Neural_Net_March_Madness_ML.ipynb

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    "colab": {
      "name": "Neural Net March Madness ML",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/rafibarash/ml_march_madness/blob/master/Neural_Net_March_Madness_ML.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "jK4aXgYCVnXO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Summary and Findings"
      ]
    },
    {
      "metadata": {
        "id": "MubsT864VrgW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "A machine learning attempt to predict result of march madness matchups.\n",
        "\n",
        "### Findings\n",
        "**Linear Regression/Classification**\n",
        "* Linear regressor/classifier on single \"Seed Difference\" feature predicted outcomes with 72.4% accuracy, log loss of 0.55, and rmse of 0.43 (BENCHMARK TO BEAT)\n",
        "* Linear regression model predicted near 100% chance of winning when seed difference is -15 (1 vs 16 seed) and near 0% chance of winning when seed difference is 15 (16 vs 1 seed)....as expected\n",
        "\n",
        "**Neural Network on Compact Data**\n",
        "* First model was only fed regular season data from one tournament team and predicted outcome\n",
        "* First Model overfit badly and had comparable classification accuracy, log loss, and rmse to simple linear regression on seed difference\n",
        "* Second model fed regular season data from both tournament teams\n",
        "* Second model predicted tournament outcomes with 73.5% accuracy, log loss of 0.45, and rmse of 0.40\n",
        "* Second model did not overfit too much\n",
        "\n",
        "**Neural Network on Detailed Data**\n",
        "* First model was only fed regular season data from one tournament team and predicted outcome\n",
        "* First model also overfit badly and had comparable classification accuracy, log loss, and rmse to simple linear regression on seed difference\n",
        "* Second model had very bad overfitting. Validation and test results were worse than linear regression!\n",
        "* Tried using the AdamOptimizer built in to Tensorflow and had a ridiculous rsme of 0.30 on training data but 0.45 on validation and test data\n",
        "\n",
        "**Ideas to Improve Performance of Neural Network Models**\n",
        "* Bucketize each feature for number of seasons\n",
        "* Keep individual stats like assists or turnovers instead of using feature crosses\n",
        "* Apply Dropout Regularization (maybe test around with L1 and L2 regularization)"
      ]
    },
    {
      "metadata": {
        "id": "MOK_-6Ifb_mk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Code Used Throughout Project"
      ]
    },
    {
      "metadata": {
        "id": "Gd2m_KyacY7B",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "from IPython import display\n",
        "from matplotlib import cm\n",
        "from matplotlib import gridspec\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import metrics\n",
        "from sklearn.utils import shuffle\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.data import Dataset\n",
        "\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ujWzzwj8uzFV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def preprocess_features(results_dataframe):\n",
        "  \"\"\"Prepares input features from tournament data set.\n",
        "\n",
        "  Args:\n",
        "    results_dataframe: A Pandas DataFrame expected to contain data\n",
        "      from the results of regular season and tournament NCAA games\n",
        "  Returns:\n",
        "    A DataFrame that contains the features to be used for the model, including\n",
        "    synthetic features.\n",
        "  \"\"\"\n",
        "  stats = STATS\n",
        "  selected_features = results_dataframe[stats]\n",
        "  processed_features = selected_features.copy()\n",
        " \n",
        "  return processed_features\n",
        "\n",
        "\n",
        "def preprocess_targets(results_dataframe):\n",
        "  \"\"\"Prepares target features (i.e., labels) from tournament data set.\n",
        "\n",
        "  Args:\n",
