65p8D9jhw5YoSFhRlLly4tg6ssP8W9z6qSP/l5/vnnjaZNmxpbt24tcJvKmj9ZWVnG9OnTjfDwcCMsLMyIjo62W1+SvKgMOVVYfIr7PDKMypFThcWnJPdYFfInP0V5HhlGxc+forx2qvrzR6oW5WzpcvYzRvJ6++23jRYtWhhhYWHGW2+9ZVuuOJfM66+/boSHhxs///yzbdnZs2eNVq1aGY8++qhtG8W4ZJ555hkjLCzM2LNnj23Zr7/+aoSFhRn/+c9/DMNQnK9Vab3PVNwdK633q4qzY2pBU0ktX76c9u3bExoaalvWuXNnGjZsyPLly0t9v4rk0qVLHDhwgD59+uDp6Wlbft111xEZGemw7cPBgwcB7OJTGRX3PqtC/uQnJiaGuXPnMnjwYNq1a1fgdpUxf9LT0xk8eDDTp09n4MCB1KpVK882JcmLip5ThcWnJM8jqPg5VZT8Kck9Vvb8yU9Rn0dQsfOnqK+dqvz8kapHOVt6yuIZI/b279/Pe++9Zxs5fCXFufgMw2DJkiVERUURGRlpWx4YGMhjjz1mW6YYl1xsbCz+/v40b97ctqxVq1ZUr16dAwcOAIrztSjN95mKe8FK8/2q4uyYCvCV0KVLlzhx4gQtWrTIs65Fixbs3r27VPeraHx8fPjuu+8YPXp0nnUXL17ExcWlwH0PHDiAyWSiUaNGGIZBSkqKE6+0/BTnPqtK/uTn9ddfx9PTk7///e8Ot6uM+ZOenk5SUhJvvPEGL7/8MhaLfWezkuRFZcipwuJTkucRVPycKiw+UPx7rAr5k5+iPo+gYudPUV47Vf35I1WLcrZ0OfsZI/ZyW8907tyZAQMG2K1TnEsmNjaWM2fO0LlzZyCnIJ+cnAzAyJEjGTZsmGJcSurXr8+lS5fs+l0nJCSQmJhIYGCg4nyNSut9puLuWGm9X1WcC6cCfCV05swZgHw/IQwMDCQpKYnExMRS26+icXFxoUGDBnnuMyYmhu3bt9O2bdsC9z148CDVqlXj+eefp23btlx//fX07Nmz0n2aV5z7rCr5c7U9e/awfv167rzzTmrWrOlw28qYPz4+PqxcuZK+ffvmu74keVEZcqqw+JTkeQQVP6cKiw8U/x6rQv5c7VqeR1Cx86cor52q/vyRqkU5W7qc/YwRex999BHHjx/nueeey7NOcS6Z48ePAzmjVadNm0ZkZCRt27alV69erFmzBlCMS8vYsWMJCgpiypQpxMTEsH//fh555BEsFgvR0dGK8zUqrfeZirtjpfV+VXEunArwlVDuJ9pXfj0kl7u7O0C+o9yKu19lkJyczOOPPw7AAw88UOB2Bw8e5PLly6SnpzNt2jT+/e9/4+XlxZQpU1i6dGkZXa3zFec+q2r+LFiwABcXF6KjowvdtjLmj9lsdjgqtyR5URlyqrD45KeozyOo+DlVlPgU9x6rYv5cy/MIKn7+XO3q105Vf/5I1aKcdb7SfMbInw4ePMg777zD448/Tu3atfOsV5xL5vLlywC8+eabrF27lieffJKXX34ZDw8PJk6cyKZNmxTjUhIcHMy4cePYunUrAwcOZMCAAWzevJlXX32ViIgIxfkaldb7TMXdsdJ6v6o4F+7aoiwVgtVqLXQbsznvZy/F3a+iS01NZfz48cTExDBhwgSHPXNHjBiBi4sLd911l21Zv3796NevH9OmTaN///6FtoyoCIpzn1Uxf1JTU1m+fDndu3cnJCSk0O2rSv5cqSR5UVVzqqjPI6gaOVXce6xq+XOtzyOoXPmT32tn27Zthe6n549UFspZ5yrtZ4zkyM7OZurUqdxwww0MGzYs322U2yWTkZEB5LSHWLFiBX5+fgB0796dXr168dprr/Hkk08WehzFuHBvvvkm7777Lu3bt2fYsGFkZ2czf/58pkyZwn//+1/8/f0LPYbiXHRFfTboGVK6Cnq/qjgXrmrffSXl7e0N5PRyulrustxtSmO/iuzSpUuMGTOGn3/+mSFDhhTaMzc6OtquUAHg4eHBwIEDOX/+PIcOHXLi1Zad4txnVcyfn376iZSUFPr06VOk7atK/lypJHlR1XLqWp9HUDVyqrj3WNXy51qfR1B58qeg146eP1KVKGedxxnPGMkxc+ZMYmJieOSRR4iPjyc+Pt42Yjs1NZX4+HjFuYS8vLwA6N27t634DuDr60v37t3Zs2ePYlwKLl++zIwZM2jRogWzZ8+mf//+DBo0iHnz5tGoUSOeeeYZxbmUFTWeinvpcfR+VXEunArwlVBwcDAA586dy7Pu7Nmz+Pr62v4hLo39KqoLFy4watQoduzYwfDhw3nhhReKfayAgACg8n+lxtF9VrX8AdiwYQOurq5ERUWV6DiVOX9KkhdVKadK83kElTunchV2j1Upf6D0nkdQsfLH0WtHzx+pSpSzzuGsZ4zk2LhxI5mZmQwdOpROnTrRqVMnBg8eDOQU5zt16kRsbCygOBdXbj/m3H/brxQQEIBhGNSoUQNQjEvi2LFjZGRk0K9fP7tvD7q6ujJgwAAuXLiAYRiA4lxaivoM1rO6dBT2flVxLpwK8JWQr68vderUYc+ePXnW7d27l4iIiFLdryJKSkri3nvvZf/+/YwePZrnnnsOk8nkcJ8TJ07Qt29f3nvvvTzrjh49CkCdOnWccr1lqbj3WZXyJ9eOHTto2bIlPj4+hW5bVfLnaiXJi6qSU8V5HkHVyKmS3GNVyZ9c1/I8gsqRP4W9dvT8kapEOVv6nPmMkRyPP/44s2bNsvszbdo0AAYOHMisWbOIjIxUnEugSZMmuLm55futttjYWNzd3QkICFCMS8jNzQ3AVmS/Um5rDqvVqjiXoqI+g/WsLrmivF9VnAunAnwl1bt3bzZv3szhw4dtyzZt2sTRo0cLnN24JPtVNM8++ywxMTHcfffdTJ06tUj7hISEkJCQwMKFC0lKSrItj4uLY/HixXTo0IHAwEBnXXKZKcl9VpX8AcjMzOTQoUM0b968SNtXlfzJT0nyoirkVHGeR1A1cqqk91gV8geu/XkElSN/ivLa0fNHqhLlbOly9jNGICIigs6dO9v9adu2LQB169alc+fO+Pn5Kc4l4OXlRffu3Vm3bh0HDx60LT9x4gRr1qwhKioKFxcXxbiEmjRpQs2aNVmyZIldC46MjAy++uor/P39CQsLU5xLWVHjqbiXTFHfryrOjpmM/D6ikwovPj7e9vWne++9l/T0dGbMmEHdunX5/PPPcXNz48SJE2zfvp22bdtSt27dIu9X0R04cID+/ftTrVo1nnzyyXwnmBs4cGC+8fnuu+946KGHCAsLY+jQoSQmJjJ//nwyMzNZsGABoaGhZX07TlGU+6yq+ZPr999/p1evXvzjH/9g7NixedZX1fzJnQByzpw5tmVFzYuqkFNXx6eozyOoGjmVX/4U9R6rYv7kKux5BJUvf4r62tHzR6oS5WzpKe1njBRdbGwsPXr0YNKkSUyePBlQbpdUbGwsQ4cOBeDuu+/G1dWVTz/9lJSUFL788ksaNGigGJeCVatW8be//Y3GjRszZMgQrFYrixcv5uDBg7zyyisMGDBAcS6BkrzPVNyLriTvVxXnQhhSaR0+fNgYO3as0aZNG6Njx47GY489Zpw7d862ftGiRUZYWJixaNGia9qvops7d64RFhbm8I9hFByflStXGnfccYcRERFhtGvXznjwwQeNQ4cOlcetOFVh91lV8yfXr7/+aoSFhRkLFizId31VzZ+bb77ZiI6OzrO8KHlRFXLq6vgU9XlkGFUjpwrKn6LcY1XMn1yFPY8Mo/Llz7W8dvT8kapEOVs6SvsZI0V34sQJIywszHjrrbfslivOJfP7778bkydPNm644Qajbdu2xv33328cOHDAbhvFuOQ2bdpkREdHG61btzZat25t3HXXXcb69evttlGci6ck7zOvZbuqriTvVw1DcXZEI+BFRERERERERERERJxAPeBFRERERERERERERJxABXgRERERERERERERESdQAV5ERERERERERERExAlUgBcRERERERERERERcQIV4EVEREREREREREREnEAFeBERERERERERERERJ1ABXkRERERERERERETECVSAFxERERERERERERFxAhXgRUREREREREREREScQAV4EZFK7MSJEyxZssT2c/fu3QkPDycrK6scr8p5YmNjCQ8P56677irRcVauXElMTEwpXZWIiIiIiIiIVFUqwIuIVFIxMTH07duXH3/80bbs7rvvZtKkSZjNlfPx7+vry6RJk7jjjjuKfYxXX32VyZMnEx8fX4pXJiIiIiIiIiJVkaW8L0BERJzj0qVLZGRk2C0bPXp0+VxMGfH19WXy5MklOsb58+dL6WpEREREREREpKqrnEMgRURERERERERERETKmQrwIiKV0BNPPMHdd98NwLJlywgPD2fx4sV5esBv2bKF8PBwZs+ezbfffsvtt99Oq1at6NKlCy+//DIZGRmcOnWKhx9+mMjISDp06MADDzzA8ePH85zz9OnTPPPMM3Tr1o2IiAi6du3K008/zZkzZ+y2W7x4MeHh4Sxfvpy5c+fSu3dvWrVqRZ8+ffjwww/z7U+fkJDAyy+/TM+ePYmIiKBDhw5MmDCBnTt32m2XXw/46dOnEx4ezm+//caHH37ILbfcQkREBFFRUfznP/8hOTnZtm14eLitZ/6YMWMIDw8v3l+AiIiIiIiIiAhqQSMiUin17NkTgCVLlhAWFkbv3r1p1qxZgdt/9dVXHDx4kFtuuYXIyEi+++47Pv74Y+Lj49m0aRO1a9dmyJAh7Nmzh3Xr1vH777+zbNkyLJacf0YOHjzI3XffzcWLF4mKiiI0NJTff/+dL7/8kjVr1jB37lwaNmxod86PP/6YvXv30qdPH7p168aGDRt47bXX2L59O++99x4mkwmAU6dOMWLECOLi4mjdujU9evTg9OnTrF69mvXr1/Piiy8yaNCgQmPy7LPPcvjwYfr06UP37t1ZsWIFs2bN4syZM7zxxhsATJo0ie+//56YmBgGDhxI3bp1ixN+ERERERERERFABXgRkUqpZ8+eVKtWjSVLlhAeHl5oX/S9e/fy3nvv0b17dwCGDRtG3759Wbp0Kf3792fatGmYTCYMw2DkyJFs27aN3377jeuvvx6Axx57jISEBD788EO6du1qO+769esZN24cU6dO5bPPPrM75+7du3n77bfp1asXAFOmTGHChAmsXbuWZcuWMWDAAACefvpp4uLieOihh3jwwQdt++/Zs4fo6GiefvppOnToQFBQkMN7PHHiBMuWLaNOnToAjB8/nt69e7NixQrOnTtHYGAgkydP5uTJk8TExDBo0CA6d+5clHCLiIiIiIiIiORLLWhERITQ0FBb8T335+rVqwMwduxY22h0k8lkK7qfPHkSgF27drF371569+5tV3wH6NatGzfeeCM7duzg8OHDdutuvPFGW/EdwNPTk6lTpwLY2sCcOXOGjRs30qhRI7viO0CLFi247777yMjIYPHixYXeY//+/W3FdwA/Pz/atm1LdnY2sbGxhe4vIiIiIiIiInKtNAJeRERo0KBBnmXe3t4kJCRQr149u+UeHh4AZGRkAPDbb78BcOHCBaZPn57nOCkpKUDOiPXQ0FDb8o4dO+bZNjw8HG9vb/bu3Qtg+29kZGS+192uXTsA9u3bV/DN/SG/e/T19QUgMzOz0P1FRERERERERK6VCvAiIoKXl1eB69zc3Bzue/nyZQC2bt3K1q1bC9zu0qVLdj8X1DLGx8eH8+fPA5CYmAhAtWrV8t22Vq1aAKSmpjq8RgB3d/c8y3JH9huGUej+IiIiIiIiIiLXSgV4EREpEW9vbwAeffRR7r///iLvl5aWlmeZYRgkJibi7+8P5BTjIacVTX5yi/+57XJERERERERERP5K1ANeRKSSyh3d7WzNmzcHcnrB52fevHm8/fbbefqs//rrr3m23bdvHykpKbRu3dru2Dt37iQrKyvP9lu2bAEgLCys+DdwlbKKm4iIiIiIiIhUfirAi4hUUhZLzpec8itcl6a2bdvSqFEjVq1axapVq+zWbdu2jZdeeonZs2fbRrXn+vrrr9m5c6ft5+TkZF566SUAhg0bBkDt2rXp0qULJ06c4N1337XbPyYmhpkzZ+Lm5kbfvn1L7X5y46a+8CIiIiIiIiJSUmpBIyJSSeX2WN+wYQMvv/wyPXr0cMp5zGYz06ZNY8yYMUyaNIkuXboQHh7OqVOnWLVqFYZh8NJLL9la1eRyc3MjOjqaW265herVq7Nu3TpiY2MZPnw4UVFRtu2ee+45Ro4cyTvvvMOPP/5ImzZtOHPmDKtXr8ZqtfL8889Tt27dUruf3Li99dZb/PLLL0ycONE28ayIiIiIiIiIyLVQAV5EpJIKCgrikUceYfbs2cydOxdPT0+nnSsiIoLFixfzwQcfsHHjRrZs2UJAQABRUVGMGzeOVq1a5dlnxIgR+Pj4sGDBAuLj4wkNDWXChAkMGTLEbruQkBAWL17M+++/z+rVq5k3bx5+fn50796d++67L99jl8SIESPYsWMHP//8M8ePH2fgwIE0bty4VM8hIiIiIiIiIlWDyTAMo7wvQkREqo7FixczdepUxo8fz8MPP1zelyMiIiIiIiIi4jTqAS8iIiIiIiIiIiIi4gQqwIuIiIiIiIiIiIiIOIEK8CIiIiIiIiIiIiIiTqAe8CIiIiIiIiIiIiIiTqAR8CIiIiIiIiIiIiIiTqACvIiIiIiIiIiIiIiIE6gALyIiIiIiIiIiIiLiBCrAi4iIiIiIiIiIiIg4gQrwIiIiIiIiIiIiIiJOoAK8iIiIiIiIiIiIiIgTqAAvIiIiIiIiIiIiIuIEKsCLiIiIiIiIiIiIiDjB/wOVtZB7yohVNQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from aeon.visualisation import ShapeletClassifierVisualizer\n",
+ "\n",
+ "stc_vis = ShapeletClassifierVisualizer(stc)\n",
+ "id_class = 0\n",
+ "fig = stc_vis.visualize_shapelets_one_class(\n",
+ " X_test,\n",
+ " y_test,\n",
+ " id_class,\n",
+ " n_shp=3,\n",
+ " figure_options={\"figsize\": (18, 12), \"nrows\": 2, \"ncols\": 2},\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "weights = stc._classifier.feature_importances_\n",
+ "fig = stc.plot_most_important_feature_on_ts(X_test[0][0], weights, 3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see the same 3 shapelets are plotted ontop of the first object in the test set. These both show the same information only in a very slightly different format. The second might be useful if trying to make plots the same as the original paper however this can be achieved through the shapelet viz module by using the plot_on_x function.\n",
+ "\n",
+ "- Differences are\n",
+ " - SAST plot doesn't normalise distances\n",
+ " - SAST plots the best shapelets irrespective of class while shapeletViz plots them specific to a class, this would become apparent in linear classifier pipelines\n",
+ "Overall I think we can remove the SAST function, since it isn't providing adiditional insight that isn't present in shapeletViz"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "aeon_dev",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/classification/classification.ipynb b/examples/classification/classification.ipynb
index 0391e35535..68ef76af3f 100644
--- a/examples/classification/classification.ipynb
+++ b/examples/classification/classification.ipynb
@@ -76,14 +76,10 @@
],
"source": [
"# Plotting and data loading imports used in this notebook\n",
- "import warnings\n",
- "\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from aeon.datasets import load_arrow_head, load_basic_motions\n",
"\n",
- "warnings.filterwarnings(\"ignore\")\n",
- "\n",
"arrow, arrow_labels = load_arrow_head(split=\"train\")\n",
"motions, motions_labels = load_basic_motions(split=\"train\")\n",
"print(f\"ArrowHead series of type {type(arrow)} and shape {arrow.shape}\")\n",
@@ -96,9 +92,13 @@
{
"cell_type": "markdown",
"source": [
- "We tend to use 3D numpy even if the data is univariate, although all classifiers work\n",
- " with shape (instance, time point), currently some transformers do not work correctly\n",
- " with 2D arrays. If your series are unequal length, have missing values or are\n",
+ "We use 3D numpy even if the data is univariate: even though classifiers\n",
+ "can work using a 2D array of shape `(n_cases, n_timepoints)`, this 2D shape can get\n",
+ "confused with single multivariate time series, which are of shape `(n_channels, n_timepoints)`.\n",
+ "Hence, to differentiate both cases, we enforce the 3D format `(n_cases, n_channels,\n",
+ "n_timepoints)` to avoid any confusion.\n",
+ "\n",
+ "If your series are unequal length, have missing values or are\n",
" sampled at irregular time intervals, you should read the note book\n",
" on [data preprocessing](../utils/preprocessing.ipynb).\n",
"\n",
@@ -293,9 +293,9 @@
"collapsed": false
},
"source": [
- "Another accurate classifier for time series classification is version 2 of the\n",
- "[HIVE-COTE](https://link.springer.com/article/10.1007/s10994-021-06057-9) algorithm.\n",
- "(HC2) is described in the [hybrid notebook](hybrid.ipynb) notebook. HC2 is relatively\n",
+ "A slower but generally more accurate classifier for time series classification is\n",
+ "version 2 of the [HIVE-COTE](https://link.springer.com/article/10.1007/s10994-021-06057-9) algorithm.\n",
+ "(HC2) is described in the [hybrid notebook](hybrid.ipynb) notebook. HC2 is particularly\n",
"slow\n",
"on small problems like these examples. However, it can be\n",
"configured with an approximate maximum run time as follows (it may take a bit longer\n",
@@ -449,10 +449,9 @@
},
"source": [
"An alternative for MTSC is to build a univariate classifier on each dimension, then\n",
- "ensemble. Dimension ensembling can be easily done via ``ColumnEnsembleClassifier``\n",
+ "ensemble. Dimension ensembling can be easily done via ``ChannelEnsembleClassifier``\n",
"which fits classifiers independently to specified dimensions, then\n",
- "combines predictions through a voting scheme. The interface is\n",
- "similar to the ``ColumnTransformer`` from `sklearn`. The example below builds a DrCIF\n",
+ "combines predictions through a voting scheme. The example below builds a DrCIF\n",
"classifier on the first channel and a RocketClassifier on the fourth and fifth\n",
"dimensions, ignoring the second, third and sixth."
]
@@ -474,14 +473,15 @@
}
],
"source": [
- "from aeon.classification.compose import ChannelEnsembleClassifier\n",
+ "from aeon.classification.compose import ClassifierChannelEnsemble\n",
"from aeon.classification.interval_based import DrCIFClassifier\n",
"\n",
- "cls = ChannelEnsembleClassifier(\n",
- " estimators=[\n",
- " (\"DrCIF0\", DrCIFClassifier(n_estimators=5, n_intervals=2), [0]),\n",
- " (\"ROCKET3\", RocketClassifier(num_kernels=1000), [3, 4]),\n",
- " ]\n",
+ "cls = ClassifierChannelEnsemble(\n",
+ " classifiers=[\n",
+ " (\"DrCIF0\", DrCIFClassifier(n_estimators=5, n_intervals=2)),\n",
+ " (\"ROCKET3\", RocketClassifier(num_kernels=1000)),\n",
+ " ],\n",
+ " channels=[[0], [3, 4]],\n",
")\n",
"\n",
"cls.fit(motions, motions_labels)\n",
@@ -612,17 +612,23 @@
"\n",
"#### KNeighborsTimeSeriesClassifier\n",
"\n",
- "One nearest neighbour (1-NN) classification with Dynamic Time Warping (DTW) is one of the oldest TSC approaches, and is commonly used as a performance benchmark.\n",
+ "One nearest neighbour (1-NN) classification with Dynamic Time Warping (DTW) is\n",
+ "a [distance based](distance_based.ipynb) classifier and one of the most frequently used\n",
+ "approaches, although it is less accurate on average than the state of the art.\n",
"\n",
"#### RocketClassifier\n",
- "The RocketClassifier is based on a pipeline combination of the ROCKET transformation (transformations.panel.rocket) and the sklearn RidgeClassifierCV classifier. The RocketClassifier is configurable to use variants MiniRocket and MultiRocket. ROCKET is based on generating random convolutional kernels. A large number are generated, then a linear classifier is built on the output.\n",
+ "The RocketClassifier is a [convolution based](convolution_based.ipynb) classifier\n",
+ "made up of a pipeline combination of the ROCKET transformation\n",
+ " (transformations.panel.rocket) and the sklearn RidgeClassifierCV classifier. The RocketClassifier is configurable to use variants MiniRocket and MultiRocket. ROCKET is based on generating random convolutional kernels. A large number are generated, then a linear classifier is built on the output.\n",
"\n",
"[1] Dempster, Angus, François Petitjean, and Geoffrey I. Webb. \"Rocket: exceptionally fast and accurate time series classification using random convolutional kernels.\" Data Mining and Knowledge Discovery (2020)\n",
"[arXiv version](https://arxiv.org/abs/1910.13051)\n",
"[DAMI 2020](https://link.springer.com/article/10.1007/s10618-020-00701-z)\n",
"\n",
"#### DrCIF\n",
- "The Diverse Representation Canonical Interval Forest Classifier (DrCIF) is an interval based classifier. The algorithm takes multiple randomised intervals from each series and extracts a range of features. These features are used to build a decision tree, which in turn are ensembled into a decision tree forest, in the style of a random forest.\n",
+ "The Diverse Representation Canonical Interval Forest Classifier (DrCIF) is an\n",
+ "[interval based](interval_based.ipynb) classifier. The algorithm takes multiple\n",
+ "randomised intervals from each series and extracts a range of features. These features are used to build a decision tree, which in turn are ensembled into a decision tree forest, in the style of a random forest.\n",
"\n",
"Original CIF classifier:\n",
"[2] Matthew Middlehurst and James Large and Anthony Bagnall. \"The Canonical Interval Forest (CIF) Classifier for Time Series Classification.\" IEEE International Conference on Big Data (2020)\n",
@@ -632,17 +638,12 @@
"The DrCIF adjustment was proposed in [3].\n",
"\n",
"#### HIVE-COTE 2.0 (HC2)\n",
- "The HIerarchical VotE Collective of Transformation-based Ensembles is a meta ensemble that combines classifiers built on different representations. Version 2 combines DrCIF, TDE, an ensemble of RocketClassifiers called the Arsenal and the ShapeletTransformClassifier. It is one of the most accurate classifiers on the UCR and UEA time series archives.\n",
+ "The HIerarchical VotE Collective of Transformation-based Ensembles is a meta ensemble\n",
+ " [hybrid](hybrid.ipynb) that combines classifiers built on different representations.\n",
+ " Version 2 combines DrCIF, TDE, an ensemble of RocketClassifiers called the Arsenal and the ShapeletTransformClassifier. It is one of the most accurate classifiers on the UCR and UEA time series archives.\n",
"\n",
"[3] Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. \"HIVE-COTE 2.0: a new meta ensemble for time series classification.\" Machine Learning (2021)\n",
- "[ML 2021](https://link.springer.com/article/10.1007/s10994-021-06057-9)\n",
- "\n",
- "#### Catch22\n",
- "\n",
- "The CAnonical Time-series CHaracteristics (Catch22) are a set of 22 informative and low redundancy features extracted from time series data. The features were filtered from 4791 features in the `hctsa` toolkit.\n",
- "\n",
- "[4] Lubba, Carl H., Sarab S. Sethi, Philip Knaute, Simon R. Schultz, Ben D. Fulcher, and Nick S. Jones. \"catch22: Canonical time-series characteristics.\" Data Mining and Knowledge Discovery (2019)\n",
- "[DAMI 2019](https://link.springer.com/article/10.1007/s10618-019-00647-x)"
+ "[ML 2021](https://link.springer.com/article/10.1007/s10994-021-06057-9)\n"
]
}
],
diff --git a/examples/classification/convolution_based.ipynb b/examples/classification/convolution_based.ipynb
index 5bd2cd2221..ec38dad826 100644
--- a/examples/classification/convolution_based.ipynb
+++ b/examples/classification/convolution_based.ipynb
@@ -3,17 +3,23 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"source": [
"# Convolution based time series classification in aeon\n",
"\n",
"This notebook is a high level introduction to using and configuring convolution based\n",
"classifiers in aeon. Convolution based classifiers are based on the ROCKET transform\n",
- "[1] and the subsequent extensions MiniROCKET [2] and MultiROCKET [3]. These\n",
+ "[1] and the subsequent extensions MiniROCKET [2] and MultiROCKET [3] and HYDRA [4] \n",
+ "and a combination of ROCKET and HYDRA, MultiRocketHydraClassifier. These\n",
"transforms can be used in pipelines, but we provide two convolution based classifiers\n",
- " based on ROCKET for ease of use and reproducability. The RocketClassifier combines\n",
- " the transform with a scikitlearn RidgeClassifierCV classifier. Ther term\n",
+ " based on ROCKET for ease of use and reproducibility. The RocketClassifier \n",
+ " combines the ROCKET transform with a scikit-learn RidgeClassifierCV classifier, while \n",
+ " Hydra and MultiRocketHydra further extend this approach by enhancing feature \n",
+ " extraction and handling multiple feature maps more effectively.The term\n",
" convolution and kernel are used interchangably in this notebook. A convolution is a\n",
" subseries that is used to create features for a time series. To do this, a\n",
" convolution is run along a series, and the dot product is calculated. This creates a\n",
@@ -38,19 +44,34 @@
"\n",
"ROCKET employs dilation. Dilation is a form of down sampling, in that it defines\n",
"spaces between time points. Hence, a convolution with dilation $d$ is compared to\n",
- "time points $d$ steps apart when calculating the distance.\n"
+ "time points $d$ steps apart when calculating the distance."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
"data": {
- "text/plain": "[('Arsenal', aeon.classification.convolution_based._arsenal.Arsenal),\n ('HydraClassifier',\n aeon.classification.convolution_based._hydra.HydraClassifier),\n ('MiniRocketClassifier',\n aeon.classification.convolution_based._minirocket.MiniRocketClassifier),\n ('MultiRocketClassifier',\n aeon.classification.convolution_based._multirocket.MultiRocketClassifier),\n ('MultiRocketHydraClassifier',\n aeon.classification.convolution_based._mr_hydra.MultiRocketHydraClassifier),\n ('RocketClassifier',\n aeon.classification.convolution_based._rocket.RocketClassifier)]"
+ "text/plain": [
+ "[('Arsenal', aeon.classification.convolution_based._arsenal.Arsenal),\n",
+ " ('HydraClassifier',\n",
+ " aeon.classification.convolution_based._hydra.HydraClassifier),\n",
+ " ('MiniRocketClassifier',\n",
+ " aeon.classification.convolution_based._minirocket.MiniRocketClassifier),\n",
+ " ('MultiRocketClassifier',\n",
+ " aeon.classification.convolution_based._multirocket.MultiRocketClassifier),\n",
+ " ('MultiRocketHydraClassifier',\n",
+ " aeon.classification.convolution_based._mr_hydra.MultiRocketHydraClassifier),\n",
+ " ('RocketClassifier',\n",
+ " aeon.classification.convolution_based._rocket.RocketClassifier)]"
+ ]
},
"execution_count": 1,
"metadata": {},
@@ -70,12 +91,17 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
"data": {
- "text/plain": "(67, 1, 24)"
+ "text/plain": [
+ "(67, 1, 24)"
+ ]
},
"execution_count": 2,
"metadata": {},
@@ -87,8 +113,10 @@
"\n",
"from aeon.classification.convolution_based import (\n",
" Arsenal,\n",
+ " HydraClassifier,\n",
" MiniRocketClassifier,\n",
" MultiRocketClassifier,\n",
+ " MultiRocketHydraClassifier,\n",
" RocketClassifier,\n",
")\n",
"from aeon.datasets import load_basic_motions # multivariate dataset\n",
@@ -104,7 +132,10 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"source": [
"ROCKET compiles (via Numba) on import, which may take a few seconds. ROCKET does not\n",
@@ -120,12 +151,17 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
"data": {
- "text/plain": "0.9689018464528668"
+ "text/plain": [
+ "0.9708454810495627"
+ ]
},
"execution_count": 3,
"metadata": {},
@@ -143,12 +179,17 @@
"cell_type": "code",
"execution_count": 4,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
"data": {
- "text/plain": "0.9689018464528668"
+ "text/plain": [
+ "0.967930029154519"
+ ]
},
"execution_count": 4,
"metadata": {},
@@ -165,7 +206,10 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"source": [
"MiniROCKET[2] is a fast version of ROCKET that uses hard coded convolutions and only\n",
@@ -186,12 +230,17 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
"data": {
- "text/plain": "0.9708454810495627"
+ "text/plain": [
+ "0.9698736637512148"
+ ]
},
"execution_count": 5,
"metadata": {},
@@ -209,7 +258,10 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
@@ -232,16 +284,84 @@
"print(\" multi acc =\", accuracy_score(motions_test_labels, y_pred))"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "HYDRA[4] (for HYbrid Dictionary-Rocket Architecture) is a dictionary method for time \n",
+ "series classification using competing convolutional kernels, incorporating aspects \n",
+ "of both Rocket and conventional dictionary methods. Hydra involves transforming \n",
+ "the input time series using a set of random convolutional kernels, arranged into `g`\n",
+ "groups with `k` kernels per group, and then at each timepoint counting the kernels \n",
+ "representing the closest match with the input time series for each group."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.966958211856171"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "hydra_clf = HydraClassifier()\n",
+ "hydra_clf.fit(italy, italy_labels)\n",
+ "y_pred = hydra_clf.predict(italy_test)\n",
+ "accuracy_score(italy_test_labels, y_pred)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "MultiRocketHydra concatenates the results of HYDRA and MultiROCKET and trains RidgeClassifierCV on the combined features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9689018464528668"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mr_hydra = MultiRocketHydraClassifier()\n",
+ "mr_hydra.fit(italy, italy_labels)\n",
+ "y_pred = mr_hydra.predict(italy_test)\n",
+ "accuracy_score(italy_test_labels, y_pred)"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"source": [
- "Convolutional classifiers has three other parameters that may effect performance.\n",
+ "Convolutional classifiers have three other parameters that may affect performance.\n",
"`num_kernels` (default 10,000) determines the number of convolutions/kernels generated\n",
" and will influence the memory usage. `max_dilations_per_kernel` (default=32) and\n",
- "`n_features_per_kernel` (default=4) are used in 'MiniROCKET' and 'MultiROCKET. For\n",
+ "`n_features_per_kernel` (default=4) are used in 'MiniROCKET' and 'MultiROCKET'. For\n",
"each candidate convolution, `max_dilations_per_kernel` are assessed and\n",
"`n_features_per_kernel` are retained.\n"
]
@@ -249,7 +369,10 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"source": [
"## Performance on the UCR univariate datasets\n",
@@ -259,9 +382,12 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 9,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
@@ -288,7 +414,10 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"source": [
"We can recover the results and compare the classifier performance as follows:\n"
@@ -296,22 +425,27 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 10,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
"data": {
- "text/plain": "(112, 7)"
+ "text/plain": [
+ "(112, 7)"
+ ]
},
- "execution_count": 8,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "from aeon.benchmarking import get_estimator_results_as_array\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
"from aeon.datasets.tsc_datasets import univariate\n",
"\n",
"names = [t[0].replace(\"Classifier\", \"\") for t in est]\n",
@@ -324,23 +458,30 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 11,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
"data": {
- "text/plain": "(, )"
+ "text/plain": [
+ "(, )"
+ ]
},
- "execution_count": 9,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": "",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -354,23 +495,30 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 12,
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
"data": {
- "text/plain": "(, )"
+ "text/plain": [
+ "(, )"
+ ]
},
- "execution_count": 10,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": "",
- "image/png": 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"
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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -383,14 +531,17 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"source": [
"## References\n",
"\n",
"[1] Dempster A, Petitjean F and Webb GI (2019) ROCKET: Exceptionally fast\n",
"and accurate time series classification using random convolutional kernels.\n",
- "[arXiv:1910.13051] (https://arxiv.org/abs/1910.13051),\n",
+ "[arXiv:1910.13051](https://arxiv.org/abs/1910.13051),\n",
"[Journal Paper](https://link.springer.com/article/10.1007/s10618-020-00701-z)\n",
"\n",
"[2] Dempster A, Schmidt D and Webb G (2021) MINIROCKET: A Very Fast (Almost)\n",
@@ -400,22 +551,30 @@
"\n",
"[3] Cahng Wei T, Dempster A, Bergmeir C and Webb G (2022) MultiRocket: multiple pooling\n",
"operators and transformations for fast and effective time series classification\n",
- "[Journal Paper](https://link.springer.com/article/10.1007/s10618-022-00844-1)\n"
+ "[Journal Paper](https://link.springer.com/article/10.1007/s10618-022-00844-1)\n",
+ "\n",
+ "[4] Dempster, A., Schmidt, D.F. and Webb, G.I. (2023) Hydra: Competing convolutional \n",
+ "kernels for fast and accurate time series classification.\n",
+ "[arXiv:2203.13652](https://arxiv.org/abs/2203.13652),\n",
+ "[Journal Paper](https://link.springer.com/article/10.1007/s10618-023-00939-3)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
- "outputs": [],
- "source": [],
"metadata": {
- "collapsed": false
- }
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
+ },
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -429,9 +588,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.10"
+ "version": "3.11.10"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
}
diff --git a/examples/classification/deep_learning.ipynb b/examples/classification/deep_learning.ipynb
index 3c08a05230..2bc8d56d8f 100644
--- a/examples/classification/deep_learning.ipynb
+++ b/examples/classification/deep_learning.ipynb
@@ -156,7 +156,7 @@
}
],
"source": [
- "from aeon.benchmarking import get_estimator_results_as_array\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
"from aeon.datasets.tsc_datasets import univariate\n",
"\n",
"names = [t[0].replace(\"Classifier\", \"\") for t in est]\n",
diff --git a/examples/classification/dictionary_based.ipynb b/examples/classification/dictionary_based.ipynb
index c1b2e308ed..c14d6a5da2 100644
--- a/examples/classification/dictionary_based.ipynb
+++ b/examples/classification/dictionary_based.ipynb
@@ -411,7 +411,7 @@
}
],
"source": [
- "from aeon.benchmarking import get_estimator_results_as_array\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
"from aeon.datasets.tsc_datasets import univariate\n",
"\n",
"names = [t[0] for t in est]\n",
diff --git a/examples/classification/distance_based.ipynb b/examples/classification/distance_based.ipynb
index b3a2506e72..ac34c11faa 100644
--- a/examples/classification/distance_based.ipynb
+++ b/examples/classification/distance_based.ipynb
@@ -388,7 +388,7 @@
}
],
"source": [
- "from aeon.benchmarking import get_estimator_results_as_array\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
"from aeon.datasets.tsc_datasets import univariate\n",
"\n",
"names = [t.replace(\"Classifier\", \"\") for t in est]\n",
diff --git a/examples/classification/feature_based.ipynb b/examples/classification/feature_based.ipynb
index 2e5c5db7d9..fdfc2c09d6 100644
--- a/examples/classification/feature_based.ipynb
+++ b/examples/classification/feature_based.ipynb
@@ -290,7 +290,7 @@
}
],
"source": [
- "from aeon.benchmarking import get_estimator_results_as_array\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
"from aeon.datasets.tsc_datasets import univariate\n",
"\n",
"names = [t[0].replace(\"Classifier\", \"\") for t in est]\n",
diff --git a/examples/classification/hybrid.ipynb b/examples/classification/hybrid.ipynb
index 542979e868..882817f168 100644
--- a/examples/classification/hybrid.ipynb
+++ b/examples/classification/hybrid.ipynb
@@ -212,7 +212,7 @@
}
],
"source": [
- "from aeon.benchmarking import get_estimator_results_as_array\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
"from aeon.datasets.tsc_datasets import univariate\n",
"\n",
"names = [t[0] for t in est]\n",
diff --git a/examples/classification/img/early_classification.png b/examples/classification/img/early_classification.png
new file mode 100644
index 0000000000..244ff259f8
Binary files /dev/null and b/examples/classification/img/early_classification.png differ
diff --git a/examples/classification/interval_based.ipynb b/examples/classification/interval_based.ipynb
index 7daad96899..8913814a6d 100644
--- a/examples/classification/interval_based.ipynb
+++ b/examples/classification/interval_based.ipynb
@@ -417,7 +417,7 @@
}
],
"source": [
- "from aeon.benchmarking import get_estimator_results_as_array\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
"from aeon.datasets.tsc_datasets import univariate\n",
"\n",
"names = [t[0].replace(\"Classifier\", \"\") for t in est]\n",
diff --git a/examples/classification/shapelet_based.ipynb b/examples/classification/shapelet_based.ipynb
index 17ea31f435..0b7f402c60 100644
--- a/examples/classification/shapelet_based.ipynb
+++ b/examples/classification/shapelet_based.ipynb
@@ -646,7 +646,7 @@
" \"RSASTClassifier\",\n",
" \"LearningShapeletClassifier\",\n",
"]\n",
- "from aeon.benchmarking import get_estimator_results_as_array\n",
+ "from aeon.benchmarking.results_loaders import get_estimator_results_as_array\n",
"from aeon.datasets.tsc_datasets import univariate\n",
"\n",
"est = [\"MrSQMClassifier\", \"RDSTClassifier\", \"ShapeletTransformClassifier\"]\n",
diff --git a/examples/clustering/img/partitional.png b/examples/clustering/img/partitional.png
new file mode 100644
index 0000000000..03363a2ea2
Binary files /dev/null and b/examples/clustering/img/partitional.png differ
diff --git a/examples/datasets/data_loading.ipynb b/examples/datasets/data_loading.ipynb
index fe97bdad28..c3b1bb49a7 100644
--- a/examples/datasets/data_loading.ipynb
+++ b/examples/datasets/data_loading.ipynb
@@ -8,7 +8,7 @@
"[Provided datasets](provided_data.ipynb). Downloading data is described in\n",
"[Downloading and loading benchmarking datasets](load_data_from_web.ipynb). You\n",
"can of course load and format the data so that it conforms to the input types described\n",
- "in [Data structures and containers for aeon estimators](data_structures.ipynb). `aeon`\n",
+ "in [Data structures and containers for aeon estimators](datasets.ipynb). `aeon`\n",
"also provides data formats for time series for both forecasting and machine learning.\n",
"These are all text files with a particular structure. Both formats store a single time\n",
"series per row.\n",
@@ -33,7 +33,7 @@
" ).\n",
"\n",
"The baked in datasets are described [here](provided_data.ipynb). Data\n",
- "structures to store the data are described [here](data_structures.ipynb)."
+ "structures to store the data are described [here](datasets.ipynb)."
],
"metadata": {
"collapsed": false
@@ -276,7 +276,7 @@
"source": [
"Train and test partitions of the ArrowHead problem have been loaded into 3D numpy\n",
"arrays with an associated array of class values. Further info on data structures is\n",
- "given in [this notebook](data_structures.ipynb). Datasets that are shipped with aeon\n",
+ "given in [this notebook](datasets.ipynb). Datasets that are shipped with aeon\n",
"(like ArrowHead, BasicMotions and PLAID) can be more simply loaded with bespoke\n",
"functions. More details [here](provided_data.ipynb)"
]
@@ -435,8 +435,7 @@
"\n",
"A further option is to load data into aeon from tab separated value (`.tsv`) files.\n",
"Researchers at the University of Riverside, California make a variety of timeseries\n",
- "data available in this format at [Eamonn Keogh's website](https://www.cs.ucr\n",
- ".edu/~eamonn/time_series_data_2018). Each row is a time series, and the class value\n",
+ "data available in this format at [Eamonn Keogh's website](https://www.cs.ucr.edu/~eamonn/time_series_data_2018). Each row is a time series, and the class value\n",
"is the first one.\n",
"\n",
"The `load_from_tsv_file` method in `aeon.datasets` supports reading\n",
diff --git a/examples/datasets/load_data_from_web.ipynb b/examples/datasets/load_data_from_web.ipynb
index 71ea7561e8..7dd4c4bca4 100644
--- a/examples/datasets/load_data_from_web.ipynb
+++ b/examples/datasets/load_data_from_web.ipynb
@@ -19,7 +19,7 @@
"numpy if `n_timepoints` is different for different cases. Forecasting data are loaded\n",
"into pd.DataFrame. Anomaly detection dataset are loaded into 2D numpy arrays of shape\n",
"`(n_timepoints, n_channels)`. For more information on aeon data types see the\n",
- "[data structures notebook](data_structures.ipynb).\n",
+ "[data structures notebook](datsets.ipynb).\n",
"\n",
"Note that this notebook is dependent on external websites, so will not function if\n",
"you are not online or the associated website is down. We use the following four\n",
diff --git a/examples/datasets/provided_data.ipynb b/examples/datasets/provided_data.ipynb
index 0d1f632569..8d917034b7 100644
--- a/examples/datasets/provided_data.ipynb
+++ b/examples/datasets/provided_data.ipynb
@@ -2,7 +2,6 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {},
"source": [
"# Provided datasets\n",
"\n",
@@ -10,50 +9,56 @@
"`datasets`. This notebook gives an overview of what is available by default. For\n",
"downloading data from other archives, see the [loading data from web notebook](load_data_from_web.ipynb). For further details on the form of the\n",
"data, see the [data loading notebook](data_loading.ipynb).\n",
+ "data, see the [data loading notebook](data_loading.ipynb)."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Time series clustering, classification and regression\n",
"\n",
- "## Forecasting\n",
- "\n",
- "Forecasting data are stored in csv files with a header for column names. Six standard\n",
- " example datasets are shipped by default:\n",
- "\n",
- "| dataset name | loader function | properties |\n",
- "|----------|:-------------:|------:|\n",
- "| Box/Jenkins airline data | `load_airline` | univariate |\n",
- "| Lynx sales data | `load_lynx` | univariate |\n",
- "| Shampoo sales data | `load_shampoo_sales` | univariate |\n",
- "| Pharmaceutical Benefit Scheme data | `load_PBS_dataset` | univariate |\n",
- "| Longley US macroeconomic data | `load_longley` | multivariate |\n",
- "| MTS consumption/income data | `load_uschange` | multivariate |\n",
- "\n",
- " These are stored in csv format in time, value format, including a header. For\n",
- " forcasting files, each column that is not an index is considered a time series. For\n",
- " example, the airline data has a single time series each row a time, value pair:\n",
- "\n",
- " Date,Passengers\n",
- " 1949-01,112\n",
- " 1949-02,118\n",
- "\n",
- "Longley has seven time series, each in its own column. Each row is the same time index:\n",
- "\n",
- " \"Obs\",\"TOTEMP\",\"GNPDEFL\",\"GNP\",\"UNEMP\",\"ARMED\",\"POP\",\"YEAR\"\n",
- " 1,60323,83,234289,2356,1590,107608,1947\n",
- " 2,61122,88.5,259426,2325,1456,108632,1948\n",
- " 3,60171,88.2,258054,3682,1616,109773,1949\n",
+ "We ship several datasets from the UCR/TSML archives. The complete archives (including\n",
+ " these examples) are available at the [time series classification site](https://timeseriesclassification.com)\n",
+ " and the [UCR classification and clustering site](https://www.cs.ucr.edu/~eamonn/time_series_data_2018/).\n",
+ " All the archive data can be loaded from these websites or directly\n",
+ "from the web in code, see [data downloads](load_data_from_web.ipynb). All\n",
+ " data is provided with a default train, test split. Problem loaders have an argument\n",
+ " `split`. If not set, the function returns the combined train and test data. If\n",
+ " `split` is set to `\"test\"` or `\"train\"`, the required split is return. `split` is\n",
+ " not case sensitive. They can also be loaded with the functions `load_classification`\n",
+ " and `load_regression`, which also return meta data. See the notebook [data loading](data_loading.ipynb) for details. The data X is stored in a 3D\n",
+ " numpy array of shape `(n_cases, n_channels, n_timepoints)` unless unequal length,\n",
+ " in which case a list of 2D numpy array is returned.\n",
"\n",
- "The problem specific loading functions return the series as either a `pd.Series` if\n",
- "a single series or, if multiple series, a `pd.DataFrame` with each column a series.\n",
- "There are currently six forecasting problems\n",
- "shipped."
- ]
+ "| dataset name | loader function | properties |\n",
+ "|-----------------------------|:-------------:|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n",
+ "| Appliance power consumption | `load_acsf1` | univariate, equal length |\n",
+ "| Arrowhead shape | `load_arrow_head` | univariate, equal length |\n",
+ "| Gunpoint motion | `load_gunpoint` | univariate, equal length |\n",
+ "| Italy power demand | `load_italy_power_demand` | univariate, equal length |\n",
+ "| Japanese vowels | `load_japanese_vowels` |
univariate, unequal length |\n",
+ "| OSUleaf leaf shape | `load_osuleaf` | univariate, equal length |\n",
+ "| Basic motions | `load_basic_motions` | multivariate, equal length |\n",
+ "\n"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
},
{
"cell_type": "markdown",
"source": [
- "### Airline\n",
+ "### ACSF1\n",
"\n",
- "The classic Box & Jenkins airline data. Monthly totals of international\n",
- " airline passengers, 1949 to 1960. This data shows an increasing trend,\n",
- " non-constant (increasing) variance and periodic, seasonal patterns. The\n"
+ "The dataset is compiled from ACS-F1, the first version of the database of appliance\n",
+ "consumption signatures. The dataset contains the power consumption of typical appliances. The recordings are characterized by long idle periods and some high bursts of energy consumption when the appliance is active.\n",
+ "\n",
+ "The classes correspond to 10 categories of home appliances: mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave ovens, printers, and televisions (LCD or LED).\n",
+ "\n",
+ "The problem is univariate and equal length. It has high frequency osscilation."
],
"metadata": {
"collapsed": false
@@ -62,61 +67,69 @@
{
"cell_type": "code",
"source": [
- "import warnings\n",
- "\n",
- "from aeon.datasets import load_airline\n",
- "from aeon.visualisation import plot_series\n",
+ "import matplotlib.pyplot as plt\n",
"\n",
- "warnings.filterwarnings(\"ignore\")\n",
+ "from aeon.datasets import load_acsf1\n",
"\n",
- "airline = load_airline()\n",
- "plot_series(airline)"
+ "trainX, trainy = load_acsf1(split=\"train\")\n",
+ "testX, testy = load_acsf1(split=\"test\")\n",
+ "print(type(trainX))\n",
+ "print(trainX.shape)\n",
+ "plt.plot(trainX[0][0][:100])\n",
+ "plt.title(\n",
+ " f\"First 100 observations of the first train case of the ACFS1 data, class: \"\n",
+ " f\"({trainy[0]})\"\n",
+ ")"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:18.616123Z",
- "start_time": "2024-09-25T22:58:08.862906Z"
+ "end_time": "2024-09-25T22:58:20.673104Z",
+ "start_time": "2024-09-25T22:58:20.238813Z"
}
},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "(100, 1, 1460)\n"
+ ]
+ },
{
"data": {
- "text/plain": [
- "(,\n",
- " )"
- ]
+ "text/plain": "Text(0.5, 1.0, 'First 100 observations of the first train case of the ACFS1 data, class: (9)')"
},
- "execution_count": 1,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 1
+ "execution_count": 53
},
{
"cell_type": "markdown",
"source": [
- "### Longley\n",
- "This mulitvariate time series dataset contains various US macroeconomic\n",
- " variables from 1947 to 1962 that are known to be highly collinear. This loader\n",
- " returns the series to be forecast (default TOTEMP: total employment) and other\n",
- " variables that may be useful in the forecast\n",
- " GNPDEFL - Gross national product deflator\n",
- " GNP - Gross national product\n",
- " UNEMP - Number of unemployed\n",
- " ARMED - Size of armed forces\n",
- " POP - Population\n"
+ "### ArrowHead\n",
+ "The arrowhead data consists of outlines of the images of\n",
+ "arrowheads. The shapes of the projectile points are converted into\n",
+ "a time series using the angle-based method. The classification of\n",
+ "projectile points is is an important\n",
+ "topic in anthropology. The classes are based on shape\n",
+ "distinctions, such as the presence and location of a notch in the\n",
+ "arrow. The problem in the repository is a length normalised version\n",
+ "of that used in Ye09shapelets. The three classes are called\n",
+ "\"Avonlea\" (0), \"Clovis\" (1) and \"Mix\" (2).\n"
],
"metadata": {
"collapsed": false
@@ -125,50 +138,61 @@
{
"cell_type": "code",
"source": [
- "from aeon.datasets import load_longley\n",
+ "from aeon.datasets import load_arrow_head\n",
+ "\n",
+ "arrowhead, arrow_labels = load_arrow_head()\n",
+ "print(arrowhead.shape)\n",
+ "plt.title(\n",
+ " f\"First two cases of the ArrowHead, classes: \"\n",
+ " f\"({arrow_labels[0]}, {arrow_labels[1]})\"\n",
+ ")\n",
"\n",
- "employment, longley = load_longley()\n",
- "plot_series(employment)"
+ "plt.plot(arrowhead[0][0])\n",
+ "plt.plot(arrowhead[1][0])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:18.803829Z",
- "start_time": "2024-09-25T22:58:18.622082Z"
+ "end_time": "2024-09-25T22:58:20.861894Z",
+ "start_time": "2024-09-25T22:58:20.689090Z"
}
},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(211, 1, 251)\n"
+ ]
+ },
{
"data": {
- "text/plain": [
- "(, )"
- ]
+ "text/plain": "[]"
},
- "execution_count": 2,
