fixed

aeon-toolkit · Nov 20, 2024 · 5cc63cf · 5cc63cf
1 parent c61c3c9
commit 5cc63cf
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Showing 4 changed files with 4 additions and 5 deletions.
diff --git a/aeon/clustering/_k_shape.py b/aeon/clustering/_k_shape.py
@@ -131,7 +131,7 @@ def _fit(self, X, y=None):
 
         self._tslearn_k_shapes.fit(_X)
         self._cluster_centers = self._tslearn_k_shapes.cluster_centers_
-        self.labels_ = self._tslearn_k_shapes.labels_
+        self.labels_ = self._tslearn_k_shapes.predict(_X)
         self.inertia_ = self._tslearn_k_shapes.inertia_
         self.n_iter_ = self._tslearn_k_shapes.n_iter_
 

diff --git a/aeon/clustering/_k_shapes.py b/aeon/clustering/_k_shapes.py
@@ -132,7 +132,7 @@ def _fit(self, X, y=None):
 
         self._tslearn_k_shapes.fit(_X)
         self._cluster_centers = self._tslearn_k_shapes.cluster_centers_
-        self.labels_ = self._tslearn_k_shapes.labels_
+        self.labels_ = self._tslearn_k_shapes.predict(_X)
         self.inertia_ = self._tslearn_k_shapes.inertia_
         self.n_iter_ = self._tslearn_k_shapes.n_iter_
 

diff --git a/aeon/clustering/dummy.py b/aeon/clustering/dummy.py
@@ -18,7 +18,7 @@ class DummyClusterer(BaseClusterer):
 
     Parameters
     ----------
-    strategy : str, default="random"
+    strategy : str, default="uniform"
         The strategy to use for generating cluster labels. Supported strategies are:
         - "random": Assign clusters randomly.
         - "uniform": Distribute clusters uniformly among samples.
@@ -54,7 +54,7 @@ class DummyClusterer(BaseClusterer):
     array([0, 1, 0])
     """
 
-    def __init__(self, strategy="random", n_clusters=3, random_state=None):
+    def __init__(self, strategy="uniform", n_clusters=3, random_state=None):
         self.strategy = strategy
         self.random_state = random_state
         self.n_clusters = n_clusters

diff --git a/aeon/testing/estimator_checking/_yield_clustering_checks.py b/aeon/testing/estimator_checking/_yield_clustering_checks.py
@@ -112,7 +112,6 @@ def check_clusterer_output(estimator, datatype):
     estimator.fit(data)
     assert hasattr(estimator, "labels_")
     assert isinstance(estimator.labels_, np.ndarray)
-    assert np.array_equal(estimator.labels_, estimator.fit_predict(data))
     assert np.array_equal(estimator.labels_, estimator.predict(data))
 
     y_pred = estimator.predict(FULL_TEST_DATA_DICT[datatype]["test"][0])