        "    results_dataframe: A Pandas DataFrame expected to contain data\n",
        "      from the results of regular season and tournament NCAA games\n",
        "  Returns:\n",
        "    A DataFrame that contains the target feature.\n",
        "  \"\"\"\n",
        "  output_targets = pd.DataFrame()\n",
        "  output_targets[\"Result\"] = results_dataframe[\"Result\"]\n",
        "  \n",
        "  return output_targets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ojr-jq4ccIQ4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def construct_feature_columns(input_features):\n",
        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
        "\n",
        "  Args:\n",
        "    input_features: The names of the numerical input features to use.\n",
        "  Returns:\n",
        "    A set of feature columns\n",
        "  \"\"\" \n",
        "  return set([tf.feature_column.numeric_column(my_feature)\n",
        "              for my_feature in input_features])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BAWLayafcLS3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
        "    \"\"\"Trains a linear regression model.\n",
        "  \n",
        "    Args:\n",
        "      features: pandas DataFrame of features\n",
        "      targets: pandas DataFrame of targets\n",
        "      batch_size: Size of batches to be passed to the model\n",
        "      shuffle: True or False. Whether to shuffle the data.\n",
        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
        "    Returns:\n",
        "      Tuple of (features, labels) for next data batch\n",
        "    \"\"\"\n",
        "    \n",
        "    # Convert pandas data into a dict of np arrays.\n",
        "    features = {key:np.array(value) for key,value in dict(features).items()}                                            \n",
        " \n",
        "    # Construct a dataset, and configure batching/repeating.\n",
        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
        "    \n",
        "    # Shuffle the data, if specified.\n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(10000)\n",
        "    \n",
        "    # Return the next batch of data.\n",
        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
        "    return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d34THLNUcPE5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def linear_scale(series):\n",
        "  min_val = series.min()\n",
        "  max_val = series.max()\n",
        "  scale = (max_val - min_val) / 2.0\n",
        "  return series.apply(lambda x:((x - min_val) / scale) - 1.0)\n",
        "\n",
        "def log_normalize(series):\n",
        "  return series.apply(lambda x:math.log(x+1.0))\n",
        "\n",
        "def clip(series, clip_to_min, clip_to_max):\n",
        "  return series.apply(lambda x:(\n",
        "    min(max(x, clip_to_min), clip_to_max)))\n",
        "\n",
        "def z_score_normalize(series):\n",
        "  mean = series.mean()\n",
        "  std_dv = series.std()\n",
        "  return series.apply(lambda x:(x - mean) / std_dv)\n",
        "\n",
        "def binary_threshold(series, threshold):\n",
        "  return series.apply(lambda x:(1 if x > threshold else 0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ceCTn8AscR5J",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def normalize_linear_scale(examples_dataframe):\n",
        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
        "  normalized_features = pd.DataFrame()\n",
        "  for stat in STATS:\n",
        "    normalized_features[stat] = linear_scale(examples_dataframe[stat])\n",
        "  return normalized_features"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3j8TGYbwUgwq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_nn_regression_model(\n",
        "    my_optimizer,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    hidden_units,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets,\n",
        "    test_examples,\n",
        "    test_targets,\n",
        "    dropout=0):\n",
        "  \"\"\"Trains a neural network regression model.\"\"\"\n",
        "\n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "  \n",