+ "execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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Tp0567rnnlJaWprCwMEnS0qVLFRAQ4A5VO3XqpG+++abQ6yxdutR9DgAAAAClz2a1avbaxMu2zV57SH/pVV9xxzJ1MqfAfTzI1+ZeszLKPbryl5GWNQN85M9Ua0nSzU1rqHeDEP3jhwN6YXmCfjh4Sq1fXq3xXeroqT6NFOhrM7tEXMHZAofGfbZDH8QdkyTd2qyG3h3WWkH8mQGoJEzd/XzTpk3q3LmznnrqKd1xxx3auHGjxo4dq7feekvDhw+XJPXs2VMnT57Ua6+9ptq1a+uHH37QuHHj9K9//Uvjxo2TJI0bN07ffPON3nvvPQUEBOihhx6SJK1fv16S5HQ61apVK0VGRmrmzJlKSUnRyJEjdd9997mnvicmJqp58+YaP368Ro8erRUrVmjixIlavHix+vbte9VrYfdzAAAAoPilnslXxFPfX7H96OM3auORdFXx9nQHmawN+fscPn1Wj3y9W5/tSJZ0br3Qvw9oqpFto2S1MiW9LNl3Ilu3v79ZO1POyGqRnu/fRFN71mPpAADl3rXka6aGmpK0aNEizZgxQwkJCYqJidGUKVMK7X6ekpKiGTNm6Pvvv9fp06dVu3Zt3X///Zo8ebL7G3ZeXp7+/Oc/66OPPlJ+fr769u2rN954wz21XJIOHz6scePGadWqVfL399eoUaP04osvytPzlw88q1at0uTJk7V7925FRUXp8ccf1z333FOk6yDUBAAAAIrXVztT1LthiKKfWaaMXPsl7UG+NqU80adUN+KpDJbuO6GJX+xQ/IkcSVKn2tU0+7bmahMVZG5hkCR9ui1JYz7ZpjP5DtWo6q2PR7RRj3ohZpcFAMWiXIWaFQWhJgAAAFA8Us/ka8LnO/TZjmR9cU97xR3L0LPLEi7p97cbG2pqr3q/axd0/LYCh0uz1hzU00v3KafAKYtFur9jbT13U2NV9/Myu7xKye50adriPXpl9UFJUve61fXRiLaKCPAxuTIAKD7Xkq/xI00AAAAAZYJhGPpwyzE1e2mlPtuRLA+rRYfTz2rGDQ30txsbutcKDPK16W83NtT06+sTaJYQL0+rpvaqr73Teumu1jVlGNK/NxxWoxdX6K0fD8vpYmxMaTqemateb653B5qP9KynZX/qRKAJoFJjpGYxYaQmAAAA8PslZeZp3Gfb9fXuVElSy8gAvTO0lVqf38U5p8Ahm9WqzDy7An1ssrtcBJql6IcDJzXxy53akXxGktQ2KlCzb4tVx9rVTK6s4luecEJ3zd2iEzkFCvTx1LvDWmlg8wizywKAEsH0cxMQagIAAADXzjAMvb/5mKYs3KWMXLtsHhY91vvcKEybBxPLyhKH06U31h/S376LV1aeQ5J0T/tovdi/icKqeptcXcXjchl6YUWCnvguXi7jXND/6d3tVC/E3+zSAKDEEGqagFATAAAAuDZHM3L1wKfb9e3eNEnnRv+9M7SVYiP4PF2WpZ7J14xv9ui9TUclSYE+nnqqbyM92LmOPAmii8XpswW6e97P+ub83417r4vWa7fFytfmYXJlAFCyCDVNQKgJAAAAFI1hGPrPT0f0yNe7dSbfIW9Pq57o01CP9KhHKFaO/Hg4XRM+36EtxzMlSbERVTV7YKy61ws2ubLybfPRDA3532YdTs+Vj6dVrw2K1ejrapldFgCUCkJNExBqAgAAAFd36PRZ3b9gm5YlnJQkdaxdTf+9o6Wa1KhqcmX4PZwuQ//56bAe/XavTp+1S5Lual1TM29uqshANrG5FoZh6N8bDmvSV7tU4HSpXrCfFtzdTq3OrysLAJUBoaYJCDUBAACAK3O5DL254ZCmL96jnAKnfDytevamxnq4W115WC1ml4c/6FROgR5bsldv/XhYhiFV8fbQ470b6uFudeXlyejbq8nJd2jcZ9s1d8txSdLA5uF6Z2grBfnaTK4MAEoXoaYJCDUBAACAyztwMkf3fbJNPxw8JUnqFlNd/7mjpRqEVjG5MhS3LccyNOGLnfrxcLokqXFYFc0a2Fw3Ngw1ubKyKz4tW0P+t1k7U87Iw2rR8zc11iM968liIewHUPkQapqAUBMAAAAozOkyNHttoh79do9y7S752Tz0woAmGt+5jqyMzqywXC5D/4s7pumLdystu0CSNDg2Qv/4v6aqXd3P5OrKlk+3JWnMJ9t0Jt+h8Kre+nhEW9YkBVCpEWqagFATAAAA+EV8WrbGfLJV6w+dG7HXq16w3r6jpeoG+5tcGUpLRq5dT34fr9fXHZLTZcjXZtWMGxrokR715FPJd/G2O136y6LdmrUmUZLUo26wPhrRRuEBrEMKoHIj1DQBoSYAAABwbnTmv344oCe+i1eew6Uq3h6aOaCp7u9Ym9GZldSO5CxN/GKne/mBusF+euXW5rq5aQ2TKzPHsYxcDZsb5w78/9Krvp7t10ieHqw9CgCEmiYg1AQAAEBltzvljEbP36qNRzMkSX0ahuqtIS1UqxpTjis7wzA0f2uSHvl6t5Ky8iRJNzepoZdvbaZ6IZVn9O6yfSc0/MMtOpFToEAfT71/Z2vd0izc7LIAoMwg1DQBoSYAAAAqK7vTpZdWHdDT3+9TgdOlQB9P/fOWZrq3fTSbnaCQM3kOPbtsn15Zc1B2pyEvD6um9qqnGdfXl5+Xp9nllRiXy9DzKxL0xHfxMgypVWSAFtzdrlIFugBQFISaJiDUBAAAQGW0PSlLo+dv1ZbjmZKkAU3CNOf2FqoZ6GtyZSjL9qad0cNf7tTSfSclSbWCfPXPW5pqUGxEhQvCT58t0N3zftY3e9MkSWM61NKrA5vLt5KvKwoAl0OoaQJCTQAAAFQmBQ6Xnl+eoOeXJ8jhMlTN16ZXBjbTiDZRFS6UQskwDENf7kzR5K926UhGriTpxoYhmjWwuRqHVTW5uuKx+WiGhvxvsw6n58rH06o3BrfQPe2jzS4LAMosQk0TEGoCAACgsthyLEOj52/T9uQsSdLA5uF6fVCsIti5Gb/D2QKHXlyxXy+tOqB8h0ueVosmda+rx3s3VFWf8jkl3TAMzdlwWJO/2qUCp0v1Q/y14O62ahkZaHZpAFCmEWqagFATAAAAFV2+w6mnl+7TzJUH5HQZCvazafZtsRraKpLRmfjDDpzM0ZSFu/T17lRJUkSAt166uanubF2zXN1fOfkOPfDZdn245bgk6bbm4XpnaCsF+tpMrgwAyj5CTRMQagIAAKAi23gkXaPnb9Xu1GxJ0pAWEZp9W6zCqnqbXBkqmsW7UzXpq506cOqsJKl73ep6dWCsWkSW/f9nxadl6/b3N2tX6hl5WC16sX8TTelRt1yFsgBgJkJNExBqAgAAoCLKtTv1xHfx+tcPB+QypLAqXnp9UKwGt4g0uzRUYHl2p/61+qCeW7ZPuXaXPKwWPdi5jp7q20hBZXTE4ydbk3Tfgq3KzncqIsBbH49oq251g80uCwDKFUJNExBqAgAAoKJZf+i0Rs/fqn0nciRJw9vU1Cu3Nlewv5fJlaGyOJJ+Vo98vVufbk+WJIX6e+nFAU00ql20rNayMfqxwOHS1EW7NXttoiSpZ71gzRveRuGsMQsA14xQ0wSEmgAAAKgocvIdemzJXr26NlGGcW5twzcHt9AtzcLNLg2V1LJ9JzTxy53am3Zu+YMOtYI0+7ZYtYsOMrWuYxm5GvpBnDYcTpckTb++vp7u20ieHlZT6wKA8opQ0wSEmgAAAKgIfjhwUvd9ss29nuE97aL1z1uaqpofozNhrgKHS7PXJuqppfHKznfKYpHu61BLz93UWCH+pb+269J9JzT8wy06mVOgIF+b3h/WSv9H8A8AfwihpgkINQEAAFCeZec7NG3xHr25/pAkKSrQR/++vYVualLD3MKAX0nKzNP0xbs19/zu4tX9bHq2X2ON7VhbHqUwJd3lMvTc8gQ9+X28DENqUzNQn9zdVnWD/Uv8tQGgoiPUNAGhJgAAAMqrZftOaOyCbTqcnivp3Oi3l25uqsAyuiELIElrDp7SQ1/s1PbkLElS65oBeu22WHWqU73EXvNUToFGztuiJfEnJJ37u/LqwObysXmU2GsCQGVCqGkCQk0AAACUN5m5dk1dtFv/+emIJKl2NV+9PaSlejcMNbkyoGgcTpfmbDisx5fsVWaeQ5I0ql2UXhzQVDWqFu+U9I1H0nXH/+J0JCNXvjar3hjUQqPaRxfrawBAZUeoaQJCTQAAAJQnS/am6f4F23QsM0+S9GDnOnpxQBNV8fY0uTLg2qWdyddfv92rdzaeC+gDfDz1ZJ9GGt+ljmx/cNMewzD05vrDmrxwp+xOQw1C/LXg7nZqEcn/+wCguBFqmoBQEwAAAOVB+tkC/Xnhbr23+agkqV6wn/5zR0v1qBdicmXAH/fT4XQ99MUObT6WKUlqHl5Vrw5srp71f9/9nZ3v0AOfbte8n8+t3zkoNlz/vaMVSzMAQAkh1DQBoSYAAADKuq93peiBz7YrOStfFov0cLe6erZfI/l5MToTFYfTZeidjUf012/26NRZuyRpWKtIzby5qaKCfIt8nj2pZzTkf5u1OzVbHlaL/j6giSZ3ryuLpeQ3IwKAyopQ0wSEmgAAACirTuUU6OEvd7pHmzUK9dd/h7ZS5xLcUAUw2+mzBXp8Sbz+veGQXIbk7+Whx3o31OTudeXl+dtT0udvPa77PtmmnAKnIgN89PHINuoaE1xKlQNA5UWoaQJCTQAAAJRFn21P0vjPdygtu0BWi/TnHvX0ZN9G8mW3ZlQSPx/P1ENf7ND6Q+mSpIah/po1sLn6NgpTToFDNqtVGXl2BfnYlO906dU1iXp8yV5J0vX1Q/Th8DbFvukQAODyCDVNQKgJAACAsiTtTL4e+mKHFmxPliQ1rVFF7wxtpetqVTO5MqD0GYahD+KOadriPUo9k6/GYVW0bkIXvbImUbPXJioj164gX5smdKmjh7rGqMcb63VbbLie7ttYHlammwNAaSHUNAGhJgAAAMoCwzA0f2uSJn65UydzCuRhtWhar/p6/MYG8vZkdCYqt8xcu55auk896wVr89EMPbss4ZI+j/VuoNHX1VKd6n4mVAgAlRuhpgkINQEAAGC2lKw8Pfj5Dn25M0WS1CIiQO8Mbak2UUHmFgaUMfkOpyKeWqqMXPslbUG+NqU80eeq624CAIrfteRrbHMIAAAAlHOGYWjulmOa9OUupefa5Wm16NHeDTTj+gYEM8BlZOY5LhtoSlJGrl2ZeXaFVmEdTQAoywg1AQAAgHLseGauHvh0uxbvSZMktakZqHeGtlKLSGYPAVcS5GNTkK/tiiM1A31sJlQFALgW/NgWAAAAKIcMw9B/fzqiZi+t0uI9afLysOq5mxrrx4ldCTSBq7C7XJrYNeaybRO7xsjucpVyRQCAa8VITQAAAKCcOZJ+Vvcv2K7v952QJHWoFaT/3tFKTcOrmlwZUD74e3lq+vX1JUmvXrT7+cSuMZp+fX352NhUCwDKumvaKOjQoUNaunSpCgoK1KNHDzVv3rwkaytX2CgIAAAAJc3lMvTWj4f1l8W7lZ3vlI+nVU/3a6zJ3evKw2oxuzyg3MkpcMhmtSozz65AH5vsLpf8vRj7AwBmKZGNglauXKmbb75Zubm5557o6al33nlHI0aM+GPVAgAAALiqg6dyNPaTbVp54JQkqUudavrv0FZqGFrF5MqA8utCgHlhUyAvVmgDgHKjyN+xH3/8cd144406fvy4Tp06pbFjx+ovf/lLSdYGAAAAVHoul6HZaxPV4h8/aOWBU/K1WfXyrc206sEuBJoAAKDSKvL086CgIK1fv15NmzaVJJ09e1YBAQFKTU1VcHBwiRZZHjD9HAAAAMUt4US27vtkm9YknpYk9agbrP/c0VL1QvxNrgwAAKD4lcj086ysLIWEhLgf+/n5ydfXV5mZmYSaAAAAQDFyugzNWnNQj327V3kOl/y9PPT3AU31QKfasrJ2JgAAwLXtfv7dd98pMDDQ/djlcmn58uXauXOn+9gtt9xSfNUBAAAAlcye1DMa88k2/Xg4XZLUu0GI3hrSUnWq+5lcGQAAQNlxTasgjxo1SgMHDnR/5ebm6k9/+pP78W233XbNBRw/flwjRoxQcHCwfH19FRsbq82bNxfqs2fPHt1yyy0KDAyUv7+/2rdvryNHjrjb8/LyNH78eAUHB6tKlSoaPHiwUlNTC53jyJEjGjBggPz8/BQWFqapU6fK4XAU6rNq1Sq1adNG3t7eql+/vt57771rvh4AAACgqHIKHCpwuJSWna8Ch0s7U7J059wt+vFwugJ8PPXWkBb67v6OBJoAAAC/UuSRmi6Xq9hfPD09XV26dFGvXr307bffKjQ0VAkJCapWrZq7z4EDB9S1a1eNGTNGTz31lAICArRr1y75+Pi4+0yePFmLFy/WggULFBgYqAkTJmjQoEFat26dJMnpdGrAgAEKDw/X+vXrlZycrLvvvls2m03PP/+8JCkxMVEDBgzQAw88oA8//FDLly/Xfffdp4iICPXt27fYrx0AAACVW57dqZkrD2j22kRl5NoV5GvThC51tPRPHfX4kr16tHdDRQf5ml0mAABAmVTkjYJKwvTp07Vu3TqtWbPmin2GDRsmm82mDz744LLtmZmZCg0N1bx583T77bdLkvbu3asmTZpow4YN6tixo7799lvdfPPNSkpKUo0aNSRJc+bM0bRp03TixAl5eXlp2rRpWrx4caGp9MOGDVNGRoaWLFly1WthoyAAAAAUVU6BQzNXHtAzS/dd0vb4jQ30l1715e91TStFAQAAlHvXkq8Vefr56tWri/R1LRYuXKh27dppyJAhCgsLU+vWrfX222+7210ulxYvXqyGDRuqb9++CgsLU4cOHfTll1+6+8TFxclut6t3797uY40bN1atWrW0YcMGSdKGDRsUGxvrDjQlqW/fvsrKytKuXbvcfS4+x4U+F87xa/n5+crKyir0BQAAABSFzWrV7LWJl22bvfaQbNZrWiUKAACg0inyj3979uwpi+XcTotXGtxpsVjkdDqL/OIHDx7Um2++qSlTpuivf/2rNm3apIkTJ8rLy0ujRo1SWlqasrOz9eKLL+rZZ5/V3//+dy1ZskSDBg3SypUr1aNHD6WkpMjLy0tBQUGFzl2jRg2lpKRIklJSUgoFmhfaL7T9Vp+srCzl5ubK17fw1J8XXnhBTz31VJGvFQAAALjgdG6BMnLtl23LyLUrM8+u0CrepVwVAABA+VHkULNatWqqWrWq7rnnHo0cOVIhISF/+MVdLpfatWvnXteydevW2rlzp+bMmaNRo0a51/G89dZbNXnyZElSq1attH79es2ZM0c9evT4wzX8XjNmzNCUKVPcj7OyshQdHW1aPQAAACgfvo9PU5eY6grytV022AzytSnQx2ZCZQAAAOVHkee1JCcn6+9//7t7KveYMWO0fv16BQQEKDAw0P11LSIiItS0adNCx5o0aeLe2TwkJESenp6/2Sc8PFwFBQXKyMgo1Cc1NVXh4eHuPr/eDf3C46v1CQgIuGSUpiR5e3srICCg0BcAAABwJYZh6PnlCbrpPz9p2b6TmtClzmX7TewaI3sJbNIJAABQkRQ51PTy8tLQoUP13Xffae/evWrRooUmTJig6OhoPfroo3I4HNf84l26dFF8fHyhY/v27VPt2rXdr9m+ffvf7NO2bVvZbDYtX77c3R4fH68jR46oU6dOkqROnTppx44dSktLc/dZunSpAgIC3IFpp06dCp3jQp8L5wAAAAB+r5x8h4Z9EKfHvt0rw5C2JWVqxg0N9LcbGyrI99yozCBfm/52Y0NNv55NggAAAK7mD+1+npiYqDFjxuiHH37QiRMnVL169Wt6/qZNm9S5c2c99dRTuuOOO7Rx40aNHTtWb731loYPHy5J+uKLLzR06FC9/vrr6tWrl5YsWaJJkyZp1apV6tq1qyRp3Lhx+uabb/Tee+8pICBADz30kCRp/fr1kiSn06lWrVopMjJSM2fOVEpKikaOHKn77rvPPfU9MTFRzZs31/jx4zV69GitWLFCEydO1OLFi9W3b9+rXgu7nwMAAOByEk+d1W3vbdL25CzZPCx67bZYje147gf0OQUO2axWZebZFehjk93lItAEAACV1rXka9ccaubn5+uzzz7TO++8ow0bNmjAgAEaPXq0+vXr97uKXbRokWbMmKGEhATFxMRoypQpGjt2bKE+77zzjl544QUdO3ZMjRo10lNPPaVbb73V3Z6Xl6c///nP+uijj5Sfn6++ffvqjTfecE8tl6TDhw9r3LhxWrVqlfz9/TVq1Ci9+OKL8vT85UPjqlWrNHnyZO3evVtRUVF6/PHHdc899xTpOgg1AQAA8Gsr95/UHf/brFNn7Qqr4qXPRrVXl5hrGwgAAABQWZRIqLlx40a9++67+vjjj1WnTh3de++9GjFixDWPzqyoCDUBAABwgWEYem3dIU1ZuEtOl6G2UYH6/J72ig66dK12AAAAnFMioabValWtWrU0atQotW3b9or9brnllmurtoIg1AQAAIAk5TucevDzHXp341FJ0og2NfXvIS3la/MwuTIAAICyrcRCzauxWCxyOp1Fq7KCIdQEAABAclaeBr+/WT8eTpfVIv19QFNN6VFXFovF7NIAAADKvGvJ14q8CrnL5frDhQEAAAAV1U+H0zX4/c1KyspTkK9NH49ooz6NwswuCwAAoEJia0UAAADgD3pv01E98Ol2FThdalqjir689zrVD/E3uywAAIAK6+pzys978MEHlZ2d7X780UcfKScnx/04IyND/fv3L97qAAAAgDLM4XRp0lc7NXr+VhU4XRrYPFwbHupGoAkAAFDCirympoeHh5KTkxUWdm4KTUBAgLZu3aq6detKklJTUxUZGcmamqypCQAAUCmcyinQ0A/itGL/SUnS325sqL/d2FBWK+tnAgAA/B4lsqbmr7PPImahAAAAQIWzIzlLA9/dpMTTZ+Xv5aH372ytQbERZpcFAABQabCmJgAAAHANPtuepHs+3qqcAqfqBvvpi3vaKzaCmToAAACliVATAAAAKAKXy9CT38fr2WUJkqTeDUL08ci2qu7nZXJlAAAAlc81hZp/+9vf5OfnJ0kqKCjQc889p8DAQEnS2bNni786AAAAoAzIyrPr7o9+1sJdqZKkSd3rauaAJvL0KPK+mwAAAChGRd4oqGfPnrJYrr7o+cqVK/9wUeURGwUBAABUTAknsjXw3U3ak5Ytb0+r/n17C93dLtrssgAAACqcEtkoaNWqVX+0LgAAAKBcWbI3TXd9uEUZuXbVDPTR56Paq32tILPLAgAAqPSKPF+mbt26OnXqVEnWAgAAAJQJhmHopZX7dfN/f1JGrl2dalfTpoe7EWgCAACUEUUeqXno0CE5nc6SrAUAAAAw3dkCh8Yu2K6Pfj4uSRp9XS29Pqi5vD09TK4MAAAAF7D7OQAAAHDekfSzuu29Tfr5eJY8rRa9fGszPdi5TpHWlgcAAEDpuaZQ87vvvnPvdn4lt9xyyx8qCAAAADDDmoOndPv7m3Uip0Ah/l76ZGRb9awfYnZZAAAAuIxrCjVHjRr1m+0Wi4Up6gAAoELKKXDIZrUqI8+uIB+b7C6X/L2Y9FJRzFl/SBO/3CmHy1CryAB9cU971a7uZ3ZZAAAAuIJr+iSekpKisLCwkqoFAACgTMqzOzVz5QHNXpuojFy7gnxtmtg1RtOvry8fG+sslmcFDpcmfrlTb/14WJI0tFWk/ntHS/kRWAMAAJRpRf60xjpCAACgMsopcGjmygN6Zuk+97GMXLuePv94aq96jNgsp1LP5GvI/zZrbeJpWSzSczc11rRe9fncCwAAUA5Yi9rRMIySrAMAAKBMslmtmr028bJtr65NlM1a5I9TKEM2H81Q+1dWa23iaQX6eOrr0ddp+vUNCDQBAADKiSIPKxg1apR8fX1LshYAAIAyxe506WROgTJy7Zdtz8i1KzPPrtAq3qVcGf6ID7cc09hPtinP4VKjUH99ee91ahRWxeyyAAAAcA2KHGq+++67kqTjx4/rs88+075956ZcNWrUSIMGDVLNmjVLpkIAAIBSlu9w6v3Nx/T2j4e16sHOCvK1XTbYDPK1yd/LU1uPZ6pVzUATKsW1cLoMTV+8R//84YAkaUCTMM29q40CfW0mVwYAAIBrdU0LQL3xxhuaMmWKCgoKFBAQIEnKysrS1KlT9a9//UsPPvhgiRQJAABQGs4WOPSfn47opVUHdDwzT5K0+sApPdS1jp5ZmnBJ/wld6uj7fSc06L1N6lKnmiZ1r6uBzSPkYWUKc1mTfrZAd87dou/3nZAkzbihvp7u25g/KwAAgHKqyKHm4sWLNXHiRE2aNEl//vOfFRERIUlKTk7WSy+9pIcfflh16tRR//79S6xYAACAknAmz6E3NxzSv344oLTsAklSzUAfTe1ZTz3rBatX/RBZZNGrv9r9fGqvenpm6T7ZPCxadyhd6w7FqU41Xz3ULUZjrqulAB9GAJYFu1POaOB7m7T/ZI78bB56Z2gr3dEq0uyyAAAA8AdYjCLuANSzZ0917dpVzz777GXbH3vsMa1du1arVq0qzvrKjaysLAUGBiozM9M9ihUAAJRt6WcLNHvtIc1ac1Dp56eX16nmq+k3NNCodlHy9vRw980pcMhmtSozz65AH5vsLpd71/OkzDy9sf6Q/r3hkE6dPXeeqt6euve6aE3sGqO6wf6lf3GQJH21M0UjP9qi7Hynalfz1Rf3tGepAAAAgDLqWvK1IoeaAQEB2rRpkxo1anTZ9vj4eLVv315ZWVnXXnEFQKgJAED5cSI7Xy+vPqjX1x3SmXyHJKlhqL9mXN9Ad7WpKZvH79vR/GyBQ3O3HNes1Qe1Jy1bkmS1SLc2C9ek7nXVNaY6u2uXEpfL0LPLEvTk9/GSpJ71gjV/ZFs2dQIAACjDriVfK/L0c6fTKZvtylOobDabnE5n0asEAAAoZUmZefrHDwf07w2HlGt3SZJiI6rqrzc00O0tIv/w+op+Xp66v2Ntje1QS9/vO6FXVh/Ud/En9MXOFH2xM0VtowI1qXtdDWkRKS/P3xec4uqy8x265+Of9fmOFEnShK4x+uf/Nf3dYTUAAADKniKHms2aNdNXX32lyZMnX7b9yy+/VLNmzYqtMAAAgOJy+PRZ/X3lfr2z8agKnOfCzHZRgXq0d0P9X9MashbzZjEWi0V9G4Wpb6Mw7U45o1fWHNTcuGOKO5apkfN+1rRFe/Rglzr6U8faCvb3KtbXruwOnsrRwHc3aWfKGdk8LHpjUAuN6VDL7LIAAABQzIo8/fz999/XuHHj9I9//EP333+/PD3P5aEOh0P//ve/NXXqVL3xxhu65557SrLeMovp5wAAlD0JJ7L1wor9mht3TA7XuY88XepU02M3NlSfhqGlOhX8RHa+/v3jYb2x7pBSzuRLknxtVo1sG61J3WPUOKxqqdVSUS3bd0LD5sbp9Fm7wqt667NR7dSpTnWzywIAAEARlciampL0yCOP6F//+peqVq2qevXqyTAMHTx4UNnZ2Zo4caJefvnlP1x8eUWoCQBA2bEzOUsvrNiv+VuP63yWqd4NQvRo7wbqXjfY1HUt8x1OfbItSa+sPqifj/+yFvlNjcP0cLcY3VjKYWtFYBiGXllzUFO/3i2XIbWPDtLn97RTzUBfs0sDAADANSixUFOSfvzxR3300UdKSEiQJDVs2FDDhg1Tx44df3/FFQChJgAA5os7lqHnlyXoi50p7mM3N6mhv/ZuoI61q5lY2aUMw9Dqg6f0yuqDWrg7VRc+kTWrUVUPd4/R8DZR8rV5/PZJoDy7Uw98ul3/izsmSRrVLkpvDm4hH947AACAcqdEQs2nn35ajzzyiPz8/IqlyIqGUBMAAPNsOHRazy1L0Dd70yRJFos0ODZCM25ooNY1A02u7ur2n8zR7LWJenfTEWXnn9t4McTfSw90qq0HO9dReICPyRWWTcczczXovc3adDRDHlaLXrq5qR7uFsNIVwAAgHKqREJNDw8PJScnKywsrFiKrGgINQEAKF2GYWjVgVN6blmCVuw/KUmyWqS7WtfU9OsbqGl4+VujMiPXrv/+dESz1ybqSEauJMnLw6o7W0fq4W511aocBLSlZcOh0xr8/malnMlXdT+b5o9sqxsahJpdFgAAAP6AEgk1rVarUlJSCDWvgFATAIDSYRiGluxN03PLE7T+ULokyeZh0d3tojWtV33VD/E3ucI/zuF06YudKXpl9UFtOJzuPt6zXrAmda+rm5sU/47t5cl/fzqi8Z/vUIHTpebhVfXlve1VN7j8/7kDAABUdiUWaqampio0lJ+AXw6hJgAAJcvlMvTVrhQ9tyxBW45nSpK8Pa26r0MtTe1ZT7WqVcwlcn46nK5Zaw5qwfZkOc/velQ/xF8Tu8bonvbRquLtaXKFpcfudGnKwl16fd0hSdKg2HC9N6x1pXoPAAAAKrISCzUDAwOvukbR6dOni15pBUKoCQBAyXC6DH2yLUnPL0vQrtQzkiQ/m4ce6Fxbf+5RTxGVZL3Joxm5em1tot7+6Ygycu2SpEAfT93XobYe6lqnwoa6F5zIztfQD+K06sApSdJTfRvp0RsaVOoRqwAAABVNiYWar7zyigIDf3stp1GjRhW90gqEUBMAgOJld7o0N+6YXlyxXwkncyRJAT6emtAlRpO6xyjE39vkCs2Rne/Q+5uP6tU1ie73xcNq0eDYCE3qXrfM7fJeHLYez9Rt723S4fRcVfH20Ad3ttGtzcPNLgsAAADFjDU1TUCoCQBA8cizO/XupqOauXK/Dqef2yynup9Nk7rX1YQuMQrytZlcYdngchn6Zm+aXll90L1RkiR1qBWkSd3ranBshDw9rCZWWDzmbz2u0fO3KtfuUv0Qf31xT3s1K4ebQAEAAODq2P3cBISaAAD8MTn5Dr3102H9Y9UBJWflS5JqVPXWn3vU0wOdarNu4m/YlpSpWasTNe/n4ypwuiRJ0UE+mtAlRmM71i6XQbDTZejxJXv14or9kqQ+DUP10Yg2qubnZXJlAAAAKCmM1DQBoSYAAL9PVp5dr687pJdXH9TJnAJJUlSgj/7Sq77GdKglX5uHyRWWHylZeXpzw2HNWX9IJ86/l/5eHrqnfbQe7la33OwMn5lr1/APt+ibvWmSpEd61tML/ZvIg/UzAQAAKrQSCTXx2wg1AQC4NqfPFmjWmkTNXpvo3vimbrCfpl9fX3e3jZaXZ/mfOm2WPLtT834+rldWH9TOlHObK1ks0s1NamhS97rqWS/4qps/miU+LVsD392o+BM58vG06u07Wmp4myizywIAAEApINQ0AaEmAABFk3omX//64YDe3HBI2flOSVLjsCr66w0NNKxVZIVYB7KsMAxDyxNO6pXVB92jHiWpVWSAHu5WV8NaR8rbs+yMhP1mT6ru+nCLsvIcigr00Rf3tlfbqCCzywIAAEApIdQ0AaEmAAC/7VhGrl5adUBv/3hYeY5z6z62jAzQozc00G2xEUwtLmHxadmateag3t98VLn2c+9/jareGtepjsZ1rq3QKubtJm8Yhl5csV+PLdkrw5C61KmmT0e1V42qlXOHewAAgMrqWvI104dCHD9+XCNGjFBwcLB8fX0VGxurzZs3X7bvAw88IIvFoldeeaXQ8dOnT2v48OEKCAhQUFCQxowZo+zs7EJ9tm/frm7dusnHx0fR0dGaOXPmJedfsGCBGjduLB8fH8XGxuqbb74ptusEAKCySjx1Vg98ul31X1ih2WsTledw6broIH11b3ttmdxdt7eMJNAsBY3CquiNwS109PEb9Xz/xqoZ6KPUM/l68vt41Xp2me77ZJt2JmeVel05+Q7d9eEWPfrtuUDz/o61tfyBzgSaAAAA+E2mbiOanp6uLl26qFevXvr2228VGhqqhIQEVatW7ZK+X3zxhX788UdFRkZe0jZ8+HAlJydr6dKlstvtuvfee3X//fdr3rx5ks6lvH369FHv3r01Z84c7dixQ6NHj1ZQUJDuv/9+SdL69et155136oUXXtDNN9+sefPmaeDAgdqyZYuaN29esm8EAAAVUHxatl5ckaC5W47L6To3MaR73ep6rHdD3dAgpMyu6VjRVffz0vTrG+jPPeppwbYkvbL6oDYfy9Q7G4/onY1HdGPDEE3qVld9G4XJWsJh86HTZ3Xbe5u0LSlLnlaLXh3YXA90rlOirwkAAICKwdTp59OnT9e6deu0Zs2a3+x3/PhxdejQQd99950GDBigSZMmadKkSZKkPXv2qGnTptq0aZPatWsnSVqyZIn69++vY8eOKTIyUm+++aYeffRRpaSkyMvLy/3aX375pfbu3StJGjp0qHJycrRo0SL363bs2FGtWrXSnDlzrnotTD8HAOCc7UlZemFFgj7ZlqQLnzL6NAzVX29ooO71gs0tDpcwDEPrD6XrldUH9cXOZJ3Pn9U4rIomdo3R3e2i5OdV/D8HX7X/pO74IE4ncwoU6u+lT0e1U7e63B8AAACVWbmZfr5w4UK1a9dOQ4YMUVhYmFq3bq233367UB+Xy6WRI0dq6tSpatas2SXn2LBhg4KCgtyBpiT17t1bVqtVP/30k7tP9+7d3YGmJPXt21fx8fFKT0939+ndu3ehc/ft21cbNmy4bO35+fnKysoq9AUAQGW26UiGbnt3o1r96wfN33ou0LylWQ39OLGrltzfkUCzjLJYLOoSU10LRrVTwvQbNKl7XVX19tTetGw9+PkO1Xp2mf76zR4dz8wtltczDEOvrU3UjW/9qJM5BWpTM1CbJnUj0AQAAMA1MTXUPHjwoN588001aNBA3333ncaNG6eJEyfq/fffd/f5+9//Lk9PT02cOPGy50hJSVFYWFihY56enqpevbpSUlLcfWrUqFGoz4XHV+tzof3XXnjhBQUGBrq/oqOjr+HKAQCoONYmntJNb/+oDq+u0Ve7UmWxSHe0jNTPU7rry3uv03W1Ll1WBmVTTLCf/nVLMx19vLdevrWZYqr76fRZu15csV8xzy3XyHlbtPloxu8+f77DqbELtmvilzvldBm6q3VNrR7fWbWq+RXfRQAAAKBSMHVNTZfLpXbt2un555+XJLVu3Vo7d+7UnDlzNGrUKMXFxWnWrFnasmVLmVt3a8aMGZoyZYr7cVZWFsEmAKDSMAxDyxNO6rllCfrh4ClJkofVouGta2r6DfXVOKyqyRXijwjwsenhbnU1oUuMFu5K0SurD2pN4ml9uOW4PtxyXF1jqmtS97q6tVl4kTd5Ss7K0+3vb9aGw+myWKQX+zfRIz3rlbnPeAAAACgfTA01IyIi1LRp00LHmjRpos8++0yStGbNGqWlpalWrVrudqfTqT//+c965ZVXdOjQIYWHhystLa3QORwOh06fPq3w8HBJUnh4uFJTUwv1ufD4an0utP+at7e3vL3ZlRMAULkYhqHFe9L03LJ9+ulIhiTJ5mHRPe2jNa1XfdUN9je3QBQrD6tFt8VG6LbYCMUdy9Arqw9q/tYkrU08rbWJpxVT3U8PdY3R6OuiFeBjcz8vp8Ahm9WqjDy7gnxsOpVToDs/3KINh9MV6OOpj0a0Vb/GYb/xygAAAMBvMzXU7NKli+Lj4wsd27dvn2rXri1JGjly5GXXuRw5cqTuvfdeSVKnTp2UkZGhuLg4tW3bVpK0YsUKuVwudejQwd3n0Ucfld1ul8127gP30qVL1ahRI/dO6506ddLy5cvdGxBd6NOpU6fiv3AAAMoZl8vQFzuT9dyyBG1NOreOtI+nVWM71tbUnvUUFeRrcoUoaW2jgvTBXW304oAmemP9If17w2Elnj6rKQt36Ynv4jWmQy1N6V5XIf5emrnygGavTVRGrl1BvjZN6FJHC+5uq9Efb9W/bm2mBqFVzL4cAAAAlHOm7n6+adMmde7cWU899ZTuuOMObdy4UWPHjtVbb72l4cOHX/Y5derUKbT7uSTddNNNSk1N1Zw5c2S323XvvfeqXbt2mjdvniQpMzNTjRo1Up8+fTRt2jTt3LlTo0eP1ssvv6z7779fkrR+/Xr16NFDL774ogYMGKCPP/5Yzz//vLZs2aLmzZtf9VrY/RwAUBE5nC59vDVJLyxP0J60bEmSv5eHHuxcR1N61FONqsxaqKzOFjj0QdwxzVqTqL3n740v72mvzccy9OyyhEv6P9a7gf7co54CfW2XtAEAAADSteVrpoaakrRo0SLNmDFDCQkJiomJ0ZQpUzR27Ngr9r9cqHn69GlNmDBBX3/9taxWqwYPHqxXX31VVar8Mgpg+/btGj9+vDZt2qSQkBA99NBDmjZtWqFzL1iwQI899pgOHTqkBg0aaObMmerfv3+RroNQEwBQkRQ4XPog7pheXJGgA6fOSpICfTz1UNcYPdytroL9vUyuEGWFy2Xou/g0vbfpqN4Z1krRzyxTRq79kn5BvjalPNFHXp6m7lMJAACAMqxchZoVBaEmAKC8+fW6h3aXSx4Wi/678Yhmrtyvoxl5kqQQfy9N6l5X4zvXYZQdflPKmTxFPrX0iu2pT/ZRaBVG9wIAAODyriVfM3VNTQAAYI48u/OSdQ8f6lpHD3Wtq9fXHdLRjDyFV/XWIz3r6U8da8vfm48MuLrqvl4K8rVdcaRmoA+hOAAAAIoH/0MBAKCSySlwaObKA3pm6T73sYxcu55ZmiDDkP51SzMdPHVWo6+Llo/Nw8RKUd7YXS5N7Bqjpy+6ty6Y2DVGdpdLXmL6OQAAAP44Qk0AACowh9OlQ+m5ik/L1r6T2Tqekaen+zXW7LWJl+3/2rpDeqx3Q/VrTPCEa+fv5anp19eXJL160SjgiV1jNP36+oTkAAAAKDaEmgAAlHOGYSj1TL72ncjRvpPZik/LUcLJbMWnZevAqbNyuH5ZPrt5eFWN7xpz2enB0rkRm5l5dtY9xO/mY/PQ1F719NcbGigzz67A8+u1EmgCAACgOBFqAgBQTmTnO5RwMufcqMsTvwSX+07mKCvPccXn+dqsahBSRY1C/dWyZqAiqnqz7iFKlL/XuY+YF8JxppwDAACguBFqAgBQhvx6unh8Wo4STpwLLo9n5l3xeVaLVKe6nxqG+KtBaBU1Cq2ihqH+ahRWRTUDfGS1Wtx9cwocrHsIAAAAoFwj1AQAoJRdy3TxXwv191LD0F8Fl6FVVDfYr8jTe1n3EAAAAEB5ZzEM48r/c0KRZWVlKTAwUJmZmQoICDC7HABAGVAc08UbXBRcNgj1V3U/r2KrL6fAIZvVWmjdwwvThgEAAACgtF1Lvsb/XAAA+AN+73Rxi0WqU83vkuCyYai/ogJ9C00XLymsewgAAACgvCLUBADgKgzDUFp2QeHg8uS50ZcHTuXI7rzypIcQfy93cHkhtGwYWkX1rmG6OAAAAACgMEJNAECFcmFKdUaeXUHXOKX6wnTxfScKr3NZ1OniFwLLkpouDgAAAAA4h1ATAFBh5NmdmrnygGb/xuY3v54uvu9EjvalFX26+K+Dy9KcLg4AAAAAOIdQEwBQIeQUODRz5QE9s3Sf+1hGrl1PL90nQ4b6NAzT2AXbrmm6+C/BJdPFAQAAAKAsIdQEAFQINqtVs9cmXrZt9tpD+kuv+jqZUyC705CPp9U92rLBRcFlQ6aLAwAAAEC5QKgJAKgQMvLsysi1X74t167MPIe+ure9agb6MF0cAAAAAMo5Qk0AQLmX73Cqqrengnxtlw02g3xtCvHzUmSAjwnVAQAAAACKm9XsAgAA+CMSTmSry+x1+j7+hCZ0qXPZPhO7xsjucpVuYQAAAACAEsNITQBAufVB3FGN/3yHsvOd+vuKBH3/p06yWix69Td2PwcAAAAAlH8WwzCuvAUsiiwrK0uBgYHKzMxUQECA2eUAQIV2Js+hCV/s0AdxxyRJPeoGa+7w1qoZ6KucAodsVqsy8+wK9LHJ7nLJ34uf4QEAAABAWXct+Rr/ywMAlCtxxzJ059wt2n8yR1aL9ESfRvrrDQ3kcX7jnwsBZmgVb0mSFyutAAAAAECFQ6gJACgXXC5Dr6w5qBnf7JHdaSg6yEcfDm+jrjHBZpcGAAAAAChlhJoAgDIv7Uy+Rs/fqm/2pkmSbmserrfvaKnqfl4mVwYAAAAAMAOhJgCgTFuecEIj5/2slDP58va06l+3NNMDnWrLYrGYXRoAAAAAwCSEmgCAMsnudOmJ7+L195X7ZRhS0xpV9NGItoqNYDM2AAAAAKjsCDUBAGXOodNnddeHW/Tj4XRJ0tiOtfTyLc3kxy7mAAAAAAARagIAypgF25J0/4JtysxzKNDHU28NaakhLSPNLgsAAAAAUIYQagIAyoSzBQ5N+mqX/vPTEUlSp9rV9OHwNqpT3c/kygAAAAAAZQ2hJgDAdDuSszTsgzjtScuWxSJNv76+nuzTSDYPq9mlAQAAAADKIEJNAIBpDMPQnA2HNWXhLuU7XIoI8Nb/7mytGxqEml0aAAAAAKAMI9QEAJji9NkCjf1km77YmSJJ6t84TO8Oa6XQKt4mVwYAAAAAKOsINQEApW7NwVMaMW+LjmbkyeZh0YsDmmhSt7qyWCxmlwYAAAAAKAcINQEApcbpMvTcsgQ9vTReLkOqH+Kvj0a0UduoILNLAwAAAACUI4SaAIBScSwjVyPn/awfDp6SJN3dNkqzb4tVVR/+KQIAAAAAXBv+JwkAKHELd6Vo9PytOn3WrireHnpjUAuNaBtldlkAAAAAgHKKUBMAUGLy7E79ZfEevbY2UZLUNipQ84a3UYPQKiZXBgAAAAAozwg1AQAlYm/aGd05d4u2JWVJkiZ3r6sX+jeRl6fV5MoAAAAAAOUdoSYAoFgZhqF3Nx3VxC926qzdqVB/L703rJVualLD7NIAAAAAABUEoSYAoNhk5to17rPt+nhrkiTphgYh+t+drRUR4GNyZQAAAACAioRQEwBQLDYeSdedc7co8fRZeVgteqZfI/2lZ31ZrRazSwMAAAAAVDCEmgCAP8TlMvSPHw7osW/3yuEyVKear+aNaKuOtauZXRoAAAAAoIIi1AQA/G4pWXka9fHPWrrvpCTpjpaRmnN7CwX52kyuDAAAAABQkRFqAgB+l+/i0zTqo5+Vll0gX5tVswY215jrasliYbo5AAAAAKBkEWoCAK5JgcOlR7/dq3/+cECSFBtRVR8Nb6um4VVNrgwAAAAAUFlYzS7g+PHjGjFihIKDg+Xr66vY2Fht3rxZkmS32zVt2jTFxsbK399fkZGRuvvuu5WUlFToHKdPn9bw4cMVEBCgoKAgjRkzRtnZ2YX6bN++Xd26dZOPj4+io6M1c+bMS2pZsGCBGjduLB8fH8XGxuqbb74puQsHgHLowMkcdXt9nTvQHNe5jn6c2I1AEwAAAABQqkwNNdPT09WlSxfZbDZ9++232r17t/75z3+qWrVzm0ucPXtWW7Zs0eOPP64tW7bo888/V3x8vG655ZZC5xk+fLh27dqlpUuXatGiRVq9erXuv/9+d3tWVpb69Omj2rVrKy4uTi+99JKefPJJvfXWW+4+69ev15133qkxY8bo559/1sCBAzVw4EDt3LmzdN4MACjj5m05pjYvr9amoxmq5mvT5/e00+uDYuVr8zC7NAAAAABAJWMxDMMw68WnT5+udevWac2aNUV+zqZNm3Tdddfp8OHDqlWrlvbs2aOmTZtq06ZNateunSRpyZIl6t+/v44dO6bIyEi9+eabevTRR5WSkiIvLy/3a3/55Zfau3evJGno0KHKycnRokWL3K/VsWNHtWrVSnPmzLlqXVlZWQoMDFRmZqYCAgKu5W0AgDItO9+hiV/s1Hubj0qSusVU19zhbRQd5GtyZQAAAACAiuRa8jVTR2ouXLhQ7dq105AhQxQWFqbWrVvr7bff/s3nZGZmymKxKCgoSJK0YcMGBQUFuQNNSerdu7esVqt++uknd5/u3bu7A01J6tu3r+Lj45Wenu7u07t370Kv1bdvX23YsOGydeTn5ysrK6vQFwBUNFuPZ6rdK6v13uajslqkv93YUMsf6ESgCQAAAAAwlamh5sGDB/Xmm2+qQYMG+u677zRu3DhNnDhR77///mX75+Xladq0abrzzjvdaW1KSorCwsIK9fP09FT16tWVkpLi7lOjRo1CfS48vlqfC+2/9sILLygwMND9FR0dfY1XDwBll2EYenXNQXV8da32nchRzUAfLX+gk57s20ieHqYvxwwAAAAAqORM3f3c5XKpXbt2ev755yVJrVu31s6dOzVnzhyNGjWqUF+73a477rhDhmHozTffNKPcQmbMmKEpU6a4H2dlZRFsAqgQTubka/TH27RoT6ok6ZZmNfTfO1op2N/rKs8EAAAAAKB0mBpqRkREqGnTpoWONWnSRJ999lmhYxcCzcOHD2vFihWF5tSHh4crLS2tUH+Hw6HTp08rPDzc3Sc1NbVQnwuPr9bnQvuveXt7y9vbu6iXCgDlwqr9JzVi3s9KysqTt6dVL93cVOO71JHFYjG7NAAAAAAA3EydQ9ilSxfFx8cXOrZv3z7Vrl3b/fhCoJmQkKBly5YpODi4UP9OnTopIyNDcXFx7mMrVqyQy+VShw4d3H1Wr14tu93u7rN06VI1atTIvdN6p06dtHz58kLnXrp0qTp16lQ8FwsAZZjD6dLjS/bqhn9vUFJWnhqF+uvHiV01oWsMgSYAAAAAoMwxNdScPHmyfvzxRz3//PPav3+/5s2bp7feekvjx4+XdC7QvP3227V582Z9+OGHcjqdSklJUUpKigoKCiSdG9nZr18/jR07Vhs3btS6des0YcIEDRs2TJGRkZKku+66S15eXhozZox27dql+fPna9asWYWmjz/88MNasmSJ/vnPf2rv3r168skntXnzZk2YMKH03xgAKEWHT59VrzfX67llCTIM6d7rorV5Une1jAw0uzQAAAAAAC7LYhiGYWYBixYt0owZM5SQkKCYmBhNmTJFY8eOlSQdOnRIMTExl33eypUr1bNnT0nS6dOnNWHCBH399deyWq0aPHiwXn31VVWpUsXdf/v27Ro/frw2bdqkkJAQPfTQQ5o2bVqhcy5YsECPPfaYDh06pAYNGmjmzJnq379/ka7jWracB4Cy4vMdybrvk23KyLUrwMdTcwa30LDWNc0uCwAAAABQCV1LvmZ6qFlREGoCKE9y7U5NWbhL/95wWJJ0XXSQ5o1oo7rB/iZXBgAAAACorK4lXzN1oyAAQOnblXJGwz6I067UM5Kkv/Sqr2f6NZLNw9QVSQAAAAAAKDJCTQCoJAzD0Fs/Htbkr3Ypz+FSjare+t+drXVjw1CzSwMAAAAA4JoQagJAJZB+tkB/+nS7Pt2eLEnq2yhU7w1rrRpVvU2uDAAAAACAa0eoCQAV3PpDp3XX3C06kpErT6tFz/dvoind68pqtZhdGgAAAAAAvwuhJgBUUE6XoRdXJOjJ7/fJ6TJUL9hP84a3VftaQWaXBgAAAADAH0KoCQAVUFJmnkbO26KVB05Jku5qXVNvDI5VgI/N5MoAAAAAAPjjCDUBoIJZvDtV987fqpM5BfL38tBrt8Xq7nZRsliYbg4AAAAAqBgINQGggsh3ODV98R7NWpMoSWpdM0DzhrdVo7AqJlcGAAAAAEDxItQEgApg34ls3Tk3Tj8fz5IkTewWo78PaCJvTw+TKwMAAAAAoPgRagJAOfe/zUc1/vMdyilwKtjPpneHtdbNTWuYXRYAAAAAACWGUBMAyqkzeQ6N/3y75m45LknqWS9YH9zVWjUDfU2uDAAAAACAkkWoCQDlQE6BQzarVRl5dgX52HTqbIHu/fhnfb/vpDysFj3Zp6GmX99AHlY2AwIAAAAAVHyEmgBQxuXZnZq58oBmr01URq5dQb42TehSRx/c1UbDPojT0/0aq0tMdbPLBAAAAACg1BBqAsXs1yPq7C6X/L34q4bfJ6fAoZkrD+iZpfvcxzJy7Xp2WYIk6fN72ivQ12ZWeQAAAAAAmMJqdgFARXJhRF34U98r/MnvFf7U93pp5QHl2Z1ml4Zyyma1avbaxMu2vbbukHxt7G4OAAAAAKh8CDWBYpJT4NALK/brmaX7lJFrl3RuRN3TS/fpxRX7lVPgMLlClBeGYWjz0Qy9uCJBSVl57vvp1zJy7crMu3wbAAAAAAAVGaEm8Ac5nC79dDhdVovliiPqXl2bKJuVv274bUfSz+qF5Qlq9tIqXTdrjf71w0GFVvFS0BWmlwf52hTow9RzAAAAAEDlw0J/wO+QeiZfS/amaUl8mr6PP6GagT76avR1vzmiLvlMnr7YmaKedYPVMjJAFgu7VEPKyrPrs+3Jmht3TKsOnpJhnDvu42nVDQ1CdDKnQBO7xujpi9bUvGBi1xjZXS558fMpAAAAAEAlQ6gJFIHTZWjjkXR9uzdN3+5NU9yxzELtIf421ajirSBf22WDzSBfm0L8vfT8sgRNydmlhqH+Gtqqpoa2jFTT8KqldRkoIxxOl5buO6G5W47ry53JyrW73G096gZrRNso3d4iwr0B0PTr60s6N+L3wu7nE7vGaPr19eXDmpoAAAAAgErIYhgXxgXhj8jKylJgYKAyMzMVEBBgdjkoBmln8vXdvjQt2XtC38Wn6fTZwmFl26hA9Wscpv6Nw3RdrWrKczj10soDlx1R97cbG+rudlGatmiPFu9JVZ7jlxArNqKq7mgZqaGtaqp+iH+JXxfMYRiGtiZl6YO4Y/ro5+NKPZPvbmsU6q8RbaM0vE2U6lT3u+zzcwocslmtysyzK9DHJrvLJX8vfi4FAAAAAKg4riVfI9QsJoSa5Z/TdW5zlgujMTcfy9DFfzuCfG3q2yhU/RqFqW+jUIUH+Fxyjjy7Uy+u2P+bI+rO5Dm0cHeKPtmapCXxabI7f3mRtlGBuqNlpO5oGanaVwi3UL4cz8zVh1uOa27cMe1MOeM+HuLvpaGtInV322i1iw5kOQIAAAAAQKVHqGkCQs3y6WROvr6LP3Fufcy9aTr1q9GYrWsGnB+NWUMdagXJ0+Pqaxdey4i69LMF+nJnij7ZlqRlCSfldP3y17FT7Wq6o1WkhrSIVGTgpQEqyq7sfIc+33Funczl+0+6w3EvD6tuaVZDI9tGqV/jMNmKcD8BAAAAAFBZEGqagFCzfHC5DMUdy9Q3e1O1ZG+aNh4tPBoz0MdTfRqGql/jMPVrHKaIy4zGLCknsvP1+Y5kzd+apB8u2jDGYpG6xwTrjlaRur1FhEKreJdaTSg6p8vQ8oQTmht3TJ/vSNFZu9Pd1jWmuka0jdKQFhGq5udlYpUAAAAAAJRdhJomINQsu07lFOj7fb+MxjyRU1CovWVkgHttzI61q5WJ0XPJWXn6dHuy5m89rvWH0t3HPawWXV8/WHe0rKnbYsNVnYDMdNsvWiczKSvPfbx+iL9GtInSiLY1VTeYtVIBAAAAALgaQk0TEGqWHS6XoZ+TMvXNnnMh5k9H0nXRrG4F+HjqxgYXRmOGqmagr3nFFsGR9LP6ZFuyPtl6XJsv2nXd5mFRn4ahuqNVpG5tFq4AH5uJVVYuyVl5mrfluOZuOaZtSVnu49X9bLqjZaTubhetDrWCWCcTAAAAAIBrQKhpAkJNc6Wf/WU05rd705SWXXg0ZmxEVd3UuIZuahyqznWql4nRmL/HgZM5mr8tSZ9sTdL25F/CNG9Pq/o3DtPQVjU1oEmY/L3ZFbu45eQ79OWuFM2NO6al+064g3Kbh0U3N6mhke2i1L9xDXl5ls97CwAAAAAAsxFqmoBQs3QZhqGtSVn6Zs+5tTE3HC48GrOqt6d6NwhRv8ZhuqlxmKKCyvZozN9jT+oZzd+apE+2JWlvWrb7uJ/NQ//XrIaGtopUv0Zh7l3Xce2cLkOrDpzU3Lhj+mxHsrLzf1kns1PtahrZNkp3tIpkGQAAAAAAAIoBoaYJCDVLXkauXUv3ndC359fGTDmTX6i9WY2quqnJuRCzS53qlWbEnGEY2p6c5Q44D546624L8PHUrc3CNbRVpHo3CK0078kftSvljD6IO6Z5W47pWOYv62TGVPfTiLZRGtGmphqEVjGxQgAAAAAAKh5CTRMQaha/C2HdhbUx1x9Ol/Oi4Zj+Xh6FRmPWquZnYrVlg2EY2nw0U/O3HdeCbUk6mvFLIFfN16ZBLSI0tGWketYLlmc5nYJfUlLP5Oujn49rbtwxbTn+y9qlQb42DWkZobvbRqtznWqskwkAAAAAQAkh1DQBoWbxyMy1a1nCCX2799z6mBfvJi1JTcKquEdjdo2pLm9PplZfictlaMPhdM3flqQF25KUetHI1rAqXhrcIlJDW0aqa0x1Wa2VM6jLtTv11c5z62R+t++EOzT3tFrUv0mYRraN0oAmNZjCDwAAAABAKSDUNAGh5u9jGIZ2JJ9xTylfd+i0HBeNxvSzeeiGi0Zj1qnOaMzfw+kytPrgKc3fmqTPtifp1Fm7uy0ywEdDWkZoaKualWLHbtf59+KDLcf06bZkncl3uNuuiw7SyHZRGtoqUiH+3iZWCQAAAABA5UOoaQJCzaLLyrNrecJJfXM+yDyeWXg0ZqNQf93UpIZuahymbjHVGSVXzOxOl1bsP6n5Pyfpi53Jysz7JdSrU81XQ1pGamirSLWuGVihAs69aefWyfww7riOZOS6j9eu5nt+ncwoNQpjnUwAAAAAAMxCqGkCQs0rMwxDu1J+GY25JrHwaExfm1XX1w/RTY3PBZkxwYzGLC35Dqe+jz+h+VuTtHB3SqHdvRuE+OuOVpEa1qqmmoVXNbHK3+9Edr4+3pqkuXHHtOlohvt4gI+nbm8RqbvbRlXq6fcAAAAAAJQlhJomINQsLDvfcX40ZqqW7E0rtGGNdC4wu7A2Zo+6wYzGLANy7U59sydV87cmadHuVOU5XO62ZjWq6o5W50ZwNizju37n2Z36eneq5sYd07d709wBuofVon6NQjWybbT+r1kN+XLPAQAAAABQphBqmqCih5o5BQ7ZrFZl5NkV5GOT3eW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RkZEtXKwWd31mZmab9zt69CjAaUtUS0pK+N3vfkdYWBgeHh6EhIRYK7lO/l0888wzHDhwgOjoaKZOncqjjz7a7As8NTUVUCXKISEhzV5vvPEG9fX11uud7lqtceLECWpqahgxYkSLfSNHjsRkMrXIl+gJoqKiWnxRBQQENPvCSU1N5eDBgy3+3cOHDwdUvkl7HD58mD179jBz5kzS0tKsr3nz5vHtt9+2KspOrbazMGjQoBbhjMzMTOLj49Hrm3+0nfo+CgsLIz4+3vp+3LhxI7Nnz2bOnDnk5uZy7NgxNm/ejMlkalW8tPZ3uXDhwhZfcKmpqWiaRnx8fIvf2eHDh5v9vrKysrjhhhsIDAzE29ubkJAQaxKz5f1ksT8+Pr6FTa29XzrCiRMnqKio6FKJd0dsdnNz4+9//zs//PADYWFhzJkzh2eeeYb8/HzrdebOncull17KY489RnBwMBdeeCFvv/12szyq1NRUysvLCQ0NbfG7rKqqsv4uO3KtrqCdkntnEW6tXddSunyyuLMVFrsGWrWe5Lz0M9zc3Fp8cINKmr3hhhv4+uuvWb16Nffccw9PP/00v/76a7di5a0xa9YsGhsb2bp1q/VLAZSo2bhxI8nJyZw4caLVLwVbcsUVV7Blyxb+8Ic/MH78eLy9vTGZTJx99tlWT4nluNmzZ/Pll1+yevVqnn32Wf7+97/zxRdfsGTJEuuxzz77bJslx5bck9Ndy5FozbsBzT+0TSYTiYmJvPDCC60eGx0d3e493n//fQDuu+8+7rvvvhb7P//88xbelLa+ALr7xTBr1izWrl1LbW0tu3bt4uGHH7Z6Azdu3Mjhw4fx9vZmwoQJXb6HyWRCp9Pxww8/tPr7tbxPjEYjixYtoqSkhD/+8Y8kJCTg5eVFTk4ON9xwQ7P3p6PQGZvvvfdezj//fL766it+/PFH/vKXv/D000/z888/M2HCBHQ6HZ999hm//vor33zzDT/++CM33ngjzz//PL/++qv1bzU0NLRND11ISAhAh67VGSzlzqd6jQIDA3FzcyMvL6/FOZZtkZGRnbpXV7DY1V4uZH9ExMsAIjExkcTERP785z+zZcsWZs6cyWuvvcYTTzwBdE65t3fs1KlTcXV1ZePGjWzcuJE//OEPAMyZM4fXX3+dtWvXWn+2MGTIEH766acWCW7JycnW/W1hSSw+cOBAm56p0tJS1q5dy2OPPcbDDz9s3W7xopxKREQEd955J3feeSeFhYVMnDiRJ598kiVLlljv5+vre1pP2Omu1RohISF4enqSkpLSYl9ycjJ6vf60IqE1euLJLC4ujr1797JgwYJOX0/TND788EPmz5/PnXfe2WL/X//6Vz744IMW4qUzDBkyhH379mEymZqJ+NbeR7Nnz+btt9/m448/xmg0MmPGDPR6PbNmzbKKlxkzZrQp6jpCXFwcmqYRGxtr9U61xv79+zly5AjvvPMOS5cutW4/NUxssb+1921r75eOEBISgq+vLwcOHOjUeR212UJcXBwPPPAADzzwAKmpqYwfP57nn3/eKmgBzjjjDM444wyefPJJPvzwQ6699lo+/vhjbr75ZuLi4vjpp5+YOXNmh4Rre9fqDIMHD8bDw4P09PRm2/V6PYmJia02LNy2bRtDhw61ebIuYLXr1KTy/o6EjQYAFRUVGAyGZtsSExPR6/XNXJ5eXl7NSpzbw9Jro7Xj3d3dmTJlCh999BFZWVnNPC+1tbX885//JC4ujoiICOs555xzDkajkX/961/NrvXiiy+i0+na9VJMnDiR2NhYXnrppRb2WLwGli+gU12/L730UrOfjUZji3BaaGgokZGR1t/VpEmTiIuL47nnnqOqqqqFPZbeKx25Vms4OTlx1lln8fXXXzcrJS0oKODDDz9k1qxZ+Pr6tnl+W7T3f9ZRrrjiCnJycnj99ddb7KutrW1WDXQqmzdvJiMjg2XLlnHZZZe1eF155ZWsW7euU/lBp3LOOeeQn5/PJ598Yt1mMBh4+eWX8fb2btZLxvK+/Pvf/87YsWOtOSSzZ89m7dq17Ny5s9vewUsuuQQnJycee+yxFu89TdOs+RStvT81TWtW6g9KCI8fP5533nmn2XtrzZo1HDp0qEs26vV6LrroIr755ptWv4hPtdtCR22uqalp0QE2Li4OHx8f699BaWlpi/tYvJqWY6644gqMRiN//etfW9hiMBis7+uOXAs6Xirt4uLC5MmTW/3dXHbZZezYsaPZvpSUFH7++ed2+7P0JLt27UKn0zF9+vReuZ+jIJ6XAcDPP//M3XffzeWXX87w4cMxGAy89957ODk5cemll1qPmzRpEj/99BMvvPACkZGRxMbGMm3atFavGRcXh7+/P6+99ho+Pj54eXkxbdo0a37C7Nmz+dvf/oafnx+JiYmA+uIeMWIEKSkpLXrInH/++cyfP58//elPZGRkMG7cOFavXs3XX3/Nvffe22bZNqgP33//+9+cf/75jB8/nmXLlhEREUFycjIHDx7kxx9/xNfX1xprb2xsZNCgQaxevbrF01RlZSVRUVFcdtlljBs3Dm9vb3766Sd27NjB888/b73fG2+8wZIlSxg9ejTLli1j0KBB5OTksG7dOnx9ffnmm286dK22eOKJJ1izZg2zZs3izjvvxNnZmf/85z/U19e32nulI5zu/6wj/OY3v+HTTz/l9ttvZ926dcycOROj0UhycjKffvopP/74Y5tNFT/44AOcnJw499xzW91/wQUX8Kc//YmPP/64WU+gznDrrbfyn//8hxtuuIFdu3YRExPDZ599xubNm3nppZeaPQkPGzaM8PBwUlJS+O1vf2vdPmfOHGv/oe6Kl7i4OJ544gmWL19ORkYGF110ET4+PqSnp/Pll19y66238vvf/56EhATi4uL4/e9/T05ODr6+vnz++eetJrg+/fTTnHvuucyaNYsbb7yRkpISXn75ZUaPHt1CTN9www0dKgd+6qmnWL16NXPnzrWWwOfl5bFy5Uo2bdrUahPEjtp85MgRFixYwBVXXMGoUaNwdnbmyy+/pKCggKuuugqAd955h1dffZWLL76YuLg4Kisref311/H19eWcc84BVC7LbbfdxtNPP01SUhJnnXUWLi4upKamsnLlSv7xj39w2WWXdeha0PFSaYALL7yQP/3pT1RUVDR7cLjzzjt5/fXXOffcc/n973+Pi4sLL7zwAmFhYTzwwAPNrmFpc9CWGDwZizf84MGDALz33nts2rQJgD//+c/Njl2zZg0zZ87sUjffPk0vVjYJncBSktlWuV9bpdJeXl4tjj127Jh24403anFxcZq7u7sWGBiozZ8/X/vpp5+aHZecnKzNmTNH8/Dw0IDTlk1//fXX2qhRozRnZ+cWtnz33XcaoC1ZsqTZOTfffLMGaG+++WaL61VWVmr33XefFhkZqbm4uGjx8fHas88+26wcuT02bdqkLVq0SPPx8dG8vLy0sWPHNitvPH78uHbxxRdr/v7+mp+fn3b55ZdbywwfeeQRTdNUefAf/vAHbdy4cdbrjBs3Tnv11Vdb3G/Pnj3aJZdcogUFBWlubm7akCFDtCuuuEJbu3Ztp6/VGrt379YWL16seXt7a56entr8+fO1LVu2NDumM6XSmtb2/9ncuXOblfhauP7661uU3zY0NGh///vftdGjR2tubm5aQECANmnSJO2xxx7TysvLW71vQ0ODFhQUpM2ePbtd+2JjY7UJEyZomtb+30Bb9mqaphUUFGjLli3TgoODNVdXVy0xMbHV8nBN07TLL79cA7RPPvmkma2enp6aq6urVltb2+z40/1dtmXX559/rs2aNUvz8vLSvLy8tISEBO2uu+7SUlJSrMccOnRIW7hwoebt7a0FBwdrt9xyi7Z3795Wy9s///xzbeTIkZqbm5s2atQo7Ysvvmj1/+rSSy/VPDw8WrQQaI3MzExt6dKlWkhIiObm5qYNHTpUu+uuu7T6+npN01ovle6IzUVFRdpdd92lJSQkaF5eXpqfn582bdo07dNPP7VeZ/fu3drVV1+tDR48WHNzc9NCQ0O18847T9u5c2cLO//73/9qkyZN0jw8PDQfHx8tMTFRe/DBB7Xc3NxOXaujpdKapt5Tzs7O2nvvvddiX3Z2tnbZZZdpvr6+mre3t3beeedpqampLY6bNGmSFh4eftp7aZrWbin+yZSVlWmurq7aG2+80aHr9id0mtbB9qWCIAhCnyIsLIylS5fy7LPP2tuUPs9NN93EkSNHmjXb7CiVlZUEBgby0ksvcdddd/WYTS+99BLPPPMMR48e7ZXKJkdCxIsgCEI/5ODBg0yfPp1jx44NuEoUW5CVlcXw4cNZu3Zti8nSp+O7777jrrvu4siRI212K+4sjY2NxMXF8dBDD7WaAN/fEfEiCIIgCEKfQqqNBEEQBEHoU4h4EQRBEAShTyHiRRAEQRCEPoWIF0EQBEEQ+hT9rkmdyWQiNzcXHx+fATeoShAEQRD6KpqmUVlZSWRkZKsz+k6m34mX3NzcLs19EQRBEATB/mRnZ592YHC/Ey+W9t/Z2dldmv8iCIIgCELvU1FRQXR0dIcGWvY78WIJFfn6+op4EQRBEIQ+RkdSPiRhVxAEQRCEPoWIF0EQBEEQ+hQiXgRBEARB6FOIeBEEQRAEoU8h4kUQBEEQhD6FiBdBEARBEPoUIl4EQRAEQehTiHgRBEEQBKFPIeJFEARBEIQ+hYgXQRAEQRD6FCJeBEEQBEHoU4h4EQRBEAShTyHiReizmEwan+7IZlNqkb1NEQRBEHoRES9Cn8Rk0vjz1wd48PN9LH1rmwgYQRjg1DUaeXV9GtklNfY2RegFbCpeNmzYwPnnn09kZCQ6nY6vvvqq3ePXr1+PTqdr8crPz7elmUIf5MnvD/PhtiwATBrc/dFusorlQ0sQBipvbkrnmVUp/N+X++1titAL2FS8VFdXM27cOF555ZVOnZeSkkJeXp71FRoaaiMLhb5IakElb25KB+CpixMZF+1PWU0jt7y7k+p6g52tEwTBHqw+VADAlqPFlFQ32NkawdY42/LiS5YsYcmSJZ0+LzQ0FH9//w4dW19fT319vfXnioqKTt9P6Fv8+5ejAJw9Opxrpg3mzIRQzv/XJlIKKvn9yr28eu1EdDqdna0UBKG3KKyoY292GQBGk8bqg/lcNXWwfY0SbIpD5ryMHz+eiIgIFi1axObNm9s99umnn8bPz8/6io6O7iUrhZ6iwWBiY+oJjCbttMceL63hf0m5ANw5Pw6AcD93XrtuIi5OOn44kM8r69Jsaq8gCI7F2uTCZj9/f0BSDfo7DiVeIiIieO211/j888/5/PPPiY6OZt68eezevbvNc5YvX055ebn1lZ2d3YsWCz3BU98f5jdvbucfPx057bGvbziGwaQxa1gwY6P8rdsnDQnkrxeOAeD5NUdYe7jAVuYKguBg/GQOGV06MQqALWlFHCmopLKu0Z5mCTbEocTLiBEjuO2225g0aRIzZszgrbfeYsaMGbz44ottnuPm5oavr2+zl9B3KK1u4OMdKvF2xZaMZjkrdY1GjpfWUFBRB0BRVT0f71Di9M55cS2uddXUwVx3xmA0De79OIm0wqpe+BcIgmBPahoMbEpT1Ya3zIklIdwHg0njrBc3MPmJnziYW25nCwVb4FDipTWmTp1KWpqEAforH27Poq7RBEBFnYHPdh0HYEdGCWc8vZZZf1/HtKfWcteHu3ljYzr1BhPjovyYHhfU6vUePm80U2MCqaw3cOt7O6mQJy9B6LdomsafvjxAvcHE4EBPRoT5cMe8OHzdnXFx0lFvMPHU94fRtNOHpIW+hcOLl6SkJCIiIuxthmADGgwm3t2aAcDU2EBAlTt+uC2L69/aTllNIy5OOnQ6+G5fHq+ZE3XvmDeszYRcV2c9r143kUg/d46dqOa+j5Pkg0sQ+hGNRhP1BiPpRdU88d1hvtyTg5Nex1MXJ6LT6bhw/CD2PbqYnx+Yh6uTns1pxaw/csLeZgs9jE2rjaqqqpp5TdLT00lKSiIwMJDBgwezfPlycnJyePfddwF46aWXiI2NZfTo0dTV1fHGG2/w888/s3r1aluaKdiJHw7kUVBRT4iPG6//ZjJznl1HVkmNtU/DrGHBvL50Mr8cKeSOD3ajaTAs1JuzRoW1e91gbzf+85vJXPraFtYmF3Ior4LRkX698U8SBKEHMZk03t2awcgIX6YNDeKVdWn846dUGoymZsc9dsFoZsUHN9sWHejJ9TOG8PrGdP72fTJz40PQ66UKsb9gU/Gyc+dO5s+fb/35/vvvB+D6669nxYoV5OXlkZWVZd3f0NDAAw88QE5ODp6enowdO5affvqp2TWE/oGmadZeLUvPGIKfpwtPXZzIe79mYNJgVIQvDy1JwN3FibPHRPC3SxJ5bvUR/u+chA59ACVG+TEtNpCNqUXszS4X8SIIfZDvD+Tx6DeHcNLruGxiFJ/sbCrIcHHSMXFwAJdOjOKKKa1Xmd49P56Pd2STUlDJmsMFLB4d3lumCzZGp/Uzn3pFRQV+fn6Ul5dL8q4DklZYRU2DgXqDictf24qbs56tyxcQ6OXa4/d67scU/rUujSsnR/P3y8b2+PUFQbAtV7y2le0ZJc223T43jjvnx+HmrMfN2em013hmVTKvrj/KpCEBfH7HDFuZKvQAnfn+tqnnRRBOxmjSuPI/WymubiDExw2ASyYOsolwARgbpbwte4+X2eT6giDYjkO5FWzPKMFZr+OiCYP4bNdxzkkM58HFIzoV/rlhRgxvbExnV2YpuzJLmDQk0IZWC72FwyfsCv2H46U1FJvbdp+oVF2Rb5wZa7P7jY/2B+BIQSU1DTI2QBD6Eu/9mgHA4jHhPHf5OLYuP5NXrpnY6byVUF93Lp4wCIA73t/NLe/uZMcp3hyh7yHiReg1LH1XIvzcmRoTyG1zhhIf5mOz+4X6uhPh545JgwM5MjZCEPoK5TWNfLVHddK+fnoMABF+Hl0e+3Hr3KG4OusprKxnzaECrntjG1uPFveUuYIdEPEi9BoW8TJpSACf3j6d5eeMtPk9raEj89wTQRAcn5W7sqltNJIQ7sOUmIBuXy8uxJtND87nnRunMm9ECPUGEze9s0METB9GxIvQa6SaxUt8qO28Lacyzhw6krwXQegbmEwa7/2aCcDS6TE9NmQ11NeducNDeO26ScyOD6amwcj1b29nlcxB6pOIeBF6DYvnZViod6/dc7x5/pGIF0HoG/ySeoLM4hp83J25aEJkj1/f3cWJ15dOZtGoMBoMJu78YBfJ+RJW7muIeBF6BU3TrOIlPqz3xMsYc9gou6SW4qr6XruvIAgdwGSCxjr1wtz/aaPq/3T5pGg8XW1TEOvu4sS/r53IrGHBmDT4fr94X/oaIl6EXqGgop6qegNOeh0xQV69dl9fdxfiQtT99uXIgDZBcBgyNsOLo+HJMHgqElYt57MdmWxKK8LFScfS6UNsentnJz0XjFeenV9SCm16L6HnEfEi9AoWr8uQQE9cnXv3bTcu2h9nDFTv+Rwq5QlLEOxGTQkkfw/bX4f3L4FKVVGEZoRfX8X3u1txpZEHzhpBTLDtH3LmDQ8B1IONeGb7FtKkTugVUgsrgd7Nd7EwKdyV812eZ37yXih6C+7YCk7y1heEXiVvL7x3CdQUNW0bfjZc9G+0o+swfH4bi3Xb+MK3jpFTv+kVk0J93RkZ4cvhvAo2pJ7g4glRvXJfofuI50XoFXo9WbehBr66E14YzRVbzmO+0161vegIJH3QOzYIgqA4vgtWnKeEi180DJ4Os+6DK98Hz0A+b5jG0oYHqdI8GNOwF6d3zoXKgqbz09bC6wvgcM+LmnkjlPdlfYpMnu5LiHgRbI6maSSZ+6z0uHjJ2Q0lx5pvqy6Gd85XIqXiOC51xZRq3nximKf2r/8bVJ0AY2PP2iIIQuus/jPUV8CQmXDHZrhxFSx8FJxcKK6q54nvDrHVNJpVU94ArxDI3w9vLoLMrbDnffjwSsjZCV/frUJPPYgldLThyAmMpn416q9fI75zweasPlTAwdwK3Jz1zBoWfPoTOsrxneoDzt0f7tkNHgGgafDZMvVB5xEAF/wLPIO44+sK9uTWcIFPCh6VufDcMHDzg/NfgjGX9JxNgiA0pyIPsraq9Uv+C+7NJ7x/uvM4ZTWNJIT7cOGSWTBjFLx3MZRmwNtnNx3o5Ap1ZbDhWTj76R4zb+KQADxcnCitaSS9qNouoW2h84jnRbApjUYTf/8hGYBbZg8l1Ne9Zy6sabDqIdBMUFsCG19Q21PXQPov4OQGy1bByPNgyHTiBw+iHle+G3QPOJttqC+Hz26EnW/3jE2CILTk8P8ADaKmgF/LnJJ95h5Ml06MwsVJD4FD4aY1EHcmeASqMNPM38FVH6oTtv8XProGfvgj1JZ22zwXJz3Dze0bjhRUdvt6Qu8g4kWwKSt3HudYUTVBXq7cNndoz114/2dwfAfozc7Dbf+BE0dgzcPq5zNuh9AE6+GWTrvvlI6FP+XDnwth8o2ABt/eCzvf6jnbBEFo4uBXajnqolZ37ze3MBg9yLdpo3co/OZL+GM63HcAFj0O8Ytg2CIwGSDlO9j2Gry5GMqyum3iiHDV9Ts5X8RLX0HEi2BTvt+fB8Ctc4bi4+7SMxdtqIGfHlHr8x6CmNlgrIdXpsCJwypcNOv+ZqfMHxGCs17H/pxyUgqqwNkNzn0BZvxWHfDt/ZD0Uc/YJwiCojK/KWQ06sIWu8tqGjheWgvA6Ei/FvtbcOnrcOErcM5z4BMJRSnwxkJVydQNhpsHxB4R8dJnEPEi2Ix6g9E6ev7MhNCuXaT4KKy8QVUqfHS1ynPZ8jJU5IDfYJh+N5z9N+VetrDgEfDwb3aZIG83Fo4MA+DTndlqo04Hi/4KU28DNPj6Ljj2S9fsFAShJQe+ADQYNBn8o1vuNk97HxLkiZ9HBx5uPAJgwnUw9Ra4+ScIHQVVBfD2OZD6U5fNTAhXXp8UCRv1GSRhV7AZSVll1BtMBHu7dS0JLnMrfHyNymmxcGy9yncBWPQYuHhA+Bh48BgY6pRL2a31wY9XTIli1cF8vtyTwx/PTlDN8nQ6JX5qS2D/Svh0KdzyMwTFdd5eQRCaMBpg27/V+rirWj3EEjIaM6gDXpdT8RukqpY++Y3Kc/vwCpWAP3Fppy9lCRtlFFdT22DEw9Wp8/YIvYp4XgSbsfWYGjd/xtDAzk+GLUqD9y5SoiJyAlz6pkrga6wBQy1EnwGjL246XqdTQqYN4QIwJz6EUB83SqobWHv4pB4Ser2qSho0WVUzfHtf52wVBKElh79W+SieQTD+2lYPOZBrFi8dCRm1hrsfXPsZjL1Kden9329V995OEuztSqCXK5rW1JNKcGxEvAg2Y8tRJV5mxHWyPNpSSWSoU/ksN3wPiZfB1Z+oD0G/aDj3OSVYOoGzk55LJqpqh2/NuThWXNxVPB0gY1OP95IQhAGFpsHmf6j1qbeCq2erhx0we14Su+J5seDsChe/ppreAax5RJVndwKdTseIMEvSrkyY7guIeBFsQl2jkaSsMgCmxwV18KQKJRz2vAdpa0DvAuf/o+mDz9kVLnoV7t0P4Yldsuus0SrvZcOREzQaTc13Bg5VMXTNqDp6CoLQNTI3qyRaZw+Yckurh5TXNpJZXAPA6EjfVo/pMDodnPmwKsdurIa1jzffbzKqBxKt7SZ0ltCRlEv3DUS8CDZhV2YpDUYT4b7uxAS1/tQFNH2gmEzwwWWw4lzl+gWYflfruSedDUGdxLgof4K8XKmsM7Azo5UeEcPNTbGOrOryPQRhwLPjTbUcdyV4tXx4WXOogIte2QxAdKAHAV6u3b+nXg9n/12t7/0Qjv5s7rZ9ATweBM/Eqk69bSDl0n0LES+CTVh7WI2YnzEsqPV8l5oS+Oou9YHy0VWw6y3I3qa6aHoEqDyXOb/vcbuc9DrmmmeZ/Jxc0PIAi3hJWyPjAwShK1QVNs0gmnxji91phVXc9t5O0ouqCfB04dHzR/fcvaMmwfjr1PoHV8Dr81UyL2aPS+qPbfaFGRmhvD87M0pJKxQB4+iIeBF6HE3T+PFgPgCLR4e3PKCmBF6bBUnvq5+PrILvHlDrCx6BP2bArevbTb7tDgsSVOjo5+TCljujJqsEw7pyJaYEQegce94HU6NKgI8Y12L3a78cxaTB7PhgNv7xTBaYWxj0GOc+pxrimRqhLBN8B8FtG9RcJYBDX7d62thBfsyIC6K20cjt7++mut7Qs3YJPYqIF6HHOZhbQU5ZLR4uTsyJD2l5wIHPVZ8W3yhVpuxkdhmHjIRpt9ncvtnDg3HW6zh6opqMourmO/VOEH+WWt/6igpnCYLQMQz1sGuFWm/F63K8tIav9uQA8MBZI/B2s0G3DhcPuOxtWPAwjDhHlVNHjGuqTmxDvOj1Ov5x1QRCfdxIK6ziudUpPW+b0GOIeBF6nFUHlNdl7vCQ1vsl7F+pltPvhDPugKs/Vh8yl74OTj3UhbcdfN1dmBwTAMD6lFa8L1NvUcnCKd/D2sdsbo8g9As0TeWrlWUq7+XJrQzM/HfDMQwmjVnDghlvHtlhE/R6mP0AXP0R+A9W20aeD+jUWJEDX8DPT0BFbrPTQnzcePzCMQCsPliA1k6Cr2BfRLwIPc4qc8jo7DGthIxKM8zhGB2MNk9zHrZAfch0sYKoK8w2e4Q2m8u5mzFokmpBDrD5JTXsURCE9tn8Euz7BHROqi/TKeXRB3LK+WCbyje5c74dmkD6hMPg6Wr9s2VqOvWbZ6ku3icxZ3gwLk46cspqySqp6X07hQ4h4kXoUdKLqkkrrMLFScf81kYCWLwusXPAN6J3jTuJWcNU75lfjxZjOLVkGlSVxFRzCGvrK71omSD0QYqPws9PqvVznoG4+c12NxhM/H7lXowmjXPHRnS+91NPkXiZWuqdwTsMyrPhrcVQ2ZS87+nqzIRo5ZndnNbKw43gEIh4EXqUbeauuhMGB7ScVVJfBUnmsfZjr+hly5ozZpAfvu7OVNYb2GdulNWC6XcBOji2TnX8FQShdX56VCXIxi2AKTe32P36xmMk51cS6OXK4xf0YHVRZ5m0DK78AO7eAbdvhuDhUH2i6aHKjKU31ZajRfawUugAIl6EHmVnpuqdMnlIQPMd9VXwweVQckyVQo883w7WNeGk11mf/jantvEBFTAEhi9W6zvf7CXLBKGPkbkVDv8PdHo464kWu00mjQ9+zQTg/84ZSZC3W29b2IReDyPPUw0pvUNg8k1qe8oPzQ6bafbMbj1ajMkkeS+OiIgXoUfZZRYvU2ICm+/4392QtQXcfOHaz9VMEjszM94sXtp7urI8RW5/HZ6Oho+vbbdLpyAMOLb8Uy0n/AbCRrXYvT2jhNzyOnzcnTlvrP1Cxa0yYolaZm1tNhJkfLQ/Hi5OFFc3cER6vjgkIl6EHuNEZT3p5tLjiYNP8ryUZcHBr9T6tZ+pRlIOgCXvZXdmGTUNbfR0iFsAIQnKJV5fAcnfqmoKQRDU5Oj0jWp98rJWD/k6SZVGLxkTjruLg01rDhgCYWPUSJDU1dbNrs56psSqB7BNbXlmBbsi4kXoMSxel+Fh3vh5npTvsvtdQIPYuTB4mn2Ma4WYIE/CfN1oMJo4mNvGMDa9Hpb9AMtWqa6/oOYvCYIA+XuhoVJ5UsPHtthdbzDy3T41JPGi8YN627qOYfG+pHzfbPPc4aoicfXBVjpxC3ZHxIvQY+zKVG7XySeHjIyNZvFCq02r7IlOp7O2BG93nolnIAyZDnFnqp9FvAiCwuJ1GTJTNXg8hfUpJ6ioMxDm68a0oR0c0NrbjDhHLdPWqiZ7ZiytHnZkllBYUWcPy4R2EPEi9BitJuum/ABVBeAVCgnn2smytrEMY0vJb8PzcjIxs9QyY5PkvQgCQIZZvMTMbnX3zgz1QHPWqHCc9F0fqGpTIsarsumGKsjcYt08yN+D8dH+aBrWcSeC4yDiRegRqusNHDCXHE8ecpLnxdKKe/w1vdI9t7MkWMVLB5Lyoqep/hDl2ZL3IgjGRlVpBE3C/hTSCquApocEh0Svh2EL1XraT812nZuoEoy/25/X21YJp0HEi9AjbD1aTKNRIzrQg+hAj6Ydx7er5SlNqxyFEWFNYaPTtgJ39VLdd0FCR4KQmwSN1ar1QdiYVg85ekIl8A8L9e5Fw7pAG+LFEjranl7Cicr6U88S7IhNxcuGDRs4//zziYyMRKfT8dVXX532nPXr1zNx4kTc3NwYNmwYK1assKWJQg+x/oiaETRveCg6ndk9XJmvKo10+qYvfQcjLtQLJ72OyjoDeeUdiGtbnjAtsX5BGKgcW6+WQ2Yq78Up1DUayS5V7fXjQhxcvMTNV59TJ5KhLNu6OTrQk9GRvpg0+PWYdNt1JGwqXqqrqxk3bhyvvNKx9urp6emce+65zJ8/n6SkJO69915uvvlmfvzxR1uaKXQTTdNYn3ICaMrQByDb7HUJHQVujuk2dnN2YmiwF9DB0NHQeWp56GsoP247wwTB0Un+Vi3jF7W6O6O4Gk0DX3dngr1de9GwLuARAFFT1Xpa81lmoyOVd9YSAhMcA5uKlyVLlvDEE09w8cUtp4u2xmuvvUZsbCzPP/88I0eO5O677+ayyy7jxRdfbPOc+vp6Kioqmr2E3uVYUTXHS2txddIzY9hJFQWWkFHUFPsY1kGsSbsFHRAvMbNh8Aww1MKaR2xsmSA4KGVZkJekvBUjWk/Et3zZDwv1bvLGOjLx5tBRavPQUXyo+nxIOyHixZFwqJyXrVu3snDhwmbbFi9ezNatW9s85+mnn8bPz8/6io6OtrWZwilYvC5TYwPxdHVu2pG9Qy2jp9rBqo7TqaRdnQ6W/A3QwYHPIGubbY0TBEfksNnrMniGarPfCkcLVb6Lw4eMLMSbR4GkroaSdOtmS77OUfG8OBQOJV7y8/MJCwtrti0sLIyKigpqa2tbPWf58uWUl5dbX9nZ2a0eJ9iO9SnmfJcRJ32IGRogd49aj3acxnStMSK8A71eTiZiHEy4Vq3/+qqNrBIEB+bwN2rZzoyyo2ZPRZyjJ+taiBirejmZGmHdk9bNFvFy7ER16xPoBbvgUOKlK7i5ueHr69vsJfQeBRV1bE5T7bMXjDxJeObvA2M9eAapIWgOzMgI5Xk5UlBJakdCR6Cm04K5sVWDjSwTBAckd4+aBQRqyGEbWMNGfcXzArDwUbXcvxLy9gKq34u7i54Go4ns0tYfooXex6HES3h4OAUFzVsxFxQU4Ovri4eHRxtnCfbkyz05mDTVmC7WnPgKwL5P1TJ6mgq1ODBRAZ4sHBmG0aTxl68PnL5kGiByomq811AJmZttb6Qg2BujAb6+C/47D9Ag+gzwi2r1UJNJ41hRH/O8gPKqJl6u1jepXEu9XsfQYPVvkKRdx8GhxMv06dNZu3Zts21r1qxh+vTpdrJIaA9N01i5U4XpLpt00odYSTrsfEutT7vNDpZ1nkfOH4Wbs55fj5Xw5Z6c05+g18Pws9T6kVW2NU4QHIFNL8Ce99X66EvgsrfaPDS3vJa6RhOuTnqiA/rYg+fUW9Xy2HowqTCRJXQk4sVxsKl4qaqqIikpiaSkJECVQiclJZGVlQWofJWlS5daj7/99ts5duwYDz74IMnJybz66qt8+umn3HfffbY0U+giSdllHD1RjbuLnnNPHnW/7ikVN447s6m02MGJDvTk7vnDAHjws318uC3r9CdZZqKkfC/jAoT+TfYOWP83tX7Rv+Hyt8Gv9UGLJpPGy2vTABga4oWzk0M9I5+eyAng6g21pVBwABDx4ojY9F21c+dOJkyYwIQJahrv/fffz4QJE3j44YcByMvLswoZgNjYWL777jvWrFnDuHHjeP7553njjTdYvHixLc0UukByfgV/+GwfAEvGRODjbm79f/RnFS+GpvhxH+G2uXGcNzYCg0nj/77cz+e7TtPHZeg8cHJTZaOFh3vFRkHodUxG+PpO0Iww5jIYd3W7hz/6zUE+2ZmNXgf3LozvJSN7ECcXGDJDrZtnN1nFi5RLOwzOpz+k68ybN6/d/IHWuufOmzePPXv22NAqobtsTy/huje30WAwEeLjxl3z49SO4qOw8gZAg4nXq/hxH8LVWc/LV0/Az8OFD7ZlsTa5gEsntR7TVyd4KQGT+iMc/BLCRvWarYLQayR/B0VHwN0fzn2+3Ry2tMJK3t2aiU4Hz18xjrPHRLR5rEMTM1uVTKdvgOl3NSuX1jStb/St6ef0MX+eYG8ajSb+78v9NBhMzBoWzA+/m82wUB8VNvl0KdSVq06V5zxrb1O7hE6nY8HIUECVRp6WsVeo5d6PrPFxQeg3aBps+adan3ITePi3e/iqA2r68tzhIVw8oR3h7+jEzlHLjM1gNBATpMaIVNUbKKiQGUeOgIgXoVO8syWDtMIqAr1ceeWaiQR7u6kdRakqPuzkBle+D85u9jW0G8SaKwsyi2swmU6Ty5JwHrj7qUnT6b/0gnWC0Itkb4PjO8DJFaaePvl+9SFVLbp4dLitLbMt4Ynq77qhEvL24uqsZ0iQJyB5L46CiBehwxRX1fPST6kA/PHsEfh5ujTtzNqillFTwCeslbP7DlEBHjjpddQ2GimoPM2wRhf3ptJKSyWGIPQXtr2mluOuOu3fdW5ZLfuOl6PTwcKRffszAL0TDDEPYf30N7DjTWu/mrTCDvaCEmyKiBehw3yxO4eqegOjIny5fNIpYxgyzU2rhvT9snYXJz2DA9VTVnpHQkcTrlPLw9+oCgVB6A801sKR1Wrd0pSxHVYfVCGjyUMCCPHpu55XK3MfBO8wqMiB7+7nLBfVtE6Sdh0DES9Ch9A0jZW7VE+Xa88YjF5/SsJaptnzMrjvixfA2nAvvbgD4iViPISMVB2Fj8gEdKGfcOwXaKwG30GqfLgdymsa+XSnqs7r8yEjC5Hj4Xf7YMylAEyqUw9oEjZyDES8CB1i3/FyjhRU4eas57yxkc13lh+H8izQOTn8EMaOYhUvHfG86HRNM16Sv7WhVYLQi6R8p5YJ57ZbYXSkoJJzX97IobwKPFycOCexj1YYtYaLu7U0fFDRZkAT8eIgiHgROoTF67J4dDh+Hi7Nd1pCRhFjwc2nly2zDVbxUtQB8QKQYG5Yl/azcrcLQl/GZISUH9S6pRljGzz1/WGOl9YyONCTT2+bTqR/H+uoezpiZoGzO67VuQzT5VBU1UBZjcwzszciXoTT0mAw8b+kXAAun9xK+aNlts+Qmb1olW3ptHiJGK/c643Vyt0uCH2Z4zuh+gS4+akv7zYwmTR2Zao8r1eumUhilF9vWdh7uHhYP9su9DwISOjIERDxIpyWzUeLqKgzEOLjxoy44JYHZGxSy36S7wJN4iWrpAaDsQP9W3S6k8YFfGdDywShF9hvHqw6/CzVcbYNjhVVUVlnwN1Fb53O3i+JXwTAfGfVVVzEi/0R8SKclh/NjacWjw7D6dRE3dJMKE5V+S7tPKH1NcJ93XF30WMwaRwv7WAYyBI6Sv4eGmpsZ5wg2JKaEkj6UK1P+E27h+7OKgNgbJR/35th1BmGLQQgoeEA3tSIeHEA+vG7TegJDEaTtfHUktZafR81TwGPnnra7pt9Cb1eR0xQJ0NHMbPBLxpqimDj8za0ThBsyM63oLFGNWqzdJptgz1m8TJhsL/t7bInQcMgJAFnrZFLnDZKubQDIOJFaJcdGaWUVDfg7+nC1NjAlgekmcXLsAW9a1gvMDxMucGTsss6doKTC5z9tFrf8k/VdVgQ+hKGetj+X7U+/bftVhkB7MlS+S4TogNsbZl90elgys0AXO+0moPHy6g3GO1s1MBGxIvQLqsO5AGwaGQYLqe6hQ0NTcmpZrdqf2JGXBAAm9KKOn5SwnkwbBEYG2DNwzayTBBsxMGvoKoAfCJhzCXtHlpVbyClQHWb7feeF4BxV6G5ehOnz2NE7W7e2pRhb4sGNCJehDZJL6pm5S7VeGpJYiuNp45vV7M/PIMhvG9NkO4Is+JVcnJSdhkVdY0dO0mng8VPqvXU1Sp/QBD6CjvfUsvJN7aZqFtZ18iyt7dz49s70DQY5O9BmK97LxppJ9x80Jm7aV/v9CMv/5xKQcVpxocINkPEi9AqDQYTv/t4DzUNRs4YGsjc4aEtDzr4pVoOWwD6/vdWigrwZGiwF0aTxtajxR0/MWQEhCWCydDUK0MQHJ3Cw5D9q0q+t4y8aIWfDhewLuUE2zOUMB8/ELwuFibdAMA8p300NtTzz7USGrYX/e8bR+gRXvzpCPuOl+Pn4cKLV45vWWWUsRl2vKnWx17R+wb2Ehbvy6bUToSOAEZdoJaHvu5hiwTBRux8Wy1HLAHftrvkHjN3nR4Z4cuSMeHcOS+uN6xzDEISwN0PFwwM1x3vXEhZ6FFEvAgt2HK0iNd+OQrA3y9NJMLvlI6ZdeXw5e2App7Q+mG+i4XZ8SFAJ/NeAEaaxcuxdVBX0cNWCUIP01gLez9W65NvbPfQo+ZKm0snDuLf101idGQ/bEzXFjqddc5Tov4YmcU1lFRLt117IOJFaEZZTQP3f7IXTYOrpkRz9qnl0SYTfHGbmmUUEANn/80udvYWZwwNxEmvI72omqziTvRuCU2A4BEqcVeGNQqOzvEdUF8O3uEwdH67h1o8L3Eh3r1hmeNhFi8zPdXIlL3Hy+xozMBFxIvQjM92HSe/oo7YYC8ePn9UywPWPQFHfgAnN7jsrX4zy6gtfNxdmBKjykBXH8rv3MmW0FHyNz1slSD0MJb5ZDEz281fM5o0a9+jASteIsYDMN4pHYAkc68boXcR8SI042CuCnFcOnEQnq7OzXcWJjc1X7vgZRg0qZetsw+W5nw/HOikeBm+RC2PrgdjB6uVBMEeZG1Ry9OM+Mgtq6XeYMLVWc+ggH42gLGjmD0vkfXHcKOh432ghB5FxIvQjENm8TIywrflznRzT5eh82Dclb1nlJ1ZPFqVie/KLO1caWTkBFVGXl8O2dttZJ0gdBOjAbJ3qPUhM9o91JLvEhPk2TKJf6DgPxg8AnHSDIzQZbP3eBmaptnbqgGHiBfBSl2j0frhNCqyFfGSZXEt958ZRh0h3M+dieZy0B8PdsL7otc3JTOnru55wwShJ8jfq6ahu/tByMh2Dz060PNdoFnS7njnDMpqGsnsTD6c0COIeBGspBVWYTBp+Hu6EH5q0ylNg6xf1Xr0Gb1vnJ2xho72dzJ0ZJ5GS+qaHrZIEHoIS75L9Bmn7dd0zPxwMzTEy9ZWOTZm8XKml8p72ZlZak9rBiQiXgQrh/JUyGhUhC+6U2ealGVBZR7onQdMrsvJnD1GhY62pRdTXFXf8RPjzgSdHgoPQvlxG1knCN3A4lEd0n6+C0ilkZWhcwGYU/cLo3UZPPq/g6xLKbSzUQMLES+ClXbzXbK3qWXEOHD17EWrHIPoQE/GDPLFpMEa85TtDuEZCFFT1bp4XwRHw9gImZvV+uD2812gKedl6EAXL7FzYNSF6DHyiteb1NXXcdOKHawXAdNriHgRrBw+yfPSAsvT2WmqEfozXa46ktCR4KgcXQe1peAVclqPakVdI4WVyus44MNGAOc8Bx4BxBiO8tTgPZg0uO+TJHLLau1t2YBAxIsAgKZp1rBRq56XLLPnJXpaL1rlWFhCR1uOFlFe24nS5/iz1PLYejB0IuQkCLZm/0q1HH0JODm3e+gecz+TqAAPfN1bH9o4oPAOhRm/BeDSgFRGR/pSWtPIbz/ag9Ek1Ue2RsSLAEBOWS2VdQZcnHQMCz3FJVyRB4WH1PrggZesayEuxJvhYd40GjXWHu5E6Cg8UXUubaxuctELgr1pqIbk79R64uWnPXx7uhpOOi02yJZW9S3MxQtOubt59dqJeLs5syuzlJ+TJXxka0S8CAAk51UC6gva1fmUt8XBLwBN/aF6tzJdegBhGZewqjOhI51OQkeC45HygxLUATEQNfm0h287pqZITxsaaGPD+hCR49UU7spchriUc90ZQwB4a1O6fe0aAIh4EQBIKVDiJSG8lXb/Ftdy4mW9aJFjMsc8ZdrSibjDWEJH0u9FcAQ0DXatUOuJlyuB3Q51jUbrDJ9psSJerLh6Qah5jMrxnSydPgQnvY6tx4qtOYSCbRDxIgCQahYv8WGniJeiNMjdo54uRl9sB8scC0uVRU5ZLXWNxk6cOE+VmRenQfpG2xgnCB0ldTVkbFQzyiYuPe3hu7NKaTRqhPu6Mzhw4FUbtsugiWqZs5NIfw9rbpx4X2yLiBcBgJQCVQI54lTxcuAztYw7E7yCe9kqxyPA0wU/D5WsmFFc3fET3X1VUiTAR1dDzi4bWCcIHcBogNV/Uetn3K7a3Z+G7ekqZDQ1NrBlD6iBjiXkdlz9Td84MxaAz3cfZ+vRYntZ1e8R8SJgMJo4WqjEy/CTxYumnRQyOn1C30BAp9MRG6zKRNNPdEK8AFzwT4iZDQ2V8OFVYGiwgYWC0A7GRvhxORSlgEcg5ZPuYfkX+7jzg13UNrTtSZR8l3YYZBYvuXvAZGTSkAAumxSFSYN7Pt7DiUqpMLQFIl4EMktqaDCa8HBxIurkSbF5SSrM4ewBCefYzT5HY6hZvBwr6qR4cfGAqz9WwxqrC5sa/wlCb1BfBe9cANv/C8C2Yfey5D97+Wh7Nt/vz+etza2HORoMJnZnqfb3ku/SCiEjwNVbJT+fSAbg8QtHEx/qzYnKeh775qCdDeyfiHgROJKv8l2Gh3mjP3lS7H5zyGjEEnBrJZF3gGL1vHRWvAC4easQHMDRn3vQKkE4DbtWQNYWGp29uaPxXq7cMYzc8joCPFUY9N/rj7Y6+mLf8TLqDSaCvFxlLEBr6J0g2txFO+lDADxdnXnxyvGAqkwU70vPI+JF4Ig536VZsq7JCAc+V+sSMmpGbEg3xAuIeBHsw/5PAfgm5BZ+ME5laIgXDywazoYH55M4yI+qegP/XJva4rRtku9yes64Uy13vAlVJwAYM8iPCYP9MZg0Ptslc816ml4RL6+88goxMTG4u7szbdo0tm/f3uaxK1asQKfTNXu5u7u3ebzQfY6YK42aJetmblaDGN39YdhC+xjmoHTL8wIQN18t8/ZCdVEPWSUI7XDiiHq/6Z351qi6ZN+7cDi/XRCPj7sLy89JAOCDbVkt3tcW8SIho3YYthAiJ4KhFrb8w7r56ikqGfqTHVlomnTd7UlsLl4++eQT7r//fh555BF2797NuHHjWLx4MYWFbXcg9PX1JS8vz/rKzMy0tZkDmhRrmfRJLuGDX6nlqAvB2bX3jXJgYoKUeCmpbqC8phNjAiz4hEPoaEBTIwMEwdaYvS7ELWB/qQoTxQQ1lTzPiAtm/ogQDCaNZ39Mtm43GE3syrB4XqSzbpvodDDvIbW+402oUz1ezhsXgbebMxnFNWw9JpVHPYnNxcsLL7zALbfcwrJlyxg1ahSvvfYanp6evPXWW22eo9PpCA8Pt77CwsJsbeaApd5gJMP8pNWs0sgyiNHSXE2w4uXmTLiv8gamd6Zc+mQs3pdfnoEv74ACSeoTbMRJVYN1Iy+15l8MCWo+XPGhJSPR6+D7/fnWBN2DuRVUNxjxdXduvYGl0ET8WeA3GBprVOURKvfl/HGRAHy/P8+e1vU7bCpeGhoa2LVrFwsXNoUd9Ho9CxcuZOvWrW2eV1VVxZAhQ4iOjubCCy/k4MG2P9jr6+upqKho9hI6TlphFQaThp+HCxF+5vBcbRkUHlbrA3gQY3s0hY6qunaB4YvVsigF9n4IPz3aM4YJwqnk74PSDHDx4ljQHAACvVyt/YosjAj34fJJ0QDc/M5O3vs1ky92q1yNqbGBzZP5hZbodBA5Tq3n77Nunjtc9cfamVFqD6v6LTYVL0VFRRiNxhaek7CwMPLzW58NM2LECN566y2+/vpr3n//fUwmEzNmzOD48dYTnp5++mn8/Pysr+jo6B7/d/RnDuVaJkn7NCXj5ewENAgcCt4h9jPOgYmxlEt3tteL9QKz4dI3YfYD6ueMzdL3RbANaWvVMnYO6eVq9eSQ0cn8fvEIRoT5UFLdwF++OsA7W1XIXoYxdpBws3jJaxIvk4aoXKGUgsquhZmFVnG4aqPp06ezdOlSxo8fz9y5c/niiy8ICQnhP//5T6vHL1++nPLycusrOzu7ly3u2xwyz98YFeHXtDHbnFAtXpc2iTdP3u7y/BKdTs2Kmv9n1felsRqOt53ILghdxlLVNmyBtSt0zCkhIwshPm58e88s/nzuSIaHeTNmkC/nJkZw6aSo3rK2bxMxVi1P8ryE+LgxNNhLjZPKKrGTYf0PZ1tePDg4GCcnJwoKCpptLygoIDw8vEPXcHFxYcKECaSlpbW6383NDTc3t27bOlCxfPmOivRt2mhpnmbpXSC0YMJgfwB2Z5WhaVrXS0j1epX/sn8lHF0HMbN6zkhBaKiGrF/VetyZZPyswpwWz2FruDjpuXn2UG6ePbQ3LOxfhJvFS9ERaKgBV+XhmhwTwLGianZklHJmguRw9gQ29by4uroyadIk1q5da91mMplYu3Yt06dP79A1jEYj+/fvJyIiwlZmDlg0TWsWNgJUf5fjO9W6eF7aZHSkH67OekqqG8gorunexYaak3el74vQ02RsBlOjml8UOJRM83t1SBthI6Gb+ISDVwhopmZJ+JNjVOhoR7p4XnoKm4eN7r//fl5//XXeeecdDh8+zB133EF1dTXLli0DYOnSpSxfvtx6/OOPP87q1as5duwYu3fv5rrrriMzM5Obb77Z1qYOOHLKaqmoM+DipCM+1CxeCg9BQxW4+kBIgn0NdGBcnfUkDlKhtt2Z3UzEs1Qe5e6BGvlwE3oQiyCOOxN0Omt1XGw7nhehG+h0EGFJ2t1r3TzFLF72HS/v3DR6oU1sLl6uvPJKnnvuOR5++GHGjx9PUlISq1atsibxZmVlkZfXVEJWWlrKLbfcwsiRIznnnHOoqKhgy5YtjBo1ytamDjgsXpdhoT64OpvfCpaQUdRk1fZaaJOJ1tBRN8WLb6RZKGqQ/ku37RIEQDVATP5WrcedSXW9oalMOlDEi82whI5OStqNCfIk2NuNBqNJqo56iF5J2L377rvJzMykvr6ebdu2MW1aUzhi/fr1rFixwvrziy++aD02Pz+f7777jgkTJvSGmQOOw3mqOZ01ZASSrNsJJg4OAFTeS7eJX6SWB7/s/rUEoTQD3lwE5dngFQpD51uTdQM8XfDzdGn/fKHrtJK0q9PpWDgyFIDHvjko3pcewOGqjYTe41CeqpscFSHJul1h4hAlXlLyK6iqN3TvYolXqGXKKtVnRxC6iskIn1wHJcdU07RlP4C7L1nWfBfxutiUiPFqmX9AJUybefDsBIK93UgtrOKZVSn2sa0fIeJlgJJdUsOmVDVXZ4w5d4PKAvXEhk6FjYR2CfN1Z5C/ByYN9maXde9i4YkQMhKM9XDo6x6xTxig7H4X8veDux/cuAqChwFQUFEH0NSMUrANATHgF60SpbOamrEGernyzGWJALy1Od36/yF0DREvAxCjSeOBT/dS3WBkakygNZnM2mckdJT64BNOy+QY5X3ZerSbc0t0Ohh3pVrf92k3rRIGLLVl8PNf1fq85eA3yLrrRJXKdwn1kdYSNkWng9i5av2U2WVnJoRZGwR2ebCrAIh4GZB8uC2T7RkleLo68dzl43CytP2WkFGnmROvOhCvP9L2oNEOk3g5oIPMTVAmzRaFLrD1X1BTDMEjYErzCs3CCiVeQkS82J6h89TyWMsE/EEBHgDkltX2okH9DxEvA5Cfk9UX7d1nDmPwyf0eJFm308wZrsTLgZwKCiu76Qb2i2pqUrdfvC9CJzEaYPd7an3+cnBqnpTb5HmRsJHNiVUzpMjfB9XNvbKRfkq85JSKeOkOIl4GIDlmxT868qTQkKEecpPUunheOkyIjxtjo9Tv8ZeUE92/4Fhz6GjvJ2oasCB0lNTVUJWvxk2MOLfFbvG89CI+YSr8DpCxodkuq+elXMRLdxDxMsDQNM2q+Af5ezTtyN+vkkU9g9VARqHDzDN7X9b3hHgZdQE4u6tp03l7T3+8IFjY/a5ajr8anF1b7LZ4XkS89BJt5L1Emj93c8okYbc7iHgZYJTXNlLdoHoMNBMvBQfUMmKsSjgTOsy8BNW/YWPqCQxGU/cu5u4HI5aodUncFTpKRS6k/qjWJ17fYrfRpFEsCbu9S8xMtczZ3Wyz5XNXcl66h4iXAcZxs9clyMsVD9eTOugWHFLLUOlk3FnGRfkT4OlCRZ2BTWlF3b/g2KvUcv9KaJSnM6EDJH2g5ukMngHB8S12F1fXY9LUc0mgV0uvjGADLJ+lRUdU7x0zkSeJF01Cw11GxMsAw5LvYom7WikU8dJVnPQ6LpqgSlLf3ZrZ/QsOWwA+kVBdCL++2v3rCf0bk6kpUXfi0lYPsYwFCPJyw9lJPvZ7hYAYFQI21Jn7ZyksfXZqGoyU1TTax7Z+gLyLBxit5rtoWpN4CRPx0hWWTo8BYF1KIZnF3ezf4OQCCx9V6xueg4q8dg8XBjgZG6AsE9z8YNSFrR5SWCn5Lr2O3gmCh6v1wsPWze4uTgR7q/+HHAkddRkRLwMMyx9L1Mmel+oTqjcEOpkk3UVig72YOzwETYP3esL7kng5RE2Bxmr4+YnuX0/ov+x6Ry3HXg6unq0eYvG8SL5LL2PxZJ843GzzIH/lfZG8l64j4mWA0arnpeCgWgYOBRePVs4SOsINM2IA+GRHNsdLa9o8rkNxbr0eFj2u1g993SxmLghWKvObJke3ETKCJvEinpdeJtT8MFjYXLxEStJutxHxMsBoynk56QnN8oclIaNuMXd4COOj/amsN3DXh3toMLSsPMorr2XJPzay6IVfTj9SIHoauPpAQ2WTwBSEk/npMTA2QNRUiBjX5mHiebETISPVsjC52WareCmXhPyuIuJlgGEVLyd7XgrNX4ySrNst9HodL189AV93Z/Zml/HMquYfWIWVdVz7+jaS8ytJLazi6td/5Z9rU9u5oFNTw8CsX21oudAnOb4T9n6o1s9+ut1DxfNiJ0LN4qU4FYxNybmWz1/pstt1RLwMIGoaDJRUNwCnVBtJmXSPER3oyfNXjAfgzc3p7MwoAWBXZgmXvLqFY0XVDPL34MrJ0QC89NMRkvMr2r7g4OlqedJ0WkFA02DVQ2p9/LWnnQJvGV0h4qWX8YsGFy/lHSs5Zt3c1KhOxEtXEfEygLDEV33cnPHzMM89MRnhhNlDIOKlR1g0KozLJ0WhafDg5/tY/sV+Ln9tK8dLa4kO9ODDW6bx98vGck5iOCYNnvj2cNt5MIPPUMusrTIuQGgiezsc36FKcRc8ctrDm8JGMteoV9HrIWSEWj8p72WQiJduI+JlAGFpUNfM63J0HTTWgEeAjAXoQf587ihCfNw4dqKaj7ZnYdLgkgmD+O6e2QwJ8gJg+ZKRuDrp2ZRWZB2W2YJBk0DvDJV5UJbVi/8CwaHZ8bpaJl6m5uicBimVtiOWh8KT8tYGB6qcwxOV9VTVG+xhVZ9HxMsAotV8l6T31TLxCnBytoNV/RM/Txeeu3wcwd5uLB4dxie3nsELV47H171p0m90oCc3zIwB4KPt2a1fyNWzKRFT8l4EgKpCOPiVWp9yy2kPr643UGMeCSIJu3YgeopaHv3ZusnP08Xa6+XYiSp7WNXnEfEygCgwZ7aHmzs8UlMCyd+p9QnX2smq/svc4SHs/PNC/vObyUwbGtTqMWePCQdgd1ZpO6Ejc97L0bW2MFPoa+x+B0yNqg9Q5PjTHm7xuni6OuHlJg8ovU78WWqZswuqm8aHDAtVHti0QhEvXUHEywDC8iEW5msWLwc+V4lkYYntllkKtmNMpB9uznpKqhs4VtRGZ15L19T9KyH/QO8ZJzgeRgPsfFutT7m5Q6cUVKiHFuvfvdC7+EZCeCKgQeoa6+a4EG9AxEtXEfEygLB8iFldx3s/VkvxutgNV2c946L8AdiVUdr6QdFTYdRFavDeqockcXcgc+QHqMgBzyD1nugATeJFQkZ2Y/jZammZ/A0MCxXx0h1EvAwgmnle6soh1zyqfeQFdrRKmBQTAMDOzJK2D1r0ODi5QcZGOLKqlywTHI7t5kTdiUvBpWOelMKKUzyuQu8Tv1gt09Za+71YxYvkvHQJES8DiGYVB1m/qif5wKHgN8jOlg1sJg9R4mVXZhueF4CAIXDG7WpdJk0PTE6kQPovoNPD5Bs7fJqEjRyAQROVt6y+wpp4bwkbZRXX0Ghs2Y1baB8RLwMEg9FEcZW514OvG2RsUjuGzLSjVQLAxMFKvBw9UU2puYlgq0y5RX1xpW9QX2TCwCFnF3x8jVoffjb4D+7wqfmnhouF3kfvBHFnqnXzZ2+Enzterk4YTFr3J9EPQES8DBCKqxswaaDXQZCXG2RuVjtiZtnXMIEAL1fiQlTlQbveF/9oGL5Ere98qxcsExyC9A3w5llQnAY+EbDg4U6dLmEjB8HyoGj+7NXpdMRJ3kuXEfEyQLB8gIX4uOHUWAW5SWqHeF4cgqmxqpR6Q+qJ9g+ccpNaJn0I9fKB1+9prINvfgcmg/K43LGlaV5OBymolLCRQ2D5rD2+Awzq83iYVBx1GREvAwTLbJNQH3fI2gaaUbme/aPtbJkAsGhUKACrDxa03e8FYOh8CIxTsfOfTt8WXujjbHpBzcTxiYBLXgfPwE6drmmaNeclXMSLfQmOB68QMNRBjiqWsHhejp6QsFFnEfEyQCi0zjZxg0xzvkvMbDtaJJzMjLhgvFydyK+oY39OedsH6vVw7nNqfccbkPx97xgo9D5l2bDpRbV+9t/A3bfTl6ioM1DXqJJBQ6VU2r7odDBkhlo3h44s4WLpstt5RLwMEKw9XnzdIG+v2hg1xY4WCSfj7uLEvBFN3pd2iTsTpt+t1v93N9RX2tg6wS5se001kYyZ3dSosJMUmv/u/TxccHdx6knrhK5wSt5LtHnGUXapDGjsLCJeBgiFJ0+VLUpTGy3TTgWH4KzRasDejwfzT3/wgodVmXtNMez5wMaWCb1OXQXsfletz/ydemrvAvnSoM6xsIiXrG1gNFjFS0l1gwxo7CQiXgYIloTdCC8TlJunEwcPt6NFwqnMGxGKs15HamEVGW2NCrDg7AbT71Lrv74KJqPtDRR6jz3vq7ym4BEQt6DLlymQSiPHInQUuPlBYzUUHsLX3QV/TzWsNbukxs7G9S1EvAwQTpgTdodgfqp391dNkwSHwc/DhdGRKq8hpaADoaBx14BHAJRlNg3YFPo+mqZCRgDT71R5Tl1EGtQ5GHq9ec4RUHAQgMGW0JGIl04h4mWAYHkCi2g8rjYED++yK1qwHVEB6oPseEdi4K6eMNlcOr3pRTBJl85+QckxJUidXCHxim5dqlDCRo5H2Gi1LFBDVqMDJO+lK4h4GQCYTBpF5u66QXWZamNwvB0tEtoiKsADgJyOfpBNvRVcvdWcqt3v2NAyodcwt48ncqISqN1AwkYOiFW8KM9LVKD6mxfPS+cQ8TIAKKlpwGDS0OnAq/KY2ijixSEZZBYvx0s7+EHmEwZn/lmt//QIVJ6mUklwfLK2quXgM7p9qYKT+zsJjkHYGLWUsFG3EPEyALAk6wZ5uaIvTlUbg0S8OCKD/M2el7JOuJCn3goR49Wk8A+vgJJ02xgn9A4Wz8vg6d2+VEG5hI0cjtAEQAfVhVBVaA0bZYl46RS9Il5eeeUVYmJicHd3Z9q0aWzfvr3d41euXElCQgLu7u4kJiby/ffSiKs75JWrL8IwHzcoPqo2SqWRQ2LJeemUeNE7wYWvqOTdvCT4zxzIP2AbAwXbUl0ElgeM6KndulRVvYE8c86L5X0lOACuXqrNAUDBQavn5XhpbfvdtYVm2Fy8fPLJJ9x///088sgj7N69m3HjxrF48WIKCwtbPX7Lli1cffXV3HTTTezZs4eLLrqIiy66iAMH5MO4q2QUK0U/zr8WGqpA5wQBMfY1SmgVS9iorKaxc30fwsfA7ZsgcoIqsd31to0sFGyKxesSMrLTowBO5UBOOZqmpheHyERpx+KkvJdIfw90OqhtNFJU1c5UeaEZNhcvL7zwArfccgvLli1j1KhRvPbaa3h6evLWW61Pxf3HP/7B2WefzR/+8AdGjhzJX//6VyZOnMi//vUvW5vab7GMWx/rbhaMATHg7Go/g4Q28XZzxs9D9X3ocNKuBb8omHWfWs/c0sOWCb1CD+a77D+uxkyMjfLr9rWEHuakvBdXZz0R5oRqCR11HJuKl4aGBnbt2sXChQubbqjXs3DhQrZu3drqOVu3bm12PMDixYvbPL6+vp6KiopmL6E5mWbPS7w+V22QkJFDE9XZpN2TseRJFB6CmpIetEroFaz5Lt0XL3uPlwEwNsq/29cSephwi3jZDzSNCejS3/wAxabipaioCKPRSFhYWLPtYWFh5Oe33gI9Pz+/U8c//fTT+Pn5WV/R0TIl+VQsnpeoBnMiZ2iCHa0RTkeXknYteIc2JWNnb+tBqwSbY2iA/H1qvQfmju0ze17GiXhxPCxhoxMpYGy0ipesYhEvHaXPVxstX76c8vJy6ys7O9veJjkUjUaTteFZQGWy2hg+1o4WCadjUGd7vZzKELP3xTz8TegjFB5Ugxjd/ZsSOrtIaXWDNQSRKGEjx8NvMLj6qP/v4jRig83TpU83FkSwYlPxEhwcjJOTEwUFzXtPFBQUEB4e3uo54eHhnTrezc0NX1/fZi+hidyyWgwmDS8XDZeiw2qjiBeHplNddlvDOrm29VCr4KDk7FLLQZO63f16X47yusQGe1lzqAQHQq+HsFFqveAgw8N8AEjJlwnxHcWm4sXV1ZVJkyaxdu1a6zaTycTatWuZPr31HgbTp09vdjzAmjVr2jxeaB9LpdEMvzJ0hjpw8er2U51gWyxho+NdCRtBU95LXhI0yJNcnyFnt1oOmtjtS+3LLgMkWdehOWlMwAizeEk7UYXBKGM+OoLNw0b3338/r7/+Ou+88w6HDx/mjjvuoLq6mmXLlgGwdOlSli9fbj3+d7/7HatWreL5558nOTmZRx99lJ07d3L33Xfb2tR+iSXfZZpnjtoQPqZbg94E29PpEQGn4j8YfKPAZGhKABUcH6t4mdTtS+21Vhr5d/tago04qVw6KsADT1cnGgwmMorlgaMj2Pxb7Morr+S5557j4YcfZvz48SQlJbFq1SprUm5WVhZ5eXnW42fMmMGHH37If//7X8aNG8dnn33GV199xZgxY2xtar8ko0h5XkbrzTONJGTk8FjES1FVPTUNnej1YkGng6Hz1HraTz1nmGA76ivhhDknLbIHPC/mSqNx4nlxXE4ql9brdcRbQ0dVdjSq79Arj+B33303mZmZ1NfXs23bNqZNm2bdt379elasWNHs+Msvv5yUlBTq6+s5cOAA55xzTm+Y2S+xeF5iGs2ddS3j2AWHxd/TlUAv1YfnaGEXn8KGn6WWR37sIasEm5KbBGjKY+YTdrqj2yW/vI7Cynr0OhgVKTmADkvoSLWsyIGaEhKs4kXafXQEiR/0c5QLUiO46ojaIOKlTxAf6g3AkYIuJvANnQ96Fyg52jQSQnBcrMm63fe6WPq7DA/zwdPVudvXE2yEu58K8QIUHmJEuFm8dPVvfoAh4qUfYzRpZJfUEkYpLvUlaixA6Ch7myV0AEv1wZHCLn6Qufs2lUyL98Wx0TTY/5la74FhjPuszekkZOTwnBQ6sooXqTjqECJe+jH5FXU0GE1MdD6mNoQkgIu7fY0SOsTwMOV5SS3oRvw7frFapop4cWjSf1GdVl08YdxV3b7cPknW7TtYxcsBq3jJLKmhtsFoR6P6BiJe+jGZ5oZHZ3qkqQ090HJc6B0syXtdDhsBDDeLl4zNUFfeA1YJNmGLeW7bhOu6PYxR0zT250hn3T6DJYyftY1gbzeCvFzRNEjtqsd1ACHipR9j6fEyRWduTjdkhh2tETqDJWx0vLSW6s5Mlz6ZoGFqOrGpEQ580YPWCT1GYTKkrQF0cMYd3b5cVkkNZTWNuDrprU/yggMTOxv0zlCUAsVHSYhQ/2e7M0vtbJjjI+KlH5NZXI0PNQxuMCdsinjpMwR6uRLsrSqO0gq7GDrS6WD8NWo96YMeskzoUY78oJbDF/dI80hLf5eRkb64OsvHu8PjEdD0uXxkFfOGhwKw6mDrs/yEJuTd3Y/JKK5mkv4IekwQEAu+kfY2SegE8aE9EDoae6VK1D6+Qw2BExwLS2M6y0iHbvDe1gz+/KWaUjwh2r/b1xN6iRHmViDJ37MkUY3B2Z5ewonKejsa5fiIeOnHZBbXMFVvbnzVAx+OQu9icfundtXzAqpnSLy558ue93vAKqFHyU1Sy8gJ3brML0dO8JevD1JRZ2BUhC+3z43rvm1C7zBiiVpmbSXKrY5xUX6YNFh9SLwv7SHipZ+iaRoZxdUniRcJGfU14sO62evFwoRr1fLgl920SOhRqougPAvQQcS4bl1q27FiAJaMCeeb384i3E+qCvsMATEQOho0I6SuZkliBADf789r/7wBjoiXfkphZT1aYx1jdZLv0lexJO12q1waVMM6nR7Ks6EitwcsE3qE3D1qGRyv+vJ0g2Rzb5AZcUE46bs3kVqwA/GL1DJrK+eMUeJl69FiSqsb7GiUYyPipZ+SUVRNou4YrjojeIUqdS/0KYabc15yymqprGvs+oXcvNWTHcDxnT1gmdAjWMRLN0NGAIfzVEv5hAgZB9AnsTQPLT7K4CBPBgd6YtJ6wOvajxHx0k/JLK5hgt7c3yV6qqo8EfoUfp4uhPq4Ad3MewGImqyWx3d00yqhx7Ak63ZTvJTVNJBXXgcg5dF9laBhalmsPrOHBHkCqvRdaB0RL/2UjOLqJvESNcW+xghdpil01M0nMMt7QDwvjoPV89K9eUaWkFFUgAe+7i7dtUqwB0HmBOvKPKivJDpQiZdsES9tIuKln5JZXM1Efar6QcRLn6Upabe7nhfzeyB3Dxi7EYISeoaKXKjKV7lI3RyWag0ZhUvIqM/i4Q9eIWq9+CiDA8XzcjpEvPRTqgozCdeVYtI5QeR4e5sjdJHhPTEmAJRb2t0PDLVQcLAHLBO6hcXrEjISXD27dankPPXeGBUhIaM+zUmhI4t4yRTx0iYiXvohdY1GfIuTADAEjwRXL/saJHSZHhnQCKDXwyDJe3EYLOJlUA8k6+ZLsm6/wBI6Okm8SNiobUS89EOSsssYiwoZuQyRYYx9mWHmiqP8ijrKa7sZ7pGkXcehh5J1jSaNFHPOy0gRL32boHi1LE6z5rwUVTV0fbZZP0fESz9kR3qJNVlXFy35Ln0ZPw8XIswNx9K6O2k2eppaZm7pplVCt9C0HiuTTiusot5gwsPFyfq0LvRRTgob+Xm44Oehkq+zS8X70hoiXvohOzJKGK7LVj90s3OnYH/irXkv3QwdRU9TE2zLs6E0swcsE7pEWRbUloDeBcLGdOtSm9OKAJgcEyDN6fo6FvFSlAaa1pS0WyzipTVEvPQzjCaNzKwMfHW1aOh6ZFKtYF+Gh6q8F0t4oMu4eTc96Wdu7qZVQpfJNYeMwkaDs1u3LrXJLF5mDQvurlWCvQmMVdVnDZVQVSgVR6dBxEs/43BeBaENx9UP/tHd/nAU7M+YQX6AmjTbbWJmqWXGpu5fS+gaPRQyajSa+NU802imiJe+j7Mb+A9W68Wp0uvlNIh46WfsyCghVq8GeuksbkihTzNneAg6HRzKqyCvvLZ7Fxvi+OJF0zQ0TbO3GbbDkqw7qHvN6fZklVHTYCTIy5VRkqzbP7Ak7RYdEc/LaRDx0s/Yk1VGrM48Sl3ES78g0MuViYMDAPg5ubB7Fxs8DXROUJYJZdk9YF3PUtdoZOlb25nz7DoqujPPyVExGSFvr1rvpudlU+oJAGYMC0Yv+S79g9AEtSxMFvFyGkS89DOyS2tEvPRDzkwIBeDnw90UL24+TU0LHSzvRdM0/vTlATamFpFdUsuOngiTORq5e6C+Atz8VIO6brDRnO8yW0JG/QfLe6LwUFOvl9La/u2J7CIiXvoZuWW1xOpU2IjAOPsaI/QYC0Yq8bIprYjaBmP3LjZkhlpmb+umVT3Le79m8vnu49af9x4vt6M1NiJ1jVrGzQMn5y5fpqiqnr3ZZQDMjBfx0m8ItYiXw4T5qXzFBoOJ0pp+6IXsJiJe+hENBhMnKmuJ0RWoDUEiXvoLI8J8GOTvQb3BxJajRd272KBJamnJvXAAKusaeX71EQASzQnK+4+X2dEiG5H2k1oOW9ity6w6kI9Jg3FRfgzy9+gBwwSHIGSEWtYU4VZXQrC3K6AeSoXmiHjpR+SX1xGhFeOma0TTuzRlrgt9Hp1Ox/wENbhtw5ET3buYRbwUHIDGum5a1jO8uzWT8tpGhoZ48egFowHYd7y8f7nLa0ogZ5daj1vQrUt9t095V89JjOiuVYIj4eoFATFq/cRhIvyUMM0vd4y/U0dCxEs/Iqeslli9ynfRBcaC3snOFgk9iaWXx+ajxd27kF80eAaDyaAEjJ2prjfwxsZjAPz2zGGMjvTFWa+juLqB3P70oX30Z0CD0NHgN6jLlzlRWc+2dPUeEPHSDwkdpZaFyYSbu2vnVfSjv4MeQsRLP6JZvosk6/Y7zhgahE6nWsIXdOfDTKc7KXS0q2eM6wYrd2ZTWtNITJAn54+NxN3FiRHhqqvwPnNeR78gba1aDuue12XVQXPIKNrf2gtE6EeEWCqODllHg+R3t0VCP0TESz9CiRdLpZHku/Q3/D1dGROp8kG6n/di7jHiAOJlvTkMdu20ITg7qY+ksVH+AOzL6SdJuyZTj+W7fL0nB4BzE8O7a5XgiFg9L4ebPC/9yQPZQ4h46UfkltcSp8tVP4jnpV8yY1gQAJvTuhk6cpCk3UajyVoSbfm