        "  # Create a DNNRegressor object.\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
        "      feature_columns=construct_feature_columns(training_examples),\n",
        "      hidden_units=hidden_units,\n",
        "      optimizer=my_optimizer,\n",
        "      dropout=dropout\n",
        "  )\n",
        "  \n",
        "  # Create input functions.\n",
        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
        "                                          training_targets[\"Result\"], \n",
        "                                          batch_size=batch_size)\n",
        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
        "                                                  training_targets[\"Result\"], \n",
        "                                                  num_epochs=1, \n",
        "                                                  shuffle=False)\n",
        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
        "                                                    validation_targets[\"Result\"], \n",
        "                                                    num_epochs=1, \n",
        "                                                    shuffle=False)\n",
        "  predict_test_input_fn = lambda: my_input_fn(test_examples, \n",
        "                                                    test_targets[\"Result\"], \n",
        "                                                    num_epochs=1, \n",
        "                                                    shuffle=False)\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training neural network model...\")\n",
        "  print(\"RMSE (on training data):\")\n",
        "  training_rmse = []\n",
        "  validation_rmse = []\n",
        "  test_rmse = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    dnn_regressor.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "    # Take a break and compute predictions.\n",
        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
        "    \n",
        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
        "    \n",
        "    test_predictions = dnn_regressor.predict(input_fn=predict_test_input_fn)\n",
        "    test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
        "    \n",
        "    # Compute training and validation loss.\n",
        "    training_root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
        "    validation_root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
        "    test_root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(test_predictions, test_targets))\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_rmse.append(training_root_mean_squared_error)\n",
        "    validation_rmse.append(validation_root_mean_squared_error)\n",
        "    test_rmse.append(test_root_mean_squared_error)\n",
        "  print(\"Model training finished.\")\n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.ylabel(\"RMSE\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(training_rmse, label=\"training\")\n",
        "  plt.plot(validation_rmse, label=\"validation\")\n",
        "  plt.plot(test_rmse, label=\"test\")\n",
        "  plt.legend()\n",
        "\n",
        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
        "  print(\"Final RMSE (on test data): %0.2f\" % test_root_mean_squared_error)\n",
        "\n",
        "  return dnn_regressor, training_rmse, validation_rmse, test_rmse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A1Hu8l3fUba2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_nn_classifier_model(\n",
        "    learning_rate,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    hidden_units,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets,\n",
        "    test_examples,\n",
        "    test_targets,\n",
        "    dropout=0):\n",
        "  \"\"\"Trains a neural network classification model.\"\"\"\n",
        "\n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "  \n",
        "  # Create a linear classifier object.\n",
        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)  \n",
        "  nn_classifier = tf.estimator.DNNClassifier(\n",
        "      feature_columns=construct_feature_columns(training_examples),\n",
        "      optimizer=my_optimizer,\n",
        "      hidden_units=hidden_units,\n",
        "      dropout=dropout\n",
        "  )\n",
        "  \n",
        "  # Create input functions.\n",
        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
        "                                          training_targets[\"Result\"], \n",
        "                                          batch_size=batch_size)\n",
        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
        "                                                  training_targets[\"Result\"], \n",
        "                                                  num_epochs=1, \n",
        "                                                  shuffle=False)\n",
        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
        "                                                    validation_targets[\"Result\"], \n",
        "                                                    num_epochs=1, \n",
        "                                                    shuffle=False)\n",
        "  predict_test_input_fn = lambda: my_input_fn(test_examples, \n",
        "                                                    test_targets[\"Result\"], \n",
        "                                                    num_epochs=1, \n",
        "                                                    shuffle=False)\n",
        "  \n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training nn classifier model...\")\n",
        "  print(\"LogLoss (on training data):\")\n",
        "  training_log_losses = []\n",
        "  validation_log_losses = []\n",
        "  test_log_losses = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    nn_classifier.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "    # Take a break and compute predictions.    \n",
        "    training_probabilities = nn_classifier.predict(input_fn=predict_training_input_fn)\n",
        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
        "    \n",
        "    validation_probabilities = nn_classifier.predict(input_fn=predict_validation_input_fn)\n",
        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
        "    \n",
        "    test_probabilities = nn_classifier.predict(input_fn=predict_test_input_fn)\n",
        "    test_probabilities = np.array([item['probabilities'] for item in test_probabilities])\n",
        "    \n",
        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
        "    test_log_loss = metrics.log_loss(test_targets, test_probabilities)\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_log_losses.append(training_log_loss)\n",
        "    validation_log_losses.append(validation_log_loss)\n",
        "    test_log_losses.append(test_log_loss)\n",
        "  print(\"Model training finished.\")\n",
        "  \n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.ylabel(\"LogLoss\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"LogLoss vs. Periods\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(training_log_losses, label=\"training\")\n",
        "  plt.plot(validation_log_losses, label=\"validation\")\n",
        "  plt.plot(test_log_losses, label=\"test\")\n",
        "  plt.legend()\n",
        "\n",
        "  return nn_classifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iD8OKnhyUcFH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
        "                                                  validation_targets[\"Result\"], \n",
        "                                                  num_epochs=1, \n",
        "                                                  shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "79hBZ8E7ew2P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Linear Regression/Classification"
      ]
    },
    {
      "metadata": {
        "id": "ouAwOJEBb9IZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Load datasets (IF ONLINE)\n",
        "\n",
        "t_compact = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/NCAATourneyCompactResults.csv\", sep=\",\")\n",