3AYyNUiJtX39J2i04ANWF4OIFg7s+6f1QbgU7M0tx1uu4cHzXQ0+CA2OpODpxmAhfVXEkOS8tEfHSj8gpq2O43lxq2s0eEoJjMjPOnPeSVtS9ZNZIs+elOBVqy7pvWBfZn1NOdYMRf08XRoY3dYltEi/lmEz9IGnX4nWJndOtkR3vbs0A4Owx4YT5uveAYYLDERyvGknWlTPYpQIQ8dIaIl76EeWlJ4jQmRt7WTo1Cv2KKTGBuDrpySuv41hRddcv5BUE/kPUuh37vVhm80yLDWzWJXZEmA8eLk5U1hlIO9HNadqOgEW8xHc9ZFRW08BXSSpkdP2MmB4wSnBInN2sFUdRRvUwmlde178q73oAES/9BE3T8C5PBcDgHQnufna2SLAFHq5OTI5RowLWdXdUwPDFarn73W5a1XW2miunpg8Narbd2UnPhMH+gJrX1aepK28SiF0skdY0jX+uTaOu0cTICF8mDwnoQQMFh8Mc9g+sUyM8ahuNlNdKo7qTEfHSTyivbWSIMQsAXaiEjPozZ40KA+DHg/ndu9Dkm9Qy5Xsoz+mmVZ2nwWBiZ0YpANPjWnaJtXxB7zIf02dJ36DK0oOGQWBsp0/XNI2/rUrmrc3pANw1Pw6dTmYZ9WvM4sWl7BiBXqpRnSTtNkfESz8hp6yWeJ1yMTqFj7KzNYItWTRaVZnszCylqKq+6xcKTVBTpjUT7FrRM8Z1gp2ZJdQ2Ggn0ciU+1LvF/kkxgebj+rh4ObpOLbvodfnf3lz+84vqg/PI+aM4b2xkT1kmOCpBQ9Wy5CjhvpZyaREvJyPipZ+QU1rLCJ15SrAk6/ZrBvl7kDjID02Dnw4VdO9iU8zel93vgLH33NKapvGPn1SYc9HIsFanIk8c7I9ep6bqFnahr82Kzenc+u5O1hwqoLy2kdSCSuoauzkXqivk7FRLy0ypTmA0afxjrfo93XPmMJbN7LznRuiDWKpFi9OsvV7E89Icm4qXkpISrr32Wnx9ffH39+emm26iqqr95Lt58+ah0+mavW6//XZbmtkvyC2rbao0krBRv8cSOlrdXfGScJ6acFxVAAUHe8CyjrH2cCHb0ktwddZzz8L4Vo/xcXdhhLkCqbPel5yyWv763WFWHyrglnd3Mu6x1Sx6cQPXv7W9dxMfG+ug4JBat/TW6QTf78/j2Ilq/DxcuGXO0B42TnBYLOKlNINIXzXAUxrVNcem4uXaa6/l4MGDrFmzhm+//ZYNGzZw6623nva8W265hby8POvrmWeesaWZ/YKighyCdaqszjrcS+i3LB6jQkeb0oqoqjd0/ULOrjBoglrPS+q+YR3AYDTxt1XJANw4M7bdwYKWvJedncx7WbE5HaNJIyrAAx+3punN29JLWHOogLpGIx9vz+LCf21i/nPr+XBbFkZblGQXHARTI3gGqbEMncBk0nhlXRoAy2bG4OPu0vP2CY6JTyQ4e4DJwAj3MkA8L6diM/Fy+PBhVq1axRtvvMG0adOYNWsWL7/8Mh9//DG5ubntnuvp6Ul4eLj15evr2+7xApgKDgNQ6RGlhnsJ/Zr4UG9igjxpMJj4JaWbgxojzeIld0/3DesA3x/IJ62wCn9PF+6Y134naEtlVWc6ClfUNfLRdhVC/euFY9j1l0UcfGwxd81X9/rbqmQueXULD32xn73Hy0kvqub/vtzPFf/ZSnV3hGBr5JqbAEZOVGMZOsHKXdkk51fi7ebMshkSLhpQ6PUQqDxtsaiRLyJemmMz8bJ161b8/f2ZPHmyddvChQvR6/Vs29Z+X4kPPviA4OBgxowZw/Lly6mpqWnz2Pr6eioqKpq9BiIeZSkANARJf5eBgE6nY7E5cXf1oW5WHfWieNE0jX+vPwrAshmx+Hm0702YOzwEZ72O5PxK0go71u/lk+3ZVNUbiA/1Zu7wEFyd9Xi5OXPb3Dj8PV04dqKaQ3kVBHm58n/nJPCX80bh4+7MrsxSfvvRnp71wFg6GHcyZFRUVc9T3yvv1D0LhuHnKV6XAYd5xEuUSVUCZpW0/T04ELGZeMnPzyc0NLTZNmdnZwIDA8nPb/vD9pprruH9999n3bp1LF++nPfee4/rrruuzeOffvpp/Pz8rK/o6M65ZvsDRpPG0Fo1HdjZEgIQ+j1njVZ5Lz8nF9JgMHX9QhbxUnBI5WjYkF+OnOBwXgWerk5cP2PIaY/393Rldrwqo/52X/seW1Di6KPtqmXATbNimyUC+7q7cP+i4YAaavjtPbO4dU4cN82K5d0bp+LmrOfn5EL++u2hrvzTWsciCCM7J16e+u4w5bWNjIzw5UZJ0h2YmPNeQhqVeDleWtO9v/N+RqfFy0MPPdQiofbUV3JycpcNuvXWW1m8eDGJiYlce+21vPvuu3z55ZccPXq01eOXL19OeXm59ZWdnd3le/dV8spqmKpTH7jeI+ba2Rqht5gQHUCwtxuVdQZrp9ou4RetcjJMjVBou6TdtMIqnjZ7E66ZOhh/T9cOnWcpDf5mb+5pk22Tsss4VlSNh4sT541rWVK8dHoM638/j89vn06EX1OuzYTBAbx05XgAVmzJ4G1zT5VuUV8FRcojahWIHWD/8XK+2JODTgdPX5JoHVYpDDDMnhePynQ8XJwwaUrACIpO/1U88MADHD58uN3X0KFDCQ8Pp7CweQdQg8FASUkJ4eEdn4Y6bdo0ANLS0lrd7+bmhq+vb7PXQKMw/SAhunIacMEpeoq9zRF6Cb1ex6KeaFin00HEeLVuo9DRyp3ZLPnHBlIKKvFxd+am2R33JiwaHYars56jJ6pJzq9s99gvdqun1LPHhON9UqLuycQEe7UqCJYkRrB8iQq7Pv7tIdaldLODcd5e1UPHdxD4hHX4tGdXK8Fz0fhBjI/2754NQt/F7HnRFaUxJMgTgMxiES8WOi1eQkJCSEhIaPfl6urK9OnTKSsrY9euXdZzf/75Z0wmk1WQdISkpCQAIiIiOmvqgKHh2EYAjrmNBBcZ1jaQWDy6qWS6W7kaNsx7qa438MR3h2k0aswfEcJ3v53dzOtxOnzdXZg3PARQpcNtUW8w8r+9KrR06cSoLtl665yhXDUlGk2D535M6V5Z9fHtatkJr8uvx4rZcOQEznod9y0c3vV7C32fkBGg00PFcSb5KtGe3p15Zv0Mm/kjR44cydlnn80tt9zC9u3b2bx5M3fffTdXXXUVkZHKnZuTk0NCQgLbt6s/8qNHj/LXv/6VXbt2kZGRwf/+9z+WLl3KnDlzGDt2rK1M7fN45/0KQEHg5NMcKfQ3ZsQF4+fhwonKerand2MGkOULNqfnxcunO7Mpr20kJsiTN66fwmDzU2RnWDBS5c+1929cl1xIeW0j4b7uTI8LavO49tDpdPzx7ARcnfUczK1g7/HyLl0HUGMBAGJmd/iUf/2sPMxXTx3cpd+T0I/wCIDBqrHhfNR3ZGaxiBcLNg2mfvDBByQkJLBgwQLOOeccZs2axX//+1/r/sbGRlJSUqzVRK6urvz000+cddZZJCQk8MADD3DppZfyzTff2NLMvo2mMahcVTTUDzrDzsYIvY2rs97qfelIQmubRJmFb+EhqOm5QYgGo4k3N6n8kZtnD8WplU66HWHCYFUyvT+nHIOx9aTFtYdVmOf8cRFdvg9AgJcr5yUqT+8Hv2Z27SKGesjcqtaHdiwPrbS6ga3m3KVbZktDOgEYeR4AYyuVEE6XsJGV1oPCPURgYCAffvhhm/tjYmKauWWjo6P55ZdfbGlS/6M0nQBjEQ2aEx5Dp9vbGsEOnDc2kk93HmfVgXwSwn14d2smz14+rnP5Ej7hEDxCJZhmboaR5/eIbasO5nO8tJZAL1cum9S1UA7AsBBvfNycqaw3cKSgilGRzXPbNE1jc5rqBTM7PqRbNgNce8ZgvtiTwzf7cvnzeaNOW9LdguM7wVALXiEQ0nr7gvSian49VszRwioumRhFSkEFRpNGQriPeF0ERcJ5sOohQkr3EEw5GUXyvrAgaex9HNNxlVN0QItlcFjXXOVC32ZGXBABni4UVzfwl68PklpYxWvrW6/Oa5fYOWp5rOceICw5KtdMHYy7i1OXr6PX6xhnFmN7slt2280oriG3vA5XJz1TzAMdu8PEwQGMCPOhrtFkLb3uFOnm32HsnFab06UXVbPohV9Y/sV+3tiUzp0f7OL7/SrpeuHIjif3Cv0c/2iIGI8OjYVOu6Rc+iREvPRxarL3AZCsDWm3zbrQf3F20nP2mOYJ7T8nF1Je08lBi5bwhiVXo5sYjCY2pSpviCVnpTtYPEl7sspa7LN4XSYM9sfDtesiyYJOp+Nmc0XUv9cfpby2k79Ly+8wtvWQ0ac7szGYNAYHehLo5UpGcQ1rzHOqFo4S8SKchNkLerbzLimXPgkRL32chlzVnK7QI076QQxgbpoVQ0K4D38+dyQJ4T40GE18f6DtypxWGTIT0KnQUWU3u/YC+3LKqagz4OvuzNgo/25fb8JgdY2k7LIW+yziZdaw4G7fx8IlE6OID/WmvLaR/27ohCervgqO71DrFm/WSRhNGl+aS7r/eHYCf1jcNIssxMeNsYP8umW30M8wJ3yP1qseZlIurZBvuz6OW4lq+lXlJ2WVA5lhoT6suncON88eykUTBgFYvyA7jGcgRIxT6z3gfdl4xCwo4oO7lUBrweJ5SSusauYJMZo0a6LrjB4UL056nVVYvLkpncKKDnYf3voKmAwQEKNep7DlaBH5FXX4ebiwYGQol0+KYniYNwALEkKbdQUWBILV1PUQrQgvaqVc2oyIl75MXQVetarCxBA8ys7GCI7CheMj0elge0YJx050bB6QFYunIOWHLt27qt7A1f/9lTs/2MWaw8p7M6cHEmgBgrzdGByoEhZP9r7syiylrKYRbzdnxkX1rNdi0agwJg72p67RxH83HDv9CYXJsOFZtX7mX1rNd/l813FAVUW5uzjh7KTnpSsncNH4SO4+c1hPmi/0BzwDVeI3MFSXR8ppGjUOFES89GUK1STpPC2QgKDu5xQI/YMIPw/mmpu6PbByL41tlBa3yqgL1fLgF11K3H1nSwZbjxXz/f58DuSoIamz4nvOGzI1ViXjrktWZdENBhMPf61Cp4tHh/d46FSn03HPAvXk+8G2LIqr6ts/4dt71ZiF+MUw5tIWu8trGlll7oZ8ciO9UZG+vHTVBKICpJpEaIVg5VmP0+WyIfVE95on9hNEvPRlzHNoUkzRRPpLZ12hib9eOAYfd2f2ZJXxj59SO35i1GSYfKNa/99vVf5GB6muN/DGRuWdcHNWHy1DQ7x69Av5nEQ1WuS7/XkYTRqvrEsjOb+SQPOEaFswd3gIY6P8qG00WnvWtEpFLmRtBZ0TnPdCq16XT3dmU9doIiHcR1r/Cx3HLF5GOOeSV17HkYJOelT7ISJe+jIFahhjshYtlUZCM6IDPXnq4kQAXlmfxtajnRjcuOhxNayxLBN2vtXh097dmklpTSNDg7344XezOXdsBA+d3bOCYtawEGtH4RVbMnhlnepI+/iFownyduvRe1nQ6XTcPV+Fc17feIxH/3ewdQ9MjmoWSehI8GvZ08Zo0nhnawYAy2bGoGtF3AhCq5jFyxRvlUfW7blb/QARL30YrZnnRcSL0Jzzx0VyxeQoNA3u+ySJ0uqGjp3o5gNTblbrHZx1lFpQyatmIXH3mcMYGuLNK9dM5KzRHR/C2hFcnfWcbb7mX789hMGkcfbocM5NtO3ss0WjwjhvbASNRo0VWzKY++x6/vFTKnWNxqaDcsxz3NqYZbTmUAHHS2vx93ThwvGDbGqv0M8IUeJlmE7lOK4X8SLipc+iaWhmz0uKFk24n4SNhJY8esFohgZ7kV9Rx+KXNnDVf7dae6+0S9hotTTnVbXHicp6lq3YQWW9gSkxAVwwLrKbVrfPeeOahIq/pwt/vWiMzb0YOp2Of10zkfdvmkbiID+q6g28+NMR7v5wT1P+Qa7Z8zJoYrNz16cUcubz67n9fSVuru5mwz5hAGL2vPjVZuOEkZ0ZpVTUdbL3UD9DxEtfpTIPfV0ZBk1PqedQ+TAUWsXT1Zl/Xj0BH3dnCivr+fVYCX/+aj+m002gtrS0L04FQ/sem4e/PsDx0lpigjz5z28m27zf0PShQYT6qBDRYxeMJsTHNuGi1pgVH8zXd83kH1eNx8VJx0+HC/g6KRc0rclLFdkkXg7mlnPH+7s5dkKVtyaE+7BsRkyv2Sv0E3yjwMUTnamRmUGVGEwaW9I6EQruh4h46asUqJBRuhZBaICPnY0RHJkxg/zY9OCZfHLrGfi4O5NRXMOG1BPtn+QXBa4+ql9JSdsN2qrqDaw1V/7865qJBHq59qTpreLspOedG6fy+tLJNvfytIZer+PC8YO450xVhfToNwcpzj4MdeXg5Gb1WhVX1XPru7uobTQyOz6YPX9ZxKp75xDqK15SoZPo9RCk8q7mB6rxGGmFA7tkWsRLX8UsXpK1aCL8JN9FaB8/TxemDQ3i8knRgEqubRedDkLN3pd2QkcbjpygwWAiJsiT0acMS7QlIyN8WTQqzK5Jr7fPi2N0pC9lNY18+e03amN4IjipIY7/Xn+UnLJaYoO9+NfVEwnoBWEn9GNCVMPEEc6qc3Z60cDutCvipa9SaK40Mg2WZF2hw/xm+hBAVStkFp+mU2foSLVsR7ysNvcsOWt0+ICrnnFx0vPsZeNw1uvQWUJG5nyX8tpG60DHh88fhZ9nJ6dSC8KpmPNeoo2qyeFp/377OSJe+ionJetKjxeho8QGezFvRAiaBl/uOc34gBCLeDnU6u5Go8kaMjprgA4THBXpy13zhzFWr0Jr35dEsDurlI+2Z1HdYCQ+1Jt5w3umw7AwwDGPCQiqU17TjAE+48jZ3gYIXcDYqIbnocJGl4rnRegEC0aGsT7lBLsyS9s/0OJ5OZHc6u5tx0qorDMQ7O3KhMEBPWxl3+GuuTGYNqsvlOcPenP0wBZrf7pb5gwdcB4pwUYEq7CRR/lRQKOoqp6qegPebgPza1w8L32R4qNgbKAad3K0YAkbCZ1ikllo7Mkqw9he1ZFFvJQcg8aWQwktU6sXJIT1yODFvopryRHcqafR2YvRiZPwcHFC0yDc150Lx/d+QrHQTwmKA50eXX0FIzyV12Ugh44GpmTr61ib00WhoRfxInSKEeE+eLk6UVVvILWwkoTwNhJtvcPAIwBqS6HoCESMte6qNxj5bp8SLxcM9C9oc3M6l6iJ/POaSVTVG9iUeoIR4b64OUsLA6GHcHZTU8pLjjHNt4iUGi8yimoYHdmzw0j7CuJ56YtYxgKYovFxdyZIqhiETuCk1zHOPFdnd2ZZ2wfqdBBqblaXt7fZrnXJhZTXNhLu684ZQ4NsY2hf4ZTmdN5uzpw9JoLYYC87GiX0S8xJu2PdCgDIGMCeFxEvfZFCS7LuYM4YGoR+ALvsha4xaYgKHZ0278XSLTZnZ7PNn+9Wyb4XTRg0oENGQNNMo8iJ7R8nCN3FLF6GOSmv50AOG4l46YuYe7ykaNHMGhZsZ2OEvshEa97LacRL9FS1PN4kXkqqG6yzVS6ZOMBn9DTWNVVjDRLxItgYs3iJbFRl+AO54kjES1+jvlJN+0WFjWYOG+Aue6FLTBjsD8CxompK2hvYOGiyWhYeUu894OukHBqNGqMjfRkeNsC7O+fvV12IPYPVJG5BsCVm8eJfkwFARpF4XoS+QqEqWy3Q/HHzDSYuxNvOBgl9EX9PV4aFqvfOc6tTmoYLnopvhPpS1kyQsxtN0/hgm3rqu2rq4N4y13E5Od9FSqIFW2Pu9eJanYcXtRRW1lPTYLCzUfZBxEtfw1ppFM3MYcHSQ0LoMg8sGo5OBx9uy2LZih3c90kSX7XSuM5o9r58+vWXfL8/n7TCKjxdnbhooFcZQVM4TfJdhN7AMxC8VNPDcR5qPlnmAA0diXjpa5grjQ5rg5kZJ/kuQtdZkhjBM5eq8uf1KSf4ck8O936SxAsneWI0TeOHUpXXElCyl7s+VJ6GC8dH4uM+wFveaxpkblbrg8+wry3CwMHcrG6qt0W8DMzQkfR56WNoBQfQoTwvi8x5C4LQVS6fHM2gAA+2p5dQXNXAe79m8s+f02gwajy0JIH/bjjGqowQznODyU5p0KgBOq6ZOsTeptuf0gyoyAG9S1NisyDYmtCRkLmJRJccYNKATdoV8dKX0DS0gkPogCMMJirA094WCf2AGXHBzDB78eLDvHn464O89stRskqq+X5/Pq7EYNS5EKBVMDOgDN9BI0mMGpiNsZqRsUktB00EV+npIvQSYar3UpwpAxi4SbsiXvoSlfno60oxaHpqfONwdZaon9CzLJ0eQ3lNI8+vOcL3+9XE6GVzR6DPmwxZW3l/QSO6yZPsbKWDYAkZxcyyrx3CwCJsjFrUqmGgA7VRnXz79SXMyboZWjgRwf72tUXot9x95jCumaYqiW6YEcNDZyegi50DgM7ibRCaPC9DZtrXDmFgEZoAgEf9CQKpkIRdoQ9gGQugRTM4UEJGgm3Q6XQ8dXEi2/+0gEcvGK0q2izehYyNKlF1oFOaCeXZoHOC6Gn2tkYYSLj5qBlHwAh9NnnlddQ1Gu1rkx0Q8dKXKDgAqGTdwYESYxdsS6iPe9MPUVPByQ2qCqA4zX5GOQrH1qvloIngJr2WhF7GHDqa4HocGJjl0iJe+gqGBkhdDcBObQRDgsTzIvQiLu5NFTXpG+xri73RNNj5plofcY59bREGJuak3YluucDAzHsR8dJXOLYeakspwp9tppESNhJ6n5NDRwOZrF/VlG1nd5h0g72tEQYiZvEyHDUqZiD2ehHx0lc4+AUA3ximYULPYPG8CL1NzGy1PLZeDSQcqGz7t1qOvUJ1PBWE3sYcNopszESPaUD2ehHx0hdorIPD3wLwrfEMAjxd8B3o3U2F3id6GvhGQW0pHPjM3tbYh4pc698i0263ry3CwCUgBlw8cTbVM0qXIZ4XwUFJWwMNldR6RLBbi2dwkCTrCnbAyRmm3arWf/33wKw6SvsJNCNETbG67gWh19E7QfwiAJY6rSGjSDwvgiOSoZphHQ2ai4Ze8l0E+zFxKbh4qsq3gZj7YuntMnSeXc0QBKbfDcCFTptpKM8bcOXSNhMvTz75JDNmzMDT0xN/f/8OnaNpGg8//DARERF4eHiwcOFCUlNTbWVi38FcIp2siwNgiIgXwV54BMD4a9X6tv/Y15beRtOaxIt01RXsTfRUtKipuOkMXOe0huOlA8v7YjPx0tDQwOWXX84dd9zR4XOeeeYZ/vnPf/Laa6+xbds2vLy8WLx4MXV1Azg5UNOs4iWpQU33lWRdwa5MXqaWqauhrsK+tvQmJcfUIEYnV9X3RhDsjG76XQBc67SWjBNVdramd7GZeHnssce47777SExM7NDxmqbx0ksv8ec//5kLL7yQsWPH8u6775Kbm8tXX31lKzMdn4pclSCpc+LXCjU8T8JGgl0JHQXBI8DYACnf29ua3sM6iHEyuMrfoOAAJJxLo86FYF0FxceP2NuaXsVhcl7S09PJz89n4cKF1m1+fn5MmzaNrVu3tnlefX09FRUVzV79igI1z0gLjie9XMU0pUGdYFd0Ohh9sVo/+KV9belNLDk+EjISHAUnF4o9hwJgzNtvZ2N6F4cRL/n5aoJtWFhYs+1hYWHWfa3x9NNP4+fnZ31FR0fb1M5ep0C9IWsCRmI0abg66wk7uW27INgDi3hJW6s8g/2dk/NdYmfb1xZBOImawFEAeJQcsrMlvUunxMtDDz2ETqdr95WcnGwrW1tl+fLllJeXW1/Z2dm9en+bY/a8FHoOAyA6wAO9XmdPiwRBTbYNHQWmRkgeAKGjwsNQmae66kZNsbc1gmDFKWIsACHVAyts5NyZgx944AFuuOGGdo8ZOnRolwwJDw8HoKCggIiICOv2goICxo8f3+Z5bm5uuLm5demefQKzeMlwjgFgiPR4ERyFkRdA4SGVuDvhWntbY1vMc8WImQ0uHva1RRBOwjd2AmyHWEM6DQYTrs4OE1CxKZ0SLyEhIYSEhNjEkNjYWMLDw1m7dq1VrFRUVLBt27ZOVSz1KxrroEiVih8wRANVkqwrOA5x8+GXv6lcEJMJ9P34Q9MiXoYvtq8dgnAK/jETABikKyI9L4fY/pY60QY2+7TJysoiKSmJrKwsjEYjSUlJJCUlUVXVVM6VkJDAl1+qhD+dTse9997LE088wf/+9z/279/P0qVLiYyM5KKLLrKVmY7NiWTVzdMjkIMVyuMi4kVwGCIngosX1BRD4UF7W2M7akvVMEawdjUVBEdB5+FPnl7lipYe22Nna3qPTnleOsPDDz/MO++8Y/15wgSlDtetW8e8efMASElJoby83HrMgw8+SHV1NbfeeitlZWXMmjWLVatW4e4+QBNU8/epZfgYMktrAak0EhwIZ1cYMkONrzj2C4R3rC1Cj7D5n5C/H857Edy8bXuvoz+rh4jgEWqmjCA4GAUew4ioLqAhZy9wgb3N6RVsJl5WrFjBihUr2j1GO2U2ik6n4/HHH+fxxx+3lVl9i1ylorWI8WQfU90TRbwIDsXQuUq8pP8CM+7unXse/RnW/EWt6/Rw8WuqfNtWHLGEjM6y3T0EoRtU+o+C6s24FfVjD+gp2Ey8CD1AbhIAVYFjqKo3ABAVIOJFcCBi56hl5hYwNoKTjaed11fC/+5p+nnfx+Dmo5JoR54P0e10vm2oVg8ERUdg5IXgFXT6+6VvhP0r1frwJd2zXRBsRfgYyIGgyhR7W9JriHhxVIyN1kqjLPfhQC7hvu64uzjZ1y5BOJmwRPAIhNoSyNkFg8+w3b0MDfDVnVCeDf6DIfEK2Pgc7Hhd7d/7Mdy7H1xOCjObTHB8B+x+Bw58Doa6pmOX/aCm87a4Tz0kfwu1ZbD+aRUyGnulCpEJggMSPHwa7ILIhgzqaipx9/Sxt0k2R8SLo1J4GIz14OZHakMIkCszjQTHQ69XoaODX0Lqmp4XLw3VsOFZMBmUJzJjI+hd4MJXYchM0ExQmgGZm6GqAPa8B1NvUedt/ifsfhcqc5uu5xMJdWWQvQ02/0OJoLJMNSU6YoIaefDxNXB0bdM5YYlw3ku2DU0JQjcYMWwERfgTrCsjadcmJs/u/15CES+OSl6SWkaMJaPEnO8ilUaCIzL8bCVejqyCBX/p/vWK0qA8CwbPgE+vVzk1Fly84Kr3m7rcLnxELbf9F374gxIszm6w/m9qiCKAqw+MPA8m3wRRk5Wg+eYeWPtY03XXPg6eQWpqdnEauHjC0PngGQhz/yizjASHRu+kp9hvDMHlm8g5uFnEi2BHzPkuRE4gtUCVl8eH2biqQhC6QvxZKnG24ACUZSlvRlc58AV8dYcK77j6QEMlOHvAxN+oUOrkZRAxruV5E3+jPDTlWfC/36ptfoOVuEk4r3koaeJSOPyNEkXe4TBoIqRvUCXfNcVKuFy7UmYYCX0Kj5gpsHcTzvlJGIwmnJ36cd8lRLw4LhbPS+R4kverYZMjwn3tZ48gtIVnIERPg6ytcORHFbbpCpv/AWseVutOrkq46Jzg8hUw4uz2z3XxgDm/hx8eVIJkxt0w5ebWu+HqdHDl+5C7W02IdnZVeS75+1VCb/Q0MLdcF4S+wqBRM2DviySY0tieXsKMYcH2NsmmiHhxRIyNkH8AgPqQsWQUqy67I8L6fxKW0EcZvtgsXla1FC8mY+uJsSez+Z9NwmX63TBvOaT8AL4RHfeATL0VYueqXiwup+kN5eLePAHX2U2FlKImd+xeguBgOEVNAiBOn8dn+4/2e/HSv/1KfZXcJJWs6+5PmiEYo0nDz8OFMN9+PMNJ6NtYyojTN8D+z9QUZoCkj+Bvg+HnJ5q2nUxNCXxzb1Pflvl/hsVPqsZzYy/vXOhGp1MDI08nXAShP+IVRJVnFACNx3fb2RjbI+LFEUlfr5axs0kpqAZgRLgPOql2EByVkBEqwdbYAJ/fBO9dDOXH4Yc/QkOVykf54Y+q3NlC4WF4eRLselv9PPchmPsH+9gvCP0AQ9h4APxK99vXkF5AxIsjkr5BLWPnklJQCUjISHBwdDq47nPlOXHxhGPr4N8zob5c5aAAbP8P/HM8/PqaGjr65W2qP0zISLjhe5i/3K7/BEHo63gMVa0KxjQeoKS64TRH921EvDgajbWQtU2tD51HSr5ZvISLeBEcHFdP5Tm5diU4ual+KgCXvw2XvqlETEUOrPoj/GMc5O0Fd39Y+hXEzLSj4YLQP3AbfiYAZ+gPk5ZbZGdrbIuIF0cje5vKd/GJhKBhVvGSIOJF6CvEzIJL31ACZsJvVGJs4mVw7z4493lw84WqfHXsOc+CT7h97RWE/kLoKMqcAvHQNVB+ZJO9rbEpUm3kaBz7RS1j51BeZyCvXLUzj5ewkdCXGHUBxGeC80nJs85uqnw5/ixY+1dzi//L7WejIPQ3dDqyA87Av+h73DN/Aa6wt0U2Q8SLo3FsnVoOncsRc75LpJ87fh42HngnCD1Naz1WQImWS1/vXVsEYYBQGz0Xir4nqnSrvU2xKRI2ciRy96iX3hnizmRnRikAYwb52dkwQRAEoS/gPWoRALGNR6HqhJ2tsR0iXhyJLS+r5ZhLwSecX48VA3DG0CA7GiUIgiD0FWKHxHDAFANA1f5v7WuMDRHx4iiUZsLBr9T6jN9iMJrYmVECiHgRBEEQOoaHqxOb3dTgUt22f7feHLIfIOLFUdj2H9CMapJteCIHciuobjDi5+EilUaCIAhChzkYcQnVmhteZSlwdK29zbEJIl4cAZMJDn6h1qfdBmANGU2NDUSvl866giAIQscYPGgQnxjnqx82/9O+xtgIES+OQN4eqMwDFy/leQG2Sb6LIAiC0AVGRvjypmEJRvSQ/gsUpdrbpB5HxIsjkPy9Wg5bAC7uGIwmdpgrjc4YGmhHwwRBEIS+RkKEDzmEsE0bpTYcW29Xe2yBiBdHIMUsXhLOVT8WVFJVb8DH3ZmEcF87GiYIgiD0NWKCvHB30bPVkKA2ZG62r0E2QMSLvSlJh8JDoHNSnUeBPVllAIyP9sdJ8l0EQRCETuCk1zEi3JftppFqQ8bmfld1JOLF3qT8oJZDZoCnChFZxMuEaH/72CQIgiD0aUaG+5CkxWHQuUJ1IRSn2dukHkXEi72xjAMwe10A9mSrfJcJgwPsYZEgCILQxxkZ4Us9rhx1NYeOMvrXoEYRL/bE0KDceQBxqsqorKaBYyeqARU2EgRBEITOMjJC5UtuNoxQG/pZ3ouIF3uSswsaq8EzCEJHA5CUXQZAbLAXAV6udjROEARB6KuMMDc3/ak2Xm3oZ3kvIl7siaV8LXYu6NV/xcnJuoIgCILQFfw8XIgK8GC3KR6jkwdU5kLubnub1WOIeLEn6b+o5dB51k0Wz8uEwf69bo4gCILQfxgf7U8dbhwNmqM27P/Mvgb1ICJe7EV9JRzfodbN4sVo0tiTZU7WjZZkXUEQBKHrWDz4q3RqUCMHPgeT0X4G9SAiXuxFxmYwGSAgBgKGAJCSX0lFnQEvVydGRsgwRkEQBKHrWCpWPyyK4//bu/e4Ju97D+CfBEhEkETkEhBQboqtioqKaG11MMFjVzut1dae1dbZ1eLO2lo33TmVdWc79rZur/W4bu3pKe1ZZ1vXqqu9rIrirYiKokWFGoWGW0CxCSB38jt/PCQ2JXKRJA+hn/frlVcen98v4ZsvMfnyPL/n9xO+I4HGGqDskMxROQeLF7lc+Kd0H5dm23W87CoAYNqYkfD24q+GiIhu3q3hAfDxUsB4TeBa7J3SzjPb5Q3KSfgNKQchgC8/k7bj0227j5VKxUtyNNczIiKigRnm44Vbui6ZPq3t+kO55OMhceqIxYscas4C9RWAty8QLZ2LFEIgv6t4mRnNlaSJiGjgrKeOcpqigWEaoPkqUHFC5qgGjsWLHL78VLqPuQPw8QUAlNU14UpjK1ReSkyO0MgYHBERDRXWQbunKhqB2FRpp3XYggdj8SKHC9ZTRteXBDhWWgdAeqMN8/GSIyoiIhpirNNunK2sR3vs96Wd1mELHozFi7tdvQSUH5O2x10f73L9lBHHuxARkXNEBQ5HoJ8KbZ0WnPdPBqAAar4AzJVyhzYgLitefvvb32L27NkYPnw4tFptnx6zatUqKBQKu1tGRoarQnQ/IYCPNwAQQMx8QBNha7LOrJs0lvO7EBGRcygUCtupo4LLSiBihtRwdod8QTmBy4qXtrY2LFu2DGvXru3X4zIyMlBdXW27bdu2zUURyuDcLkC/F/BSAf/ygm23qakNpVe6FmOM0MoUHBERDUVTreNeDCZgwg+knZ/9O/Dxz4HODtniGghvVz3xM888AwDIzs7u1+PUajV0Op0LIpKZxQLseVranvM4EBRva+JijERE5CpTusa9nCr/Glj2E+DrMuDE68Cxv0jfRTPXyBrfzRh0Y15yc3MREhKC8ePHY+3atairq+uxf2trK+rr6+1ug1JlAWAyACp/4LYn7JqsxQsXYyQiImdLjNRCoQDKrzbjSguAO18CUjdLjWfekzW2mzWoipeMjAy89dZbyMnJwXPPPYcDBw5g4cKF6Oy88YQ6W7ZsgUajsd0iIyPdGHE/nNsp3Y/LAFTD7Zqs4124GCMRETlbwDAfxAb7AwAKu75vkHg/AAVQcQwwlcsW283qV/GycePGbgNqv30rLi6+6WBWrFiBu+66C5MmTcLdd9+N3bt34/jx48jNzb3hYzZt2gSz2Wy7lZcPwl+CEMC5f0jbtyz+VpPA6QoTAB55ISIi17COe7Ee6UdAGDBmjrTtgYN3+zXmZf369Vi1alWPfWJiYgYST7fnCgoKgl6vR2pqqsM+arUaarXaaT/TJapOAWYD4OMHxH/frqmsrgmmpnaovJVI0AXIFCAREQ1lU6NGYntBBU58dfX6zok/BL46LK02Peff5AvuJvSreAkODkZwcLCrYummoqICdXV1CAsLc9vPdIlzu6T7cQtsM+paFZZ/DQCYNFoDlfegOotHRERDREqstOzM8bKvUdfYilH+amDCYmn6jupCoO4iMCpW3iD7wWXflgaDAYWFhTAYDOjs7ERhYSEKCwvR2Nho65OQkIAdO6TDVY2NjdiwYQOOHj2KsrIy5OTkYPHixYiLi0N6evqNfoxnKPlEurdeovYNH56uBgBMH8P5XYiIyDWig/wwabQGnRaBj4uM0k7/YGnOMQA4/Y58wd0ElxUvmzdvxtSpU5GVlYXGxkZMnToVU6dOxYkT1xeEKikpgdlsBgB4eXnhzJkzuOuuuzBu3DisXr0aSUlJOHTo0OA/LdQTkwG4UgIovK6vK9FFX9uIfcW1UCiAFTOjZAqQiIi+C+5KDAcAfFhYdX3nlPul+9PbpCk9PITL5nnJzs7udY4XIYRt29fXF//8p+cvFtWNfq90HzkT8NXaNb1+uBQAkDYhFNFBfm4OjIiIvkvuTAzDf31yHsfKrqLS1IzRWl8gYRGg1gDmcqDskLRgsAfgIAtXu9BVvMTZH3Wpa2zFBycrAABr5jpvkDMREZEjYRpfzBgrrZ+3+3TX0RcfX2DiEmm78G8yRdZ/LF5cqaMNKD0gbcfZX2X02qFStHZYkBihwQyuZ0RERG6waJJ0Acz+ktrrO6eslO7P7QJazDJE1X8sXlypPB9oawT8ggHdZNvuK42teCuvDADw0+/FQ6FQyBQgERF9l8yNDwIAnPzKhOa2rglgI6YDwROAjmaPGbjL4sVVhACOviJtx6UByuupfvXgJTS1dWJyhAapE0JkCpCIiL5rooP8EKYZhrZOy/U5XxQKYMZqafv469L31yDH4sVVCrKBko8ApQ8w6zHbblNTm+2oyxPfH8ejLkRE5DYKhQJz4qSjL4f1V643TF4uTaR6pQQoOyxTdH3H4sUVas4Cn26SttOygLDrp4z+cboKLe0WJOhGYN449034R0REBABz4qQJ6z7Xf2Ph42EBQOJyaXvnWuCjp4DGyzJE1zcsXpzNXAH89R7p3GHMPGBWpl3z3wukK4zunR7Joy5EROR2c2KlIy9FVWaYmtquNyQ/Cnippcumj78G/P0hmSLsHYsXZ2pvAd5eBjRUAUHjgXvesBvr8mVNA85UmOGtVGDxlHAZAyUiou+qkIBhiA/xhxDA5xe/cfQleDzw5Dlg6evSkIeyQ4DhqHyB9oDFizMVvg3UnpOuLnrgfWB4oF3z+11HXeYnhEjrShAREclgbrw0bCH3m5dMA4BfEDDpHmDKfdK/D77g5sj6hsWLs3S2A4f/IG3fvgHQRto1WywCOwsrAQD3JEW4OTgiIqLrvpcgXem6v+QyLBYHVxfd9qS0rI1+L1B50s3R9Y7Fi7OceQ8wG6SjLtN+1K25qMqMmvpW+Km8MG88B+oSEZF8ZkYHwk/lhcsNrSiqcjAxXWC0dAQGAE78r3uD6wMWL87Q2Q4c+p20Pfun0nTL35JzXjo0Nzc+GGpvL3dGR0REZEflrbSdOtpXXOu4k/UP8bM7gbYm9wTWRyxenOHkm8DVi8DwUcD0hx12sb45vsdJ6YiIaBCwnjq6YfESNRvQjgHaGoDij9wYWe9YvAxUawOQ+6y0fcdGQD2iW5ea+hZ8UWmGQgHMH8/ihYiI5DcvQTrycqbCjCpTc/cOSiWQ2DVw9/TgWrSRxctA5W0Frl0GAmOApFUOu1ir2sQILYJH8CojIiKSX8iIYZjZtcr0i5+VOO6UuEK6v7gfMFe6KbLesXgZiI5W4Nir0vb3/gPwVjnsZi1eUhN41IWIiAaPXy6aAAD44GQlCqxrHX1TYDQw5jYAAjj1V/cG1wMWLwNxbhfQVAcEjAYmLHbYpdMikH9JmgRoLpcDICKiQWRKpBb3Tpem78j6x1kIR4syWs8qnHwT6OxwX3A9YPEyEMdfl+6TVgFe3g67FBvrUd/SAT+VFyaGB7gvNiIioj74eUYC/FReKKqst59x1+qWu6QLUuorgQufuT9AB1i89FV9FbBjLfD3h4F3HwB2PgaUHwWU3g7ndbE6ekk6DDcjOhDeXkw3ERENLkH+aiztmjz1zc/LunfwVgNTVkrbx18DHB2dcTN+m/ZVW5M02rrofeD8h9JSAACQsAgYobvhw6ynjJKjR7kjSiIion77UcoYAMDe8zWo+NrBnC7WU0cX90l/wDc5GB/jRo7PdVB3fkFA2jNSBar0BhqMQPPXwG2P3/AhFovAsTLpFzwrJvCG/YiIiOQUFzICc+JG4Yi+Dn89asDGhQn2HUbFAot+B3yyESjeDVSdAh493G0NP3dh8dJXvtoeCxVHSmoaYGpqx3CVFyaO1rgkLCIiImd4MGUsjujr8O5xAx5Pi8cwn2/NBj/jx8Do6cD7q4Gxt8lWuAAsXlxqf9dqndPHBsKH412IiGgQS50QitFaX1SamvHh6Sosmx7ZvVP4FOCRA4BC3u80fqO6yKXLjXg5Rw8ASL81VOZoiIiIeualVOCBWdLYlzfzyhxfNg0Aan9ANdyNkXXH4sUF2jstePzdQjS3d2J27CjcNyNK7pCIiIh6tXxGJFTeShRV1uOkwSR3ODfE4sUFXjt0CWcqzND4+uB39yZCqVTIHRIREVGvAv1UWJwYDgB4K69M3mB6wOLFySpNzbbTRZvvvAVhGl+ZIyIiIuq7B2ePBQB8/EU1ahta5A3mBli8ONl/fngOze2dmDk2EEumjZY7HCIion6ZOFqDaVFatHcKbMsvlzsch1i8ONHuM1X49KwRXkoFfn33rVAoeLqIiIg8j/Xoy9v5X6G90yJvMA6weHGSanMz/n1HEQBg7R2xSNBxHSMiIvJMCyeGIchfjdqGVvxf3ldyh9MNixcn+cX7X8Dc3I7JERr8LC1e7nCIiIhumspbibXzYgEAv/noHD47a7S1tXdaYLHIu74RixcnOGX4Gge/vAwfLwV+v3wKJ6QjIiKP9/CcsVgxIxIWAfx02ykc0V9BbUMLVr6Wj//er5c1Ns6w6wT/c6gUALB4ymjEBvvLHA0REdHAKRQK/ObuibjS2Ia952vwcPZxBPj64HJDK4qN9fjXWWMw0k8lS2w8RDBAhromfFJUDQD48dxomaMhIiJyHm8vJbaunIq0CSFo7bDgckMrxoX6Y2fmHNkKF4BHXgbEYhF4aU8JLAK4fVwwB+kSEdGQo/b2wp9WJuG5T4thEQJPLRgPP7W85QOLl5vQ1NaB2vpWbN2vx87CKgDAY10Dm4iIiIYalbcST995i9xh2LB46aMqUzPeO1GOg19eRmG5CdaB1koF8OKyRMyKGSVvgERERN8RLhvzUlZWhtWrVyM6Ohq+vr6IjY1FVlYW2traenxcS0sLMjMzMWrUKPj7+2Pp0qWoqalxVZh9Zmpqxx/2XsBJg1S4+Pp4IS7EH688kIQl0yLkDo+IiOg7w2VHXoqLi2GxWPCXv/wFcXFxKCoqwpo1a3Dt2jW8+OKLN3zcE088gY8++gjbt2+HRqPBunXrsGTJEhw5csRVofZJgm4E7p0egWlRI3FbfBAiRsq7HDgREdF3lUII4baZZl544QW88soruHTpksN2s9mM4OBg/O1vf8M999wDQCqCJkyYgLy8PMyaNavXn1FfXw+NRgOz2YyAAA6gJSIi8gT9+f5266XSZrMZgYGBN2wvKChAe3s70tLSbPsSEhIQFRWFvLw8h49pbW1FfX293Y2IiIiGLrcVL3q9Hi+//DJ+8pOf3LCP0WiESqWCVqu12x8aGgqj0ejwMVu2bIFGo7HdIiMjnRk2ERERDTL9Ll42btwIhULR4624uNjuMZWVlcjIyMCyZcuwZs0apwUPAJs2bYLZbLbdyssH5/LdRERE5Bz9HrC7fv16rFq1qsc+MTExtu2qqirMnz8fs2fPxquvvtrj43Q6Hdra2mAymeyOvtTU1ECn0zl8jFqthlqt7nP8RERE5Nn6XbwEBwcjODi4T30rKysxf/58JCUl4Y033oBS2fOBnqSkJPj4+CAnJwdLly4FAJSUlMBgMCAlJaW/oRIREdEQ5LIxL5WVlZg3bx6ioqLw4osv4vLlyzAajXZjVyorK5GQkIBjx44BADQaDVavXo0nn3wS+/fvR0FBAR566CGkpKT06UojIiIiGvpcNs/Lnj17oNfrodfrERFhP4mb9ers9vZ2lJSUoKmpydb2+9//HkqlEkuXLkVrayvS09Pxpz/9yVVhEhERkYdx6zwv7sB5XoiIiDzPoJ3nhYiIiGigWLwQERGRR2HxQkRERB6FxQsRERF5FBYvRERE5FFcdqm0XKwXT3GBRiIiIs9h/d7uy0XQQ654aWhoAAAu0EhEROSBGhoaoNFoeuwz5OZ5sVgsqKqqwogRI6BQKJz63PX19YiMjER5eTnnkHEh5tk9mGf3YJ7dh7l2D1flWQiBhoYGhIeH97qc0JA78qJUKrvN6OtsAQEB/I/hBsyzezDP7sE8uw9z7R6uyHNvR1ysOGCXiIiIPAqLFyIiIvIoLF76Qa1WIysrC2q1Wu5QhjTm2T2YZ/dgnt2HuXaPwZDnITdgl4iIiIY2HnkhIiIij8LihYiIiDwKixciIiLyKCxeiIiIyKOweCEiIiKPwuKlj7Zu3YqxY8di2LBhSE5OxrFjx+QOyaP96le/gkKhsLslJCTY2ltaWpCZmYlRo0bB398fS5cuRU1NjYwRe46DBw/iBz/4AcLDw6FQKLBz5067diEENm/ejLCwMPj6+iItLQ0XLlyw63P16lWsXLkSAQEB0Gq1WL16NRobG934Kga/3vK8atWqbu/xjIwMuz7Mc8+2bNmCGTNmYMSIEQgJCcHdd9+NkpISuz59+awwGAxYtGgRhg8fjpCQEGzYsAEdHR3ufCmDXl9yPW/evG7v6UcffdSuj7tyzeKlD9599108+eSTyMrKwsmTJ5GYmIj09HTU1tbKHZpHu/XWW1FdXW27HT582Nb2xBNP4MMPP8T27dtx4MABVFVVYcmSJTJG6zmuXbuGxMREbN261WH7888/jz/+8Y/485//jPz8fPj5+SE9PR0tLS22PitXrsTZs2exZ88e7N69GwcPHsQjjzzirpfgEXrLMwBkZGTYvce3bdtm18489+zAgQPIzMzE0aNHsWfPHrS3t2PBggW4du2arU9vnxWdnZ1YtGgR2tra8Pnnn+PNN99EdnY2Nm/eLMdLGrT6kmsAWLNmjd17+vnnn7e1uTXXgno1c+ZMkZmZaft3Z2enCA8PF1u2bJExKs+WlZUlEhMTHbaZTCbh4+Mjtm/fbtt3/vx5AUDk5eW5KcKhAYDYsWOH7d8Wi0XodDrxwgsv2PaZTCahVqvFtm3bhBBCnDt3TgAQx48ft/X55JNPhEKhEJWVlW6L3ZN8O89CCPHggw+KxYsX3/AxzHP/1dbWCgDiwIEDQoi+fVZ8/PHHQqlUCqPRaOvzyiuviICAANHa2ureF+BBvp1rIYS44447xM9+9rMbPsadueaRl160tbWhoKAAaWlptn1KpRJpaWnIy8uTMTLPd+HCBYSHhyMmJgYrV66EwWAAABQUFKC9vd0u5wkJCYiKimLOB6i0tBRGo9EutxqNBsnJybbc5uXlQavVYvr06bY+aWlpUCqVyM/Pd3vMniw3NxchISEYP3481q5di7q6Olsb89x/ZrMZABAYGAigb58VeXl5mDRpEkJDQ2190tPTUV9fj7Nnz7oxes/y7Vxbvf322wgKCsLEiROxadMmNDU12drcmesht6q0s125cgWdnZ12vwwACA0NRXFxsUxReb7k5GRkZ2dj/PjxqK6uxjPPPIO5c+eiqKgIRqMRKpUKWq3W7jGhoaEwGo3yBDxEWPPn6P1sbTMajQgJCbFr9/b2RmBgIPPfDxkZGViyZAmio6Nx8eJF/PKXv8TChQuRl5cHLy8v5rmfLBYLHn/8ccyZMwcTJ04EgD59VhiNRofvd2sbdeco1wBw//33Y8yYMQgPD8eZM2fwi1/8AiUlJfjggw8AuDfXLF5IFgsXLrRtT548GcnJyRgzZgzee+89+Pr6yhgZkXOsWLHCtj1p0iRMnjwZsbGxyM3NRWpqqoyReabMzEwUFRXZjY0j17hRrr85HmvSpEkICwtDamoqLl68iNjYWLfGyNNGvQgKCoKXl1e30es1NTXQ6XQyRTX0aLVajBs3Dnq9HjqdDm1tbTCZTHZ9mPOBs+avp/ezTqfrNhi9o6MDV69eZf4HICYmBkFBQdDr9QCY5/5Yt24ddu/ejf379yMiIsK2vy+fFTqdzuH73dpG9m6Ua0eSk5MBwO497a5cs3jphUqlQlJSEnJycmz7LBYLcnJykJKSImNkQ0tjYyMuXryIsLAwJCUlwcfHxy7nJSUlMBgMzPkARUdHQ6fT2eW2vr4e+fn5ttympKTAZDKhoKDA1mffvn2wWCy2Dyvqv4qKCtTV1SEsLAwA89wXQgisW7cOO3bswL59+xAdHW3X3pfPipSUFHzxxRd2heKePXsQEBCAW265xT0vxAP0lmtHCgsLAcDuPe22XDt1+O8Q9c477wi1Wi2ys7PFuXPnxCOPPCK0Wq3diGrqn/Xr14vc3FxRWloqjhw5ItLS0kRQUJCora0VQgjx6KOPiqioKLFv3z5x4sQJkZKSIlJSUmSO2jM0NDSIU6dOiVOnTgkA4qWXXhKnTp0SX331lRBCiGeffVZotVqxa9cucebMGbF48WIRHR0tmpubbc+RkZEhpk6dKvLz88Xhw4dFfHy8uO++++R6SYNST3luaGgQTz31lMjLyxOlpaVi7969Ytq0aSI+Pl60tLTYnoN57tnatWuFRqMRubm5orq62nZramqy9ents6Kjo0NMnDhRLFiwQBQWFopPP/1UBAcHi02bNsnxkgat3nKt1+vFr3/9a3HixAlRWloqdu3aJWJiYsTtt99uew535prFSx+9/PLLIioqSqhUKjFz5kxx9OhRuUPyaMuXLxdhYWFCpVKJ0aNHi+XLlwu9Xm9rb25uFo899pgYOXKkGD58uPjhD38oqqurZYzYc+zfv18A6HZ78MEHhRDS5dJPP/20CA0NFWq1WqSmpoqSkhK756irqxP33Xef8Pf3FwEBAeKhhx4SDQ0NMryawaunPDc1NYkFCxaI4OBg4ePjI8aMGSPWrFnT7Q8e5rlnjvILQLzxxhu2Pn35rCgrKxMLFy4Uvr6+IigoSKxfv160t7e7+dUMbr3l2mAwiNtvv10EBgYKtVot4uLixIYNG4TZbLZ7HnflWtEVNBEREZFH4JgXIiIi8igsXoiIiMijsHghIiIij8LihYiIiDwKixciIiLyKCxeiIiIyKOweCEiIiKPwuKFiIiIPAqLFyIiIvIoLF6IiIjIo7B4ISIiIo/y/4qDjQdfSbDEAAAAAElFTkSuQmCC"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 2
+ "execution_count": 54
},
{
"cell_type": "markdown",
"source": [
- "### Lynx\n",
+ "### BasicMotions\n",
"\n",
- "The annual numbers of lynx trappings for 1821β1934 in Canada. This\n",
- " time-series records the number of skins of predators (lynx) that were collected\n",
- " over several years by the Hudson's Bay Company. Returns a pd.Series"
+ "The data was generated as part of a student project where four students performed our activities whilst wearing a smart watch.\n",
+ "The watch collects 3D accelerometer and a 3D gyroscope It consists of four classes, which are walking, resting, running and\n",
+ "badminton. Participants were required to record motion a total of five times, and the data is sampled once every tenth of a second,\n",
+ "for a ten second period. The data is multivariate (six channels) equal length."
],
"metadata": {
"collapsed": false
@@ -177,50 +201,56 @@
{
"cell_type": "code",
"source": [
- "from aeon.datasets import load_lynx\n",
+ "from aeon.datasets import load_basic_motions\n",
"\n",
- "lynx = load_lynx()\n",
- "plot_series(lynx)"
+ "motions, motions_labels = load_basic_motions(split=\"train\")\n",
+ "plt.title(\n",
+ " f\"First and second dimensions of the first train instance in BasicMotions data, \"\n",
+ " f\"(student {motions_labels[0]})\"\n",
+ ")\n",
+ "plt.plot(motions[0][0])\n",
+ "plt.plot(motions[0][1])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:19.214127Z",
- "start_time": "2024-09-25T22:58:19.016116Z"
+ "end_time": "2024-09-25T22:58:21.053382Z",
+ "start_time": "2024-09-25T22:58:20.879846Z"
}
},
"outputs": [
{
"data": {
- "text/plain": [
- "(, )"
- ]
+ "text/plain": "[]"
},
- "execution_count": 3,
+ "execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 3
+ "execution_count": 55
},
{
"cell_type": "markdown",
"source": [
- "### PBS_dataset\n",
+ "### GunPoint\n",
"\n",
- "The Pharmaceutical Benefits Scheme (PBS) is the Australian government drugs\n",
- " subsidy scheme. Data comprises of the numbers of scripts sold each month for immune sera\n",
- " and immunoglobulin products in Australia. The load function returns a pd.Series."
+ "This dataset involves one female actor and one male actor making a motion with their\n",
+ "hand. The two classes are: Gun-Draw and Point: For Gun-Draw the actors have their\n",
+ "hands by their sides. They draw a replicate gun from a hip-mounted holster, point it\n",
+ "at a target for approximately one second, then return the gun to the holster, and\n",
+ "their hands to their sides. For Point the actors have their gun by their sides. They\n",
+ "point with their index fingers to a target for approximately one second, and then\n",
+ "return their hands to their sides. For both classes, The data in the archive is the\n",
+ "X-axis motion of the actors right hand.\n"
],
"metadata": {
"collapsed": false
@@ -229,49 +259,53 @@
{
"cell_type": "code",
"source": [
- "from aeon.datasets import load_PBS_dataset\n",
+ "from aeon.datasets import load_gunpoint\n",
"\n",
- "pbs = load_PBS_dataset()\n",
- "plot_series(pbs)"
+ "gun, gun_labels = load_gunpoint(split=\"test\")\n",
+ "plt.title(\n",
+ " f\"First three cases of the test set for GunPoint, classes\"\n",
+ " f\"(actor {gun_labels[0]}, {gun_labels[1]}, {gun_labels[2]})\"\n",
+ ")\n",
+ "plt.plot(gun[0][0])\n",
+ "plt.plot(gun[1][0])\n",
+ "plt.plot(gun[2][0])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:19.433684Z",
- "start_time": "2024-09-25T22:58:19.242052Z"
+ "end_time": "2024-09-25T22:58:21.247394Z",
+ "start_time": "2024-09-25T22:58:21.075323Z"
}
},
"outputs": [
{
"data": {
- "text/plain": [
- "(, )"
- ]
+ "text/plain": "[]"
},
- "execution_count": 4,
+ "execution_count": 56,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 4
+ "execution_count": 56
},
{
"cell_type": "markdown",
"source": [
- "### ShampooSales\n",
- "\n",
- "ShampooSales contains a single monthly time series of the number of sales of\n",
- "shampoo over a three year period. The units are a sales count."
+ "### ItalyPowerDemand\n",
+ "The data was derived from twelve monthly electrical power demand time series from\n",
+ "Italy and first used in the paper \"Intelligent Icons: Integrating Lite-Weight Data\n",
+ "Mining and Visualization into GUI Operating Systems\". The classification task is to\n",
+ "distinguish days from Oct to March (inclusive) (class 0) from April to September\n",
+ "(class 1). The problem is univariate, equal length.\n"
],
"metadata": {
"collapsed": false
@@ -280,62 +314,59 @@
{
"cell_type": "code",
"source": [
- "from aeon.datasets import load_shampoo_sales\n",
+ "from aeon.datasets import load_italy_power_demand\n",
"\n",
- "shampoo = load_shampoo_sales()\n",
- "print(type(shampoo))\n",
- "plot_series(shampoo)"
+ "italy, italy_labels = load_italy_power_demand(split=\"train\")\n",
+ "plt.title(\n",
+ " f\"First three cases of the test set for ItalyPowerDemand, classes\"\n",
+ " f\"( {italy_labels[0]}, {italy_labels[1]}, {italy_labels[2]})\"\n",
+ ")\n",
+ "plt.plot(italy[0][0])\n",
+ "plt.plot(italy[1][0])\n",
+ "plt.plot(italy[2][0])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:19.594227Z",
- "start_time": "2024-09-25T22:58:19.439671Z"
+ "end_time": "2024-09-25T22:58:21.419932Z",
+ "start_time": "2024-09-25T22:58:21.266319Z"
}
},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- },
{
"data": {
- "text/plain": [
- "(, )"
- ]
+ "text/plain": "[]"
},
- "execution_count": 5,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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vb+73/pycHEWj0SjPP/98iceUJDY2VgGUN954o8zPvZVWrVoV+d6pLIMHD1ZcXFyUGzduFHnst99+UwBl7969pT5fucbwvvrqKzZs2FDgdjtPPfVUgf8bs+h169aRnp5enjDKzMnJqUD9ho2NDV27duXixYtFjn344YcL1D8cOXKEc+fOcf/99xMfH09cXBxxcXGkpaUxaNAgtm/fjsFgQFEUli5dysiRI1EUxXRcXFwcoaGhJCUlFRg+Lmz58uUYDAZmz55dZIg1f1Fg/thSUlKIi4ujT58+pKenc/r0aaB0X+Nly5ZhMBgYP358gVh9fX1p0qQJW7ZsKfW5inPgwAGuX7/O1KlTC4wcDB8+nObNmxeYkqlMgwcPLjCi1rZtW1xcXEyvdUVfp5KU5bxubm5ERESwf//+SvmcK/P1riyrV69Gp9MxY8aMAvc///zzKIrCmjVrCtzfr18/WrZsWakxQMGfl5ycHOLj42ncuDFubm63fZ0nTpzI3r17uXDhgum+X3/9lcDAQPr161fg2NOnT+Pl5YWXlxctWrTgiy++YPjw4aZp6dWrV9O1a1d69+5teo6TkxOPP/44ly9f5tSpUwD06dMHvV7P7t27AXXkq0+fPvTp04cdO3YAcOLECRITE02j/wkJCWzevJnx48eb3hPi4uKIj48nNDSUc+fOFSgHALXepTSF1YU5OTmZVk+W57qTJ08u8H7WrVs3FEVh8uTJpvt0Oh2dO3cu8v6c/7W8ceMGSUlJ9OnTp9jX8XbvA3q9nnXr1jFq1CgaNGhgOq5FixaEhoaW+etitHTpUtq1a1dkJBAKvo8Xlv9zy8zMJC4uju7duwMU+Pzc3NzYu3cv165dK/Y85n7vT0hIQFEU3N3dS/zcaov33nuPjRs38p///KfYmljj1+BWU9CFlSsR69q1K4MHDy5wuxUrK6si9QfBwcHMnDmT77//Hk9PT0JDQ/nqq69KVW9UXgEBAUV+CNzd3blx40aRYwuvCj137hygJmjGN1rj7fvvvycrK4ukpCRiY2NJTEzk22+/LXLcpEmTgJtF6cW5cOECWq32tr+MTp48yejRo3F1dcXFxQUvLy9Tkmn8Gpbma3zu3DkURaFJkyZF4g0LCzPFWt7X68qVKwA0a9asyGPNmzc3PV7Z8r+pGuV/rSv6OpWkLOd9+eWXcXJyomvXrjRp0oRp06YVW29YWpX5eleWK1eu4Ofnh7Ozc4H7W7RoYXq88OdQFTIyMpg9e7apTs3T0xMvLy8SExNv+z187733Ymtra+qNlJSUxMqVK3nggQeKvJ8EBQWxYcMGNm7cyM6dO4mOjmblypWmKYwrV64U+7NQ+OvRsWNHHBwcTEmXMRHr27cvBw4cIDMz0/SYMak7f/48iqLw+uuvF3lt33jjDaDo93R5v96pqamm17Q81y3882n8ZR8YGFjk/sLvzytXrqR79+7Y2dnh4eGBl5cX33zzTbGvY2neBzIyMmjSpEmR44p7nUrrwoUL5SoWT0hI4JlnnsHHxwd7e3u8vLxMr1H+z++DDz7gxIkTBAYG0rVrV958880CCaul3vuVQvV8tc2iRYt47bXXmDx5cpHBJSPj16AsPejKVSNWVra2tsUW0H300Uc88sgjrFixgvXr1zNjxgzmzp3Lv//+W6rC0bIq6S+/4r55Cq8GMvYB+vDDD0tsjeHk5GQqqnzwwQdNxeeFVXTZd2JiIv369cPFxYU5c+YQEhKCnZ0dhw4d4uWXXy7Qs+h2X2ODwYBGo2HNmjXFfn3y1wWZ+/WqiNu91savUWW/TmU5b4sWLThz5gwrV65k7dq1LF26lK+//prZs2fz1ltvlfnaULmvtyVU1Sq8p59+mh9//JFnn32WHj164OrqikajYcKECbft8eXu7s6IESP49ddfmT17NkuWLCErK6vA6LqRo6Pjbf8wLQ1ra2u6devG9u3bOX/+PNHR0fTp0wcfHx9ycnLYu3cvO3bsoHnz5qaaQePn8cILL5Q4mtO4ceMC/y/P1zsiIoKkpCTTucpz3ZJ+Pou7P//7844dO7jrrrvo27cvX3/9NfXr18fa2poff/zRtMilNNeprgnD+PHj2b17Ny+++CLt27fHyckJg8HAnXfeWeD7dPz48fTp04e//vqL9evX8+GHH/L++++zbNkyU72zOd/7PTw80Gg0xQ5q1BYbNmxg4sSJDB8+nPnz55d4nPFrcKu6+cLMkojdSps2bWjTpg2vvfYau3fvplevXsyfP5933nkHKFtWWZVdkI3D2y4uLrd8o/Xy8sLZ2Rm9Xl+uN+SQkBAMBgOnTp0qMeHbunUr8fHxLFu2jL59+5ruv3TpUrHH3+prHBISgqIoBAcH07Rp09vGd7vXq7CGDRsCcObMGQYOHFjgsTNnzpgeL6uKvtYVfZ1KiqGs53V0dOTee+/l3nvvJTs7m3vuuYd3332XWbNmmXYXKKvKer0r4+epYcOGbNy4kZSUlAKjYsbp8/K+/iUpKeYlS5bw8MMP89FHH5nuy8zMLHWj3IkTJ3L33Xezf/9+fv31Vzp06ECrVq3KHF/Dhg05c+ZMkfuL+3r06dOH999/n40bN+Lp6Unz5s3RaDS0atWKHTt2sGPHDkaMGGE6vlGjRoCaxFVGMliShQsXApiSLnNdF9QpPzs7O9atW4etra3p/h9//LFc5/Py8sLe3t4045Ffca9TaYWEhJS5w/qNGzfYtGkTb731VoGmoMXFBlC/fn2mTp3K1KlTuX79Oh07duTdd98tsPDMXO/9VlZWhISElPg7qKbbu3cvo0ePpnPnzvz5559YWZWcOl26dAmtVluqr6mRxRpjJScnk5ubW+C+Nm3aoNVqycrKMt3n6OhY6jdLYw+cquhC3qlTJ0JCQpg3b16BpeJGxqXpOp2OMWPGsHTp0mJ/EItbBp/fqFGj0Gq1zJkzp8hf6sa/4ox/veT/qy47O5uvv/66wPGl+Rrfc8896HQ63nrrrSJ/JSqKYhrhK+3rVVjnzp3x9vZm/vz5BY5bs2YNYWFhRVY5lVZFX+uKvk7GGApfvyznLbwk3cbGhpYtW6IoCjk5OaZrQOk+z8p8vY3XrmipwLBhw9Dr9Xz55ZcF7v/kk0/QaDRFVitXVEnvFzqdrsjn+8UXXxRp91KSoUOH4unpyfvvv8+2bduKHQ0rjWHDhrFv3z727Nljui8tLY1vv/2WoKCgAiUJffr0ISsri08//ZTevXubksw+ffqwcOFCrl27ZqoPA/D29qZ///7897//JSoqqsi1S/M9fTubN2/m7bffJjg42NSGyBzXNdLpdGg0mgKv2+XLl8u9m4JOpyM0NJTly5dz9epV0/1hYWEVajI6ZswYjh49yl9//VXksZJG44p7XweK7G6h1+uL/Fx6e3vj5+dn+jm3xHt/jx49OHDgQLGfW01m/D0VFBTEypUrbzuKfPDgQVq1anXLTgKFWWxEbPPmzUyfPp1x48bRtGlTcnNzWbhwoekXmVGnTp3YuHEjH3/8MX5+fgQHB9OtW7dizxkSEoKbmxvz58/H2dkZR0dHunXrVil1J1qtlu+//56hQ4fSqlUrJk2ahL+/P5GRkWzZsgUXFxf++ecfAP7zn/+wZcsWunXrxpQpU2jZsiUJCQkcOnSIjRs3kpCQUOJ1GjduzKuvvsrbb79Nnz59uOeee7C1tWX//v34+fkxd+5cevbsibu7Ow8//DAzZsxAo9GwcOHCIj9Mpfkah4SE8M477zBr1iwuX77MqFGjcHZ25tKlS/z11188/vjjvPDCC6V+vQqztrbm/fffZ9KkSfTr14/77rvP1L4iKCio3NvgdOrUCYBXX32VCRMmYG1tzciRI8vUKLgir5MxhuK+N0t73iFDhuDr60uvXr3w8fEhLCyML7/8kuHDh5tGj8ryeVbm62289qJFi5g5cyZdunTBycmJkSNHlvrrCzBy5EgGDBjAq6++yuXLl2nXrh3r169nxYoVPPvss2XqPl0aJb0mI0aMYOHChbi6utKyZUv27NnDxo0bTS0Mbsfa2poJEybw5ZdfotPpuO+++8oV3yuvvMLvv//O0KFDmTFjBh4eHvz0009cunSJpUuXFijh6NGjB1ZWVpw5c8bUjgOgb9++fPPNNwAFEjFQF1L17t2bNm3aMGXKFBo1akRMTAx79uwhIiKCo0ePljrWNWvWcPr0aXJzc4mJiWHz5s1s2LCBhg0b8vfffxdYfFOZ172V4cOH8/HHH3PnnXdy//33c/36db766isaN27MsWPHynXOt956i7Vr19KnTx+mTp1Kbm6uqb9f4XO++eabvPXWW2zZsuWW2+e8+OKLLFmyhHHjxvHoo4/SqVMnEhIS+Pvvv5k/fz7t2rUr8hwXFxf69u3LBx98QE5ODv7+/qxfv77IKFNKSgoBAQGMHTuWdu3a4eTkxMaNG9m/f79pxNcS7/133303Cxcu5OzZs6UaDVq4cCFXrlwxLQDYvn27aWbloYceMo0Ob926lQEDBvDGG2/cdhuk7du3m9q+xMbGkpaWZjpn3759C8wgaTQa+vXrd8t9ZVNSUggNDeXGjRu8+OKLRRaXhYSE0KNHD9P/c3JyTL35yqTU6yuV2y9rLql9haOjY5FjL168qDz66KNKSEiIYmdnp3h4eCgDBgxQNm7cWOC406dPK3379lXs7e0V4LatLFasWKG0bNlSsbKyKhBLcUvdjfHlX6JvXL5e0lLdw4cPK/fcc49Sr149xdbWVmnYsKEyfvx4ZdOmTQWOi4mJUaZNm6YEBgYq1tbWiq+vrzJo0CDl22+/vWX8Rj/88IPSoUMHxdbWVnF3d1f69eunbNiwwfT4rl27lO7duyv29vaKn5+f8tJLLynr1q0rsKS+tF9jRVGUpUuXKr1791YcHR0VR0dHpXnz5sq0adOUM2fOlPlcxVm0aJHp8/Hw8FAeeOABJSIiosAxZWlfoSiK8vbbbyv+/v6KVqst0OIBKHZpd8OGDYt8/1TkdbrV92Zpzvvf//5X6du3r+l7KSQkRHnxxReVpKSkUn2ehVXm660oipKamqrcf//9ipubmwLctpVFST9jKSkpynPPPaf4+fkp1tbWSpMmTZQPP/ywQPsCRSn5dSvL9Up6TW7cuKFMmjRJ8fT0VJycnJTQ0FDl9OnTRb4nimtfYbRv3z4FUIYMGVKmz7+wCxcuKGPHjlXc3NwUOzs7pWvXrsrKlSuLPbZLly5FlsJHREQogBIYGFji+SdOnKj4+voq1tbWir+/vzJixAhlyZIlpmNu9bNWuE2RjY2N4uvrq9xxxx3KZ599ZmqpUZnXfeONNxRAiY2NLXB/cb8//ve//ylNmjRRbG1tlebNmys//vij6fn5leV9YNu2bUqnTp0UGxsbpVGjRsr8+fOLPefzzz+vaDQaJSwsrNivQX7x8fHK9OnTFX9/f8XGxkYJCAhQHn74YVNbm+J+X0ZERCijR49W3NzcFFdXV2XcuHHKtWvXCrR3yMrKUl588UWlXbt2irOzs+Lo6Ki0a9dO+frrr03nscR7f1ZWluLp6am8/fbbt/3aKMrNllLF3fL//P3zzz8KoMyfP/+25zS+ZsXd8rfHSElJUQBlwoQJtzyf8TUq6Vb4+2jNmjUKoJw7d65UXwMjjaJU06pFIYSoRo4ePUr79u35+eefeeihhywdjrCArl270rBhQxYvXmzpUKqlt99+mx9//JFz586Vqy1KcV566SV+//13zp8/X6AusCJWr17NiBEjOHr0KG3atKmUc4JaWqTRaIqdkr4V2TxRCCFK4bvvvsPJyYl77rnH0qEIC0hOTubo0aNl2kOwrnnuuedITU3ljz/+qLRzbtmyhddff73SkjDjOSdMmFCpSVhYWBgrV67k7bffLvNzZURMCCFu4Z9//uHUqVO8/vrrTJ8+nY8//tjSIQkhahFJxIQQ4haCgoKIiYkhNDSUhQsXFmlOK4QQFSGJmBBCCCGEhUiNmBBCCCGEhUgiJoQQQghhIRbf4qiqGQwGrl27hrOzc5VugSSEEEKIyqMoCikpKfj5+RW7X3VtYdZEbO7cuSxbtozTp09jb29Pz549ef/992+5y/2CBQuYNGlSgftsbW3JzMws1TWvXbtGYGBgheIWQgghhGWEh4cTEBBg6TCqjFkTsW3btjFt2jS6dOlCbm4u//d//8eQIUM4derULbemcXFxKbABa1lGtowrnMLDw3FxcSl/8EIIIYQwm+TkZAIDA2v9SmWzJmJr164t8P8FCxbg7e3NwYMHC+wBVZhGo8HX17dc1zQmbS4uLpKICSGEEDVMbS8rsuikq3EHeQ8Pj1sel5qaSsOGDQkMDOTuu+/m5MmTJR6blZVFcnJygZsQQgghRHVksUTMYDDw7LPP0qtXL1q3bl3icc2aNeOHH35gxYoV/PLLLxgMBnr27ElERESxx8+dOxdXV1fTTerDhBBCCFFdWayh61NPPcWaNWvYuXNnmYrwcnJyaNGiBffdd1+xezplZWWRlZVl+r9xjjkpKUmmJoUQQogaIjk5GVdX11r/+9si7SumT5/OypUr2b59e5lXQlhbW9OhQwfOnz9f7OO2traVujmoEEIIIURVMevUpKIoTJ8+nb/++ovNmzcTHBxc5nPo9XqOHz9O/fr1qyBCIYQQQgjzMeuI2LRp0/jtt99YsWIFzs7OREdHA+Dq6oq9vT0AEydOxN/fn7lz5wIwZ84cunfvTuPGjUlMTOTDDz/kypUrPPbYY+YMXQghhBCi0pk1Efvmm28A6N+/f4H7f/zxRx555BEArl69WqCD7o0bN5gyZQrR0dG4u7vTqVMndu/eTcuWLc0VthBCCCFElbBYsb651JViPyGEEKI2qSu/v2vv5k1CCCGEENWcJGJCCCGEEBYiiZgQQgghhIVIIiaEEEIIYSGSiAkhhBDVWLY+m59O/sT2iO3oDXpLhyMqmUU66wshhBCidJafX868A/MAqO9YnzFNxjC6yWi8HbwtHJmoDDIiJoQQQlRjl5IumT6OSoviyyNfMmTJEJ7d8iy7IndhUAwWjE5UlIyICSGEENXYtdRrALzQ+QXq2ddj8ZnFHLp+iE1XN7Hp6iYCnAIY03QMoxqPwtPe08LRirKSREwIIYSoxq6lqYlYsGswfQP6MqLRCM7fOM+Sc0v4+/zfRKRG8Nmhz/jqyFcMDBzIuGbj6OrbFa1GJr1qAumsL4QQQlRjvX7vRXJ2MsvuWkYT9yYFHsvIzWDd5XUsPruYY7HHTPc3dGnI2CZjubvx3bjbuZs75EpRV35/SyImhBBCVFMp2Sn0/L0nAP/e/y+O1o4lHnsm4QyLzy5m5cWVpOWkAWCttWZww8GMbzqeTj6d0Gg0Zom7MtSV39+SiAkhhBDV1JmEM4z9Zyxutm7smLCjVM9Jz0lnzaU1/Hn2T07FnzLd38i1EWObjuWukLtwtXWtqpArTV35/S0TyEIIIUQ1ZSzU93PyK/VzHKwdGNN0DItGLOKPEX8wpskY7K3suZh0kQ/2f8CgxYN45993yDXkVlXYogwkERNCCCGqKWOhvp9j6ROx/FrVa8WbPd9k87jNvNbtNZq5NyNLn8WiM4tYdXFVZYYqykkSMSGEEKKaKs+IWHGcbJy4t/m9LB65mMfaPAYgiVg1IYmYEEIIUU1VViJmpNFoGN14NAB7o/cSlxFXKecV5SeJmBBCCFFNVXRqsjgNXBrQ1rMtBsXA2ktrK+28onwkERNCCCGqqcoeETMa1mgYINOT1YEkYkIIIUQ1lJaTRmJWIlD5iVhoUCg6jY4T8Se4knylUs8tykYSMSGEEKIaMo6Gudi44GzjXKnn9rT3pHv97gCsvri6Us8tykYSMSGEEKIaikqLAip/NMxoeKPhAKy6tIpa3tu9WpNETAghhKiGIlMjgcot1M9vYIOB2OnsuJJ8pUAHfmFekogJIYQQ1VBVFeobOVo70j+wPwArL66skmuI25NETAghhKiGjCNi/k7+VXYN4/Tk2str0Rv0VXYdUTJJxIQQQohqKCpVrRGr71S/yq7Ry68XrrauxGXEsTd6b5VdR5RMEjEhhBCiGjI2c63KETFrnTVDGg4BZPWkpUgiJoQQQlQz6TnpJGQmAFVXI2ZknJ7ceHUjmbmZVXotUZQkYkIIIUQ1Y2xd4WztjIuNS5Veq4N3B+o71ictJ41tEduq9FqiKEnEhBBCiGrGuGKyKuvDjLQaLUODhwIyPWkJkogJIYQQ1UxVt64ozDg9uSNyB0lZSWa5plBJIiaEEEJUM5FpVd+6Ir+m7k1p4t6EHEMOG65sMMs1hcqsidjcuXPp0qULzs7OeHt7M2rUKM6cOXPb5y1evJjmzZtjZ2dHmzZtWL1ahk6FEELUXqYRsSrqql+cYcHDAFh9SX7HmpNZE7Ft27Yxbdo0/v33XzZs2EBOTg5DhgwhLS2txOfs3r2b++67j8mTJ3P48GFGjRrFqFGjOHHihBkjF0IIIczH2EPMXFOTcDMROxB9gOi0aLNdt67TKBbc6TM2NhZvb2+2bdtG3759iz3m3nvvJS0tjZUrb26/0L17d9q3b8/8+fNve43k5GRcXV1JSkrCxaVqV54IIYQQlaH/ov7EZ8azaMQiWtZrabbrPrzmYQ5dP8TMTjOZ1HqS2a5bnLry+9uiNWJJSWpBoIeHR4nH7Nmzh8GDBxe4LzQ0lD179hR7fFZWFsnJyQVuQgghRE2RmZtJfGY8YL4aMSNj0b5MT5qPxRIxg8HAs88+S69evWjdunWJx0VHR+Pj41PgPh8fH6Kjix82nTt3Lq6urqZbYGBgpcYthBBCVCVjR30HK4cq7yFW2JCGQ7DSWHE64TQXEi+Y9dp1lcUSsWnTpnHixAn++OOPSj3vrFmzSEpKMt3Cw8Mr9fxCCCFEVcpfH6bRaMx6bTc7N3r79wZg1cVVZr12XWWRRGz69OmsXLmSLVu2EBAQcMtjfX19iYmJKXBfTEwMvr6+xR5va2uLi4tLgZsQQghRU0Smmrd1RWHDGt1cPWnBMvI6w6yJmKIoTJ8+nb/++ovNmzcTHBx82+f06NGDTZs2Fbhvw4YN9OjRo6rCFEIIISzG3M1cC+sf2B8HKwciUyM5GnvUIjHUJWZNxKZNm8Yvv/zCb7/9hrOzM9HR0URHR5ORkWE6ZuLEicyaNcv0/2eeeYa1a9fy0Ucfcfr0ad58800OHDjA9OnTzRm6EEIIYRbGGjFz9hDLz97KnkENBgGw8uLK2xwtKsqsidg333xDUlIS/fv3p379+qbbokWLTMdcvXqVqKgo0/979uzJb7/9xrfffku7du1YsmQJy5cvv2WBvxBCCFFTWXpEDG5OT66/vJ4cQ47F4qgLrMx5sdLMNW/durXIfePGjWPcuHFVEJEQQghRvRgTMUvViAF0r98dDzsPEjIT2HNtD30Diu/1KSpO9poUQgghqoksfRaxGbGAZUfErLRW3Bl0JyCrJ6uaJGJCCCFENWHcWsjeyh43WzeLxmJs7rolfAvpOekWjaU2k0RMCCGEqCaMrSv8HM3fQ6ywNp5tCHQOJCM3gy3hWywaS20miZgQQghRTVSHQn0jjUZj2ghcpierjiRiQgghRDVRnRIxuLl6cve13SRkJlg4mtpJEjEhhBCimjD1EKsmiVgj10a08GiBXtGz/vJ6S4dTK0kiJoQQQlQT1W1EDG4W7cv0ZNWQREwIIYSoJkz7TDparodYYUODh6JBw5HYI0SkRFg6nFpHEjEhhBCiGsjR5xCbbvkeYoV5O3jT1bcrAGsurbFwNLWPJGJCCCFENRCdFo2Cgp3ODg87D0uHU0D+6cnS7JIjSk8SMSGEEKIaiExTpyXrO9W3eA+xwgY3HIyN1oYLSRc4c+OMpcOpVSQRE0IIIaqB6liob+Rs42zab3L1xdUWjqZ2kURMCCGEqAbyd9WvjozTk6svrcagGCwcTe0hiZgQQghRDUSlRgHVc0QMoE9AH5ytnYlJj+FgzEFLh1NrSCImhBBCVAOm1hVO1ad1RX62OlvuCLoDkJ5ilUkSMSGEEKIaqG5d9Ytj3Hty/ZX1ZOuzLRxN7SCJmBBCCGFhOYYcrqdfB6pvjRhAZ5/OeNt7k5Kdwo7IHZYOp1aQREwIIYSwsJi0GAyKARutDfXs61k6nBLptDqGBg8FZPVkZZFETAghhLCw/K0rtJrq/at5WCN1enJbxDZSs1MtHE3NV71fbSGEEKIOMLWuqMb1YUYtPFoQ7BpMlj6LjVc3WjqcGk8SMSGEEMLCjIX69R3rWziS29NoNAwPzuspJtOTFSaJmBBCCGFhxqnJ6tq6ojDj6sm90XuJy4izcDQ1myRiQgghhIVV5+2NihPoEkhbr7YYFANrLq2xdDg1miRiQgghhIXVtBExwDQ9Kc1dK0YSMSGEEMKCcg25xKTHADWjRswoNCgUnUbHyfiTXE66bOlwaixJxIQQQggLup5+Hb2ix0prhZeDl6XDKbV69vXo7tcdUDcCF+UjiZgQQghhQabWFY7Vv4dYYfmnJxVFsXA0NVPNesWFEEKIWqamFernN7DBQHwcfOhavysZuRmWDqdGsrJ0AEIIIURdVhM2+y6Jo7Uj68eur3EjedWJfOWEEEIICzKNiFXjzb5vRZKwipGvnhBCCGFBNXlqUlScWROx7du3M3LkSPz8/NBoNCxfvvyWx2/duhWNRlPkFh0dbZ6AhRBCiCpWk/aZFJXPrIlYWloa7dq146uvvirT886cOUNUVJTp5u3tXUURCiGEEOajN+iJSVN7iNWkZq6i8pi1WH/o0KEMHTq0zM/z9vbGzc2t8gMSQgghLCg2I5ZcJRcrjRVe9jWnh5ioPDWiRqx9+/bUr1+fO+64g127dt3y2KysLJKTkwvchBBCiOrIOC3p6+iLTquzcDTCEqp1Ila/fn3mz5/P0qVLWbp0KYGBgfTv359Dhw6V+Jy5c+fi6upqugUGBpoxYiGEEKL0pFBfVOs+Ys2aNaNZs2am//fs2ZMLFy7wySefsHDhwmKfM2vWLGbOnGn6f3JysiRjQgghqiVJxES1TsSK07VrV3bu3Fni47a2ttja2poxIiGEEDXZ2ZgUftt7leORSbwzqjUt6ruY7do1uZmrqBw1LhE7cuQI9evXnN3phRBCVD9ZuXrWnojm13+vsu9ygun+WcuOs+ypnmi1GrPEYawRkxWTdZdZE7HU1FTOnz9v+v+lS5c4cuQIHh4eNGjQgFmzZhEZGcnPP/8MwKeffkpwcDCtWrUiMzOT77//ns2bN7N+/Xpzhi2EEKKWuBKfxm/7rrL4QAQJadkA6LQaBrfwZue5OI6EJ/LPsWvc3d48iZFxarK+owww1FVmTcQOHDjAgAEDTP831nI9/PDDLFiwgKioKK5evWp6PDs7m+eff57IyEgcHBxo27YtGzduLHAOIYQQ4lZy9QY2hl3n171X2HEuznR/fVc7JnRpwISugfi42PHVlvN8uO4M7685zZCWvtjbVO0qRoNiICotCpARsbpMoyiKYukgqlJycjKurq4kJSXh4mK+eX8hhBCWFZWUwR/7wvlj/1VikrMA0GigX1MvHujWkAHNvLDS3WwekJmjZ9BH24hMzOD5O5ry9KAmVRpfTFoMg5cMRqfRceDBA1hpa1y1UJWqK7+/5VUXQghRaxgMCjvOx/HLv1fYFBaDIW+ooZ6jDeO7BHJflwY0qOdQ7HPtrHW8PLQ5M34/zNdbLzC+izpSVlWMhfq+jr6ShNVh8soLIYSo8eJSs1h8IILf9l0hPCHDdH+3YA8e6N6Q0FY+2FrdfqpxZNv6LNh1iUNXE/lw3RnmjWtXZTEbC/WlPqxuk0RMCCFEjaQoCvsuJfDr3qusORFFjl4d/nKxs2JMpwAe6NaAxt7OZTqnRqPh9REtGf31bpYeiuCRnkG09netivCJSlXrw6R1Rd0miZgQQogaJzUrl4n/28uhq4mm+9oFuvFAtwaMbOtXoUL7Dg3cGdXej+VHrjFn5SkWPd4djaby21lI6woBkogJIYSogVYevcahq4nYW+sY1cGPB7o1rNSRq5fubM7ak9Hsu5TAupPR3Nm68qcPpau+gGq+16QQQghRnA2nYgCY2j+Eufe0rfTpQz83ex7v0wiA91afJitXX6nnB0ytK/wcJRGryyQRE0IIUaOkZ+ey87zaD2xwS58qu84T/ULwdrblakI6P+2+XKnnNigGGRETgCRi5RadFs3nhz7nvb3vWToUIYSoU3aciyMr10CAuz3NfctWjF8WjrZWvBjaDIAvNp0nPjWr0s4dnxFPtiEbrUaLj2PVJZOi+pNErJzSc9P57vh3LDm7hLScNEuHI4QQdcbGvGnJO1r6VEkRfX5jOgbQ2t+FlKxcPtl4ttLOayzU93bwxlprXWnnFTWPJGLl1Mi1EUEuQeQYctgZudPS4QghRJ2gNyhsPn0dgDtaVP1Iklar4fXhLQH4be9VzkSnVMp5pT5MGEkiVgEDAtU9Lzdf3WzhSIQQom44dPUG8WnZuNhZ0SXYwyzX7NaoHne28sWgwDurTlEZOwNK6wphJIlYBQxsMBCAHZE7yDHkWDgaISwnOimTx346wMw/j5CrN1g6HFGLGaclBzT3xlpnvl9hs4Y1x0anZce5OLaeia3w+aRQXxhJIlYBbTzb4GHnQUp2CgdjDlo6HCEs4tDVG4z8cicbw2JYdiiyUutohChsQ776MHNqWM+RR3oFAeqoWE4F/+CQREwYSSJWATqtjv6B/QGZnhR1058Hwpnw33+JTcnCz1XdHPnrrRfYeS7OwpGJ2uhCbCoX49Kw1mno19TL7NefPrAxHo42XIhN47e9Vyt0LuOG35KICUnEKmhgoDo9uSV8S6XUDQhRE+TqDbz1z0leWnKMbL2BIS19WD+zH/d1bYCiwLOLjhCbUnlL/YWAm6Nh3RvVw9nO/CsNXeysee6OpgB8svEsSenlK0lRFMU0IubvKDVidZ0kYhXUrX437K3siU6LJiwhzNLhCFHlbqRl8/CP+/hx12UAnhnUhPkPdsLJ1orZI1rS1MeJuNQsZv55BINB/jgRlceYiA0x87Rkfvd1CaSpjxOJ6Tl8vvlcuc4RnxlPlj4LDRp8HX0rOUJR00giVkF2Vnb08usFqKNiQtRmZ2NSuPurXew6H4+DjY5vHujIc3c0RatVeznZ2+j46v6O2FmrRc3/3X7RwhGL2iIuNYtDV28AMMgMbStKYqXT8lpeO4uf91zmUlzZ+0gaR8O8HLyw1kkPsbpOErFKMKCB2sZiy1VJxEQNcu0wrHoeTiyFrNTbHr7uZDSjv9rF1YR0AtztWfpUT4a2KboRchMfZ966qxUA89af4eCVG5Ueuqh7NoddR1Ggtb8Lfm72Fo2lb1MvBjTzIkev8N7qss+EGOvDpHWFAEnEKkVf/77oNDrO3DhDREqEpcMR4vYMelg6BfZ/D0sehQ9D4I8H4NhiyEwueKhB4bON53hi4UHSsvX0aFSPv6f3pkV9lxJPP75zICPb+aE3KMz4/XC5a2mEMNoQlrdaskX1mMp7dXgLdFoNG07FsPtC2RanyIpJkZ8kYpXAzc6Njj4dAdgavtWisQhRKmF/Q/w5sHEGj0aQmwmnV8Kyx+DDxvD7fXD0D9KSEpj22yFTS4pHegbx8+SueDja3PL0Go2G90a3pmE9ByITM3h56TFZzCLKLSNbz45zau+uwS29LRyNqrG3Mw92awDA2yvD0JehHtKUiElXfYEkYpXG2GVf6sREtacosOMj9eMeU+HpQ/DkTujzAtRrDPosOLMa/noCm0+aMPbM89xrtZ2P72rAm3e1KnUTTWc7a764rwPWOg1rT0bzy79XqvCTErXZrvNxZOYY8Hezp+UtRmLN7dnBTXGxsyIsKpnFB8JL/TxjV30ZERMgiVilMSZiB2MOkpSVZOFohLiFcxsg+jhYO0K3J0GjAd82MOh1mH4AntpDeNunuUAA1uQySHeY963mc8/G/vDLGDj0M6QnlOpSbQPcePnO5gC8vSqMk9fkZ0OUnXG15OAW3lW+yXdZuDvaMGNQEwDmrT9LalZuqZ4XlZq3z6QkYgJJxCpNgHMATd2bolf0bI/YbulwhCieosCOeerHnSeBQ8G9+hTg54sO9D/Qk0GZHzDV9RuSu78I3i3BkAvnN8LfT6vTlz+PgoMLIO3W9TGTewczqLk32bkGnv79MGml/GUlBKibfG86beymXz3qw/Kb2COIYE9H4lKz+HrL+dseryiKFOuLAiQRq0QyPSmqvSu7IHwv6Gyh59MFHsrONfB/fx1n9oqT6A0Kd7f34+Pp9+Jy52swdQ9M2w8DXwOfNqDo4eIW+OcZmNcEfhqpFv6nXi9ySY1Gw4fj2uHrYsfF2DRmrzhprs9W1AJHwhOJS83G2c6Kbo3Ms8l3WdhYaZk1VB31/X7nJcIT0m95/I2sG2TkZgBQ37HoqmNR90giVomMbSx2Ru4kSy9dxUU1tD1vNKzDg+B8c3QhNiWL+7/7l9/3haPRwKyhzfn03vbYWetuPterKfR9EZ7aqdaVDXoD6rcDxQCXtqutMOY1hR+Hw77vICXa9FQPRxs+m9AerQaWHopg2SFZXSxKxzgt2b+ZeTf5Los7WvrQo1E9snMNvL/29C2PNfUQs/fCRnfrRS+ibqie39U1VEuPlvg4+JCRm8HeqL2WDkeIgiIPqqNYGh30mmG6+3hEEnd9uZMDV27gbGfFj4904Yl+IbeuxakXAn1mwhPbYcYRGPwW+HUAFLiyE1a/AB81hx+Gwr/zIfka3RrV45lB6vYwry0/wcXY2/cuE2JjmBk3+S7nyl6NRsNrI1qg0cDKY1EcvFJyDaW0rhCFSSJWiTQajWl6UjYBF9XOjo/Vf9uOB/cgAFYciWTs/N1EJWXSyMuRFdN60b9ZGdsDeARD72fh8a3wzDEY8g4EdAEUuLob1r4MH7eA/w3haYd1DG+oJz1bz/TfDpOZo6/ET1DUNpfi0jh/PRUrrRk2+T7wA7zjA191hxXT1UUp18PAYCjV01v5uTK+UyAAc1aGlbi9lyRiojArSwdQ2wxoMIA/zvzB1vCtGBQDWo3kuqIaiDml9glDA72fIyNbz/trT7Ng92UABjTz4rP7OuBS0Y2U3RuqtWc9n4akCDj1N5xaAeH/QvhetOF7+Qp40q4Jf1/vwvzlWTw7bnBFPztRS23Mt8m3q30VbwW0/we1dUtsmHo7vFC939YF/Duqf1wEdIWAzkUWuRg9H9qUlceucTQ8kb+PXmNUh6LF+KbWFdJDTOSRRKySdfHpgrO1M/GZ8RyLPUZ77/aWDkkI2PmJ+m+LkRzK8OaFn3ZwMW+PvKf6h/DCkGbotJXcFsA1QO1T1mMqJF+DsH/UpOzKbtpwjjbW5+DkbyRda41rp3HQ8m51dE2IPPnbVlSplBiIOa5+fM/3cP0UROyHyEOQlQwXt6o3I4+QvMSsMwR2Be9WoLPC29mOqQMa8+G6M7y/9jShrXyxt9EVuJRxxaSMiAkjScQqmbXOmt4BvVlzaQ2bwzdLIiYsL+EinFgCwI9WY3n7m90YFPB1seM/Y9qUfSqyPFz8oNsT6i0lBsL+5vKO3wlMPoTrjROw8QRszCv+b3k3tByl1qGJOishLZsDebVWg6u6Puxi3kr3+u2g7bib9+tz1dGx8H0QcUBNzuLPQcIF9XbsD/U4awe1RjKgM1Pqd2ataw7Hk+C7HRdNfcaMjFOT0rpCGEkiVgUGBg5kzaU1bLm6hZmdZlo6HFHX7fwUFAP7rDrx1n71R/6eDv68MbIVrg5VPN1THGcf6DoFv46TmfT1agJiNnOv4yHa5hxDE3UUoo7Cpjlqm4yh/4Gg3uaPUVjc5tPXMSjQsr4LAe4OVXux85vUf0MGFrxfZ6U2O/ZtA10mq/elJ6gLXyL2590OQlaS2hrmyi5sgH+ACFtP/tnWl7iu3+DpbAfk9RCTGjFRiCRiVaC3f2+stFZcTr7MxaSLNHJtZOmQRB2VcyMC7eFf0QEfpA2nnqMN745uw52tLd8Y08ZKyzsPDGT459b8ljyIF3rXY3r9M+r05aVt6lTRr+PhkX/Av5OlwxVmtuGU2v6kykfDDIabI2Ihg25/vIMHNLlDvRmfH3c2X2J2AOX6KQI0cTylWcaqv1oyfOKLACRlJZGeq/YZkx5iwsisleTbt29n5MiR+Pn5odFoWL58+W2fs3XrVjp27IitrS2NGzdmwYIFVR5nRTnZONHNtxsAW65Kc1dhGWdjUlj93/9Dp+Sy19Acr1b9Wf9c32qRhBk1qOfA3DFtAPhoVzw7XYbDQ8vghXPQqD/kpMGv4yDunGUDFWaVmaNn+1l1x4YhVZ2IxRyHtFh1y6/AbmV/vlYL3s2h40Nw1+cwdTeaV65yseU0AHpf+ITEmKvAzfqwenb1sLOyq7RPQdRsZk3E0tLSaNeuHV999VWpjr906RLDhw9nwIABHDlyhGeffZbHHnuMdevWVXGkFSdd9oWl6A0K/912gYc+X80dGWvV+3rN5OsHOlLPydbC0RU1oq0f93VtgKLAs4uOEJuSpY463PuLWneTHg8L74HkKEuHKsxk94U4MnL01He1o5VfFW/yfSGv1VBwH7CqpAardi4Ej3mLs7omuGrSiF00HfJNS0p9mMjPrInY0KFDeeeddxg9enSpjp8/fz7BwcF89NFHtGjRgunTpzN27Fg++eSTEp+TlZVFcnJygZsl9A/sD8Cx2GPEZdx6Lz4hKsuluDTG/3cPc9ec5kHNahw0WeT4tKPnkPHVarPkwmaPaElTHyfiUrOY+ecRtQeTrTM8sERdoZZ0FX65BzJuWDpUYQY3V0v6VP33rak+rBTTkmWg0VlzfcA8chQdTRK2kXZ46c3WFVIfJvKp1k2u9uzZw+DBBXsMhYaGsmfPnhKfM3fuXFxdXU23wMDAqg6zWD6OPrSu1xoFha3hWy0Sg6g7DAaFn3ZfZuhn2zl45Qa+ttk8Yaf+grHu/yJU4yQMwN5Gx1f3d8TOWsuOc3HM335BfcDREx76C5x81ZYCv98HORmWDVZUKYNBYWOYumdplXfTz0qFq/+qHzeu3EQMoGfPfiyyy1uFueZFriVeBKC+k9SHiZuqdSIWHR2Nj0/BH0QfHx+Sk5PJyCj+zXjWrFkkJSWZbuHh4eYItVjGvSdlelJUpYgb6Tz4v7288fdJMnMM9Aypx7peZ7DJTQGv5tBsuKVDLJUmPs68dVcrAD5af5aDV/JGv9wbwoNLwdYVru6BxZPUtgKiVjoakUhsShZOtmbY5PvKLjDkgFsD8Kj8RVVarQaPO2dx1uCPY04CkZfU3wX+jjI1KW6q1olYedja2uLi4lLgZikDA9Wl0P9e+5f0nHSLxSFqJ0VRWLT/Knd+uoPdF+Kxt9Yx5+5W/DKxDa5HvlMP6j1TLSauIcZ3DmRkOz/0BoUZvx8mKT1HfcC3Ndz/B1jZwdk1sPKZcu8LKKo3496S/Zp5YWulu83RFZR/WrKKRo1D2zXkc6dnMSgaotLVz02mJkV+1fod2tfXl5iYmAL3xcTE4OLigr29vYWiKr0QtxACnQPJNmSz69ouS4cjapGY5EweXbCfl5ceJzUrl04N3Vn9TB8m9ghCe3ghpMeBW0NoPcbSoZaJRqPhvdGtaeDhQGRiBq8uP37zwYY9YeyPoNHC4V9g01uWC1RUGWN92B0tzLDJt7FQv3D/sEqk02q4Y8hw/qcP5Zq12jHK38a1yq4nap5qnYj16NGDTZs2Fbhvw4YN9OjRw0IRlU3+TcCljYWoDIqisOJIJEM+2c6WM7HY6LT837Dm/PlED4I9HSE3G3Z/rh7c+1m1IWUN42xnzRf3dUCn1bDyWBSrj+dbLdl8GIz8TP145yew52vLBCmqxJX4NM7GpKLTahhQ1Ts+JF5Vu+RrdNCoX5VeakRbP5a43Utq3ui0777/Ven1RM1i1kQsNTWVI0eOcOTIEUBtT3HkyBGuXlV7rMyaNYuJEyeajn/yySe5ePEiL730EqdPn+brr7/mzz//5LnnnjNn2BUysIH6l9a2iG3kGqSuRZRfrt7A9N8P88wfR0jKyKGNvysrZ/Tm8b4hN/eJPPo7JEeCc31o/4BlA66AdoFuPNlPrdl5ffkJ4lOzbj7YcSIMmq1+vG4WHPvTAhGKqmAcDesW7FH1uz4YpyUDuoBd1Y5Q6bQaRnavB4CHXo/DgQVwZXeVXlPUHGZNxA4cOECHDh3o0KEDADNnzqRDhw7Mnq2+qUZFRZmSMoDg4GBWrVrFhg0baNeuHR999BHff/89oaGh5gy7Qtp7tcfd1p3k7GQOxRyydDiiBltzIppVx6Kw0mp4bnBTlk3tSVMf55sH6HNvbu7d82mwqn49w8pixqAmNPNxJj4tm9krThZ8sPdM6PaU+vHyp+DcRvMHKCpd/rYVVc4M05L5NQ1Q6x1tc/K2a1oxXVYAC8DMiVj//v1RFKXIzdgtf8GCBWzdurXIcw4fPkxWVhYXLlzgkUceMWfIFabT6ugXqA57y+pJURE7z6n96B7uGcQzg5tgrSv043tqOdy4BPYe0OkRs8dX2WytdMwb1w6dVsOq41GsPHbt5oMaDYS+B23GgSEX/nxI3ZRZ1Fg30rI5kLdStsrbVuhz4eI29eMqaFtRnJh0dYo9KrsR1/FQNw3f+h+zXFtUb9W6Rqy2yN9lX5GVXqKcdl1QE7HejT2LPmgwwI6P1I+7TwUbRzNGVnXaBLgyrX8IALNXnCQu/xSlVgt3f62ueMtJV7dCij1roUhFRW05cx29QaG5rzOBHlW8yXdk3kbddm7q7g1mYOyqb6Otz/9lT1Lv3P0FXDtsluuL6ksSMTPo4dcDO50dkamRnL0hvyhE2V2NTyfiRgZWWg1dg4vprXR2rdrw1MYZuj5m/gCr0PSBTWju60xCWjavLz9R8I8ZKxsY/7O6KXhGAiwcDUmRlgtWlJuxbUWVj4bBzWnJRv1BW8UtMvIYE7F+jZqy0dCJ9dpeoOjVKUp9jlliENWTJGJmYG9lTw8/daXn5vDNFo5G1EQ7z6ujYR0auOFoW2glpKLAjnnqx10fA3t3M0dXtWystMwb1w4rrYY1J6L551ihPSdtneD+xVCvCSRHqFshpSdYJlhRLlm5eradiQXMlYjlFeqbaVoSbm74PbxlS3xd7Hgl/SEyrd0g5gTs/NRscYjqRxIxM5E2FqIijNOSPUOKmZa8tE2darGyg+7TzByZebT2d2XagMYAzF5xguspmQUPcKwHDy0DZz+IPQ2/T4BsaaJcU+y5EE9ath4fF1ta+1Vxj62MG+rPC5itUB8w7TPZwCWAp/qHkIAL/yFvinL7B3D9tNliEdWLJGJm0i+wH1qNlrCEMKJSo27/BCHyGAwKey7EA9C7STGJ2Pa80bCOD4OTlxkjM69pAxrTsr4Liek5vPbXiaL1lm4N1K2Q7FwhfC8sfkSmfGqI/Ksltdoq3hf14jZQDODZDFwDqvZaeVKyU0jJTgHUrvr3dgnEy9mWBSmdifTqC/ps+PtpMOjNEo+oXiQRMxMPOw/ae7UHZPWkKJuw6GQS0rJxsNHRLsCt4IPh++DyDtBaQ68ZFonPXPJPUa4/FcPfR68VPcinJdz/pzo6eG4d/D1DtkKq5tRNvvMSMXNOS5pxNMxYH+Zm64ajtSN21jqe6NsI0DAj5SEUG2eI2Af7vjVbTKL6kETMjIzNXaVOTJTF7vPqaFi3YA9srAr9yBpXSrabYLa/7i2ppZ8LMwY1AdRVlNeTM4se1KA7jPtJ7Zh+9DfYMNvMUYqyOHEtiZjkLBxtdPQMqVe1F1MUuJD3h7A568PyErH8e0w+0K0hnk42HEx05FCzvCblm+bAjctmi0tUD5KImZGxTuxg9EGSs5MtHI2oKYz1Yb0Kt62IPq6ultRooXfN2W2iop7qH0JrfxeSMnL4v7+OF98SptmdcNcX6se7P1fbBIhqyTgtaZZNvuPOQVI46GygYa+qvVY+xkJ9P8ebiZi9jY4pfdTdI1682AGlYS+1Dcs/sqF9XSOJmBk1cGlAY7fG5Cq57IjYYelwRA2QnWtg70V1BWCRQv0dH6v/thoN9ULMHJnlWOvUKUprnYaNYdf563AJ7So6PACD8zYGX/8aHPndfEGKUrNIN/0GPcCminuV5WMs1M8/IgbwYPeGuDtYczE+g42NX1On1C9uVTe1F3WGJGJmZhwV23xVpifF7R0JTyQjR089Rxua++bbzijuPJz8S/2490zLBGdBzX1deCZvivLNv08SU9wUJUCvZ6DHdPXjFdPUmjpRbYQnpHM6OgWdVsPA5lW8yTdYpG0FYFqgVTgRc7S14rG8UbH/7MvG0P9V9YF1r0KyLOqqKyQRMzNjIrYzcifZ+mwLRyOqu115/cN6hNQruJps5yeAAk2Hgm9rywRnYU/2C6GNvyvJmbnMWlbCFKVGA3e8rY4aKnpY9jhkpZo/WFEs42hY54buuDnYVO3FcrPg8k714xDzJmLGETF/J/8ij03s0RAXOysuxKax2nEU+HVUu/6vel6mKOsIScTMrJVnK7ztvUnPTWdftPx1Lm7NmIgVqA9LDIdjf6gf93neAlFVD1Z5U5Q2Oi2bT19n6aESpii1WhjxKbgEqHtxrptl1jhFyczaTf/qv2oNlpMP+LSq+uvlY6wRq+9Yv8hjznbWTO6tjop9sfUyhpFfgNYKzqxS948VtZ4kYmam1WjpH9gfkOlJcWupWbkcCU8ECu0vuftzdaPr4L4Q2MUywVUTzXydeWawOkX51j8niU4qYYrS3g1Gzwc0cOhnOL3KbDGK4iWl57D3klr/aNZu+iED1ZFSM0nLSSMpKwkoOjVp9EivIJxtrTgTk8L6+Ho3/8Ba/aLsElEHSCJmAQMaqNOTW8O3YlAMlg1GVFv7LsWTa1AI9LC/uQly6nU1kQDo84LlgqtGnujbiHYBrqRk5vLKsmPFT1ECBPeBnk+rH//9NKTEmC9IUcTWs+om3019nGhYzwyb1BsL9c3YPwxutq5wsXHB2ca52GNc7a15pFcQAJ9tOo/SeyZ4tYC0WFgrI7i1nSRiFtDVtyuO1o7EZsRyMu6kpcMR1dSuvP5hvfKvltzzFeRmQkAXdURMFJii3HomlsUHIko+eOBr4NMG0uPh7+lSg2NB60+ZcVoy9bra7gWg0YCqv14+xkSsuPqw/B7tFYyjjY6wqGQ2nkuCu79UW9Mc+wPOrjdHqMJCJBGzABudDb39ewPS3FWUrEh9WMYN2P8/9eM+z5t1eqW6a+LjzMwhTQF4e+UpriVmFH+glS3c8y3obOHcejjwgxmjFEbZuQbTJt9mbVtRv53ZtwEzFuoXVx+Wn7ujDRN7BgHwxeZzKP6doPtU9cGVz0Gm9J6srSQRsxDZBFzcSlxqFqej1b3pTN3Gz2+C7BR1j7ymd1owuuppSp9GtA90IyUrl1dKWkUJ6jZIg99QP17/mtoKRJjVvxfjSc3KxdvZtui2XVXBQtOSAFFpxbeuKM5jvYOxt9ZxLCKJrWdjYcCr4B4EyRGw8c2qDVRYjCRiFtInoA9WGisuJF3gSvIVS4cjqpndeZt8t6jvQj0nW/XOiP3qvyEDZDSsGDqtRp2itNKy/Wwsi/aHl3xwt6cguJ+6im7ZFNkc3MyMbSsGmWOTb4MhXyJm3rYVcOvWFYXVc7Llwe4NAPhs4zkUa/ubO0Qc+B9c3lVlcQrLkUTMQlxsXOjs2xmQUTFR1G7jtGT+vfciDqj/BtTtlZK30tjbiRfypijfWRVGZElTlFotjPoG7Fzh2iHY9oEZo6zbFEXJ17bCDE1cY46rRe/WjhDYreqvV0hx+0zeypS+jbC10nIkPJGd5+PUWtCOD6sP7phXVWEKC5JEzIJM05PhkoiJgnYWrg/LzYLoY+rH/p0sFFXNMLl3Izo2cCM1K5dXlt5iFaWrP4z4RP14xzzpum8mJ68lE5WUib21rui2XVXBOBoW3AesqrhpbDHKmoh5O9txfzd1VOzzTefU79+eM9QHL22HjMSqCFNYkCRiFjSwgVqvcPj6YeIz4i0cjagursanE3EjAyuthq7BHuqd0cdBnw0OnmrNiCiRTqvhw3HtsLXSsuNcHL/vu8UUZesx0GY8KAbpum8mxmnJvk09sbOu4k2+Qa2tBItMS6bnpHMj6wZQ+kQM1F0jbKy07L98g38vJoBnY7U21JAL5zdWVbjCQiQRsyBfR19aeLRAQWF7xHZLhyOqiV0X1NGwDg3ccLS1Uu801ocFdJb6sFII8XLixdBmALy76hThCeklHzzsQ3ANlK77ZrLB1LbCt+ovlp2mdtQHixbqO1s742LjUurn+bjYcW/nQEAdFQOg+XD137B/KjVGYXmSiFmYsbmrtLEQRsZpyQLTNqb6sM4WiKhmmtQrmM4N3UnL1vPy0mMYDCVMURbuuh+20pxh1ikRN9I5FZWMVoN5Nvm+vBMMOeDWAOqFVP31CjG1rnC6deuK4jzZPwRrnYY9F+PZfzkBWoxQHzi/EXJK2EFC1EiSiFnYwED1r7Q91/aQnnOLv9pFnWAwKOzJWzHZu0n+RCxvRMxfErHSMk5R2llr2X0hnl/3XS354KDeN7vu/zNDuu4bKYpak1RJjW83hV0HoHNDDzwczVCvlX9a0gIjyWWtD8vP382esZ3yjYrV7wDOfpCdqtaKiVpDEjELa+reFH8nf7L0WeyJ2mPpcISFnY5OISEtGwcb3c3+SqmxkHgF0IB/R0uGV+MEezryUmhzAOauDuN68i1GEqTrvjqVd3kX7PwU/ngAPmoG7zeEda9WyunNusk3WLR/GNzc7Ls0rSuKM7V/CFZaDTvOxXEoIgmaD1MfOC3Tk7WJJGIWptFopLmrMDF20+8W7IGNVd6PZ2TetKRXM7XdgiiTR3oG0S7AlfRsPYsP3mL7o7rWdV9RIP4CHP0DVj0P8/vA3EBYMAw2vgGnV0Jq3sjgv1/BiaUVulxaVi57L6obWA9sYYZpycSrEH8ONDqLbQdmGhFzLPuIGECghwOjO6hJ3BebzkHzvOnJM2vAoK+UGIXlSSJWDRhXT26L2EauIdfC0QhLMhbqm9pWgNSHVZBWq+HB7g0BWLQ/vORaMcjruv+m+vG6VyHuXNUHaC6ZyXBhC2z7EH4dBx80gi86wl9PwP7v1fYoih6c60OLu2DIO/DoOuj1jPr8v2dUaBeCPRfiydYbaODhQCNPM27yHdBZrQO0gIpMTRpNG9AYrQa2nInlmHVrsHVV+6IZyxVEjWdl6QAEdPDugKutK4lZiRy5fsTU6FXULdm5BtOIQcFCfakPq6jhbevz1j+nuJqQzr+X4m/dv6rbk3B2LVzapnbdn7wBdNbmC7YyGAwQd0b93onYD+H7IfY0UCgJ1dmCX3u1SbDx5lpoGs2/M0QchCs74c+J8NhGsHEoc0hbz6r1Yf2aeqExR72WBdtWGBmL9SuSiAV5OjKqvT/LDkfy0cZL/NQ0FI7/qa6ebNC9skIVFiQjYtWAldaKfgH9AFh6rmLD/6LmOhKeSEaOnnqONjT3dVbvNOgh8pD6sXTULzcHGytGtlN/Gf55q62PoFDX/cM1p+t+xg04/Cv8dq9a1/V1d/j7aXUlaGwYoIBbQ2g9FoZ+AFM2w6wImLweQt+FVqOKJmEAOisY+z9w9ILrJ2HNi2UOTVEUtuZt8t2/mRk23dbnwsVt6seNLZOIZeZmkpCp/mFV3hoxoxmDmmCt07DtbCwnXPuod55eVTfrGGshScSqiQnNJqBBw8qLK9kfLUPOdZGxPqxHSL2b++/FnVU3+rZ2BO8WFoyu5ru3i7oCbc2JaJLSb7O3ZE3pup+eoCZav4yBDxvDiqnqaF5Wsvo9E9QHej8HE36HF87Bs8fUpKrbE+oODaXtNO/sC2P+BxotHP5FTfjK4GJcGhE3MrDRaemRf9uuqnLtEGQlgZ0b+HWo+usVF0Jeob6jtWOZeogVJ8jTkYd7BAEw66g3is5W7Xt3PayiYYpqQBKxaqKNVxvGNxsPwJw9c8jWZ1s4ImFuuwpvawQ368P8O4LWDF3Ia7F2Aa4083EmK9fAiqORt39Cka77KVUfZGmkxcHBBfDzKDX5+vtptbeUIRe8W8GAV+GJ7fDKVXhkpVrz1nwYOFWwQL5RP+j/f+rHq56HmFOlfuq2vNGwrsEeONiYoSLGOC3ZqL/Ffm6M9WH1HetXylTs04Oa4O5gzfFYPREeeVOSp6XnXW1gkUTsq6++IigoCDs7O7p168a+fSX/tblgwQI0Gk2Bm52dnRmjNZ8ZHWdQz64el5Mv8+OJHy0djjCjtKxcjoQnAtC7cXH1YbK/ZEVpNBrTqNii201PGuXvur/Wgl33U6/D/v/BTyNhXhP45xm4uEUtrvdtAwNfh+kHYOpu6PcS1G+nTilWtj7PqzVXuRlqvVgpt4TaetaM05Jg8bYVcDMRq+i0pJGrvTXP3aFuaP+/uJbqnZKI1QpmT8QWLVrEzJkzeeONNzh06BDt2rUjNDSU69evl/gcFxcXoqKiTLcrV66YMWLzcbFx4aUuLwHw7bFvuZp8iwaUolbZdymBXINCoIc9gR75CqEjD6r/Sn1YpRjdwR8bnZaT15I5EZl0+yfk77p/eKF5u+6nRMO+7+DH4TCvKayaqTbyVAxQv7060vX0IXhyJ/R9ATybVH1MWq3a4sPZT20NsfLZ29YpZebo2XtRbVLcr6kZErGMGzdbvlioPgwqZ8VkYfd3bUBjbyf+zmiHAS1EHVXbdIgazeyJ2Mcff8yUKVOYNGkSLVu2ZP78+Tg4OPDDDyX37NFoNPj6+ppuPj5magZoAUODh9Kjfg+yDdm8u/ddFCnGrBOM2xoVGA3LSoXredM/0rqiUrg72nBHK/X9488DpRwVM2fX/aRI+Hc+/HAnfNQcVr+grlZEUUdF75gDzxyFJ7aptV8W2LYHR08Y96Pan+v44tv2W9tzMZ6sXAP+bvY09naq+vgublOTVc9m4BpQ9dcrQWWPiAFY6bS8OrwFCbhwwKCOjnF6daWdX1iGWROx7OxsDh48yODBg28GoNUyePBg9uwpuat8amoqDRs2JDAwkLvvvpuTJ0+WeGxWVhbJyckFbjWJRqPhte6vYaO1Yfe13ay9vNbSIQkz2FXc/pLXDqu/UFwD1WJpUSkm5E1P/nU4ksycUjbFrIqu+9npal+ui9tg95fw/R3wSUtY+zJc3QMoENAVhrwLzx5XVzn2egbcgyp+7Ypq0P1mv7W1r8C1IyUeaqwP62uuthXVYFoSIDItb59Jx7LvM3krA5p507epF+v0eX+cyfRkjWfWPmJxcXHo9foiI1o+Pj6cPn262Oc0a9aMH374gbZt25KUlMS8efPo2bMnJ0+eJCCg6F87c+fO5a233qqS+M2lgUsDprSdwldHvuL9fe/Ty79XhVfdiOorLjWL09FqIXjP/CvKpD6sSvQK8cTfzZ7IxAzWnohmVIdSjFgYu+5/2z+v6/7/oMtjJR+flQLJ1yA5Mu/fQh8nRUBmYjFP1KhJTsu7ocVIi47o3FbPp9WE8cxqWPwwPL6t2Map28xZH6YoNxMxC05LAkSlRgGVOyJm9NrwFkz5rAuv8wvKld1o0hPAwaPSryPMo9o3dO3Rowc9evQw/b9nz560aNGC//73v7z99ttFjp81axYzZ840/T85OZnAwECzxFqZHm39KKsuruJy8mU+P/Q5r3V/zdIhiSqyO2+T7xb1XajnZHvzAVN9mExLViatVsO4zgF8uvEci/aHly4Rg5td99fNgnWvqa0RstOKT7iySjkSb+2otspwawBNQtXky6VyR1CqjEYDo76G//aFG5dhxTS495cCm2tfiU/jUlwaVlpNwT8yqkrcOUgKB50NNOxZ9dcrQVpOGrEZagJamTViRk19nOnTtROnDjWkpfYKhjNr0HZ4oNKvI8zDrImYp6cnOp2OmJiCNRYxMTH4+pZu6sXa2poOHTpw/nzxW23Y2tpia2tb7GM1iY3Ohte7v87k9ZP588yf3B1yN2282lg6LFEFdhvbVuT/RaUoN0fEpFC/0o3rHMhnm86x52I8V+LTaFivlFvu5O+6v3TyrY+1dQUXv3w3fzXpMn7s4ge2LgUSlxrH3h3G/QQ/hKpTZP9+Az2mmh42joZ1DnLH2c4MuxMYR8Ma9AAbM2yjVILD1w8D6miYu517lVzjucFNWXSkCy25wrV/lxAgiViNZdZEzMbGhk6dOrFp0yZGjRoFgMFgYNOmTUyfPr1U59Dr9Rw/fpxhw4ZVYaTVQ9f6Xbkr5C7+vvA3c/6dw+/Df8dKW+0HMUUZFbu/ZFKEuuGy1kptRSAqlb+bPX2aeLH9bCx/HgjnxdDmpXuiVquuolz0EORkFE2sCiRZzlX7SVQX/h0h9D11YcGG19UR3MCuAKZu+v2ammGTb4ALef3DLDwtaWzK3dmn6kaz6znZ4tNlLOxfgmfMTlJTk3FykhKWmsjsqyZnzpzJd999x08//URYWBhPPfUUaWlpTJo0CYCJEycya9bNfj1z5sxh/fr1XLx4kUOHDvHggw9y5coVHnvsFvUZtcjznZ/HxcaF0wmn+TWsbN2sRfV3NT6d8IQMrLQaugbnq/Ewjob5tAZre8sEV8vd21ktWVhyMIJcvaH0T3Txgymb1J5dDyyGkZ+pvbs6PKgWiHs1qztJmFGXx6DVaLWp7OJHIC2ezBw9e/Km3c1SH5abBZd3qh9buFD/QIzaPqOLb9WOZo8YcgdRGm/syGbjP79X6bVE1TF7Inbvvfcyb948Zs+eTfv27Tly5Ahr1641FfBfvXqVqKgo0/E3btxgypQptGjRgmHDhpGcnMzu3btp2bKluUO3CA87D2Z2UmvevjryFdFp0RaOSFQm42hYhwZuONrmG+2U+rAqN7ilN+4O1sQkZ7H9XKylw6nZNBoY+Tl4hKg1cn89wf5LcWTk6PFxsb25d2pVuvov5KSDk4/6B4yFpOekczJOXdnf2bdqf35tra3IChkKgCFsJZGJGVV6PVE1LNJZf/r06Vy5coWsrCz27t1Lt27dTI9t3bqVBQsWmP7/ySefmI6Njo5m1apVdOhgmb3DLGV0k9F08O5ARm4Gc/fOtXQ4ohIV27YCpD7MDGytdNzTUV2V+Me+UvYUEyWzc4HxP4OVHZzfQM62jwG1iat52lbkTUuGDLRo3d2R60fQK3r8HP2qZMVkYQ17jQNggOYQH64+UeXXE5VP9pqsAbQaLa93fx0rjRWbwzez5eoWS4ckKoHBoJhWTPZuki8Ry81WO2YD+MuIWFUybnm0+fR1YlOyLBxNLeDbGobNA6Bf5Ld004TRv5m56sOqR/+w/TF59WFVPBpmpGnQg1w7D9w1qcSc2MKhqzfMcl1ReSQRqyGauDdhYquJALy37z3Sc9ItHJGoqNPRKSSkZeNgo6NdgNvNB2JOQG6m2h7BEp3T65CmPs60D3Qj16Cw7FCEpcOpHTo8SFqL8egw8IXNF/T2LWXT3IpIvQ7Rx9WPGw2o+uvdwoFotT6sKgv1C9BZYdVcXbw2RHuAt1eekh1ZahhJxGqQJ9s9ib+TP9Fp0Xxz9BtLhyMqyDgt2S3YAxurfD+K+evDanJrgxrCtBH4gXD5BVYZNBr+CZjJGUMA3ppEXFY9CYYqTsYu5M0S+LYFJzNtLF6M9Jx0TsSp04PmGhEDoPlwAEJ1Bzl89QZ/H71mvmuLCpNErAaxt7Ln/7r9HwALTy3kTMIZC0ckKqLYthUg9WFmNrKdHw42Oi7GpnHgikzrVIZNF9KYmvMM2Vp7uLwDtv6nai9YTdpWHI09Sq6Si6+jLwFOZtwVIWQAWDvgp4mjleYy7685Xfrtu4TFSSJWw/QN6MsdDe9Ar+iZ8+8cDEoZlt2LaiM718C+SwlAcYX66tSG1IeZh5OtFcPbqN3sF+2Xov2Kys41sPt8HBcUf2L6va/euf1DOL+xai5oMOSrD6s+/cPMskDByNrelISOcTjCtaRMvt9x0XzXFxUiiVgN9HKXl3G0duRY7DGWnF1i6XBEORwJTyQ9W089R5uCS/vTEyDhgvqxf0fLBFcHGacnVx2LIiUzx8LR1GwHriSQlq3H08kG/z4TofOjgALLHoekyMq/YMwJSItVt4sK7Hb746vQwRi1rKCq+4cVq/kIAMY6HgHg660XuJ6caf44RJlJIlYD+Tj68HSHpwH49NCnxGXEWTgiUVbG+rAeIfXQavP95WysD6vXWDbxNaNODd0J8XIkI0fPymNRt3+CKJFxW6O+Tb3U7+3QuWrtVno8LHkU9JWc6BqnJYP7gJVN5Z67DDJyMzgWdwwwY6F+fk1DQaPDJfkcQ/3SSc/W8+E6KV+pCSQRq6EmNJtAy3otSclOYd6BeZYOR5TR7hLrw/KmJaU+zKw0Go1pVOwPmZ6skG2mbY3yiuat7WD8T+q+muH/wvrXIacSR2rO5+sfZkHHYo+Ra8jF28GbQOdA8wdg7w5BvQF4NUSdllxyKIITkUnmj0WUiSRiNZROq2N299loNVpWXVzFnmt7LB2SKKW0rFwOX00EoHdJhfr+ncwblOCejgFYaTUcDU/kTHSKpcOpkaKTMjkdnYJGA32b5Fu96NEI7v5K/XjvNzA3AL4dAKtfguNL4MZldaP7sspOUzvqQ92tD8svb3oyIHozd7XzQ1GQdhY1gCRiNVgrz1ZMaDYBgHf3vkuWXhpS1gT7LiWQa1AI9LAn0MPh5gMGQ77WFTIiZm6eTrYMaqE2H5Wi/fLZdvY6AO0C3HB3LDRN2PIuGPIuOHqBIQeuHYJ9/4Wlk+GzdjCvCfx+H+z4GC7tgKzU21/w8k71XG4NLN5zz1z7S95SXj8xwvcyq289bK207L2UwLqTMZaLSdyWJGI13NMdnsbL3osryVf43/H/WTocUQrG+rAio2EJFyAzUd0ixqeV+QMTTOjSAIC/DkeQlSvL/8tqa960ZImbfPecDi+cg2eOwZj/Qbcn1dFfrbVacH9mNWx6C34aAf8JhG96wz/PwpHfIO6c+sdKfvm76Vuw515mbibHYi1YH2bkGgB+HQCF+tGbmdKnEQBz14TJ93M1ZnX7Q0R15mTjxMtdX+aFbS/w/fHvGRo8lGDXYEuHJW5hZ4n7S+bVh9VvDzpr8wYlALXA3NfFjujkTDacimFEWz9Lh1Rj5OgN7Dynfm+b6sOKo9GAe0P11mZs3pMzIfoYhO9Tp+cjDkByBMQcV28Hf1SPs3NTGx0HdFX/NbbEsPC05PG44+QYcvCy96KhS0OLxkLz4XDtMJxexVNjH2TRgXCuxKfz8+4rTOnbyLKxiWLJiFgtMKThEHr79ybHkMO7/74r9QDVWFxqFqfz6o96htQr+KCpkav0D7MUnVbD2E5qI06Zniybw1cTScnKxd3Bmrb5t+wqDWs7COyqjpiN/wlmnoSZYTB+IfR8Ghr0UEeKMxPV5Gvre/DLPRB/HjQ6CO5bFZ9SqVWL+jCj5iPVfy9uxZEMXhzSDIDPN58jPlXKV6ojScRqAY1Gw6vdXsVWZ8ve6L2svLjS0iGJEuzJ2+S7RX0X6jnZFnww0rhiUhIxSxrfWV3xtvN8HBE3ZE/X0jLWh/Vt6oVOWwnJiItfXl3ZO/DoWpgVAY9vVTcVbzMe3PNG/psPB3u3il+vAoz1YWbd1qgkXs3AIwT02XBuA2M6BdCyvgspmbl8uvGcpaMTxZBErJYIcA7gyXZPAjDvwDySsmTJcnVkrA/rVXg0LDsdotU96qRQ37Ia1HOgZ0g9FAWWHJSNwEtra+G2FZVNZ63WP3WdAmO+g2eOqMnZ+J+r5nqllKXP4uj1o0A1ScQ0GtPek5xehU6r4fURLQH4bd9VzsXIiuDqRhKxWuThlg8T4hpCQmYCnxz8xNLhiGKUuL9k1FFQ9ODkCy7+FohM5GfsKbb4QAR6g0z13871lExOXksG1BExs7F1tmiRPsDx2ONkG7KpZ1ePYJdqUp/bIm968tx6yM2mR0g9hrT0QW9QeGdVmGVjE0VIIlaLWOuseb3H6wAsPbeUI9ePWDYgUcDV+HTCEzKw0mroGlyoa37++jBL15gIQlv54mJnRWRihmkUU5Rs+1n1a9TG3xXPwlPutVz+aUmL14cZ+XcGR2/ISobL2wH4v2EtsNZp2HY2li1nrls4QJGfJGK1TCefToxuPBqAt/a8RbY+28IRCSPjaFiHBm442hZasCz1YdWKnbWO0R3UkclFB6Ro/3aM2xqV2LaiFjsQnZeIWbJtRWFa7c2eYqdXARDk6cgjPYMAeHdVGDl6QwlPFuYmiVgtNLPTTNxt3TmfeJ45e+bIKspqwlQfVnhaEmRro2pofN705IaTMSSkyR80JdEbFHacq+L6sGoqW5/N0Vi1PsyijVyLY1w9eXq1qf/a9IFN8HC04fz1VH7fd9WCwYn8JBGrhdzs3PhP3/+g0+hYcWEFP5+ybDGrAINBYXfeiskiiVjyNUiOBI1W7SEmqoVWfq609nchW2/gr8ORlg6n2joSnkhieg4udla0D3SzdDhmdSLuBJn6TDzsPGjkWs16dAX3ARtnSI027djham/Nc4ObAPDJhrMkZVTyBuyiXCQRq6V6+vXkxS4vAvDxwY/ZHrHdwhHVbaejU0hIy8bBRke7wj2WjKNh3q3A1snssYmS3ZvXaf/P/eEyslwC47RknyZeWOnq1q8UY31YJ59O1ac+zMjKFpoOUT8+fbOl0X1dG9DE24kb6Tl8u/2ChYIT+dWtn5o65v7m9zOmyRgMioGXt7/MxcSLlg6pztqdVx/WLdgDG6tCP3am+jDZ6Lu6uaudH7ZWWs7EpHA0QlrCFGdbXuF3vzpYH5a/kWu1lK+NhZGVTssLoWqT1x92XuZ6SqYlIhP5SCJWixkbvXby6URqTirTN08nMTPR0mHVSTulPqxGcrW3Zlib+gAs2i81NYXFp2ZxLFJNUOtafViOPqf61ocZNb4DdDYQfw5iz5juHtLSh/aBbmTk6Plq83kLBihAErFaz1pnzSf9P8HfyZ/wlHBe2PYCOQapCzCn7FwD+y4lAMXsL6nPVfeFA3XJuah2jJ32/zkaRXp2roWjqV52nItDUdSdInxc7CwdjlmdjD9JRm4GbrZuhLiFWDqc4tm5QHA/9eN805MajYaX8kbFftt3lfAE2UHCkiQRqwPc7dz5fODnOFg5sDd6L+/ve9/SIdUpRyMSSc/WU8/Rhua+zgUfvH4KctLB1gU8m1omQHFL3Rt5EFTPgdSsXFYdi7J0ONVKnW5bEXOzbYVWU41/lRqnJ8MKbn3Xs7EnvRt7kqNXZOsjC6vG3z2iMjV1b8p/+vwHDRoWnVnEotOLLB1SnbHznDot2SOkHtrCe/AZ68P8O6q9f0S1o9FoGJc3Kvan9BQzMRgUtp+tm20rIF99WHXY1uhWmg0DNHDtECQVXP1rrBX763CEbH1kQfLOX4cMaDCAGR1nADB331z2Re2zcER1w+6StjUCqQ+rIcZ2CkCrgf2Xb3D+eqqlw6kWTlxLIj4tGydbKzo1dLd0OGaVY8jh8HW1pKDaFuobOftAYFf14zOrCzzUPtCN0FY+GBSYt/5MMU8W5iCJWB0zufVkhjcajl7RM3PbTMKT5S/8qpSWlcvhq4kA9L5VIib1YdWaj4sdA5t7A7BYRsWAm5t892pcD+s61rbiVPwpMnIzcLV1pYl7E0uHc3um1ZMrizz0wpBmaDWw7mQMR8MTzRuXACQRq3M0Gg1v9XyLNp5tSMpKYvrm6aRmy1/4VWXfpQRyDQqBHvYEejgUfDAjEeLy/gqVrY2qPWPR/tJDEbI9DLA1r21F/2beFo7E/IzbGnXy7lS968OMmo9Q/728EzJuFHioiY8zozsEAPDhOhkVs4Qa8B0kKputzpbPBnyGt4M3F5Mu8tL2l9Ab9JYOq1YybmtU7GjYtUPqv+5B4FjM46JaGdDcG08nW+JSs9l8um5vmpyYns2RvNGTOlkfFlND6sOM6oWAVwsw5MK5DUUefnZwE6x1Gnaej2O3bHJvdpKI1VFeDl58PuBzbHW27IjcwWeHPrN0SLWScVujIm0rQKYlaxhrnZYxnfI2At9ft6cnd5yLw6BAUx8n/NzsLR2OWeUacjkco9aHVdv+YcUxrZ78p8hDgR4O3N9V3UXi/XVnZBcJM7NIIvbVV18RFBSEnZ0d3bp1Y9++WxeNL168mObNm2NnZ0ebNm1YvXr1LY8XpdPKsxXv9HoHgB9P/siK8yssHFHtkp6dy+noZEDtqF+EFOrXOPfmTU9uPXOd6KS625F8Wx1eLRkWH0Z6bjrONs40casB9WFGxkTs/CbIySjy8LSBjbG31nE0PJENp2LMHFzdZvZEbNGiRcycOZM33niDQ4cO0a5dO0JDQ7l+vfih/t27d3PfffcxefJkDh8+zKhRoxg1ahQnTpwwc+S1053Bd/JE2ycAeGvPWxy5fsSyAdUiJ68lY1DA18UO78LNLhUFItTpDakPqzkaeTnRNcgDg6LWitVFBoOSr39YHawPy7e/pE6rs3A0ZeDXAVz8IScNLm4r8rC3sx2TegUB6gpKvUFGxczF7InYxx9/zJQpU5g0aRItW7Zk/vz5ODg48MMPPxR7/Geffcadd97Jiy++SIsWLXj77bfp2LEjX375pZkjr72mtp/KoAaDyDHk8MyWZ4hKlaaVleFY3t6EbQJciz544xJkJKjbj/i2MXNklU9v0PPS9peYvG4yf1/4m8zc6jFalGvIZfPVzUzbNI3H1j1GUlbF94sc30UdFVt8oG5uBB4WnUxsShYONjo6B9WtthVQA/aXLIlGk2/1ZNHpSYAn+obgYmfF2ZhUVhyJLPYYUfnMmohlZ2dz8OBBBg8efDMArZbBgwezZ8+eYp+zZ8+eAscDhIaGlnh8VlYWycnJBW7i1rQaLe/1fo9m7s1IyExgxpYZpOfIlhcVdSwiEYC2/sUkYhEH1X9924KVrfmCqiJHYo+w5tIa9kXv49WdrzJo8SDe3/c+F5Mss9F8dFo0Xx/5mtCloTyz5Rm2R2xnb/Refjr5U4XPPbS1L/bWOi7Hp3M8su5tBG5sW9EzpB62VjVoRKgS5BpyTf3DalR9mJExETuzBopZoOXqYM2T/dXtmj7ZeJbsXFkdbA5mTcTi4uLQ6/X4+PgUuN/Hx4fo6OhinxMdHV2m4+fOnYurq6vpFhgYWDnB13IO1g58PvBzPOw8OJ1wmtd2vYZBkR/CijieNyLWNtCt6IOmacka+GZejC1XtwDQxL0Jfo5+JGcn80vYL9y9/G4eWfsIqy6uIlufXaUx6A16tkds5+lNTxO6NJRvjn7D9fTruNu6c0fDOwD47fRvFR4Vc7S1YnBL9T1pxZFrFY67pjHVh9XBackzCWdIzUnF2dqZZu7NLB1O2TXsBXZukB4P4XuLPeSRnkF4OdsSnpAhG92bSa1bNTlr1iySkpJMt/Dwur26qSz8nPz4dMCnWGmt2HBlA/OPzrd0SDVWUkYOF+PSAGhT3IiYcWujWlAfpigKm8M3A/Bk2ydZfc9qvh70NQMCB6DVaDkYc5BXdrzC4MWD+ejAR1xJvlKp17+efp3/Hv0vQ5cNZdqmaWyN2IpBMdDFtwsf9P2AjeM2Mq/fPJq6NyUtJ42FpxZW+Jp3t/MD4J+j1+pULU1yZg4Hr6h9qPrXwUJ9Y31YR5+ONas+zEhnDU3vVD8OK9rcFcDBxoqnBzYG4PPN52WjezMwayLm6emJTqcjJqbgioyYmBh8fX2LfY6vr2+Zjre1tcXFxaXATZReB+8OvNHjDQC+OfoN6y6vs3BENdPJvCmrQA97PBxtCj6YkwlRx9SPa0EidiHxAuEp4Vhrrenl3wudVkefgD58PvBz1o1Zx9R2U/Fx8OFG1g0WnFzAiL9G8Ni6x1h3eR05+pxyXdOgGNgduZtntzzLkCVD+PLIl0SlReFq68pDLR9ixagV/BD6A0ODh2Kjs0Gr0fJkuycB+DXs1wqPivVt6oWrvTXXU7LYezG+QueqSXadi0NvUGjk5Vi0QXEdUGPrw/LL32W/hBrHCV0aEOhhT2xKFgt2XzZfbHWUWRMxGxsbOnXqxKZNm0z3GQwGNm3aRI8ePYp9To8ePQocD7Bhw4YSjxcVN6rxKCa2nAjAaztf41T8KQtHVPMcy0vE2vq7FX0w+jgYcsDBE9wamjewKrAlXJ2W7F6/O47WjgUe83X05an2T7F2zFo+H/A5ffz7oEHD3ui9vLDtBQYvGcynBz8lPKV0I9dxGXF8f/x7hi8bzhMbn2DT1U3oFT0dvTvyXu/32DRuEy91eYlGro2KPHdQg0E0dmtMak4qv4b9WqHP2cZKy7A29YG6NT1Zl9tW6A16DsWoTZhrTCPX4jQeBFZ2kHgFrhZfa21jpeW5wU0BmL/1AkkZ5fuDSZSO2acmZ86cyXfffcdPP/1EWFgYTz31FGlpaUyaNAmAiRMnMmvWLNPxzzzzDGvXruWjjz7i9OnTvPnmmxw4cIDp06ebO/Q6ZWanmfTy70WmPpMZm2cQlyHdlsvCWKhf7IrJ/PVhGo35gqoixkRsQIMBJR5jpbViQIMBfD34a9aOWcvjbR/H096ThMwE/nfifwxfNpwnNzzJpiubyDEUfNNXFIW9UWridseSO/js0GdEpEbgbO3M/c3v56+7/uKnoT8xMmQktrqSFz7kHxX75dQvJGdXbCHP3e3V6ck1J6LIyq39O1MoimIq1K+LbSvO3jhLSk4KjtaONPdobulwys/GEVqPVT9eMQ2yit/i7u72/jT1cSI5M5dvt18wY4B1j9kTsXvvvZd58+Yxe/Zs2rdvz5EjR1i7dq2pIP/q1atERd1sn9CzZ09+++03vv32W9q1a8eSJUtYvnw5rVu3NnfodYpOq+PDvh8S7BpMTHoMz2x5hix9lqXDqjGMrSvaFpeImerDOpkxoqpxPf06x+OOA9A/oH+pnuPn5MfTHZ5m/dj1fNL/E3r69URBYde1XTy79VlCl4TyxeEvOJNwhgUnFjBy+UgeW69OZeYacmnr2ZY5PeewafwmZnWbRWP3xqWO946Gd9DYrTEpOSkVHhXrGuSBr4sdyZm5bMtLUGqzszGpRCdnYmulLb5BcS1nnJbs4N0BK62VhaOpoNB31J5iCRdh/avFHqLTanh+iLog4Yedl7meUj1a0tRGFinWnz59OleuXCErK4u9e/fSrVs302Nbt25lwYIFBY4fN24cZ86cISsrixMnTjBs2DAzR1w3Ods488XAL3CxceFY7DEeXvMw/1z4p9r0iKpsiqLw08mf6L+of4Vq4+JTs4i4oXaubl1s64ras2Jya/hWANp6tcXLoWzTVdZaawY3HMx/7/gvq0ev5tHWj+Jh50FsRizfHvuWsf+M5aODanG/o7Uj9za7lyUjl/Dr8F8Z3WQ09lZl31pHq9GaGhgvPLWQlOyUMp/DdC6thpHt8qYnj9b+6UnjJt89QuphZ10DC9UryFioXyPbVhRm7w6jvlE/PrhAbWdRjCEtfWgf6EZGjp6vNp83X3x1TK1bNSkqV0OXhnzU/yNsdbacjD/J/+38P4v3iKoKmbmZzNo5i3kH5hGfGc+qi6vKfS5jb6lGXo642FkXfDD1OiReBTTg17ECEVcPxtWSAwJLnpYsjUCXQJ7r9Bwbx27kw34f0s1X/eOsZb2WvNHjDTaP28xr3V+jmUfFWwbc0fAOGrk2IiU7hd/CfqvQue5ur+49ufFUDKlZtXt1WV2uDzMoBg7GqL3/anShfn6N+kGPvBKfv5+G1KKjuhqNhpdC1Z+53/ZdJTxB+ktWBUnExG11r9+dNfes4ekOT1PfsX6BHlGT1k5i9cXVVd4jqipFpUYxcc3EAsnXuRvnyn0+U/+wYkfD8qYlvZqDXc1e0ZuancreKLUX0cDAgZVyTmudNXcG3cn3od9z+KHDLBqxiLFNx+JgXXkr9HRanWlU7OdTP5OaXXyNTGm08nOhkZcjWbkG1p8svrdhbZCalcv+ywlA3awPO3fjHMnZyThYOdCiXgtLh1N5Br4O3q0gLVZNxopZRdmzsSe9GtcjR6/w6cbyvy+KkkkiJkrFy8GLx9s+zpp71vDVoK/oH9gfrUbLgZgDvLzj5SrrEVXVDkQfYMKqCYQlhOFu685H/T4CICI1oty7Cxw1bW3kVvTBWlQftvPaTnINuQS5BBHsGlzp56/KOpzQoFCCXYNJzk7m99O/l/s8Go2Gu9upo2K1efXkngvx5OgVGng4EFSv7rat6ODdAWut9W2OrkGs7eCeb9Wt1s6uUacpi/FiqLo44a/DEZyLKf90viieJGKiTHRaHX0D+vLFwC9YN2YdT7V7Cm8H74I9otZXrEeUOSiKwh+n/2DK+ikkZCbQ3KM5f4z4gyFBQ/CyV6deziWW76+/45GJALS73YrJGs7YTX9A4AA0NWz1p06r4/G2jwPw06mfSMtJK/e57spbPbnzfBzxqbVzQYuxPqx/M68a91pXBmN9WI1uW1ES39YwaLb68br/g/iiKyTbB7oR2soHgwIfrT9r5gBrP0nERLn5Ovoytf1U1o1ZV7BHVFT5ekSZS7Y+mzf3vMm7e98lV8llaPBQfh76M35O6i/UJu5NgPJNT8YkZxKTnIVWAy39Ck09GvQQqe5Th3/NfkPPMeSwI2IHcOu2FdXZnUF30tClIUlZSRUaFQv2dKRtgCt6g8Lq41G3f0INoyiKqT6sf7O6WR9mSsRqS31YYd2nQVAfyEmHZY+Dvmi94wtDmqHRwNqT0RwNTzR/jLWYJGKiwvL3iFozZg1T2kwpdY8oc7uefp1J6yax7NwytBotMzvN5P0+7xdYgdfErfyJmLFtRVMfZxxsCk2txZ6B7BSwdgTvml1nciD6ACk5KXjYedDWs62lwykXK63VzVGxkz9VaKP7u/K2PKqN05MXYtOIuJGBjU5L90b1LB2O2Z1PPE9SVhL2Vva08mxl6XCqhlYLo+eDrataPrFjXpFDmvg4M7qDOg3/4boz5o6wVpNETFQqfyd/ZnScYeoR1aN+j2J7REWlmn/k4GjsUSasnMCx2GM42zjz9aCvmdR6UpGplqYeakfpszfKPgR/3NjI9Vb7S/p3hJq4T10+xiau/QP718w99/IMCx5GA+cGJGYlsujMonKfZ2Q7PzQaOHDlRq1bWWacluzWyKPoHxd1gLE+rL1X+9pVH1aYawAMV2tk2fbBzYVF+Tw3uCnWOg07z8ex+7w0+a4skoiJKmHsEfXtkG+L7RF157I71Q2aw7eiN1R9V/K/zv3FpLWTiM2IpbFbY/4Y/ge9/HsVe6xpRCzxHEoJe7GV5OitGrka68P8a3ahvqIopkSsslZLWoqV1oopbacAsODkgnKPivm42NEjb7Ton2O1a1SsLretAExtK2pF/7DbaTtO7bqv6GHZlCJd9wM9HLi/awMAPlh3pszvj6J4koiJKlegR1TfD+nq2xWDYmB7xHae3vw0dy67k2+OfkNMWsztT1ZGOYYc3tv7HrN3zybHkMOgBoP4ZdgvNHBpUOJzGrk1QqfRkZSVRGxG6TumK4pi6iHWtrgVkxHqG3pNL9QPSwgjOi0aeyt7utXvdvsnVHMjGo0gwCmAhMwEFp9dXO7zGLc8+rsWTU9mZOvZe8nYtqLuJWKKonAguhYX6hdn+Lxbdt2fNrAx9tY6joQnsuFU5b9n10WSiAmzsdZZc2fwnfwv9H/8M+ofHm75MG62bkSnRfP1ka8JXRrKjM0z2BGxo1JGyRIyE3h8/eOmQuxp7afxcf+Pi2xMXZitzpaGLupm3GWZnoy4kUFCWjbWOg3N6zsXfDArBa7nbZ4eULPf0I2jYT39emJnZWfhaCouf63YDyd+ICM3o1znubNVfax1Gk5Hp3AmumYv8U/JzGH9yWhmLTtGdq4Bfzd7QrycLB2W2V1IvMCNrBvY6exoXa+ObKt3m6773s52TOoVBMC89WfQG2RUrKIkERMWEeQaxAtdXmDjuI3M7TOXjt4d0St6toRvYeqmqQz/azjfHfuu3JuNn4o/xYSVEzgQcwBHa0c+H/A5T7Z7Eq2mdN/y5Vk5aRwNa+7rgq1Vobqpa4cBBVwDwdm31OesjjZfVbvpD2xQs6cl8xsRMgJ/J391VOxM+UbFXB2sTc1O/z4aWZnhVblcvYGDV27w2cZzjP1mN+3nbODxhQdZnje6d2dr3zrZtmJ/jFpO0M67Hda6WlwfVlj+rvsrpqs7guTzRN8QXOysOBuTyoojNet7vTqSRExYlK3OlhGNRvDT0J9YfvdyHmzxIM42zkSmRvL54c+5Y/EdzNw6kz3X9mBQDKU656qLq3h4zcNEpUXR0KUhvw37rcwtFsqzcvKYqZFr7a0Pi0iJ4OyNs+g0Ovr697V0OJXGWmvNlDZqrdiPJ38s936qxunJFUeuVfv6mSvxafzy7xWeWHiADm9vYMw3u/lk41kOXLmB3qAQVM+Bh7o35NuHOvHK0OaWDtcijNOSXXxqdjlBuRi77qfHFem67+pgzZP9QwD4ZONZsnNL994silf3lsCIaivELYSXu77MMx2fYd3ldSw+u5ijsUfZcGUDG65sINA5kLFNxzKq8Sg87DyKPF9v0PPpoU9ZcHIBAH38+/Cfvv/BxabsWwk1dS/7ysljeSsmi9/aqHbUhxmnJTt4d8DNzs2ywVSyu0Lu4ttj33It7RpLzi7hwZYPlvkcg5r74GijI+JGBoeuJtKpoXsVRFo+SRk57LkQx/Zzcew8F8fVQqs7Xeys6N3Ek96NvejTxJNAj7rXQT8/RVFqdyPX27G2gzHfwbf94exadZqy8yTTw4/0DOKHnZcJT8hg0f6rPNQjyFKR1niSiIlqx87Kjrsb383dje/mTMIZFp9dzKqLqwhPCeeTg5/w5eEvGdxgMOOajaOzT2c0Gg1JWUm8tP0ldl/bDcBjbR5jevvp5W6tYJyavJh0kRxDzm2XrRsMtyjUV5R8HfVr9hu6abVkLZqWNLLWWfNY28eYs2cOP5z4gXHNxmGrsy3TOextdIS28mXZ4Uj+PhJp0UQsR2/gSHgiO87GsuN8HEfDE8lfzmOl1dCxoTt9GnvSp6kXbfxd0Wnr3vRjSS4lXSIhMwFbnS1tPNtYOhzL8Gmldt1f/5radT+4L9RTR8IcbKyYMagxs1ec5PPN5xnTKaBOtjepDPJVE9VaM49mvNb9NWZ2msnay2tZfGYxJ+JPsObyGtZcXkOQSxB3hdzFsnPLiEiNwN7Knjm95nBn0J0Vuq6fkx+O1o6k5aRxNfkqIW4htzz+cnwaKZm52FppaeJTqKg5KRzSroPWCuq3q1BclpSYmcihmEOAuq1RbTQqZBTfHfuOqLQolpxdwgMtHijzOUa292PZ4UhWHY/i9REtsdKZtwJkU1gMv+8L59+L8aRmFeyQHuLlSJ8m6ohXt0b1cLKVXwElMfYPa+fVDhudjYWjsaDu0+DsOri8Q21p8eg6yKuXm9ClAd9uv0jEjQwW7L7M1P6NLRxszSQ1YqJGcLB24J4m9/D7iN9ZNGIRY5uOxcHKgcvJl/n88OdEpEbg7+TPwqELK5yEAWg1Whq7qW8qpZmeNI6GtfRzwbrwL17jaJhPa7C2p6baHrkdvaKniXsTApwDLB1OlbDWWfNYm8cA+OH4D2Tpy753ZO/Gnng42hCXms3uC/GVHeItHbxyg8k/HWBjWAypWbm4O1gzom19PhjTlt2vDGTT8/15865WDGrhI0nYbdTpacn8CnTdPwjbb3bdt7HS8txgtYxj/tYLJGVU3/2FqzNJxESN07JeS97o8Qabx2/m9e6v09arLQMDB/LH8D9o5tGs0q5TlpWTxkL9drW4f5hxk++a3sT1dkY1HoWPgw/XM66z7NyyMj/fWqdleJv6gHm3PMrVG3ht+QkABrfw4Z/pvTn42h18eX9HxncJxM+t5v4RYG6KophGxGrt/pJl4RoAIz5WP97+YYGu+6M6+DO4hTdvj2qNsyT35SKJmKixHK0dGd9sPL8O+5XPBn5W6cXjxoL90iViiUAJWxvVgvqwzNxMdl3bBdTcTb5Ly0ZnYxoV+9/x/5Gtzy7zOYyrJ9edjCYzp+p3jgBYsPsyYVHJuDlY88HYtrQJcEUrNV/lcjn5MvGZ8dhobWjrVTP3Uq10bcYW23Vfp9Xw/cNduLu9v3y/lZMkYkKUwNjC4nZTk3qDwonIZADaBRZKxHKzIeqo+nENHhHbG7WXjNwMfBx8aOnR0tLhVLl7mtyDt4M3Mekx/HXurzI/v2MDd/zd7EnNymXz6eu3f0IFRSVl8MkG9fv0lTub4+FYh2uaKoFxWrKtV9syL9io1W7TdV+UjyRiQpTAODV5Le0aqdmpJR53/noqGTl6HG10BHsWKtSPOQ76LLVbtUejqgy3ShlXSw4IHFAnGnva6GyY3HoyAN+f+L7Mo2JarYaR7Yw9xaq+4eWcf06Rlq2nYwM3xncOrPLr1Xamacm6Xh9WWOGu+6dXWzSc2kISMSFK4Grrio+DDwDnE8+XeJxxWrJVccv/jfVh/p2hhiYweoP+ZiJWy6cl8xvTdAze9t5Ep0Wz/PzyMj/fOD255XRslRYxbzlznTUnotFpNbw7uo1MD1WQoigcjM7b6LsuNnK9nfxd9/9+ukjXfVF2kogJcQvGUbFbTU8aV0y2K66jfmReUWsNrg87HnechMwEnKyd6tQvJludLY+2eRSA749/T46+bMlUc19nmvo4ka03sO5kdFWESGaOnjdWnATg0V5BtKhf9ubFoqCrKVe5nnEda6211IeVZNDsErvui7KTREyIWyhNInbUtLWRW8EHFAWu7lE/9q+5idjmcHVvyT4BferWfnvAmCZj8LT3JCotihUXVpTpuRqNhrvb+wPwdxWtnvxqy3muJqRT39WOZ/PaCIiKMW5r1MazTa3Y1L5KWNmqXfd1Nje77otyk0RMiFu43crJ7FwDYVF5hfqFR8SuHYLEq2BlDw26V2mcVamutK0ojp2VHY+2zjcqZijbqNhdeXViuy/EcT25fPtXluT89VTmb7sAwBsjW+IorQMqhXGjb6kPuw1j131Qu+7HX7BsPDWYJGJC3EL+zb+L28T5bEwK2bkGXOysaFB4b77jS9V/mw8DW6ciz60JLiZd5HLyZay0VvT2723pcCxibNOx1LOrR2RqJP9c+KdMzw30cKBDAzcMCqw8FlVpMSmKwuvLT5CjVxjQzIvQVr6Vdu66TFGUmxt9+9adafhy6z4NgvpATrra0qKM0/dCJYmYELfQyLURVhorUnJSiEmPKfK4sZFr2wC3gqsJDXo4kZeItRlnjlCrhHE0rJtvN5xsamYyWVH2VvZMaq1udvztsW/LPCp2t3H15NHKm55cceQaey7GY2ulZc7drevESlZziEiJICY9BiutFe28au52ZGZj7Lpv5wq2zpCVYumIaiRJxIS4BWudNUGuQUDxdWLHIxMBaFt4WvLyTkiNBjs3CBlUtUFWofxtK+qycU3H4WHnQWRqJCsvrCzTc4e39UOrgaPhiVyOS6twLEkZObyz6hQAMwY1IbDwSKwoN2P/sDaebbC3kp0ISsU1AB7bBA/+BQ4elo6mRpJETIjbuFXB/tFw44hYoUTsxBL135Z3g1XNbK4ZlxHHsdhjAPQP7G/ZYCzMwdqBR1o9AsB3x78j15B76yfk4+VsS6/GngD8UwmjYvPWnSEuNZsQL0em9Km5vemqI9nWqJw8m6ijY6Jc5CsnxG2UVLCfmaPnbIw6FN82/4rJ3Cw4lbfCrs1Yc4RYJbaGb0VBoXW91vg4+lg6HIu7t9m9uNu6E54SzupLZWtkaVw9ufxIZLG1hqV1JDyRX/ZeAeDtUa2xsZK38MqiKIps9C0sQn6KhbgNUyKWWDAROxWVTK5BwdPJhvqu+Za5n98EmUngXB8a9jJnqJWqLjZxvRUHawcebvUwoNaKlWVULLSVDzZWWi7EpnEqb5VtWekNCq8tP46iwOgO/vQM8SzXeUTxIlMjiUqLwkpjRXuv9pYOR9QhkogJcRvGlZOXEi8VaOp53Ng/zN+1YLH08cXqv63HgFZntjgrU3pOOv9e+xeQ+rD87mt+H262blxJvlKmFZTOdtYMau4NlL+n2MI9lzkRmYyLnRX/N6xFuc4hSmYcDWvl2QoHa6m7E+Zj1kQsISGBBx54ABcXF9zc3Jg8eTKpqSXv4QfQv39/NBpNgduTTz5ppoiFAF9HX5ytnclVcrmUfMl0f/4VkyZZqXBmjfpx6zFmjLJy7bq2i2xDNoHOgTR2a2zpcKqN/KNis3fP5uE1D7Pq4qpS7UVp3PLo76PXMBjKNj15PTmTj9arNYov3dkcL2fZiLqy7bmmNl+W+jBhbmZNxB544AFOnjzJhg0bWLlyJdu3b+fxxx+/7fOmTJlCVFSU6fbBBx+YIVohVBqNxlSwn79OzLjHZIFC/TOrITcDPELAr4M5w6xUm6+q3fTryibfZfFAiwcYFjwMnUbHoeuHeGXHKwxaPIh5++dxOelyic/r38wbZ1sropIy2X85oUzXfHtVGClZubQLdOP+rg0q+BmIwtJz0k1T8XV9YYowP7MlYmFhYaxdu5bvv/+ebt260bt3b7744gv++OMPrl279VC9g4MDvr6+ppuLi+ynJsyr8MrJtKxczseqo7lt8idixmnJNmNr7CbfOYYctkdsB2Rasjj2Vva83/d91o1Zx9T2U/Fx8CExK5GfTv3EyOUjeWzdY6y9vLbI3pR21jrubK02Xv27DKsnd5yL5Z+j19Bq4N1RrWVT7yqwNXwrGbkZBDgFSP8wYXZmS8T27NmDm5sbnTvfHPYdPHgwWq2WvXv33vK5v/76K56enrRu3ZpZs2aRnp5e4rFZWVkkJycXuAlRUfk77AOciExCUaC+qx3eznmF+mnxcEEdSaJ1zV0teTjmMMnZybjbutPeu72lw6m2fBx9eKrdU6wbs44vB35J34C+aNCwN3ovL257kcFLBvPJwU8ITwk3Pce4enLV8Siycw23vUZmjp7Xl58AYGKPIFr7F7OxvKgw4yrYYY2GyQiwMDuzbU4WHR2Nt7d3wYtbWeHh4UF0dHSJz7v//vtp2LAhfn5+HDt2jJdffpkzZ86wbNmyYo+fO3cub731VqXGLkRTj4IrJ49H3izUNzm1HAy5UL8deNXcDZiNm3z3DeiLlVb2L7wdnVZHv8B+9AvsR1RqFEvPLWXZuWXEZsTyw4kf+OHED/T068m4puPoHdwXTydb4lKz2Hk+loHNb90WZP62C1yOT8fb2Zbnh9Tc76nq7EbmDXZF7gJgePBwC0cj6qIKv8u+8sorvP/++7c8JiwsrNznz19D1qZNG+rXr8+gQYO4cOECISEhRY6fNWsWM2fONP0/OTmZwMDAcl9fCMBUsB6dFk1SVpKpUL9doNvNg47nNXGtwaNhiqLc3OS7Qd3b5Lui6jvVZ3qH6TzR7gm2h/9/e/cd31S9/3H8laTp3qV00c0oq+yWgiBQZCkXruhPXIDiZHjBvQAVuFzx3p8I4t5XRC/yAxEVBQo42MUylNEBlE5aSiedyfn9EVrkgqUjyUnbz/PxyMM0OTl510PIh+/8kTUn1rAza2fdzdfJl/BO13HucGe+SsqqtxA7lV/GG9tNGynPH98NN0e9tX6NNuWHUz9Qo9TQ1bsrEZ6yQK6wvmYXYo899hjTpk2r95iIiAj8/f05e/bsZY/X1NRQUFCAv3/DN6yNjY0FICUl5aqFmIODAw4OMqNImJebvRsBLgFkl2WTUpjCoQxTl3ddi1hRBqTvBDQterbkifMnyCrLwlHnSFxgnNpxWiy9Vk98aDzxofFklGSwNnkt65LXkVeeRx7rcOmoYUtBFzalPcjIsGFXtDwqisK8r45QVWNkSKd23NgzQKXfpPWr7Za8MUJaw4Q6ml2I+fr64uvre83j4uLiKCwsJDExkX79+gGQkJCA0WisK64aIikpCYCAAPmLSVhXZ6/OZJdlcyj3GKfOeQJ/KMRqN/gOHQweQeoENIPa2ZIDAwfKXntm0sGtA3/r+zdm9JpBwpkE1hxfw56cPWhcjvHET3PxS/RjUudJTO02tW79qo2HsvkpOR97Oy0LZVNvi8kqzeLA2QNo0DAmbIzacUQbZbXB+l27dmXMmDHcf//97N27l19++YVZs2YxefJkAgNN6+tkZmYSFRXF3r17AUhNTWXhwoUkJiZy6tQpNmzYwJQpUxg6dCjR0dHWii4EcGnm5L6s3wAI8XbGy+XiPpJ1syVbbmsYXFpNf0SwdEuam16nZ3TYaN4b/R43+66g6txQ7HAl90IubyS9wV3f3UVGSQYlFdUs3Gja1HvGsEjC2rmonLz1qm0NG+A/QLbxEqqx6jpiq1atIioqivj4eMaNG8d1113HO++8U/d8dXU1x48fr5sVaW9vz5YtWxg1ahRRUVE89thjTJo0ia+/bviK1kKYS93MyYsD9uuWrcg7DjmHQWsH3SaqlK75skuzOVpwFK1Gy/XB16sdp1W7q18/Ks+Oo+TEM8yLWYSPow/J55OZ/M1knvpmLWdLKglv58JD1185/EKYzzdp3wDSLSnUZdUpUd7e3nz22Wd/+nxYWNhlG+IGBwezY8cOa0QT4ppq95zMqzwFKPSqLcRqB+lHxoOztyrZzKF2tmRv3954O7bc36Ml6OTnRtcAd45mF1NT3JPPb/qcudvmcuTcEX6sWILeaxwv/eVRHPUtc4usluB4wXFSClPQa/WMDB2pdhzRhslek0I0UKhHKHZaOwxUoNGfp2eQJygKHLlYiPW8VdV8zVXXLSmzJa2idsujr5Iy8Xfx5/3RH+JaPRCNxoij/0a+y32VipoKlVO2Xt+cNLWGDe0wFHd7WSRcqEcKMSEaSK/VE+IWDoDOMYceQe6QdQAK0kDvDF3Gqpyw6Yoqi0jMSQRkNX1rGd/LVIjtPVVAdlE5axNzyU6ZAPkT0Gp0fJ32NdM2TSOn7M/XWRRNY1SMfHfStCesdEsKtUkhJkQjeNuFAtDOq8C0rlNtt2SXseDgqmKy5vkp8ydqlBoiPSIJcZe9DK0hyNOJmDBvFAU++uUUSzcdAzQ8NnA679zwNp4Onvx27jdu23gbibmJasdtVQ7kHiCnLAdXvStDOwxVO45o46QQE6IRNNWmZVNc3fLAaIAjF3d4aOndkrKIqyrGX+yefPvHNEoqaugR5M7dcWHEBsSy+sbVdPHqQkFFAfd9fx//Of6fy8bQiqar7ZYcGToSB52sOynUJYWYEI1QXOwDQI1dFpz6GUpzwNHTNFC/haoyVPFz5s+AdEta2409A7C7uIm3RgOLJ/ZEd/HnDm4d+GTsJ4wJG0ONUsPC3Qt5cdeLVBmq1Izc4lUbqvnh1A+AdEsK2yCFmBANpCgKp7JMMyXPV2VRdegL0xPdJoCdvYrJmmdP9h4u1FzA18mX7u26qx2nTfF2sef6zqYFse+KDb18yyzAWe/M0qFLmdN3Dho0rE1ey/Tvp5N3IU+FtK3Dz5k/U1xVjK+TLwP8BqgdRwgpxIRoqNziSvKLHFEMThgxkJZiWgyyxXdLXpwtOTx4OFqN/JVgbX+/uSd//2tPnrux61Wf12g0TO85nZXxK3HTu5GUl8TkjZM5nHfYyklbh9puyTHhY9BpZXkQoT75W1eIBjqYUQhocDCatjBKphLcAiB0kKq5msOoGNl+ZjsAw0OkW1INfu6O3BEbcs01w4Z0GMLqm1YT4RHB2fKzTN00lfUp660TspUoqy6r+/Mu3ZLCVkghJkQDHc4oAiDAybSERbLe3rTBdwv+V/WR/CPklefhonchxj9G7TjiGkLdQ1k1bhXDgodRbaxm3i/z+Mfef1BtrFY7WouwNX0rlYZKwtzD6ObdTe04QgBSiDXL71nFbD2aq3YMYSWHMk2FWDevMABO2Ouh5y0qJmq+2m7J64Kuw17Xcse5tSWu9q68Nvw1Hu71MACrjq7iwc0PUlBRoHIy21e7pdG4iHGykbqwGVKINdFPyXmMW/4TT609TEW1Qe04wsIUReFQRiEA8dpiAJIdnSCgt3qhzCAh3bStkcyWbFm0Gi0zes9g2fBlONs5sy9nH7dvvJ1jBcfUjmaz8svz2Z29G4Abw6VbUtgOKcSaaGCEDx28nMgvrWT13nS14wgLyzhfTuGFavQ6DXF5BwA4q4XCyiKVkzXd6eLTpBWlYaexY0iHIWrHEU0QHxLPqnGrCHELIassi7u/vbtuxXhxue9PfY9RMdKzXU9ZtFjYFKtu+t2a6HVaZgzryLPrDvPWjlRuj7n2YFvRch282BoW66fgnradoCA/MvV2JBcmM8C/ZU6Br13Etb9/f9lrrwXr6NWRz278jKd+fIpfsn7hyR+fZGPaRlz0LmpHuyp/Z39m9Zll9a7w2m5JGaQvbI0UYs0wqV8Qryckk1VUwZr9Z7g7LkztSMJCagfq3+qUCAUGOmmdyKSaE+dPtMhC7EL1BT49+ikgq+m3Bh4OHqyMX8nyX5fzwZEP+DHjR7Uj1ctZ78xDvR6y2vulF6dzOP8wWo2W0WGjrfa+QjSEFGLN4GCn4+Fhkcz76jfe3J7KbQNCsLeT3t7WqLZFLK7c1IrU2bcH2wt+Jfl8soqpmm5l0kpyL+QS5BrExI4T1Y4jzECn1TG331yGBA3haMFRteNcVXZZNv/+/d+8e+hdxoaPJdQ91CrvW7t22MCAgbRzameV9xSioaQQa6Zb+wfz+rYUsooqWHsgg9tjZOxBa2M0KhzJLCaQfNoXHAA0dIocCy20EDtWcIxVR1cB8FzsczjZOamcSJhTf//+9Pfvr3aMq1IUhdTCVHZm7WTR7kW8c8M7Fp+9qCgK36aZFl+Wbklhi6T5ppkc9ToeHBoJwMptKVQbjConEuZ28lwZpZU1TLTfY3ogdDCdA2MBSC5Mxqi0nGtuMBp4addLGBQDo0JHySB9YVUajYbnYp/DXmvP7uzdVplY8Pu53zlVfAoHnQPxIS13T1jRekkhZga3x4TQztWBjPPlrPs1U+04wsxql624xX6X6YGetxDiHoK91p7ymnIyS1vONf/yxJcczj+Mi96Fp2KeUjuOaINC3EN4IPoBAJbuW0pxVbFF36+2W3JY8DCbncAg2jYpxMzAyV7Hg0MjAFOrWI20irUqhzKKiNRkElGTBlo76DYBO60dkZ6mltAT50+onLBh8svzee3AawDM7jOb9s7tVU4k2qp7etxDuEc45yrO8VriaxZ7H4PRwKaTmwBZO0zYLinEzOTOgSF4u9hz+twFvj6UpXYcYUaHMor4i26n6YeOI8HZG4BOXp0AWsw4saX7llJSXUI3n25M7jJZ7TiiDbPX2TNv4DwA1pxYw8G8gxZ5n705e8krz8Pd3p3rgq6zyHsI0VxSiJmJs70d9w0x7UG4IiEFg1FROZEwhxqDkd+yCpmgvViI9bi0pVFnr85Ay2gR25m5k+9OfodWo2V+3Hx0LXh/TNE6DPAfwF8i/4KCwku7XqLGWGP29/j2pGmQ/qiwUeh1erOfXwhzkELMjKbEheHprCctr4xvDmerHUeYQUpeKZ1rUgjT5qLonaHL2LrnOnm2jBaxipoKFu1ZBMDtUbfT3ae7yomEMHm8/+N4OHhw4vyJupm85lJpqGTL6S2AdEsK2yaFmBm5OtgxfbCpVez1hGSM0irW4h06U8SEi92Smi7jwMG17rnarsn0knQqaipUydcQ7x5+lzMlZ2jv1J5ZvWepHUeIOl6OXjzW7zHAtLZddqn5/gG748wOSqtL8Xfxp69fX7OdVwhzk0LMzKYODsPN0Y4TuaV8/1uO2nFEMx3OOMd43aXZkn/UzqkdXg5eGBUjqUWpKqS7trSiND448gEAT8c+jau96zVeIYR1Teg4gb7t+1JeU86SvUvMdt7absmx4WPRauSrTtgu+dNpZu6Oeu652Cr22lZpFWvplJO/0F5TSJXeAyIvX4NIo9HY9IB9RVFYuGshNcYahgQNYWTISLUjCXEFrUbLvIHzsNPYse3MNhLSE5p9zqLKorptnqRbUtg6KcQs4N7BYbg62HEsp4QtR3PVjiOaqLLGQHThZgCqOt8EdlduUmzLhdiG1A3sz92Po86R5wY+Z/EVzIVoqo5eHZnWYxoAS/Yu4UL1hWadb8vpLVQbq+no2ZEu3l3MkFAIy5FCzAI8ne2ZOsi0h9ryhGQURVrFWqLkzHOM0ZhW03fpf/XlHmx15mRhRSH/2v8vAB7q9RBBrkEqJxKifg9EP0CQaxA5ZTm8kfRGs85V2y0pWxqJlkAKMQuZfl0EzvY6jmQWs/14ntpxRBPkJ32Lu+YCBbp2aEIHX/UYW505+eqBVzlfeZ6Onh2Z0n2K2nGEuCYnOyeei30OgE+PfsrxguNNOk9uWS77cvYBMC58nNnyCWEpUohZiLeLPXcPNLWKvbZVWsVaIq/UrwBIbT8K/mTdrUjPSDRoOFdxjnPl56wZ708l5ibyf8n/B8D8uPnotbJ+kmgZhnQYwqjQURiUi3uiGg2NPsd3J79DQaFv+74EugZaIKUQ5iWFmAXdNyQCR72WpDOF/JScr3Yc0RiVJXQp/hmA6m6T/vQwZ70zwW7BgGkDcLVVG6pZtNu0ZtikTpPo076PyomEaJynYp7CRe/CofxDrE1e2+jX13ZLSmuYaCmkELMgXzcH7oy9OFZMWsValKrfNuJIFanGAMJ7Dqr3WFsasP/x7x+TUpiCl4MXc/rOUTuOEI3W3rk9s/vMBmBZ4jLyyxv+j9i0wjSOFhzFTmPHqLBRlooohFlZrRBbvHgxgwYNwtnZGU9Pzwa9RlEU5s+fT0BAAE5OTowcOZLkZPW/7BrjwaER2Ntp2X/6PLvSbKPrSlxbeeLnAGzVDcHfw6neY2sH7KtdiGWUZPD2wbcBeHzA43g6eqqaR4immtxlMt19ulNSXcLSfUsb/LqNaRsBGBw0GC9HL0vFE8KsrFaIVVVVceutt/Lwww83+DVLly5l+fLlvPXWW+zZswcXFxdGjx5NRYXtrmL+39q7O3L7AFPX1fKtLauIbLPK8nHL+gmAU4Fjr7nsQ22LmJozJxVFYfGexVQYKojxj2F8xHjVsgjRXDqtjvlx89FqtHx38jt2Zu285msURZFuSdEiWa0Qe/HFF5k7dy49e/Zs0PGKorBs2TKef/55JkyYQHR0NJ988glZWVmsX7/esmEbQlHgt3Ww7/1rHvrQsEjsdVp2pxWwR1rFbN/v69EqBg4bw2gf3uOah9fOnEwtTG3S4GJz2Hx6Mz9n/oxeq+f5gc/LmmGixevm0407ou4AYPHuxVQaKus9/mDeQTJLM3Gyc2JY8DArJBTCPGx2jNjJkyfJyclh5MhLq4F7eHgQGxvLrl27/vR1lZWVFBcXX3aziJStsGYabJ4PpfUvTxHg4cSt/TsAsCIhxTJ5hPkc/hKArwyDie7gcc3Dg92CcdQ5UmGoIKM0w9LprlBaVcrLe18G4N4e9xLuEW71DEJYwszeM2nv1J70knTePfRuvcd+k/YNAPEh8Tjrna0RTwizsNlCLCfHtE+jn5/fZY/7+fnVPXc1S5YswcPDo+4WHBxsmYAd4yGwD1SVwo+vXPPwh4dFYqfV8HNKPomnz1smk2i+wjOQvgujomGjYSA9gzyv+RKdVkekZySgTvfk60mvc7b8LCFuIdwffb/V318IS3G1d+Xp2KcBeP/I+6QVpV31uGpjNT+c/gGQbknR8jSrEHv66afRaDT13o4dO2aurA3yzDPPUFRUVHc7c+aMZd5Io4GRL5ju7/8ACk7We3gHL2cm9a1tFZOxYjZr33sA7FWi0HoE4evm0KCXqTVz8rdzv7H62GoAnhv4HA66huUVoqUYGTKSoR2GUmOsYdHuRVedfb47azcFFQV4O3oTFxinQkohmq5Zhdhjjz3G0aNH671FREQ06dz+/v4A5OZevldjbm5u3XNX4+DggLu7+2U3i4kYBhHDwVgN2/5+zcNnDI9Ep9Ww/XgeB88UWi6XaJrsQ7DrdQA+qBlDzwZ0S9ZSY+akwWha9NKoGBkbPpZBgfUvsyFES6TRaHg29lkcdY7sy9nH12lfX3HMNydN3ZKjw0Zjp7WzdkQhmqVZhZivry9RUVH13uztr9wouSHCw8Px9/dn69atdY8VFxezZ88e4uJs6F88ta1ih9dAzuF6Dw31cWFib9Oef9IqZmMMNbBhFhhr+NV1KD8YBxDdwbPBL1dj5uTnxz/n93O/46Z348kBT1rtfYWwtiDXIB7q9RAA/9z3TworCuueu1B9gYT0BEC6JUXLZLUxYunp6SQlJZGeno7BYCApKYmkpCRKS0vrjomKimLdunWA6V9Bc+bMYdGiRWzYsIHDhw8zZcoUAgMDmThxorViX1tgb+h+M6DAlhevefjM4ZFoNbDl6FmOZBZZPJ5ooF0rIPsgOHryouEegAYN1K9VO3PyTMkZLlRfsEjEP8oty2XFrysAmNNvDu2c2ln8PYVQ05TuU+jo2ZHzledZdmBZ3ePbz2ynvKacDq4d6OXbS7V8QjSV1Qqx+fPn06dPHxYsWEBpaSl9+vShT58+7N+/v+6Y48ePU1R0qTh58sknmT17Ng888AADBgygtLSUTZs24ejoaK3YDTPiedDaQcpmOPVzvYdG+Loyvpdp/7PXZQalbchPgW1LACgbsZCk86ZxVtENGKhfy8fJBx9HHxSUPx1QbE4v73uZsuoyottFc0vnWyz+fkKoTa/VMz9uPgBrk9dyIPcAcKlbclzEOFm2RbRIVivEPvroIxRFueI2bNiwumMURWHatGl1P2s0Gl566SVycnKoqKhgy5YtdO7c2VqRG84nEvpONd3fvMC0xlg9Zg3viEYDm37L4ViOhZbXEA1jNJq6JA2VEBnPAc8xAIT6OOPh3LjNsq3VPfljxo9sPr0ZnebSopdCtAV92vdhUifT3q8Ldy8k70IeOzNNi73eGH6jmtGEaDL5G9xcrn8K9M6QuR+Obaz30E5+bozrEQDIumKq2/8+pO8CvQvKTa+yJjETgJ5BDe+WrGWNmZPlNeX8fY9pYshdXe+ii3cXi72XELZobr+5eDt6k1KYwoytM6hRaujq3ZUIz6ZNDBNCbVKImYubH8TNNN3f+pJp8Hc9Zo3oCMC3h7NJOVti6XTiagrPwJYXTPdHvsB7hw1sOJiFTqvh7oGhjT6dNWZOvn3wbTJLM/F38WdG7xkWex8hbJWHgweP938cgGMFpuWRboyQ1jDRckkhZk6DHgEnb8g/AQc/q/fQrgHujO7uh6LIWDFVKApsnGNakDd4INs9/sKS744C8PyNXYmN8Gn0Kf/YNXm1tY6a67f83/j4t48BeCbmGVk9XLRZN0XcRIx/DAAaNIwJG6NyIiGaTgoxc3J0h6Gmf6mxbQlUl9d7+OwRpi/uDQezSMsrrfdYYWaHvoCULaBzIH3Iy8z+/CBGBW7rH8y0QWFNOmWkRyRajZbzlec5V2HePUXPXjjLIwmPUKPUMDJkJCNCRpj1/EK0JBqNhnkD5+Hj6MNNETfh5+J37RcJYaOkEDO3/tPBIxhKsmDvO/Ue2iPIg/io9hgVWLkt1UoBBaVnYZNp25SK655k2teFlFTU0C/Ui5cmdm/yzCtHO0dC3EIA8w7Yr6ip4G8Jf+Ns+VkiPSJZOHih2c4tREsV5hFGwv8k8Pch115MWwhbJoWYuekdYfizpvs//QvK699Xcna8qVVsfVIm6ecsv/6UAL59AsrPo/hHMyNtEGl5ZQR4OPLWXf1wsNM169TmHrCvKAoLdi7gyLkjeDh4sGLEClztXc1ybiFaOpkxLFoD+VNsCdG3QftuUFEEPy+r99DewZ5c39kXg1Hhje0yVszijn4Nv68HjY4PfB4jIfk8jnot707p3+B9Jetj7iUs3j/yPt+e/BY7jR3/e/3/EuxuoU3shRBCqEIKMUvQ6iDetPAge96C4qx6D3/kYqvYl4kZZJyXVjGLKT8P3zwGwLGO01mYaFon7JVbetGjCctVXI05Z04mpCew/MByAJ6JfYaYgJhmn1MIIYRtkULMUjqPgeCBUFMB2/9R76H9Qr0Y3NGHGqPCfR/v50yBFGMW8cPzUJpLhUcktxy9DjAtrlu704E5dPY0FWKphanUGOtfwqQ+J86f4OmfnkZB4bYut/E/Xf7HXBGFEELYECnELEWjgRsu7j3566eQX38LyfybutPO1YFjOSVMWPkLu9PMO+uuzUtNgF8/RUHDrNJ7Ka2xY2RXPx69wbw7NQS5BeFk50SVsYr0kvQmnaOgooBHEh6hvKacWP9Ynop5yqwZhRBC2A4pxCwpZCB0HguKwbTIaz26+LuxYdZgegZ5UFBWxV3v7eHfu05ZZD2qNqeyFL7+GwDfOI1nS1k4nf1cefW2Xmi15t2bTqvR1m0A3pTuyWpDNXO3zSWzNJNgt2D+Nexf6LWN22pJCCFEyyGFmKXFzwc0cHQDZCTWe2igpxNrHopjQu9AaowK8776jWfXHaGqxmidrK1VwkIoTKdA78+T5yfi6azn3Sn9cXO0TIHT1AH7iqKweM9iDpw9gKvelddHvI6Hg3nGrgkhhLBNUohZml836HW76f6Wa28I7qjXsey23jwzNgqNBlbvTefO93aTV1JphbCtUPoe2PM2AH8rm0al1omVd/Ql1MfFYm/Z1CUsPjv2GWuT16LVaFk6dKnsnSeEEG2AFGLWMPwZ0NnDqZ8gdes1D9doNDx4fSQfTBuAm6Md+06dZ8LrP3Mks8gKYVuR6grYMAtQWGMYyk/GaObf1I3BHdtZ9G2bMnNyZ+ZOlu5bCsCj/R5lSIchFskmhBDCtkghZg2eITDgftP9LS+AsWFdjcO7tGf9zMFEtHMhq6iCW97ayYaD9S+FIf7gx1cg/wR5iicLq+9i8oBgpsQ1fjPvxqodI5ZRmkFZddk1jz9ZdJLHdzyOUTEyIXICU7pNsXREIYQQNkIKMWsZ8hg4uEPOYfjt/xr8skhfV9bNHMzwLr5UVBt5ZPWvvLzpGAajDOKvV/YhlF+WAfB89TS6hAXz0oQeTd6+qDE8HT3xdfIFIKWw/kV6iyqLeCThEUqqS+jt25v5cfOtklEIIYRtkELMWlx8YNAjpvsJC6GmqsEv9XDS897UATx0fSQAb25P5b6P91FcUW2JpC2foQZlwyw0xhq+McRw2G0ob97VD3s76/1xb0j3ZI2xhid2PMGp4lP4u/jz6vBXsdfZWyuiEEIIGyCFmDXFzQCX9nD+FBz4uFEv1Wk1PD02itcm98bBTsu243lMXPkLaXmllsnaku1agSb7IIWKC0u4l3em9Keda/O3L2qMhsyc/Nf+f7ErexdOdk6sGLGCdk6WHbsmhBDC9kghZk32LnD9k6b7O142rW/VSBN6B/HlQ4MI8HAkLa+MCSt/Ydvxs2YO2oLlp2BI+DsAL1XfzTO3DjPb9kWNca2Zk2tPrOXTo58CsPi6xUR5R1ktmxBCCNshhZi19ZsGXuFQlge7VjbpFD07eLBh1nX0D/WipKKGez/ax9s7UmXxV6OR0jUPoTNWscMQTdD193BjdIAqUeq6JguTr7gu+3P2s2jPIgBm9p7JDaE3WD2fEEII2yCFmLXp9DDiedP9ncuhLL9Jp/F1c2DV/bFMHhCMosCS744x94skKqoNZgzbshT9/DauufsoVRz5Nuxp5t7QRbUsER4R6DQ6iiqLOHvhUotlRkkGj25/lBpjDaPDRvNg9IOqZRRCCKE+KcTU0P1m8I+GqlL48Z9NPo2DnY4lN/fkpQnd0Wk1rE/K4ta3dpFdVG7GsC1DRf4p7LeZ9vb80Gkq8+4abfbtixrDXmdPqLtpqYzkQlP3ZFl1GbMTZnO+8jxdvbuycPBCmSEphBBtnBRiatBqYeQLpvv734fzp5t8Ko1Gw5S4MD6dHouXs57DmUWMX/ELiacLzJO1BVCMRk5+eD9OSjkHiGLCffNxdbBTO9ZlMyeNipGnf3qalMIU2jm1Y/mI5TjZOamcUAghhNqkEFNL5AgIHwqGKtj292afLi7Shw2zriPK34380komv7ObL/almyGobTMYFTZ/vpyuZXupVPTwlxWEtHNVOxZw+czJFb+uYPuZ7dhr7Xlt+Gv4u/irG04IIYRNkEJMLRrNpVaxQ19AzpFmnzLY25m1Dw9ibA9/qg0KT609zOsJVw4Wbw3ySipZuS2Fu/7xKTHHTVsD/db5Yfr2jVE52SW1K+xvO7ON9w6/B8ALg14g2jdazVhCCCFsiBRiagrqB90mAApsfcksp3RxsGPlHX2ZPaIjAP/84QQvbzreKooxRVHYmZrPzM8OELdkKx9/v5tXKl/AU1NGjlsP+kyer3bEy3T2NnVN1m5zdG+PexkfOV7NSEIIIWyM+gNp2roR8+HoRkj+Hk7vhNBBzT6lVqvhsVFdcHfUs/jbo7y1I5ULVTW8ML67qgPYm6rwQhVfJmbw2d500vJMRY07paxxWUoHQz5G70j8p39lmpFqQwJdAnHVu1JaXcqwDsN4pM8jakcSQghhY6QQU1u7jtD3bkj8CDYvgOk/mLotzeD+oRE4O+h4fv0RPtl1mgtVBv5xc0/sdLbfEKooCr+eKWTV7nQ2Hsqissa0UbqLvY5be/nw5NlXcc49Da7+aO9eBy62tyq9RqNhbr+5HMw7yDMxz6DT6tSOJIQQwsZolNbQZ1WP4uJiPDw8KCoqwt3dXe04V1ecDcv7QE05jH0FYu43WzEGsO7XDB5fcwiDUWFcT3+W3dbHqvsuNkZpZQ1fJWXy6e50jmYX1z3eNcCduwaGMCHaD9d1U+HEJnDwgHu+Bf8eKiYWQghhCS3i+9sMpEXMFrgHwKBZ8OMr8N0TkLIFxi8D90CznP6vfTrgpLdj9uoDfHs4h/Kq/bx5Vz8c9bbTQvN7VjGr9pxm/a+ZlFWZFqV1sNNyU3Qgdw4MoU+wJxqAr2aZijA7R7jjcynChBBCtGjSImYrDDXwyzLTHpSGKnBwh9GLoc/dZmsd23Eijwf/vZ+KaiMDI7x5b+oAVdfbqqg28M2hbFbtOc2B9MK6xyN8XbgzNpRJfYPwdLa/9ILNC0z/jzRauG0VRI2zemYhhBDW0WK+v5tJCjFbc/YofDUTMhNNP0eOgPGvgWeIWU6/J+0c0z/eT2llDb2DPfn4nhg8nK07yD3j/AU+/OUUXyZmUFReDYCdVsPoHv7cGRtCXITPlSvO71oJ3z9ruv+XFdB3ilUzCyGEsK4W9/3dRFYbKLR48WIGDRqEs7Mznp6eDXrNtGnT0Gg0l93GjBlj2aBqa98V7v0BbngJdA6QmgBvxMG+98BobPbpYyN8WHVfLJ7OepLOFDL53d3kl1aaIfi1FV6oYvE3vzPinzt4/+eTFJVXE+TpxBOju7DzmRGsvKMvgyLbXVmEHfziUhEWP1+KMCGEEK2G1VrEFixYgKenJxkZGbz//vsUFhZe8zXTpk0jNzeXDz/8sO4xBwcHvLy8Gvy+Lbqizk82tY6d2WP6OWyIqTXIO7zZpz6WU8xd7+0lv7SSCF8XVt0XS4CHZbbcqag28OEvp3hjewolFTUAxEX48MDQCIZ29kVX35IayZth9WQw1kDswzBmiVknMgghhLBNLfr7uxGs3jX50UcfMWfOnAYXYoWFhaxfv77B56+srKSy8lILT3FxMcHBwS33QhoNsPcd2PKiaVal3hniF0DMA6Y9K5vhZH4Zd767m6yiCjp4OfHZfQMJ8XE2U3DT9kNrD2Tw6uYTZBdVAKbZj0+PjWJop6u0fP23jP3w8XiovgA9b4W/vtPs31kIIUTL0FYKMZv/Vtu+fTvt27enS5cuPPzww5w7d67e45csWYKHh0fdLTg42EpJLUSrg4EPw4ydEHqdqSjZ9BR8OBbyU5p16vB2LvznoTjCfJzJOF/OrW/vJOVsSbMjK4pCwrFcxr32E09+eYjsogqCPJ343//pxTezr+P6zr7XLsLyjsOqW0y/b2Q8THhDijAhhBCtjk23iH3++ec4OzsTHh5Oamoqzz77LK6uruzatQud7upLL7S6FrE/Mhph//um2YPVZaYlHIY/B3EzTQVbE50truCu9/dwIrcUbxd7Prk3hh5BHk06V9KZQpZ8e5Q9JwsA8HDSM2t4R+6OC234chlFGfD+aCjOMG0DNWUDONjGRt5CCCGso620iDWrEHv66ad5+eWX6z3m6NGjREVF1f3cmELsv6WlpREZGcmWLVuIj49v0Gta5YU8fxq+/hukbTP9HNQfJqyE9lH1v66+U5ZVMeWDvRzOLMLN0Y6P7omhX2jDx+Kdyi/jle+P883hbADs7bTcMziMGdd3bNyszAsFpta+vGPg0wnu/R5cfBr76wghhGjhWuX391U0qxDLy8u7ZldhREQE9vaX1oJqTiEG4Ovry6JFi3jwwQcbdHyrvZCKAgc+gR+eh8pi0NnD9U/B4Dmga9raYMUV1Uz/aB/7Tp3H2V7He1P6M6hj/VsH5ZdWsnxrMp/tSafGqKDRwKS+HXj0hs4EejZy8H/VBfhkAmTsBbdAmP692ZbtEEII0bK02u/v/9Ks1Tx9fX3x9fU1V5ZrysjI4Ny5cwQEBFjtPW2WRgP9pkLHkbBxDiT/AAkL4egG03iqJqw47+6o5+N7Y3jw34n8lJzPtI/28eadfYnv6nfFsWWVNbz300ne+TG1biX84V18eWpsFFH+TfjAGKphzVRTEeboAXetlSJMCCFEq2e10c/p6ekkJSWRnp6OwWAgKSmJpKQkSktL646Jiopi3bp1AJSWlvLEE0+we/duTp06xdatW5kwYQIdO3Zk9OjR1opt+zyC4I7/wMS3TAVM9kF4Zxhs/wfUVDX6dM72drw3tT+juvlRVWPkwX8n8vXBrLrnqw1GPt19mutf2c6rW05QVmWgVwcPVt8/kA/viWlaEWY0wobZpmLSzsn0+/h1a/x5hBBCiBbGaoP1p02bxscff3zF49u2bWPYsGGmMBoNH374IdOmTaO8vJyJEyfy66+/UlhYSGBgIKNGjWLhwoX4+V3ZQvNn2krTJgAlObDxUTj+jeln9yBo18n0X/fAi7egSz87ef3pmlzVBiOPrznIV0lZaDXwj5ujcXeyY+mm46TllwEQ6uPME6O7cGPPgGvPgqzPD8/DzhWg0cHtq6GzFNpCCNHWtZXvb9niqLVRFDiyFr59AsoL6j/WzunyAs0j6A/FWiAG10Ce/yGb1fvOXPYyHxd7HonvxO0xIdjbNbNR9ZflsHme6f7EN6H3Hc07nxBCiFahrXx/SyHWWlUUQ9YBKM6G4kwozrr434v3L9Q/yaKWonOg0K4dx8vdOYsP/sERRHfrhqN38KWizcW3aWt8Ja2G9Q+Z7t/wEgz+W+PPIYQQolVqK9/fUoi1VdUVUJJ1sUDLulSgFf2hWCs727BzafXgFvCHFrXAP3SHXvyvq9/la52d+B5W3w6KAeJmwejFlvk9hRBCtEht5fu7WbMmRQumdwTvCNPtz9RUQUn25YVacZZpodXa+yU5YKyGonTT7c9odODmbyrK3AJMe0gqBoieDDcsNP/vJ4QQQrQAUoiJP2dnD16hptufMVRDae5VirXMi61rWaZiTjFc6hqt1WkUTHhdti4SQgjRZkkhJppHpwePDqbbnzEaoPTs5cWasRr6Tze9XgghhGijpBATlqfVgXuA6UY/tdMIIYQQNkP6hIQQQgghVCKFmBBCCCGESqQQE0IIIYRQiRRiQgghhBAqkUJMCCGEEEIlUogJIYQQQqhECjEhhBBCCJVIISaEEEIIoRIpxIQQQgghVCKFmBBCCCGESqQQE0IIIYRQiRRiQgghhBAqkUJMCCGEEEIldmoHsDRFUQAoLi5WOYkQQgghGqr2e7v2e7y1avWFWElJCQDBwcEqJxFCCCFEY5WUlODh4aF2DIvRKK281DQajWRlZeHm5oZGozHruYuLiwkODubMmTO4u7ub9dyi4eQ62Aa5DrZBroNtkOvQfIqiUFJSQmBgIFpt6x1J1epbxLRaLR06dLDoe7i7u8sHzQbIdbANch1sg1wH2yDXoXlac0tYrdZbYgohhBBC2DgpxIQQQgghVCKFWDM4ODiwYMECHBwc1I7Spsl1sA1yHWyDXAfbINdBNFSrH6wvhBBCCGGrpEVMCCGEEEIlUogJIYQQQqhECjEhhBBCCJVIISaEEEIIoRIpxIQQQgghVCKFWBOtXLmSsLAwHB0diY2NZe/evWpHanNeeOEFNBrNZbeoqCi1Y7V6P/74I+PHjycwMBCNRsP69esve15RFObPn09AQABOTk6MHDmS5ORkdcK2Yte6DtOmTbvi8zFmzBh1wrZSS5YsYcCAAbi5udG+fXsmTpzI8ePHLzumoqKCmTNn4uPjg6urK5MmTSI3N1elxMIWSSHWBF988QWPPvooCxYs4MCBA/Tq1YvRo0dz9uxZtaO1Od27dyc7O7vu9vPPP6sdqdUrKyujV69erFy58qrPL126lOXLl/PWW2+xZ88eXFxcGD16NBUVFVZO2rpd6zoAjBkz5rLPx+rVq62YsPXbsWMHM2fOZPfu3WzevJnq6mpGjRpFWVlZ3TFz587l66+/Zs2aNezYsYOsrCxuvvlmFVMLm6OIRouJiVFmzpxZ97PBYFACAwOVJUuWqJiq7VmwYIHSq1cvtWO0aYCybt26up+NRqPi7++vvPLKK3WPFRYWKg4ODsrq1atVSNg2/Pd1UBRFmTp1qjJhwgRV8rRVZ8+eVQBlx44diqKY/uzr9XplzZo1dcccPXpUAZRdu3apFVPYGGkRa6SqqioSExMZOXJk3WNarZaRI0eya9cuFZO1TcnJyQQGBhIREcGdd95Jenq62pHatJMnT5KTk3PZ58PDw4PY2Fj5fKhg+/bttG/fni5duvDwww9z7tw5tSO1akVFRQB4e3sDkJiYSHV19WWfh6ioKEJCQuTzIOpIIdZI+fn5GAwG/Pz8Lnvcz8+PnJwclVK1TbGxsXz00Uds2rSJN998k5MnTzJkyBBKSkrUjtZm1X4G5POhvjFjxvDJJ5+wdetWXn75ZXbs2MHYsWMxGAxqR2uVjEYjc+bMYfDgwfTo0QMwfR7s7e3x9PS87Fj5PIg/slM7gBBNNXbs2Lr70dHRxMbGEhoayn/+8x+mT5+uYjIh1Dd58uS6+z179iQ6OprIyEi2b99OfHy8islap5kzZ3LkyBEZpyoaTVrEGqldu3bodLorZr3k5ubi7++vUioB4OnpSefOnUlJSVE7SptV+xmQz4ftiYiIoF27dvL5sIBZs2axceNGtm3bRocOHeoe9/f3p6qqisLCwsuOl8+D+CMpxBrJ3t6efv36sXXr1rrHjEYjW7duJS4uTsVkorS0lNTUVAICAtSO0maFh4fj7+9/2eejuLiYPXv2yOdDZRkZGZw7d04+H2akKAqzZs1i3bp1JCQkEB4eftnz/fr1Q6/XX/Z5OH78OOnp6fJ5EHWka7IJHn30UaZOnUr//v2JiYlh2bJllJWVcc8996gdrU15/PHHGT9+PKGhoWRlZbFgwQJ0Oh2333672tFatdLS0staVU6ePElSUhLe3t6EhIQwZ84cFi1aRKdOnQgPD2fevHkEBgYyceJE9UK3QvVdB29vb1588UUmTZqEv78/qampPPnkk3Ts2JHRo0ermLp1mTlzJp999hlfffUVbm5udeO+PDw8cHJywsPDg+nTp/Poo4/i7e2Nu7s7s2fPJi4ujoEDB6qcXtgMtadttlQrVqxQQkJCFHt7eyUmJkbZvXu32pHanNtuu00JCAhQ7O3tlaCgIOW2225TUlJS1I7V6m3btk0BrrhNnTpVURTTEhbz5s1T/Pz8FAcHByU+Pl45fvy4uqFbofquw4ULF5RRo0Ypvr6+il6vV0JDQ5X7779fycnJUTt2q3K1//+A8uGHH9YdU15ersyYMUPx8vJSnJ2dlb/+9a9Kdna2eqGFzdEoiqJYv/wTQgghhBAyRkwIIYQQQiVSiAkhhBBCqEQKMSGEEEIIlUghJoQQQgihEinEhBBCCCFUIoWYEEIIIYRKpBATQgghhFCJFGJCCCGEECqRQkwIIYQQQiVSiAkhhBBCqEQKMSGEEEIIlfw/4On2ykAlntQAAAAASUVORK5CYII="
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 5
+ "execution_count": 57
},
{
"cell_type": "markdown",
"source": [
+ "### JapaneseVowels\n",
+ "\n",
+ "A UCI Archive dataset. See this link for more [detailed information](https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels)\n",
"\n",
+ "Paper: M. Kudo, J. Toyama and M. Shimbo. (1999). \"Multidimensional Curve Classification Using Passing-Through Regions\". Pattern Recognition Letters, Vol. 20, No. 11--13, pages 1103--1111.\n",
"\n",
- "### UsChange\n",
+ "9 Japanese-male speakers were recorded saying the vowels 'a' and 'e'. A '12-degree linear prediction analysis' is applied to the raw recordings to obtain time-series with 12 dimensions and series lengths between 7 and 29. The classification task is to predict the speaker. Therefore, each instance is a transformed utterance, 12*29 values with a single class label attached, [1...9].\n",
"\n",
- "Load MTS dataset for forecasting Growth rates of personal consumption and income. The\n",
- " data is quarterly for 188 quarters and contains time series for\n",
- " Consumption, Income, Production, Savings and Unemployment. It returns a pd.Series to\n",
- " forecast (by default, the series Consumption) and a pd.DataFrame containing the\n",
- " other series."
+ "The given training set is comprised of 30 utterances for each speaker, however the\n",
+ "test set has a varied distribution based on external factors of timing and\n",
+ "experimental availability, between 24 and 88 instances per speaker. The data is\n",
+ "unequal length"
],
"metadata": {
"collapsed": false
@@ -344,17 +375,27 @@
{
"cell_type": "code",
"source": [
- "from aeon.datasets import load_uschange\n",
+ "from aeon.datasets import load_japanese_vowels\n",
+ "\n",
+ "japan, japan_labels = load_japanese_vowels(split=\"train\")\n",
+ "plt.title(\n",
+ " f\"First channel of three test cases for JapaneseVowels, classes\"\n",
+ " f\"({japan_labels[0]}, {japan_labels[10]}, {japan_labels[200]})\"\n",
+ ")\n",
+ "print(f\" number of cases = \" f\"{len(japan)}\")\n",
+ "print(f\" First case shape = \" f\"{japan[0].shape}\")\n",
+ "print(f\" Tenth case shape = \" f\"{japan[10].shape}\")\n",
+ "print(f\" 200th case shape = \" f\"{japan[200].shape}\")\n",
"\n",
- "consumption, others = load_uschange()\n",
- "print(type(consumption))\n",
- "plot_series(consumption)"
+ "plt.plot(japan[0][0])\n",
+ "plt.plot(japan[10][0])\n",
+ "plt.plot(japan[200][0])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:19.784741Z",
- "start_time": "2024-09-25T22:58:19.608190Z"
+ "end_time": "2024-09-25T22:58:21.705366Z",
+ "start_time": "2024-09-25T22:58:21.437860Z"
}
},
"outputs": [
@@ -362,41 +403,42 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\n"
+ " number of cases = 270\n",
+ " First case shape = (12, 20)\n",
+ " Tenth case shape = (12, 23)\n",
+ " 200th case shape = (12, 13)\n"
]
},
{
"data": {
- "text/plain": [
- "(, )"
- ]
+ "text/plain": "[]"
},
- "execution_count": 6,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 6
+ "execution_count": 58
},
{
"cell_type": "markdown",
"source": [
- "### Solar\n",
- "Example national solar data\n",
- " for the GB eletricity network extracted from the Sheffield Solar PV_Live API.\n",
- " Note that these are estimates of the true solar\n",
- " generation, since the true values are \"behind the meter\" and essentially\n",
- " unknown. The returned pandas DataSeries is half hourly."
+ "### OSUleaf\n",
+ "\n",
+ "The OSULeaf data set consist of one dimensional outlines of leaves. The series were\n",
+ "obtained by color image segmentation and boundary extraction (in the anti-clockwise\n",
+ "direction) from digitized leaf images of six classes: Acer Circinatum, Acer Glabrum,\n",
+ "Acer Macrophyllum, Acer Negundo, Quercus Garryana and Quercus Kelloggii for the MSc\n",
+ "thesis \"Content-Based Image Retrieval: Plant Species Identification\" by A. Grandhi.\n",
+ "OSULeaf is equal length and univariate"
],
"metadata": {
"collapsed": false
@@ -405,107 +447,55 @@
{
"cell_type": "code",
"source": [
- "from aeon.datasets import load_solar\n",
+ "from aeon.datasets import load_osuleaf\n",
"\n",
- "solar = load_solar()\n",
- "print(type(solar))\n",
- "plot_series(solar)"
- ],
- "metadata": {
- "collapsed": false,
+ "leaf, leaf_labels = load_osuleaf(split=\"train\")\n",
+ "plt.title(\n",
+ " f\"First three cases of the test set for OSULeaf, classes\"\n",
+ " f\" ({leaf_labels[0]}, {leaf_labels[1]}, {leaf_labels[2]})\"\n",
+ ")\n",
+ "plt.plot(leaf[0][0])\n",
+ "plt.plot(leaf[1][0])\n",
+ "plt.plot(leaf[2][0])"
+ ],
+ "metadata": {
+ "collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:20.194929Z",
- "start_time": "2024-09-25T22:58:19.800676Z"
+ "end_time": "2024-09-25T22:58:21.910360Z",
+ "start_time": "2024-09-25T22:58:21.726272Z"
}
},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- },
{
"data": {
- "text/plain": [
- "(, )"
- ]
+ "text/plain": "[]"
},
- "execution_count": 7,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 7
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Time series clustering, classification and regression\n",
- "\n",
- "We ship several datasets from the UCR/TSML archives. The complete archives (including\n",
- " these examples) are available at the [time series classification site](https://timeseriesclassification.com)\n",
- " and the [UCR classification and clustering site](https://www.cs.ucr.edu/~eamonn/time_series_data_2018/).\n",
- " All the archive data can be loaded from these websites or directly\n",
- "from the web in code, see [data downloads](load_data_from_web.ipynb). All\n",
- " data is provided with a default train, test split. Problem loaders have an argument\n",
- " `split`. If not set, the function returns the combined train and test data. If\n",
- " `split` is set to `\"test\"` or `\"train\"`, the required split is return. `split` is\n",
- " not case sensitive. They can also be loaded with the functions `load_classification`\n",
- " and `load_regression`, which also return meta data. See the notebook [data loading](data_loading.ipynb) for details. The data X is stored in a 3D\n",
- " numpy array of shape `(n_cases, n_channels, n_timepoints)` unless unequal length,\n",
- " in which case a list of 2D numpy array is returned.\n",
- "\n",
- "| dataset name | loader function | properties |\n",
- "|-----------------------------|:-------------:|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n",
- "| Appliance power consumption | `load_acsf1` | univariate, equal length/index |\n",
- "| Arrowhead shape | `load_arrow_head` | univariate, equal length/index |\n",
- "| Gunpoint motion | `load_gunpoint` | univariate, equal length/index |\n",
- "| Italy power demand | `load_italy_power_demand` | univariate, equal length/index |\n",
- "| Japanese vowels | `load_japanese_vowels` |
univariate, unequal length/index |\n",
- "| OSUleaf leaf shape | `load_osuleaf` | univariate, equal length/index |\n",
- "| Basic motions | `load_basic_motions` | multivariate, equal length/index |\n",
- "\n"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "source": [],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:58:20.220860Z",
- "start_time": "2024-09-25T22:58:20.216870Z"
- }
- },
- "outputs": [],
- "execution_count": null
+ "execution_count": 59
},
{
"cell_type": "markdown",
"source": [
- "### ACSF1\n",
- "\n",
- "The dataset is compiled from ACS-F1, the first version of the database of appliance\n",
- "consumption signatures. The dataset contains the power consumption of typical appliances. The recordings are characterized by long idle periods and some high bursts of energy consumption when the appliance is active.\n",
- "\n",
- "The classes correspond to 10 categories of home appliances: mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave ovens, printers, and televisions (LCD or LED).\n",
- "\n",
- "The problem is univariate and equal length. It has high frequency osscilation."
+ "### PLAID\n",
+ "PLAID stands for the Plug Load Appliance Identification Dataset. The data are intended for load identification research. The first version of PLAID is named PLAID1, collected in summer 2013. A second version of PLAID was collected in winter 2014 and released under the name PLAID2.\n",
+ "This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models.\n",
+ "For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available).\n",
+ "The classes correspond to 11 different appliance types: air\n",
+ "conditioner (class 0), compact flourescent lamp, fan, fridge,\n",
+ "hairdryer , heater, incandescent light bulb, laptop, microwave,\n",
+ "vacuum,washing machine (class 10). The data is univariate and unequal length."
],
"metadata": {
"collapsed": false
@@ -514,25 +504,27 @@
{
"cell_type": "code",
"source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "from aeon.datasets import load_acsf1\n",
+ "from aeon.datasets import load_plaid\n",
"\n",
- "trainX, trainy = load_acsf1(split=\"train\")\n",
- "testX, testy = load_acsf1(split=\"test\")\n",
- "print(type(trainX))\n",
- "print(trainX.shape)\n",
- "plt.plot(trainX[0][0][:100])\n",
+ "plaid, plaid_labels = load_plaid(split=\"train\")\n",
"plt.title(\n",
- " f\"First 100 observations of the first train case of the ACFS1 data, class: \"\n",
- " f\"({trainy[0]})\"\n",
- ")"
+ " f\"three train cases for PLAID, classes\"\n",
+ " f\"( {plaid_labels[0]}, {plaid_labels[10]}, {plaid_labels[200]})\"\n",
+ ")\n",
+ "print(f\" number of cases = \" f\"{len(plaid)}\")\n",
+ "print(f\" First case shape = \" f\"{plaid[0].shape}\")\n",
+ "print(f\" Tenth case shape = \" f\"{plaid[10].shape}\")\n",
+ "print(f\" 200th case shape = \" f\"{plaid[200].shape}\")\n",
+ "\n",
+ "plt.plot(plaid[0][0])\n",
+ "plt.plot(plaid[10][0])\n",
+ "plt.plot(plaid[200][0])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:20.673104Z",
- "start_time": "2024-09-25T22:58:20.238813Z"
+ "end_time": "2024-09-25T22:58:22.119236Z",
+ "start_time": "2024-09-25T22:58:21.932521Z"
}
},
"outputs": [
@@ -540,72 +532,72 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\n",
- "(100, 1, 1460)\n"
+ " number of cases = 537\n",
+ " First case shape = (1, 500)\n",
+ " Tenth case shape = (1, 300)\n",
+ " 200th case shape = (1, 200)\n"
]
},
{
"data": {
- "text/plain": [
- "Text(0.5, 1.0, 'First 100 observations of the first train case of the ACFS1 data, class: (9)')"
- ]
+ "text/plain": "[]"
},
- "execution_count": 8,
+ "execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 8
+ "execution_count": 60
},
{
"cell_type": "markdown",
"source": [
- "### ArrowHead\n",
- "The arrowhead data consists of outlines of the images of\n",
- "arrowheads. The shapes of the projectile points are converted into\n",
- "a time series using the angle-based method. The classification of\n",
- "projectile points is is an important\n",
- "topic in anthropology. The classes are based on shape\n",
- "distinctions, such as the presence and location of a notch in the\n",
- "arrow. The problem in the repository is a length normalised version\n",
- "of that used in Ye09shapelets. The three classes are called\n",
- "\"Avonlea\" (0), \"Clovis\" (1) and \"Mix\" (2).\n"
+ "## Regression\n",
+ "\n",
+ "We ship one regression problem from the [Time Series Extrinsic Regression]\n",
+ "(http://tseregression.org/) website and one soon to be added."