        "t_seeds = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/NCAATourneySeeds.csv\", sep=\",\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zqQQzoo6gYmC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Load datasets (IF OFFLINE)\n",
        "\n",
        "t_compact = pd.read_csv(\"datasets/NCAATourneyCompactResults.csv\", sep=\",\")\n",
        "t_seeds = pd.read_csv(\"datasets/NCAATourneySeeds.csv\", sep=\",\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CRewA4gItSGe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "STATS = ['SeedDiff']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "l9jzXHzdg2gx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Run data through pipeline to make usable"
      ]
    },
    {
      "metadata": {
        "id": "kpTIrJv3co5U",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Clean Data\n",
        "t_seeds['Seed'] = t_seeds['Seed'].apply(lambda seed: int(seed[1:3]))\n",
        "t_wseeds = t_seeds.rename(columns={'TeamID':'WTeamID', 'Seed':'WSeed'})\n",
        "t_lseeds = t_seeds.rename(columns={'TeamID':'LTeamID', 'Seed':'LSeed'})\n",
        "t_compact = t_compact.drop(labels=[\"DayNum\", \"WScore\", \"LScore\", \"WLoc\", \"NumOT\"], axis=1)\n",
        "t_compact = t_compact.rename(columns={'WTeam': 'WTeamID', 'LTeam': 'LTeamID'})\n",
        "\n",
        "# Merge Seeds with Team ID\n",
        "t_dummy = pd.merge(left=t_compact, right=t_wseeds, how='left', on=['Season', 'WTeamID'])\n",
        "t_concat = pd.merge(left=t_dummy, right=t_lseeds, on=['Season', 'LTeamID'])\n",
        "t_concat['SeedDiff'] = t_concat['WSeed'] - t_concat['LSeed']\n",
        "\n",
        "# make DataFrame that summarizes SeedDiff and Result\n",
        "t_wins = pd.DataFrame()\n",
        "t_wins['SeedDiff'] = t_concat['SeedDiff']\n",
        "t_wins['Result'] = 1\n",
        "\n",
        "t_losses = pd.DataFrame()\n",
        "t_losses['SeedDiff'] = -t_concat['SeedDiff']\n",
        "t_losses['Result'] = 0\n",
        "\n",
        "# concat seediff with results into DataFrame to use for predictions\n",
        "full_results = pd.concat((t_wins, t_losses))\n",
        "full_results = full_results.reset_index(drop=True)\n",
        "full_results = full_results.reindex(\n",
        "    np.random.permutation(full_results.index))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5DpKHg3KgsKf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Partition into training, validation, and test dataframes"
      ]
    },
    {
      "metadata": {
        "id": "KCgHxKsDct1L",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_total_examples = len(full_results.index)\n",
        "num_training_examples = math.floor(num_total_examples*0.7)\n",
        "num_validation_examples = math.floor(num_total_examples*0.15)\n",
        "index_validation_examples = num_training_examples + num_validation_examples\n",
        "num_test_examples = math.floor(num_total_examples*0.15)\n",
        "index_test_examples = num_training_examples + num_validation_examples + num_test_examples\n",
        "\n",
        "# Choose the first 65% of examples for training.\n",
        "training_examples = preprocess_features(full_results.iloc[0:num_training_examples])\n",
        "training_targets = preprocess_targets(full_results.iloc[0:num_training_examples])\n",
        "\n",
        "# Choose 15% after training examples for validation\n",
        "validation_examples = preprocess_features(full_results.iloc[num_training_examples:index_validation_examples])\n",
        "validation_targets = preprocess_targets(full_results.iloc[num_training_examples:index_validation_examples])\n",
        "\n",
        "# Choose 15% after validation examples for testing\n",
        "test_examples = preprocess_features(full_results.iloc[index_validation_examples:index_test_examples])\n",
        "test_targets = preprocess_targets(full_results.iloc[index_validation_examples:index_test_examples])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qi-560wfcuiV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Double-check that we've done the right thing.\n",
        "\n",
        "print(\"Training examples summary:\")\n",
        "display.display(training_examples.describe())\n",