],
"metadata": {
"collapsed": false
}
},
{
- "cell_type": "code",
+ "cell_type": "markdown",
"source": [
- "from aeon.datasets import load_arrow_head\n",
+ "### Covid3Month\n",
"\n",
- "arrowhead, arrow_labels = load_arrow_head()\n",
- "print(arrowhead.shape)\n",
- "plt.title(\n",
- " f\"First two cases of the ArrowHead, classes: \"\n",
- " f\"({arrow_labels[0]}, {arrow_labels[1]})\"\n",
- ")\n",
+ "The goal of this dataset is to predict COVID-19's death rate on 1st April 2020 for each country using daily confirmed cases for the last three months.\n",
+ "This dataset contains 201 time series, where each time series is the daily confirmed cases for a country.\n",
+ "The data was obtained from WHO's COVID-19 database.\n",
+ "Please refer to https://covid19.who.int/ for more details"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from aeon.datasets import load_covid_3month\n",
"\n",
- "plt.plot(arrowhead[0][0])\n",
- "plt.plot(arrowhead[1][0])"
+ "covid, covid_target = load_covid_3month()\n",
+ "print(covid.shape)\n",
+ "plt.title(\"Response variable for Covid3Months data\")\n",
+ "plt.plot(covid_target)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:20.861894Z",
- "start_time": "2024-09-25T22:58:20.689090Z"
+ "end_time": "2024-09-25T22:58:22.385200Z",
+ "start_time": "2024-09-25T22:58:22.146164Z"
}
},
"outputs": [
@@ -613,41 +605,40 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "(211, 1, 251)\n"
+ "(201, 1, 84)\n"
]
},
{
"data": {
- "text/plain": [
- "[]"
- ]
+ "text/plain": "[]"
},
- "execution_count": 9,
+ "execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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tN2Fg/MUvfoHXXnsNo0ePxqRJk3DVVVcJAzIzxmbNmmU719/+9rfo6urCjh07HK/lkCFDcNxxx+G+++7jn917771Ip9M45ZRT+GelUgm//OUvsd9++yGXy2HIkCEYOnQoXnnlFeX+99prL8djUqxevRrnnHMOBg0ahJaWFgwdOhRTp04FANt+/dwDGZs2bcL27dtxyy232K7P7NmzAVh9IQiC3mu/16N///4ATC2V33bsv//+ts/ldvi9z6r9A8B+++0nfJ7JZGz3gmH48OGYNm0aTj31VNx000344he/iOOPPx7r169Xbn/IIYdg7NixuPvuu3HXXXdhxIgR+NznPufYnv32248b2E7nyyA/Y8w4CWJ8eO0jl8vh5z//Of7yl79g+PDhOProo/GLX/zC8XzpuQD2awtAeU8fe+wxHHHEEWhoaMCgQYMwdOhQ3HTTTa7PN0VHRweuuOIKrgFjz/D27dt970MjOLSmRsMTkyZN4tFPJ598Mo466iicddZZePvtt9HS0sKZie985zuYPn26ch/Mv3/00Ufjvffew6OPPoq//e1v+O1vf4tf/vKXuPnmm/H1r3890vNw0kwYRGh62mmn4bOf/Swefvhh/O1vf8O1116Ln//853jooYdw4okn8nO99tprMWHCBOX+vJLWnXHGGZg9ezZWrVqFCRMm4L777sNxxx2HIUOG8G1+9rOf4Yc//CG+9rWv4cc//jEGDRqEZDKJSy65RMkE+WFpisUijj/+eGzduhXf/e53MXbsWDQ3N2PNmjU455xzXBkmv2D7+OpXv4pZs2Yptzn44IN7fBwv+LkeADB27FgAwKuvvhp6SK6f+xwFTj31VPz3f/83Hn30UVxwwQXKbc466yzcdNNN6NevH04//XSb0VIp/DxjYezjkksuwZe+9CU88sgj+Otf/4of/vCHmDdvHp588kkccsghwRqtwDPPPIMvf/nLOProo/HrX/8aI0eORCaTwe23364MlVfhoosuwu23345LLrkEU6ZMwYABA5BIJHDGGWeE8qxpqKGNGo1ASKVSmDdvHo499ljceOON+N73vsdXkJlMBtOmTfPcx6BBgzB79mzMnj0bu3btwtFHH42rrrpKMGpKpRLef/99zgwAwL/+9S8AVnTHnnvuiSeeeAI7d+4UVvAssmPPPfes6BxHjhyJb37zm/jmN7+JjRs34tBDD8VPf/pTnHjiidx91b9/f1/nqsLJJ5+MCy64gLsm/vWvf+Hyyy8XtnnggQdw7LHH4tZbbxU+3759e8WT4quvvop//etfuPPOO3H22Wfzz2nkGYWfeyBj6NCh6NevH4rFYsXXR4Wo7vVRRx2F3XbbDX/4wx/w/e9/31MsvOeee+Ltt9+2fa5qh5/7rNo/YDKClD3J5/P44IMPMH78eM9zYpFsbmzAWWedhSuuuALr1q1zFOyy9rzyyisolUqC4dOT666KdqoE++yzD7797W/j29/+Nt555x1MmDAB119/PX7/+98rt6fXVoZ8Tx988EE0NDTgr3/9q8BK33777bbfOp3PAw88gFmzZuH666/nn3V2diqj0jTCg3Y/aQTGMcccg0mTJmHBggXo7OzEsGHDcMwxx+A3v/kN1q1bZ9t+06ZN/P2WLVuE71paWrDvvvuiq6vL9jsW9gqYq7Qbb7wRmUwGxx13HAAz50axWBS2A4Bf/vKXSCQSOPHEEwOdV7FYtE0Ew4YNw6hRo3j7Jk6ciH322QfXXXcddu3a5XquThg4cCCmT5+O++67D/fccw+y2ayNJUilUrbV7f3339+jTM5swqb7NQwDN9xwg+NvvO6B6hgzZszAgw8+iNdee832vZ/ro0LY95qhqakJ3/3ud/Hmm2/iu9/9rpJR+P3vf4/ly5fzdixfvhzLli3j37e1teGWW27BmDFjBG2Gn/ss47DDDsPQoUNx8803o7u7m39+xx132CbDzZs3K9v729/+lu/LCfvssw8WLFiAefPmOUbrsfNdv369oA0qFAr41a9+hZaWFu66DILm5mYA6pBzP2hvb0dnZ6fw2T777IN+/fopxxGGkSNHYsKECbjzzjuF53zx4sVcC8WQSqWQSCRQLBb5Zx9++KEyc3Bzc7PyXFTP8K9+9SthnxrhQzM1GhXhsssuw8yZM3HHHXfgG9/4BhYuXIijjjoKBx10EM477zzsvffe2LBhA5YtW4ZPPvmE51cZN24cjjnmGEycOBGDBg3CSy+9xEOoKRoaGrBo0SLMmjULkydPxl/+8hf8+c9/xve//30MHToUAPClL30Jxx57LP77v/8bH374IcaPH4+//e1vePTRR3HJJZcIomA/2LlzJ/bYYw+ceuqpGD9+PFpaWvDEE0/gxRdf5KutZDKJ3/72tzjxxBPxmc98BrNnz8buu++ONWvW4KmnnkL//v3xpz/9yfNYp59+Or761a/i17/+NaZPn25LNvfFL34RV199NWbPno0jjzwSr776Ku666y5HXYUfjB07Fvvssw++853vYM2aNejfvz8efPBBR72Dn3ugwjXXXIOnnnoKkydPxnnnnYdx48Zh69atWLlyJZ544gls3bo1cNvDvtcUl112GV5//XVcf/31eOqpp3DqqadixIgRWL9+PR555BEsX74czz33HADge9/7Hv7whz/gxBNPxMUXX4xBgwbhzjvvxAcffIAHH3zQ5sbxus8yMpkMfvKTn+CCCy7A5z73OZx++un44IMPcPvtt9vu/e9//3vcfPPNOPnkk7H33ntj586d+Otf/4rFixfjS1/6kqNOhkEOx1bh/PPPx29+8xucc845WLFiBcaMGYMHHngA//jHP7BgwQLfAmuKiRMnAgAuvvhiTJ8+HalUCmeccYbv3//rX//Ccccdh9NOOw3jxo1DOp3Gww8/jA0bNnjuZ968eTjppJNw1FFH4Wtf+xq2bt2KX/3qV/jMZz4jLFJOOukkzJ8/HyeccALOOussbNy4EQsXLsS+++5r0wBOnDgRTzzxBObPn49Ro0Zhr732wuTJk/HFL34Rv/vd7zBgwACMGzcOy5YtwxNPPMHTR2hEhKrHW2n0GrCQ7hdffNH2XbFYNPbZZx9jn332MQqFgmEYhvHee+8ZZ599tjFixAgjk8kYu+++u/HFL37ReOCBB/jvfvKTnxiTJk0yBg4caDQ2Nhpjx441fvrTnxrd3d18m1mzZhnNzc3Ge++9Z3z+8583mpqajOHDhxtXXnmlLcx5586dxqWXXmqMGjXKyGQyxn777Wdce+21Qri1YdhDVRn23HNPHo7Z1dVlXHbZZcb48eONfv36Gc3Nzcb48eONX//617bfvfzyy8Ypp5xiDB482Mjlcsaee+5pnHbaacaSJUt8XdvW1lajsbHRFi7L0NnZaXz72982Ro4caTQ2Nhr/9m//ZixbtsyYOnWqENbLQoLvv/9+2z5UId1vvPGGMW3aNKOlpcUYMmSIcd555/HQ9ttvv51vF+QeQArpNgzD2LBhg3HhhRcao0ePNjKZjDFixAjjuOOOM2655RbPa6MK6TaMnt9rLzzwwAPG5z//eWPQoEFGOp02Ro4caZx++unG0qVLhe3ee+8949RTTzUGDhxoNDQ0GJMmTTIee+wx5T697rPqHhmGYfz617829tprLyOXyxmHHXaY8fe//91271988UVj5syZxqc+9Skjl8sZzc3NxqGHHmrMnz/fyOfzwv5oSLcbVNduw4YNxuzZs40hQ4YY2WzWOOigg4S+YhhWOLYqzFruH4VCwbjooouMoUOHGolEgod3+93H5s2bjQsvvNAYO3as0dzcbAwYMMCYPHmycd9997meG8ODDz5oHHDAAUYulzPGjRtnPPTQQ8asWbNsId233nqrsd9++xm5XM4YO3ascfvtt/PrSPHWW28ZRx99NL/PbDzZtm0bv24tLS3G9OnTjbfeeksYczTCR8IwAii4NDSqgHPOOQcPPPCA0r2joaGhoaHhBK2p0dDQ0NDQ0KgLaKNGQ0NDQ0NDoy6gjRoNDQ0NDQ2NuoDW1GhoaGhoaGjUBTRTo6GhoaGhoVEX0EaNhoaGhoaGRl2gzyTfK5VKWLt2Lfr16xdamm4NDQ0NDQ2NaGEYBnbu3IlRo0Z51inrM0bN2rVrMXr06Fo3Q0NDQ0NDQ6MCfPzxx9hjjz1ct+kzRg1L5/3xxx+jf//+NW6NhoaGhoaGhh+0trZi9OjRvspy9Bmjhrmc+vfvr40aDQ0NDQ2NXgY/0hEtFNbQ0NDQ0NCoC2ijRkNDQ0NDQ6MuoI0aDQ0NDQ0NjbqANmo0NDQ0NDQ06gLaqNHQ0NDQ0NCoC2ijRkNDQ0NDQ6MuoI0aDQ0NDQ0NjbqANmo0NDQ0NDQ06gLaqNHQ0NDQ0NCoC2ijRkNDQ0NDQ6MuoI0aDQ0NDQ0NjbqANmo0NDQ0NDQ06gLaqNHQ0NDQ0NAIhJc+3IrfP/8RDMOodVME9Jkq3RoaGhoaGhrh4PKHXsU7G3dh8l6DsN/wfrVuDodmajQ0NDQ0NDQCoa2rAADYVX6NC7RRo6GhoaGhoREIJUN8jQu0UaOhoaGhoaERCKWyliZumhpt1GhoaGhoaGgEgmZqNDQ0NDQ0NOoChmZqNDQ0NDQ0NOoBzP2kmRoNDQ0NDQ2NXg1my2imRkNDQ0NDQ6NXo1SmaOJl0lRo1CxcuBBjxoxBQ0MDJk+ejOXLlztu+/rrr2PGjBkYM2YMEokEFixYYNuGfSf/u/DCC/k2xxxzjO37b3zjG5U0X0NDo4f4y6vrcN1f347dKk1DQ6M6MLhQOF5jQGCj5t5778XcuXNx5ZVXYuXKlRg/fjymT5+OjRs3Krdvb2/H3nvvjWuuuQYjRoxQbvPiiy9i3bp1/N/ixYsBADNnzhS2O++884TtfvGLXwRtvoaGRgj42V/exI1PvYt3Nu6qdVM0NDRqgLrR1MyfPx/nnXceZs+ejXHjxuHmm29GU1MTbrvtNuX2hx9+OK699lqcccYZyOVyym2GDh2KESNG8H+PPfYY9tlnH0ydOlXYrqmpSdiuf//+QZuvoaERAjrzJQBAV/lVQ0Ojb6FUD0xNd3c3VqxYgWnTplk7SCYxbdo0LFu2LJQGdXd34/e//z2+9rWvIZFICN/dddddGDJkCA488EBcfvnlaG9vd9xPV1cXWltbhX8aGhrhgPnT4zagaWhoVAf82Y/ZEBCooOXmzZtRLBYxfPhw4fPhw4fjrbfeCqVBjzzyCLZv345zzjlH+Pyss87CnnvuiVGjRuGVV17Bd7/7Xbz99tt46KGHlPuZN28efvSjH4XSJg0NDRE8m2iN26GhoVEbsGc/bgub2FXpvvXWW3HiiSdi1KhRwufnn38+f3/QQQdh5MiROO644/Dee+9hn332se3n8ssvx9y5c/nfra2tGD16dHQN19DoQyhqpkZDo0/DiKmmJpBRM2TIEKRSKWzYsEH4fMOGDY4i4CD46KOP8MQTTziyLxSTJ08GALz77rtKoyaXyzlqeDQ0qoU317Wiq1DChNEDa92UUMGZZ23UaGj0SZRiOgYE0tRks1lMnDgRS5Ys4Z+VSiUsWbIEU6ZM6XFjbr/9dgwbNgwnnXSS57arVq0CAIwcObLHx9XQiAKGYeCs/30eZ9yyDB3dxVo3J1QUY7pK09DQqA7iGv0U2P00d+5czJo1C4cddhgmTZqEBQsWoK2tDbNnzwYAnH322dh9990xb948AKbw94033uDv16xZg1WrVqGlpQX77rsv32+pVMLtt9+OWbNmIZ0Wm/Xee+/h7rvvxhe+8AUMHjwYr7zyCi699FIcffTROPjggys+eQ2NKGEYwLb2PACgrbuAxmyqxi0KD3xAi9uIpqGhETkMw4gtWxvYqDn99NOxadMmXHHFFVi/fj0mTJiARYsWcfHw6tWrkUxaBNDatWtxyCGH8L+vu+46XHfddZg6dSqWLl3KP3/iiSewevVqfO1rX7MdM5vN4oknnuAG1OjRozFjxgz84Ac/CNp8DY2qgepN6k17EtcKvRoaGtGDDmdxGwMqEgrPmTMHc+bMUX5HDRXAzBbsx5L7/Oc/77jd6NGj8fTTTwdup4ZGLUEf9jqzaawU6fV2YhoaGp4whPfxGgN07ScNjYhQ30xNPP3pfQGd+aI2JjVqCnFsq2FDFNBGjYZGRIjzg98TGIZhRT7EbJVW79jRnsfkny3BN+9aWeumaPRh0LEtbga2Nmo0NCICNWTqSVAbZ396veODLW3Y0ZHHqo+317opGn0YRoxd69qo0dCICOJqpoYNCRn17FaLO3gmZ33ZNWqIOI8B2qjR0IgIBqn1GLcHvycoxph6rndYWVz1ddeoHeLM1mqjRkMjIsR5NdMTCAOaLtJdVehQeo04IM5jmzZqNDQiQr0KhYul+A5o9Q4dSq8RBwjjWcy6ojZqNDQigpinJmZPfg9Qr8Zab4DF1OgLr1E7GJqp0dDoezDqdPKvV2OtNyCulZE1+hZKWlOjodH3ID74MXvyewAanl4/Z9U7ENfKyBp9C0JkZ8xGAW3UaGhEhDiL6XqCej2v3gAd0q0RB+joJw2NPoh6zVNTrFO3Wm9ASYd0a8QAhjC2xasvaqNGQyMiGHXqfjK0pqZmYJdbG5MatUScs6Vro0ZDIyLUa5SQdj/VDpqp0YgDRE1NvKCNGg2NiFCvQmEhT41OvldVWELh2rZDo28jzgs2bdRoaESEOFey7Qnq1a3WG8CFwrFbH2v0JcTZBa2NGg2NiFCveWooUxOz8azuofPUaMQBukq3hkYfRJzFdD1BnHNU1Dt0RmGNOCDOujpt1GhoRASxRlINGxIy4pxNtN5B89TEjfbX6DvQmhoNjT6IetXUxHmVVu8oxZj21+g7iHMQhDZqNDQiQpyzbvYEcV6l1TviXEhQo+8gzos0bdRoaESEemU0RKFw/ZxXb0Cc84No9B3EWS+ojRoNjYgQZ4q2JzBiPKDVO2heoHrqUxq9CzRAIG5DgDZqNDQiQr3WftLup9qhXvtUrdCZL2L1lvZaN6PXgRrXcYuA1EaNhkZEqFf9gxjVVT/n1RugEx+Gi+/c/08cfe1TeHNda62b0qsQ54WNNmo0NCJCvYY+19O59DbEeTLpjVi91WRp3t/UVuOW9C7ojMIaGn0QpTplNOpVAN0bUK86rVqBsY4d+WKNW9K7EOcxQBs1GhoRoRTj1UxPUKrTpIK9AVpTEy5Y/+3oLtS2Ib0MtOvFrR9qo0ZDIyLUbe2nGK/S6h2GYNToa99TlDRTUxHi7AbVRo2GRkSoV1dBnIvZ1TvqVadVKzADvb1bGzVBEGfjWhs1VUJrZ17n9OhjiPNqpicQzqueTqwXIM5aht4IztRooyYQBNd67ZqhhDZqqoC12ztw2E+ewCX3rqp1UzSqiHqt/VSvhTp7A+qV/asV2DXU7qdgEHR1MRsEtFFTBXywuQ3dhRLe0LkQ+hTqNadIvZ5Xb4ChhcKhQrufKkOc3aDaqKkC2MAfN4tWI1qIbpoaNiRkCLWfatiOvggd/RQu2HOpmZpgEMskxKsjaqOmCmBzQDFmN18jWlAbtp7ufb261XoDtPspXBS1pqYixLnrVWTULFy4EGPGjEFDQwMmT56M5cuXO277+uuvY8aMGRgzZgwSiQQWLFhg2+aqq65CIpEQ/o0dO1bYprOzExdeeCEGDx6MlpYWzJgxAxs2bKik+VUHG3yKmqnpU6jXyV9PrLWDFgqHC66p0UZNIMS5HwY2au69917MnTsXV155JVauXInx48dj+vTp2Lhxo3L79vZ27L333rjmmmswYsQIx/1+5jOfwbp16/i/Z599Vvj+0ksvxZ/+9Cfcf//9ePrpp7F27VqccsopQZtfExjaqOmTqNckdfUa1dUboMPpwwXry+3a/RQIcV7YBDZq5s+fj/POOw+zZ8/GuHHjcPPNN6OpqQm33XabcvvDDz8c1157Lc444wzkcjnH/abTaYwYMYL/GzJkCP9ux44duPXWWzF//nx87nOfw8SJE3H77bfjueeew/PPPx/0FKoO5rfVRk3fQpwf/J4gzqu0eke9lt6oFSz3k84oHARx1nYFMmq6u7uxYsUKTJs2zdpBMolp06Zh2bJlPWrIO++8g1GjRmHvvffGV77yFaxevZp/t2LFCuTzeeG4Y8eOxac+9SnH43Z1daG1tVX4VytwoXDc7r5GpKhXRkMQCtfRefUGxDnqpDdC136qDHHOlh7IqNm8eTOKxSKGDx8ufD58+HCsX7++4kZMnjwZd9xxBxYtWoSbbroJH3zwAT772c9i586dAID169cjm81i4MCBvo87b948DBgwgP8bPXp0xe3rKbhQOG53XyNS1KumRgjp1n26qqjXPlUrsEuoNTXBoKt0e+DEE0/EzJkzcfDBB2P69Ol4/PHHsX37dtx3330V7/Pyyy/Hjh07+L+PP/44xBYHg9bU9E3U6+RfrwxUb0CcV8i9EUUtFK4IcXatp4NsPGTIEKRSKVvU0YYNG1xFwEExcOBAfPrTn8a7774LABgxYgS6u7uxfft2ga1xO24ul3PV8FQTrAPoQahvoV4nfzFPTR2dWC9AvVZ+rxVYX27PF2EYBhKJRI1b1DtQN5qabDaLiRMnYsmSJfyzUqmEJUuWYMqUKaE1ateuXXjvvfcwcuRIAMDEiRORyWSE47799ttYvXp1qMeNCqwDFOopA5uGJ+K8mukJdARO7VCvhnKtwC6nYQBdBT0++0WcGcNATA0AzJ07F7NmzcJhhx2GSZMmYcGCBWhra8Ps2bMBAGeffTZ23313zJs3D4ApLn7jjTf4+zVr1mDVqlVoaWnBvvvuCwD4zne+gy996UvYc889sXbtWlx55ZVIpVI488wzAQADBgzAueeei7lz52LQoEHo378/LrroIkyZMgVHHHFEKBciSlgZhWvcEI2qIs6rmZ6gKAxodXRivQD1aijXCrQvd3QX0ZBJ1bA1vQdxZgwDGzWnn346Nm3ahCuuuALr16/HhAkTsGjRIi4eXr16NZJJiwBau3YtDjnkEP73ddddh+uuuw5Tp07F0qVLAQCffPIJzjzzTGzZsgVDhw7FUUcdheeffx5Dhw7lv/vlL3+JZDKJGTNmoKurC9OnT8evf/3rSs+7qmD3vJ6yymp4w6jTyV+HdNcO9dqnagXqSu3IF7FbDdvSmyAs2GrYDhUCGzUAMGfOHMyZM0f5HTNUGMaMGeNpyd1zzz2ex2xoaMDChQuxcOFC3+2MC3RG4b6Jeg2/rdfz6g2oV/avFpDF+7qopX/EuahtLKKf6h3CJKBngT6DemU0aB+OG/Vc7xBp/9q1ox4gP5M6Aso/4qzt0kZNFUA7gHZB9R3E2e/cE9Rr9fHegHo1lGsBeSzWCfj8QzM1fRx0QtMuqL6DOEcI9AS0D8dtQKt3xHky6W2QDfJ2XSrBN4S+F7NuqI2aKoBOaNqo6Tuo1zo9MR7P6h5xpv17G2SmplMzNb4R5yg8bdRUAdr91DdRr4Ja7QKpHXSZhPAg910tFPaPOI8B2qipArRQuG+iXiegonBeNWxIL8c7G3bif//+ProK/ifTkmbJlCgUS/jtM+/jjbX+CxfLY7HW1ARAjAXr2qipArSmpm+iXvUP9Xpe1ca1f30bP338TSx9e5Pv3wg6LT2WcDz//lb85M9v4mePv+n7N/JYrKOf/CPOblBt1FQBdPDRRk3fQZwf/J5AFArXsCG9HLu6TGHqzk7/AlUqbtXX3sLOzjwA65r6gSwF0O4n/4hzZKc2aqoAQSgcsw6gER3qtZxAnP3pvQlWUk7/cfH16tLsKQrlQTbINZE31e4n/4jzGKCNmiqgpN1PfRL1Wvgxzqu03gR2HYsBcv3Uq/i8p+D19QJcE+1+qhz0uY9bN9RGTRWgk5X1TQgh3XU0A4kZhWvYkF4OowKmRtd+UoMZKEGuiTZqKge9cnEb2rRRUwVo91PfRL2uquNMPfcmsEm1EKBzxLmQYC3BrmUQJtwW0q3dT74R51Ip2qipArT7qW+iXid/UStUw4b0cljupyATMX2vLz5DkWtq/P9GvuyaqfGPONcg00ZNFUBvujZq+g6MOhV1ilqh+jmvaoNdu4qZGn3tOYpcU9MD91Nel0nwizgv2LRRUwXokO6+iXp1P+mQ7nBQCVMj5AjS+jyOUgWammpnFF63owOPrlqDQhBleEwR51xV6Vo3oC9AU8Z9E3FezfQE9Xpe1Qa7doViZROxvvYWKnE/VVso/JPH3sSfX12H/g0ZHDt2WKTHihpxzsGlmZoqQGtq+ibqlakx6vS8qg1L3FpZnhp97S0wF16QQAzZKIw6T8229m7htTfDcPyj9tBGTRUglEnQq6s+g3rV1BRjHPnQm8AuXTBNjbCHUNvTm1GqQFMj25JRMzWVRGjFFXFmDLVRUwXogpZ9E3F+8HsCUaxaw4b0clgZhYNoajRTowKTqQTRGckLzKiNGna4enhm4qyp0UZNFUBvepBVmUbvRr26n+rVWKs2uKZGh3T3GOxaBGEOmTGZTZvTYNTuJ2ZE1QNbX4pxsIA2aqoAzdT0TdTr5C8WVayf86o22KWrNGGcHkosMLF1kGvCDKCWnBkvUygZ6C5EF5lUCTMXVwh5amrXDCW0UVMFaE1N30T91n7SE2sYKFYwyem6W2r0JE8NM2qAaNkatqCth/sW53xJ2qipAnT0U99EvTI1xRgPaL0JlbifdO0nNSrJU8P6cS6dRDqZABCtrqaSvERxBT2DuPVDbdRUAdoP3jdRr4yGDukOB8yNV2lItx5KLFhMjf/fsMueSibQmEkBANq7o8sqzKOf6uC+GTHuh9qoqQJEpqaGDdGoKurVmBUzCtfPeVUbFZVJEPRMYbeo96KSKt1s22QigcasadRE6n6qQMwcV8R5waaNmipArP2krZq+gnrNUxPnAa03obKCltqgVIEbNQGuJWN3kklYRk2k7qc6FQrHrB9qo6YK0ExN34TAaNTRfRfGsJgNaL0JlWlq6Ht97RkqqtJd/k0qYbmfomVqxNfejDgb19qoqQIEoyZmHUAjOvQN91MNG9LLwVfuFdd+Cr1JvRb8WlYQ/ZRMJtCUZZqa6KOf6mEsiHNkpy5oWQXoPDV9E/U6AcV5ldabwPpEsOR7+tqrUKhIU2O+JhMJNGTM9X1nhExNJSH8cUWco/A0U1MFCHlq6qBDa/hDvboK6tVYqzYsjUWQ6CfrfR11qR7DYkEC/Mag7idzfR8pU1NBLp24Is79UBs1VQAds7T7qe+gXlfVcRYJ9iawibjSPDX62luwNDWVuJ8soXC07if22vvvm5BaoIbtUEEbNVWATr7XN6FrP2m4gV26Slwm8vu+jory1DCmJplAU1koHKX7qRLdT1yhC1r2cdAHTRs1fQf1OvlroXA4YJNboWKhsL74DDyxXQX6JJqnphrJ9+rhmYlzP9RGTRUQZ1HVq5/swBNvbKh1M+oScc662RMEXaV1dBfx3LubUdD5DARUkrekNzA1Kz7aiq/f+SI+2tJWtWPSa+jXBcW6oykUZkxNlAUty69xvXEBIIwBMXustVFTBcTZ/fSfd63A1//vJWxo7ax1U+oO9VrNWujDPk7rf558B2f99gU8smptdI3qhagk+qk3aGruWf4xnnhzIxa9tr5qx6xEvM7z1CQTSJVnwijH5/oSCsf3HLRRUwXE2f20vT0vvGqEhzhTtD1B0PNav8M0mLXhLMKoiKmJP/uXL1MgQYy1nqKS0h3U/ZRKJAL9thJYzFxkh6ga4pyDqyKjZuHChRgzZgwaGhowefJkLF++3HHb119/HTNmzMCYMWOQSCSwYMEC2zbz5s3D4Ycfjn79+mHYsGE4+eST8fbbbwvbHHPMMUgkEsK/b3zjG5U0v+qIM1PD2pOvhyctZugNroJKELSgZSUp7PsCKstTQ9/H83oWanC/KzFqeJmEhJmAT95P2KikPlVcEWdJRWCj5t5778XcuXNx5ZVXYuXKlRg/fjymT5+OjRs3Krdvb2/H3nvvjWuuuQYjRoxQbvP000/jwgsvxPPPP4/Fixcjn8/j85//PNraRJ/seeedh3Xr1vF/v/jFL4I2vyYQaj/FrANwsaKecEJHb3AVVIJiwAGNJx2ro2sQBixxa2VVuuP6yFrVqGtj1Pg9rOB+qgJTU0m0W1wRZ8YwcEbh+fPn47zzzsPs2bMBADfffDP+/Oc/47bbbsP3vvc92/aHH344Dj/8cABQfg8AixYtEv6+4447MGzYMKxYsQJHH300/7ypqcnRMJLR1dWFrq4u/ndra6uv30UBYSCK2UhUqmBg1fCH3jABVYKgA1pJMzU2UCO30tpPcZ0crey+1TsmDSDzy7bQMgnVZGrixtZXgjiz0IGYmu7ubqxYsQLTpk2zdpBMYtq0aVi2bFlojdqxYwcAYNCgQcLnd911F4YMGYIDDzwQl19+Odrb2x33MW/ePAwYMID/Gz16dGjtC4o4F7Rkq6l8gLBSDX/oDa6CSlAKSPXXYuUed1Sqsyv1AvavFkZs0D5pbme+phIJpLhRE3rTyPHqyf1E38frfAIxNZs3b0axWMTw4cOFz4cPH4633norlAaVSiVccskl+Ld/+zcceOCB/POzzjoLe+65J0aNGoVXXnkF3/3ud/H222/joYceUu7n8ssvx9y5c/nfra2tNTNshAEsRh2gVDJ45wySK0PDH+qXqaHvvU+sngSSYYFet8rz1ITapNBQSR2mnh/T6ly+o5+IpqaaQuF6IMXjrKmJXUHLCy+8EK+99hqeffZZ4fPzzz+fvz/ooIMwcuRIHHfccXjvvfewzz772PaTy+WQy+Uib68fCKuIGI1ERYECr4MnLWaI82qmJwiafK8Wk1zcUWlkHH1M43o5a5FkTrwu8XQ/sV3HaWFbKeqmTMKQIUOQSqWwYYOYrG3Dhg2+tS5umDNnDh577DE89dRT2GOPPVy3nTx5MgDg3Xff7fFxo4awKouTUUPaopma8NEXQrr9nFY9aQnCAp2EK81TE9c+xRZI1WxfsQIGi/0mlUgglbDvJ2zUU/STwNbG7LkOZNRks1lMnDgRS5Ys4Z+VSiUsWbIEU6ZMqbgRhmFgzpw5ePjhh/Hkk09ir7328vzNqlWrAAAjR46s+LjVQly1FSXN1EQKgdGoo8sblIGqJHNuvaPSNA+9oZhoLUL4qWHo93qyy5ckmpqo2hxXtr5S1FX009y5czFr1iwcdthhmDRpEhYsWIC2tjYeDXX22Wdj9913x7x58wCY4uI33niDv1+zZg1WrVqFlpYW7LvvvgBMl9Pdd9+NRx99FP369cP69WYmygEDBqCxsRHvvfce7r77bnzhC1/A4MGD8corr+DSSy/F0UcfjYMPPjiUCxEl4pqnRmBqYtSuekFviFSpBIFDuutohRoWRE2NDunuKaihEEf3kzAHxPS+BYGwsKldM5QIbNScfvrp2LRpE6644gqsX78eEyZMwKJFi7h4ePXq1UgmLQJo7dq1OOSQQ/jf1113Ha677jpMnToVS5cuBQDcdNNNAMwEexS33347zjnnHGSzWTzxxBPcgBo9ejRmzJiBH/zgB0GbXxMIeWpiNBIJFHg9PGkxQ5xXMz1B0ImV9bM49f1ao/LoJ/o+nteTnU81m1dJkVX2m1QyeqFw0IVA3GEgvudTkVB4zpw5mDNnjvI7ZqgwjBkzxtNy9vp+9OjRePrppwO1MU6Iq7aCPmg6o3D4iOt97ykCh3TXUShrWKg8T038mRp2PtU0Yit51th2qUT0TI3A2sb1xgVAnOva6dpPVUBvcD/FqV31gt6wqq4Eoq7De/taTHJxRyh5amJH/JuohbuxENDQptslSO2nqAjrSso4xBlxdoNqo6YKqHQAixq0Y+Zj1K56gVgmoYYNCRlBV8VW1urImtTrIOvZ/OpAghqUtUAtjBpRiOvvN6w/mlW6IxYKxzgBayUQLlPM+qE2aqoAI6ZMjRAxUA9PWsxQt0xNhRmF6+ka9BSyERM0YRwQXzeGFf1UxWP2xP2UTCDBQroji36yH7c3I86pBbRRUwXEOaMwg45+Ch9xpmh7gqBsgQ7ptkO+FH5TKogRdSE2KETUItliRVW6WfQTDemOqM31pq+L8/loo6YKiOvqig4EuvZT+KhXpoYa5oGS79XRNegp5P7g1+CL82TCUOsq3XEsk0CvRT0Y9zH2PmmjphoQmZratUOG+KBVjyt+/NV1+LdrnsQ/P95etWPWAvWqqQlKPfPopzoYzMNCGEZN3JPv1Sqk27c+iYd0VzdPTVyN0SCQ2do49UVt1FQBRkyZmlKNmJon3tyANds78Oy7m6t2zFqg3gYyhqAMlC6TYIe8hvBv1Fjv43o1ax/S7e83Rc7URB/9JGhq6kC+KBsxcRretFFTBcS1HEGtClrWIo16LVC37qeAVL8WCtshXwu/mrY4CzQZirWo/VRBeoqaRT/F9L4FgXxv49QXtVFTBVB7IU5BRrUqk8COVe/i5PoVCsurNPeTK2mmxobK3U/q93FCLYTCleSpMThTg8jdT5W4x+IMeQ0cpzPSRk0VEFc3RK3KJPSVSU6ojxKj+95TyKtZr9vIVqZx0pPVGvbop/rT1FSTlBZrP/n7Da39FLVQmO62HsY9zdT0ccS1QwvupypSSJqp6d2Qz8VrQGNdq97djUEgGyRFHxafYRhSuv2wWxUOauFurCRPDfuNWSah/FlUTI3gforkEFWFfAoxsmm0UVMNxJWpoRFP1TQwLOFoTEflkBDX+95T2N1P/raPk0Ffa8i6Cj+aNvk6x7VP1SajMHlfQfSTJRTW7ic/0ELhPo741n6y3lfT/VTsK0yNEPFQP+calHpmLGA9CCTDQiXRTzZjMswGhQTDMIimpnrHpUah/4zC5msiEb1QOK5Z5StFULa2mtBGTRVA73ecJnIh+V4Nop/q4eF2Q73mqZFvmzdTU36t8/sdBJVEP8V5ImGoRZ07wzAqElBb7iciFNbJ93xBa2r6OOKaUbhWDBJbVcXJwIsClfj5ewPkvuKtqWFC4fq5Bj2FfCkqYmpieDlrUY3aZuz5FV0r3E9Rre3ofuN434LCbmDXph0qaKOmCohr7SchpLuq0U/l49eDYs4FvSH8NihUegBPo0ZnFLahEqamN2hqamHU2I3sYL+j7qdqZBSO0xxQMeRziNEpaaOmCogrU0MfrnxVo5/6BlPTGxKlBYXqlnndxpJmamyQr0UlTE0cHx9B21KlIUW+dr7LJJQ3SyUTSEYsFK63oIE4u0K1UVMFCCHdMbr51MCqpvspbtFPL324FV/61bNY8dG2UPcr10epNt7duBNfvvFZPPHGhtD2qeonXpMIz1MTj9sdC9hCuisyauIzljAImX2rxdTI19K3UcM0NdELhQUGK47WKMw2nnP7clzzl7c8t41zX9RGTRUgaldq2BAJolC4ikZN+XrEhalZ9Np6vLpmB/72xvpQ91vr1dnStzfhlU924E+vrA1tn6rzcLuNpZKVWyWug3ktYE++5z0w2C5fDC9nLUKXe+p+SiYTSLE8NZExNeS4MTIAKD7c0oalb2/CXS985LmtLVggojZVAm3UVAGiURMfq0aoSVVN91ORrdzj8SjwAnwha3zoJF4LoyaK0HnVebhNXmLSsXjc7zhANvD8PAvydY7T6pghaF2wMGDPcB2MqUkmYLmfqqCpidEUICBIVGqc+6I2aqqAWoQ5+gGd7GqRfC8uTE1U0TlC9tcanGoUAt2gmppKCg32BdiZGj/uJ/nv+F3PQg3ut3zt/Gtqyu6nahS0rPECxw+CjMtxjsTTRk0VIApGa9gQCWL0U9/NU8Mm/7AfzKCFH8MGG0jDHESDMjVxTTxZa1RSJqE3CIVr4X6yXRefQxl3PyWiFwr3hjw1pQCLoDhH4mmjpgqIK1MjuJ/6sKYmqgKbtc7lwOzUMO1VOuCV5wHN1FQAe5mESlbH8bueAlNTM02NT6am/FykkgmefC8q11CtWVs/4Kk2fFw/zdT0ccR1tVqrMgmWpiYezuWo3E+1jhDg7qdQmRrrfTrpXdm4kpo8fQHyMOBPx+C+jzhAjPKp/jGByjQ1UVfprkX+nqCgjLUXW6OZmj6OWoVOe4G2y0/0RVjgvtuYJN+LKjlcrR98ZjSG2efovpI+JoLeQLvXAvbke36in+LP1PSq5HvcqCFVuqvgfoqTAUARJBxfMzV9HHHNU0PbUtWCljxvSTyuRVQan1o/+IyJC3MQZRMpTVjmtvtKCg32JlRqCFeWp8b97zigFvdbHlN7UqXbMKIxFntDQcsgHgVbSHeMTkkbNVWAYKXHqEMLQmEd/RT6JFFr9xMX/oV4XGt1a/7z2j8lIOI6mFeKpW9vxME/+hv+/Mq6wL+ViRlfmpoK3SzVRC00VPbr4u93vC+T6CcgmnZTV3+c5gCKIB6FWo9tbtBGTRUQ17oftctTE75bpCeIYvI39+f+d9SIgoFiu6IRI26XrZ7dTy9+uBW7ugpY9v7mwL+1sQsVaGpiNJRwiNFP9u8Nw0B3IdyxRr6WvkO6y80w3U/EqIngwoqJOEPffSgIklOq1q51N2ijpgqIa/QTdTlVM6MwO1TcmJow700lhR/DBmegIoh+SiYSJPrJjamJ/2BeKdg6oBLXrdw/Kol+itNEwuClqfn2/f/E5J89ga1t3aEdU77+fp9joUxCwjJqopAXCixIDO8bILGqHn3annwvihZVBm3UVAFCnpoY3f1aRWUVuIA1LtFP5deIooQYjCqfLru/4Z4XcT/x6Cfn7es5pJtdi3wFRk0l0U+9wajxCule/sFWbGvP471Nu0I7ZqX5e6wyCRDdT5EwNdb7OAq8gWBMjf0ax+ectFFTBdAOEBd2AhAH0mpW6Y6fpsY893Az78aHqYnE/ZRMgE0DfbVMgtWPgz879ugnP0aN+HccL6dXSDdjVcIMTKg0pLtImJpkImJNTS9wwwqsamBNTSRNqgjaqKkCal3Y0AlRRz99tKVNqdWJX0bh8mudGTXseGGuDFVZWP0yNXFiKcNAT1IT2Jkab8NIvo8xGko4vNxPbPEUqni9JF8Xf/tmm8lC4Sj6qZxVPo5sTZBkrHa9YHzORxs1EcMwDDGkO0YDe5T5c557bzOmXrsUP/nzm7Zjck1NTPLUWOUEwtyn4rMqn24USQVpSHfCT56aXqAlqBSW+6kCpkbqDPVS+8mvURMmS9vjKt2JBIhNE0k/tRteoR+ixwjiKo6zga2NmohhV4nHx0qnY3E+ZH3Lh5vbAcDmO48jDWuFdEfL1FT7vkdSJoGtbn2GdMvRMHHp+2GgJ25U+Zr5qf1UqZulmhALWtq/z0eQTVw2QvyOK2w7ZqDz/hzBuGRj5mJ474J4FORv49QXtVETMdRuiBo0RAH6YBlG2JlnzUGrM1+UPvdPcVYL0WhPan/fgxSo8wt2jRI+Q7ptk3dM7nkY6BFTI12GyqKfAh82clBjRWXAMv1RmCytbB8FrdLNjBnmgopEKFyK/3MglM0JqKmJkU1TmVGzcOFCjBkzBg0NDZg8eTKWL1/uuO3rr7+OGTNmYMyYMUgkEliwYEFF++zs7MSFF16IwYMHo6WlBTNmzMCGDRsqaX5VoeobcenQdgo8vNUTeyg6XIya2EQ/RZCnhl5aP4xGFIjC/UTDYNl5ueapkQfzOI1+PQQ7t8qMmuCTnD1PTfyuJZ0MVWUdLKYmvLbL45bfXVtGTUJ4jWJ8jrMRwBAkSWycE0EGNmruvfdezJ07F1deeSVWrlyJ8ePHY/r06di4caNy+/b2duy999645pprMGLEiIr3eemll+JPf/oT7r//fjz99NNYu3YtTjnllKDNrzpUNzsuRo2tUnAEEQmdeXHAKfQRpoZOOOlyYZmaFbSMgIFKJuBLU2NjF+Jhx4aCWuepicfTI8JNlxHVs19pqDu7f4yhSUVYqdvmIouREcAQJJeO3cCOokWVIbBRM3/+fJx33nmYPXs2xo0bh5tvvhlNTU247bbblNsffvjhuPbaa3HGGWcgl8tVtM8dO3bg1ltvxfz58/G5z30OEydOxO23347nnnsOzz//fNBTqCpUNzvMDr2jI4/n3ttc0cTlNuj0FJyp6RaZmjgW94wiozA9NTZYVvvBZzqNKM4rmbSKALrt35YYLU6jXw/B3U8VPXvy38ELWtrCwoslPPfuZrR1FQK3Jyy4ZRSmjFa40U/i30GZGm7UJCJ0Pxny3/F7DsQM88HcT3E6n0BGTXd3N1asWIFp06ZZO0gmMW3aNCxbtqyiBvjZ54oVK5DP54Vtxo4di0996lOOx+3q6kJra6vwrxaImqm5+k9v4Kz/fQF/f2dT4N/ajJoQFaVsX10F0aiJM1MTauZdct/5CrBGTE00yfcSYJlqXEO661hTY4V0h+F+8vMb6W/pN39+dR3O+u0LmL/4X4HbExbcku/RJIVR5qnx65azop/Mv1kyyUjcT7K7JobPQZAK6+xbK6t4RI2qAIGMms2bN6NYLGL48OHC58OHD8f69esraoCffa5fvx7ZbBYDBw70fdx58+ZhwIAB/N/o0aMral9PoRSMhtgDPtrSBgDY2NoV+Le1YGrimGE2Su2J6aZhn4W2e39tiMJYIxOBpalxcT/Jq+iY3PMwwPpLGO6nSvLUyGPLhtZO4bUWcJsYKVMTblBCZYazrKmJcvFRaRuriWBVusssF09aGJ/zqdvop8svvxw7duzg/z7++OOatEMpFA7xoWntzFe8z0qymvoF19RIxevkkO44iB2j0dSYr2KSuhoxNaHqF8zXZNJn8r1eoCWoFMxAqyQdQmXRT+Lf8qVk+6hl/ie3jMK0XaGONRVGhbH7x4yZagqFY2jTCGyht1FjvqZ8lEqpNtJBNh4yZAhSqZQt6mjDhg2OIuAw9jlixAh0d3dj+/btAlvjdtxcLueo4akmlIUNQ+wBrR2m/7ySBzFS9xMxFPLFEjIp036W83EUSwbSqYTt99VENNFP1irQD6MRBaJgoGjCMgPe16030O6VomcZhYOv3L10DOzZqqVb1z9TE95YU2kkTlFiathzWh2jJn7PQZAcYmwsSycT6EK8nutATE02m8XEiROxZMkS/lmpVMKSJUswZcqUihrgZ58TJ05EJpMRtnn77bexevXqio9bLdB7nS4/NWEOOjs68uXj9HxgraQwnxOogUTDuuXwyzjoakrEAAttn+VdJRLwxWhEgSjKJPCMwj4ZqHoO6WanVqs8NfIvOFNTwxAzd01NSbldT1GppoZdpqQU/RRFF5VPN47uJ3rd/Ba09FPUttoIxNQAwNy5czFr1iwcdthhmDRpEhYsWIC2tjbMnj0bAHD22Wdj9913x7x58wCYQuA33niDv1+zZg1WrVqFlpYW7Lvvvr72OWDAAJx77rmYO3cuBg0ahP79++Oiiy7ClClTcMQRR4RyIaICHYjSqQQKJSO0Dt1dKHGDIQymJtzcEda+OvNF9G/IAFCvUA3DwDfvWolRAxvxwy+OC60NfsHaGuaDaWlP/JUTiAJRFrRMJIAkK2npsnt58orjYF4pelSlu4LrIncfJ7anltdYTL5nTpSs/9PrFKWmJnD0k5ynpgqamlgyNQH0jiXC1ADgrG0cENioOf3007Fp0yZcccUVWL9+PSZMmIBFixZxoe/q1auRTFoE0Nq1a3HIIYfwv6+77jpcd911mDp1KpYuXeprnwDwy1/+EslkEjNmzEBXVxemT5+OX//615Wed9VABaOpkCe3nWU9DVCpUSP+HWalbtqezm7nFVqhZGDtjk785bX1SCcTNTFqLEFtmIyG+SqUE6jyApodL9QJhITBFn0Yg/Wdp6ZyZqSyKt0yIyF+z/YR5nMcFPKhDcMSykcmFK4wwo67n8rTVSrC6CebyDuGz0Gw2k/ma6p88eJkowU2agBgzpw5mDNnjvI7ZqgwjBkzxhcd6LZPAGhoaMDChQuxcOHCQG2tNQTBaMgPTWunlY8iDPdTFNFPANBJwrpteUtKBvIFq8hdoVhCOlVd/XrUoc+1FgqHykCR8zIC1n6ibaoHsPOuVpVu+Td2pqZUfo0HUwOY95sxenFyP9FCw6mqRD+Jf8eRqfEb/USvb8pHrqpqo26jn+ICOgmE/dC0dlCmJvjv7a6BEJkaMtDTsG67IVUSBrvuGqwy2SGjyLybEEKfQ9u9L0TifiIh3QkfRk09134Ks0yCH8PIM/keb0/trrE8pggJ3QIwAUFQSWQRPX4thMJxNO79Rj/Rr5hBGKfT0UZNxKAaBJ6xMqR5u1VwP1UwsEodN8zBkIa5dgpCYQVTQ47bXai+UcMGnHCZGvM1maydpibKTMk0pNtt9yotgWEYuOSel/HzRW+F1q5aoCdVug2+2BH35ec3DE45gGrL1Di3MV+IhqmRDUI//Z0eXhYKR1OlW74uMbICyvBbpVtILJqqzdjmBm3URAwqGE3y6KdwJu4dPWRq7CHd0VDCNPpJdUy60u2qgVHDIrXCfDCFKKEaUbSW5iUqt5r4mVsb6N/rdnTikVVr8b9/fz+0dtUC3BguBc+3xLo8S3XgS1PDonUcMiAUemBkhQU3poaWkwg1pLsCpkaV8TtKoXBvyFNDDS23PiQEv8RQU6ONmohBBaNpvhIIZ98sRw1Q2YNoK2gZQZVuQCxqqdLU0ON25WvB1JRfQy2TYL4mEv6S1EWBKN1PKSGqy7sN9G9mxDINVW8FPbegLCebGLIp/8VOrYgT9W96UrYhLNiiuqhRQxYsYTaxksgi0f1kvkYpFLbX+oqRFVCG3zw19PL6WdhUG9qoiRgqwWhYKwHqfqqEzpR/EypTQ/ZF3U8qHYDgfiqKZRWqgSgmf1pXxnLT1IqpCe/YQki3H6ZGoQOhBm8tNFRhgT4uQXU17H5k0mWmxpemxnx1Yv4KEfTjoJBX+EaJfhdN8j1bH/Nx/vQ31SiT4FXiIg6g182tffQrzdT0QVDBaNgrAUEoHDumJoimhm5bA6FwxNFPtar9RM8nrGMXFUa622VT5WOh/b8WzFxYEOj6wEyN+ZpJ+R8TrCyujKkRv+fC5RrGC7uxJt1RlUmwaWq8f0ONLXuZhNCaxtEr8tQY/vozbXsyQkOwUmijJmJQYWXo0U89ZGqqUdASkDU1ckbhkvAA1Sb6qcxohHj+Yp6aGgmFfa68goBrhQShsAtToxjMa62hCguC+ymgIcHuh6Wp8R/SbWW+dWBqahr95FzrrVClPDW+3E9UUyMxNdFEP0nHj6H7iTbJt1CYs4ZRtSo4tFETMQxhZWt+FlaH3kE1NZW4nyoIK/ULIfle3tmXXiwZgiFTi5V7lILahE9BbRQIUsvF9z5LFvPoh4GSu1SxJLalq1B9d2NYKPlc2arA7k227H4KUvuJa/Ns19Z8dvI1nGHc8rFEladGXoz4cbXS6836cdjJUSl6m1DYbW0phHTzRLvxOSFt1EQMztQkLNo4Lu4neSAOMxNpwUFTY2dqDGHbak9y4oMcgfspCV9umihAL3VYAzVlC3zlqVG4nwqCUVMnTE1gTY35mg0S/URYMnMf6kVJnJLvCSHd5DkPkxWtpEo3Dalngnc2P0cjFO4F7ief0Wm03zkZ2LWENmoihrBiDz2jcM/KJESZGE1kavxraqqdpyYK3QndVxxqP8nve4KgmZJVIb6CEduLNTWCayXg9WWTOq9e70tTY746MzXmB7WMfnIN6Y6IqbGNKQHcTykSHx+lULg3JKH0y+yKTE38NDUVlUnQ8A+h9hNbCYSlqenoae0ny5VgGOHS1k7J9+KWpyaKiR9Qux1rKhQO6bLSjMK+hMKKwTyRoPe7F7ufBKFwUE2N+cqEwoGYGoeEZ3HIU+NWJJcas1GkGWAI4n5ifZi+r0ZG4TgZAQwCa+3SPKFMQo3SVbhBMzURwxKMJqwOEBpT07PaT6zj5nhYaTQFLV2T75VKNc0oLBg1oWpqzNdETITCYZ2byECxz5z3rar9JLobey9TQ0+t0jw1QZga7vpzMCZ7kuE4LNjrMFnvuyNiamw6Hh9dykpkaGdqIjFqKmhjtSEyNc4NpJeHueyqna7CDdqoiRh0RRC6+6mHTA2b9HLpVKjtAmRNjXuVbiH5Xi3dT1FoaghTU/U8NYJrLZxjBw3ptqfNl0K6ezFT0xNNDU++lw4S/SS6TFR11IDaup/cCpiKTE34GYXTAVwh8rUEohUKVxKhVW3QW+fWhageqVZ6QTdooyZiCHlqQqQ3O/NFwQDoSZkExtSEWfvJiamxCUeLYvK9WgqFoyknYAkRq706o/cgLINNDOku79tn+CdrkyAU7sWaGrFYY89Cuv2EYdvz1KhdPSUjXAM9CHxrakIca9i1T6f8u0KK5PlksBadoTWNozcUtPQ7FlK21s/21YY2aiIG7QCc3gyhA1CRsHmcCpia8m9ymfDdT36T7xVqLRQWHuTw2BTqdqxVSHck7ieiw6q0oGWhHvPU9DT5ni92wXy1op/E7+mzVSsXlN39RK4RGRPCfBbkOlq+mJqSM1MTSe0nBWMZNzjpn2SoggViZNNooyZqUKouTJ8trftU6T4tpsZ0P0UVkeAmFC6WajvJVRIO6gdinpoa1X6KIk8NMdb8RHXZ7zekkO54up8Mw8C7G3e6Tj49yVPDyyQE0tSUmZqUu6bG7/6igD260XqfL0RjdMmRZEGS7wlC4Qj1IfLpxtCmEccLl2vAvvFbKqXa0EZNxFAxNWF0AJmp6UmZhGyArKZ+4aSpUQmFu2MiFFb9XSms+1672k9CnpqQLit1P/lKvqcSCtdQQ+UXd72wGtPm/x13LvvQcZseZRQubx4sT4356qipKVbenrBgYyQcXHRRZBQOkjOFC4UJUxNl9JM8PscxpFtwP7kZ81QnqpmavgeqqQmztggVCQP+fPIyOFPD3U9hUsLWvtyin+xMTXVX7lElxYpD7adCBHQ/TQNQUUHLUu/IU/PRlrbya7vjNn7pehVkoXAQTY2ToFVgampUKsFWJsFBTB3FWBPI/SRdSyDa6KdeUdCSNMnNyGZNF7OKx+d8tFETMejkxldYITw0O2SjphKmRhIKV8P95KWpqfbK3RZqGZqmxnI71iKk2zAMMZoh5JDuVMJf7SfPgpYxdT8xjYwbeylMAgFXKux+ZIIwNZIORP4JbWt8NDXW+3xEeWrYvpiBGCRPTdWinyJaPIUJv9GS4oKtPAZE27RA0EZNxBAFo/4TbXmB5qgBKjOUSnwwKGtqIhMKOzMGZkbhGrqfIqKF2embmaTLn1VxILP58ENzq9mpZ1f3kyLqIy8YNfFkapih7cYoiO6nYNeXXZZK8tQwTY25H7WhEKYrOQjcsvuKGYVDzIkluZ/8XEv2G0LURBz9JB0/5u4n94zCwdjaakMbNRGDdoB0mNFPZaamJxFV7Dc8pDvM1ZND7Sd5kqg1U+NWq6YnKCmYmmo+9275QnoC7k9PWhOCe5Vu+++LvSD6ifVJt6gmYcIOeB48pDvNFjpB8tRYwza99EL0U43cT66aGsrUhNg8NtakufvJ+zdUG8YQaUFLFwYrLvArNKc60QR0RuE+B6VQOBSmxjRqBjZmAFQa/WS+5gL49f3COfpJ8rkXS8Jg54epWfLmBvz0z2+EwizZqoaH7aZJ1qb2U1S1Zmh/9sXUyPfbkPPUxNv95JpZtQfMCPtplkzEXuMCu6VpMhHT+yy2pzazjC1PDfmbZhQOM/me5crzL8hnTaGamrCTo1L0htpPJQfWzw6yYONFuuNzPtqoiRhCteYIQrp3a84Kx6mkbSykO8yIiaI0wLKVr2w0FEqGcFw/Gotr//o2/veZD/DPT7aH2k7V35VCDOlmn4Wya19w0zb0aL+C+8n8zD2kW/y7VOodVbo5U+Ny00TXSrALLCffk/fn9puUYNRY39PrGqbREAR27Yj1vhCRUNge0u39Gyp4Z+C1+SKJfpL/jo8RwOBXKCwwNbr2U98DLWyYCtH/yNxPuzX1hKkpGzURRD/JK1fG1qirdAeb5Nq6C+V99nzgjqrQnMr9VE2mJiqtkJhRuLKClsVeZNQ4sYGGYYiun6BlEqSJGPC+R1SkbX2mXl2HmR3cDcWSgf+49QVc+ehrALwyCvtlAgK2QWJq4lgmoZKim9WG3wzkqoheranpQxAFoyEyNcz91JQVjhOsbYyp8S9W9LtfeVcsrFt2cRVKwZPvsSReYVDs0YV0m6+1qv1kizoK2VhLJABUVNASkoYq3u4nJ0Pfnm+pUqbGmlS99iFX6QacNTXVcm98sq0dz7yzGfe8+LHZRhf3U15wP4Vo1FTA1FDBO0N13U+hH6LHEAxk14hG8zWRYIoazdT0KdAVO/OFhxL9JDE1lUQT8OR7vPaTv31s3NmJC+9eiWXvbVF+rzo/lo9ExSAEFQpzV1YIFLuKOQoDlKGrBUWrKiQZzn7N10qjn2wFLWOap4YzNQ4nJ59XpWUS2LMHePc9q/aTN1NTLU0NO05XoQRD0kuZ7bPei9FP4Rs16QCaRbeQ7ihcQ1EJ98OEf6EwZaHNz+LEPKVr3YB6R3RCYVFTUwnbzAaWoFW6F7+xAX9+ZR2KRQNT9hls+57uJ5dOoqtQspgaxQo3aEh3t49wW7+wuZ9Cjn6qVdhjddxP5meuOS0Ug3lv0tQ4LRbkUw7qfpKLUwIB3E9kIqa/EPLUVIkKkF2JrlW6I2KSepJ8jzI1YY7PMliTEgnzfZyMAAa/Ro0qTUmcTkczNRFDlYQtzJDu3bj7Kfg+ZfeT35Du9i7TQOl0cB3QwbVfg2k3O2tqSoHdERZTE777KbzQZ/PVL6MRNuS5OKzzEjMKe0ebqJiwWmaQ9gtmaDsxMHK/CZoOgW2eJsahF9taiiNTI+nh2DmoMs3SBUuYRo0suvZV+0lVJiHCPDVc95MM19UfJuhlc6/9RAxCnaem74H13UTITM3OLpOpGdCTkG4pT43f1R2biPzoDVpyplHT0e2mqQnmjmCDYxgDtyrjbRhQ56mpHVMT1qGpWDXhRygsRZn0tugnp2fC7n4KmFG4fA2SyQRna/wzNSRPDTlsLfLUyNmheXZfZmA4GFpRZC+3hMLev7HKJFifRSoUZgZpgDb2BNvbu7HkzQ2BGDuhoKVrlW7zVVjYVNbMSKCNmohBa4yEydSwztqQ8b86oaDRG0HLJLCoo26HB4aubpuyZaam4KCpKRrCfpz2ybcnIuQwDBD5nMMyPChFSyf0aiF6Y81fqLpcDqBo9I7aT1aeGvXJydc3cPQTMXrZYsfLELFcf/b9lEpSNFaVQrqF4qT5kt2oIW0ShcLhtY/dC5Z8z88zLJecACIWCleg++kJfr7oLZx750v46+sbfP9GcD+5uZRr7Fr3gjZqIgbNU5MKid6k0UVB9TAMgu4lE6xMgsXUOKxiyQPcmDX3zZkaNugRQ0ocGN3dEWGLDVVp/MOAmKem+u4nt3T1PUHQUHXVyl1kauLqfnLPKGxzPwVkRqjR6ze9v+V+Sto+s9VUqxFTU5Ceb6eMwqEKhWXD2Y9QmDyfDFEKhVmTqHEfJdZs7wQAbN7V5fs3TokcZRjCwkZravoc1INXz6waOiBkK3xI6PaVMjVOAzkzUlLJBGeSuCEk6XiKkvvJi6kJOytpdIyG+Zqs0Womqugny0j3F6puq0ZtGMJ9i737yaGP2ZNIVsbUJBJWiLZ3SLf5SsWtTqxltaOfAHNcsIl2S+pnO1yhMMrH9L944NFPglDYfI2CReELvQC5dHqCtrI8IUg/8KvJov2wFiy0F7RREzGEFXtITI3IstgHD1/tIm2wNDV+jRrTQHHSEdCBraHMJLHfyOLkQkl0P7HQUCfQGjuR5KkJvfZTbVYzUaVlpyHdfkLVrb5g9X1ZXBpHFDzy1Mj9JCgzQvtHUKZGCKUFY2rEBlXL/SRHP3F9S9reNwqRGTXmfhmD5cf9xDapmvtJFjNHbAQwoybIwk9IvucjojGRgK7SXe+4+A8v4+t3viQ8VHTFHpYQjQ5YjKkJOsGLTE3Ktl83sInIyahhDE4qmUCD5H5ix7DcZmLtJ8NwP5ewV3u2yT80TY3ldqxF7Se3dPU9AQ3pVkW4OLWDRqb0htpPPG2Aw4XrcZ4aVnsoaQUQeD1/nPVVZHOOqtyHF2RXopwp2amURCQh3QpDyus3NPopWveT7CIL/RACWNb1IPOCEP3k5n4qv/otlVJt6Dw1IaGrUMQf/7kWALCjI29l+qUr9pBWAtQI4L7roEaNYh/BmRqHAZ9oajhTw4TCCqZGNo66CiUhfTwFyyYcpL1uiCr5nhUhEI/aT2GXfxBTpHu3I0v0DoXe5H5ymH1sQuEK3U90sePJ1JTka2/UXFNTEgxUi6nJKkS7sh7OMAxB01JxG8qHyAYJ6SbXn4EZl1HMz6yNXCgcuftJHW3qBqecQjJo362bKt0LFy7EmDFj0NDQgMmTJ2P58uWu299///0YO3YsGhoacNBBB+Hxxx8Xvk+UqWz537XXXsu3GTNmjO37a665ppLmRwKnxEWGavDqMVNj/b5S4VlPNDVsInIa8KmmpjFr7ttJKCzXfgLcV+9hMzX2wo/hTv5inprqPfnVcD/50dSwW0tFnLJgNI45O5ib0yn/jE3DUqH7KZFIVKapkQxlu6amWtFP1nHbu63nViXalRcvYd12axHlXyjMo5+IUZXwaVxWAjlCK46aGmqguguFzdcErDEgTkrhwEbNvffei7lz5+LKK6/EypUrMX78eEyfPh0bN25Ubv/cc8/hzDPPxLnnnouXX34ZJ598Mk4++WS89tprfJt169YJ/2677TYkEgnMmDFD2NfVV18tbHfRRRcFbX5kcMqWSVfsYQnR2ICVSSUqjqiibcwGzFPDmJruQEyNWiisYmrcxMJhRz9FNfkLQuHyfa9qnpqImBrufqJaIZftuTsibRl2ctv8ZJGuNpgx4zdPjZfAXQY1UNhk7DUuqDQ17Dc2pqZKhiLVbDCXB2AX7dJUDAxhGV6yCNdPV+fXX3A/lfcXqfsp+gVOoVjiC88g45mQp8ZnSHddVOmeP38+zjvvPMyePRvjxo3DzTffjKamJtx2223K7W+44QaccMIJuOyyy3DAAQfgxz/+MQ499FDceOONfJsRI0YI/x599FEce+yx2HvvvYV99evXT9iuubk5aPMjQ9EhXFHMR1GZ/kVGgWhWKqUzaZVaNqj6zYrKmRqHQanAB5kkD+nulJgarqkpGraJwy13Sdi5LuS5KHRNTYS1n3a05x2/i4ypUQ1oLvuWNTXFkmHrZ3EL6zYMA161n3qap0ZgcH3WhHMLpbUltayS+0lgaroso0Z2i6v0d+Gxh8ENhiIxzhmiLJNgDzsP/RAcbYQxqzT6yb32k/laKxbaC4GMmu7ubqxYsQLTpk2zdpBMYtq0aVi2bJnyN8uWLRO2B4Dp06c7br9hwwb8+c9/xrnnnmv77pprrsHgwYNxyCGH4Nprr0WhUFDswURXVxdaW1uFf1HCi6lJhsjU8Ic4maxYp0NDGtOpYPvgmhqHFTYbUNPJBBoyLPpJXDlkBabG/6o39Dw1kmHUW2o/PfXWRoy/+m+45e/vKb+XL2FY4zTbj1j7yW378v0mtLt8zeOmqymWrER2jlmzbSHdQRcV5muw6CfzNSGwZIypkaOfqsXUWMdpU7ifnDQ/8m8pFjzxL5x4wzPY2elstAv7kSOLAiTfIyl/Ik6+Z75WQ1PTRozLIAs/v0Jhmnst4WMMqDYCCYU3b96MYrGI4cOHC58PHz4cb731lvI369evV26/fv165fZ33nkn+vXrh1NOOUX4/OKLL8ahhx6KQYMG4bnnnsPll1+OdevWYf78+cr9zJs3Dz/60Y/8nlqP4RTjr1pd9VxTU9aspBJWRFWFRk0yaT1oflO9d3noDcQ8NeXop7zM1LAVSwl54k7LFw1XpqYr5PoxUTE1bL+pRDTJ995YZxrpb67b6XB8yf0UllutZO/PbgO0nIxN1tQA8csqLKbzd09bwNCTMgl+mRpBoOmlqalSQUt6L9uJ+0nOKKxqj9Pz+8dVa/H+5ja8umYHjtxniGcb5IgrP49wkfRjhmqUSahGSDe9D1EwNczfLOjqYhTUHbvop9tuuw1f+cpX0NDQIHw+d+5c/v7ggw9GNpvFBRdcgHnz5iGXy9n2c/nllwu/aW1txejRoyNrNx38qHVMIxZSIa0EuHuHDIhBJ2NavoE9aH4p6y6feWpo8j25oCXLr9NdLPFBqDmXxvb2vKs7Iu/g5qsU8nULO0pIdBWE9+B7ukciClVXTayuyfds7id7m+PmfqJMYb6ojtKRT7nyPDXwnZTTjfavnaaGMDVdCqFwuX3smrIq1W5tZIscv+OkvfaTD6aGuN8ZomRqZN1PlBmFd5H7ULGmxgdTk1C4QeOAQEbNkCFDkEqlsGGDWE9iw4YNGDFihPI3I0aM8L39M888g7fffhv33nuvZ1smT56MQqGADz/8EPvvv7/t+1wupzR2ooITUyO6n0LKU8PdO5b7KXCeGsVK0bf7qcyWGIb5Gzow0LbQ5HuMqZHdER2Esm7OmkaNm3A0HzJTI6+YwlpBiXlqyvsO8cm3ksP5YxJCr/2U9Jl8TyGQtBs18WJqZLdqyRALHwIKpibg9TUU44Lf2k8qg7Kn0ViVQox+MhkCmnuHtY+1J5NK8lIZTn2S6YP8jmlWXSX/kaB00cFgRaf6OmwgsFOxEgSGfwwG6n6qNPrJXShsvib4f9EyT0ERSFOTzWYxceJELFmyhH9WKpWwZMkSTJkyRfmbKVOmCNsDwOLFi5Xb33rrrZg4cSLGjx/v2ZZVq1YhmUxi2LBhQU4hMjhVyFXVygmLqUklK3c/CULh8oid95t8j4RcKwWARMjMhMLMxcCuTS4tam0Aq6K32yQnaGpCGH2im/zN16hqP3kxNVExUKqQ7iDJ90z3k6ypiRdTI19TZR+XNTUVFrRMJPyHIrvVE7P342plFKbRT+Z9pIkZWbvYNcwkE54LMWYg+s2xwg1nLk72027zVTBqIhQKy9FPUaYxEDQ1Feap8aWpIXNafEyaCtxPc+fOxaxZs3DYYYdh0qRJWLBgAdra2jB79mwAwNlnn43dd98d8+bNAwB861vfwtSpU3H99dfjpJNOwj333IOXXnoJt9xyi7Df1tZW3H///bj++uttx1y2bBleeOEFHHvssejXrx+WLVuGSy+9FF/96lex2267VXLeocM5T435GkQQ6H0sS4PChG5B6UxuGCUSyJDVg4p5kdFZoPR8ietmrH2ztOXE/VSQNDWSWwoAmnJlA8jFqAm79lPUSeqiqv3EroPXape3J2QGyq9WiB2X1iiT2xw3TY3MFKomX3v0U2WLCoGp8SkUVhmUtXI/qaKfqFucfc3cxpl0EkahhG44T7gFbrD76xdW4IR/N6+1qLM+q0aZhHSFecWCoK1CTU3JYQ6TIepEy7+Nkf8psFFz+umnY9OmTbjiiiuwfv16TJgwAYsWLeJi4NWrVyNJJOVHHnkk7r77bvzgBz/A97//fey333545JFHcOCBBwr7veeee2AYBs4880zbMXO5HO655x5cddVV6Orqwl577YVLL71U0MzUGk4VaGk12LAeGlqGIEV8mkEydAruJ8KtF0olpJIpp58hXywJ7VdlFaZMEhcKs5Buyf3EjJpUMoHGDDNqaqepCUtfqdI/hPncFzwoenv18XCOS0O6LdvXhalh95uE+Mbe/SR1Aj8i16BCYfZzypQGqf2UkPqUPfledSYZVfSTYPBKTE06mUQxWWZiHB4I1na/52AvxeH9mxIZoxiiLJNQieEl45l3NmFDaxdOnbiH63ZtgqbGf7+k181P8r24VumuSCg8Z84czJkzR/nd0qVLbZ/NnDkTM2fOdN3n+eefj/PPP1/53aGHHornn38+cDurCSemRpkO3WcH2LKrC6+u2YGj9xsqJImiGTRT0udp2fnvABpimCFGaKFoIOfSK+QJyC3/RDpFQrodmBqmtUknE3zic2VqQo9+ioapUeapCXGiyXOmxl8a//CqdJuvKaqpcRk33dxPTDQaN/eTbKgrw5GlfuLXdcugovC989Sg/BsVUyMbYjVgapimJmWvC8bak00lUCy5C6PZtn6f76JhjTf0mH5+kxDcT+U2R2AQsiapMi37xaX3/hObd3Xh6P2GYFj/BsftKtXUOOlCZViudS6piRVTowtahgQ6qND3KkGg34fmqj+9gXNufxHPvrtZOpb1EAtGTYCOVSSTLjWEvAbDTqmEgcqooULmRs7UiJSyrKnJppI8zNtVKBx2RuGI3U9inppQdg3Amnj95lGJIqmgn5Bu+yraYmqas94aqlrAztTYzy+sKt0JwS3tFf1E+5QXU1MtTQ0xarqsBYrMejB3aTqV9ExCytrup0ioYVg5hYLUflKVSQgr5YYKtuinCm4Py9uzs8s5Pxsg56kJPicA7tewpFiwxcim0UZNWBCsXCoU5m6e4D7bT7a1AwDWt3YKnxc4lSsaNUHGMRp2nSb78BoM7UyNyv1ENTXMeCkKbWSsDGNw0qkEN3T8CoVDYWps7qdwGY2osm56CoUjEkCz/fg11rhRk7b6Pns+mnOiiDwukI0aN6EwmxMrrdJNFzteE51bn6qZpoacdxuJfpKNLjZmZVLWeONkCLK2+3Gd0H6dDuJ+IowjQ3WEwvZCn37B3XIefa3SjMJ+NTXqorbxsWq0URMS6KDmVPspqFB4V6c5SDgJF9OppKDeD7LCoNFPiQBiRZmpUekNRE0NcymxPDWMqRFDGzOpJDd03Jia7pCZmshDnyMSCrPr6DdPTXjuJ9pvzM9c89RIq+iiAQVT0/vcT+x6spQFQZkRGp5trd79MTVUx8+aZi+TUP3oJ8bUpIh+0NLUWJO6WwoJIZuzjz5bEIya4O4nOn7yRWcEEzQXCld4DIMI7L36WqVMDb1ubteeeh/imFFYGzUhwTlPDZ3cgnVo1jltRg0VClP3U5DwvZI4QPrNKiyvqlUlDSjVytiXfNF8KOXaTwwZ4n5yFQoXqPEY3+gn8cEPfzXTXXBfzUZWJkEI6a7A/VSyyiQ0+wjhjwKGYeD9TbscjTHZIFBdY1kbFtT9RCdVvzXhrNxHVtRjzZkaQShsaWrYuMLO08oannRdQAVlYmnfywbI1luSxj+AaB4j6I6sSUHYJArf2X5RGVNjGGLBUbdrqHKtV7NYrxe0URMSnDIKG8LgFYypYb5T2XCg7p1UpUyNpP73yyJ1FmSmxr69ZXQlOVMDmMaKXPuJIZ3yJxQOPU9NVNFP3E0TTZ4aztQ4XAN5UAo/o3Cw5HuqMglNWW93YxT43fMf4XPXP427Xlit/F5+3lSuJXZezBCvNPopUO2n8iFUmVyjYhy9oGKlaQAD+4wlNEyn3Ivw0knYj0uPHr+SMgnVcj9Zxn1lxwhyXSqp/SQ3x228UC3YYmTTaKMmLPjLKMw+8+4BhmE4MjUWE5JURkX5aq9Ev7IVhNcD40coTMMXsyQRRFe+ZJVJkIwak6kxJzm/7qdIMgo73Jv27gL+9M+12OUh0rP2Y74mI1rNWNFP6n3aku/VyK1mK5NAhcKMqclX1/30weY2AMCH5VcZNveTUigsMo5BmRGacbqS2k9y6Y1aRT+p+h/V1LDrRLOMu2VQLgpu/KCaGv9MOH8+aZmEiITC9Nnzm2hRhlPBZBWE6Ce/CQwDGMWqfqg1NXUI5yrdlKrzz9R05Iv8wXNyP7EVTyXlF6juBbBWEGEIhRnVbObgSPJ2dhWsHDeyUZNOJny5n+i1CINil/fhdA3vfO4jXPSHl3Hjk+/62q9Q+4mvAHvQUAl57n5yMGoiYmrY7RZyVLhszwtakhVqocbuJ/b8OLErtugnlftJYmqKJSOQ4VgRUyOskMXPahX9pHoGUwrXZJ4Ihd0Yaxoa7+f5FpiaZIDoJ6YNq0JGYdqeILofCmrseWpquoNramy14rSmRsMx+klgavwbNUwkDKjcT5JRU0H5hZK8D5/1Z+RVtVuZBPYA0wgoJ/dTNu1PKBx29JPNTeOwzw3lCLRn393ka7+qOj2hRj/VSCjMMwonE54MFD2mkqmpkfuJHb/boa/bjRoFo1D+jGbTDpKrRshfVUHtJ8/op5ozNeXvuVFjMTVplwWUkMTUj/uJ9L0UNxj8t1vQ1FSYnd3zWCrdT8BjCMaep/spuKbGri10ea5VmpoYFUrQRk1IcGJqDMXg5WcyprkI7NFPln8aqKz6N80oDFi0qNdD4Cf5nswC5YhWxkkoLDI1fvPUhCAU9rlCYYnF3ljbitZyvgg3sN1EXvvJZ0HL8Go/WQOaV/I9em1pJXj2cVONop/YNXNmagzpb/t2JYmpMfcb/PmrLE9Nwpb0rFYZhVXHSacsdpLda5pR2G0RVgjI1NAkokHcvNz9rnA/he1Kobvzmz1aRiChcAXRT6pzdvqtamyLkfdJGzVhoejwMAo1XgL4bGnHtDE1RIhrvgZ/GGX6lbufPASPdk2N88PADCU28Hfmi5amJqPQ1GS885Y4hc5XCnuUkHqfHeU2lQxgxUfbPPcruJ8i0NR4lkmwMVDhHJftVtBNOJyX4BpQsHAtNcpTw9rlxAgGSb6XrdCoYZeMhj8Hq/0k5YGpkftJZYgpNTXla5pNu6ePKATU1LDzTJKgCT/DgiGNfwAxakK+dJXm0qGgfdJLlF5JRmHVOTvNU+rUAvGxarRRExKcHkZq1Qbx2QruJwehMKsjwqneQEyN+coGVP95avwzNWwFyus/EYNIKRQuP/CqMHGGsPPU2IXC6u06iJ96+QdbvffLJ6BoVoCeBS0jYmrE8g9s3+ptxXBbpquiBUxro6lhVaCdJgd7SLfCcJeiusz9VuJ+CqKpsSYTK0dQuT2k9IR5DjVkahTup26uA0zyxY5qHCwEcLOY+zBfUwFTJ8hMNVAZ4+0HtD2ZChagcptcRbwlA+354LWfVAaM00/ZlsmIWOieQhs1IcFXnpoAiZfc3E9UiEtf5c7+v39/H99/+FUlQ1C0MTXO+TYMw+ATgOwqUPvFy+0rT2Rs4G8nhoFNKJxKcPbGr1A4jMFHHpSd3U9Wm154f4vnfktk0PQT+hwUgQtahnRwVUZhJwaKHlOll7I0NdV1P7GB3smokbU2SvcTMdz9amKE3xNdgv88NearW0Zh9lzVoqAlA9UPciaJC4Xd89QUHMZRx+MrNV7erChf1FWhTAIdItMVamr8XpeOfFFwBVWqqTF/q34+VFW6dZ6aOoSzpsZ8pe4nP8Zzm4tRIwtxUw7G0g1L3sHdL6zGh1vabfuX89SwV9Vq8/zfrcCR1zyJnZ15O1NTcB6Y2AqUuZWogE2VfI+J6HzXfgphNepXUEtZplc+2cGrjnvtV1V8MAx4hXRHnafGz8pYlUOEMm1NNar9xFyYPRIKK5iWILlqOJOXDM7UJBKwJd+TtWq11tSwvsHaRaOf3HQlhYDuZZpvhhooXj+1MmNbn1Uj+ilToaZGEFC7TCA08inIcVTPsNNhrBxc9mrxcYA2akKCH6YmUPSTi6Ymz42GZHnf6v2yFfBOhbDVJhRm0SmKgf7FD7di084uvLepzbaqVhlB1kBj7rPBB1OTEZia6kU/+Q197pCydL68epvrfqn7SV61hgFe0NJnRuGwVlJ0MvainlVGDdPPJBJAY7Y2mhouFHboZ7L7yY2pSZFcTEEMiUoCCERDWa2pYckuw8i27Qeq8SJFxMBWSLf5miGlXbwyCrO+vbG1Eyf9zzO464WP7Md3NGr8X0ur3eV9Rhj9ZLnegu3Dr1uOLhy9tqWg5U8YnDU15msiogVbT6GNmpDgnafGf30lANjpqqlRu5/og2K6jMzjqBLG8cGg3CkzvG3OYZY7OlRMjbOmJuPC1Mgh3TT5nqtQmDBDUUQ/Oa3SmPtpj90aAQAveOhqRO1J+JoaK/rJgSWJyP2kFEA7bCsO5mVNDVuxJ/2VxYgCrH/6dT851SgCykxLKjhTYz1/pMBjAKGwU/QTZ2pioqmR89TQjMIqw0tws5TPYfmHW/H62lY88vIa2/ZWaHYCCTKkeD1r9HcMQfKIBYHoavTXPhlOi2YZbV2VMTVWcId1PZzGV1HbpZmaukXRwZKmA1GQKCU395OVnZMJhe3uJxolJFvvdFs5gkr1wDCmaEdHXqGpUVHIoqYmp2BqbGUSkiRPjU+hcChMjZTI0Gl1woyaY/YfCsBkr9ygyuUQVZVuFQtjz78TznHdstra22C+ppJWhA/ry6mkv6rsUSBw8j1lmQTzNZVIkGzcwd1PiUSCPydekYeGYjJh+5GZmuppauxtViWctJIwemhqBDeLeJ9U7kLqRqKRTF6PmoqZiMz9RMTMlep28orrooJs1ASNfkoRd6ijUJjMaTqjcB1DZGqs3qBKmBXU/dTlMMjKBolozVu/kTs63ZatHNyEwmywbVUwNa4FLaXoJ1ZoLZmwjseQTZM8NS5p80V6OgSjxrBoccDZlcJC2Q8Y2R8AsLWt23W/JeWD39PWWlAZzhQ2Bios9xMRWHolFaQiTjrhALIwvEYh3U6aGgdhPgV1P3GW0yc7Qo3AZMLKhOuXqaG0P9uXrWxDlap0OzM14uTNjNk00dR4RT9ZehzzVXVONH0EZV28xlhV9FNkQmGa1qNCw0mIfnK5t0xT01gec4NGP4kpBhyEwrAWbDqjcB2j6GBJ80kgGX5INzMaVAXiqJtG5X6SVypOFHqxZFVvNd1PkqbGRSickvLUtJfboZzk/DI1IUc/WbWJnO+NYRicZRrQmLG1Q7lfwZi19hMGDMOQQtvdJ10gfPeTmIvEYVviYqErYkBKtljl2k/MSOl2cHv5yc4rCIW5psbfBEJ3b/7en/tKXXNHbHMcop9SKXudO3ZtzOgn5zbSay27WN3SRyST1gRLj+sEdmh1mQTXnwZGkbSR3rdiycDVf3oDf3l1nec+Cj4Xc4yV79+Y9txW3UbvgBZxwWa+19FPdQg/tZ+CJN+jhoj8MFP/NEBCxclx6aTnxtTwgpYOkx89dmtHnq+qsy4DuWx0sYGWMTXMHUHnOd+amrDdTzzfCFvZ2PfZVSjxB5kbNZ6uAvM1ipBuP8XneC6jAEX+ghxbXKWp900zSyclIzaVTNbM/SQzADKcypKo9pFK0gWBT6pfYGqIUeTxexX7xyYTtiJnrGjtNTWi1oItfjKkFpw6jNg+jrpVpKfReIGin/j4Z33mFEXaUwjuGnLur63Zgdv+8QGu/evbnvvwG9LNxno2TvkdI6lHwcsVr9TU+DpKdaCNmpDgHP1kvpod2r6tE3a5hXRLRoMq7Xjep1FjFbRUsySCUdNpMTUtDWnl9oA1CMm1nxjbwSIA2Kt5fL9lEtTGY6XgtahcJn/KTvVvyJTb4W9VLZZJCOfRt1WRVk26klst7OinlGLisrWBTBwyU+P3fkeBvJemxlZA1sv95M8oYaD9Npl0F+lT0MreMu0fK6YmoQjp5kyN1W/c9Hj0eysE33kBJS+SPPPUGNbvGKISCguuHbIQYONyhw+m0m/yPbZwZOOUb6aGMrAuQm7Aet4TsAvW4wBt1IQEr9pPZpSD/7LzbkYNzwNTnrBUTA0dsHcphMJymQSWM6S92zkkcAdhalrK2WDVGhwHpqbL0tQA4kSXTiUE95PToBR2RmHOaKSds5yya5JJJdBUDkP2WpWr3E9hjZWyxkOdxp8Za/77nB8Eyb9DXVWShMoUCvtIthgFvGo/2UsOOBuNyUQCmXSZqfHpt6CXizI1fvtUQqFnYve3IaCWoqdQVulOJnhUpRzSbWYUdjYe8opxNO9yv1RJEM3jurebRp/Rdsv7DQOU3aSh7l38vLyP5bdMQqVMDWXuLZe1elsrsajdDRoHaKMmJIi1n6z3qoRZQd1P9tpPZSaEh3Tb9+vN1JivzCBqLtfhaZe2pfvZ0ZHn+gdm1LgXtGSaGompSTGmxhpETPeT1R2dVu9R5amxdEn2bZhR05hJcebDKccJg5uroKewMQkqF6DE1EQR/eQ3+Z7K/ZQm0U/5ohH66tgNXrWf3NhKBsrUpAMyNbL7iddd8ypoSUTanhmFq1al295mWtCSt48k30u5JKCj+5OLtioXUGQyTpA+5tWf2C0Qop/o70NkHgwlC2Lw/udHi+WbqVEYNX7GHRr95KXDY58miPdBa2rqEM6aGvOVZhQ2DO9O4CYUlqtgW8IuatRY73d1uwiFJaamrVtOrmftp7WjwKOf+jUwo0Y1MImGAgszZUwNb3dKNmqsLMNOmhU6oYeRp6YkT/6K+8IS7zVl01ZhxkDht+Kxego/pR1Y8xiLEF70E2Vq3P3p1KixCYUlI9ZLeB0mGKPitELOS7oxt9pP1CjxG9JN70UiYblhg7F/4vVkgQosJ1T13E/ma5ZQcSoRuZVR2GJq1Mn3VEyNi/tJCnjwK1yl7AkD8YaHamTTvkJdtmzh5scALTiM7TJY9FP/slED+DsX2ka3KuqAU1oHz0NUDdqoCQmCpkYIt7U6gJCt0aOj+dLU+HQ/qZgaHhZeHpBZxWRbngOZqSm7CiyjxltTI+epYQ+NyNRYkwPgLBYOPU+NtMJV0c7M592UTfmewNhuBE1NSPO2zchVGpbWJGL+HZb7yXwV3U9O21qGs5qpocxc9VxQRQ9NjZzzRW24m6+UqfFv1FjvafSTd54a6zeyoWzX1FTHSGT9jDG9gBhezSZL7n4ieWqUyfcUUaSuQmFpgefXHSIbQ/L7MDUiYgSsdXz2HPvpN2Kou5v7iUU/WUaNHwOXz1NJb8E0HQPk38cB2qgJCZ61nwj1CLjTm8WSIWhbZI1JXtKsyCnJzW083E9+mRrJqGFMjaWp8Y5+kvPUyJmQzW2TSCQSrllmaZZkIGRNjcvkz4yxhkyKr0hLhvskpEppH55QWG3kip+Zr1Z7wzJq7C4lp1WxFW6rYmoSwgRXTbFwnk+WhtKIZdeXPROq+yxEi3CjxKf7iQqFEyDuJ39MTYKskNn8xia6XJWT77HjsGsFMF2G+d7gRo3lfnJjasRyAKIxo5r85YAHv88aTTdA2y3vNwyoQ/EDGjUBk+/1b7Duh59zUaVfcHQ/RTi2hQFt1IQExzw1RNxHmQm3hZRclAyQGQpZU2MXdnULeWoUQmFpMGh2YGqoEbGzM89ZixYX9xP7jGtqMmKeGjYJCNFPZWNGVc1Z1RbANBh7KuizaU9c3U8pIWmgGw1srWYoJd6jpnL4EbKyfscTndUgpJv2MdmokXMYVbP+k1PqA/5Zue+x2lRuId3JZMJKXOk7T421P/PaBHU/2UXanF0qu3ANI/woHhXYMQSmRlHQkk3KmVTSYpZdggzob606Z3Z9iCz4lQt9OsFiJpyEwq4/DwQVY1kqWQu3ko9xTFU+QoU2KZ+W/FsnCHlqPIwaWRMGaPdTXcIrT43sB3cbAKmehqFb0JKIE5aKLvTL1LA2NZeZF7tRQ0XPJlsDAC0559Bme56aslC4bBCxAYgOIhlpW9XK3U2UXCks7Yl39FNjNiWUd3DT1URZ+8nJHan6LGz3k+gCcXer0QlH5X4CUJX6T3f84wP8/vmP+N9ekSTsM57zxUWITWs3+c9TY71PJGhG4iDuJ3Ey4W7UjNU/q+GCYhMsZWpock12rt2cqXHX1AhMjeR+AuzXWA7N9jvJFsm15O2OSChsLQREg4E+x16Rc37z1OxSuJ+CaGr8MDUloR+a77VRU4dwjn4yX2moHODB1HTZrW3BqHEsk6AerFVGjbWKNv9udgjpdhqo3TU1IgvEtAlyxAFlrpg+KOfK1DgbUJXCCn129sVTTQ1tsxttHGXtJ3lQU14XiYEKa34T3U/iZ05tULqffBixYaCtq4AfPfYGrvzj67xPidoz1cTKJmrnRHZCnhoeceR8Dp35Ir52x4v4v2UfCq4rACQjsV+mhrB/UDM1Tu0OG+xaMnc0UE6+55BROJ2ymClVvxHcy9ztpF6s0eNzpsZnrhl5/ANE1iZUTQ0Z9yi7qRrTnVBUMPUqMDa8X0OaH8uPcStEP3kswizjOvyxLQxooyYkeOWpSSb8rwR2Mr9oY5oP/iqBbCYpP8jWPoToJ7eMwkmmqXFyP6kfCPfoJ1konBK+V2lqMpKoWDXJuSX6qxRB3E+NmRQSiQTXqbgaNUwcKPide9RUDj+aGluemrDcTwpdh9Ou2TVIJ9W1nwBEnqumM1/krphuSaMBqO8hm2zcchIJ0U8uzAPDytXb8ORbG3HHPz60CS39l0kwXxMJe5ZqNVMT/UQjG4CAWmvBMgpnPZgaMYkpu1/OzJrsZvUbaUhdeRRRFLUU8zVZ16UrgFHj2/1UHr+bs2nXfEAyrOea1n5yMmqsuUNnFK5jeGUUZp2FV6h1GcCY+6k5m+buDlpjiVGVsnEghnRb++8qlGzHk4XC3P3ULUc/qbura54akmgLgBDlomo3YBkVbpoa9hmNkuopUyO7adzdT2nh+Kq6V3y/VP8Qci4HWxVpV/dTeK4vwzAstk0RgWNrg+IaMLDVOs9k7XItewJhlc+EmcQQdmMEXd1PNPrJR/I89kx3F0s212/GZ1JOdUJHkamhC4jqaGpY9JPE1Mgh3UQHyJlll+K55r7FcwMU2bSL4rX0Hf1UEn/HwK5rmO4nNqbQ5HvUyAbCdD+V545cmsw13udCq52nPQw7pWBdMzX1ByemRn542CTjpsdoIxSilWXXWsnKE7Gquqw88bVJYmG7ULjsfupyjn6iYD50f+4nkanhhThTdqPGKtdgX7mzAU2g2EM2alQTAXU/AfCVq8Ygg0TYtZ9sugIXobCbsRYUdBf+8tRYxretTEL572zK+1r2BHlp4iiVDIFZcuu/jS45XwT3kw+mhS0W8sWSkJEV8F87impq5MnbCuG33ILVqNTNrxVhalKkFIIV0l1uX9ojT41i8u72wdQEzVMj/44hilIJdAFJxwJayNXT/aRgsGQUiiW0lo3n3Zoyjhnsr/rj6/j3hf8Q2FEa/aRKEUIhaGqkz+IAbdSEBD95agBrknEbwJj7qSWX5oM+pSqt6CLGeNjbID/8cgI+OU9DM3M/dRek8HH7A5RLJ11rzNiFwk5MjfU5G9jdo59KfJsg1KobeO0nlyR1HeVrx40aP+4nytCFHdLtJ6MwM9bS4bmf5KgHr/OiWaudaP4MZyKrYNQUDduKWBm9J7mflGkLCNtiZRR2YV/Li4VC0RCME4CGdHu5nwjzJYXTF8iYwNtTFabGPEZzluapsYd08+inpFeeGrtLRvWZdXzzlV0Py73jr93O7if33wcBvd+UVXcz1mTkXa4Bw/ZyEEciYeoxUw7G46Or1uCfH2/Hexvb+GdC9BPX4ngwNYDOKFzP8MxTkxBXpm6dmLufcmnlJO8U0k0nl26p48taGVlgx5iakgGei0Y+L4aGTIobISrjoyC5x5w0NVR0y66LtXK3H9dyPyUdH9igsDEait0x9xNjnHgbXSZidfhtj5rKIU9+6oy35quVFbfnxxWMmqR3qLrVx1QFLcuuyYiZGlGLULJdK6WmpiiyD+5CYcK0uNxg9vx1F0s2PYffMguU9mdLZNYOupAImjenJ2DXl7qfaHSWXL8pk064PrsqNws9D7mfMOOSjSXycZ0gBy0wpCSGKQyIBoODpsajvX7KJGxv7wZgFrNMu1RDt0LkaUCLNR+kXYTcgHsUXhygjZqQ4Kf2E0A1BAHdT8qQbhb9ZKcZ5ZWvLBaWhcKNxEVEdTWqQb8hkyS5OZyZGrZNQ8aJqbEGFPYA+mFqMmkrjFbllw8CX8n3ZPeTD3eDRdHCtqruKWSDz23S5ZqaMNxP5HT9hKoLAklHpsafSLZSCGGzxZKNmXFLHcCeCZWxIpZJ8GZq2DNdKBq2MSGoUFjl+qP11iyjIXr3k8XUiJoaOaRbVdBS1W/EPDVMA+XMHLOoINn95MWKWvdP/NzL9VIJxHpp1vG7pL7pBkEb5tC2rW0mUzOoOQsAjv2AGYZiHT2U22jp37yT70FX6a5nqJJGAXaVPRvE3VamuxTup24F/WjVfrIfV+7IdqYGwj6SyYQyAkpFz+fS7uUCZPeYzNSwlYAqpNtP8j2Rqelh9JMsFFY8nJ3dolGTDaCpUaW07ynkydOPYRnGseWkcZ5CYULxJ6XZgxuxCvdqmJBFpn6YGnZ92f1WuUloJtuMD00Me6bzxZIt+sltgUBhRZ3Axv5Rpsbv/noKw7CuZ1OOamqSNrcEdx2nknwRpi5QGdD9xK6lFAnqmadGWtTxtrsYXJVCGf1Ushvc7u21M/UytraZTM1uTWYqECemxqpSr5inaJkED02N6Fp3bX5VUZFRs3DhQowZMwYNDQ2YPHkyli9f7rr9/fffj7Fjx6KhoQEHHXQQHn/8ceH7c845h4cpsn8nnHCCsM3WrVvxla98Bf3798fAgQNx7rnnYteuXZU0PxJ4RT/JA5ibhsAyajLKvC2yZkVd+0nsZbJRIxe0BGgCPktA5snU+ChomZOYGtZeytRkbUaNXSjMrkE2leRGUI81NeXrwPLUqMskiO4nP7oodYRAj5rKYQ/pdtZ8uIWqBwXdh3BeDl1ZDrcVmLmU9DxUQyhctEcBuhnl7H6r7jOdqCwNi7dLuVAyiLFnfkcnHzc2T8nUSNFPdOKM2v1EnxWZqZGFwjwikpRJUD1rytpPLu4nS+Bq/t2TMgn096EKhctNlvV1QfLU+AnpZu6n3ZrKTI2i/EaxZBD2TOF+SiaUzD+Fm7YrDghs1Nx7772YO3currzySqxcuRLjx4/H9OnTsXHjRuX2zz33HM4880yce+65ePnll3HyySfj5JNPxmuvvSZsd8IJJ2DdunX83x/+8Afh+6985St4/fXXsXjxYjz22GP4+9//jvPPPz9o8yODnzw1ANXUeK/qmnMppbuKdUY+WSj8wDLTsbPT3f0EWGK/duJ+UlHquXSKD+RuuWMspkbsZjz6STHJuWksaKXfsDQ1wdxPaWFbN8NUnacmnAffFtaqaHMU7idDcj95RZrI4bZ0AmEDpxszFwbyksDeK3GhYVgCzkY3oTBhoTI+NCzUpcvOlWvLfJfesLsx2GdUZ2flzYnW/USvJdXUyFW66TX1enZV42heYG/UmhrWn/yXSbDaSqEKumD4aEsb5t67Cv/asNN13/ZjWYYXd+0YhhB95HWv/NR+2sqMmrL7SRX95JRNm/ZnFfMvng/K23rr6mqBwEbN/Pnzcd5552H27NkYN24cbr75ZjQ1NeG2225Tbn/DDTfghBNOwGWXXYYDDjgAP/7xj3HooYfixhtvFLbL5XIYMWIE/7fbbrvx7958800sWrQIv/3tbzF58mQcddRR+NWvfoV77rkHa9euDXoKkcBPnhrAGsT9CIXFkG4VU8M0NfaJyx7S7RD9RJ5pNmkLFcL5qtXqKg2ZJI8Wcsvyyyb/RCIhlBdQRT/ZQrpdku+FGf3EJ/9y+1QPp8395Cv6ia5m4LjvSuAn+V5BNtbCdj/5oJ7lCDuaq4YZAn6uZU8g6zFkw0POj0OvZZNr7Sfz1XeeGsJ+sskswdlb6yF0m9ysfCeqkG6y0vZZILOnEJka4n6SDHm6XSblnoZfiPJRVOd2SmfAjBG/CwiaRJLCLZvuAys+wUMvr8Eflq923beMkmAwWO4xVUSrE/zkqdkmuZ9UjB2dR2jfF/oPuz+OQmFy7UJ2rYeBQEZNd3c3VqxYgWnTplk7SCYxbdo0LFu2TPmbZcuWCdsDwPTp023bL126FMOGDcP++++P//zP/8SWLVuEfQwcOBCHHXYY/2zatGlIJpN44YUXlMft6upCa2ur8C9K+Kn9BNA8LP7cT1lFhl259pPlfrL2YTNqut3z1JjHs5dKYCujwc05/llDJuXqfpLz1AAiW8MebMrUZAKEdNOBsacDt9Pk/9KHW/Hih1sBAO1583408jw13rooK0IAnoLawG32IxS25anp+XFl9xObEAyHTDUyxS8yNd73OwzkJYrfHtIt/22dS2MmzX8ngz7XVu0nb6EwYJ2r5X7yx9SIeWrEdhTJmOA3mV9PQftDE02+lxLLJNBz8tLDiWOn2YfcanUVpbHGr6tXNf4B7kLh7e2mELej2zn79YbWTvzoT6/j/U2WNIIzG1JqAyHK1DNPjTNbxbCt3D6LqbGfi8j42N1PNOzcy/0URbqKMBDIqNm8eTOKxSKGDx8ufD58+HCsX79e+Zv169d7bn/CCSfg//7v/7BkyRL8/Oc/x9NPP40TTzwRxXICtvXr12PYsGHCPtLpNAYNGuR43Hnz5mHAgAH83+jRo4OcamD4qf0E+Iucoe4nVfgw69Q8pFuZfE/sZE7RT5QtYWK/XV326KchLVn+WS5taVpsIZYlKw8HNVpoAj62kkylqFEjuSOU1b8tCtt6YHs2GbKHkWpqugsl/Mety/Eft76Aju6iUCaBttVPSHdCMQH1FPI1d5sc2PUMY4IT3R/+mRqVhordP8vIj2ZQFIsheod002trVem2X186Cfip0k2Nmi5u1CiYGl/sH8DSnrEupYp+ior9YqCRhwJTQzU1JdGQpJoa1eVSuVZFsbfaqGHH4245j/4uZ1SnbQfUzyobF92e+7tfWI3b//Eh/m/ZR7ZjJROiu7+TJN/zzCjsEIhCwZiaQWVNDQ/td6gJKBcrNtvobdSIxnW5H7q2vrpIe28SPc444wz+/qCDDsLBBx+MffbZB0uXLsVxxx1X0T4vv/xyzJ07l//d2toaqWHjJOSiEQuAP2HkLkVIt0jLiquMitxPEm0L0KzC9uinIS0WU5PLpIiOwHmCpUYLZWr4hKbQ1Pgpk5Clq72QQrrZcUuGgY58kWcRXrejgzNX9uR7zsdWVWcPS+JQqzIJVCdEX500NTamJmC0Wxjolqh3OzMjXUvyd0PamY2kDIGfjMC7BKPG7E/WRGxOJMWSXfNDIQiFpegiKs73mpTCAmtrIiEuWmgbDENkyzIeIeeqHExyAkX5e3ZMIDhTY4t+4saY/TdMl9jlMnZ/vLUdgDjeWtnFxczaHd3+Mwr7cj+VNTUDmVBYwdgJ2kyl+0n9OwqqqeFsbYysmkBMzZAhQ5BKpbBhwwbh8w0bNmDEiBHK34wYMSLQ9gCw9957Y8iQIXj33Xf5PmQhcqFQwNatWx33k8vl0L9/f+FflHDW1FgrdsBf4rY2hftJlafGT5mE/g12nQzdltKhVlZhe/TTYImpYRR3yXBODEWZGpX7ibJE9uR7iugnEtLtlAI8CAzDigSg+6PXet2OTm7gNEoh3W6GKV/NJMOPELBnVXV2j3gl0gqCksS8yGHFMvhAqYp+kpiaqFgFORxYvlbyc2ilDbCqb6tW0TRaJEieGoAwNWT09ePCohF1cukNutBxcw2HCWpQUKOGuseKhiG0LUmMQK/oJ8C89k4uE7Z/gLCBPp81mUHnbXcxCHd2mu4dt7F7zfYOs90OIlxqQ3Xk7W5+JxQCuJ8GSe4nJ6arW2BqiOHlwSzTdBVhs9BhIJBRk81mMXHiRCxZsoR/ViqVsGTJEkyZMkX5mylTpgjbA8DixYsdtweATz75BFu2bMHIkSP5PrZv344VK1bwbZ588kmUSiVMnjw5yClEBsc8NdLq1g/dvrPTcj/JId00N4QVPaFiasz3zL/qzNRYTxkTCtNt2QMxsCkr1HLKECNFxSIBolZAcD8po5985KlhGYXT4UQ/0Z9miJuGns9HW9q5gcKuTzChMI1UqbipAuzsggtTE5H7CfCuiMwnM4nZAaqoqZHodnmlLz+HPLIwaRnuqgSPyugnl2tM0yR05UX3EwBfhogvTU0yPL2ZF2iUI03bkE6pQ5cz3Lh1zqNjM1qK4vMo9xOZDfT7rMkZ1RnchMI015AT1u5gRg1d2Jqv1F0DiEaNWzZqwGf0E3M/NYtCYacFt6p4KM0p5VkmgRjXMbJpgkc/zZ07F//7v/+LO++8E2+++Sb+8z//E21tbZg9ezYA4Oyzz8bll1/Ot//Wt76FRYsW4frrr8dbb72Fq666Ci+99BLmzJkDANi1axcuu+wyPP/88/jwww+xZMkS/Pu//zv23XdfTJ8+HQBwwAEH4IQTTsB5552H5cuX4x//+AfmzJmDM844A6NGjQrjOvQYRYfOYhMKe9S6KZWsTJNNWXvyPSGSgIUxJuydkG3PqEhbQUspMgVQC4WtQT7BWZ+GdEowSJzqkjgxNWmFpkauE+Va+ymVdF3tUby3aRcuf+gVTgtT0AGUaWoMQzwfKvizNDXmtm5CYdasSGo/+WJqzFe3/DtBIRvonC3wGPxcq7L7zKZbKYRMrAE0NVSMrppw2LWgeWqczqGrUBT6CnNf0AlOpX+wHVORH4QzNSSzrpNrOGzQKEyBiU0mBZdrN3luAeri8WZq7Joadd+XhcJe0X4WwyN+7sbUeGlqiiUD63d0AhADO6zoJ/Gei64g93vlVSbBLGZpMjVszFcxNWJqEPs+zVpm7mNGlGNbGAisqTn99NOxadMmXHHFFVi/fj0mTJiARYsWcTHw6tWrkSQ95cgjj8Tdd9+NH/zgB/j+97+P/fbbD4888ggOPPBAAEAqlcIrr7yCO++8E9u3b8eoUaPw+c9/Hj/+8Y+Ry1k6jrvuugtz5szBcccdh2QyiRkzZuB//ud/enr+oSGs2k908Mulk7aVLD2ORevbj8sekoGNptXuWCaBPGRMKNymEApnUkkMaMxgW3seOZJ8D1Ar6hOSKI5mFbZq3tgnObfoMKugpf/V6N0vrMYfln+Mof0aMPf4Twvf0fmDRj/RB//9zW3lY1rskJWnxvnYwgTEI0Fcm+obwTQ1lpuwp5ATNnplby1KRlDtmRrDnnxPLg5a7svZdNLVOKDu22RK/K0MeUHBqjNTksAyjPz0KbueiRoY1WNqLIMim0oikTD7AmWLzOgn9tyKKSi8qnSb25QkJljd97lL1GeeGkOxqDP/Nl9VRhFj0J366uZdXfz+OSe2Syh/65WnJu9i2AHAjo48fw7ZmK8qHOrkfmK/FbMeO7ifyq+mYF38fRxQkVB4zpw5nGmRsXTpUttnM2fOxMyZM5XbNzY24q9//avnMQcNGoS77747UDurCcHnqdTUmH+75XcBRAufGjVdCqNGjn6iDzLbjuUssGUUJitNBpYVtE1IvscmxwQGlB+WhnSKd37ZXSML9xhonhu5EGcy4W+SEzU19gdWBZZIsF06f0AcuLhQuCRWz/2gbNQ0kegOPzoQGtIduqZGHthdcgVRAXRPwfpFrsxYefnTrcEc5VdnTU1kBS0lg1ueNJ2Ew+mkFeHH9nHVH19HZ76Ia2YcLDAELBLJyYUgP3ty9BNAKnW7GjXma4L8zhb9lLI0NZGHdJNnPZFIIJdOojNfspUGoQJ/QO0ut/ZpNzKFPCs2DZS4b7/PmmpRB5CxVMXUMKPGoa9+sq3D1i6A5sQRNTXieXi1t6R8z7CNF7NM837LmRqHPD+qNlIW0Mv9FEVi0TAQ2P2koUZRQeUBYgcAvDU1VmSEqTORB/2iwr2jLpMgu598CIUVZRJoJtD+ZaOG+c/Z8VV1qdISr0uZGuZTlyc2wL0WUCVVulmCKbckgbS9RSmvxuqy24oW/PRT+0ldpTucB19OGKfMKCwJhcOY4Dbu7AIADOtnMqhe4ZyyayBoAdMwICYbK9kmBPk55P2dFE0tlAx0dBdxx3Mf4p4XP8aO9rxgsPHoJ4dzkFlS1repnsOq9K3eB52khYg6F01N1CHdcg069oxTTU2xRNheH0yNSgQvhiSL38sifssl6txuGiDgJBSWm0ZdiE59de12tVHD9pVKsDJA9t96CoWpca6YO2SRMKCOYnKqo0Wjn9wKjgJSQcsE+8y1+VWFNmpCghxyx2687H7yWuUzESEbIORBnw56VkFLeydk27M6IE7uJ8qoqMsklLdLJTGifwMA68HJSitZp/0CYv0na+Uuupzo+apr8lhGjd/Jmg9EHkYNY9CcmKdGgakpG3MuEzH7nSpSpadQhb06HZ+dVxgG1abWslHT3zRqvITCdr2D9V1KcjdWo/ZToWTYJkUn0XWG6LYKpZItJJuu9C1DXJ2Uzc7U2N1PTB/nxNTQWywayuYkQ11BYWXb9oL8rOeI0WKFdBvcCM9KDIJXRmGAuZ/UkzJgjZcs/N4rcsf8znpvcz856H1omRmnxQw1aroVmhp5vKbwWpx5hXTzYpbEqFFqapzy1JA2elUqZ8MPNdA0U1OHkDsA+9Mxo7DDhMhWccwIkEPAZcoXcGdqdisr4du6i8JqTyUUZllBdykKWmZTCVx6/Kfx43//DL54sBmVllaIPHlEREp8cBtcmRprWz8ZhbMp/0X72OpZZjcANVNjGOpju7mf7n1xNWbc9By27Ori2/DVWVKkaN/btAtfv/NFrPp4u2u7Xc9J0ii4MTV+XRGGYeAXi97CIy+vcdxmQ6spghzezzRuPZPvSREmglBYFoZXwf2UL9iFwm7uDKpzkUOyqcE2tMxcMSZLhryg6Fa4n9Ie4t6SwNRYEw9lHQDzmeLtjlxTIz7rB+0+AE3ZFPYc1CSEdFO2l7Wf/p5Cvj8yYysbPTJT48cdUpJYLwqLqRF/v4saNT6YGsoAyhIEOTcOEKxMgupZtkokEKZGESRAjyOU3iEuMjchN+AuWI8DYpF8rx5gF7CVkEqm7LWfPKI92CqODfZyNBD7HZ0gLEGYtR/WeZn7qVgyo6pYaDUvNihEP9mZmjxhakYNbMR/TBnDv1MloSsQo4si56KpSSuYGnehsP/aT25MjUrAV5Q0NQxNGetRkdmkPyz/GKs+3o7lH2zFiQeZBp8ql4NhAH9ctRZPvLkRw/o3YMLoga5tdzynsoHWkE4qXSrsPABRKGwYhqDHoHh/cxt+vfQ9DGjM4ORDdlduw91P/SX3kxNTY8trY++zfkTXPYEgFJZYOPl7wOon6VRCMLbllToVTY8c2Mi32dVV4FGEDDahsCJPTcrDEKGTbCIpsmQFib3lk1mVop/YJHjL2YehI19ESy6NHR2mO6RUMvjCgj03bpGL8uROs+4C9uvDvm/IyEaNd7sBu4HBjTFpB7sUZS5krNneabVTwfaqGEsGb/cT7ccqTU25REKTO1PjmFGY3EuVMURhzWn0mY6PVaOZmhBQKhm2h4h1CCemxksobHM/SSHdQo4XxeqC7Z+JewHxwZQjWQCap8bO1FAXEYPqXGQ/O4OQnIsZM2xio1FSbgUtC9ZE7V9Tw5gau2uADso0FFSljWggTI1caZ0NrEIYpyqjsGHweyC7JIKATWKNLgUX5QSNgLvfu718z3d05B1r22zcaQ7aTFPjlRPELaOwFdJddt3UKKRbNmB5LqRUUjC2t3d0K/eTTCbQkkujXzndwTqyWmewuZ+UeWrcmRp67+QVMj2nVJJW6Y6aqZHcKuVrwdrI2i2HdKsmW2ufEhPTrb4/DOzZY+OlH3eIcL1kobADU8PCpQF/7idV9JOqWj3f3uNeyRG1spCZCYVZjhrAO/qJLiQos6wquyOCjm3W719bswPz/vIm7n/pY9dziRraqAkBqpvPHlhbSDdnXtQdxtLUiMnoWGe0illat06lVmfbN6STXORKB1drULaO3axMvscGefuDmFHk1qChpRSqMglsRUAT+fkraEnz1LhPhmwAUtG79BrQMEbVtk0Zu/uJ7ZsNrIIfnTB0dKBlOYDaXYrieYHf23KblHlquFFj3Te33B00gzMzXmQwpmZo2f0UVFOjYhe98jb1FKLBba/Srao1BIgRdgA48wCY95lOAgAwaoDJ1qzdYb92dqGwWKUboBmFvZkaOZS2IBk1bsntwoTTs87aATDhvSW+Nr9zdonK98fG1NjcT6KB74epYVF8iYQYAEB/b2Nq/LifdjhoaqQ5QBYnA34yCqv7KQPT1Az0ZGrIezpuE8MrSJ4aOga8tX4nfvP0+/jTK+tczyVqaKMmBCiFmkWZqZHodo88NWxyz6bMh46HdPPoIvsEocoonEkneVQTHVxVBd2ay+6njrwlhMwTdkSGpQ8y8PLqbXj45U+4gZOWjCAxOZfI1Kjy1bjVfjIzCvsbuC2mxtlFk0qIad1VJRqUmpryPplfv8thdUYHWubac6v06wV2b9mArDTYDPt9c3PVUZbJSRuysdXJ/aTep5v7iUc/RR3SLUXOyP1FNqZoJXg3o0YOCR410DT01iqYGufoJ+szHj7uYKTbhcIWoyBGRLrn1wkTMlNDQSc7dr5+hMLy/enwcD+xnD8NUmSVW0g3M1Basml77SeHtqmK/FK0dRV4FW95G6uvmH+rNTX+3U+AvZ9s50yNIvrJIYxbyHrM76V7AkJAHNtoAk45dL9W0EZNCHCj/2WRWMYlugewHlKZqbGS79mNBjehcDaV5FoZ6laSFfmAFdINWJMvs+ZljYzZBmsgvuTeVbj03n/irfU7bfsF1GUS3KKfXDU1Kf8RHq5GDdHU0Guo0ncoo584U2M/hlOeGnYPqG4pKNh1YG1SsVXsI3pt3Sh52nZmvFAYhoFNDiHd7Ht7G0QDXF3QkjEU0UzAlBHNF0u2ycCmqVGkDQAgTFiiUWN+xnQ1ftxPKqGwV54aQVNDaH8a+cTaU63ke0WHBQxAF1p2F7Z7SLfIVNuYGuk5tod0s7a5GDVdrAyNXVLqVCaBaqryRcPm/pGNWXEscGYs6T7d4JepYXnJAB+aGtJGaqS7lYowP0d5WzGtA3PxU/1kLaCNmhDgVhtGph69hcIOmhobU0OYD4UPlCcRSyVI/hk7UyNm/U3ywZK5R2iIqwx6LuvKIrmPtrSX2+fM1MgMjdKoUSbfs084XgM3uw5KoTAx7FKEdVBtq8pTw/bdoXQ/UaaGfQZ05MtMTb7ySZzdExZRpswoXD5+1idTIxg1CvfT9vY8vy5DuVFjfa/adYEMlICUl4UzNSnb8cNEQdIT2Gs/yStgq78nEpZYuJUwNV3Fkm2iGjWgzNQEcD+JzJU782iQZgorZMMeEVmt5HtO+jnWRrN9ZAUvsSlKoXD5M2akdMoZn0MQCrP70dJgN2qsxY36Nwxyv2GFLPuVx1ohsqj8lker2i+XZ0Zh+VrJxq9KKKy6zp7J94jQ3MnApkEQ1A3KzjmnmZreD6GGUDopfMY7QPlKswFHlVyOfm4L6ZY0NaI+wXylqwe64lS5n1iTqfGRSNgNIDehMFtt7+iwJrx15UE95Zp8T1yxCCHdvsokJD3DDhncEmZRpoYOAHKWUsDJ/WQItKvKqBHz1FhMTUeoTI2zUc10DIB7QjJ6vVXuJ/bZwKYMEWWSfauYGmniF6N9mKYmWqZGptsZu+CUD4m6n2g7qfspXyjZkleOYkzNDh9C4XI/EfLUBA3pJn2KFpakr/mige/c/0+ceMMzjjl0egIrwaOz+4lqarIpu1EjM3ys3zKDvVNy08rjQifPU+MvyzVA3E8uTI2sP6NMjaoda8uLuj2HNAGQXDtSZm21psZrcSYzNeLxtyncT55MjSQ+BsrRTx5CYTq2uRmvtYI2akIApaKz0ipJZmq8o5/c3U98VUbdT0qmxlqp91NpaqRVNEOzFAFFGR8ZbCCm7or15UFdFhaLyfdEpoaKnt0LWlqraK/VBP+NFApPwcPaSd4PWvtpj0GNfNvGrDUAUqFwJ5ksqBaH3nca0s3LNoQiFFav7qlxG5b7SY58AmSmxtmwUmYUTjGmxt3I7ynklamsR5L7GV0MAFZSPEFTUyzx1Tc7p5FMKLxdxdTItZ/s7iev3DKyUJiyf3JEpCU6LuGRl9fgzXWt+HCzvaCrHzyw4hM89+5m5XdumhqB+ZRDusn2YlSPwf9m44Wb+8kwDM6SNmTFHDiumpryONhPwdQ41T3aSaKfAHu/Ye6nMYOb+XnZI2Ardz/JLmY5SzDrnwOVeWrULiel+ynpo/ZT+WMqFDYM6xnWRk0dwMrNYneLOAuF1R3GllFYEs6ylZxKKCx09JI1OA8o+1npwFw01AMSL2rJNDUu7if22SaysreYGtn95MzUZBXup5JhX7WKidHsD6wKfvLUpIn7qUSYmk8NauLbUqaGrvI7iRtJ5Uenq2oAhKnpuVCYRz/JqeXJgJ4h9Ihr9JOH+4mLhMuRT4CsqbHvk11yVdRHWtJTVYepsbQw7H66RT8BlvEla2pkFooKheUJlTE1jBlgCxeVkefM1FjvxVT7hs14ZPta39rJz0c2DvxgzfYOfOf+f+KSe1cpv3eLfqL3mj0jsqYGUC/EAMvo7JQYJmrAU0OYu594SLbTWVn3oznr5n4SdyC7n+T+yoyavYY027aR5wAlU+Mxjtmql5O/hWKWQTQ1CvdTKuFdLFhIV0FC4NkYktNGTe8HHVRkASstOw94CyNt0U+ScLagGEjk3AqGYRBjJIGBjab1zihKuc0UbOBljAIbZNUh3WYbqLuCZZ31k3xvn2EtAIB9y6/0fOk5878rqP3UpXANMdDViRj9ZH4+amAjP05jxi4UzhdLQnSGOoxTHMTYiq89X3RdTbqBu59YIkWXVVwySVb1fqOflEyNKBIGxPNydz+h/GqfxN2YuTAg51Bi/bmRGzWS4SwxNdTFyqCKfhpR1tR0FUpc38DAFggsZxTr16L7KcnbqAKtt2P+lhnhlL1lRoP5SgssVmLUbCw/y9ul82Fw1dSQz9ixLaZGrfOi71nR1E5Je0bvFz2nRp9FVgFgp4umxqnMwi7Z/ST1V7aYG00WQlZuMZTbZnfDMngxzm5CYTau92tIC4tPVeh83sHAofOUU64evi1hoammRjM1dQSaRVdO32/LU+MhjHTKU+MW/SQfk654MukkV8RvbyNMjYP7ia1gGWXe7crUmL+lTE3eYaCjZRLYMQ/91G5Y/v3jcMUXx5F9EqNGdg0owm29NDXswVUZkXS1baWdt47bmElhaIs5iTc6JN+jAys1wiy/cwIJcunaygyNU+ZiP5BdKDb3ExmMBL1QT5ia8mdD+1tGDe06qtsg9zGVUJhmPI5C2CqnhWfXivVzW/I9yYhn7RQ0NcWSLXowl05hSLmvyJEwbJXPSpao3U9lQ9kjpFte7ZcMw+YGYsn3PtlquZxkwa0fsHPuViQtBNSJQBnoR12SoUjHBoFFIOfOxj85pJv2U2bwpJKWODpJGFcnuGlqnJgam6ZGup7McB1MNC3MQDYk417F1HgxlXJ7KEP93qY2AMDo3ZqEbZRMDXU/KWr2mQs8d9c+NbAFTQ1n0lPK31UL2qgJAayDpRSTrd395M7UWJoa9+gnQSgsheDRfWeSSU5J0qyoqpBugDA15YHYLflemjM19klQNoIEpobsa1j/BmFVZ0ZwQDhnBlrtN2ieGpVmg+siiDjO3LbIz4GxSMP7W24XmkuHupG6yCCmCuk2j2m1t7O7UqNGZBvsAx5hahK0YrKLUUP6zLb2vO3aW0yNdR1Eo8Z70qP3mbse085GbBiwMTXMqCmXvbCXSVC7n4ToJyIUpv3GKVcNm0RZZAq71vTRS3swNXYXBvvcfp2ZlmJLm/W8V+LubCUTuYrpcdXUqJgaSXwNiK5Teu4NnKlxdj91SjlqAJ/J99w0NU5CYQext7zPllyajPHmPtyMe9V5qSAbu9QgeWNtKwBg3Kj+4rmwucgxT42dWabeBi+mJkH1grBrp2oFbdSEAIGpkbLsWh3AfLXy1Kg7jFv0ExXSUa2EvLoQjJpUgovHKC2uGpQBUiqhPAjKdDxFVqGpYbBrauy0qAqJRMJRPJon1X79MDWFopX5VSkUJlEjSro8lcA1Mw7CzV89FIeP2Y1/T4XCNKqkmxs11jGoUFhGe76yCCjmQpHreDHQ8U+smOy8T/lab9ol3lPmihjeX+1+UmtqxEmP9jUuxKXMXAS6moI0oHORNXM/STmJLHerKBSmkxplLmhXZlmF10lh3WzCY+4n1r+ClEmwFUUkglg7U2N/viqJfqLslMqocctTI2hqpMmOPg9C9Wli7LExwC2jsJyjBvBXJmGnW54aB5Hsri5JKCzdJyb8b85ZLiBLU2Nuk3Qx7t2YmlLJWiSxcZSOe2+uKxs1I0WjRl2l28H9ROYDOp+s29GB/1v2oZBXi/dF/p+UZFEbNb0flD1JS35MOR+MLPyV4RT9BJiGUF6xOrJWF9Z2gPmAp5IJvkLc0a5wP0k9oJkn6mPJ90R/PQV7aFo77ZOzTEkLyfcUKxUKr3DbbJpqapwHA8H1oLje7Kd04gcsWjubTmKP3ZpwwoEjhfBlqouitWnYMZzCb2VUGgFlEwrLTA11P/llamRmplWcmFVMjVfyPbnvK8skkAnRi6nZ1taN+1782CbadIMcwsqFwhkHTY3sflJM2CqhMACMZEwNCesulQy+QGDPoVXQkjKU7tFPshubTt7cuFBcZ4ZKNDWUnVK5ryxjyksobDGfZtvV7mNaAoZ9LzNM9JmW6z7R47oRH/7cT+LnsvtJTgLIxsumbMqWtkOu/0dvT7ODtouCGiUNin77RtmoOUAyalS1n4TCmA7uJ2oM/c+Sd3DFo6/joZVr+La0Lwo1vqS5q1bQRk0IoFEAsoBVFvh5hnRL0U85STirWh3JqwuaWyaRSHD3ExUKW5E/YhfgId08+snZ/ZRx6bzuTI27UZOTxNEMNPmeH6ZGFu7Kq2Caa4Q2SR6EZdAyCSpNjRCpkhTdNBSVuAQMw+BUtBXSLZ4X/VsWQTtBNig2ELGwYRgk+skppNu+T7mgpexmBERmzktX8Ju/v4//evAV3LN8tet2FHIGVa5HctDUyO4n1YRNhcKC+4kxNSSsu530D6Zto65JBr9MjSwUNmB3SasMscrcT15MjT9NjSwUpm2lfZKWgGGMt7tQWHTD0v26ifDZ2ObX/WQYBjeE2FhK+41hGB5MjfQckD7D2CI3oTB9ntkzz679jo48F4T7YWq8op9koTAbB2j+JZp7jZ6LZmrqCJT+tWtqzG1kTY0T1S53DBru3F0oQVX7ycn9xH5raWryvEM6MTVNJPlekdCeKko742Kc2Atapsh3HkyNA5tlRT95Z70EVCJQ2U1j3Te3laUMGpbfqXA/RcnU0HviKBQmDBR9dRNPyvWuNhGd1K6uAqf6hwlCYeu8lJoamakhl4Eyf25ZpClYZJ1TbSoV6D0vlKxFATdq5Iy1svvJkakx31NDjTE1dAJgK/hkAujXYIXbmp8RI0/SYciwC4Wtz+XQalWIdSVC4VYP95ObpkaY7KSQbrONCr0HYZzY951c32Z301jZhK398qR/PoTCqpBu1bPSVbAE5iy5He03sgA9JzHNlAWhx2Dbm+fuZtRY3+WkLOJvlVma3Qc28tQd/FwUmaWdMgqrxsJiyeB9gEoXVJoagDJn2qjp9aArFqc8NVxT41XQUor1p3RgN3m46ArSSSjMBgKmqekumCHIhmFYwjBb8j3zoWnvKgptVCffc+4+8vYNiuR7TnCa5NR5avwbNfL+VAmnAJJXw+HhpGH2dAWsNmrU0Q5AZfWf6KDEBkTZsJP1UkGjnwDRcGDvW3JprrlisCZXhVHjg6kBvMXzDMwFEOS6yXQ7u1a+3U+Kvkr1R6JQ2J6Aj9YZkg0kdZmEYJoaVfSTqs2VuZ+oUNjeLlemhj5PBWemhp4v1wumkvx68IzBCv2YJRQO5n5yC+lOSmMpYDFWiQQRe5M+0E6SKzZl07b+7JanpoUzNS7upyI1akRBuZPrCfDD1Fif0zbSsZWd+7Y2O8tvhnRb58JKv+iClnUAKjilSeHEKBjz85ynUNhu7dJJ3koORpkalI9plLcTdTDN2RTffnt7Xnjg7cn3LPeTKDhWMDXSpE8TP/lJvucEVaXuYskyxMw8Nd7RT/JE3SWxEYLAO2EfhJ1qmNBrsUsSkALigOomFK5kohHCXh00NTznhBRC6lomoXyt+pcHepqrRuV6YnCr1C1nv1aFdANW//bKKsxz/ARguGRhpBzSTZkOcxvR/aTSktEwYzp5M/fT+tZOvk8hKkZ6XsQ8Ne7MoyGxXjzqhAQPpF10QHJotB9QoXCXiqlxyVND22gJ7xXuJ8WES8dR9ltVCL5KKOxmZDO4lklgYyn5Pd1e5Rpn7qyGTFIIL+eaGok5pX2GLRL8uJ+SCXsZHqfIJ3q8goPLSVWfSi7uywxbUboA3h6arqJL4WasBbRREwJolAfNGSNHwQDWYEnTaFNY0U/2WkPdxSLvoEIiM0mcLLufTF2NlYBP1lxQsBVse3dReNCURo30208P60faJH6XSVmh2p7uJzbJOTyM2bQ/psa+Cpcmf4XiH6BMjbqddHCmq1kn91PCkampwKihWVQ5Fa1moGSmxrVMQvlaseRhNEyf56hRGDVu0Sa2UFZFnwX8ZxVmBmQQfYisIWDXqoFMhCqdATdqFH1VMGrI10P75ZBImOfNqiYLTI3kFhLdT+6LHdn9RJPvyUyNysjoqqCAqqCpUURPyQJlGSlumNi1FqpFCS2ea2lqmFFjZzQ6Jf0hIEaFOcEtpJtnyCXt4mUVcmkli8z1NOU2ylnjizaWzToeC8xwyk8EiO5Fedx7wyHyydxexdTQ6001h9aYQQNPWB+gCRjZtU1AdK3LxZhrBW3UhIACWSlRA0N2QwAiu6EaxLsk9xMgrmRVlK+8ulDllhlYDifd3p4XQ34dku91dFvuJyoeo5ANnf2GW5mBZYFlIpGwis5V4H7qllgjP9FPTpocBllXxJrV5ampsdpPB34e0k0Oo4p4YKjEqKETGIvCcop+4ivDANFPe+xmsg3U/cRC9tVGjTPdby9oSfosLWLqU1PDJpe2ANfNy/0EiH1LrnWmYmpokUU5oqtFqrPG9Ru5tM1IFo089/5sj6CxDFXZuFD120pYQTGk294uXoXd4XlmfYPmfWJwj35K8AWTXIVbFf0kMDVJ5/7IjtdGRL0yrGfF+oy5PVtIxl6BqWGRTzkxt1heWuTwRUZQoTBhxGix0nyxhHc27AKgNmpUbJiYIJRE6VJXfLnvd+WLfIyiTA0VutM7rxKE1wLaqAkBTOwmRz8JUTCSUBhQi4W7FP5nKpylYY8M8qTF3E90EGG+4O3teTHkVxqQ2ADR3l10DedWfU7LHahWbyz3jl+hsECVFqhR4y+jsFO2WAZ50mWvHQq6nCKVtFgnOSkb3S+g9qMzVBKRIoilHVxwcmLFpGT0qtDFjRrG1FhGDWMdaLZUBuoGkeEnozAgZmh2AzMQglQ4l4WR7FrRiVCVZZW1SSWGp6yFfF958dhOZoAx10XKJuAV3U+iVkIGs3USnKkx/zbgj6mpxP3kJRR209QA1v22aj+JBiDdBwCBhWZ9u8OH+0lMvie2TUYb6TtuVbrpM7yTuJ/8MDVZyfApSc8BNQKb/Whq6KKZMP3vbdqF7mIJ/XJpvhihSCvZMPWYSI1mdg2oIbOt3QoyodnSVQEW2qipA1BtBlvhOTI1ZGCTcx0A9jIJ9L0Z/WSnfN1CuhkGkLBuOeMsBaN5O/JF3j6nyZ0OUv1yaYwcYOUwUfn1qfjZDaqBg0aFJRLeRdfk36v+tlZA5XbxlaW7UDiRsPzmNH9Fd9Fu1MjCToqeMDUZQkU7CoXZJJcQ+4cKMlOzeVcX72vcqGkJqKkpf6as0q3Q1MgRWDKYwNPvdaPh74CYfC+dTNoyv5ptkNxPin5MWQtbRu6yS2NnOVkbKzfSnE3bGBR19JNfpsY6R1tBS8V1DsrUGIYhZRR2ZmqckmnaNDXkeeIFPBVMTSaZ5N/zsG0uFCZjglJT4+5+YqxKJpVQRumoyiRYVb0zXGdHxxKaowaw3NaOyfdIl2GGlVv0U1ERFVYolbie5oCR/ZVjqlq3JB7Hqk9lZ4O2SUVcmRFJ+yId1jq1ULh+oNTUlNSaGhrNpFqZsk5G/ZI02oZa7fK+2WSmyi2zG6nUTSc3eZXVxJmagrLOFAUdpAc2ZwT3hGr19qWDR2HsiH4YO6Kf7TsKVZFDOSU6NR6dYBMKy+4nTgubf6ekCcDt4WTfqdxPVsijfWVNUUlGYVoqQjUxAHaGxE/lYtbvRg5oQDJhGikszf7mXebrICVTY1/ZMliMkbit+Zn13hKGuzFJRX59/TJcRekZLBRFUa1Ky8MXDTz6SSEUpu4n6ca2yEwNibTJSu4n+ohkPITv9uR71j2VjXPKoLJK8yqjxA1t3UWxpEcFTA3rd12KxZGbUDhNmFiGRoWwu0NyTQHe0U/UHajSuqny1DCBunkPXZianKipYdsUJYNUGdLtwtTkyf2l5TQYm7rHIDtLA3hHP7H9AGJEE2ufPH6yxQ3vi0mJqVF4GWoBO/+mERh08qfRTyo3BGB2+kKpqNbUKJgaIfqJuLoYrAHC/FvF1DD307a2buGBlS186n5SubEo5P0PbbGYGtXq7QekcKUb5MrkgDWRsAGMu15cXBZO4boMspvGosvdNTW8jV0SUyMVsKP3vKfup/964J/4eGsHLv/C2HLbxL4mnheU5+XHAGzImIUZN+7swsbWLgzv34CtbebgqXI/WUJh+z7llTy7nGZ9L7v7ya1MQhsJm/XL1KgqG4tMTRJAUTB25RBiNVOjjn4CgJZyLhpZU9OSS9sMpMqYGtGdWaLRTwqmZq8hzXh/c1tg9xN1PQFOQmHx+ZEh93lBKJywGAd5f5SRYGiShN2pZMq6V2S/bsJ1gIRzK1xP5rmUf0+Zmk67UJjeJ+bSYm2U3amGxJwKyfd8RD9Zoe6i251rijJqYW4qZR8fnFzyTtnVKba357HHbs5MjVzKoVbQRk0IKJLBW9TU2N0QgPlwd+SLrpoamteFakxUCa/k6BZVZe0BJAGfUzFLQBQKc8GxQycXhMhNWQzpZ016XroZN6iS77HrwoyuoBmFVX8XpYlCnqDdVhzs3AWmRqabySWg7xsySXTmS76NGsMw8ODKNSiWDPyrLAw09Vvq1b18Xm5sCm87ce8N6182anZ2Ahjgy/3kytRIjJHc7zKSsFKFXcR49JunRvV8sYkwnUooJ6h2aYJSMjXlyUT1/PSThMJslT+gMWN3PymeYa+ClnIETcmwa2roszdmSDMAdUi2G3bIRo2L+8mRqZE+zngyNYxFS9oWRbJR05BJWewtzSjswdS0eRg17u6nNN8vjcxsJy5GwOrPrGwA21dCeh4Bi91xZ2qo1qhs/JYsd5CTUaNyT8v9y84mOScLZRobMqspt621UaPdTyFA0NQI0U/WNjJTA3hFP9ndT2b0k90lJAuFC8RFwWAJhbsdi1kCVvXiQslK/e2kLRGZmgyasmmevM/JZeUHcp4HgKyg04ypsa/0ZHgKhT0ElqpssnIbKVPDwvSpkI6B3v8hZeOg3edEkyduE1YB2i2sXV5BqwZqGZbbM8nrOzF6m7mh1O4n89W99pP5N+tv8uTuh6nZSQoK+mZqFAYCmwjSSXV5BrZvro8gfYB9xiZT1fPDJkvWL5gupV+DKvme9Z4LhR2jn9hvxInRUEQ/0WePGTU9ZWpURhE7bsrhOZGfJ29NDdGOSPtszFhGCDN+LKGwPfrJSVOzi9wPZZsV7qdWzrZllO4nztTk1EwNO0U5xQJghXS7aQNpUsIMcbt3Sayi7VxcXHzy35bL2tlIZRobytSoNq21+0kbNSHAOU8NdT9Z22cZ1azQEKjqZ9AHia9mFKs8e54awqSQkG422Kv0fVR0xwY2p06ucm8xXU2PmBrFwMHdTyEyNV5RI740NdLgT4sd0t3R+Y8xHn6ZGjohMaNGlb2awR7VZX7uJ6NwNpXiSfY2tnahu1DiE7Ta/eS8MpaZGsfrLAkrVaBMTaFkeIZ/O+2PXfN0igqF7f2MCeZpW1kfZ/dD9fwwoTBb3bP+0b9BwdQoosGcIsDkGnI8+knJ1FjH2WuwadQE1dTYmRrnMglOz7qsWRE1NeZ76uaheid5n9l0kp87W7Spaj+xQzqNC24Vus12JWzt2kV1UQoW2X/0E4RXwOpnpv5L3eaCML9YbnfWV+n5U8j5ywCFpqYkamrc3U/d5fNh56HOwaWNmjqAyNRYk60jU+MQ7VEoWnloBE0NeUjkOi/suOyY5nZ29xNNvifnTaCgDAAb2Jy0JWnB/WQaTcyocYqI8AOVpob59Jn/3Ff0k1wmQfrbxmi4aABkWCtrO50rizoB0dUwpGwc+HWj0AllTdmocSvqyY3W8iGDRD9l00lu1GzY2cldT6lkAgMaM7bfWW4QZ6ZGvr62Ccujcj0AW2VuPwZhnrBPDJ2EqZEFyoZh2PQRguHenCnvo8xQuDA1uzhTUzZqGjM29kHF3vplaixj0q6pYf22f0OatDkgUyNVpXYrk+D0rMvXR2BqlCJWa2xTsaZyjhhV7ScvobCn+4kknmPYVb6H/RyEwlb0E3M/icYyX+SoNDU56lZzMGqK9vmlUCpZmc8dxinVGOm00KNtdDJqLKGw2m3PoKOf6gBFmt6b0KpOmho6mHYXSnj2nc3o6BYFi9T9lCEPUkGRxZO7F1j0U4G5qOwD8vb2vK3Amgxm+bOBzcmoySqYGua6cHPdeEFOXgXYhcKqVYgMWaMhP9C2pFi2gdTFqHHINtxVLNpEnfL7wS3lFb9PpkZp1KSTQs4KuspzSnrnGv1EjJqh/cvup9YubCmLhHdryir7C68Wrdi3UyE/eXJXJTSTIRs1fiLHaM4Z1h87FEYNm3y6SKHKJokRBKw+zqC6Hv1sTI352r8hYxvs6ZjAx43AmhrDZlx8eng/nHLo7vjO9P358xKFUDhsTY1VTNTO1FjCbuu+qms/ma9O+jFP95PE1BiGgY/LVbD7N2RsxSoBGv0kGsKWXoW1TeF+IrXUnAxaVSBKoeSHqVFcY2kQsNxP5fNPuAuFATG6k54XOzenvGbVgjZqQoBQQ4hPtiVhIEooVmX5Ygn3vfQxvnrrC7jp6feESZeuamhuBB6+SRNZSStxVUj3wMaypqYjb2MoZLABnTE1TvoYFVPz1SP2xLH7D8Xnx41Q/sYPcopJjlUZbszImpqeMDXmq2oFBXgYNQ7f0QrOdHf0UnNNTQ/cTxlpRUWvgy2k20f0ExM+UqZmE2FqVK4nc9/mq2oSsVxaIrsmi2/9ZBTeKTEHfq4dDX9n94sbNamkJVAub0eNTLbqTisMdwbV82PX1DCmxp6nRkxG6KyzA9yin+zGRSqZwPzTJuDsKWP48xK0TAJ79tkpKkO6vWo/uWhqrOgn+4SbJqHLDGYIPjP8ykxNQXRJm+1lRraDURNQKLxy9Ta8u3EXcukkjth7kJJFlpkaWavlltqAusGcmBpqtFLjl42JDQ5lCVS6Q9amRilDs6pKNwPLP8aEwva+aG1ba5YG0NFPoUDodGSyVbkhAKKpKZbw8dZ2AMDqLW1W0reUOGHRQZ+LxsjEIGeMlWs/AZbRQSuvOq2wzIezi6/W/IZ0A8CUfQZjyj6Dldv7hTL5nkQ1V5KnxnQNGbjk3lXIppLYc7CZw4NdB5lFd3tA3Ywa1iJ63xMCU1PW1PhcPdPJ1srOmhTuX7FkgOkFHZPvOQz0hmEIBsjw/pZQeKuLSBhwT77XIYluufvJgalx1dT0wP2USSVQLCUBFHk7TaGw6CZgrqdsOkkMMMrUiO43pfuJMzXms0M1NTb3E9l3xqM/y2MJFWjz8Uex+GBMDXNdexWTZWBjBAvvryz6ye5CYrCeX2u/PKOwgqlRuZ84exugSrenpkYSCv/++dUAgC+PH4WBTVllsV2ZqbGi6kS9ijqjsNV2p6zCecH9ZLm9O72YGqlPGYbB99WcS6Ejb9USpPWp5Gv/qUFNWLejkwuF2aVlt9es1G0I515L1L4FdQCRqbEsaZVgFBAHcU5TdxZIhW6xk9LVQV7BsqTIxEI7Lp14GzIpbhCsLhtSqnBVwLLi2cDm5EpSGTVhwE0ozB5gf9FPdqp1e3sej65ai/tXfMIf0qQ0+cvtUMHJj91dLNlEnfL7IQHdTyrjJ51yZmocyyQ4jPR0hSgyNV287hNzmclwCuk2DINHd1miWwjtYlAlW5SxS2Jq2rr8u59o9mAGGtLNFhMdXPCZErZjGOjD/URrPxWKJV5nqH+jl/tJnAhlyO6nhAtTQ0H1JkF0NYypYQauOvme5XZXwebOdcmtBdCMwnYXCHU/Fbj7yS4UlhORyvDS1NA8NVt2deHPr6wDAPzHlD0BWAsdKhWwdFhi8j22jZ05tY6XS6esMG0PpobOL0WiqfEM6eYMvrV/1lZZzKxyP7HFHxMKu2lqeq1Rs3DhQowZMwYNDQ2YPHkyli9f7rr9/fffj7Fjx6KhoQEHHXQQHn/8cf5dPp/Hd7/7XRx00EFobm7GqFGjcPbZZ2Pt2rXCPsaMGYNEWW3N/l1zzTWVND900JUS7UiWG0I9WXYXDf6Q7ezMKyOfAFFIyQYSVR0V1hZV8j3AMjweWrkGAHDongOV58NW1kwP4Kew48Amu4i0UvBBQSEUZgYf9xe7JK1SMTW09gsLWZbzqFjtcF7VyteE3WLqfnLS1AwN6H5STShZmakh10F2L3pV6aZUei6d5O6xQsnAe5vaADi7n5yS71FROxt0nYTCslZCBbumxvvaFYhLTb5fKo1GuxT5RNsG2NkqFVPDNTWdBcFl1q8hbXOpqKKfehLSrTIuKIsRxKhhz/7w/mZf6FQYnKrs5hSijjAhPF+qxJE0T418vzLE/cTGN7VQ2HytNKSbGkX3vfQJuosljN9jAA7eYyAA9YLLylMjampYO2WWjfabHAnMcGIqlZoaEv2Uy6jHZy6FKF9X2resTMYim5RKJmzj4J7lCDrG2lp9UTwvdj61RuAW3HvvvZg7dy6uvPJKrFy5EuPHj8f06dOxceNG5fbPPfcczjzzTJx77rl4+eWXcfLJJ+Pkk0/Ga6+9BgBob2/HypUr8cMf/hArV67EQw89hLfffhtf/vKXbfu6+uqrsW7dOv7voosuCtr8SCAwNYRWlUP5GHinL5R4bZidnQVlNmFAzFNDU2Yz0E5YNAyoNDWAtdJ85p3NAIATDhypPB8uFOYh3T7cTw6TXiVQDRxyple+YvERpsz/JgMBAGxo7RT2RQebRMJ5BQrYry1b+VEXoVOeGup+cotIYujotg92dqbG2kYWQHsl3xO0XKkksukkn8DfXNcKABjUbE+857Zvep1lds1JUyOXsaCQNTV+WC5mrKnynqjKJDCDl678qQEmG+6q7tGSszIKM6azKZsScoyofu9Z0FLK+ZPgn7szNcmkxUipDBMnsLYPLQv/1Xlq7GORcGyFjpBBpYmj+XZsTE3K0tnYop8UeWqcCFy/Id3FkoG7XvgIgKkTZHDNKMzLJIhGiuUOFtvI9ucUSclAy2CkybaBmRqSQqSRGzViG2kZHwZWamO7lKdGVQKmVzI18+fPx3nnnYfZs2dj3LhxuPnmm9HU1ITbbrtNuf0NN9yAE044AZdddhkOOOAA/PjHP8ahhx6KG2+8EQAwYMAALF68GKeddhr2339/HHHEEbjxxhuxYsUKrF69WthXv379MGLECP6vubm5glMOH3Sl5EdTQ33DFlND3U8OQkohpNvufjLbonY/AVauGsCcvI7df6jyfGT3k1yzxjqPBH9tdvDrVgJViK+cPdNX9JMsFC6UBHZENmrofcqmksocDAzytWXhzl2+8tRYBqAqqkSGyv2USTkX9nQSQBdLwLfv+ydOWPB3YdXOrnOarNKYC+rt9TsBAIMc3E9WvhTJqKFJ7qQw/Mo0NWI0jh+Wq0CeAztTY0++J2uAaJsBf9FPvKBlZ0GIfGLtEH5PmRqPMgky5c/sCMMwiGBXPZyzNAhBynK0cvdTmampIE8NHZfkyU6dQ8ViflRsHjX8DMMSyqry1DgZ8N7uJ3MH//x4Oz7Z1oGWXBpfGj/Kdh6U3WyX3JZyhJSTsJbpVyzxr/reWzpKMWUIW+z4Tb5H22wJhZn7qfybhF0oPKbM1OzqKphMtC3vjjhu1hqBWtDd3Y0VK1Zg2rRp1g6SSUybNg3Lli1T/mbZsmXC9gAwffp0x+0BYMeOHUgkEhg4cKDw+TXXXIPBgwfjkEMOwbXXXotCwdmv3tXVhdbWVuFfVFBHPxm2zsxAk41Zmpq8MpswIJcusFO+svtJrjTMwMK6AeCz+w1Bvwa1y6jJJ1Ozx25NGD2oEcfsP8zVAAgK1WpIpporrdLtatQk/T+c8vds4nLMU0NWNXRy9DM5q4wadnxVvg85YzQNU/3La+vw1vqdeH2t9Tx0K9yeLN8QO/YQD6GwfBvaFSJGxzw1fjQ1NqGwH02NxVhmpD5Mk++x48rZhM3fOuvG3KKfugolbC6Hw/dvFLUWDAKT4VHQkk061urYuu5exkWjlAnZD9izz1I0uOep8eN+ks6duFEYrHQV9uinTEoUdtMs25Sp8SqTQEseqMDuCUtn8bmxwwSjQbXgYvtscihoaRkB4vOYS5sLEyvyzUHzRhfN5Bp05f0yNSX+G3YOspiZ6n7k+7nHbo3cgNne0a0ormptGwf3U6Dop82bN6NYLGL48OHC58OHD8dbb72l/M369euV269fv165fWdnJ7773e/izDPPRP/+/fnnF198MQ499FAMGjQIzz33HC6//HKsW7cO8+fPV+5n3rx5+NGPfhTk9CoGXSmJmhpR3MdAOz1bOezqKvBBR/aR0sR5PEKAMjXkfalEyySIBx7QaA3K0w90DrluLGsKvJLvNWRSWPqdY5U0fE/gWqU7419Twx7ipmwK7d1mAVGa8I4N1NZgY/3WqTQE/55snE0lubFFmRp6X9jbpowpDAxS/6lTsY1VRTqBLojXwSYUJuGzbOL+ZFs7Ju65GwArCSQ1athkxuAU/WQxNeLnbqyHzf3kwVIAlhZiQGMGOzryPkO6LaZGdlNmktT9xDQ1ouATEBcP/RrSSCUT1mSuin4iDMC67abRzAxeM1zWro+hx/F0P0mrYzFPjfpBZM9Mlw9WkGEHN2qYpqYCpsZlkeCWQ0VV0JIaOvmiIbSnIUs1Ne6u1l2k5IFXmwHgRGmclA3wfLHE38uamm6uVxH3zdrIrgkPVXfwmdEyCQJTo6hSrjqXkmGOCRZzSQ0p2f0kXoNEwnzmBjRmsK09j+3teVv0k8DU9DajJmrk83mcdtppMAwDN910k/Dd3Llz+fuDDz4Y2WwWF1xwAebNm4dczu7vv/zyy4XftLa2YvTo0ZG0WxX9VCwaysELEIWRzMo3DKvGjmzt0grbOcn9AkjuJxL9JA8kLCQ1lUzg+ANEQ5OCTUQscsNNMOs3RDQIlBmFWaRDgDw1TGjcnEuXq46XlJMhj8oRNADu50UNxoZMUmizOk+N+QczGJuyaXTmu32FdatW2KwPqaLA7EnvzM+pC+eTckIxgJTmIP2FuR0YgkY/caYmY19FO9UEcs1T02UJV3d05HnfdAOvyJ1KIF2yazrkPDUqdokyPM05M01+R4mVSbD3kVQywY1ollOoP3H7plNJfp7053wF7lsobH3uZVwwJkOlzVKBRm31JPrJbbJTaeIKRChs19SIQmFm6CcSYr/1cj/t4poatSFA29yQSWKq5KKXmRo6njRJBS1Z8k9LYyceI8uCHvh5eWlqLE9AZ95i7Z2ZGuu6FA2LwU+nkrbSJLSSOL32Lbk0kskEdmvKYlt7Htvaum0eCJGpCU+GUCkCmVVDhgxBKpXChg0bhM83bNiAESPUK/8RI0b42p4ZNB999BEWL14ssDQqTJ48GYVCAR9++KHy+1wuh/79+wv/ogJdKYmaGg+hMNHUAMDmXSZdLQ8Ag8puo20kG7BQ0NKn+4mtuo7Ye5CrsLdJ0sf0pDhlJcimyrk1FExNroI8NS2kEq6KGVFFP3mtOORweTZAibWfxBUPYA2mbCCq1P3Ejk+zCjNY7ifzbzZIUbEtNWpU7ifWVxgGOwqFzVebUJhR44T16FFG4XLbGYPkx/3Eq8ynknamgHwmGzXNDpqa5lxKjDp0cLmy/saNGuLqoO0Q8tSUJyDDUPdpeSxRRj85PKcNAd1PtEQCc0N25ks23RSdbFVwc+fKrj9zf5a70N5HRGE3LXCrEuSrhoXuQokb8P18MDXHfHqYwNoBYuQqYLF7GZIiwJZ8z1aLzXI/mb9lWiGP6KekZdhRd6xj9FNKnBdoVKzMUlL2kRrHjGVkInlaZieuTE2gFmSzWUycOBFLlizhn5VKJSxZsgRTpkxR/mbKlCnC9gCwePFiYXtm0Lzzzjt44oknMHiwd/K2VatWIZlMYtiwYUFOIRIomZqSOrQXsDpzV6EorDg372RMjWhUDCRMDev48qqMhu1aZRLEbU6ZuAfO++xe+PG/H+h6PnIyJ7fMulFANeDJQmE/eWrYQ8x1DpL7iYGtftyiNWTQh7cxmxJWcOo8NQmh/cxw9FP/iZ27HCJrtt3OWMnuJzboi0ZNO3+vNGr6W+4np7pP9Lzs7iexhhJgJmYcv8cAnHLo7sK2QTQ1zNjy5X4qEKGw5IqlAmZr1e3sfkokzHuXVUTayGBiYVbSgmrX6DMp5qmx/lC54eT0EGxrWtDSmakpr+59up+YnqY5mxIYDTk6TeVmpaAfy9efGclsIQeIhRtlF6Wcp4ZH/khjlZORDYi5jZyZGuv9CQoXvdVniuV92tMAMBakWzJqZGaDGzUeeiqqo2TXmj0PiYSzjoX2hwIxarKKyD/K7tJxkLGM3FvQnndkDc19196oCex+mjt3LmbNmoXDDjsMkyZNwoIFC9DW1obZs2cDAM4++2zsvvvumDdvHgDgW9/6FqZOnYrrr78eJ510Eu655x689NJLuOWWWwCYBs2pp56KlStX4rHHHkOxWOR6m0GDBiGbzWLZsmV44YUXcOyxx6Jfv35YtmwZLr30Unz1q1/FbrvtFta1qBiqTJiipsa+6gDslXDZAy530kHlDrWzq8BXKLKQLpVIoAjDNU9N/4YM/vukcZ7n05SprVGjCvGVQ7rZoMf8xaoJhvu6cxaLonJbqNxPQYTCDekU0QEVXfPUsFBSKv72Althj+zfgLU7TJ0Gu/80bwUDGwTlvBgsmg2QmBpFBmrK1OzWlHGcwGlhRQqV6HbUwEY8Ouco2z68op+KRAs0tL//CufdZOVflNxP1J3R7eZ+KretOZtGIpEQnk2nLtKPMTU7mPtJnfcm5WBEqyY3WVOjKmjpGP2U8d/XAKufDGjMCHqNznxR+JuWNVDB6fwAYEQ59f66cn8GxGg1N/dTN2FcGxzcWqo8NcwQaMjYhcgMrJ9nUgl87gD7glmuwM0M4WZFn7GKRZb3LT2PbJzzinwrKDSbbIEiM1UUQgBJkUTFpkmFekUbUwJTY/ZdquuUI/EsEzseTE1go+b000/Hpk2bcMUVV2D9+vWYMGECFi1axMXAq1evRpJ08iOPPBJ33303fvCDH+D73/8+9ttvPzzyyCM48ECTLVizZg3++Mc/AgAmTJggHOupp57CMcccg1wuh3vuuQdXXXUVurq6sNdee+HSSy8VNDO1hJDxkbgDvDIKs4y2DE5GTf/GDBIJc1XGtpFXZckkgKIY0l2p1SxTrj0pTlkJVJqaLkkUJzywhoEk7G2UmRon9xMPfSaXK5D7KZsSNTWSD52+Z5N8QxD3U3mb0YOauFFjuZ/sjJUT3U2ZmjXbOrgxyAbfnOB+spgaJ5EwIGo7KFSaGieo7jcFpdpZu/yFdFsLgIy0azH5nhjSLWQUTjJjVEyBD7i4n8oTwfodolAYkNxPVChMV9VKpkacSCgj4Rn9VL4HfvPUsMVW/8YMNzCKJcMWAeUlUHYL9WX1hNYToyZP3CyqkO40uV88cEBiarixpzhVq+6Tc6LQsSP6YUBjBl84aKRw3/h5SOweZ2pydsNVrv3ETkl2P3llkxYE1OVt2bk4lUgAxP5ZKJUsjVnSmakxQ7qtfTCmZhApiGxPvmdt3yuNGgCYM2cO5syZo/xu6dKlts9mzpyJmTNnKrcfM2aMY/ZHhkMPPRTPP/984HZWC4WS3ZL2k6dmW1kYzGAZNWJHTSUTGFhWn28tp6qWBxJes8SFqfEL+UFxWolFBVX0kz1PjegvVs2dFlNjJcZTCoUVAlavaycYNemk4H5yY2qYUROEqWHnvufgJrzwwVYAVsSQKt+HLBRmx95JmJruYgmbdnVheP8GB/eTxdQ46WkAZ2GmVwVhCq5BKKjHATaAZ9NJ7gbzk1GYGvc0yWEiURYKS8e1mBq7+6lZKlYIuLifuBFt7lcUCqvdT7Tv5YsGNu7sREd3kWdzdaofZBhW5JtTe3hknl9NDcuvU253QzqJtu6iTZPDhb2OLJ71Xp7s3JgaGrrMQPMKuRVzdCuT4BXODQAjBzRi5Q+PdzTUWBtKhml8qpgap9pPsjs4y91PjG11YiotOQHbB3OluS0akiTazklTw7RBNPqJ5b8qlgyiqbEkEHy+5kaNda3iENJd+xbUAdS1OZyZGtaZ7UxNWVOjEH4xnybrT7bspCSawCmjsF/IQuFqW99MKOwnTw3g7IvuUhg1HXmVpkac/AEf0U/k+0bK1BSsFZEQ0l2+hM0k+gnwV9Syo7xCZpk9AWt1p06+Z626zG3Mz+WsvKyYKnc/kfvckElx6tkp8R4gClbFNtvdT07wZGpYavtc2tIi+an9VLKvTNnfgF3Q2a7QAY3ezbzmew81jQuqDXEWCosrfLrizzgwNYkEqUJdKmHmzctwwoJnuCFq5alhvzVfTaZGrbNjqNT9xO5/A2d6xN97MTVuId0jBzQCMBdyzKgWQ5dlYbG1YKTuJydNjdL91Oke+aRqtwz6jOSLBndnC5oayf1UlGQI7FLk5Ognj4zCVCjMnmUnkTADLYCZJ8+5LE42ZHa33FbmOmVs7Za2bndNjTZq6gMFh4zCsriPgXVMViCMgZV2V7mN5Ggl2X/OjltyySjsF3ampkbuJwVTI+epAZxz1bCHuB9xPzG6mEJ20wDBhMINacuo6SqW+IBDJzMrpDslvPpxo3QS95PcPlW+D2ZMpyVjTTZqmK5GFdINWGJhp7pPgNW35TnEcj95k8GqyscUbGJvaSBGTRChcDoprPzZQO+kqaFGzX7D++GJuUdjwRmHAPDH1MhMANXU0H4r/zzNw3VL+GhLOzryRe6esYtNy9cdVKyqnqydjBIn0Mriwu8l95NX7Sc34f1uTRn+zGzcaZ4jDcFXamrSFlPDcu40SJO6W/QTM9acIp/8gD733YUSN66poWR3P5mfywYDZ2o8o58sTwAb99kz6+XepYtsOi/YSjnYFkLmK2NGR5THAhbRB9j7Ij2nWqL2LagDOFVRdVrJWJoa0ahhE4MbU8PgFP0kMjVhaWpqIxQulAyUyqHxTrWfzO3UgwGbrJpJlld1nho7U+NFo9JrIjM1LB8MndzYg28XCnszDmwy6t+Q4bmG3KKfbGUSuKZGZAZZBJTK/QRYYmE395OTpkYV/eQEWXwpYydJbR+E4WL9IiuFdLNJ2ClPjdz/9x3Wj7uUfGlqpBT8gqZGEBrbJ25AHBdYeLXlyjZf2S9LhoGdXaIRIsPJKHHCTqnoIxuPZPdT0YMhckuRkEgk+ETJDDeqHZGZUhqWT7VxsvvJLU+NH/eTF9LJBD9GV7GoZmok5tFJ5G3LCl4u/9AqPacWg2XXGjkl3qPtBUSmhjKX3eVjygtw1jdZn9pjN5NZY+yueD7W8XIxiH6qfQvqAKpQRNZBAWf3k9Mgo0pgtJtUTE9eHVn1fQwh6qMSyBNRrYTCgDkw0CgotjKjdY+cctXImpp8Ue1+kisfAwE1NRlRU8MmhRYyeLI+0FQBU0Or8TKhrJ2pUQiFpWgLJ6bGMmrE+z5upJnbaf8RLY5t80y+58v9JK4aZVhZYNOBQuGdClra3U9izhE3Q4xeIyc3RYuNqSGaGvIbO4Nr19px95NNKMyYWbsRIoOt5v0YggCd/JmmRp3nhjIIKtDLo3qeZF0NjaayLQSTYjVrJ6EwzaIrw7pOlTM1ppvQetbVTE2Cfw+IJQhoG62cW+V+WCrh54vexiFXL8aqj7fz/VF3tmzUeDI1pMCyyv2UJ8lCadvYYVjf3b1s1NDo0YRi3Mz5CAyIGtqoCQE0IZ6oqTG/dxIKO0HFEsgRKI55akqk5k2FVKD8oNQqTw1gsit0MKUrE6+swmyyYu6n7qLF1AxpsdgHy/1E2xDA/ZQRmZpWxSRjEwqX3TJ+BK9UJD1yYIOwH1XkhD2jsHidmFbi4zJT4+R++q8TxuLxiz+L6Z9xLqnhpGFoD6KpUSRbpKAr7CDGYEEIYaVMjfnenqfGu81ZB6EvhZ2pUYd0y+MCe6a3CkaNee7ySppqmbwm6wYHpsUJ3E3DNTXqRZiquC6FW0FLwB4BVSDuJ1uemhTNAO0tFC4pxoSdnXYGtRLkiFHjpqmxku+JbWMGNms711IVDaxcvQ3FkoFX1+zg+xO0RtICU3a/yRCYGpq3iRTRpIvCFG+juV/Wd5uyadvCWsXUxCFPTe1bUAdQ5RFwq/0k33j5e5VRM9DmflL7kouGVeOj8pBuOaNwtYXCot+a5+ZxEHz6ZWrMlZU5CH1qUCPfTqWp8fIN08mtIUPy1BRLlrBV0NSYr2zwayzXqwmSp6Yxm8Lc4z+N84/eG8eVy1yo2CrL7cmOLXawsSNMBsbO1IjnnE0nMW5Uf9dipbSwotDmACHdmQqYmo7uomfUJDfuSeQMYPUbOZNxh4P7icLNfcQgT5r9HNxPdk2N3f1kGTWyC8N8LRqGoDlSgdd+Cuh+apGEwnLtKMpQqyBoLRRsr42pKTpP3mkpYZwlFFaPpSr3kxej5Rc0usktTw2LOpKjn074zAhM/fRQzDxsD2H7fLHE9UytJIdZnkSFyayYl/uJL2iKBhfOi9mZDeFaJaUxg7KMjK1hUDE1WlNTJxDz1FCmRk5SZEJmAYa2iJoFFYU3qFm0km0h3YIgLGxNTXXdT4lEQtBZyMUsGbyYGktTw6KpDLSX3U80kiileDiDuJ8aJaZGtSJMSEwNCxv2FdJNDISD9xiI73/hAM4GqKp029xP0qnsP6IfAFP0Z7ory+6tCgakpMMkEsj9RAdY1Qq7y5pkGcNVIG5WJ9DnQHA/pSSjhuUc8eN+ckieR0GZGto3AFkoLE/cTGtnTWhMnyUnPGOvnfmSxUiG5H6y+q8sFFZHPzkXtLTeK91PTFPTahrXqoAL/vtk0gp9LpW4zsyRqamCUWMWJLbnqREjpEo2g3TvoS2482uTcNiYQQDECCXWRmrUUO1SRrou3pqaZHkfNNN8UjDo6bViY+qQcsTj7gMtQ4a+p+fjFrpfC8SqoGVvhVP0k3OeGvHvkQMbsXGnlS5cxbDITI08SFCjprtg0biVoCGT5Mn+VMeqBrLpJLrLFXCdjBqVnoTBMKzrwAax7kKJP4iCUaMQCqtWlhSumhrDCkFmOGzP3fDqJztw8B4DAFhZm73cT4Zh2HL0UKQU18ApTw3DXkOakU4meD4UJ6bGDxJQMzWW+8lH9BOdBEol5JLiedLKytRI6uguuhbQY5N9OpVEhgzcbKBnxsfOzjxKJLmcu6aGMC1Omhpy32nkEyD2G9km4kJhF/cTX0mXX5mrKJEAWhyutZPQ1wny5K9yPxk+qoN7reBt7iciiHWLfuouGJwJlMcEt+gn2VirFJYQuOjK1ACma1d+HmXQ7L6cqSFiYVUZHga/0U8FKSkrjX6iLC+7fgu/cijWbOsQIi53H2i9p9u6JVmsBWrfgjqAU/STk/tJ1rqMJHV2AHX0k6ypkTs3+zOMjMKJREJ4WKod0g1Qirdky1HDwKjYj7d14HPXLcVtz37Av6Mak2aFpuZT5aRmgF17AvSAqSmWeDQKHTz/64SxWHXl8dh3mMmS+I1+olW/ZVEkoC6TUJSYGnkw7d+Y4dqcj7d2WEZNBf2FTa62PDUVRD8Bal0NjSbLkhTvXroamq9J5bZkxRo37ewSWAw3Q8ypzAEFdQPJEUnC7xVsBOCkqRFznTBjkl32lmzaJflesIKW/7+9Lw+zqjjz/p279t5N792sDYKA7CgtrknoERyjoowrGZchEA0kZnAIH3kiZJvg6Bc1GqOTZ9zm02jG7zOYGEMeFNFEWlSWGFwQCIvS3WxNL/R2t/r+OLfq1KlbZ7kLvVzr9zw8t7n33HurbtWpeuv3/t73NVISCHlquPfzG6FVck4n5rM6nquGGjXMCPV4Eq7n89TwBS1FJtBrMR8BSLVuqYC2rc9CU8MfWsPRGBsjq/lCjdm+SIyxkjQBIsDJG7yJ5R3camrEABK+jhZ/JqRjNq6iAJdMMFcoT3Q/xR+555zy5vQHBr4FWQBp9JONUDghEVWJYNS4iX4STzKsFlL67ifAvBmlKjhOBzzzYcVU0N9gyyfH8PcTXXj5r03sNV6fQRPe8RmFR3I3qJjPBXChqeGSsAU5o6YvwmtqzIsnP65uBa+9IaMfcqbGoJcpxIKW4mKaH/CypHKftXZLk++5hVVBy1TcT4A8VfxpLqQbcF/hPCLJywEYvws1ajp6I8yQ0DT7jcKVpobLg1KUKxo1du6nRE1NB4t+Mr9H/Gq7jdpwP7nV1Fi5n4z38+5Oq+rgTlXvKVNztLMP0RgxFetNYGo8RgFS3v0kukyp0SfT2WUi+gkwr02y6Cc+kSLPhIjjTUHXbn7c+bqApjIJKTM1MeF+MA5hUYn7SYYRw0T3k2JqshYGU+MxuYGYL1X4lUVjg97cFDJtQ0KeGouMwpEYMdG4qcJU1K+fyyQA5qKWfUKOGgr6W38WF7zyyQz5E78YjQIAo8oMKtUItTReT5qpkYV0S76XgrJHXQ6ZcalBJ4qkjXbEF09uEY+I7idhocoL+lAVZwdPnO4zop9ScT9ZaBh6bVxmIjzcYi1jasTfM4/TI/3ur03404ct0s8N8Zoabg7T37EozvwARv6NXL91gUDApfuJMzBEY8Pe/WTN1IjpIcQ22m3UhlDYmakhhCTkc2Eh3RErpsbK/WT8LdvsyguCLB3/idN9XBSpJ8EQ58tahCLEJqOwtftJlj8qFfBCYRlTA5hLf1jtAxT0Hj55OtGYBcyaGtHocAqhttJa8kaXSShss2UkamrirOEg09QMfAuyAHxmTTGREiBhahLqoJgni8yoKc4VmRrRFaM/8gtXOgxLnl9Op/YX+FwPVu4natjRJHK8FoFuaB5NLqarLMxhC09K0U+uQrqtN5qS+Hi2CZXaRVjpiSjYosUxU4ypEfLUUOQHvMxIPtUdTs/9FP/oBE2Ni/BoHlaVurv6ImyxL2DhpfpnHjzZhbte2IlvPb9TGjnFhwjz9wKdN5qmMZH+obhR46QB4pOLWd0W/KlddD/x7oMErR2LfjLmhHWeGvN32m3UyYR090V44TFlahLfb2JqUgzp9no0VMXZsub2XqOshVdjdYsAfS3QNF6vGLMJ6dYfZe4n0a2WKvh7nWlqhNILfk53YxUwQiEzZjtkTI038WDjdGgwCt6ay+fwiSf5gpt2Br3I1NArVUbhLIRl7SdWr0VYvITVsCTXb9qwZda3z+sxGTYiU0MXEL4SbzoMC38C6u+QbsBIchaKxhJKJFAwpqZVZ2o6eiNsI+PFr+KNluv3wuvRUBZX+NPuaUnQqHaaGjcnQlr2or0nbJkeHUgsDyFCFv1kFKeTb4D5QSPnxKmuUFpCYVm0iUnc7NqoMXQFgG6Y3frku5jygz/ho+YOAIaBQD/zvYOtIEQfa7E4LGAuaMlHjfDMAnVBHTzZBcDZCDPlmbHYzINc2QxRKBxw4X5qk4R0077IKH/ArfvJ2ajhhcdUzC5zP8lym4jg7ycr5rOKEwuLBTLpukMPcCadXZJCYT6fj12VbjcIcgaLUaJCzsjxQmEr44/OzZNdRrBIR69EUyNJSuh0fzH3dJSYmEu6N0SixFgvbAwaQD9Y84JoejnfJFXQMktgin7iLGPLgpbCDZ4f9JlzWVgsALyuRlxI6ALLn6bSYVj4xX0g/KT8aUgskUBBFz9+saa+aKYT8Xri+R2M34L2jRYppIn4vKZF2P6343+THL+H3czdfRHWXqu09YDB1BBi9p+LMIwD+RjINDXURmJaIWEC5gd8zKg61R1KU1OjP/In496wIY50E/2kfzcNu9fbcri1G29+ehyE6KG/184cjlmjS1j7AeD9g6fY+1u7ZUYNx9SYhMLG39SoOXySMjX2m4SbMgmAEflmx9SItxWf34SCbsTU0KGpHRKNGmf3k5syCbyrj84bWZXvKHe6tzLu+P5ZzS0jAqrHlFFYf9Q/l96L9PlwhDBXmHhf0LYkVI0PR1mbMyUUNjE1Fu6nU11hNp5W64EVU0PvKbq/+CWammSS78k0ZqGoc3QWhaZpTCysafI8NXbRiP0FFdKdAUQ51T4fZuw2o3BB0IfCHB+Ox8O6rRTkw/IDOHiyW7qQ0AWWLjyaZi/6ckKeianpf/cTn7XTOk9N4u90qjuMsoKgKSU4oC+M9Oalp5ufXTcDnx7txNThxfHP44yaJGo/5fi9bBM/yS1MdtWAKfPW3hPGqe4Qygrk9ZWckthJ89SIQuEETQ3vfgqxvqRyypIl3+NLGLjR1AAGg0FZo5YOPSJmTFketqz6sulaOn4ft3Sw51pPWxs1fq/HrAHh5jM1ag6lYtTY3F8FOT6c7ApJhMK8pkZgaiSfR/Ut1AVXGq/DJdpTVon3AC6kO6InLLRzMciKscoKYjqxD4AY/SS/rrpI3ySbO3oZYynWNaO/maEfi+FEfK0UjTkrdyjtl9ejuXaJWoEaLF19UeZmFe91Ok+a23UWOS/gtWRVGEMnuJy6Q1HkB33mQBThd0yuoKWV+yl+rQNTA+i6mk+PnjaNrUk7pZia7AA/6bxc9JNV7Sdxw8wPek2LiNXmQjcimTvIYGqMhdxu8XJCrilEceCYmnCUj34SNDWSBZWeaEWdCM+s0EWtujgHl0yokJ44nIXCxrW8poa6T/ICXke3HQ3Tb+1ywdRYaWq4ZI8UIp2cqKnxYVj8xM9ralIyauKP/MmYtjngS6TLrcCPNwAcjRs1VUK6A8AYP/4wLmNqeMG8LKMwYCS+PMTcT/bnPDdCYcAQNSeGdNu5nxJ//85e/cROjWVaMV28td24nwiBqY6aDLLEkUbtJz76yWCnrWCKfrK4F2ok7ifRmBELkDa39aKpvReaBkyMJ5Jk32lRJoFlXQ760loXAWMO/P3EaQD6fBRTbtBxpuHq1NUt/bx4P0UZEHUFmsokCAc5J6Ewr0Pi3U8yIbObe5UyNaZLVfRT9oGv/cTnBRDrtVCIpxbK1FBYUXjMqJFMPqapoRtKmpMrj7tZBkIozLuf+hw0NTyoyFLUifCFCK02Lv4nc6zSzb3OV+mmcENxU3diq0QPQuGkqWFZVmVC4XiT+M2FhiwzpqYrXfdTIlPTk6RIGEjM7kuNmuriRKNGduKV/Yb0s/gQVsDM8FGmpstlCLqbjMKAYYxVF5sZOHPtJwivJX5eOErQF4mx/tHNUTSI7Fyd/NxxKpUgy7rLJ+873tmHZ985xNYZqxw1ehuNv63mFhWf7mnp5IxQs4CffgfVgVAW76yKggSmxqpMQqZy1ABGXz49qhs1o8vyLYuTNsfbWmpT6d4qeozmquHLJKTH1HDGkdcwdqIWh28ZRsRTQWiDmKlR7qcMwMzUpKapMTE1Vu6n+CYoNWooUxOniNM1RAZcKEyFdlHnPDU8aK4H/lSif16ipkZEMkyNSVMjuZHd5MIo5XQtVrAKXaXgmUEKkanhf6b8gH5Spd/d1hNmmiJaWDIZsD2N20RY5FMSFXt5oTUAtLTr7oVqG6aGx8kk3E9+ifuJIj9D7qe1X52MyyZX4ZLx5gRm9gUt5XOuozeMk6f134OOWzJCYX9cVxaN6VqUYljPTVndMj753r1//AT/b8fnuO2CMQDsfwOvi/upfmwZPBrwSUsne87HNDRmTY24pk0fWZLweVZlEmT9ShWGUaO3ua48z/Kao3GmpjzfmqmxWl8NpoarNp+gqXHL1Bh5gPgCr6EIF/3khqmJh3XzVw622k/KqMkA+OgnM1PjHNJNy8Dzi5KlUDjf2v3EjBpa/DFdpiYwsEyNn2NqaJ9EqlW2oCa4nxhTk+h+EsHf1G4qqV8wrgw94SiG5QVMWhrAPkcNBa9rsYJTvhefzP1ko6mhvv+SuIEcjREWdZFenhrjuWQS71FYMTVy91Pibyv7DXn3UywmN0ZEoybXyf1kY5TwGFOejzHl+QnPm9xPwvy10q519kbY/KIGaDIh3YA+f073RRxrjYkVugGz0HjnYV2c/cHnbXqbbTZCN6G+pfkBzBo1DO8fMkTfjKGhxg1zQ5k/w86oEV05mar7BBhzoC3OCo8pk40z1dRQpsbaqLFaX2lYtxHSLYl+cmRqjEACWYHXMBf95EpTw9xPHPvLvT4Yop+UUZMB8P5lafI9G6Ew873nutfUyDZz+hz1HbsVaFrBZNQMRPI9qVBY0NRIFgPqfmLhvBKjxmrjcsqrwUPTNDz39Xr2dyruJ8bU2LifWDp4hzw10oKWLKSbM2rifQ/6vMgPeNEViuJEnOXIVEh3T5iWSHC/vPAJzQDDxWCnqQF09vJUdzjBqATACvjpTI3xPH//icVkkxEKp6LDt3U/CfdZeUEQJ0734Wh7L9PClDJNjcDUOIQp5/g9ON1nFvvKIEscSZnI1q4Qjnbq47I37npxLxS2nltfmVRpMmr8LPqJPuqfIx72ZowoSfgsekksXr28qa0XZ1cXGlohF4cNJ4j3icx4pW2l89gqEACwZuhoVCSvNRJ/x2Sin0KSjMLhJKKfAGB8ZQGKc/2m2nmDjakZ+BZkAfiMwvwEpQt0QuZQycmZ3mwBn7XAl4ZzipVaAWNiNbXFtQiSzSAZmITCAzBRqVHQ3hO2dD/x2gh6c4tMjRE5YVxr5WIwMzXON7imaWysREPUTuNAQYuUuhEKWwkCeWbwx698hAUPvYUdh9oA8JmSOdcbF6UhFknNVPK9npC9ISaDUZVd7y8VWIqaFMBseFx4VjkAuWEY5u5LfjxleWooMuV+soLPVihsFlzSSskH45FZQZ+H9T1ZpiYoEfvKICslQJmalo5exoDQGkV2TI1bjdq8iVWm/9PfISH6yWfePM8WRMIAVyaBENz1wi7Mf+gtfNTUcUaYGoo6G0auVRB4yyAezuh7KVMjy4NG4cjUUCY3GjOMfJ/HlHqEMbsumJrCHD/e+u6X8eIdc9lzpozCSiicHTBpaoRiZkDi4qVpBv1HT870ZrO7+anYTGZk0LlEs+uK9aSShSmkO43Q8FRRG29/c3uPY54aADi7ugiAnhcCMDbHoJSpsWA90lDxi9e7cT+VsggkZ6GwE1PTG47iqbcP4JOWTnY6ZBmTuX7x7IlIiadyyqIbDp8fiYZ0J+N+YkxNRI8aPNZpzdTwBjctuicTChth/UKeGiFyjd/onNxPbpLvuX2/uIfwrxXmGMEDNDFgeUHQKGiZRJ4awBgLJ/eTNPrJZuO0qvsEuGdqJlQVmLLVsqgn0Q3FHWLOqS2Szlcj+gn425F2AMDupvaMVegGJEyNxP0kXmMX/ST+NlS30sESLxr5lsS12H30E2EehYCQt4keAN0a6cW5ftOcoL95IM2I20xBGTVpIhYj7PQiCrmoZSybK9Qap5sfvdnskhfNGFmCr06rwbJLxia8RifkkTY9L4IsaiQZmDU1/T9NauM3dlNbD/oiVkyN8cNOrokbNXEDIRwxR1LwfbAWCht/J9tnjzD27qKfKFPjRigsbw9t5+enehAj+sJy43kj0TCpCg2T9RMwv87wTESJUCQ1FX94bXyeNcXnHWAYYslFP+mN7IvqkT6U5awslLif4vOgND/Axl3mfopYVH0W6f5Kjq3JVPI9K9iGdAvzh64JB0/oRg1vhCbL1ORwuWrsICslYOfisI1+4kO6beaWpmmYN7GS/V/U1FB3FH9wmC5xPQHG70IjtQDgyKmeMxL9BOjrd7nEYBHXD3v3k3kwaYRRIlOjGw38updM9BPvfuJ/S3poTNUeoe8bDK4nQGlq0oZYrZafcGJqcx5+nweIJ1cCDE2N3cYS8Hnwi5tnSV+j30G/s1aoJ5Us6M3i0dJL4pcqDKOml+WyEBdXflOZXKNT0VS818dlFAbMv6uV1sPtImyFgM+DSNwIyVT0k5NQmI4NPc0PH5aLexdNk14D6MUsxe/n258s6AL8+SnDqElFKMwyCkdijGkqLwhI2zQ6Xoz0/LGlJl2SmFguxEV72FUirigMYv9xd2USAhlkauzy1BTm+NkGTBMD8qf9RKbGwajxuStqaRSzTHQ/Ge023I32mhrjbyd37lcmVeGZxkMAjE2eupeNPDXGZ8yQiIT179Sv4Y3cI2097OCZEaaGG6cx5XlSdkJkbu3cT6IBRFkrKtqOSIT/0RiBR3P+XXmmhh6yfV6zO5YeGlNd5z0WLviBgjJq0oRYrdYrWVRlk55OZJoJlC7OqZ4kxAkpVv5OFnTjHwiWBjCMspaOXsPgE5ka7sacJDA1CdFPXD+cDAQgtX4HfB62obtiavJdMDUuaz9RpkSspAuY+1XAGXRi5fdU/OF0AaZuTyDVPDV6G0PRGIt8krE0ADB79DC88q2LMLosj41TJEbQ0Rsx1UeLcNEeMa5v4kZQwX2PY0HLtJkaXpMjviYyNXGjptWZqXFydzL3k4NRY5d8j+LCs8rx570nADhoapIQkJ4/thTTR5agPD/A1ksjpNssHAbkkU+A/AB55FQP608mmBre/S9zPQEypsa9UTMyLsI1hMLmTMt+j4YQnCvKA+boJ1ZuQTh8U6YmlfkMKKYm60AnCqBvHrS6bIzwmprE99ENhG4yM0eWYOU/TMDs0cNSaoc4IWvSZWrii+BAGTUVhUH4PBoiMYLDcRbCKk9NSZ4fNfHNvK1bz8AajtoIhS3KF6SjqRHfY5e2nqI0blR09kYQjsakv7Vznpq4hiBuW4uVdAHzQs8LhROMmjSZGsqUGO4n98sLFcqf6OxjImsrF6qmaZgSL22hf48X3aEoTnWFmFHDJ7/0ez0mIbNYXoOPgEqmoGUqtwZvuCSWSRA1NXpf6KZTZjJqeJ2Uc/Zq6tamIm4e+4514mhHHy48q1wqFObzZhXl+HDxeMOosTvdJ1MgNujz4uXlF5qeE/PUlOYHcFZlAfICXowpS8wNA3B5kzg0tfdguKbfF5kwavhK7TKRMJB4L9mFdItCYaapiSffkzE1gDsm1MTUcAVeqa4zFDWiS1NhHgFjnAeLUTM4WjGEYWZqzCcKK6EwYEwA6n7yeDR8e954Fs2RLBKYmjSFwnTjH6iJ6vVobFPrsqjIS/tcU5zLEhOGojF0h6K2eWqsxKCZcD9RFLlYPIty/czgtXJBOQmFxZOyE1PDF96jpRIoUulzdXEOPJqefp+GhlOhsFNiMB4zRpUAAN47dMo2R40MdMPgXQ5hLobb5zVnYk1kalLT1GTe/cQzNf6EDZjXZfDvdbNRVxXp7+UZNQA43tmHa365FV97YhsOnuhi2hOe+Qn6POw0Prm2COMqCow220Y/pcd8igJhr0fDxrsuxsvLL7RkKGRrbXNbL2M9Mq2pccPUFAZ9tlpJE0MX9DGtm6xMAmC4Kd0UjzTyWMUkEaFxHVskPaaGDvNgiHwClFGTNnhNDR1cejMzTY3kVzaEwpmpasovIAGvx9aH6wbjKwtxzczhuPPScek2LWXUCht0Qp4aZtTkIJerv3SqO8Q2NVn0k1Wm22Q0ADLw3+HGd+/1aCys+pRFWHefY54awR9fKmNqjL+tmBpNSy3KLeDzMOODbpjdKbif5tSVAQB2H2nHgbgw1m1agjKJG483asQyCaK41WzUuK/9lMomwH93Qp6aBPeTeQ7xp33+q93MtfGVuiFCU/tT/MfGT9DZGwEhwF8/b5O6nzRNY/fRObXFJnbCraYmFYNZ1NTof9tH2MiaE4rGmNg609FPshw1gFn/U2rjegLMc6Io18/c7R3xul8iU+NLmakxoqj0NurfSzU1qQqFmabGIWdOf2FwtGIIg88hIPqCqWVsp6nJz0AyKMB8aqwuzkk7tM7r0fDgDTOwVBJp1V8QWQerPDU18f5StqaNK9JopFh3jn5Kpkq3DPxJxe2J0Kn+E9PUOFT4pRhekkjLy5Lv6d9tLLbphGMauhpd15OKpmZ4SS6Gl+QiGiPY/MkxAPIcNTIMkyQxpAcKgBo1/MZozdQ41n5KM09NgNvs7MokFOb4E9i+MkujxnmuTajShfT7jhnlCLYfOoX/u/1z9v/dR9rZqV3Ms0RZt3NqizCyNM8It7aJfuLnUyoGM83HlQzLYzWHu5LQujkh4Mb9xF3jdMD0CcYs/e3bu8MJmk3+0SnxHsBpaqKGcUTbRseOaWpSFgrD9LkDjcHRiiEM0YoGDAFryMb9lGmjhj81pisSHiyoFVxoojuDbqZUX8GXHegT3E+m6CeL39yThAZABv473OSpAZxLJTCjxoJqTgwHtXc/mbLxcu6ndNyMYgSUk8vMCvV1pQCMCJzKNNxPEU7PJmZiFX8zXlNjpbeicFsmwQqu3U9BX8IcsnY/ObMPZ1XpTM2h1m70hqOIxgjW/W43AMOw5rP6ipqwEcNy4fVoOHd0KfxeD0bFNS22tZ88htYiFYNZZCZcvcemJA2QGaaGHniKcnzstxPBzxO7YpaAeU4U5/qZLqyzL8L2ECCxXISb+0sW/STWw6OamtSjXJWmJqsQjRpMDQX92y5PDZ0Abjc/J/ATUnTbDFUkup/MN/E3LhmL3624ENefOxKAkXflVHdYIhSWb+w8MhH9ROF28RzmENbtVigM6PNOpkPhWTx+vvFMTTrhmNSQOtImup+Sm9tz4kYNhVv3UynL99PHngsxqp2eSrlDhySkmyLP7772U0oZhXn3k8f6NZn7yUoo7IZ9qCgIoiTPD0KA/cdPY9dnbdh9pAMFQR9+snAqAJ2pAfRcRmLfnrz1PLy8/EJmzIyNMxRW9ar0NuqPqZ7gxdpPbiAaijOFKKlMMDVjy/Ph82ioH1tmaazxTK8sjw0Pfm4W5RpaKkKMCCj+OoOpcTZqzHlqjBQH/CPNXZSKka6/T38MuND49AdU9FOa4Os+URiaGmum5oZzR4IQkrIwWAT/Hekm3hsscNTUeD2YxiXhopt0W3fIXihspU/hygqk5lpI3v1UmpfoOuHhWNCS2wiri3PkdcFM0U/W7qdUYeV+skoYaIWUjZqCxHITkaiZatc0DX6vhnCUJIbb5gcwc1QJYsR53GiSxUiMpCQUtnU/OQqF5SHdbuoZaZqG8ZUFeO/gKew7dpqN1cXjy3HBOF3PRF12MoO8sijHxJyNrSgAPj7mqvZTqid4OreT0bfxP6nXo2HW6GHYdqCVvVaQpKEtw8jSPDSumZeQvJKH38TU2Bs1/LVFOXq23qDPg75IzOSWFqOf3Bg1Mk2NXzCO+tJ2P8XHeZC4n5RRkyaYpkYiRLSq/QQAi2aPwKLZIzLWDn4+1WaLUcOFpWua803Di25DQvK9gNdYAKyYGjpOqVYlD3CskFvmw8hVIxcKuy2TAMhdT4CoqTE+JzfgRa7fi55wNKPuJ1YmwYH1EFFXns+KOAZ8HttNg4chFO5DLEbw6bFOZsiZaz55EI5GExZvj0fDS3deAELcRTTRJItnXihszurLM19akkwNAIyvKsR7B0/h06Od+OBznZWpryvFsPwAKguDOBbPwOvm8ybG6y7Z1TjzSMYgGbjR7SR8J68tLMoxFV4sCPhSDlsWIdYMExHg+myXTRgwG7NFuUYy1uOdfSajxigbof8erpgaKoWIxEwpDvjHdKOf6NsGi1BYGTVpQqqpoUJhG6Ym0+Bv1nRz1AwW8JoaN4mmhjH3U4ir+0OpVt79ZFGlm2oAUjxx0O8qzPG71hDY1X8ihHBCYXmbeOpaJhIGzG4Ose/D8vzoaU/XqDES8BFCUhIKA/pGXT+2FH/4oBnVRe7F7qzcRHcYj2zehwdf+xSLZukHBv6w4fdq6AnLN1m9OKm7dtIki6nsj7xbwm2eGgAok+gyNE13Ubh1ddIIqI+bO7E9rp85P87SnF1dyIwaNzmWvjqtFt2hKC6N196SgdfUpAKjoKX7H5ofk+HDck1sr5t+ZQqBpNxPZqZGf/SZjBqvJBAl14URQa/ly2P4BfaaZplOO/ppkDA1g6MVQxh89BOFGP2UocOBLbxZ6H7iKXg3pxKp+4kxNdQNYR01kO4iTH3Kyfjt7eo/9UViLL27pfuJW/CtmBp+bohCWMoUpWPU1BTnQtP0KIqTXaGUaj9RULGwKBK3A3XLHOvoxf955yAAYMOuIwDMBirtoxgGnyzoCTelPDXce8TDjl9wP/H6J1lGWvp+t/ONRkC99elxdIeiKMnzY0Kl/txEruK1GyMp4PPga+ePZtlvZaBdTTWBp6GpScao4e6HeEQdRSb0NG6RjPuJn6NUJEwf6bog04S509TEdTNcIVMxIrQ3zTIJKqNwlsGOqbHT1GQa/KTPFqEwoIf6ftLS6UrpzwuFaSCkGP2UZ8P4GHR5ikaNlzI17m8ru/pPfOVr6zIJRluHWxk1pugnkamJGzVpnLICPg+qi3LQ3N6Lz0/1pFT7ieK62SPx9+NduGJajev30D40t/ey5wy3sNn9BKTuDqGQVT93C1NGYhumpijHB69HQ0HQh9N9EenG6NGAKJJnauiaVV9XygwzWuUeyNzm70mT+SyPu20qHNw3pu/UzOug2ahJP/LJLfhxlrFsPMzuJ7/pUWbU0PnrZk2kbrDDrUbSRT9LEpthTc0gMWpSasWjjz6KMWPGICcnB/X19Xj33Xdtr3/xxRcxceJE5OTkYOrUqXj11VdNrxNCsHbtWtTU1CA3NxcNDQ3Yu3ev6ZrW1lYsXrwYRUVFKCkpwZIlS3D6tDmR1EAgGhcKyyzpsE3tp0yDLiBBn8cyzHAoghpobvy1ZqZG31hF/7FVNmGAp7vTcz8lE9FmV/+J5o/wezXLNrnS1FhEP/Hfn+6CRDePQye7mI8+2ZBuQDeEfnDVOThvTKnzxXGImwYvnOV/N+qCTEajIUOQMT4pGDUm95P5NVEorD/qfZG7n5JjaioKg6baWPXxhIeAmalxkw3bDdLd7JZdMha/XDwLN84ZlcR3Gn8PH5aL3ICXaa76k6nh+2xX9wkQjJp4G6kbinc/UdC/xVp4MlwyoQIBrwd7j51m7/UIjHRfJL3DN2Nqhqr76Te/+Q1WrlyJdevWYceOHZg+fTrmz5+PY8eOSa/funUrbrrpJixZsgQ7d+7EwoULsXDhQuzevZtdc9999+Hhhx/G448/jm3btiE/Px/z589Hb69x8lq8eDE+/PBDbNq0Ca+88greeustLFu2LIUuZxY0ysIUWsvy1NAq3We+HfTUV5OBxHuDCdQNYZWnhcewfIOpsYp+snOJpLsIBzlNjVvYRT855agBBPeThaaGzg2Z640awOmGY1KDat8x46CRbEh3qijK9ZnuvwdumMH+Np1wPakbIzzo/EjF/cS3R3y/UbhRY+PEjBqp+wmma5xAI6Aozh9rGDVnVRZwn5eZQ1G67qfCHD/+cWpNUuU2+I2ZGtr0YNSfTA2/wYs11kT4hYzC+qM+ptSoMRnnSeSpGV2Wj69fXMe9l2d84u6nNPPUDPmMwg888ACWLl2K22+/HZMnT8bjjz+OvLw8PPnkk9Lrf/7zn2PBggVYtWoVJk2ahB//+MeYNWsWfvGLXwDQWZqHHnoI3//+93H11Vdj2rRp+O///m80NTVhw4YNAICPP/4YGzduxH/913+hvr4eF110ER555BG88MILaGpqkn5vX18fOjo6TP/OBAxNDZ+/Qv/78zjl159C4WwRCVPQBcmNK4NGPx3t6GV0a3JGjf6YLlOTiqamKxTFD373IX74e+Pfz1/7FIB1NmHAWIg0zVpLRedffsCXYPBmwv0EGBFQr/6tmbXHTcbTTEDPJq334+Lx5fiHyVWYGa8lxRuospxFqcCfIfeTuIdQg6cwx8exMPomJ3c/6dfYRSCJGB/X1RTn+k3sTI7fy+oYuQkRd4OBCPU1GTVxQ3s4M2r6X1NTlONzPCTRIsj69X7T418/awMgZ2rcCIUBYMVXzmIRsWbjSP+cg/GCwana+gZTMzjy1CQ120KhELZv346GhgbjAzweNDQ0oLGxUfqexsZG0/UAMH/+fHb9gQMH0NLSYrqmuLgY9fX17JrGxkaUlJTg3HPPZdc0NDTA4/Fg27Zt0u9dv349iouL2b+RI0cm01XXkGlqSuLWNs1wSq3uMwlq4VvVIhmqoEm+nMR2gFHZmy+uSDc7+mgXikmp+VTdd/Q73BZiBPSFlrqEnt56EE+9bfzbsEs32O3SrNM2jy7Ns1w86TWyKAy64KfrshxXqY/T/uP6AlmWH+xXxnBkvObVTXFXxc3xR37eUM0V74JJBdRll8rneD0a08uITBY1yisLjflDC1HKCpXSTdopvJjH1Hj27QvPKktgiqaO0F+rLHL/eXaga5JYOPVMIuj3IOD1IOjzsN+Mzs3KJH6ndEHn2vBh1kJq8/UBaJoxlvSA0hTXifFzje4vpS61RnkBH+756mT9Pdz9QNcrulYWpXhfFDHDe3DIHpLabU+cOIFoNIqqqirT81VVVfjkk0+k72lpaZFe39LSwl6nz9ldU1lZaW64EPcQ1AAAENNJREFUz4fS0lJ2jYg1a9Zg5cqV7P8dHR1nxLAZWZqHFV8+y7SwrL1yMl7e1YRoLIa8gA83nndmDCoe18wcDkII5p9Tfca/qz/RMKkKP144BRe5SFJYlOPHY1+bjV2f6eGqI4blYXp8oT5/bBl+es1UzKkbZvn+88aU4t5rp2L2aOtr7HBz/SjkB734x6nuRa4ej4Zf/fNsvL3/hPR1DRoWTLEe03EVBXjg+ummyskiRpXl4ec3zjDl7KC4cnoteiNRzJtUJXmne/zj1Bq0doVZVt9LJ1Q6vCOz+I9F0/Bxcwcuj/9W/zR7BLweDTO4jLI/XjgFOw6dwqxRqY0vxborJ2Pb31tx/lj3uh8ej//zbHT2RhKMoglVBfjf1003MSj/a8EkzB1bJr2vf3HzLBzr6EvKiL7uXD3UvWFS4visXjARs0cPw5XTa11/nh2+fHYlfrJwCi4en5kEo26Q4/fisa/Ngs/rYW6rJReNRUVBEFfPGN5v7Tintgj3/dM0nFNb5HwxgF8unoVTXSG2j1w7awR6w1G094ShQcNl5xj3592XnY3ZY0px2WT39+yCKdV4bPEs1HDG8bfnjUdNcS5C0Sj8Xg/+KcW8ad/80jiMrcjHNbMyl3ctLZAkcOTIEQKAbN261fT8qlWryJw5c6Tv8fv95Ne//rXpuUcffZRUVlYSQgh5++23CQDS1NRkuua6664j119/PSGEkH//938nEyZMSPjsiooK8stf/tJV29vb2wkA0t7e7up6BQUFBQUFhYFHMvt3Uu6n8vJyeL1eHD161PT80aNHUV0tP01WV1fbXk8fna4RhciRSAStra2W36ugoKCgoKDwxUJSRk0gEMDs2bPx+uuvs+disRhef/11zJ07V/qeuXPnmq4HgE2bNrHr6+rqUF1dbbqmo6MD27ZtY9fMnTsXbW1t2L59O7tm8+bNiMViqK+vT6YLCgoKCgoKClmKpBWsK1euxK233opzzz0Xc+bMwUMPPYSuri7cfvvtAIBbbrkFw4cPx/r16wEAd911Fy699FL87Gc/wxVXXIEXXngB77//Pn71q18B0CMXvvOd7+AnP/kJxo8fj7q6Otxzzz2ora3FwoULAQCTJk3CggULsHTpUjz++OMIh8NYsWIFbrzxRtTWZsb/q6CgoKCgoDC0kbRRc8MNN+D48eNYu3YtWlpaMGPGDGzcuJEJfQ8fPgwPF958wQUX4Ne//jW+//3v43vf+x7Gjx+PDRs2YMqUKeya7373u+jq6sKyZcvQ1taGiy66CBs3bkROjiGAe+6557BixQrMmzcPHo8HixYtwsMPP5xO3xUUFBQUFBSyCBohtLpMdqOjowPFxcVob29HUZE7RbqCgoKCgoLCwCKZ/XtwpABUUFBQUFBQUEgTyqhRUFBQUFBQyAooo0ZBQUFBQUEhK6CMGgUFBQUFBYWsgDJqFBQUFBQUFLICyqhRUFBQUFBQyAooo0ZBQUFBQUEhK6CMGgUFBQUFBYWsQNIZhYcqaI7Bjo6OAW6JgoKCgoKCglvQfdtNruAvjFHT2dkJABg5cuQAt0RBQUFBQUEhWXR2dqK4uNj2mi9MmYRYLIampiYUFhZC07SMfnZHRwdGjhyJzz77LCtLMKj+DX1kex+zvX9A9vdR9W/o40z1kRCCzs5O1NbWmmpLyvCFYWo8Hg9GjBhxRr+jqKgoaycroPqXDcj2PmZ7/4Ds76Pq39DHmeijE0NDoYTCCgoKCgoKClkBZdQoKCgoKCgoZAWUUZMBBINBrFu3DsFgcKCbckag+jf0ke19zPb+AdnfR9W/oY/B0McvjFBYQUFBQUFBIbuhmBoFBQUFBQWFrIAyahQUFBQUFBSyAsqoUVBQUFBQUMgKKKNGQUFBQUFBISugjBoFBQUFBQWFrIAyatLEo48+ijFjxiAnJwf19fV49913B7pJKWH9+vU477zzUFhYiMrKSixcuBB79uwxXfOlL30JmqaZ/t1xxx0D1OLk8YMf/CCh/RMnTmSv9/b2Yvny5SgrK0NBQQEWLVqEo0ePDmCLk8OYMWMS+qdpGpYvXw5gaI7fW2+9hSuvvBK1tbXQNA0bNmwwvU4Iwdq1a1FTU4Pc3Fw0NDRg7969pmtaW1uxePFiFBUVoaSkBEuWLMHp06f7sRfWsOtfOBzG6tWrMXXqVOTn56O2tha33HILmpqaTJ8hG/d77723n3sih9P43XbbbQltX7BggemawTx+gHMfZfekpmm4//772TWDeQzd7A1u1s7Dhw/jiiuuQF5eHiorK7Fq1SpEIpGMt1cZNWngN7/5DVauXIl169Zhx44dmD59OubPn49jx44NdNOSxptvvonly5fjnXfewaZNmxAOh3HZZZehq6vLdN3SpUvR3NzM/t13330D1OLUcM4555ja/5e//IW99q//+q/4/e9/jxdffBFvvvkmmpqacO211w5ga5PDe++9Z+rbpk2bAADXXXcdu2aojV9XVxemT5+ORx99VPr6fffdh4cffhiPP/44tm3bhvz8fMyfPx+9vb3smsWLF+PDDz/Epk2b8Morr+Ctt97CsmXL+qsLtrDrX3d3N3bs2IF77rkHO3bswEsvvYQ9e/bgqquuSrj2Rz/6kWlcv/Wtb/VH8x3hNH4AsGDBAlPbn3/+edPrg3n8AOc+8n1rbm7Gk08+CU3TsGjRItN1g3UM3ewNTmtnNBrFFVdcgVAohK1bt+KZZ57B008/jbVr12a+wUQhZcyZM4csX76c/T8ajZLa2lqyfv36AWxVZnDs2DECgLz55pvsuUsvvZTcddddA9eoNLFu3Toyffp06WttbW3E7/eTF198kT338ccfEwCksbGxn1qYWdx1111k3LhxJBaLEUKG/vgBIL/97W/Z/2OxGKmurib3338/e66trY0Eg0Hy/PPPE0II+eijjwgA8t5777Fr/vjHPxJN08iRI0f6re1uIPZPhnfffZcAIIcOHWLPjR49mjz44INntnEZgKx/t956K7n66qst3zOUxo8Qd2N49dVXk6985Sum54bKGBKSuDe4WTtfffVV4vF4SEtLC7vmscceI0VFRaSvry+j7VNMTYoIhULYvn07Ghoa2HMejwcNDQ1obGwcwJZlBu3t7QCA0tJS0/PPPfccysvLMWXKFKxZswbd3d0D0byUsXfvXtTW1mLs2LFYvHgxDh8+DADYvn07wuGwaTwnTpyIUaNGDcnxDIVCePbZZ/Ev//Ivpqr0Q338eBw4cAAtLS2mMSsuLkZ9fT0bs8bGRpSUlODcc89l1zQ0NMDj8WDbtm393uZ00d7eDk3TUFJSYnr+3nvvRVlZGWbOnIn777//jND6ZwpbtmxBZWUlzj77bNx55504efIkey3bxu/o0aP4wx/+gCVLliS8NlTGUNwb3KydjY2NmDp1Kqqqqtg18+fPR0dHBz788MOMtu8LU6U70zhx4gSi0ahpkACgqqoKn3zyyQC1KjOIxWL4zne+gwsvvBBTpkxhz998880YPXo0amtr8cEHH2D16tXYs2cPXnrppQFsrXvU19fj6aefxtlnn43m5mb88Ic/xMUXX4zdu3ejpaUFgUAgYbOoqqpCS0vLwDQ4DWzYsAFtbW247bbb2HNDffxE0HGR3YP0tZaWFlRWVppe9/l8KC0tHXLj2tvbi9WrV+Omm24yVUD+9re/jVmzZqG0tBRbt27FmjVr0NzcjAceeGAAW+sOCxYswLXXXou6ujrs378f3/ve93D55ZejsbERXq83q8YPAJ555hkUFhYmuLWHyhjK9gY3a2dLS4v0PqWvZRLKqFFIwPLly7F7926T3gSAyY89depU1NTUYN68edi/fz/GjRvX381MGpdffjn7e9q0aaivr8fo0aPxP//zP8jNzR3AlmUeTzzxBC6//HLU1tay54b6+H2REQ6Hcf3114MQgscee8z02sqVK9nf06ZNQyAQwDe+8Q2sX79+0NcZuvHGG9nfU6dOxbRp0zBu3Dhs2bIF8+bNG8CWnRk8+eSTWLx4MXJyckzPD5UxtNobBhOU+ylFlJeXw+v1Jii8jx49iurq6gFqVfpYsWIFXnnlFbzxxhsYMWKE7bX19fUAgH379vVH0zKOkpISTJgwAfv27UN1dTVCoRDa2tpM1wzF8Tx06BBee+01fP3rX7e9bqiPHx0Xu3uwuro6QbgfiUTQ2to6ZMaVGjSHDh3Cpk2bTCyNDPX19YhEIjh48GD/NDCDGDt2LMrLy9mczIbxo/jzn/+MPXv2ON6XwOAcQ6u9wc3aWV1dLb1P6WuZhDJqUkQgEMDs2bPx+uuvs+disRhef/11zJ07dwBblhoIIVixYgV++9vfYvPmzairq3N8z65duwAANTU1Z7h1ZwanT5/G/v37UVNTg9mzZ8Pv95vGc8+ePTh8+PCQG8+nnnoKlZWVuOKKK2yvG+rjV1dXh+rqatOYdXR0YNu2bWzM5s6di7a2Nmzfvp1ds3nzZsRiMWbUDWZQg2bv3r147bXXUFZW5vieXbt2wePxJLhthgI+//xznDx5ks3JoT5+PJ544gnMnj0b06dPd7x2MI2h097gZu2cO3cu/va3v5kMVGqgT548OeMNVkgRL7zwAgkGg+Tpp58mH330EVm2bBkpKSkxKbyHCu68805SXFxMtmzZQpqbm9m/7u5uQggh+/btIz/60Y/I+++/Tw4cOEBefvllMnbsWHLJJZcMcMvd4+677yZbtmwhBw4cIG+//TZpaGgg5eXl5NixY4QQQu644w4yatQosnnzZvL++++TuXPnkrlz5w5wq5NDNBolo0aNIqtXrzY9P1THr7Ozk+zcuZPs3LmTACAPPPAA2blzJ4v+uffee0lJSQl5+eWXyQcffECuvvpqUldXR3p6ethnLFiwgMycOZNs27aN/OUvfyHjx48nN91000B1yQS7/oVCIXLVVVeRESNGkF27dpnuSxoxsnXrVvLggw+SXbt2kf3795Nnn32WVFRUkFtuuWWAe6bDrn+dnZ3k3/7t30hjYyM5cOAAee2118isWbPI+PHjSW9vL/uMwTx+hDjPUUIIaW9vJ3l5eeSxxx5LeP9gH0OnvYEQ57UzEomQKVOmkMsuu4zs2rWLbNy4kVRUVJA1a9ZkvL3KqEkTjzzyCBk1ahQJBAJkzpw55J133hnoJqUEANJ/Tz31FCGEkMOHD5NLLrmElJaWkmAwSM466yyyatUq0t7ePrANTwI33HADqampIYFAgAwfPpzccMMNZN++fez1np4e8s1vfpMMGzaM5OXlkWuuuYY0NzcPYIuTx5/+9CcCgOzZs8f0/FAdvzfeeEM6L2+99VZCiB7Wfc8995CqqioSDAbJvHnzEvp+8uRJctNNN5GCggJSVFREbr/9dtLZ2TkAvUmEXf8OHDhgeV++8cYbhBBCtm/fTurr60lxcTHJyckhkyZNIj/96U9NRsFAwq5/3d3d5LLLLiMVFRXE7/eT0aNHk6VLlyYcCgfz+BHiPEcJIeQ///M/SW5uLmlra0t4/2AfQ6e9gRB3a+fBgwfJ5ZdfTnJzc0l5eTm5++67STgcznh7tXijFRQUFBQUFBSGNJSmRkFBQUFBQSEroIwaBQUFBQUFhayAMmoUFBQUFBQUsgLKqFFQUFBQUFDICiijRkFBQUFBQSEroIwaBQUFBQUFhayAMmoUFBQUFBQUsgLKqFFQUFBQUFDICiijRkFBQUFBQSEroIwaBQUFBQUFhayAMmoUFBQUFBQUsgL/H8ATZtdflhg3AAAAAElFTkSuQmCC"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 9
+ "execution_count": 61
},
{
"cell_type": "markdown",
"source": [
- "### BasicMotions\n",
+ "### CardanoSentiment\n",
"\n",
- "The data was generated as part of a student project where four students performed our activities whilst wearing a smart watch.\n",
- "The watch collects 3D accelerometer and a 3D gyroscope It consists of four classes, which are walking, resting, running and\n",
- "badminton. Participants were required to record motion a total of five times, and the data is sampled once every tenth of a second,\n",
- "for a ten second period. The data is multivariate (six channels) equal length."