        "print(\"Validation examples summary:\")\n",
        "display.display(validation_examples.describe())\n",
        "print(\"Test examples summary:\")\n",
        "display.display(test_examples.describe())\n",
        "\n",
        "# print(\"Training targets summary:\") \n",
        "display.display(training_targets.head())\n",
        "print(\"Validation targets summary:\")\n",
        "display.display(validation_targets.head())\n",
        "print(\"Test targets summary:\")\n",
        "display.display(test_targets.head())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Rom966aXhAyg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Train linear regressor"
      ]
    },
    {
      "metadata": {
        "id": "2haP5BCetHCX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_linear_regressor(\n",
        "    learning_rate,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets,\n",
        "    test_examples,\n",
        "    test_targets):\n",
        "  \"\"\"Trains a linear regression model of one feature.\"\"\"\n",
        "  \n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "\n",
        "  my_feature = \"SeedDiff\"\n",
        "  my_label = \"Result\"\n",
        "\n",
        "  # Create feature columns.\n",
        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
        "  \n",
        "  # Create input functions.\n",
        "  training_input_fn = lambda:my_input_fn(training_examples, training_targets, batch_size=batch_size)\n",
        "  predict_training_input_fn = lambda: my_input_fn(training_examples, training_targets, num_epochs=1, shuffle=False)\n",
        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, validation_targets, num_epochs=1, shuffle=False)\n",
        "  predict_test_input_fn = lambda: my_input_fn(test_examples, test_targets, num_epochs=1, shuffle=False)\n",
        "  \n",
        "  # Create a linear regressor object.\n",
        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  linear_regressor = tf.estimator.LinearRegressor(\n",
        "      feature_columns=feature_columns,\n",
        "      optimizer=my_optimizer\n",
        "  )\n",
        "\n",
        "  # Set up to plot the state of our model's line each period.\n",
        "  plt.figure(figsize=(15, 6))\n",
        "  plt.subplot(1, 2, 1)\n",
        "  plt.title(\"Learned Line by Period\")\n",
        "  plt.ylabel(my_label)\n",
        "  plt.xlabel(my_feature)\n",
        "  sample = full_results.sample(n=300)\n",
        "  plt.scatter(sample[my_feature], sample[my_label])\n",
        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training linear regressor model...\")\n",
        "  print(\"RMSE (on training data):\")\n",
        "  training_rmse = []\n",
        "  validation_rmse = []\n",
        "  test_rmse = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    linear_regressor.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "    # Take a break and compute predictions.\n",
        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
        "    \n",
        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
        "    \n",
        "    test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n",
        "    test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
        "    \n",
        "    # Compute RMSE loss.\n",
        "    training_root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
        "    validation_root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
        "    test_root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(test_predictions, test_targets))\n",
        "    \n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_rmse.append(training_root_mean_squared_error)\n",
        "    validation_rmse.append(validation_root_mean_squared_error)\n",
        "    test_rmse.append(test_root_mean_squared_error)\n",
        "    # Finally, track the weights and biases over time.\n",
        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
        "    y_extents = np.array([0, sample[my_label].max()])\n",
        "    \n",
        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % my_feature)[0]\n",