+ "By combining historical sentiment data for Cardano cryptocurrency, extracted from\n",
+ " EODHistoricalData and made available on Kaggle, with historical price data for the\n",
+ " same cryptocurrency, extracted from CryptoDataDownload, we created the\n",
+ " CardanoSentiment dataset, with 107 instances. The predictors are hourly close price\n",
+ " (in USD) and traded volume during a day, resulting in 2-dimensional time series of\n",
+ " length 24. The response variable is the normalized sentiment score on the day\n",
+ " spanned by the timepoints."
],
"metadata": {
"collapsed": false
@@ -656,119 +647,69 @@
{
"cell_type": "code",
"source": [
- "from aeon.datasets import load_basic_motions\n",
+ "from aeon.datasets import load_cardano_sentiment\n",
"\n",
- "motions, motions_labels = load_basic_motions(split=\"train\")\n",
- "plt.title(\n",
- " f\"First and second dimensions of the first train instance in BasicMotions data, \"\n",
- " f\"(student {motions_labels[0]})\"\n",
- ")\n",
- "plt.plot(motions[0][0])\n",
- "plt.plot(motions[0][1])"
+ "cardano, cardano_target = load_cardano_sentiment()\n",
+ "print(cardano.shape)\n",
+ "plt.title(\"Response variable for cardano data\")\n",
+ "plt.plot(cardano_target)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:21.053382Z",
- "start_time": "2024-09-25T22:58:20.879846Z"
+ "end_time": "2024-09-25T22:58:22.582032Z",
+ "start_time": "2024-09-25T22:58:22.410134Z"
}
},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(107, 2, 24)\n"
+ ]
+ },
{
"data": {
- "text/plain": [
- "[]"
- ]
+ "text/plain": "[]"
},
- "execution_count": 10,
+ "execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 10
+ "execution_count": 62
},
{
"cell_type": "markdown",
"source": [
- "### GunPoint\n",
+ "## Segmentation\n",
"\n",
- "This dataset involves one female actor and one male actor making a motion with their\n",
- "hand. The two classes are: Gun-Draw and Point: For Gun-Draw the actors have their\n",
- "hands by their sides. They draw a replicate gun from a hip-mounted holster, point it\n",
- "at a target for approximately one second, then return the gun to the holster, and\n",
- "their hands to their sides. For Point the actors have their gun by their sides. They\n",
- "point with their index fingers to a target for approximately one second, and then\n",
- "return their hands to their sides. For both classes, The data in the archive is the\n",
- "X-axis motion of the actors right hand.\n"
+ "Two of the UCR classification data have been adapted for segmentation."
],
"metadata": {
"collapsed": false
}
},
- {
- "cell_type": "code",
- "source": [
- "from aeon.datasets import load_gunpoint\n",
- "\n",
- "gun, gun_labels = load_gunpoint(split=\"test\")\n",
- "plt.title(\n",
- " f\"First three cases of the test set for GunPoint, classes\"\n",
- " f\"(actor {gun_labels[0]}, {gun_labels[1]}, {gun_labels[2]})\"\n",
- ")\n",
- "plt.plot(gun[0][0])\n",
- "plt.plot(gun[1][0])\n",
- "plt.plot(gun[2][0])"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:58:21.247394Z",
- "start_time": "2024-09-25T22:58:21.075323Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "execution_count": 11
- },
{
"cell_type": "markdown",
"source": [
- "### ItalyPowerDemand\n",
- "The data was derived from twelve monthly electrical power demand time series from\n",
- "Italy and first used in the paper \"Intelligent Icons: Integrating Lite-Weight Data\n",
- "Mining and Visualization into GUI Operating Systems\". The classification task is to\n",
- "distinguish days from Oct to March (inclusive) (class 0) from April to September\n",
- "(class 1). The problem is univariate, equal length.\n"
+ "### ElectricDevices\n",
+ "\n",
+ "The UCR ElectricDevices dataset series are grouped by class label and concatenated to create\n",
+ " segments with repeating temporal patterns and characteristics. The location at which\n",
+ " different classes were concatenated are marked as change points.\n",
+ "\n",
+ "this function returns a single series, the period length as an integer and the\n",
+ "change points as a numpy array."
],
"metadata": {
"collapsed": false
@@ -777,63 +718,60 @@
{
"cell_type": "code",
"source": [
- "from aeon.datasets import load_italy_power_demand\n",
+ "from aeon.datasets import load_electric_devices_segmentation\n",
"\n",
- "italy, italy_labels = load_italy_power_demand(split=\"train\")\n",
- "plt.title(\n",
- " f\"First three cases of the test set for ItalyPowerDemand, classes\"\n",
- " f\"( {italy_labels[0]}, {italy_labels[1]}, {italy_labels[2]})\"\n",
- ")\n",
- "plt.plot(italy[0][0])\n",
- "plt.plot(italy[1][0])\n",
- "plt.plot(italy[2][0])"
+ "data, period, change_points = load_electric_devices_segmentation()\n",
+ "print(\" Period = \", period)\n",
+ "print(\" Change points = \", change_points)\n",
+ "plt.title(\"Electric Devices Segmentation\")\n",
+ "plt.plot(data)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:21.419932Z",
- "start_time": "2024-09-25T22:58:21.266319Z"
+ "end_time": "2024-09-25T22:58:22.990281Z",
+ "start_time": "2024-09-25T22:58:22.610956Z"
}
},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Period = 10\n",
+ " Change points = [1090 4436 5712 7923]\n"
+ ]
+ },
{
"data": {
- "text/plain": [
- "[]"
- ]
+ "text/plain": "[]"
},
- "execution_count": 12,
+ "execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 12
+ "execution_count": 63
},
{
"cell_type": "markdown",
"source": [
- "### JapaneseVowels\n",
- "\n",
- "A UCI Archive dataset. See this link for more [detailed information](https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels)\n",
- "\n",
- "Paper: M. Kudo, J. Toyama and M. Shimbo. (1999). \"Multidimensional Curve Classification Using Passing-Through Regions\". Pattern Recognition Letters, Vol. 20, No. 11--13, pages 1103--1111.\n",
+ "### GunPoint Segmentation\n",
"\n",
- "9 Japanese-male speakers were recorded saying the vowels 'a' and 'e'. A '12-degree linear prediction analysis' is applied to the raw recordings to obtain time-series with 12 dimensions and series lengths between 7 and 29. The classification task is to predict the speaker. Therefore, each instance is a transformed utterance, 12*29 values with a single class label attached, [1...9].\n",
+ "The UCR GunPoint dataset series are grouped by class label and concatenated to create\n",
+ " segments with repeating temporal patterns and characteristics. The location at which\n",
+ " different classes were concatenated are marked as change points.\n",
"\n",
- "The given training set is comprised of 30 utterances for each speaker, however the\n",
- "test set has a varied distribution based on external factors of timing and\n",
- "experimental availability, between 24 and 88 instances per speaker. The data is\n",
- "unequal length"
+ "this function returns a single series, the period length as an integer and the\n",
+ "change points as a numpy array."
],
"metadata": {
"collapsed": false
@@ -842,27 +780,19 @@
{
"cell_type": "code",
"source": [
- "from aeon.datasets import load_japanese_vowels\n",
- "\n",
- "japan, japan_labels = load_japanese_vowels(split=\"train\")\n",
- "plt.title(\n",
- " f\"First channel of three test cases for JapaneseVowels, classes\"\n",
- " f\"({japan_labels[0]}, {japan_labels[10]}, {japan_labels[200]})\"\n",
- ")\n",
- "print(f\" number of cases = \" f\"{len(japan)}\")\n",
- "print(f\" First case shape = \" f\"{japan[0].shape}\")\n",
- "print(f\" Tenth case shape = \" f\"{japan[10].shape}\")\n",
- "print(f\" 200th case shape = \" f\"{japan[200].shape}\")\n",
+ "from aeon.datasets import load_gun_point_segmentation\n",
"\n",
- "plt.plot(japan[0][0])\n",
- "plt.plot(japan[10][0])\n",
- "plt.plot(japan[200][0])"
+ "data, period, change_points = load_gun_point_segmentation()\n",
+ "print(\" Period = \", period)\n",
+ "print(\" Change points = \", change_points)\n",
+ "plt.title(\"Gunpoint Segmentation\")\n",
+ "plt.plot(data)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-09-25T22:58:21.705366Z",
- "start_time": "2024-09-25T22:58:21.437860Z"
+ "end_time": "2024-09-25T22:58:23.230150Z",
+ "start_time": "2024-09-25T22:58:23.046130Z"
}
},
"outputs": [
@@ -870,107 +800,136 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " number of cases = 270\n",
- " First case shape = (12, 20)\n",
- " Tenth case shape = (12, 23)\n",
- " 200th case shape = (12, 13)\n"
+ " Period = 10\n",
+ " Change points = [900]\n"
]
},
{
"data": {
- "text/plain": [
- "[]"
- ]
+ "text/plain": "[]"
},
- "execution_count": 13,
+ "execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 13
+ "execution_count": 64
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ }
},
{
"cell_type": "markdown",
"source": [
- "### OSUleaf\n",
+ "## Time Series Forecasting\n",
"\n",
- "The OSULeaf data set consist of one dimensional outlines of leaves. The series were\n",
- "obtained by color image segmentation and boundary extraction (in the anti-clockwise\n",
- "direction) from digitized leaf images of six classes: Acer Circinatum, Acer Glabrum,\n",
- "Acer Macrophyllum, Acer Negundo, Quercus Garryana and Quercus Kelloggii for the MSc\n",
- "thesis \"Content-Based Image Retrieval: Plant Species Identification\" by A. Grandhi.\n",
- "OSULeaf is equal length and univariate"
+ "Forecasting data are stored in csv files with a header for column names. Six standard\n",
+ " example datasets are shipped by default:\n",
+ "\n",
+ "| dataset name | loader function | properties |\n",
+ "|----------|:-------------:|------:|\n",
+ "| Box/Jenkins airline data | `load_airline` | univariate |\n",
+ "| Lynx sales data | `load_lynx` | univariate |\n",
+ "| Shampoo sales data | `load_shampoo_sales` | univariate |\n",
+ "| Pharmaceutical Benefit Scheme data | `load_PBS_dataset` | univariate |\n",
+ "| Longley US macroeconomic data | `load_longley` | multivariate |\n",
+ "| MTS consumption/income data | `load_uschange` | multivariate |\n",
+ "\n",
+ " These are stored in csv format in time, value format, including a header. For\n",
+ " forcasting files, each column that is not an index is considered a time series. For\n",
+ " example, the airline data has a single time series each row a time, value pair:\n",
+ "\n",
+ " Date,Passengers\n",
+ " 1949-01,112\n",
+ " 1949-02,118\n",
+ "\n",
+ "Longley has seven time series, each in its own column. Each row is the same time index:\n",
+ "\n",
+ " \"Obs\",\"TOTEMP\",\"GNPDEFL\",\"GNP\",\"UNEMP\",\"ARMED\",\"POP\",\"YEAR\"\n",
+ " 1,60323,83,234289,2356,1590,107608,1947\n",
+ " 2,61122,88.5,259426,2325,1456,108632,1948\n",
+ " 3,60171,88.2,258054,3682,1616,109773,1949\n",
+ "\n",
+ "The problem specific loading functions return the series as either a `pd.Series` if\n",
+ "a single series or, if multiple series, a `pd.DataFrame` with each column a series.\n",
+ "There are currently six forecasting problems\n",
+ "shipped."
],
"metadata": {
"collapsed": false
}
},
{
- "cell_type": "code",
+ "cell_type": "markdown",
"source": [
- "from aeon.datasets import load_osuleaf\n",
+ "### Airline\n",
"\n",
- "leaf, leaf_labels = load_osuleaf(split=\"train\")\n",
- "plt.title(\n",
- " f\"First three cases of the test set for OSULeaf, classes\"\n",
- " f\" ({leaf_labels[0]}, {leaf_labels[1]}, {leaf_labels[2]})\"\n",
- ")\n",
- "plt.plot(leaf[0][0])\n",
- "plt.plot(leaf[1][0])\n",
- "plt.plot(leaf[2][0])"
+ "The classic Box & Jenkins airline data. Monthly totals of international\n",
+ " airline passengers, 1949 to 1960. This data shows an increasing trend,\n",
+ " non-constant (increasing) variance and periodic, seasonal patterns. The\n"
],
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:58:21.910360Z",
- "start_time": "2024-09-25T22:58:21.726272Z"
- }
- },
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
"outputs": [
{
"data": {
- "text/plain": [
- "[]"
- ]
+ "text/plain": "[]"
},
- "execution_count": 14,
+ "execution_count": 73,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 14
+ "source": [
+ "from aeon.datasets import load_airline\n",
+ "\n",
+ "airline = load_airline()\n",
+ "plt.title(\"Airline data\")\n",
+ "plt.plot(airline)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
},
{
"cell_type": "markdown",
"source": [
- "### PLAID\n",
- "PLAID stands for the Plug Load Appliance Identification Dataset. The data are intended for load identification research. The first version of PLAID is named PLAID1, collected in summer 2013. A second version of PLAID was collected in winter 2014 and released under the name PLAID2.\n",
- "This dataset comes from PLAID1. It includes current and voltage measurements sampled at 30 kHz from 11 different appliance types present in more than 56 households in Pittsburgh, Pennsylvania, USA. Data collection took place during the summer of 2013. Each appliance type is represented by dozens of different instances of varying makes/models.\n",
- "For each appliance, three to six measurements were collected for each state transition. These measurements were then post-processed to extract a few-second-long window containing both the steady-state operation and the startup transient )when available).\n",
- "The classes correspond to 11 different appliance types: air\n",
- "conditioner (class 0), compact flourescent lamp, fan, fridge,\n",
- "hairdryer , heater, incandescent light bulb, laptop, microwave,\n",
- "vacuum,washing machine (class 10). The data is univariate and unequal length."
+ "### Longley\n",
+ "This mulitvariate time series dataset contains various US macroeconomic\n",
+ " variables from 1947 to 1962 that are known to be highly collinear. This loader\n",
+ " returns the multivariate time series as a numpy array or a pandas DataFrame wit\n",
+ " the following columns:\n",
+ " TOTEMP - Total employment\n",
+ " GNPDEFL - Gross national product deflator\n",
+ " GNP - Gross national product\n",
+ " UNEMP - Number of unemployed\n",
+ " ARMED - Size of armed forces\n",
+ " POP - Population\n"
],
"metadata": {
"collapsed": false
@@ -978,71 +937,39 @@
},
{
"cell_type": "code",
- "source": [
- "from aeon.datasets import load_plaid\n",
- "\n",
- "plaid, plaid_labels = load_plaid(split=\"train\")\n",
- "plt.title(\n",
- " f\"three train cases for PLAID, classes\"\n",
- " f\"( {plaid_labels[0]}, {plaid_labels[10]}, {plaid_labels[200]})\"\n",
- ")\n",
- "print(f\" number of cases = \" f\"{len(plaid)}\")\n",
- "print(f\" First case shape = \" f\"{plaid[0].shape}\")\n",
- "print(f\" Tenth case shape = \" f\"{plaid[10].shape}\")\n",
- "print(f\" 200th case shape = \" f\"{plaid[200].shape}\")\n",
- "\n",
- "plt.plot(plaid[0][0])\n",
- "plt.plot(plaid[10][0])\n",
- "plt.plot(plaid[200][0])"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:58:22.119236Z",
- "start_time": "2024-09-25T22:58:21.932521Z"
- }
- },
+ "execution_count": 66,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- " number of cases = 537\n",
- " First case shape = (1, 500)\n",
- " Tenth case shape = (1, 300)\n",
- " 200th case shape = (1, 200)\n"
+ "(6, 16)\n"
]
},
{
"data": {
- "text/plain": [
- "[]"
- ]
+ "text/plain": "[]"
},
- "execution_count": 15,
+ "execution_count": 66,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 15
- },
- {
- "cell_type": "markdown",
"source": [
- "## Regression\n",
+ "from aeon.datasets import load_longley\n",
"\n",
- "We ship one regression problem from the [Time Series Extrinsic Regression]\n",
- "(http://tseregression.org/) website and one soon to be added."
+ "longley = load_longley()\n",
+ "print(longley.shape)\n",
+ "plt.title(\"Total employment\")\n",
+ "plt.plot(longley[0])"
],
"metadata": {
"collapsed": false
@@ -1051,12 +978,10 @@
{
"cell_type": "markdown",
"source": [
- "### Covid3Month\n",
"\n",
- "The goal of this dataset is to predict COVID-19's death rate on 1st April 2020 for each country using daily confirmed cases for the last three months.\n",
- "This dataset contains 201 time series, where each time series is the daily confirmed cases for a country.\n",
- "The data was obtained from WHO's COVID-19 database.\n",
- "Please refer to https://covid19.who.int/ for more details"
+ "The annual numbers of lynx trappings for 1821β1934 in Canada. This\n",
+ " time-series records the number of skins of predators (lynx) that were collected\n",
+ " over several years by the Hudson's Bay Company."
],
"metadata": {
"collapsed": false
@@ -1064,123 +989,77 @@
},
{
"cell_type": "code",
- "source": [
- "from aeon.datasets import load_covid_3month\n",
- "\n",
- "covid, covid_target = load_covid_3month()\n",
- "print(covid.shape)\n",
- "plt.title(\"Response variable for Covid3Months data\")\n",
- "plt.plot(covid_target)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:58:22.385200Z",
- "start_time": "2024-09-25T22:58:22.146164Z"
- }
- },
+ "execution_count": 67,
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(201, 1, 84)\n"
- ]
- },
{
"data": {
- "text/plain": [
- "[]"
- ]
+ "text/plain": "[]"
},
- "execution_count": 16,
+ "execution_count": 67,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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IswlHkx069rVSGokQj64+MHaG+sAQBEFYx//30hY8s74BAPCH70/BD6ZRZSihDMP7wBAEQRBENsKRpO+FjLyEEZCAIQiCIIQjN+7upxQSYQAkYAiCIAjhyD0wFIEhjIAEDEEQBCGcUJRSSISxkIAhCIIghCNPIR1o60EsdlTWixAWQgKGIAYwb29vwge7Dlu9G8RRiFzAhKMSmjuCFu4NcTRCAoYgBigdvWFc97eNuPZ/N9LqmBBOOJL6ndrfRlOpCbGQgCGIAUpnMIJITEJnMJLiVyAIEaR/p6iZHSEaEjAEMUCRr5CDYRIwhFhYCqmyyAuAjLyEeEjAEMQARb5C7o1ELdwT4miECZgxg4sAUASGEA8JGIIYoMhNlhSBIUTD+sDUJgQMNbMjREMChiAGKKEIRWAI42DfLy5gKIVECIYEDEEMUCgCQxgJTyFVJiMwR+nsYMIiSMAQxACFPDCEkTABM7qyEADQE46itTts5S4RRxkkYAhigCKfVUMRGEI07PtV7HNjSIkPAPlgCLGQgCGIAUpY7oEJUwSGEAuL8HncTowYVAAA2NdKzewIcZCAIYgBijyFFIxQBIYQhyRJPIXkcTkwvDwuYMjIS4iEBAxBDFDkJl6KwBAiicYkML+u1+XEcB6BIQFDiIMEDEEMUKiMmjAKub/K43JiBEVgCAMgAUMQAxQy8RJGIU9PemQRGDLxEiIhAUMQA5QwlVETBhFOETAODC+Pl1KTiZcQCQkYghigyFNIFIEhRCI38DocDh6BCfRG0NFLvWAIMZCAIYgBCjWyI4yCTTr3uOK3mGKfG+WFHgDkgyHEQQKGIAYoNEqAMAreA8aVvMXwUmrywRCCIAFDEAOUFAFDERhCIOFcAoYiMIQgSMAQxABFXoXUSxEYQiBMwHhdDv4Y9YIhREMChiAGKCkmXorAEAIJy8YIMKpL/QCAw51BS/aJOPogAUMQA5QUEy9FYAiBhNJMvABQ4HEBIL8VIQ4SMAQxQAlTBIYwiEweGL8n/m/6rhGiIAFDEAOUMEVgCIPI5IHxuRMRGBocSgiCBAxBDFBSRgnQqpgQSKYIjC/hh6HBoYQoSMAQxABFvhKmCAwhklC0rwfG76EIDCEWEjAEMUChPjCEUTB/lbwKiUVgyMRLiIIEDEEMUMgDQxhFRg9MwsRLYysIUZCAIYgBSqqAoZsKIY7MHhgqoybEQgKGIAYooRQTL91UCHFk9sBQBIYQCwkYghigyDvxhiIxxGJSjq0JQjkUgSHMgAQMQQxQ5CkkILUzL0HogZl4ve6+HphgJApJIrFM6Ee1gNm/fz8uv/xyVFZWoqCgAJMnT8aGDRv485IkYdGiRRg6dCgKCgowc+ZM7Ny5M+U1WlpaMGfOHJSWlqK8vBzz5s1DZ2dnyjaffvopvvnNb8Lv92PkyJG46667NL5FgiAykS5gyAdDiCJXBCYmpfYgIgitqBIwra2tOP300+HxePDGG29g+/btuPvuuzFo0CC+zV133YX7778fjzzyCNavX4+ioiLU19ejt7eXbzNnzhxs27YNK1aswLJly/Dee+/h2muv5c8HAgGcf/75GD16NDZu3Ig//OEPuP322/Hoo48KeMsEQQCpowQA8sEQ4sjlgQGobJ8Qg1vNxr///e8xcuRIPPnkk/yx2tpa/m9JkvCnP/0Jt912Gy6++GIAwF//+ldUV1fj5ZdfxqWXXorPPvsMb775Jj766CNMmzYNAPDAAw/g29/+Nv74xz9i2LBhePrppxEKhfDEE0/A6/XihBNOwObNm3HPPfekCB05wWAQwWByymkgEFDz1ghiwBFKWwVTBIYQRaYIjNflhMMBSFK8bL/Eb9XeEUcLqiIwr7zyCqZNm4Yf/OAHqKqqwsknn4y//OUv/Pk9e/agsbERM2fO5I+VlZVh+vTpWLt2LQBg7dq1KC8v5+IFAGbOnAmn04n169fzbc4880x4vV6+TX19PXbs2IHW1taM+7ZkyRKUlZXxn5EjR6p5awQx4AilrYKpFwwhikx9YBwOR7KZHUVgCAGoEjC7d+/Gww8/jGOPPRbLly/HT37yE/z0pz/FU089BQBobGwEAFRXV6f8XnV1NX+usbERVVVVKc+73W5UVFSkbJPpNeR/I51bb70V7e3t/Gfv3r1q3hpBDDjSfQh0UyFEkSkCA9BAR0IsqlJIsVgM06ZNw+9+9zsAwMknn4ytW7fikUcewdy5cw3ZQaX4fD74fD5L98EI/rFxHx5fswePXjkVIwYVWr07xFEEu8kUel3oDkUpAkMIIxRJeGDc6QKGBjoS4lAVgRk6dCgmTpyY8tiECRPQ0NAAAKipqQEANDU1pWzT1NTEn6upqUFzc3PK85FIBC0tLSnbZHoN+d8YKLy4aR+2Hwxg9ReHrN4V4igiFpMQSfR9KfHH1zEUgSFEkS0CQwMdCZGoEjCnn346duzYkfLYF198gdGjRwOIG3pramqwcuVK/nwgEMD69etRV1cHAKirq0NbWxs2btzIt3nnnXcQi8Uwffp0vs17772HcDjMt1mxYgWOP/74lIqngUB3KH5TaekMWbwnxNFEOJa8gZT4PQDIA0OII5MHBqAIDCEWVQJm4cKFWLduHX73u99h165deOaZZ/Doo49i/vz5AOImrQULFuA3v/kNXnnlFWzZsgVXXnklhg0bhu9+97sA4hGbCy64AD/+8Y/x4Ycf4l//+heuv/56XHrppRg2bBgA4Ic//CG8Xi/mzZuHbdu24bnnnsN9992HG2+8Uey77wf0JATMkS4SMIQ45F14i30UgSHEQhEYwgxUeWBOPfVUvPTSS7j11luxePFi1NbW4k9/+hPmzJnDt7n55pvR1dWFa6+9Fm1tbTjjjDPw5ptvwu9P1sw9/fTTuP7663HeeefB6XRi9uzZuP/++/nzZWVleOuttzB//nxMnToVgwcPxqJFi7KWUB/N9CRWKi0kYAiByA28LIVEERhCFJn6wADJCAyNEyBEoErAAMBFF12Eiy66KOvzDocDixcvxuLFi7NuU1FRgWeeeSbn35kyZQref/99tbt31JGMwATzbEkQymErZLfTgQK+KqYIDCEG1iSxj4nXQ2XUhDhoFpLN4QKGPDCEQFgKyeNy8rA+RWAIUWTzwPhpoCMhEBIwNodSSIQRJD0KDjJWEsLJ2gcmEYHppQgMIQASMDYmFInxUteWrhBNcCWEwSZPe91OMlYSwsnugaEIDCEOEjA2pke2Io7EJAR6IhbuDXE0EY4kbzBJYyWtigkxZK9CIg8MIQ4SMDaG+V8YZOQlREERGMJIuAfGnd4HhvxWhDhIwNiYnrQVMflgCFHIV8hsVUweGEIU4UhuDwxFYAgRkICxMd2h1JTRYapEIgQhFzA0YI8QTT4PDEVgCBGQgLEx6StiisAQomBl1F6XgyIwhHCyT6OmCAwhDhIwNqY7lC5gyANDiCFTBIYEDCGKZB8YGiVAGAcJGBuTbuKlFBIhChbi97qdMl8C3VQIMXCB3MfES9E+QhwkYGwMmXgJo5CbLCkCQ4hEkiQ+a4uGORJGQgLGxqRHYEjAEKLIVIVENxVCBPJBodk8MCSWCRGQgLExzANTVuABABwhAUMIIiTr05GchUQ3FUI/TBwDfT0wSRMviWVCPyRgbAxLIQ0vLwAAHOkkEy8hhlBKColuKoQ45ALGkz7M0UOjBAhxkICxMSyFNGJQXMC0dtM8JEIMLMzvpWnUhGBYdM/hAFzOLCZeKqMmBEACxsbwCExCwISjEgK9NA+J0E+ySsRJvTkIocgNvA4HRWAI4yABY2OYB6a8wItinxsApZEIMcj7dNBNhRBJOJK5BwxAowQIsZCAsTHMVFngdaKiyAuAKpEIMSQ9MA4egQlFY4jGKEVJ6CNZ4ebo8xwfW0FimRAACRgbw2YhFXjdXMBQJRIhglBKGbUr+TgZeQmdhLKMEQBAJfuEUEjA2JiexCqlwOPC4GKKwBDi4CkkmQcGoFJqQj/ZmtgByQgMRfsIEZCAsTG9CQ9ModeVjMCQB4YQQDiSvMm4XU64E9UitDIm9CIXx+nIxTJF+wi9kICxMd3hRArJ40JFkQ8ApZAIMaQP26NmdoQowpFcHpjkLYeMvIReSMDYGNYHpsDrQiWZeAmBBNOMltTMjhBFLg+MPNpHfYcIvZCAsTFcwHhcqCxmKSQSMIR++CrZTREYQiy5PDCAfKAjfdcIfZCAsTGskV2KB4YiMIQA0lNINGSPEEX6dyud5HeNIjCEPkjA2BjWyM7vcaEy4YFp6SITL6EfPkogcTPx8VUx3VQIfSS7PPf1wACgzs+EMNxW7wCRmVhM4jeTQq8LzkTeuKUrPg8pvUU3Qagh3adAERhCFPJBoZnwk1gmBEERGJvSI7uRyE28NA+JEEH6TYYajBGiyOeB8ZJYJgRBAsamyAWM3+2C3+NCkTe+cqFKJEIv6e3eycRLiCKfB4ZmbxGiIAFjU3q4/8XJ00cVvBsv+WAIfaQ3G6MyakIUuWYhAbJ0JXlgCJ2QgLEpyQqkpE2JN7OjUmpCJ9zES43sCMHk6gMDyAzjFIEhdEICxqZ0y3rAMAZTKTUhiHQPDEVgCFHwMRUZRgkAgJ++a4QgSMDYFHkXXkYFdeMlBJG+Sk76EigCQ+gjbx8YivYRgiABY1N6ZHOQGBXUjZcQRNIDkzpKoJdWxYRO8nlgKAJDiIIEjE3pCcVPbnkEZjAf6EgmXkIfbJSA1xX/flEEhhBFfg8MlVETYiABY1O6QxkiMJRCIgTBe3W408uoaVVM6COcT8C4qZEdIQYSMDalVzYHiUEpJEIEkiRl7cRL7d0JvTATrzebiddD3zVCDKoEzO233w6Hw5HyM378eP58b28v5s+fj8rKShQXF2P27NloampKeY2GhgbMmjULhYWFqKqqwk033YRIJLWz7KpVq3DKKafA5/Nh3LhxWLp0qfZ32E/JVIVUSREYQgAs+gLIBAxFYAhB5O8DQ981QgyqIzAnnHACDh48yH/WrFnDn1u4cCFeffVVvPDCC1i9ejUOHDiASy65hD8fjUYxa9YshEIhfPDBB3jqqaewdOlSLFq0iG+zZ88ezJo1C+eccw42b96MBQsW4JprrsHy5ct1vtX+BesDI/fAVBYnPTCSJGX8PYLIB7vBAH2nUdOqmNBLXg8MfdcIQage5uh2u1FTU9Pn8fb2djz++ON45plncO655wIAnnzySUyYMAHr1q3DjBkz8NZbb2H79u14++23UV1djZNOOgl33HEHfvnLX+L222+H1+vFI488gtraWtx9990AgAkTJmDNmjW49957UV9fr/Pt9h96ckRgwlEJHcEISv0eS/aN6N+kCBh3eiM7WhUT+sjngaFRAoQoVEdgdu7ciWHDhmHs2LGYM2cOGhoaAAAbN25EOBzGzJkz+bbjx4/HqFGjsHbtWgDA2rVrMXnyZFRXV/Nt6uvrEQgEsG3bNr6N/DXYNuw1shEMBhEIBFJ++jM9GTwwfo+L/7+FfDCERtgK2ekAXM70MmpaFRP6SO/ynA5FYAhRqBIw06dPx9KlS/Hmm2/i4Ycfxp49e/DNb34THR0daGxshNfrRXl5ecrvVFdXo7GxEQDQ2NiYIl7Y8+y5XNsEAgH09PRk3bclS5agrKyM/4wcOVLNW7MdzAPjlwkYIFmJRKXUhFbSu/ACtComxMEjMO4sfWA8VIVEiEFVCunCCy/k/54yZQqmT5+O0aNH4/nnn0dBQYHwnVPDrbfeihtvvJH/PxAI9GsRwyMwnlQBU1nsw77WHqpEIjSTaYXspwgMIYhMAlkOj/ZRHxhCJ7rKqMvLy3Hcccdh165dqKmpQSgUQltbW8o2TU1N3DNTU1PTpyqJ/T/fNqWlpTlFks/nQ2lpacpPfybTKAEAKCuI+14CvZE+v0MQSkiukJOnPw3YI0SRtw8ML6Om7xqhD10CprOzE19++SWGDh2KqVOnwuPxYOXKlfz5HTt2oKGhAXV1dQCAuro6bNmyBc3NzXybFStWoLS0FBMnTuTbyF+DbcNeY6CQFDCpQTI/rV4InYR4F155Col8CYQY8nlg/G6ahUSIQZWA+cUvfoHVq1fjq6++wgcffIDvfe97cLlcuOyyy1BWVoZ58+bhxhtvxLvvvouNGzfiqquuQl1dHWbMmAEAOP/88zFx4kRcccUV+OSTT7B8+XLcdtttmD9/Pny+eInwddddh927d+Pmm2/G559/joceegjPP/88Fi5cKP7d25jucN8qJEBeLUInP6GNTB4F3h2VIjCETigCQ5iFKg/Mvn37cNlll+HIkSMYMmQIzjjjDKxbtw5DhgwBANx7771wOp2YPXs2gsEg6uvr8dBDD/Hfd7lcWLZsGX7yk5+grq4ORUVFmDt3LhYvXsy3qa2txWuvvYaFCxfivvvuw4gRI/DYY48NqBJqAOgN9a1CAuQrZTr5CW1kNvGSB4YQQ0hhIzsSy4ReVAmYZ599Nufzfr8fDz74IB588MGs24wePRqvv/56ztc5++yzsWnTJjW7dtTRnZhG7c8SgaGhe4RWMoX42U0lHJUQjUm8vJog1JLJYyWHxDIhCpqFZFPYNOr0CEyyXwetXghtZArxs5sKQD4YQh98FlK+YY4UgSF0QgLGpvRkmEYNkAeG0A8L8cuH7bGbCkDdeAl9RGLKPDC9kSiNRCF0QQLGhkiSlLETL0AChtBPpmF7LqeD/58iMIQekh6r3B4YSUodLEoQaiEBY0OCkRhiifM6vRNvsgkUrZIJbWRrNOanKcGEAJgoyT4LidKVhBhIwNgQeXSFUkiEaFgEJt2j4KNeMIQAwhlSlHLk3zsSy4QeSMDYEDYHyeNy9F0lMwFDJl5CI6EsK2QfRWAIncRiEiKx3BEYh8NBAx0JIZCAsSE9WZrYAbISRIrAEBoJRzKvkHkEhr5bhEbCsaT4zeaBAeSRZBLLhHZIwNiQbHOQgKRPgW4yhFaydUrlHhiK7hEakZtys0VgAFAEhhACCRgbkqxA6ttnkFYuhF74LCR36grZR9E9QifhiDwCk0PAUEdxQgAkYGwI88Ckd+GNP0ZdLAl95IvA0E2F0Ar7brmcjpzdnGmgIyECEjA2pCfLHCRAPkqAbjKENrKZeMlfRegl3xwkBkVgCBGQgLEhPeHMXXgB+SgBuskQ2shW5uqjCAyhk3w9YBh+GidACIAEjA1hc5AymnipDwyhk6wpJKpCInSSrcdQOtRziBABCRgb0p1lDhIgN1rGaI4IoQlu4k0L81MEhtBLti7P6dBAR0IEJGBsSG+WOUhAqrGXbjSEFkJ5IjAU3SO0wqN77tweGCpGIERAAsaG5KxCkk0NptULoYVsPgWfhyIwhD6UemAoAkOIgASMDck2iRqIu/tZdSKtXggtZOvE63dTBIbQh1IPjJ88MIQASMDYEN6JN0MExuFwkJGX0EX2YY70vSL0kS09mQ7N3SJEQALGhvBZSBkiMAB14yX0EcriU0i2d6fvFaGNcERhHxgaJUAIgASMDenOMQsJoFA/oY9slSIU2ctNdyiCq5d+hOc+arB6V2yLYg8MLcIIAZCAsSG5qpAAutEQ+sjWB4YiMLn5cE8L3vm8GY+v2WP1rtiWbE0S06EIDCECEjA2pDuHBwagahFCH2yV3MfES8I4Jx298f5MXUE6PtlQ6oHx0zWMEAAJGBvCTbwZplED1K+D0EdWEy9FYHLSFYwLmB4677ISVjoLidLghABIwNgQbuLNFoHh85DoRkOoJ78Hhr5XmehMCBjWKZvoS1hxJ14Sy4R+SMDYkFzTqAEK9RP6yDYxmN9U6HuVEZY66g3HEIvRGI9M8PSkwhQSXcMIPZCAsSFshZepEy8gn+RKJz+hnuzDHMmXkIsuWeSF0kiZUd4HhiIwhH5IwNgQFsLPHoFxpmxHEGpgq2QfmXhVwVJIQNJoT6SifBYSjRIg9EMCxmZEojG+isnmgaEbDaGHbD4FWhXnpksmYHpIwGQkW3QvnaSPj44joR0SMDZDHprO24mXTn5CA0G+SqYIjBrkAqY7TEbeTCj1wPgoAkMIgASMzWArO4ejb4if4aMUEqERSZKylrqyvjCRmEQm1QxQCik/2Src0qFhjoQISMDYDD6J2uOCw5E5j+x300qZ0EY0JkFKaJP0VbJc0IRjJI7TkTewoxRSZpSnkCgCQ+iHBIzNyDcHCaB+HYR2WIgf6NuJV37TkW9HxOmiCExelJp4yW9FiIAEjM3IN4kaoPAroR1mEAf6rpJTBAzdWPqQmkIiD0wm1PaBCUVjiFK6ktAICRib0ZNnDhJAERhCOyGZMHE7U1fJLqcD7KFwlL5b6VAVUn7U9oEBUr+TBKEGEjA2I98cJIAmuRLakc9ByuSxYjeeEAmYFGIxCV0y0UIppMyoHSUAkJeP0A4JGJvRzecgZf9oqNyV0Eq+YXss9E8emFS608416sSbGaXDHN0uJ48Akg+G0AoJGJvRqyiFRGXUhDZ4BCZLiT7rDUMppFTk6SOAPDDZ4B6YLN8vORRJJvSiS8DceeedcDgcWLBgAX+st7cX8+fPR2VlJYqLizF79mw0NTWl/F5DQwNmzZqFwsJCVFVV4aabbkIkknpBWLVqFU455RT4fD6MGzcOS5cu1bOr/QZ2YSzMkUKiMmpCK6FI/AaTLcTPVs4kYFLp7CNg6NzLhFIPDEBePkI/mgXMRx99hP/5n//BlClTUh5fuHAhXn31VbzwwgtYvXo1Dhw4gEsuuYQ/H41GMWvWLIRCIXzwwQd46qmnsHTpUixatIhvs2fPHsyaNQvnnHMONm/ejAULFuCaa67B8uXLte5uv6EncTJnG+QIJLtYUideQi35bjAeSiFlJD0CY0cTbygSw/992ICGI92W7YPSPjAARWAI/WgSMJ2dnZgzZw7+8pe/YNCgQfzx9vZ2PP7447jnnntw7rnnYurUqXjyySfxwQcfYN26dQCAt956C9u3b8ff/vY3nHTSSbjwwgtxxx134MEHH0QoFAIAPPLII6itrcXdd9+NCRMm4Prrr8f3v/993HvvvQLesr3p4REYSiER4smXQkp6YOi7Jac/RGDe+bwZt764BYuXbbNsH5R6YACKwBD60SRg5s+fj1mzZmHmzJkpj2/cuBHhcDjl8fHjx2PUqFFYu3YtAGDt2rWYPHkyqqur+Tb19fUIBALYtm0b3yb9tevr6/lrZCIYDCIQCKT89EeU9YGhFBKhjWSVSOYbDI/AkLEyBXkXXsCeAqa1O74A/Oxgh2X7EI4o6wMDJEU0RWAIragWMM8++yw+/vhjLFmypM9zjY2N8Hq9KC8vT3m8uroajY2NfBu5eGHPs+dybRMIBNDT05Nxv5YsWYKysjL+M3LkSLVvzRZ0q+gDQ224CbWE8pp4HSnbEXH6pJBsOMwxmFjQ7G/rsSzFFc4yKDQTNNCR0IsqAbN371787Gc/w9NPPw2/32/UPmni1ltvRXt7O//Zu3ev1bukCUURGHeyVwd1sSTUwLwt5IFRB0shuRKlv3aMwMjLkXcf7rRkH1SZeBPXMfLyEVpRJWA2btyI5uZmnHLKKXC73XC73Vi9ejXuv/9+uN1uVFdXIxQKoa2tLeX3mpqaUFNTAwCoqanpU5XE/p9vm9LSUhQUFGTcN5/Ph9LS0pSf/ghbOeX2wCSfo/AroYZ804I95IHJCBMwlUVeAPY18TJ2H+qyZB/UeGAoAkPoRZWAOe+887BlyxZs3ryZ/0ybNg1z5szh//Z4PFi5ciX/nR07dqChoQF1dXUAgLq6OmzZsgXNzc18mxUrVqC0tBQTJ07k28hfg23DXuNopktBCknexZJOfkIN8k68mSATb2ZYCmlIiQ+A/SMwXx6yJgKjdBYSQBEYQj/Zm41koKSkBJMmTUp5rKioCJWVlfzxefPm4cYbb0RFRQVKS0txww03oK6uDjNmzAAAnH/++Zg4cSKuuOIK3HXXXWhsbMRtt92G+fPnw+eLXxyuu+46/PnPf8bNN9+Mq6++Gu+88w6ef/55vPbaayLes61pTxjxygu9WbdhXSwjMYlOfkIVoTwrZPY4zadJpbNfCJjkPlkWgVE4SgCgCAyhH1UCRgn33nsvnE4nZs+ejWAwiPr6ejz00EP8eZfLhWXLluEnP/kJ6urqUFRUhLlz52Lx4sV8m9raWrz22mtYuHAh7rvvPowYMQKPPfYY6uvrRe+u7WjtDgMABhV6cm7n97jQGYxQCSKhinxl1G7ywGSER2CK4wKmx4adeO0QgQmpMfHyKiS6hhHa0C1gVq1alfJ/v9+PBx98EA8++GDW3xk9ejRef/31nK979tlnY9OmTXp3r9/RqiACA8R7wXQGqZSaUEe+FTKlkDLDyqh5BCYchSRJGQdiWkW6ByYWk+B0mrt/6vrAsH5WdA0jtEGzkGxEJBpDR298ZZcvAuOjcQKEBkJ5PDA0SiAzLIVUlRAwkmS/yIF8f3rCUTQGek39+9GYBFYUqcQDw65hdjuORP+BBIyNaOsJ83+XFeRLIVE3XkI9SsuoqQ9MKiyFNDghYAD7+WDSKxLN9sHIRa8dRwmEIjGs232EKjePIkjA2Ii2RPqo1O/mXoRs+GkeEqEBXkbtzmLiZdOoI+SBkcMiMKV+D/cP2W0idbrx2mwfTEilgDF7lMBTH3yFSx9dhyf/9ZWi7Td81YIfPPIBtu5vN3bHCM2QgLER3MBblNv/Asi78ZKAIZSTLKPOXKbPQv+RGEVg5HQlxEqRz817NNmtFwxLxVSXxqNEu00WMPLxE3b0wDBB99lBZWNmXtiwDx991Yo3th40crcIHZCAsRGtXcoMvAClkAhtJFu95ymjphRSCszEW+xzozCxeLBdCilxLZg4NN7Ec/dhs1NILD3pUGRuLvDGa0jMimSxAokDbZnH0aTT1BH3EFGZt30hAWMj2AmWz8ALAH4y8RIaYGmG7CZeSiFlgqWQinwuPubDdgImkU6ekBAwXzabHIFRMUYAABeCPSYJhNaueIT7QJsyc3NTIAiAxLydIQFjI1gKqUJRBIYEDKGekOJZSHTRZoSjMS78in1uFCYiB3Yb6MhSSOMTAuZAe6+pPh01c5AAyFJx5kZgGgO9imbINSequKipo30hAWMjlPaAAQCfh5pAEerJt0pmBlUSMEnkk6iLfG7bRmDYjbam1I+KhI/OzEoktREYv8nHkV1fozEJzR25ozDBSBRHEil9isDYFxIwNqKtS1kXXsB8Bz9xdJCvEy95YPrC0kdetxMel5NHDuwmYNhixut2YuzgIgDm+mBY2tGrwMALyFJIJhxHSZLQ1p1sU5HPB3OoI8j/TREY+0ICxkbwCIyCKiQfDUIjNJCsQspm4qVRAunIDbwAbF+F5HM7MXZIXMCY6YNRM0YAAE/FmSEEO4IRRGRpo/15fDDM/wKQgLEzJGBsRJvCOUgAeWAIbYTyjBJImnjpos2QG3gBoMBj3o1XDczE63M7ccyQYgAmR2BUppBYKq7HhGsYq/Bk5IvANMu6GFM00r6QgLERySokBSZeN6WQCPXkM/HSLKS+MA9MkTc9AmNPE6/P48LYhIAxMwKjugrJxEhWqyx9BAAH8wiYJpmAoXPBvpCAsRHsJCtXFIFJmHgpAkOogA9zzDqNmjww6TABk55CslMERpKklBL5YxIppD2H40MdzSBfejKdgkQUORSNIWLw9y09ApMvhdRowxRSW3cIb25tJEElgwSMTYibzFREYGiUAKGBcN5hjhSBSSeZQooLGF6FZKPFg1xw+jxOjKwohNvpMHWoYyiSO7qXDjuOgPHHkkW3XYnp3KpSSDYRMH98aweu+9tGvLxpv9W7YhtIwNiETpnJTJmAoU68hHqSVUhk4lVKtgiMnUy88nYKvkS11KjKQgDmzURSm0LyuZ1I6An0GnwsWxIRmHGJ1NqB9jwpJFmZtV1aVTS0xPd5R2OHxXtiH0jA2ARm4PW5nSkrk2yQiZfQQjCPiZcJG4rAJOlK3Fy5idfkFvhKkLe7Z9E1buQ1qRdMWGUVksPhMK0SiV1fTxhWyv8v7++TTkoVkk3OBRah39vabfGe2AcSMDaBrRAqFJRQA4CPRgkQGsi3SqYITF/SU0h2nIUUkvX3YXOIeCm1yREYpR4YAKY1BWQppJEVhShJfI4Hc0RhmtrtZ+JlImxvi7JZTgMBEjA2QU0XXoBSSIQ2wjRKQDWdvf0ghRROllAzxlTGBczeFnNW7Pkq3DJRwOchGRvNks+ZG1ZeACD7TKSuYAQdsuiMXTww7D3sbemGJNECAyABYxvU9IABkimkIJl4CRUwYeLL2omXBEw6XdlMvHYSMLyJXTL9zISWWYscVuHmViFgzKroYoMcBxV5MazcDyC7kbdZ1oUXsIeAiURj6EgI6Y5gBO094Ty/MTAgAWMT1PSAAWiUAKGNvLOQqJFdH/qkkPgwRzsKmOTnyv5tloeDVUT6FXpgAFkzO5NSSIMKvbIITGYBw3rA8LEaNjgX0gULpZHikICxCWp6wADJi5PVEZiGI91Yvq2RQpr9hKSJN0sVkpv1gaHPk9GVMOuW9OkDYx8TbyijgHGlPGc0rJJISRECo9CkbrxMwFQUJQVMtl4wTMAMT2xnBz9YW7qAISMvABIwtkFNDxjAPhGYn7+wGf/vfzfi033tlu4HoQzlJl7rV512oTPIqpDsnEKK74t8SKfX5EVOb0IoMV+LEswYyyBJEk8hlRd6uDDJF4EZWREvQw9FY5Yv0Nj9gWGWrykXt764BRc98D7e3t5k2T6QgLEJaiMwSROvtRfRnYlW5bkc/YR9YKvJbNOoaZRAX7rSZiHZ08SbHCPAYJ+xWREYdjx8agSMCWKwOxTlabSKIi+GlsU9MNmuWayEesSgQv6Y1aXUbd32i8B83hjA1v0BRGLWHRsSMDZBdQQmER6OxCTD23BnoycU5ScWW6US9iUakxBNNEukTrzK6dPILhE1iMQkW/gjgOQN1if7XNlnbJqASSym1ERgWEm6kXOlWPrI63aiwONKemDaezOOWUhGYAr4Y1Z/zumznOzggTmYSMENLSvIs6VxkICxCdxkVqSuCglIhm7NRt7NMldTKMIeyEVJtmZjzBsTjkqWh83tQrZRAoB9ojB8ErVH5oHxmGviTQoYDSZeAyPJvAKp0AOHw4GaMj8cjrgoOZI2IwlICpiUCIzFAoYtcAcX+wBYH4GJRGNoTnQrHpqo6rICEjA2IZmjVdrILvnRWZVGOigzwXWSgLE98htZdhNv8ntlB/Oi1UiS1CcC43U74U70wO82uH+JUngKyd03AhM0ySenx8RrZAopvcLT43KiuiR7KTVLIQ0t86cIeithke4pI8oAAPtae0wb0pmJ5o4gYlL8OjK4yGfZfpCAsQlMYVcoFDBOp4PnuK0SMPIIDAkY+yNfRXqcWSIwTrmAsUd6xEp6wzGw+wSLwAD2M/Ky6rKMJl6zy6jVpJBM8BNlalGRrReMJEk8AlNT6ucpVasjMOw9TBxaCmcienSoM5jnt4yD+YeqS/1wOpV3XhYNCRgbEIxE+bwVpR4YINlvwapKJPnJTykk+8M+o0KvK+tFRx6ZIQGTKswLPX2bxNklhRTK0MhOXkZtRjqQHQs1AsZvwliG1gxjWobyUupUARPoiXAxOKTElzRCR639nFkZ9eBiL/ecWFmJdID7X6xLHwEkYGwBCw86HUCJ351n6yRWD3RMSSH1koCxO11p5cCZcDkdSIzSsbzywg7wCqQ00WfWEEKlcA9MhggMYE4KpCesvozajOPYkqHCk5VSH2xP7QXDplCXF3rg97iSaTibeGDKC73cXGylD6ax3XoDL0ACxhbI5yCpCcclxwlYb+KlFJL9YQ3ZinJ4FBwOBw10lJFu4GUU8MiBPb73uTrxxp83XmixhZQWD4yRi7C27r4RmGFlmVNI7MZcUxp/3uxS9Gy0yUTYyIS5uOGIdZVI7NpvpYEXIAFjC+RNltTAesEErYrAyFYvXTa5kBPZSZ/pkw226rSqPN9OpBt4GXZNIXkzmHjlzxsJOxaqGtmZ0NU42WNL7oHJ3MyO+V+q0gSM1WK+TfYeWIM9W0RgSknADHjU9oBh8BSSBeMEJElKOfmpD4z94Skkb24Bk6y8IAHDo1bpERibmnjlHhin05Gc52PCZ5k08dprmCPzwMgH5WYbJ8AGOVaXxCtrzO6lk4022TRtnkKy0gPDBEw5pZAGPK0qJ1EzfBaaeAM9kZSLTmcvTUe1O8mbce4VcrLyglJIyTECqceM33htMtAxkwcGMLeUWouJl0VrDO0Dw3tsJReIzANzuDOYkl5jEZjq9BSShSbeUCTGizzKC7w8hbSv1boU0sHE4pVMvESKB0YNVpp4D6S14e6iCIzt4VVIeVJI1I03SfYUUmIitU1Sp8lRAmkCxqSJ1LGYxKNA2lJIZkRgktfXuEk3fmwaZalw9u/qxI3ZDhGYtp74/rMij1GJFNLB9h5LztFwNFnCTSZeIiU8qAYWLrYiAsP6ADBDKJVR2x92kyjOk0JK5v1JwGTzDdkuhZT4rNJHRJhlQpWnsdWZeOPHtdfQPjDx6LC8x5bD4ZClkZKLsab0FBIXgNZFI5n/pazAA6fTgSElPvjcTsSk7AMpjaQp0AtJin/XKovULbpFQwLGBmQymSnByoGOrA/AuOoSAEBnKEKt521OtoqadMz0TdidbMcsOcPHJgImwzBHILnIMbpSUX4c/G71VUjd4agh14/ecJSnp8rTxrQkp1InIzDNaSkkOzSya+MWg/j9weFwYMQg5oMxX8Ako1Q+S5vYASRgbEGmMj8lWGniZcr/uKpiAIAk2Wc1SmSmO6jOA2N15YUdyFeFZJfvfFYPDOvGa/A1oldWBaXmpsaiNdGYZIhgZul5t9OBkrTPcFgi/fHZwQCAeBqMmXhryuxTRs3eQ5ksQm9lJdIBm/SAAUjA2AKtJt5kBMaKFFL8Szx2SDHY9YrSSPamU0EjO0AmYGwyadlKOrNUbhXYrJFdpjJqwDwPh5YS6vTtjYhmyWfMORypwur0YwcDAJZ+8BXW7T6Cw11BRGMSnA7w1EhSwFj3ObenRWAAcCOvFZVIdjHwAioFzMMPP4wpU6agtLQUpaWlqKurwxtvvMGf7+3txfz581FZWYni4mLMnj0bTU1NKa/R0NCAWbNmobCwEFVVVbjpppsQiaTe+FatWoVTTjkFPp8P48aNw9KlS7W/w36AZhMvCw9bkkKKf4mHlfv5DbGDBIyt6VbQyA5I3vTIAyP3wGSuQuqxyzDHDGXUgIkemLA2AeNxOXnK0ohKpNYc/sLvTBmK7508HNGYhOuf2YQt+9oBxCc+uxPngM9ljgk6F/z+UCCPwLBuvOankA721wjMiBEjcOedd2Ljxo3YsGEDzj33XFx88cXYtm0bAGDhwoV49dVX8cILL2D16tU4cOAALrnkEv770WgUs2bNQigUwgcffICnnnoKS5cuxaJFi/g2e/bswaxZs3DOOedg8+bNWLBgAa655hosX75c0Fu2H5lc8kqwsgqJfYmHlRfw8DpFYOyNYg+MmzwwjM4sKSTbmXgzdOKV/9/oz7JHQxdeRoGB85AylVAzHA4Hfve9yRhfU4LDnUHc+PwnAJL+F8AejezYHKRyu0Rg2pOLV6tRPngHwHe+852U///2t7/Fww8/jHXr1mHEiBF4/PHH8cwzz+Dcc88FADz55JOYMGEC1q1bhxkzZuCtt97C9u3b8fbbb6O6uhonnXQS7rjjDvzyl7/E7bffDq/Xi0ceeQS1tbW4++67AQATJkzAmjVrcO+996K+vj7rvgWDQQSDyemcgUBAzVuzjFhMQnuPvhSS2aMEYjFJNgsjGYGhcQL2JjnMMfdp73Zaf9G2C1lNvLYTMHk8MAanmbX0gGEUet0I9EYMSiHlrvAs8LrwyOVT8Z0/r+HXYbmA8dhgFlJyDlJfD8w+Czww6eMWrESzByYajeLZZ59FV1cX6urqsHHjRoTDYcycOZNvM378eIwaNQpr164FAKxduxaTJ09GdXU136a+vh6BQIBHcdauXZvyGmwb9hrZWLJkCcrKyvjPyJEjtb41Uwn0hhFL3Cf6Sx+Yw11BhKIxOBzxk52tTmmgo73hZdTUB0Yx/XmUAGBeBIZdg9R04WUYGc3iJdQ5CiTGDC7CPf9+Ev9/damP/9sOJt62DB5JFoE53BkyPfJ9QBZ9txrV37YtW7aguLgYPp8P1113HV566SVMnDgRjY2N8Hq9KC8vT9m+uroajY2NAIDGxsYU8cKeZ8/l2iYQCKCnJ3u+79Zbb0V7ezv/2bt3r9q3ZgnsBCvyuvpcfPLh81jTB4ZNoa4q8cHjciZTSDZp6kVkppM3ssvjgXHTKAEGFzBpU+ILPMzEa4/vfDYPDPu/4SZejR4Y+e8YcSxbupT5C781sRoLZx4HhwOYPraSP24HAZOsQkq+h7JCD0oS30kzO/KGIjEc7kyt1LISVSkkADj++OOxefNmtLe34+9//zvmzp2L1atXG7FvqvD5fPD5fPk3tBlaDbyAbJSAyQ75ZA40rsCZwZHmIdkb9REYSiFlq0KyWwSGGfmzduK1qYkXMHYiNW9RoeD6+rOZx+Kab9ampAvtYGjPFIEB4lGY7QcD2NvSjeNrSkzZF97Ezm19EztAQwTG6/Vi3LhxmDp1KpYsWYITTzwR9913H2pqahAKhdDW1payfVNTE2pqagAANTU1faqS2P/zbVNaWoqCAutDVqLhXXiL1PlfAOtSSKzxE+ujUOyL7zulkOwNj8DkMVpSCilJ3j4wNpmFxFJEWWchGbzI4R4YLSZeE1JI5Qr9heleJztEYPgk6oJUwcCa2aWPdTGSgzLvY3pZuhXo7gMTi8UQDAYxdepUeDwerFy5kj+3Y8cONDQ0oK6uDgBQV1eHLVu2oLm5mW+zYsUKlJaWYuLEiXwb+WuwbdhrHG2wPgVqK5AAwG/RMMcDaX0Ain00TsDuhKMxfhFWHIEZ4H1gojGJp0bSy6jtVIUkSck5RH36wJh0A+4Jq5+DxDDSEN2qsUkow2uDMmo2CyldhLF7RqDHvEG6LPpuBwMvoDKFdOutt+LCCy/EqFGj0NHRgWeeeQarVq3C8uXLUVZWhnnz5uHGG29ERUUFSktLccMNN6Curg4zZswAAJx//vmYOHEirrjiCtx1111obGzEbbfdhvnz5/P0z3XXXYc///nPuPnmm3H11VfjnXfewfPPP4/XXntN/Lu3AXpSSFZFYA6mjVKnKiTlbN3fjmAkhqmjB5n6d7tl6b18VUheF3lggFRPV98qpPj/Q5EYojEJLgtbqoejElgX/r4emEQExs4mXgPHMui5vgLWR2B6w1G+QE0XMMwD02Fi5PugjQy8gEoB09zcjCuvvBIHDx5EWVkZpkyZguXLl+Nb3/oWAODee++F0+nE7NmzEQwGUV9fj4ceeoj/vsvlwrJly/CTn/wEdXV1KCoqwty5c7F48WK+TW1tLV577TUsXLgQ9913H0aMGIHHHnssZwl1f6aNDxrTnkIyu8SPhSyHsQiMnwSMEva2dOOShz+AA8CHv5qJsgL1n7lW2M3Y63LmNYvz+S8D3APDIopup6NPakaehusORVDiN++zTEeeHrKqjFqPB8bIrsbJCLe2zyc5isEaAcPuD26no0/klH3nAr0mRmBs1IUXUClgHn/88ZzP+/1+PPjgg3jwwQezbjN69Gi8/vrrOV/n7LPPxqZNm9TsWr9FXwTGmmGOrAqJqXBqZKeMO9/4nK/k9rZ0o2x4mWl/u0thBRIAeGgaNYDUSdTp+X6f2wmHIz4DrCcUtVTAyKMDWadRm9XITkcKSXQn3lAkxhdVelNIVp0LrbIeMOnfQRaBCVgQgbGLgKFZSBaTzWGuBCtSSOFoDM0dLIUU/xKzCg2KwGRnw1cteG3LQf5/1gzKLLpCmatpMkEm3jisAimTZ8jhcPCJ1Fb7YLj/xdV3kKJpHhgdJt5kRZfY6wcrkHA6gFKNAtNjcQqJG3gzLHCtTCHZYYwAQALGclj3xzItAsZtfh+YpkAvYhLgcTkwuCjuW6IUUm5iMQl3LNue8tjBgMkCRuEkaoA8MIyORGg+m+nZLgMds40RiD9mTh8YNo3a77ZPFVKr7OavZkK2HKtNvG0Z5iAxWNSvw8wUEjPxUgSGAJL5Sy0rhOQogSgkyRy/AlPgNWV+flGgFFJu/vnJfnyyrx1FXhe+PTneLqDRxNJHIDUdkg/ugYkMbA9MUyDesKuqNHN/KbsMdGTiJL0HDCD3cJhTRq1rFpLgSHKyiZ329J7P6ghMhjlIjFKTIzDBSBSHO+PH1C4mXhIwFsNK4Eo1GDpZJ96YZF7TsWQJdfILXMQFjPUlpXajJxTFXW/uAAD85znjMHl4OYCkEDSLLj6JmlJISmnMUzJql3lITJyk+18A2TTlftDITnQVkpomdtmwugqpNcMcJIbZEZim9rig97mdmk3RoiEBYzFMPeuJwADmdePlZXSyECKLwJgZyuwv/OX93TjY3ovh5QWYd0YtN7+Z7oEJZu5nkgky8cZpDCSjjZmwSy8YnkLKIB7MNvFqGebIUnGiBUxrDv+IUrwWnwvtOTySpQXmRmBY+sguTewAEjCWIklSMoVUoHqqA7yueCUEYJ6Rl0Vg5CHE5Cwk81JZ/QFJkvCX93cDAG65cDz8Hhe/GZoegQmqiMA4yQMDyKbuZhEwdhknwEqkM3tgzDXxakkhFRqUQmLRCz3RAqunUeeqUmURmO5QFBETzlW7GXgBEjCW0huO8dSPljJMhyPZn8LoPg8MNkZgqFzAJHKx0Zhk6dh5u9ERjPDV0bcmxgeUDuUCpsdUscerkNR4YAZ4HxgegcmSQkoOdLRWwISiiRRSBgFjVh8TFgH25+kxlAmjq5D0eGCsN/FmH4VQIhswakYU5kC7vXrAACRgLIVFX5yO+DRqLZhdSr2vtRtAagqpUBY2NrOkz+60JZpoFXhc/HOqTtwMe8MxXoFmBlr6wJixqrMzSiMwVk+kzhWBMW2Yo44IjN+gVFygJ/656GkYabUHJtscJCC+0GA2AjOuu43tqe0z7AAJGAvp6E0aeLXmFM0spe4NR7GzuRMAMGFoKX/c6XRwAUaVSEkyhbD9HhdvqmVmGondZIsVpJCojDp+w2IVF/lMvJankHgZdV/xYFYZtYhGdqIXYYFe7QUSDJ/FHhg2BylbGszMbrw8+k4pJAIA2hMrBHkoUC28G68JJt5tBwKIxiQMLvb1CSNSL5i+tPBJ46mrJ3ZDNNPI2xmkFJIamhLpI6/LmbWLa4FNJlKHcvSBMSuFpMfEW2hQKk5PiwqG1REYZkTO1ifMzGZ2hzvjVUhDSjK3FbACEjAWIuIEMzOF9Om+NgDAlBFlfSJGNNCxL208ApN6AxxqgZG3W0UjO5pGnRQw1WW+rNFR+0RgcnhgTDChSpLEI8Ca+sDIRgmI9IWxFJKWAgkGOxciMQmxmLmCXpIkWRVSZhFtZil1m87J3kZAAsZC9JRQM3we81JIn+5rBxAXMOlQM7u+tLBBcukRGF5KbV4zu04NjewGcgqJV1yUZg+XF/JOvBZ7YBREYEIGRmjl4khTBCYhYCRJ7HVMZAQGMN/I2x2K8r+ZzYhsZjM71hgwm5iyAhIwFpJsYqcjheQ2b6DjJ4kIzIkjyvs8V0wRmD60ZSnjtCQCo2IWktdNHphkBCa7YbHAZrOQMntgjK+ikUegtFQhyUWPSDGop0koQ94c0OwKS+ah87qdWb1FpSZFYMLRGB8aSREYAkByhaBnkm2BQQa4dAK9Yew+1AUgcwTGrimkQG+YN2Aym2wrFmaCazRxHpKWUQJmdXe2I0qm7tonhZR9lIBP5oExqmyf+V+8LifcGboB58PlTLaDEDWRWpIkIRFujyuZPjRb0CcrkLIXeZjlgWH74nDoq+oSDQkYCxFxghWY5IHZmkgfDS8vQGVxXxOXXVNIP/zLOpx11yocSRjQzCTbpHErIjBslEChAo+Cx+LeF3aAicvqLBVIgJ068WYfJcBSIJIU93EYQdLAq/12IloM9oSj/P3qiXA7HI5kLxiTIzBtefwvQFLAGF2F1CobKunSOBjTCEjAWIiIFJJZF9FP98cFzIkj+0ZfAHkKyT7zkI50BrF1fwChaAx7W82PwvAITBYPzME285rZsVEC2SYryyEPjKznRc4ITMIDY3EVEu8Dk2OYI2DcDZiJDi3+F0ah4MnezMDrdjo0lXbLsaoSiZVQZ6tAAuQmXmMXjq029L8AJGAsheUUdaWQPEkHv5GwCiQ2jDAdnkKyUSO7LQnRBVgTGWrNUoXEBExXKIoOE/ZLkqRkBEZBFZKXqpC4gMkVgeH9SyzvxJvdAyOPyhh1A2YRIC0VSAzRC7GAgB5bDLPmSaXTmmMOEsOsFFJrlpYQVkMCxkJ4BEZHHxizBMwnexMRmAz+FwAo9tmvkd2WfUkBY0WH4Gwh4EKvm+eRzegFEy9Pjf9bUQSGm3gHpgcmFpO4iTdXBMZv0rmXDxaByVRG7XY5ecjfKBNqTyhRQq0j0pG8jok5T0VcWxlWpZDaedomVwrJnEZ2vKKSIjAEo0NAp8gCE4yERzqD2J8Y4jgpq4BJRGAsLimV86mFERhJkmSN7Pp+vmb6YJix2uFQdpORe2AG4nDOI10hRGISHI7cTbvMWjzkg0VAMpVRA8bfgPU0sWMkr2Ni9lFEF16G1RGY8gzXD4bZEZiKHPtiBSRgLCSZQtLvgTFSwLD+L2OHFGU1HNsyhSSLwJhdHdUTjvIbRqZVi5m9YLqDyRJqJeF0jzN5WTDK+GlnWPRlSLGPi7lMFNgkhZSrjBpIemPY0EfR6BkjwBA9V4o3sdORnmewSiSrTLy5qn7MKqPO5uezGhIwFpIMc9rbA5Or/wvDblVIzR29KWXKZgsYdsJ73c6MlT9WRGCUVCAByRQSMDCNvAfzDHFk2CUCk2uUAGB8N97ekMAqJEHHMhmBEZBCMmmeVDps2GvuFJLJHhhKIREM9qXTU1dvRhn1lhwdeBl2m4W0VZY+AswXVvIS6kxRj5pEh1czPDDMGKnE/wIgJeowEH0wTPhmG+LIYOdeJCZZKvRyjRKQP26YgBFh4hU8D0nE4pBhVRUSE2HKIjDmVCFVkIAhgPjJwFYbuiIwBpdRS5KETxQIGLs1svt0X6qAsSoCk23FYmYEhok3JRVIQLz0lDEQIzAsrZcvAuP3Ji+fVkZhco0SAIy/AYsooy5IHEtxVUhsDpJ+AeOzqK2AkjYbLALTE44aun8t3ZnHolgNCRiLkOcsi21chXSwvReHO4NwOR2YODRHBMZmKSQWNWJCwWwBky/kmvTAmCBgEr4CJWMEgNTmXQNTwMSbHuYTMF6XE0zrWemD4SmkLALCZ3AKRIwHJv7dFBVJFlmFxFKqZpt4lUSR5PcOI6MwPAJDJl4CSH7Zin1uXZ0NjTbxsv4vx1WX5AwRJwWM9Y3sJEniFUh1x1QCsDCFlOWEH1bOIjDGm3jVjBFgMONiODIQU0iJCEyeFJLD4bCFD4ZFYDJ14gWMTyGJEDDJuVKCTLwiq5BMmOidCSVRJI8rOSfJSCMvNbIjUkhOStW3QjD6AsrSR9n6vzDYzTEUjfGcvFU0BYI41BGPGp02pgKA/VJINYl5SIHeiOHiiolKVQLGotJRO9Co0MQLyBYQlgqYRBl1FhOtz+Ay6l4hnXgFN7ITWIVkhQcmEo3xa1Y+j6TRRt5wNMYbbpKAIQAkTzA9XXgB4yMwzAw7JUcFEgAUyaIzVkdhWNTo2KpiPrfJ7BEHbXlSSMU+N0oSgsJoHwyPwKgwWQ7kcQJcwOSJwACyZnYWppD4KIF8HhiDyqh7E39fj4lX9Cyk5KDc/lmFJF9w5XsPRs9DYulwp0NMREskJGAsQlSZn9ERmOZA3A8wqqIw53ZuWSjTah/Mlv1J0zFvsGdwn4R0lJjezPLBdIXUR2Cs8sC8ta2xTwWZmXT0hvnxUhSBsUEKKdcogfjj9m9kJ7qrcdIAKy6FZOa5wEqoC72unL2IgOR7NCoC05rowlte6LXVIEeABIxl8C68giIwRpVRdydaeytZXbEbpBVt++WwCqTJw8ss8+YkIzDZP18+1NFgH4y2CAwbJ2DeRXvP4S5c+78b8ZOnN5r2N9NhYrLU7+bG0lyw84JFQaxAaQTGzh4Y4cMce0WmkMxvZKcmBWb0QMdkOtxe0ReABIxlJFNI+iIwhYn+CeGoMb0oevjqPf/Fic9DsnCcgCRJPAIzeUQ5d+mbHRVS0rlyqGkRGC0mXrZqN8/Eu6MxAADY19pjWeqqkc9AKlC0vR3mIeUdJWBwBIYtngq82m8nIlNIkiQpKkFWitdlvh9MSQ8YRtIDY2wKqcJmJdQACRjLEOWSN7oXBYtcMKGUCzs0s9vf1oOWrhDcTgfG15Rw4dUZipg61yfbIEc5zMh7MGCSB0aDgDFTSHx1pBsAIEnA4c6gaX9XDvMjVStIHwGyFJJFHphINAY27SFbCsnoKhreBybL31dCgcBRAj3hKB+B0V9NvGoEWKnBJl67duEFSMBYRoegEKeRvShiMSkZHlaSQvJaPw+J+SeOrymB3+PiKSRJMq7ZXyZaFHSuNCsC060iisZgVUimCpjDXfzfzHtlNk2Jz2KoAgMvkGyfb1UERi5KslYheQyOwCQiQH6bmHhZdNvldCgen5ELK4Y5tqvoJMwnUvcYFIGxaQk1QALGMkSFOI3sRdErK4dWlkKyvpndp2ldgws8Li7wzIoM9Yaj/LPINUm2xqRuvJ3cA6PGxGuNB4bRZHBUKhssGqY2AmPkKI9cyAVM1j4wLlefbUXCRIeIPjAirmHyFhVKhpfmgx0/UyMwKiL0JQZ7D1u67NmFFyABYxnJMj/9Ic4CwQY4hvz1lISH7ZBC4v6X4eUA4gLP7DEHLH3kdjr4xSUTQ02aSN2toQ+M28lWneal3b46IhMwHRZHYJQKGBOmweeC+V88LgecWSpEjPfAJMqodY0SENcHRmQFEiDrxGuBiVeVByZotAeGTLxEApEueWaeEx2BYTe+Ao8r68VRjh3mIX3e2AEAOGFYKX+sxOTIEEsflRd6c64AhyYGOrZ2hw1dwXdq8cAkbnoRkyIw3aEImmRpo0NWRWBU9IABrDfxJidRZxcPPoP7wIgoo2ZVSMFIDNGYPtEsKj3PsNLEq6TRqdFl1PmacloJCRiLEOmS52Fs0RGYRAm10jyy2UIhHUmS+MlWVerjj3NhZZI3R0kJNRD/7NngRLbKMYLukPoyarNTSF8nDLyMJqs8MAHlXXgB6/vA8DECWSqQ5M8ZPcxRXxl18nf1HktRPbYYRvfRyUS7iigS98AYdH1royokIh2mlvtDCknpFONkBMaai3mgN8JXb/LVgtmRoZbu/CXUQDy9ZXQbcEDjKAGXuSkkuYEXAJo6zI/ABCNRHEkIYKURGMs9MHl6wMifM8IDI0mSzMSr/XbiczvBgpV6K5GUDEFUg9cCQ7ua92B0GTW7npX39wjMkiVLcOqpp6KkpARVVVX47ne/ix07dqRs09vbi/nz56OyshLFxcWYPXs2mpqaUrZpaGjArFmzUFhYiKqqKtx0002IRFK/tKtWrcIpp5wCn8+HcePGYenSpdreoU0ROS21wKBKCLayUlJCDVifQmIrhQKPKyWczU5ws/rTtPIS6vwXn2KDLz6hSIyHvtWYeHkZtUmrzj0J/ws7ZlZUIbG/6XM7Ua6waZfVHhiWFsolYIyMwAQjMbDuBHoiMPJihN6Qvv0UmZ4HkueCmcMclQxyZBi9CGKdePt9BGb16tWYP38+1q1bhxUrViAcDuP8889HV1dy9bRw4UK8+uqreOGFF7B69WocOHAAl1xyCX8+Go1i1qxZCIVC+OCDD/DUU09h6dKlWLRoEd9mz549mDVrFs455xxs3rwZCxYswDXXXIPly5cLeMvWE4tJ6Awp/4Lmw6gwNksFKZ1xYnUKKZtwMLu8Ozl6Pv8JX+IzNn8tX80qjaQB5veB+fpwPIV0Wm18+GazBREY1sSuutSvuHrFag9MMgKjwANjwA1YHnnS44EBZAMdw4IiMIJSSPbvA8OuIeIXQcFIlC9Ic7WEsApVn/Cbb76Z8v+lS5eiqqoKGzduxJlnnon29nY8/vjjeOaZZ3DuuecCAJ588klMmDAB69atw4wZM/DWW29h+/btePvtt1FdXY2TTjoJd9xxB375y1/i9ttvh9frxSOPPILa2lrcfffdAIAJEyZgzZo1uPfee1FfXy/orVtHRzDCVy0iho0ZNU6AXZSVemCsjsC0Zgl1JqujzLnJZNuPTBi9emJzfbxuZ96ZKnJY+3SzBAyLwJxWW4nl25pwuDOEcDSmap/10q5gflU6ycWDNZ2D1XhgjIggsGuE2+nQ/VmJqkQKCBrTwrDCxKuuD0z8GtIbjiEUieX8LqiFVVS6nA4h9yrR6Hqn7e3xktWKiviqaePGjQiHw5g5cybfZvz48Rg1ahTWrl0LAFi7di0mT56M6upqvk19fT0CgQC2bdvGt5G/BtuGvUYmgsEgAoFAyo9dYera53bmXDkppcBjsAdGYeqBd721qJFdtsgHH+hoUJlhOuykV7JiMXqOCYuGFavwvwDWeWBOGVXOjc1md+NlNw0lpasMvniwuIw6ZwrJwD4mIgy8DJaq1puO43OEBJVRW+KBUTFKQH5ui47CtMoKEpRUopqNZgETi8WwYMECnH766Zg0aRIAoLGxEV6vF+Xl5SnbVldXo7GxkW8jFy/sefZcrm0CgQB6ejL3zFiyZAnKysr4z8iRI7W+NcPpUJHfVAIvoxZ8EWU3P8VVSCZ7TdJhKaR0/wITVmYNdEyWUVtvwFP7GTLMTCF1BSNoTvR9GTu4GFUl8QoysyuR1JSuMuxShZStCy8gi8AY8FmyHjB6uvAyhEdg+mkKKRiJ8uOq5B7hdjn5+S16ISRvCWFHNAuY+fPnY+vWrXj22WdF7o9mbr31VrS3t/OfvXv3Wr1LWdFyocyFUZUQ3MSrMoVklQemLcvMjuKEz8S8RnbK+yaUGNz8j4k2rREYM0y8rIR6UKEHZYUeVCUqgMzuxqtl5W65B0ZNHxgDU0giIjCixKDwKiSXuQKGiRCHAzkbYcopNSiSyw28R5OAuf7667Fs2TK8++67GDFiBH+8pqYGoVAIbW1tKds3NTWhpqaGb5NelcT+n2+b0tJSFBRknhLr8/lQWlqa8mNX2AkmooQaMLCMWsUcJCBpljWyJDgX2SZAF5uc2lJaRg0Y74Hp1BiBMbMPDOvAO2ZwEQDwCEyzyd14tXgnrK5C4h6YHP6TpAdG/D72ChQwyXlIOk28giPcZkdgWCqz2OdWnLYxKpKbvJbZrwsvoFLASJKE66+/Hi+99BLeeecd1NbWpjw/depUeDwerFy5kj+2Y8cONDQ0oK6uDgBQV1eHLVu2oLm5mW+zYsUKlJaWYuLEiXwb+Wuwbdhr9HeEp5AMWgWyi7LS8lt2EgUjMdM6uMppy1KFVGxyaqutS3kZdbIJlTEpJN7EzsYeGDYDaUxlXMBUJyIwzaZHYNSnHqzuAxNSkUIy0gPjz/H3lSIshWRQHxizTLwBDV4sdu0V3cxOTUWlFai6qs2fPx/PPPMM/vnPf6KkpIR7VsrKylBQUICysjLMmzcPN954IyoqKlBaWoobbrgBdXV1mDFjBgDg/PPPx8SJE3HFFVfgrrvuQmNjI2677TbMnz8fPl985XXdddfhz3/+M26++WZcffXVeOedd/D888/jtddeE/z2rUF8CsmYPjBqy6jlN8muYBRlheb2Scw29t3MyFAoEkMHKztUcNIXGzyIrUvDIEfA3GnUX6UJGB6BMdkDo8nEa3kKSYmJ18AyatbETmAERo+AkSRJvAfG5BSSlj42JQaVUme7ptoFVXeYhx9+GO3t7Tj77LMxdOhQ/vPcc8/xbe69915cdNFFmD17Ns4880zU1NTgxRdf5M+7XC4sW7YMLpcLdXV1uPzyy3HllVdi8eLFfJva2lq89tprWLFiBU488UTcfffdeOyxx46KEmogmWsXl0IyapSAOg+Mx+XkqxWjBovlIpuJ18wp2W098RPe6VBXAmlUeouVUWuNwJibQioEkIzAmN2NV0sKyS+bQyZJ5g2+ZLA+MLlKZ1l0xIgIAq9CEmDiZdWOetJxveEYwomoYX+PwKgRYEalolttPAcJUBmBUXKC+v1+PPjgg3jwwQezbjN69Gi8/vrrOV/n7LPPxqZNm9TsXr9B9ArBKA+M2hQSEDedHYmETKv4kZO1jNpvnoBhprfyQq+i/DU33xkk+HgERkUTOyA+3RgAIiakkL5KmHhrmQem1KIqJA0mXhaBkaR46lREJEINSky8rIw6aECvGpEeGBGGaHZtdTkdqn1f2TA7AqOmBwzDqFR0i4beSGZCs5AsoENwoyWjwtjMP6FmdWVlM7usKSSWpjFDwHQrL6EG5KMEjK1C0u6BMfai3RmM4FDCrDuap5DiEZhDlkVglB8ruWCxwgeTnEatwANjRARGwCRqRjKFpP1cSBZIuBV3U84HO34xyZzp7Gp6wDBKDY7AVBwNJl5CDKIbLRllJOxWWUYNyJvGmStgekJRvhpNFw/yEQdGh/n55FaFIVfDO/FyD4w9+8Aw/0tFkZdfsKsTERjWjdcsAiomADM8LiePVlnhg0l6YPKXUUdjEh92KoqexNwiEQKmWMAwWNFdeIHU9FzYhIiklvuDYVVINk8hkYCxAOEmXkHu/XS6NeS3jW7Mlg0W+fC4HH16nrDoQ0wy/ibTIkshKYGFfo3zwGitQjKnjJr7XyoL+WODCr387x8yqZQ6FpN4hE7tzY+nPiwopVYzSgAQnwZhJl4RKSQ+8kPHtSN58xfX9l5eom5GGkmLCGNiR/RCiC/IKIVEMDoET0u1Sxk1YHxEIRvyjpHpoeNCrwvsIaMjQ/LW20pgxysUjRmSgkh6YNRd0L28kZ2xK07WxI71gAEAp9OBIcXm9oKRzydTe/OzshJJTQoJEN8LJmni1X8rKREQvTUiAuNyOvj1Ixg1/jNut4mJtzcc5UUA5IEhOOJNvAZVIYXUN0EzqpwvH9l6wACAw+FAsZelkYy9AKntmyAXh0aIvi4NIhQwzwPDesDUVhalPG52N16WPvJ71M8nM2qYqhJ4CilHHxa37AYsPAIj0MQrYi6Y6B4wQPz6YaaRV1MfGJ/46y67prqdDsUdgc2GBIwFCO/Ea9AKsEtXCsncCEy+CdDcXGzwfiVLuZUJGJfTYahvSHMVkkl9YJgHZvTgNAHDe8GYJGB0rNz5+Rcyv3mjkk68DoeDR2hET6QWaeItFjBWI9mFV+wN18xuvNr6wIi/7uaKatsFEjAmE2+0JDiFlBAYkZgk7ASLyl5L6TRqwHoBk808K+LiqGo/VLj2jfQNdWvuA2OuByY9AsO78ZqUQtJjrLdyHhIrjfblERBM4AgXMCGBAkZAU0cjIjBAMkVnjolXvZk8WUYt7vqm5VpmNiRgTKYnHOWVAMJSSLKLh6iLqLyUUUsKyajW+Nlg/Veyzewwa9BkvkhQJowUfZ0aO/FyD4yBF+z2njAOd8aPF2tix6jmvWDMjsCoPyct9cBE83tgAMCbSIuJN/HGX09MCim5yNBaLSh6DhLDihSSNg+MuOuu3SuQABIwpsNWei6nQ8hJD8RXy65E0zRReXi2snI68l8c5VgdgckmHPhAR4MFDMsbq3HtJ1eeBkRgNDeyM/aC3RuO4qf/F29UObKioE86lfWCMSsCo2WMAMMoD5oSlIwSkD8v2tPUK7ATLzsPojEJvRqb7omu8GR4+PEz9jOWj0JQ1wcmvm0wEhNm1LZ7BRJAAsZ05CeYqLyiw+FAoUdsKXUX7wGjbj+tM/Hmrv4xqz9NctWiZY6J2H2LxSRbjhLoDUfx479uwOovDqHA48Jds0/ss43Z3Xi1hO0ZVkZglIwSAGQCxiAPjKhp1Kx5tdbO1Ho+x1wYlYJLR+sohGK/+GIAtS0hrIAEjMnwLryCTzC/V2wvCi0VSICFZdS8CimPiddAAROOxvhKXk3Y1ahjJr+hqk4huY3xwDDx8v7OwyjwuPDkVaei7pjKPtuZPZFajy/Nb+FE6mQKKY8Hhpt4BZdRh/NXQSnF4ZAZ2jWeC6L9hQyzTLzs+qF2FIK8GEDUdYQ8MEQfuFlQ8AkmehXYo6ELL2BcS+t8tOWZmmrGQEcWfXE5HbYQMOxi6HU5+UA/pXgM8MBIkoT/978bU8TLjLF9xQuQrEI60mVON14tvgNGgWygo9lwE29eD4xBEZiQuAgMIGvsqPE87TAqAmOSiVdPhF60D4Y8MEQf2Be0RHCOVvQ4gWQJtbr9tCqFxBvIZVktmJFCYl1jK4uUDXJkGHXMkmWQHtUXQ7cBfWB2Nndi9ReH4HU5sTSHeAHM78arp4za77YwhRRhVUB5BIxBnib290V4YAD9lUiie2wxjPaEMbT0gGGIXggd7oyfd+SBIThGhThFjxPoEZBCMnrukJy2LoUpJAMjQ4cSJ/zgRBdZpYjoQJqJVh0mvOQ0anEX7IPt8XTQ2CFFmJ5DvACp3XjNqETS5YGx1MTL+sDkPk9Zike0iVd0BEbPcFNJkgyLcPtMMvEGdFgMRC+EGlriXbJHVRTm2dI6SMCYjJ5QdS5Ep5C0DHIEkidRREclgVpCkRifY5NNwDBh1aVj0m0+DiciBYNLVAoYg1JIekLAbMUekyBsACATIszfko8qE3vBsBuflpWvlX1g+CgBhRGYoMBzUpIkoSZeQF+kNBiJcYHWX8uo23X0sWHXERG9YIKRKA609QAARlWSgCESJFNIxkRgRK0CtQqYIgGVBGpp64nfqB2O7BcuZmI10pvDepoMURmBKTaodw4fr6DBhOeRdXYV5UFp5gJG2fFh25lh5BXSidckwc6IRGOIJMSlUg9MUGAEJhSNgWnbfI30lKJnoCNbHDod6qev54N7iIz2wOgYRsnEd3u3/uvIvtYexKT49V/t9cxMSMCYjGEmXp5CEnODTlYhqTuR5JUEZhl52Y26rMDD++GkY0Yju0M8AqMu4mHHCIxcwIhKO7CSaMURmBI2D8mMCIweE68106jln0v+Mmrxjex6ZaMTREVgSnV0zJanX0S3vjerCkmPB4alio8kzns9NCSGrI6uLLLtGAGABIzpdBhkMhO9CuzW0aDKqL4m2WA36mxjBOL7ZPwwR2Z6U7tiKTFozIEIDwwAhAVdtFkKqUqhgOERmA4zIjDaFxaiDfRKkd9Mc81CAoy5AfcmDLwupyPl+6IHPYufdoMWh4CJJl4dkcDKxHne0qVf8LMRH2NsnD4CSMCYTnPAGGe3UWXUWkKxRs72yURbd7LaJhtm9IHhAkalB6bUoCqk1jy9cXLhcDhk85AEe2AUHp/kRGpjIzCRaIx/L/rTLCRm4HU7HbxqLBtG9IGRG3hFrdKL2VRlXREY8ZOTze4Do+V7WFEUP69aBERgvpZFYOwMCRiT+bolMX1X8BeDe2AEm3jVllED5jezU3KjNrOMWm0VklEpt9au3KXl+RDdjVd9ColFYIwVMPLjrqW9gVUpJKU9YABjTKjJSdTibiNJD4wGAZO4+Zf4xEdg+PEzugpJx1BRkSkkFoEZTREYgtEbjvKL+GjBpWkFHrEemC6NZdSA+b1gkj1g8guYLh2D4vJxWGsZdeKi3R2KCqv4AfQ3ovII7AUTjUm8zFypgGHbNbb36P77uWAr9yKvK8X7oxSrUkgsmpLP/wLIyqgNETDiDLN6WgokBzmKj8CYNY1azyynymKWQhIZgSEBQyRgdfUlfnfOdIcWkqtAMRcoMSkkkyIwCuYPsWGGkZhkyDyTcDTGI0GDi9UJBvkcE5F9avR4YACxEZgjXUFEYxKcDuXHZ1h5AYB4hE2UMM+EnlUvYN0sJPY9zjdGAAB8Bszy6RXcAwYQE4ExwgNjmolXRx8Ydp63dOoTMJFoDHsT96oxlEIiGHJVK9rZLXoVKCKFJKIfgRKYcMg1dEw+C8iINNKRTm1jBID4DYhdIEWWUrfmGa+QDy/zwET0rzqZ92twsS+vX4NRVuDh36X9rcZFYfQYJwHrRgkEFfaAAYw18YrqwgvIFj8azlEWAc0VidWKx6Rhjnr6wDATb0cwosvrdLC9F5GYBK/biRqF0VKrIAFjIl+zvGKFeFUruoxa6ywkwPwUUr45SEC8syuLJhnRjZddPNWOEWCIniHVE4ryRoJaL+get7gUktomdowRg+Ih7H1GChidzSW5iddsDwxLISkQhF6BnyWDRXtFppCSfjD11w4j0x7mlVFrb6hY6k+2kWjt0n7t5f6XikJN1zIzIQFjIrw1swEnmOgwNvPAaCujtsbEm29qarFB5cqA9jECDL1D7NJpSYg6r8upuamXyBRS0sCr7viMGBRPI+1rM07A6Fn1AslzLxiJISbQw5QPpV14AWP6wBjigdFxjn51mJX+il8gegUb2jMRi0m62mw4ZdHfIzpKqb/qJ/4XgASMqfAVggGzJUT3gUl6YLSkkEw28fKhhbkjDUY2s2MVSGpLqBmiS8/lFUha05ViBYy6HjCM4QkfzL7Wbt37kA2WQtKy6gVSRX6vwDLlfKjxwBhSRs3HCAisQkpUEHWqnKUWicawN/EdGTPYAAFjQgSmKxThnY21iulkLxjtPpivDxtTKWsEJGBMxNAIjE1GCQDi0yH5UOr1MLKUWmsFEkN0KbVe/wsgH+gowAPTwXrAqE0hxQWMoR4YnSZev0xAmJlGSg5yVF5GLdLDwcyiWoVfJliUVK3Z/mB7L8LRuG9jqAG+Da8B09nTYZFAr9upOapVIUDAsAiM3ZvYASRgTCMak/gq0ghlyz0wYbGjBOyeQorGJH7i56pCAgwWMB3xC4baMQIM0REYvSXUgNgy6sZ2dXOQGDyFZIqJV5sHxul08DJbM4287QmRqiTdYEQEYffhTgBA7eBiYa9Z6HGBBQzVXD/2HDbWt2FGBEaP/4VRkajwO6KjEol7NSkCQzAOtPXEVwguY5zdPIUkqIxaTwTGzBRSoCfMw65KU0hGRmC0Dj7jx0zQvrEUkp6Oz8lOvCI9MNpMvPsN9MAEdHQ/ZYhuJKmEQ4mblJKon88AEy8TDbUCUzZOp0PTQsPom64pAkankAb0p5BiMYlnCuxeQg2QgDEN9qUYUVGQdeCgHkSWUYciySm3aoc5AuZGYFiqpNjnztvQq6RfeGBEpZBYabn2m7JIDwxLIVWpjMAwD8yhjqBh4kDPHCSG6AWEEtSkLbkHRpBHTpIk7D4UFw3HDBF7oyvRUIm053D8+lo72Ji0hxkpJBFCWm833qaOXgQjMbidDgwrt3cJNUACxjSMNPACyUhJd0h/p1l5Hl9fBMYMAaP8Rp2MwIi/Eer1wGi5aOdCbxM7QFZ5obMPTDgaw+FEtEBtBKa80MOrqIyKwrTrLKMGrGlmd5hPP1cuYETdgA91BtEZjMDpEO/p09LM7miIwOithgP0D3T8KiEERwwqUNyvyUrsv4dHCUbNQGL4Exf5mKT/IsV8NB6XQ1NrdRYCDkVjhofU21TcqLmAMbAPjO4yakH7ZicPDItOeVyOnBPDM+FwODDcYCOviJWvFQMdk2nL/MdUdBk1i76MGFSoqApKDdzQriJSuueI+HSWHJF+sGywSKAuD4zOgY79yf8CkIAxjYZEBGaUQREYeTvvXp1h7K4g879oW5GyCxBg7PBEIHmi5vO/AMk0jegUknyMgH1SSAI8MG4xKSReQl3i12SwNLqZnd5OvIA1Ax1ZVEvJd84nuIzaCP8Lo1ilmI/GJN763qjeJeaYePVHAvWmkPpTBRJAAsY0jB6O5XE54U7cHPSuAvV04QXi7fSNmrCcTlu3sgokIDnXqVPwXB35GIFyjaunYtECpkuEB0aMiZcZeNX6XxjMB7O/zZheMCKqP6wY6KjFAyMuAhOvQBor2P8CqB/oKC+QGFpWIHx/AJkJ2hQTr34PDEVgCGFIUtLZbWR3Q1HjBPSUUDNElwVnQ02/E7UrO6XoHSMAJFNIomYhCfXA6OwDo7UHDMPIUupQJMYFv54bh9kppK5ghFcKKhIwLrE3YBaBGWtABEbttYO1vh9VWWhIgQSQFIBGduJt7xZn4m3rDiOiYV/7UxdegASMKbR0hdAZjMDhSIbDjUCUkVBPCTXDrEqkVh6BUSBgEhOpRaeQmMdDq/8F0NdCPR1JksR6YHTe9JJzkDRGYAz0wMgFY7GO8lWzU0hMNBd4XNzblQs2bkCUh4N5YMYOEdcDhqHWA2NG2kO0AMxEo87zBEiNRLNro1IkSUIDRWCIdL5ORF9qSv1C54akI6oXRVLAaL+gm9ULRt4yPx9G9YFhc5C0+l8Asd2Le8JR3sVUz2ReUWXUje0shaQ1AmOcB4b5Dkr8bl2rd9ZO36wIDE8fKWycKI+m6Z3XFI7GeETZGA+MOrO9kTOQGMwPFjQwAnMw0exRTxrM7XLytLHaNNLhzhC6QlE4HMDICmNScaJRLWDee+89fOc738GwYcPgcDjw8ssvpzwvSRIWLVqEoUOHoqCgADNnzsTOnTtTtmlpacGcOXNQWlqK8vJyzJs3D52dnSnbfPrpp/jmN78Jv9+PkSNH4q677lL/7myC0QZehqheFCyFJCICEzA8AqPcxGtUJ169FUiAbAZMUH8ZPFt56RnkCAAetxgPDE8haRQwzAPT1NErfAUsogcMYL4H5hDr/KzwOyfvkaQ3CrO3pRuRmIQCj8uQppxqz1Pu2zCoAglIjcDoPT8zIUkSDiTaBAzT6ePR6oNhx3FYWYHwyjKjUC1gurq6cOKJJ+LBBx/M+Pxdd92F+++/H4888gjWr1+PoqIi1NfXo7e3l28zZ84cbNu2DStWrMCyZcvw3nvv4dprr+XPBwIBnH/++Rg9ejQ2btyIP/zhD7j99tvx6KOPaniL1mO0gZchygPDVpH6BIw5vWCYiVdJeW6xQY3seApJ4xgBICn4ojGJR8C0ImKQIyDOA6M3hTS42Auf2wlJAg62i43CiCihBpJtDMxOIWkRMHrnITH/y5jBRYa07S9RGYHhFVEGRmDkxy9iwMTx1u4w/1yqy7QvhADt3Xh5Ks6gZoBGoDpHcOGFF+LCCy/M+JwkSfjTn/6E2267DRdffDEA4K9//Suqq6vx8ssv49JLL8Vnn32GN998Ex999BGmTZsGAHjggQfw7W9/G3/84x8xbNgwPP300wiFQnjiiSfg9XpxwgknYPPmzbjnnntShE5/wegeMAxRHhhWRl3g0ZNCMt7EK0kSX90rMasWC/SZyOHlrDoiMIVeF1xOB6IxCZ3BiCJfQzZE+F8AcSkkrWMEGKwXzO5DXdjf2iP0PBLRvh2QRWBMmkatWsDI+jnpjWIl/S/GXM9YNFKJByZeQh0XtUYuEH3u1OOnpT9WLpgwj4t1fdGPCo3N7FgEZlRF//C/AII9MHv27EFjYyNmzpzJHysrK8P06dOxdu1aAMDatWtRXl7OxQsAzJw5E06nE+vXr+fbnHnmmfB6kxfg+vp67NixA62trRn/djAYRCAQSPmxC2ankPSGsXsSEZwin71NvIc7Q2jtDsPhUJaLZ6IgHJWE9cMAkh1R9XhgHA556bk+0SdiEjUAuAWUUfeGo7zDqNYqJMA4H0y7oAiM2aME1DSxA+LfL6+gXjC7DaxAAtRdOw609SAUjcHrcmJYuXG+DY9AAZiJg236/S8M1sxObS+Y/tYDBhAsYBobGwEA1dXVKY9XV1fz5xobG1FVVZXyvNvtRkVFRco2mV5D/jfSWbJkCcrKyvjPyJEj9b8hQXxtQgk1IE8hiTHx6imjLjXBxLuzqQNAXBgq2dcimSlZZCm1CA8MkExx6fUNiRjkCIhJITUnoi8+t1NXgy7mg9kneJwA6wGj1wPjN9kDk5x+rvw75xNUSWNkDxhAHinNf+1g6fmRBs2YY7icDv76RnTjZRGYoWX6PUVaU0if7msDABxXXaJ7H8ziqKlCuvXWW9He3s5/9u7da/UuAYj7UZhHYrTBoTlhZdTMAyMkhWRcBOaLhIBResK5nA7u6+kSOA/pkCABI+qYtbDScgWVWbkQ0T69KZHiqynz6/LjJHvBiG1mx1JIeprYAebPQtIimkWVUie78IovoQZkjewUnAesB4wZk5ONLKU+kKhAEhFF0tKNt7G9F18f6YbTAUwdM0j3PpiFUAFTU1MDAGhqakp5vKmpiT9XU1OD5ubmlOcjkQhaWlpStsn0GvK/kY7P50NpaWnKjx1g5YZlBR6U6eiKqgReRq03AhPsHymkHU3xleBx1covpCytwrwzeglHY9xIrCeFBCSjAHqjQ62iPTA6LtjcwKsjfQQY18xORPt2wDoTr5rvnIgbcEdvGM2JBZlRc4fkXrV8FT9fyQzFRiN6IKacg20CIzCJtGJLp3IBs37PEQDAxGGluqORZiJUwNTW1qKmpgYrV67kjwUCAaxfvx51dXUAgLq6OrS1tWHjxo18m3feeQexWAzTp0/n27z33nsIh5MhxBUrVuD444/HoEH9Rx0C5lUgAeIb2enqxOszL4WkJuTJPgf2uehFxBgBRrEg47MoD4yIUQJ6xwgwRhjUzE50GbVZERgtzROTHhjtnyebVjy42Ks7apWN4hSvWu59NdO3YeQ8JBaBqREgYLSUUa/f0wIAmF5bqfvvm4lqAdPZ2YnNmzdj8+bNAOLG3c2bN6OhoQEOhwMLFizAb37zG7zyyivYsmULrrzySgwbNgzf/e53AQATJkzABRdcgB//+Mf48MMP8a9//QvXX389Lr30UgwbNgwA8MMf/hBerxfz5s3Dtm3b8Nxzz+G+++7DjTfeKOyNm4VZBl5AnAdGRBm16Nk+6UiShB2aBEx8pcZ8SXphNxI9YwQYoqJWIsYIAPL26do9MMkSan0X5uHl8fOnMdCrqUV6NkSVUZvZB6YnFEUXHyOg/DMWcQPefTjhfzEofQTEvWos25ivYpCnkMyIwBiYQmq0OIX0YULAnFZbofvvm4nquOmGDRtwzjnn8P8zUTF37lwsXboUN998M7q6unDttdeira0NZ5xxBt588034/ckL2NNPP43rr78e5513HpxOJ2bPno3777+fP19WVoa33noL8+fPx9SpUzF48GAsWrSon5dQ98MIjAAPjFGN7JoCQXT0RuByOlSZCdnnwFpm60VLKD8bXMDoLPNu6WIeGDEpJF0eGAHt0QGgqsQHj8uBcFRCY6BX2EgOXoWkt4zaa14nXvad87mdKZPf88HKc3UJmEPGTaFmOJ0OFHvd6AhG0NEbyRplisYkvkA0xQNjUAopFpO4gBFj4o0fr9buEGIxKe/C6nBnELua48L0tDFHuYA5++yzc+YlHQ4HFi9ejMWLF2fdpqKiAs8880zOvzNlyhS8//77anfPdmzZ1w4AOMaAmSHpiBol0CXAA2N0FRIz8I6pLFTVN2F0IhL2laAUkigDLyBu/ELSAyPGxKsvhSQmAuN0OjC8vABfHenGvtYeYQJGlImXD3M0wQMj/86pMUaLKKPmJdQGVSAxiv1xAZPLD3awPV5C7XE5DC2hZngFeMIycaQrhFA0BodD/3kCJM370ZiEQG84b5fyjxLRl/E1JboXPWZz1FQh2ZFDHUF8khAwZ4wbbPjfYxEYO6SQWDQhGIkZEnJVW4HEYCmkBsEpJBECJtkHRnsERpIkW3lgWBl1lU4TL2DMUEdeRt2PqpAO887P6r5z7AasxwOzJ5FCMjICA8gHOmYX88kSauOmUMvxGjQPiZVQx6OM+m/JPreLV3IpSSOt76fpI4AEjKG898UhAMCk4aWaB9mpQdREXBEpJHlo24gojFYBMyqRQmrpCqVMItaK2qF6uSgVYOKVD3IU1gcmIsIDo1/gjSgX38yOd+LVK2AERT+VkOz8rO7z1euBkSQJewycQi1HyUDHPSYMcZTDBL3oBdkBgU3sGBXFyo2863bHK5D6m4EXIAFjKO/uiJeLn3N8VZ4txSCuE6/+CIzb5eS/L7p1PyAvoVYnYIp9bh4taRCQRhIxRoDBUkh6jhe7YHndTl2fH5CcwKs1AtMZjHCzqQgBzyMwbWKiZ73hKL8ZiRolEI5Kukcv5ENr40SfTg9Hc0cQXaEoXE6H4UUJSgY6Mt+G0dEghlFVSCKb2DG4kTdPKXVbd4gXQ5xa278qfAESMIYRicZ4BOZskwWMnjC2JEnoYtOodXhgAON6wUiShF2Jk+74GvUrQWbk/UqAkfdQop+MUBOvjuPFetIMKtQ3yBHQb+JlTedKfG5VZtNsiO4FwyqQnI7ULs1aYB4YwPgojFYBw1MgYW2f55eJDrwjBxWkDDc0ghIFc8u2H4iPizlhmDk9v7wCTNCZONguPgKjtBvvR1+1QpLiniYRaV6zIQFjEJv2tiHQG0F5oQcnjSw35W+KKKMORmJgHu1CnRd1FlEQkaqRs7+tB12hKDwuh6bBfiJ7wbAIjF08MKIGOQL6PTDsBjNhqJgbDBsnsFdQN172vSzxe3SXwPvcTl76a7QPJilgNKaQNH6eX5pQgcRI9pHKfC7EYhK2HYj7C08YVmb4/gDy0RrGCJhh5eIjMPkGOn64h6WP+p//BSABYxirEumjs44bYorBDBCTh5eLnwKPPSMwzP8ydnCxJtMbG+nwtYAIjNgyav1VSKJ6wADJC3ZEYx+YrfvjAmaioBUy813sa+1Bd0j/d4qF1/V24QXi1Zc8hWvwQEctc5AAWQpJYwRhc0MbAHMEQ74+Ul8d6UJXKAqf24ljDK6IYuhNwWUj2YVXoAdG4UDH/trAjkECxiDe/Zylj4aY9jcLBJRyshuDz+3ULbySN2TRAibhf6nRNnRszGAxEZimQC/ausNwOpLRAT2IEHwiIzBunSkktkKeNFzMDW9IiQ+Di32QpOR3QA//2nUYAHDCUDH7Z1Yl0iE+iVqtgNGXAtnU0AoAOGV0uabfV0PSA5NZzG+TRffcAip3lGCUiZenkARGYJSkkDqDEWzdHz9H+2MFEkACxhAa23ux/WAADgdw5rEmChiWQgpH884QyQaLwBQJ8CyUCGqNn84XjYkKpCptlRDMgKi3lPrjr+MX9ONrSoUeLz2l562CBjkC+lJIsZhkiEdhwtC4aP3sYED3a721PT5f7VsTq3W/FiDrBWN0CklrGbWOPjCtXSHeA+bkkcabPUvyVCFt5ekj82beiRjFkE40Fm/MCIg18Q5SIGA2fNWCmBSf5G1GHx0jIAFjAKu/iKePThxRjkoB3gilsBWgJGk/yZIl1PrSR4C8LFhwBKY5IWC0RmASvpmD7b260m0fsxXpqHLNryGnxO/haRtWmaAW1sSuQkAEhpUW94ZjaO9WJ0IbWrrREYzA63ZinEahmYnxic/8c50CZm9LNz5v7IDL6cC548WY7EW1MchFbzjKOzWrNvHqaIW/aW/8uz52cJEpzc7yVSExcSwquqcEIwTMoY4gojEJLqdDqIm2UkEV0of9PH0EkIAxBJY+Mqt8miGiEoKlkPSW4ALiOsvKicYkXj6ptoSaUV7o4Su8vTqiMB8nPAGnjBKzInU5HZiQWFFu3tum6TVaWBM7ATeZUr+HV/5sO9iu6ndZiH98TYmQ5lyM8TXx4/NZIgqnFRZ9mTZ6kLAbshnzkJjnyutyqi791uPh+PjrNgDAyYK+6/kozjGKRJIknvowMwLDBEajxsVFJg4kXqu6xCfUK5lvoGMkGsOyTw8CAGaMJQFDJAhFYliTyK2fM9689BEQL3tlYX+tYezuYKIHjICUiIiqmnT2tnSjNxyDz+3U3IvC4XDISqm1CZhQJIYtiYvoKaPFXdRPTlSsaRUwrQI9MEDyBsFWvErZalCFCKto+vxgQHOaFABWbG8EAJx/Qo2Q/QLM8cAkq968qsvk9UQQPjbR/wLIeiJluHYcaO9Fa3cYbqdD8yJGC3qvGZngM5AEp3DkAibTefL61kY0tHRjUKEH354s7hwwGxIwgtnwdQs6gxEMLvZikknlfXL0jhPoZmMEBKSQjKhCYhVI46qKda1Y+FRqjZVI2w60IxSJYVChB2MEDuo8cWT8O6NZwHAPjCgBE9+fbSoFzDYe4he7Qj6mqghupwOB3gg3P6qlrTuEj76K35DPF+R/AQC/CSkkrf4XQLuAicYkfJL4PoqKNuYjVwppW2LhcGx1SUrU2Wj4GBKBAuZAm/gmdgBQmSixD0VjfY6hJEl4ZNWXAIC53xiju12GlZCAEcyqHfH00VnHVenuLaEFvXn4HgNSSCL7wGgdIZAOG+qotRKJpY9OHjVId8M4OSclDJLbDgQ0eRVEemCAZASGVRQpQZIkfpMRHYHxuV18MKpWI+87nzcjGpMwvqYEIwV2lC3wGD+RWmsTO0B7I7sdjR3oCkVR7HObFvHI1chuq8kN7BhsodIY6BUmUpM9YMRGYAq9bvgT38f0NNL7Ow9j+8EACjwuzK0bI/Tvmg0JGIFIkoQVidy62ekjht48PDfxChEwRkRg9PlfGMzI+7VGD4xoAy9jTGUhygs9CEViqm/QkiRxD0y5zknUDCZAvjzUpfg71RjoxZGuEFxOBzfdimR8ohLpc40+mBWCq48YZnpg1Daxi/9OXPQ0BtR5ONh3/cSRZab1tOIRmAzXju2sPN9kAVNe6OW+I1HDYJlZv8aAWXmVWXrBPJyIvlx62sh+N306HRIwAvnyUCf2HO6C1+XEWcdZJGAS4UDNKSRWRi0grGhEGXUyAqOvsmUU78arLYW06WsmYMSG1B0OB04cUQ5AfRop0BPhURsRjeyA+BDGyiIvojFJsWDYlmhgN25IsSEhfuaD0RKB6Q1HsTox4kO4gDEjhaSj8/PxCdG/s6kT0Zhy/1BSrJs3K4eZeEPRWJ+yb9Yg0cwKJMaYweKaYALJQY4iu/Ay2DWgVSZgPtnbhrW7j8DtdOCab44V/jfNhgSMQJZvi6/svjGukqdPzIaFsbULmPiKR0QEplRwI7tAb5gLGL0XLxaB2d/ao7rPSWN7Lw6098LpAE40YEwEGz3xiUoBsy7RFnzskCIhfWmAuKCaqDKNxA28gv0vDF5KrSEC88GXh9EdiqKm1I/Jgm+AZvSBOaQjhTSyohB+jxPBSExVBGGT4Go7JcgXUPLrx+HOIBoDvXA4xI2oUEPSOyc2AiOyCy+D+WD+9PZOPnH6kdXx6Mu/nTRMSPNNqyEBI5C3tiUqGyZa5+pmFzZmDlNLt4BJ1AzRKSTWeGlMZSGqdYZcq0p88LmdiMQk1ceKdSQV1cAunZMSaSm1EZg1O+PVb98cN1jo/qg18nIDr0Emdnbj2n2oU3W6Rp4+EuldAsypQjrUoX10hcvp4D15digUfy1dIexhDewEp0tz4XI6MqaR2HerdrA4ka4G5p0TMQg2HI2hOfF5iuzCy/j3aSNR4HFhy/52XProOvzwL+vwZuIedd1Zxwj/e1ZAAkYQB9t78Mm+djgcwMyJ5vZ/kcNuNltVmC7l9AgVMPEITE84KmQA2rrd8cZLIvoWOJ0OzUMdjfK/ME5KpJB2H+5S1UCOle+fLlzAsAhMXwHz6icH+NR1xjaDe3RUlfgwqNCDmBRPhyglFpPw9mfxJpOi00eA2R4YbQ0ymXeMRTLzwcT62CFFKBdkDFdKpkok1v/FigpPIFlKLcID09wRhCTFO14PLhLf8PTbk4di9U1n44oZo+F2OvDBl0cgScDMCVWmlp8bCQkYQbydWNmdPLLc0rHkrGyV+RDU0sUFjDgPDJC9Jbga1ifCoNPHipnbMapCm5FXdAO7dAYVeXnFw+Z9bYp+Z19rN/Yc7oLL6cCMY8Q2pmJC5PODAURkQnTr/nbc8H+bcNXSj7ioa+kK4UCiskLUEMd0HA5H0gfTqPx7vmlvKw51BFHicxvSvMsUDwyPwGgTE8erFjBtAMwZH5BOpoGO2ywYISCHeWBERGDYEMeaMr9hFatVpX7c8d1JWPnzs3DxScMwdnARflF/vCF/ywpIwAiCdfYU2RhLCywCs0tDeB0QW0btcTl5KZ/eNFJHb5iXT4pqfc1EwteHlV+MjGpglw7z1rAJwPlg6aOTRpZz75EoxlQWodDrQjAS4/NwAOCFDXsBxPuE/OzZTejoDfMbzJjKQkN9YKwj7+cHlftgXv0k3nn0vAlVvKRYJEZ7YIKRKO9MqzkCU6NOwJjdwE5OpgjMNgtGCMhhKaT9rT26hzoyoT+01HgvyujKItx36cl45xdn83PnaIAEjADae8JY+2U8OiCyMZYWqkt9GFysrmpEjsgyakA2TiDLVFmlbPi6FdGYhFEVhcJ6JvAUkooIDGtgVyGLkhjBSbwjb6ui7d9PpI/OEJw+AuLpNhbxYAIlGInin58cAAAUeV3Y29KDRf/cxitETjD4BpMspVYWgYnGJLy2JS5g/u2kYYbsU9IDI3ZaMYPNtfG4HCgr0CYOWQRm96GuvDdgKxrYyUn2golfOwK9YZ7utSoCM6TEhwKPCzEJ2K/RZ8hgERgj/C8DBRIwAli1oxmRmIRjq4oxdoi4wXVacDgcSR/MfnU+mK5gBF8einsKRK3i2YW2KaCtaypj/W42eEzc2Hct3Xh5A7uR5cJNoHJOko0UyNcyPxaT8EFCwHzzWPECBpD5YBIC5e3tzWjrDmNomR9PXnUanA7gpU37sfSDPQCM9yhMqEmWUisZKbB+zxEc6giirMCDM8YZ0+KAiX6jPDDM/1JZ5NP83Rta5keJz41ITOLm3GxY0cBOTvooEjbOYnh5gel+HEbqGBJ9aaT9bcZVIA0USMAI4K1tLH1kbfSFwX0wKo289674Ak2BIIaXFwjzmZw6Jv46KxPmSa2wMkCR3gW5ifeva7/CzX//BLPufx9XPL6el5OnkwypG7sinTisFF6XE63d4byGwe0HA2jtDqPY5zakrBvoa+R9YWM8fXTJKcNxWm0FfnresQCApkAwZXujOLa6GE5HfHQCq+TIBUsfXTipxpD0EZBMu2YboKeXQ3yMgPabt8PhwLGJHko78qSR2DlnZgM7OSwC09YdxutbDuKOZdsBiB9PoZbRGlLP6fSEong9ERE0yis2ECABo5PecBSrdsRvzlaWT8tJRmCUGxw/3deGJ/4VXz3/9nuThM3HuGBS/Jgs39akqnmWnK5ghPtORAkrIN6+2+V0IBiJYdE/t+H5Dfuw7UAA7+88jMff39Nn+1Akho8SI+iNLin1uV2KJ1O/n/C/zBhbIXTys5xkKXU7Gtt7eeXR96eOBABcf844TJOJOqMFjN/j4tHOfA3twtEY3tgav1l850Rj0kdA0pexq7mTR0tEwiqojtEZ5T2e+WDypJhf/TSeIjzneGuqKot98ejtfSt34j+f/hjbDgTgdTnxvZNHWLI/DL1dvAHg/z5swOHOEEYMKsCFk+xx3+iPkIDRydovj6DLoMZYWmHh+x2NHYrKl8PRGG75xxbEJODik4bhbIEXrLqxlSjxu3G4M8hLMtXC/C8jBhVgxCBxvhOPy4k500dhWJkf5xw/BDecOw7zz4n3R/if93bjSNpN6LE1u9HcEURlkdeUqgylk6nX7IqLCSP8L4xjq4v5EMUH3tmJmAScOmYQahNVGW6XE3+69CTUlPoxY2wFKjWaTNWgtKHdml2H0dYdxuBinyHVR4zBxT6+T8wTJ4rWrhBe/HgfAGDO9NG6XktJKXXDkW5samiD0wH8m4GiLxcs/RyNSRhU6MFPzx2HNbecwxdFVqG3mV0wEsX/vBdvKPefZ48zbNExEOi/YyhtwOa9bfjd658BiPeVsGJ4YyZGVhSgxO9GR28EO5s684Yon1izB9sPBlBe6MF/XTRR6L543U7MnFCNlzbtx5tbGzFtjPoICi+fFlR9JGfxxZOw+OJJ/P+xmIT3vjiMLfvb8cA7u3D7v50AIF6mfP/KnQCAX82aIMzknIuTFAiY3nCUT1Y+41jjxlf43C4cW12Czw4G8H8fNgAAfpCIvjBGDCrEezefA4/LnPNgwtBSLPv0YN4IzKub45GEWZNrDE+FfOOYwfi8sQMffHlYaLTnmQ8bEIzEMGl4KU4do088KymlfuWT/QDi76fKgDk9SvjeycOx61AnptdWYPYpI0w555QwWucYkhc27ENTIIihZX7Mnjpc5K4NOEj6aaCjN4zbX9mG7z30L+xs7kR5oQdzv6FvVSQSh8PBozD5Gto1HOnGvW9/AQD41bcnaC7PzEV9orT8zW2NigyX6ST9L+LSR9lwOh245cLxAICn13/NL1K3v7IdveEYptdW4Hsnm3PRYQJm6/52Pr8nnQ/3tCAUiWFomR/HDCkydH9YWigmxStuvj1laJ9tvG6noeZmORMSlUgbvmpFV4apxUBc4LEWB0amjxinj4uL7H/tEheBCUdj+N+1XwMArvpGre7jy0qpv27pztizRpIkvJwQfUZVbClhVGUhHrjsZFw+Y7RtxAuQFDB7W3pUp8XD0Rgfpvj/zhwLn9s+76s/QgJGJcu3NeJb97yHpR98BUkCLjl5OFbeeBbGVdmrs2GyaiS7gOkNR7Hw+c3oDcdQN7YS359qTG75rOOGwO9xYl9rj+J29IzuUASf7ou/ByPD/3JOHzcYZx43BOGohD8s34EV25vw9mdNcDsd+M13J5l2gx5dWYi6sZUIRyX86MkPcc9bO/pcMOXdd43eL7mv5duTh/IqEas4dUwFBhf7sL+tBz97dnPGm8mqHc3oDEYwrMxvSinwabUVcDkdaGjpxl5BE4vf2NqIxkAvBhf7cNGJfUWjWgYXxwd0SlLcr5PO9oMB7GruhNfttDxdY0eGlhXA63IiFI3xWUZKeXnTfuxv68HgYi8uPW2UQXs4cCABowJJkvDEmj1oDPRidGUh/jZvOu75j5NMyferhRkKswmGWEzCTX//FBu/bkWJz40ll0w27AZY4HXh7OPivprliVkcSvn46zZEYhKGlfkxYpB55Ya3XDAeDgew7NODuOUfnwIAfnzmWBxrYjmpw+HAk1edih9OHwVJAu5/ZxeufGI9tu5vxwe7DuMfG/fx42lU+bScE2Sl0T+YZq2REoj3GHr0yqnwup14+7Mm3PXm5322YdVH3zlxmCkp3hK/ByeOiB8nUT6YJ9bEDeVXzBgtbMWeqxLplUT05bzxVcKbIh4NuJwOjKiIX4saVPhgojEJDyWiLz/+5lhDJrUPNEjAqMDhcOB3l0zG/HOOwfIFZ+IME24aWmGlhtsPBjKuTP/w1g68+skBuJ0OPHLFVN4i2yjYSu7NreoEjLx82qzIBxAvbWSpoiNdIQwvL8AN544z7e8z/B4Xfve9yfjTf5yEAo8L/9p1BBc9sAY/fGw9fv7CJ/j6SDecjrhXwWimjCjDMUOKcFptBU7T4GUyglNGDcIff3AigLjx+rmP4v6cjt4wXtq0Dys/Ny99xGCzqP715WHdr/VxQys2722D1+XEnBniVuzZfDCxmIRXEg0KL7YwfWR3WCXSVyoEzKufHMCew10oL/Tg8hn2sRz0Z8jEq5JjhhTjpvrxVu9GXmoHF6PA40J3KIo9h7v4FFoAeGZ9A8/D3jl7ivDhf5k4Z3wV3E4HdjZ3YldzZ8r+5ILdBESWTyvl5+cfj2WfHkQoEsPt/3aCsNJyLXz35OE4YVgpfvH3T7GrqQM1ZX4MLStAdakfZx8/RNN0YrX4PS6s/PnZkCTJVDGZj387cRi+bO7EfSt34lcvbcXybU1Ys+sw7zQ7cWipqZ1bv3HMYDzwzq7E8Dx9x4pFXy4+aZhQf1q2kQIffdWCg+29KPG7hVYjHm2oNfK2doXwm9fiBR/zTq+1ZJL20QgdxaMUl9OBCUNL8HFDG7YdaOeC4d0dzfivf24FAPzsvGMN872kU1bgwTfGDcZ7XxzC8m2NGFeVP5qx+otD2NTQBo/LgW8aWGGTjeHlBVh61ak43BkyZHqxWo6tLsE/559u9W7YSrwwFsw8Fl8e6sSyTw/inc/j/VLGDinCRVOG4YoZo03d51NGl8PnduJQRxA7mzs1d7Hd39aDNxIRy6tOrxW5i8kITFoJOjPvXjiphlIcOWAzkZSWUt/2z6043BnEuKpi/PjMsUbu2oCCBMxRzKThZQkBE8DFJw3H+t1H8JO/bUQ0JuGSU4ZjwcxjTd2fC06o4QJm/jm5BUwkGsNvEp0359aNETb/SC1mpGYI/TgcDvzxByeiptQPv8eFWVOGYnxNiSViy+d24dQxFViz6zA+2HVYs4C549XtiMYk1I2tFN6tlXm5DrT3ItAbRqnfg1AkxrvDXnwSlffmYrSKqdSvfnIAr316EC6nA/f8+4kkDAVCHpijmEmymUibGlpx9dKP0BuO4Zzjh+DOS6aYfnH/1sRqOBzAp/vasas5d/Ox//uwATubOzGo0IMbzjNXaBH9E7/Hhdsumohf1B+PCUNLLY0UfYOVU2s08r65tRFvbmuE2+kQ3psJiEdEaxL9XXY2daCtO4RF/9yK9p4wqkqMbfh3NMAiMA0t3TlbQzQHennE+/pzxmHKiHIzdm/AQALmKOaEhJH3k71tmPvEh+gKRfGNYyrx8OVTDZsHk4shJT6cfVw8FXT9M5uyzhtq7w7jnhXx3jQ3fus4zZN3CcIqTk9E7tbtPoKIgm7Yctp7wliUuOn9v7PGGjYrh/lgHnr3S5z9x1V49qP4fKtrzxxryeyj/sSIQYVwOoDuUBSHsoyNkCQJt7y4BW3dYUwaXorrLSgCONohAXMUc2xVCTwuB7pCUQR6I5g6ehD+cuU0S0OYv589BYOLffi8sQO/emlrxtXL/e/sRGt3GMdWFeMy6pVA9EMmDS9DaaIb9laVvY9+/+bnaO4IYuzgItxwrnHRx+MTpdQrP49PFj++ugTP/Hg6rvkmeTTy4XU7MTzR1iHdBxOMRPH29ibMf+ZjvPN5M7xuJ+7595NoZIABkAfmKMbrdmLC0FJ8uq8dk4eX4cmrTrXc/V5V6seDPzwZP3xsPV7atB8njyrHlXVj+PO7D3XiqQ++AgD810UT4aaTnuiHuJwOzBhbibe2N+Ffuw7zrsr5WL/7CJ5ZHy8F/90lkw1dbMQb++1BeaEHP//WcbjstFF0vqlgdEUR9rb04P6VO3HMkGI4HQ60dofw9mdN6OhNRpdvuWC8Zh8UkRsSMEc5t82aiLcSplm7NKWaPrYSt1wwHr99/TPcsWw7jqsuQTQm4b0vDuH1rQcRiUk4d3wVzjzO/MojghDF6eMG463tTXh41Zc41BHEnOmj+jRC7A1Hsb+tB/tbe7C/rQf/szre3uCy00Ya7kOpP6EGL1xXh+OqSlBWaI9rQ3/iuOoSrNl1GO/vPMwnwjOqS32YNXkY/u2kYYrFK6Eeh6RlOI1JPPjgg/jDH/6AxsZGnHjiiXjggQdw2mmnKfrdQCCAsrIytLe3o7TUvB4QhDIkScL8Zz7G61v6NrYrL/TgHz/5Bo4ZoqxXDEHYkfbuMC79y7qUYZOn1VZgcLGXC5bDnaE+vzekxIe3bzyLvF82p6UrhL9v3IuuYBTRmISoJMHjdOCMY4dg2uhBthnu2x9Rev+2rYB57rnncOWVV+KRRx7B9OnT8ac//QkvvPACduzYgaqq/A2WSMDYn85gBN97MD4Qc0iJD2ceOwRnHT8EZx47GOWFXqt3jyB0E4tJ+NeXh/G/a7/G2581IdPsvyKvC8MHFWB4eQFGDCrE3G+MUdzokSCORvq9gJk+fTpOPfVU/PnPfwYAxGIxjBw5EjfccANuueWWPtsHg0EEg0k3eCAQwMiRI0nA2JyuYARNgV7UDi6yZYM0ghDFgbYevL7lIJwOh0ywFKCswEPffYKQoVTA2NIDEwqFsHHjRtx66638MafTiZkzZ2Lt2rUZf2fJkiX49a9/bdYuEoIo8rkxllJFxABgWHkBVfgQhEBsaTk/fPgwotEoqqtT27dXV1ejsTHzMMBbb70V7e3t/Gfv3r1m7CpBEARBEBZgywiMFnw+H3w+4wfaEQRBEARhPbaMwAwePBgulwtNTU0pjzc1NaGmpsaivSIIgiAIwi7YUsB4vV5MnToVK1eu5I/FYjGsXLkSdXV1Fu4ZQRAEQRB2wLYppBtvvBFz587FtGnTcNppp+FPf/oTurq6cNVVV1m9awRBEARBWIxtBcx//Md/4NChQ1i0aBEaGxtx0kkn4c033+xj7CUIgiAIYuBh2z4weqFGdgRBEATR/1B6/7alB4YgCIIgCCIXJGAIgiAIguh3kIAhCIIgCKLfQQKGIAiCIIh+BwkYgiAIgiD6HSRgCIIgCILod9i2D4xeWHV4IBCweE8IgiAIglAKu2/n6/Jy1AqYjo4OAMDIkSMt3hOCIAiCINTS0dGBsrKyrM8ftY3sYrEYDhw4gJKSEjgcDmGvGwgEMHLkSOzdu5ca5GmAjp8+6Pjpg46fPuj4aYeOnXIkSUJHRweGDRsGpzO70+WojcA4nU6MGDHCsNcvLS2lL6EO6Pjpg46fPuj46YOOn3bo2CkjV+SFQSZegiAIgiD6HSRgCIIgCILod5CAUYnP58N///d/w+fzWb0r/RI6fvqg46cPOn76oOOnHTp24jlqTbwEQRAEQRy9UASGIAiCIIh+BwkYgiAIgiD6HSRgCIIgCILod5CAIQiCIAii30EChiAIgiCIfgcJGJU8+OCDGDNmDPx+P6ZPn44PP/zQ6l2yHUuWLMGpp56KkpISVFVV4bvf/S527NiRsk1vby/mz5+PyspKFBcXY/bs2WhqarJoj+3NnXfeCYfDgQULFvDH6PjlZv/+/bj88stRWVmJgoICTJ48GRs2bODPS5KERYsWYejQoSgoKMDMmTOxc+dOC/fYPkSjUfzXf/0XamtrUVBQgGOOOQZ33HFHymA9On5J3nvvPXznO9/BsGHD4HA48PLLL6c8r+RYtbS0YM6cOSgtLUV5eTnmzZuHzs5OE99FP0UiFPPss89KXq9XeuKJJ6Rt27ZJP/7xj6Xy8nKpqanJ6l2zFfX19dKTTz4pbd26Vdq8ebP07W9/Wxo1apTU2dnJt7nuuuukkSNHSitXrpQ2bNggzZgxQ/rGN75h4V7bkw8//FAaM2aMNGXKFOlnP/sZf5yOX3ZaWlqk0aNHSz/60Y+k9evXS7t375aWL18u7dq1i29z5513SmVlZdLLL78sffLJJ9K//du/SbW1tVJPT4+Fe24Pfvvb30qVlZXSsmXLpD179kgvvPCCVFxcLN133318Gzp+SV5//XXpV7/6lfTiiy9KAKSXXnop5Xklx+qCCy6QTjzxRGndunXS+++/L40bN0667LLLTH4n/Q8SMCo47bTTpPnz5/P/R6NRadiwYdKSJUss3Cv709zcLAGQVq9eLUmSJLW1tUkej0d64YUX+DafffaZBEBau3atVbtpOzo6OqRjjz1WWrFihXTWWWdxAUPHLze//OUvpTPOOCPr87FYTKqpqZH+8Ic/8Mfa2tokn88n/d///Z8Zu2hrZs2aJV199dUpj11yySXSnDlzJEmi45eLdAGj5Fht375dAiB99NFHfJs33nhDcjgc0v79+03b9/4IpZAUEgqFsHHjRsycOZM/5nQ6MXPmTKxdu9bCPbM/7e3tAICKigoAwMaNGxEOh1OO5fjx4zFq1Cg6ljLmz5+PWbNmpRwngI5fPl555RVMmzYNP/jBD1BVVYWTTz4Zf/nLX/jze/bsQWNjY8rxKysrw/Tp0+n4AfjGN76BlStX4osvvgAAfPLJJ1izZg0uvPBCAHT81KDkWK1duxbl5eWYNm0a32bmzJlwOp1Yv3696fvcnzhqp1GL5vDhw4hGo6iurk55vLq6Gp9//rlFe2V/YrEYFixYgNNPPx2TJk0CADQ2NsLr9aK8vDxl2+rqajQ2Nlqwl/bj2Wefxccff4yPPvqoz3N0/HKze/duPPzww7jxxhvx//1//x8++ugj/PSnP4XX68XcuXP5Mcp0LtPxA2655RYEAgGMHz8eLpcL0WgUv/3tbzFnzhwAoOOnAiXHqrGxEVVVVSnPu91uVFRU0PHMAwkYwlDmz5+PrVu3Ys2aNVbvSr9h7969+NnPfoYVK1bA7/dbvTv9jlgshmnTpuF3v/sdAODkk0/G1q1b8cgjj2Du3LkW7539ef755/H000/jmWeewQknnIDNmzdjwYIFGDZsGB0/wlZQCkkhgwcPhsvl6lPp0dTUhJqaGov2yt5cf/31WLZsGd59912MGDGCP15TU4NQKIS2traU7elYxtm4cSOam5txyimnwO12w+12Y/Xq1bj//vvhdrtRXV1Nxy8HQ4cOxcSJE1MemzBhAhoaGgCAHyM6lzNz00034ZZbbsGll16KyZMn44orrsDChQuxZMkSAHT81KDkWNXU1KC5uTnl+UgkgpaWFjqeeSABoxCv14upU6di5cqV/LFYLIaVK1eirq7Owj2zH5Ik4frrr8dLL72Ed955B7W1tSnPT506FR6PJ+VY7tixAw0NDXQsAZx33nnYsmULNm/ezH+mTZuGOXPm8H/T8cvO6aef3qds/4svvsDo0aMBALW1taipqUk5foFAAOvXr6fjB6C7uxtOZ+qtweVyIRaLAaDjpwYlx6qurg5tbW3YuHEj3+add95BLBbD9OnTTd/nfoXVLuL+xLPPPiv5fD5p6dKl0vbt26Vrr71WKi8vlxobG63eNVvxk5/8RCorK5NWrVolHTx4kP90d3fzba677jpp1KhR0jvvvCNt2LBBqqurk+rq6izca3sjr0KSJDp+ufjwww8lt9st/fa3v5V27twpPf3001JhYaH0t7/9jW9z5513SuXl5dI///lP6dNPP5UuvvjiAVsGnM7cuXOl4cOH8zLqF198URo8eLB08803823o+CXp6OiQNm3aJG3atEkCIN1zzz3Spk2bpK+//lqSJGXH6oILLpBOPvlkaf369dKaNWukY489lsqoFUACRiUPPPCANGrUKMnr9UqnnXaatG7dOqt3yXYAyPjz5JNP8m16enqk//zP/5QGDRokFRYWSt/73vekgwcPWrfTNiddwNDxy82rr74qTZo0SfL5fNL48eOlRx99NOX5WCwm/dd//ZdUXV0t+Xw+6bzzzpN27Nhh0d7ai0AgIP3sZz+TRo0aJfn9fmns2LHSr371KykYDPJt6PgleffddzNe7+bOnStJkrJjdeTIEemyyy6TiouLpdLSUumqq66SOjo6LHg3/QuHJMnaKxIEQRAEQfQDyANDEARBEES/gwQMQRAEQRD9DhIwBEEQBEH0O0jAEARBEATR7yABQxAEQRBEv4MEDEEQBEEQ/Q4SMARBEARB9DtIwBAEQRAE0e8gAUMQBEEQRL+DBAxBEARBEP0OEjAEQRAEQfQ7/n+m0cpvCjWrhwAAAABJRU5ErkJggg=="
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 16
- },
- {
- "cell_type": "markdown",
"source": [
- "### CardanoSentiment\n",
+ "from aeon.datasets import load_lynx\n",
"\n",
- "By combining historical sentiment data for Cardano cryptocurrency, extracted from\n",
- " EODHistoricalData and made available on Kaggle, with historical price data for the\n",
- " same cryptocurrency, extracted from CryptoDataDownload, we created the\n",
- " CardanoSentiment dataset, with 107 instances. The predictors are hourly close price\n",
- " (in USD) and traded volume during a day, resulting in 2-dimensional time series of\n",
- " length 24. The response variable is the normalized sentiment score on the day\n",
- " spanned by the timepoints."
+ "lynx = load_lynx()\n",
+ "plt.title(\"Lynx numbers\")\n",
+ "plt.plot(lynx)"
],
"metadata": {
"collapsed": false
}
},
{
- "cell_type": "code",
+ "cell_type": "markdown",
"source": [
- "from aeon.datasets import load_cardano_sentiment\n",
+ "### PBS_dataset\n",
"\n",
- "cardano, cardano_target = load_cardano_sentiment()\n",
- "print(cardano.shape)\n",
- "plt.title(\"Response variable for cardano data\")\n",
- "plt.plot(cardano_target)"
+ "The Pharmaceutical Benefits Scheme (PBS) is the Australian government drugs\n",
+ " subsidy scheme. Data comprises of the numbers of scripts sold each month for immune sera\n",
+ " and immunoglobulin products in Australia. The load function returns a numpy array\n",
+ " or a pd.Series."
],
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:58:22.582032Z",
- "start_time": "2024-09-25T22:58:22.410134Z"
- }
- },
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(107, 2, 24)\n"
- ]
- },
{
"data": {
- "text/plain": [
- "[]"
- ]
+ "text/plain": "[]"
},
- "execution_count": 17,
+ "execution_count": 68,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 17
- },
- {
- "cell_type": "markdown",
"source": [
- "## Segmentation\n",
+ "from aeon.datasets import load_PBS_dataset\n",
"\n",
- "Two of the UCR classification data have been adapted for segmentation."
+ "pbs = load_PBS_dataset()\n",
+ "plt.title(\"PBS\")\n",
+ "plt.plot(pbs)"
],
"metadata": {
"collapsed": false
@@ -1189,14 +1068,10 @@
{
"cell_type": "markdown",
"source": [
- "### ElectricDevices\n",
- "\n",
- "The UCR ElectricDevices dataset series are grouped by class label and concatenated to create\n",
- " segments with repeating temporal patterns and characteristics. The location at which\n",
- " different classes were concatenated are marked as change points.\n",
+ "### ShampooSales\n",
"\n",
- "this function returns a single series, the period length as an integer and the\n",
- "change points as a numpy array."
+ "ShampooSales contains a single monthly time series of the number of sales of\n",
+ "shampoo over a three year period. The units are a sales count."
],
"metadata": {
"collapsed": false
@@ -1204,64 +1079,46 @@
},
{
"cell_type": "code",
- "source": [
- "from aeon.datasets import load_electric_devices_segmentation\n",
- "\n",
- "data, period, change_points = load_electric_devices_segmentation()\n",
- "print(\" Period = \", period)\n",
- "print(\" Change points = \", change_points)\n",
- "plot_series(data)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:58:22.990281Z",
- "start_time": "2024-09-25T22:58:22.610956Z"
- }
- },
+ "execution_count": 69,
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Period = 10\n",
- " Change points = [1090 4436 5712 7923]\n"
- ]
- },
{
"data": {
- "text/plain": [
- "(, )"
- ]
+ "text/plain": "[]"
},
- "execution_count": 18,
+ "execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 18
+ "source": [
+ "from aeon.datasets import load_shampoo_sales\n",
+ "\n",
+ "shampoo = load_shampoo_sales()\n",
+ "plt.title(\"Shampoo sales\")\n",
+ "plt.plot(shampoo)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
},
{
"cell_type": "markdown",
"source": [
- "### GunPoint Segmentation\n",
"\n",
- "The UCR GunPoint dataset series are grouped by class label and concatenated to create\n",
- " segments with repeating temporal patterns and characteristics. The location at which\n",
- " different classes were concatenated are marked as change points.\n",
+ "### UsChange\n",
"\n",
- "this function returns a single series, the period length as an integer and the\n",
- "change points as a numpy array."
+ "Load MTS dataset for forecasting Growth rates of personal consumption and income. The\n",
+ " data is quarterly for 188 quarters and contains time series for\n",
+ " Consumption, Income, Production, Savings and Unemployment. It either a numpy array or\n",
+ " a pd.DataFrame."
],
"metadata": {
"collapsed": false
@@ -1269,55 +1126,85 @@
},
{
"cell_type": "code",
+ "execution_count": 70,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "[]"
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "from aeon.datasets import load_gun_point_segmentation\n",
+ "from aeon.datasets import load_uschange\n",
"\n",
- "data, period, change_points = load_gun_point_segmentation()\n",
- "print(\" Period = \", period)\n",
- "print(\" Change points = \", change_points)\n",
- "plot_series(data)"
+ "data = load_uschange()\n",
+ "plt.title(\"Consumption\")\n",
+ "plt.plot(data[0])"
],
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:58:23.230150Z",
- "start_time": "2024-09-25T22:58:23.046130Z"
- }
- },
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Solar\n",
+ "Example national solar data for the GB eletricity network extracted from the Sheffield Solar PV_Live API.\n",
+ " Note that these are estimates of the true solar\n",
+ " generation, since the true values are \"behind the meter\" and essentially\n",
+ " unknown. The returned data is half hourly."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Period = 10\n",
- " Change points = [900]\n"
- ]
- },
{
"data": {
- "text/plain": [
- "(, )"
- ]
+ "text/plain": "[]"
},
- "execution_count": 19,
+ "execution_count": 72,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "text/plain": [
- ""
- ],
- "image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
- "execution_count": 19
+ "source": [
+ "from aeon.datasets import load_solar\n",
+ "\n",
+ "solar = load_solar()\n",
+ "plt.title(\"Solar\")\n",
+ "plt.plot(solar)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
"source": [],
"metadata": {
"collapsed": false
diff --git a/examples/distances/sklearn_distances.ipynb b/examples/distances/sklearn_distances.ipynb
index f28ad3d0be..e22579828c 100644
--- a/examples/distances/sklearn_distances.ipynb
+++ b/examples/distances/sklearn_distances.ipynb
@@ -63,8 +63,8 @@
"but it is treating the data as vector rather than as time series.\n",
"\n",
"If we try and use with an `aeon` style 3D numpy\n",
- "`(n_cases, n_channels, n_timepoints)`, they will crash as `scikit-learn` expect a 2D \n",
- "numpy array. See the [data_formats](../datasets/data_structures.ipynb) for details on \n",
+ "`(n_cases, n_channels, n_timepoints)`, they will crash as `scikit-learn` expect a 2D\n",
+ "numpy array. See the [data_formats](../datasets/datasets.ipynb) for details on\n",
"data storage."
]
},
@@ -121,8 +121,8 @@
"collapsed": false
},
"source": [
- "We can use `KNeighborsClassifier` with a callable `aeon` distance function, but the \n",
- "input must still be 2D numpy array. "
+ "We can use `KNeighborsClassifier` with a callable `aeon` distance function, but the\n",
+ "input must still be 2D numpy array."
]
},
{
@@ -240,19 +240,19 @@
"collapsed": false
},
"source": [
- "Also note that using an `aeon` distance function as callable does not will not work with \n",
- "some kNN options such as [`KDTree`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html) \n",
+ "Also note that using an `aeon` distance function as callable does not will not work with\n",
+ "some kNN options such as [`KDTree`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html)\n",
"class or [`BallTree`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html),\n",
"as stated in the scikit-learn doc of these classes:\n",
"\n",
"_Note: Callable functions in the metric parameter are NOT supported for KDTree_\n",
"_and Ball Tree. Function call overhead will result in very poor performance._\n",
"\n",
- "Because of these problems, we have implemented a KNN classifier and regressor to use \n",
+ "Because of these problems, we have implemented a KNN classifier and regressor to use\n",
"with our distance functions.\n",
"\n",
- "The `aeon` kNN classifier using a 3D numpy array achieves the same performance than the \n",
- "`sklearn` one using the 2D numpy array even using time series specific distance \n",
+ "The `aeon` kNN classifier using a 3D numpy array achieves the same performance than the\n",
+ "`sklearn` one using the 2D numpy array even using time series specific distance\n",
"functions. The results achieved are the same as time series are univariate and hence,\n",
"the data can be formatted as a 2D array:"
]
@@ -307,7 +307,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "However, if the time series dataset is a multivariate one, data has to be represented \n",
+ "However, if the time series dataset is a multivariate one, data has to be represented\n",
"using a 3D numpy array. In this case, to use the `sklearn` knn approach, channels have\n",
"to be concatenated, and therefore, specific edit time series distances may compute the\n",
"distance between values of different channels, and the results may be biased by these\n",
@@ -398,7 +398,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Similar conclusions can be drawn for the kNN regressor. First of all, we load the \n",
+ "Similar conclusions can be drawn for the kNN regressor. First of all, we load the\n",
"TSER dataset."
]
},
@@ -426,7 +426,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now, we compare the prediction of the `aeon` and `scikit-learn` versions. As the \n",
+ "Now, we compare the prediction of the `aeon` and `scikit-learn` versions. As the\n",
"Covid3Month dataset is univariate, the results of both libraries should be the same."
]
},
@@ -547,7 +547,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Same conclusions can be obtained when dealing with a TSER dataset. "
+ "Same conclusions can be obtained when dealing with a TSER dataset."
]
},
{
@@ -623,8 +623,8 @@
"collapsed": false
},
"source": [
- "The SVM estimators in `scikit-learn` can be used with pairwise distance matrices. Please \n",
- "note that not all elastic distance functions are kernels, and it is desirable that they \n",
+ "The SVM estimators in `scikit-learn` can be used with pairwise distance matrices. Please\n",
+ "note that not all elastic distance functions are kernels, and it is desirable that they\n",
"are for SVM. DTW is not a metric, but MSM and TWE are."
]
},
@@ -715,7 +715,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "SVR and NuSVR also allow to use the distance function as callable as previously \n",
+ "SVR and NuSVR also allow to use the distance function as callable as previously\n",
"aforementioned. As can be observed, the results are the same:"
]
},
@@ -860,7 +860,7 @@
"collapsed": false
},
"source": [
- "You can use pairwise distance functions within the `scikit-learn` FunctionTransformer \n",
+ "You can use pairwise distance functions within the `scikit-learn` FunctionTransformer\n",
"wrapper"
]
},
diff --git a/examples/pydata/Amsterdam-2023/Lets do the time warp again.ipynb b/examples/pydata/Amsterdam-2023/Lets_do_the_time_warp_again.ipynb
similarity index 99%
rename from examples/pydata/Amsterdam-2023/Lets do the time warp again.ipynb
rename to examples/pydata/Amsterdam-2023/Lets_do_the_time_warp_again.ipynb
index 8abe8dce6a..0d2007db3b 100644
--- a/examples/pydata/Amsterdam-2023/Lets do the time warp again.ipynb
+++ b/examples/pydata/Amsterdam-2023/Lets_do_the_time_warp_again.ipynb
@@ -26,7 +26,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2023-09-24T21:09:32.118338976Z",
@@ -37,11 +37,9 @@
"outputs": [
{
"data": {
- "text/plain": [
- "108.0"
- ]
+ "text/plain": "108.0"
},
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/examples/segmentation/img/hidalgo.png b/examples/segmentation/img/hidalgo.png
new file mode 100644
index 0000000000..ca53924ed6
Binary files /dev/null and b/examples/segmentation/img/hidalgo.png differ
diff --git a/examples/similarity_search/img/code_speed.png b/examples/similarity_search/img/code_speed.png
new file mode 100644
index 0000000000..a6261417ef
Binary files /dev/null and b/examples/similarity_search/img/code_speed.png differ
diff --git a/examples/similarity_search/img/distance_profile.png b/examples/similarity_search/img/distance_profile.png
new file mode 100644
index 0000000000..dc27d4f5ec
Binary files /dev/null and b/examples/similarity_search/img/distance_profile.png differ
diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb
index 2ad7fcc5b2..86132f3a51 100644
--- a/examples/similarity_search/similarity_search.ipynb
+++ b/examples/similarity_search/similarity_search.ipynb
@@ -5,9 +5,12 @@
"id": "5083d23c-e27f-4d14-a8d2-12e11a6aff42",
"metadata": {},
"source": [
- "# Time Series Similarity search with aeon\n",
+ "# Time Series Similarity Search with aeon\n",
"\n",
- "The goal of Time Series Similarity search is to asses the similarities between a time series, denoted as a query `q` of length `l`, and a collection of time series, denoted as `X`, which lengths are superior or equal to `l`. In this context, the notion of similiarity between `q` and the other series in `X` is quantified by similarity functions. Those functions are most of the time defined as distance function, such as the Euclidean distance. Knowing the similarity between `q` and other admissible candidates, we can then perform many other tasks for \"free\", such as anomaly or motif detection.\n",
+ "The goal of Time Series Similarity Search is to asses the similarities between a time\n",
+ " series, denoted as a query `q` of length `l`, and a collection of time series,\n",
+ " denoted as `X`, with lengths greater than or equal to `l`. In this\n",
+ " context, the notion of similiarity between `q` and the other series in `X` is quantified by similarity functions. Those functions are most of the time defined as distance function, such as the Euclidean distance. Knowing the similarity between `q` and other admissible candidates, we can then perform many other tasks for \"free\", such as anomaly or motif detection.\n",
"\n",
""
]
diff --git a/examples/transformations/catch22.ipynb b/examples/transformations/catch22.ipynb
index 6bdd09a5c1..a551f67ef6 100644
--- a/examples/transformations/catch22.ipynb
+++ b/examples/transformations/catch22.ipynb
@@ -6,44 +6,43 @@
"source": [
"# The Canonical Time-series Characteristics (catch22) transform\n",
"\n",
- "catch22\\[1\\] is a collection of 22 time series features extracted from the 7000+ present in the _hctsa_ \\[2\\]\\[3\\] toolbox.\n",
+ "Catch22\\[1\\] is a collection of 22 time series features extracted from the 7000+ present in the _hctsa_ \\[2\\]\\[3\\] toolbox.\n",
"A hierarchical clustering was performed on the correlation matrix of features that performed better than random chance to remove redundancy.\n",
"These clusters were sorted by balanced accuracy using a decision tree classifier and a single feature was selected from the 22 clusters formed, taking into account balanced accuracy results, computational efficiency and interpretability.\n",
+ "More about the individual features of catch22 can be learned in the [Gitbook](https://time-series-features.gitbook.io/catch22/information-about-catch22/feature-descriptions) of the original creators.\n",
"\n",
- "In this notebook, we will demonstrate how to use the catch22 transformer on the ItalyPowerDemand univariate and BasicMotions multivariate datasets. We also show catch22 used for classification with a random forest classifier.\n",
- "\n",
- "#### References:\n",
- "\n",
- "\\[1\\] Lubba, C. H., Sethi, S. S., Knaute, P., Schultz, S. R., Fulcher, B. D., & Jones, N. S. (2019). catch22: CAnonical Time-series CHaracteristics. Data Mining and Knowledge Discovery, 33(6), 1821-1852.\n",
- "\n",
- "\\[2\\] Fulcher, B. D., & Jones, N. S. (2017). hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell systems, 5(5), 527-531.\n",
- "\n",
- "\\[3\\] Fulcher, B. D., Little, M. A., & Jones, N. S. (2013). Highly comparative time-series analysis: the empirical structure of time series and their methods. Journal of the Royal Society Interface, 10(83), 20130048."
+ "In this notebook, we will demonstrate how to use aeon's catch22 transformer on the ItalyPowerDemand univariate and BasicMotions multivariate datasets. We will go through the parameters of catch22 and how changing the default values may change results. Catch22 has also been used inside of [classification](../classification/feature_based.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Transformation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Catch22 is a feature based transformer that extracts 22 features from a time series. The input data can be both univariate and multivariate, without the need to reshape the data. It is most commonly used for interpretability of each time series data. Additionally, as the data of a time series will be reduced to 22 data values, it will increase computational efficiency of machine learning tasks such as clustering, classification, etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 1. Imports"
+ "### 1.1 Import Data and Catch22"
]
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:07.306937Z",
- "iopub.status.busy": "2020-12-19T14:30:07.306390Z",
- "iopub.status.idle": "2020-12-19T14:30:08.036353Z",
- "shell.execute_reply": "2020-12-19T14:30:08.036857Z"
- }
- },
+ "execution_count": 7,
+ "metadata": {},
"outputs": [],
"source": [
- "from sklearn import metrics\n",
+ "import numpy as np\n",
"\n",
- "from aeon.classification.feature_based import Catch22Classifier\n",
"from aeon.datasets import load_basic_motions, load_italy_power_demand\n",
"from aeon.transformations.collection.feature_based import Catch22"
]
@@ -52,69 +51,65 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2. Load data"
+ "### 1.2 Load Data"
]
},
{
"cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:08.041533Z",
- "iopub.status.busy": "2020-12-19T14:30:08.041060Z",
- "iopub.status.idle": "2020-12-19T14:30:08.210768Z",
- "shell.execute_reply": "2020-12-19T14:30:08.211258Z"
- }
- },
+ "execution_count": 3,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "(67, 1, 24) (67,) (50, 1, 24) (50,)\n",
- "(40, 6, 100) (40,) (40, 6, 100) (40,)\n"
+ "Italy Power Demand (Univariate): (67, 1, 24) (67,) (1029, 1, 24) (1029,)\n",
+ "Load Basic Motions (Multivarite): (40, 6, 100) (40,) (40, 6, 100) (40,)\n"
]
}
],
"source": [
"IPD_X_train, IPD_y_train = load_italy_power_demand(split=\"train\")\n",
"IPD_X_test, IPD_y_test = load_italy_power_demand(split=\"test\")\n",
- "IPD_X_test = IPD_X_test[:50]\n",
- "IPD_y_test = IPD_y_test[:50]\n",
"\n",
- "print(IPD_X_train.shape, IPD_y_train.shape, IPD_X_test.shape, IPD_y_test.shape)\n",
+ "print(\n",
+ " \"Italy Power Demand (Univariate): \",\n",
+ " IPD_X_train.shape,\n",
+ " IPD_y_train.shape,\n",
+ " IPD_X_test.shape,\n",
+ " IPD_y_test.shape,\n",
+ ")\n",
"\n",
"BM_X_train, BM_y_train = load_basic_motions(split=\"train\")\n",
- "BM_X_test, BM_y_test = load_basic_motions(\n",
- " split=\"test\",\n",
- ")\n",
+ "BM_X_test, BM_y_test = load_basic_motions(split=\"test\")\n",
"\n",
- "print(BM_X_train.shape, BM_y_train.shape, BM_X_test.shape, BM_y_test.shape)"
+ "print(\n",
+ " \"Load Basic Motions (Multivarite): \",\n",
+ " BM_X_train.shape,\n",
+ " BM_y_train.shape,\n",
+ " BM_X_test.shape,\n",
+ " BM_y_test.shape,\n",
+ ")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3. catch22 transform\n",
- "\n",
- "### Univariate\n",
- "\n",
- "The catch22 features are provided in the form of a transformer, `Catch22`.\n",
- "From this the transformed data can be used for a variety of time series analysis tasks."
+ "### 1.3 Transform the Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Univariate"
]
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:08.215545Z",
- "iopub.status.busy": "2020-12-19T14:30:08.215049Z",
- "iopub.status.idle": "2020-12-19T14:30:08.222937Z",
- "shell.execute_reply": "2020-12-19T14:30:08.223422Z"
- }
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -135,134 +130,248 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Multivariate\n",
- "\n",
- "Transformation of multivariate data is supported by `Catch22`.\n",
- "The default procedure will concatenate each column prior to transformation."
+ "#### Multivariate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do note that the result of the shape won't be (X , 22). This is because it's a multivariate dataset, and therefore the feature vector will be of size 22 times the number of channels. "
]
},
{
"cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:08.264541Z",
- "iopub.status.busy": "2020-12-19T14:30:08.264050Z",
- "iopub.status.idle": "2020-12-19T14:30:08.266022Z",
- "shell.execute_reply": "2020-12-19T14:30:08.266517Z"
- }
- },
+ "execution_count": 5,
+ "metadata": {},
"outputs": [
{
- "data": {
- "text/html": [
- "
Catch22()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Catch22()
"
- ],
- "text/plain": [
- "Catch22()"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(40, 132)\n"
+ ]
}
],
"source": [
"c22_mv = Catch22()\n",
- "c22_mv.fit(BM_X_train, BM_y_train)"
+ "data = c22_mv.fit_transform(BM_X_train, BM_y_train)\n",
+ "transformed_data_mv = c22_uv.transform(BM_X_train)\n",
+ "print(transformed_data_mv.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Aeon's catch22 includes a lot options for users need compared to the original catch22 implementation which we will talk about in section 2.4. Few of the parameters are shown below with examples, specifically the ones that change affect the output. More can be found in [catch22's documentation](../../docs/api_reference/transformations.rst)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.1 Features"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Catch22 takes 22 distinct features from a time series. Sometimes you may not need all the features extracted by catch22, instead you may only need some very specific features. By defining an array containing strings of features, only those specified features will be extracted. The order of these features do matter, as that will be the order of the output. Aeon's [catch22's documentation](../../docs/api_reference/transformations.rst) specifies a list of the 22 features for extraction."
]
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:08.271483Z",
- "iopub.status.busy": "2020-12-19T14:30:08.270986Z",
- "iopub.status.idle": "2020-12-19T14:30:08.413472Z",
- "shell.execute_reply": "2020-12-19T14:30:08.413974Z"
- }
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "(40, 132)\n"
+ "(67, 3)\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "Invalid feature selection.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[6], line 11\u001b[0m\n\u001b[0;32m 9\u001b[0m c22_short \u001b[38;5;241m=\u001b[39m Catch22(features\u001b[38;5;241m=\u001b[39mfeatures_short)\n\u001b[0;32m 10\u001b[0m c22_short\u001b[38;5;241m.\u001b[39mfit(IPD_X_train, IPD_y_train)\n\u001b[1;32m---> 11\u001b[0m transformed_data_short \u001b[38;5;241m=\u001b[39m \u001b[43mc22_short\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mIPD_X_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;28mprint\u001b[39m(transformed_data_short\u001b[38;5;241m.\u001b[39mshape)\n",
+ "File \u001b[1;32md:\\AeonProject\\aeon\\.venv\\Lib\\site-packages\\aeon\\transformations\\collection\\base.py:157\u001b[0m, in \u001b[0;36mBaseCollectionTransformer.transform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 154\u001b[0m X_inner \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess_collection(X, store_metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 155\u001b[0m y_inner \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m--> 157\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX_inner\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my_inner\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Xt\n",
+ "File \u001b[1;32md:\\AeonProject\\aeon\\.venv\\Lib\\site-packages\\aeon\\transformations\\collection\\feature_based\\_catch22.py:182\u001b[0m, in \u001b[0;36mCatch22._transform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 165\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Transform X into the catch22 features.\u001b[39;00m\n\u001b[0;32m 166\u001b[0m \n\u001b[0;32m 167\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[38;5;124;03m The catch22 features for each dimension.\u001b[39;00m\n\u001b[0;32m 179\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 180\u001b[0m n_cases \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(X)\n\u001b[1;32m--> 182\u001b[0m f_idx \u001b[38;5;241m=\u001b[39m \u001b[43m_verify_features\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcatch24\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 184\u001b[0m threads_to_use \u001b[38;5;241m=\u001b[39m check_n_jobs(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n\u001b[0;32m 186\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_pycatch22:\n",
+ "File \u001b[1;32md:\\AeonProject\\aeon\\.venv\\Lib\\site-packages\\aeon\\transformations\\collection\\feature_based\\_catch22.py:1300\u001b[0m, in \u001b[0;36m_verify_features\u001b[1;34m(features, catch24)\u001b[0m\n\u001b[0;32m 1298\u001b[0m f_idx\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;241m23\u001b[39m)\n\u001b[0;32m 1299\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1300\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid feature selection.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1301\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(f, \u001b[38;5;28mint\u001b[39m):\n\u001b[0;32m 1302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m f \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m f \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m22\u001b[39m:\n",
+ "\u001b[1;31mValueError\u001b[0m: Invalid feature selection."
]
}
],
"source": [
- "transformed_data_mv = c22_mv.transform(BM_X_train)\n",
- "print(transformed_data_mv.shape)"
+ "features_long = [\"DN_HistogramMode_5\", \"CO_f1ecac\", \"FC_LocalSimple_mean3_stderr\"]\n",
+ "features_short = [\"mode_5\", \"acf_timescale\", \"forecast_error\"]\n",
+ "\n",
+ "c22_long = Catch22(features=features_long)\n",
+ "c22_long.fit(IPD_X_train, IPD_y_train)\n",
+ "transformed_data_long = c22_long.transform(IPD_X_train)\n",
+ "print(transformed_data_long.shape)\n",
+ "\n",
+ "c22_short = Catch22(features=features_short)\n",
+ "c22_short.fit(IPD_X_train, IPD_y_train)\n",
+ "transformed_data_short = c22_short.transform(IPD_X_train)\n",
+ "print(transformed_data_short.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 Catch24"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4. catch22 Forest Classifier\n",
+ "Catch24 extracts 24 features from a time series. The 24 features consist of the 22 features from catch22 with the addition of the mean and standard deviation of the time series. More features does not strictly define better results, as it may increase run time and overfit the data in certain time series tasks. In certain tasks, catch24 may outperform catch22. For example in \\[4\\], catch24 significally outperformed catch22 in cross-domain anomaly detection.\n",
"\n",
- "For classification tasks the default classifier to use with the catch22 features is random forest classifier.\n",
- "An implementation making use of the `RandomForestClassifier` from sklearn built on catch22 features is provided in the form on the `Catch22Classifier` for ease of use."
+ "Catch22 extracts the most important features for machine learning tasks and therefore is more widely used."
]
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:08.431962Z",
- "iopub.status.busy": "2020-12-19T14:30:08.419431Z",
- "iopub.status.idle": "2020-12-19T14:30:08.535295Z",
- "shell.execute_reply": "2020-12-19T14:30:08.535836Z"
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(67, 24)\n"
+ ]
}
- },
+ ],
+ "source": [
+ "c24 = Catch22(catch24=True)\n",
+ "data_c24 = c24.fit_transform(IPD_X_train)\n",
+ "print(data_c24.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.3 Replace NaNs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You may find that some time series cannot extract certain features from it. This may happen when division by zero occurs, or the input value is zero. Simply, it means we cannot extract the feature from the time series. However, we may still want a number for calculations and therefore 'replace_nans' allows us to replace NaN with zero."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
"outputs": [
{
- "data": {
- "text/html": [
- "
Catch22Classifier(random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Catch22Classifier(random_state=0)
"
- ],
- "text/plain": [
- "Catch22Classifier(random_state=0)"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data with NaN: [ nan nan 1. 0. 0. 6.\n",
+ " 6. 0. nan 0. 0. 0.\n",
+ " 3. 0. 1. 1.60943791 1. nan\n",
+ " nan nan 0.08 0. ]\n",
+ "\n",
+ "Data with no NaN: [0. 0. 1. 0. 0. 6.\n",
+ " 6. 0. 0. 0. 0. 0.\n",
+ " 3. 0. 1. 1.60943791 1. 0.\n",
+ " 0. 0. 0.08 0. ]\n"
+ ]
}
],
"source": [
- "c22f = Catch22Classifier(random_state=0)\n",
- "c22f.fit(IPD_X_train, IPD_y_train)"
+ "training_data = np.array([[0, 0, 0, 0, 0, 0]])\n",
+ "\n",
+ "c22_nan = Catch22()\n",
+ "data_nan = c22_nan.fit_transform(training_data)\n",
+ "print(f\"Data with NaN: {data_nan[0]}\\n\")\n",
+ "\n",
+ "c22_no_nan = Catch22(replace_nans=True)\n",
+ "data_no_nan = c22_no_nan.fit_transform(training_data)\n",
+ "print(\"Data with no NaN: \", data_no_nan[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.4 Pycatch22"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pycatch22 is the original implementation of catch22 based on \\[1\\]. Aeon allows you to use pycatch22 by setting the parameter 'use_pycatch22' to true. The difference of the two is that pycatch22 uses C as their backend while python uses the Numba library, which assembles python code into C. Aeon also regularly maintains their catch22 library, and therefore there should be barely any discrepancy between outputs. Pycatch22 has a few issues with their implementation such as at times struggling to run on windows. If you are using the aeon library for a certain task, but want to use pycatch22 for transformation of the data, it is recommended to use aeon's catch22 with the parameter 'use_pycatch22' set to true. If you do that, you may encounter a warning that pycatch22 has not been installed and therefore will use aeon's catch22, if that happens just install the pycatch22 library.\n",
+ "\n",
+ "Currently, pycatch22 has an issue where the output features extracted using Python yield different values compared to those extracted using the native C code. Aeon's catch22 implementation extracts the same results as pycatch22's C code. Therefore, the extracted results may differ."
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:08.553299Z",
- "iopub.status.busy": "2020-12-19T14:30:08.552508Z",
- "iopub.status.idle": "2020-12-19T14:30:08.561331Z",
- "shell.execute_reply": "2020-12-19T14:30:08.561821Z"
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pycatch22 : [-0.57058807 -0.73624268 4. 0.625 -0.45833333 2.45190656\n",
+ " 6. 0.42507544 0.58904862 0.92048041 0.11344743 0.37262397\n",
+ " 3. 0.86956522 6. 1.81200059 0.75 0.15104572\n",
+ " 0. 0. 0.04 0. ]\n",
+ "aeon catch22 : [ 0.09203038 -0.73624265 7. 0.625 -0.45833333 3.\n",
+ " 6. 0.42507544 0.58904862 0.8982969 0.11344743 0.37262397\n",
+ " 3. 0.86956522 4. 1.83902118 0.75 0.15104572\n",
+ " nan nan 0.06666667 0. ]\n"
+ ]
}
- },
- "outputs": [],
+ ],
"source": [
- "c22f_preds = c22f.predict(IPD_X_test)\n",
- "print(\"C22F Accuracy: \" + str(metrics.accuracy_score(IPD_y_test, c22f_preds)))"
+ "py22 = Catch22(use_pycatch22=True)\n",
+ "data_py22 = py22.fit_transform(IPD_X_test)\n",
+ "print(f\"Pycatch22 : {data_py22[667]}\\n\")\n",
+ "\n",
+ "py22 = Catch22()\n",
+ "data_py22 = py22.fit_transform(IPD_X_test)\n",
+ "print(\"aeon catch22 : \", data_py22[667])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### References\n",
"\n",
- "[1] Carl H Lubba, Sarab S Sethi, Philip Knaute, Simon R Schultz, Ben D Fulcher*, Nick S Jones*. catch22: CAnonical Time-series CHaracteristics (2019)\n",
- "\n"
+ "## 3. References:\n",
+ "\n",
+ "\\[1\\] Lubba, C. H., Sethi, S. S., Knaute, P., Schultz, S. R., Fulcher, B. D., & Jones, N. S. (2019). catch22: CAnonical Time-series CHaracteristics. Data Mining and Knowledge Discovery, 33(6), 1821-1852.\n",
+ "\n",
+ "\\[2\\] Fulcher, B. D., & Jones, N. S. (2017). hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell systems, 5(5), 527-531.\n",
+ "\n",
+ "\\[3\\] Fulcher, B. D., Little, M. A., & Jones, N. S. (2013). Highly comparative time-series analysis: the empirical structure of time series and their methods. Journal of the Royal Society Interface, 10(83), 20130048.\n",
+ "\n",
+ "\\[4\\] Agrahari, R., Nicholson, M., Conran, C., Assem, H. and Kelleher, J.D., 2022. Assessing feature representations for instance-based cross-domain anomaly detection in cloud services univariate time series data. IoT, 3(1), pp.123-144."
]
}
],
@@ -282,7 +391,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.11.9"
},
"toc": {
"base_numbering": 1,
diff --git a/examples/transformations/minirocket.ipynb b/examples/transformations/minirocket.ipynb
index bdc32b4e72..91619248a0 100644
--- a/examples/transformations/minirocket.ipynb
+++ b/examples/transformations/minirocket.ipynb
@@ -68,7 +68,7 @@
"source": [
"### 1.2 Load the Training Data\n",
"\n",
- "For more details on the data set, see the [univariate time series classification notebook](https://github.com/aeon-toolkit/aeon/blob/main/examples/02_classification_univariate.ipynb).\n",
+ "For more details on the data set, see the [classification notebook](../classification/classification.ipynb).\n",
"\n",
"**Note**: Input time series must be *at least* of length 9. Pad shorter time series\n",
"using, e.g., `Padder` (`aeon.transformers.collection`)."
diff --git a/examples/transformations/resizing.ipynb b/examples/transformations/resizing.ipynb
index efd23cc5ee..80f2254c7d 100644
--- a/examples/transformations/resizing.ipynb
+++ b/examples/transformations/resizing.ipynb
@@ -130,7 +130,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"outputs": [
{
"name": "stdout",
@@ -165,7 +165,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-12-19T14:32:01.245270Z",
@@ -208,13 +208,13 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 7,
"outputs": [
{
"data": {
- "text/plain": "0.8212290502793296"
+ "text/plain": "0.8268156424581006"
},
- "execution_count": 12,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/examples/transformations/rocket.ipynb b/examples/transformations/rocket.ipynb
index fc2c22710d..eec06438ca 100644
--- a/examples/transformations/rocket.ipynb
+++ b/examples/transformations/rocket.ipynb
@@ -78,7 +78,7 @@
"\n",
"### 2.1 Load the Training Data\n",
"For more details on the data set, see the [univariate time series classification\n",
- "notebook](https://github.com/aeon-toolkit/aeon/tree/main/examples/classification.ipynb)."
+ "notebook](https://github.com/aeon-toolkit/aeon/tree/main/examples/classification/classification.ipynb)."
]
},
{
diff --git a/examples/transformations/sast.ipynb b/examples/transformations/sast.ipynb
index 6f4b3a7fe5..f7dbd9c251 100644
--- a/examples/transformations/sast.ipynb
+++ b/examples/transformations/sast.ipynb
@@ -65,7 +65,7 @@
"\n",
"### 2.1 Load the Training Data\n",
"For more details on the data set, see the [univariate time series classification\n",
- "notebook](https://github.com/aeon-toolkit/aeon/tree/main/examples/classification.ipynb)."
+ "notebook](https://github.com/aeon-toolkit/aeon/tree/main/examples/classification/classification.ipynb)."
]
},
{
diff --git a/examples/transformations/transformations.ipynb b/examples/transformations/transformations.ipynb
new file mode 100644
index 0000000000..db78fec3a8
--- /dev/null
+++ b/examples/transformations/transformations.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Transforming time series\n",
+ "\n",
+ "Transforming time series into different data representations is fundamental to time\n",
+ "series machine learning. Transformation can involve extracting features that\n",
+ "characterize the time series, such as mean and variance or changing the series into,\n",
+ "for example, first order differences. We use the term transformer in the\n",
+ "`scikit-learn` sense, not to be confused with deep learning Transformers that employ\n",
+ "an attention mechanism. We call transformers that extract features\n",
+ "`series-to-vector` transformers and those that change the series into a different\n",
+ "representation that is still ordered `series-to-series` transformers.\n",
+ "\n",
+ "We further differentiate between transformers that act on a single series and those\n",
+ "that transform a collection of series. Single series transformers are located in\n",
+ "transformations/series directory and inherit from `BaseSeriesTransformer`. For\n",
+ "example, `AutoCorrelationSeriesTransformer` is a `series-to-series` transformer that\n",
+ "finds the auto correlation function for a single series."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0.96019465 0.89567531 0.83739477 0.7977347 0.78594315 0.7839188\n",
+ " 0.78459213 0.79221505 0.8278519 0.8827128 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from aeon.datasets import load_airline\n",
+ "from aeon.transformations.series import AutoCorrelationSeriesTransformer\n",
+ "\n",
+ "series = load_airline()\n",
+ "transformer = AutoCorrelationSeriesTransformer(n_lags=10)\n",
+ "acf = transformer.fit_transform(series)\n",
+ "print(acf)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Collection transformers are located in the transformations/collection directory and\n",
+ "inherit from `BaseCollectionTransformer`. For example, `Truncator` truncates all time\n",
+ " series in a collection to the same length."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Unequal length, first case (1, 500) tenth case (1, 300)\n",
+ "Truncated collection shape = (1074, 1, 100)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from aeon.datasets import load_plaid\n",
+ "from aeon.transformations.collection import Truncator\n",
+ "\n",
+ "X, y = load_plaid()\n",
+ "print(\" Unequal length, first case \", X[0].shape, \" tenth case \", X[10].shape)\n",
+ "trunc = Truncator(truncated_length=100)\n",
+ "X2 = trunc.fit_transform(X)\n",
+ "print(\"Truncated collection shape =\", X2.shape)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "`Truncator` is a `series-to-series` transformer\n",
+ " that returns a new collection of time series of the same length. This can then be\n",
+ " used, for example, by a classifier that only works with equal length series:"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data seen by instance of SummaryClassifier has unequal length series, but SummaryClassifier cannot handle unequal length series. \n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "SummaryClassifier()",
+ "text/html": "
SummaryClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SummaryClassifier()
"
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from aeon.classification.feature_based import SummaryClassifier\n",
+ "\n",
+ "summary = SummaryClassifier()\n",
+ "try:\n",
+ " summary.fit(X, y)\n",
+ "except ValueError as e:\n",
+ " print(e)\n",
+ "\n",
+ "summary.fit(X2, y)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Some collection transformers are supervised, meaning they fit a transform based on\n",
+ "the class labels. For example, the shapelet transform finds shapelets that are good\n",
+ "at separating classes. This is a `series-to-vector` transformer that produces tabular\n",
+ " output shape `(n_cases, n_shapelets)`.\n"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1074, 2)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from aeon.transformations.collection.shapelet_based import RandomShapeletTransform\n",
+ "\n",
+ "st = RandomShapeletTransform(max_shapelets=10, n_shapelet_samples=100)\n",
+ "X2 = st.fit_transform(X, y)\n",
+ "print(X2.shape)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "`series-to-vector` transformers produce output that is compatible with `scikit-learn`\n",
+ " estimators"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (1074, 1) + inhomogeneous part.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "RandomForestClassifier()",
+ "text/html": "
RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
![\"bake](\"img/bakeoff2015.png\")
\n",
- "\n",
- "This displays the critical difference diagram [6] for comparing classifiers. It shows\n",
- " the average rank of each estimator over all datasets. It then groups estimators for\n",
- " which there is no significant difference in rank into cliques, shown with a solid\n",
- " bar. The published results used the original method for finding cliques called the\n",
- " post hoc Nemenyi test. Our plotting tool offers this as an alternative. See the docs\n",
- " for ``aeon.visualisation.plot_critical_difference`` for more details. To recreate the\n",
- " above, we can do this (note slight difference in names, ``MSM_1NN`` is `MSM` and\n",
- " ``FlatCOTE`` is ``COTE``."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:01:28.707811Z",
- "start_time": "2024-09-25T21:59:08.753076Z"
- }
- },
- "source": [
- "from aeon.visualisation import plot_critical_difference\n",
- "\n",
- "classifiers = [\"MSM_1NN\", \"LPS\", \"TSBF\", \"TSF\", \"DTW_F\", \"EE\", \"BOSS\", \"ST\", \"FlatCOTE\"]\n",
- "# Get columm positions of classifiers in results\n",
- "indx = [uni_classifiers_2017[key] for key in classifiers if key in uni_classifiers_2017]\n",
- "plot, _ = plot_critical_difference(averaged[:, indx], classifiers, test=\"Nemenyi\")\n",
- "plot.show()"
- ],
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\Matthew Middlehurst\\AppData\\Local\\Temp\\ipykernel_14676\\1537479541.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
- " plot.show()\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "![\"bake](\"img/bakeoff2021.png\")
\n",
- "\n",
- "We can recreate the accuracy graph by loading the results from tsc.com and plotting\n",
- "like so:"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "source": [
- "from aeon.benchmarking.results_loaders import get_bake_off_2021_results\n",
- "\n",
- "default = get_bake_off_2021_results()\n",
- "averaged = get_bake_off_2021_results(default_only=False)\n",
- "print(\"Shape of results = \", averaged.shape)"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:01:29.757184Z",
- "start_time": "2024-09-25T22:01:28.805238Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Shape of results = (26, 11)\n"
- ]
- }
- ],
- "execution_count": 6
- },
- {
- "cell_type": "code",
- "source": [
- "plot, _ = plot_critical_difference(averaged, list(multi_classifiers_2021.keys()))"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-09-25T22:01:30.001964Z",
- "start_time": "2024-09-25T22:01:29.769151Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "D:\\CMP_Machine_Learning\\Repositories\\aeon\\.venv\\lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:600: UserWarning: Exact p-value calculation does not work if there are zeros. Switching to normal approximation.\n",
- " return result_to_tuple(hypotest_fun_out(*samples, **kwds))\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "![\"time](\"img/sim_search.png\")
"
]
diff --git a/examples/transformations/catch22.ipynb b/examples/transformations/catch22.ipynb
index 6bdd09a5c1..a551f67ef6 100644
--- a/examples/transformations/catch22.ipynb
+++ b/examples/transformations/catch22.ipynb
@@ -6,44 +6,43 @@
"source": [
"# The Canonical Time-series Characteristics (catch22) transform\n",
"\n",
- "catch22\\[1\\] is a collection of 22 time series features extracted from the 7000+ present in the _hctsa_ \\[2\\]\\[3\\] toolbox.\n",
+ "Catch22\\[1\\] is a collection of 22 time series features extracted from the 7000+ present in the _hctsa_ \\[2\\]\\[3\\] toolbox.\n",
"A hierarchical clustering was performed on the correlation matrix of features that performed better than random chance to remove redundancy.\n",
"These clusters were sorted by balanced accuracy using a decision tree classifier and a single feature was selected from the 22 clusters formed, taking into account balanced accuracy results, computational efficiency and interpretability.\n",
+ "More about the individual features of catch22 can be learned in the [Gitbook](https://time-series-features.gitbook.io/catch22/information-about-catch22/feature-descriptions) of the original creators.\n",
"\n",
- "In this notebook, we will demonstrate how to use the catch22 transformer on the ItalyPowerDemand univariate and BasicMotions multivariate datasets. We also show catch22 used for classification with a random forest classifier.\n",
- "\n",
- "#### References:\n",
- "\n",
- "\\[1\\] Lubba, C. H., Sethi, S. S., Knaute, P., Schultz, S. R., Fulcher, B. D., & Jones, N. S. (2019). catch22: CAnonical Time-series CHaracteristics. Data Mining and Knowledge Discovery, 33(6), 1821-1852.\n",
- "\n",
- "\\[2\\] Fulcher, B. D., & Jones, N. S. (2017). hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell systems, 5(5), 527-531.\n",
- "\n",
- "\\[3\\] Fulcher, B. D., Little, M. A., & Jones, N. S. (2013). Highly comparative time-series analysis: the empirical structure of time series and their methods. Journal of the Royal Society Interface, 10(83), 20130048."
+ "In this notebook, we will demonstrate how to use aeon's catch22 transformer on the ItalyPowerDemand univariate and BasicMotions multivariate datasets. We will go through the parameters of catch22 and how changing the default values may change results. Catch22 has also been used inside of [classification](../classification/feature_based.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Transformation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Catch22 is a feature based transformer that extracts 22 features from a time series. The input data can be both univariate and multivariate, without the need to reshape the data. It is most commonly used for interpretability of each time series data. Additionally, as the data of a time series will be reduced to 22 data values, it will increase computational efficiency of machine learning tasks such as clustering, classification, etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 1. Imports"
+ "### 1.1 Import Data and Catch22"
]
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:07.306937Z",
- "iopub.status.busy": "2020-12-19T14:30:07.306390Z",
- "iopub.status.idle": "2020-12-19T14:30:08.036353Z",
- "shell.execute_reply": "2020-12-19T14:30:08.036857Z"
- }
- },
+ "execution_count": 7,
+ "metadata": {},
"outputs": [],
"source": [
- "from sklearn import metrics\n",
+ "import numpy as np\n",
"\n",
- "from aeon.classification.feature_based import Catch22Classifier\n",
"from aeon.datasets import load_basic_motions, load_italy_power_demand\n",
"from aeon.transformations.collection.feature_based import Catch22"
]
@@ -52,69 +51,65 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2. Load data"
+ "### 1.2 Load Data"
]
},
{
"cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:08.041533Z",
- "iopub.status.busy": "2020-12-19T14:30:08.041060Z",
- "iopub.status.idle": "2020-12-19T14:30:08.210768Z",
- "shell.execute_reply": "2020-12-19T14:30:08.211258Z"
- }
- },
+ "execution_count": 3,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "(67, 1, 24) (67,) (50, 1, 24) (50,)\n",
- "(40, 6, 100) (40,) (40, 6, 100) (40,)\n"
+ "Italy Power Demand (Univariate): (67, 1, 24) (67,) (1029, 1, 24) (1029,)\n",
+ "Load Basic Motions (Multivarite): (40, 6, 100) (40,) (40, 6, 100) (40,)\n"
]
}
],
"source": [
"IPD_X_train, IPD_y_train = load_italy_power_demand(split=\"train\")\n",
"IPD_X_test, IPD_y_test = load_italy_power_demand(split=\"test\")\n",
- "IPD_X_test = IPD_X_test[:50]\n",
- "IPD_y_test = IPD_y_test[:50]\n",
"\n",
- "print(IPD_X_train.shape, IPD_y_train.shape, IPD_X_test.shape, IPD_y_test.shape)\n",
+ "print(\n",
+ " \"Italy Power Demand (Univariate): \",\n",
+ " IPD_X_train.shape,\n",
+ " IPD_y_train.shape,\n",
+ " IPD_X_test.shape,\n",
+ " IPD_y_test.shape,\n",
+ ")\n",
"\n",
"BM_X_train, BM_y_train = load_basic_motions(split=\"train\")\n",
- "BM_X_test, BM_y_test = load_basic_motions(\n",
- " split=\"test\",\n",
- ")\n",
+ "BM_X_test, BM_y_test = load_basic_motions(split=\"test\")\n",
"\n",
- "print(BM_X_train.shape, BM_y_train.shape, BM_X_test.shape, BM_y_test.shape)"
+ "print(\n",
+ " \"Load Basic Motions (Multivarite): \",\n",
+ " BM_X_train.shape,\n",
+ " BM_y_train.shape,\n",
+ " BM_X_test.shape,\n",
+ " BM_y_test.shape,\n",
+ ")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 3. catch22 transform\n",
- "\n",
- "### Univariate\n",
- "\n",
- "The catch22 features are provided in the form of a transformer, `Catch22`.\n",
- "From this the transformed data can be used for a variety of time series analysis tasks."
+ "### 1.3 Transform the Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Univariate"
]
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:08.215545Z",
- "iopub.status.busy": "2020-12-19T14:30:08.215049Z",
- "iopub.status.idle": "2020-12-19T14:30:08.222937Z",
- "shell.execute_reply": "2020-12-19T14:30:08.223422Z"
- }
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -135,134 +130,248 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Multivariate\n",
- "\n",
- "Transformation of multivariate data is supported by `Catch22`.\n",
- "The default procedure will concatenate each column prior to transformation."
+ "#### Multivariate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do note that the result of the shape won't be (X , 22). This is because it's a multivariate dataset, and therefore the feature vector will be of size 22 times the number of channels. "
]
},
{
"cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2020-12-19T14:30:08.264541Z",
- "iopub.status.busy": "2020-12-19T14:30:08.264050Z",
- "iopub.status.idle": "2020-12-19T14:30:08.266022Z",
- "shell.execute_reply": "2020-12-19T14:30:08.266517Z"
- }
- },
+ "execution_count": 5,
+ "metadata": {},
"outputs": [
{
- "data": {
- "text/html": [
- "