        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
        "\n",
        "    x_extents = (y_extents - bias) / weight\n",
        "    x_extents = np.maximum(np.minimum(x_extents,\n",
        "                                      sample[my_feature].max()),\n",
        "                           sample[my_feature].min())\n",
        "    y_extents = weight * x_extents + bias\n",
        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
        "  print(\"Model training finished.\")\n",
        "  \n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.ylabel(\"RMSE\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(training_rmse, label=\"training\")\n",
        "  plt.plot(validation_rmse, label=\"validation\")\n",
        "  plt.plot(test_rmse, label=\"test\")\n",
        "  plt.legend()\n",
        "\n",
        "  # Output a table with calibration data.\n",
        "#   calibration_data = pd.DataFrame()\n",
        "#   calibration_data[\"predictions\"] = pd.Series(training_predictions)\n",
        "#   calibration_data[\"targets\"] = pd.Series(training_targets)\n",
        "#   display.display(calibration_data.describe())\n",
        "\n",
        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
        "  print(\"Final RMSE (on test data): %0.2f\" % test_root_mean_squared_error)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mo4Exwz5tHoZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "outputId": "d75438c4-740a-4c36-fc12-d0d641858ebd"
      },
      "cell_type": "code",
      "source": [
        "train_linear_regressor(\n",
        "    learning_rate=0.0003,\n",
        "    steps=1000,\n",
        "    batch_size=20,\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets,\n",
        "    test_examples=test_examples,\n",
        "    test_targets=test_targets\n",
        ")"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training linear regressor model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 0.61\n",
            "  period 01 : 0.55\n",
            "  period 02 : 0.50\n",
            "  period 03 : 0.48\n",
            "  period 04 : 0.46\n",
            "  period 05 : 0.44\n",
            "  period 06 : 0.44\n",
            "  period 07 : 0.44\n",
            "  period 08 : 0.43\n",
            "  period 09 : 0.43\n",
            "Model training finished.\n",
            "Final RMSE (on training data):   0.43\n",
            "Final RMSE (on validation data): 0.43\n",
            "Final RMSE (on test data): 0.44\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5O5rcZu35B9Yfa61q7hT15Gnl7+1FsfY4SxNvafq0NGWtZYs6hW2pVQqN+nUn\n0c6VWwcPkJdsPqNICCGEEAWpp02bNq08dnzq1CkmTJjAoUOHOHnyJFu3biUlJYWEhATq1atHgwYN\nmDZtGqtWrSIkJIRevXqZ3V+GFY/JK8oDzaqyIyy60OuzXuqIi4P51eNvU1Qq3IM6YV+nJsmbd3Pz\n100YdHpcO7VBUQpf8rdr4suuI4XvNn8w6gHcHIv+4VFaLRp4s+dYTKHX332uPR7O5u963ubsbF+i\n9re2ziZ1PdkXXnje/tsj2uLlav5JDd1a+bH1UOG09E9Hd8ZRaz4hqEvzqoRH3iQ9IwcD+XeoA3zz\nn35gbqX80pxHPdtWZ9vhqwXukN9++obWzDSl0tTZpLYHmdl5pKTnkJ2Th6ebA12aV+PxXoGoTJyz\nd6rr78afpwtPO3l9aEuqehT91I/S9Iu150NpjrN1Ix92Hy3873Ta8x1wNzNAABDUxp89x2PIyftn\nrZnbT6WwUxfdp9aef6U51qA2/my9axrH7advONiZ7xdrj7M08ZamT0tT1lq2qPNOJf3MLsl+72Xl\n0SaQ3y5VnLVsORJDndQo8lBwbWp6uqEoP+V13ovikz6wPemDykH6wTRz1xHl9kjQslYeqagnIuPZ\ndzqOrk2rFitDoigZp89z/rk3yLkag3ufrtSb/wEad9NprkfOxrH9SDR92gaUW4aEKfvCr7E17Cr9\n2tUocYaEtanA1ta5/XCUsZylO+Z3i4xOYv/J63RpXs3infq7pWXkEH0jnQBf83eo71aa8yg2IZ3w\nyJu0DPQqMkOiqDp3Hr1GrzbVS1xndq6OlPRs3F3sS3zXduWuc+w+FktQaz+LGRR3ioxO4khkIm0D\nPUvcL9aeD6U5zj9PxbLtyFX6tq1hMYPibjdTMjl3JZmGNauYzRy4W1pGDmk5ely1qhKdf2D9sUZd\nT+FwRDztG/uYzZAwxdrjLE282bk61Fo7dDm5Je7T0pwP1rJFnSDTN4pSXtNmbrf3+j1/Uf3HOTio\nocGc/6J2LNm/C1E69+Ij+P5tpA9sT/qgcrBFPwwZ8hBLly7n119/oXXrNjRr1sK4LSMjgxEjHmfl\nyqIXZN69ewdBQb3ZuHEdzs4u9OjRs8xjNHcdcV8PSkDZnTS5iclceOUdUvccxL5uTep//QlODeuV\nQYS2Jx9wlkkbWSZtZJm0kWXSRpbJoIRp5X0dkZ6Zyy/vL6Jr/FE8Bw/Fu/+AcqlPmCafDbYnfWB7\n0geVgy0HJZycCmcwWxqUiI2NYcGCz5gx4+NyjdHcdcQ98/SNys7OswoNf5xH9EcLiV3wPWcGPkPd\nz6bi+WAfW4cmhBBCiHLm4miputtdAAAgAElEQVSHa/cgsledIH7zZjz79EVlV7wpoUIIIYQpzz33\nJLNmzaFatWpcvx7L229PwMfHl8zMTLKysnj99Tdp0uSfKYMzZ04jKKg3rVq15p13JpKTk0OLFq2M\n27du3cTKlctRq1XUrl2Pt956h7lzZxMRcZpvv12CXq+nSpUqDB78OAsXfs7Jk+Hk5ekYPHgoISED\nGT16FO3bP8DRo2EkJycze/Z/qVYGT52SQYkypKjV1HjnNZxbNObi69OJHDUJv9HPEPDWyygW5loL\nIYQQ/3azZs0iPDwcRVGYPHkyLVr8k14aGxvL+PHjyc3NpUmTJrz//vtFlomNjWXixInodDp8fHz4\n5JNP0GrLb32m4urdpQGbdjagfdIZUg8eoErX7rYOSQghRBlZFbmeYzdOWnyfWqWgK+qZ73dp7duc\nRwMfLHJ79+492b9/D4MHD2Xv3t/p3r0n9erVp3v3II4cOcyPP37PzJmfFCq3Zcsm6tatx5gxE9ix\nYyvbt28BIDMzkzlz5uPq6sqrr77AhQuRDB8eyqpVv/Dssy/w9ddfAnD8+FEuXrzAokXfkJmZydNP\nD6N79yAAnJ2d+fzzRSxaNJ89e3YydOgTxTpWc2zy9I1/O8+H+tBk/bfY1w4g9ovvOPfUWPKSin7U\noBBCCPFvd+jQIaKioli+fDkzZ85k5syZBbZ/9NFHPPfcc6xcuRK1Wk1MTEyRZebNm8cTTzzBTz/9\nRK1atVi5cqUtDqkQTzcH9O27o0Ph+tr1GPR6y4WEEEKIIuQPSuwFYN++3+natQe//76Dl18eyaJF\n80lJMf0b8/LlizRr1hKA1q3bGl93c3Pj7bcnMHr0KKKiLpGSYvqJUWfPnqFVqzYAODo6Urt2Xa5e\nzV8kvWXL1gD4+vqSnp5eJscpmRLlxKlRIE03LuXCa++SsmM/p/uPyF9nomkDW4cmhBBCVLgDBw7Q\np0/+lMZ69eqRkpJCeno6Li4u6PV6jhw5wty5cwGYOnUqACtWrDBZ5uDBg0yfPh2Anj178s033/DE\nE6W/U1MWevZsxsEDdWmeeIFbJ8JxadXa1iEJIYQoA48GPmg2q+G2slxTom7dety8GU9c3HXS0tLY\nu3c33t6+vPvuB5w9e4YvvvjMZDmDAVSq/Cd+6f/O2sjNzWXu3I/57ruf8PLyZuLEcUXWqygKd648\nmZeXa9yf+o4ZAGW1PKVkSpQjTRU3Gnz/X/zHPU/2lWuceehZbv622dZhCSGEEBUuISEBD49/nr7j\n6elJfHw8AImJiTg7O/Phhx8yfPhw5syZY7ZMZmamcbqGl5eXcT+VQYCPC0ktOgMQs2atjaMRQghx\nr+vUqSuLFy+kW7cepKQkU716AAC//76LvLw8k2Vq1qzF2bMRABw9GgZARsYt1Go1Xl7exMVd5+zZ\nCPLy8lCpVOh0ugLlGzVqyrFjR/4ul8G1a9EEBNQsr0OUTInypqhUBEx8CefmjbgwdioXXp3CrRMR\n1HjnNRSNNL8QQoj70513VwwGA3FxcYwYMYLq1aszatQodu/ebbaMuddM8fBwQqMpn/Wd7l5R/D9D\nuhF2bBeBVy9hn3ANt8aNyqVeUdC9/oSYfwPpA9uTPqgcyrIfHn54IMOGDWPt2rVkZGTw1ltvsX//\nbp588kl27drGnj1bUatVeHu74OBgh7u7I716Pc6rr77KG2+Mpm3btqjVKgIDa9CtW1deeukZGjVq\nxKhRL7Bw4Wf88MMPzJx5niVL8teacHFxoE+fbpw8Gca4cS+Rl5fHxIlvUrOmL1qtBg8PZ3x88t+X\nm2tfJscqjwStwEe2ZP51ib+ee4OsC1G4dW1PvUWzsPPysFzQxuTxQpZJG1kmbWSZtJFl0kaWVdZH\ngs6fPx8fHx+GDRsGQO/evVmzZg0uLi7k5eXxn//8h40bNwLw1VdfYTAYyMrKMlnm4YcfZsOGDTg4\nOHDo0CGWLVvGvHnzzNZfkdcRBoOBxV+so2f4KtSNm1NvwoRyqVv8Qz4bbE/6wPakDyoH6QfTzF1H\nyPSNCuRYvw5NN35PleAepO47zOmQUG6dOGvrsIQQQohy16VLF7ZsyV/9+/Tp0/j6+uLi4gKARqOh\nRo0aXL582bi9Tp06RZbp3Lmz8fWtW7fSrVu3ij8gMxRFoX2/jkQ7+KCLOEl2zDVbhySEEEJUWjJ/\noIKpXV2o//UnxHz+Ddc+/ZIzg0ZSZ/bbeD9medEUIYQQ4l7Vpk0bmjZtyrBhw1AUhalTp7Jq1Spc\nXV3p27cvkydPZtKkSRgMBho0aECvXr1QqVSFygC89tprvPXWWyxfvhx/f38GDRpk46MrrFUDHxbX\naEPAX1u4vm49tV580dYhCSGEEJWSTN+wYXpN8vZ9XBg9BV1qOlWfe5waU19HZVf5xokkBckyaSPL\npI0skzayTNrIsso6fcPWbHEdsed4NJovP8FTl0a9jz7FztOzXGIQ8tlQGUgf2J70QeUg/WCaTN+o\npKr06UrTjUtxbFCXuG+Wc+7xV8iNv2nrsIQQQghRBjo18+dktRao9HriN8vTt4QQQghTZFDCxhzq\n1qTJhu/weLA3aX8e5VRIKOnHTtk6LCGEEEKUkp1GRc0+QaSpHUnduxtdxi1bhySEEEJUOjIoUQmo\nnZ0I/PIjAiaPJvd6PBGPvED8T6ttHZYQQgghSqlH21oc926KKjeHxJ07bB2OEEIIUenc14MSBgPk\n5FWOJTUURcF/9DM0+HEeKidHLr0xg8uTPkSfk2vr0IQQQghhJScHDVV6BJGlsiNh61b0uTm2DkkI\nIcQ9ZPfukg1oHz9+lKSkxHKKpnzc14MSl5PsWHvEwJUkOyrLcp9VgjrRdNNSHJvU58bSXzk75EVy\nrsfbOiwhhBBCWKl35/qEV2mIOiOdlP37bR2OEEKIe0RsbAzbt28pUZkNG9bec4MSle9RDxUoIPYP\nap/ew7laDxEe0JzGVXOw19h+dMKhVgBN1n7DpTdmkLh6C6dDniJw8WxcO7SydWhCCCGEKKEqLvao\nOvYgb+MZrq9fT5XuPVBU9/V9ISGEEMUwd+5sIiJO8803i7l4MZK0tDR0Oh3jxr1JYGB9li37jt9/\n34VKpaJLl240btyEvXt3c+nSRWbM+Jhq1arZ+hCK5b4elLCv4o6dLoOW538gLqE54YGDqRugxdtZ\nZ+vQUDs5Um/BDJxbNubqB/M4+9hL1Hz/DXxHDEZRFFuHJ4QQQogS6NWjCfv21aVlciRpR4/g1q69\nrUMSQghRAvErfiYt7LDF90WpVeh0+mLt07Vde3weG1bk9uHDQ1m16hdUKhUPPNCZhx4axKVLF/n8\n80/57LOF/PzzMlav3oxarWb16l9p374jgYENGD9+4j0zIAH3+aCEvnoDXEZMJHX9MqreOInH0QtE\nJA8msU5T6nnnorbxTQxFUfB78SmcmzYk8qW3iXr7I26Fn6H2rLdQOdjbNjghhBBCFJuflzNprbph\n2BNJ7Np1uLZtJzcZhBBCFMvJkydITk5iy5aNAGRnZwEQFNSbceNeoW/fEPr1C7FliKVyXw9KAKiq\neJPb71lU5w5hd3SrMWviZKNHCQyww8Xe9tM53Lq2p+nmH/hr5Jsk/LyWzLORBC75GPvq987olxBC\nCHG/69GnFWeO1KRhzBUyz53FqVFjW4ckhBCimHweG2Y2q8H4Ph9X4uPTyrRuOzsNr7/+Js2atSjw\n+htvvE1U1GV27tzGa6+9yOLF35dpvRVFJjQCKCr0jTqS89BodL61qZp4ktaHPuXa8bNcS1ZXikUw\n7QP8aLL6K7weG8it42c4HRJK6oEjtg5LCCGEEMVUz9+dmIYPABCzZq2NoxFCCFHZqVQqdDodTZo0\nY8+e3QBcunSRn39eRnp6Ot9+u4RatWrz7LMv4OrqTkbGLWOZe4kMStzJ1ZO8fs+S234gGkMOLc4v\nw3n//zgblUNOJehXlaMDdT+bRq0Zb6JLSeXs0Fe4/tXPGCrDqIkQQgghLOoU/ABXHKqi/yuC7KtX\nbR2OEEKISqxWrTqcO3eW5OQkrl27yiuvPM/s2TNo1aoNLi4uJCcn8cILIxgz5iWaNm2Gm5s7rVq1\nYcqUt7h48YKtwy82xXCP/KIt6xQYALJSsdOlkqv1BDungtvSElHv/w1N/GVyNM78VW8QXk0b4+Fc\nOZor9c+jRI6aRF5CIl5DBlBn9mRUjg7lUld5pCD920gbWSZtZJm0kWXSRpaVVxv5+LiW+T4rUnmd\nNyVtb4PBwJdzVtDr7Ea0bTpQ+5VXyiWu+418Ntie9IHtSR9UDtIPppm7jri/MyUMenJvpULSZUi7\nDoY7Vkl19UQX/Cy57fKzJpqe+xFl93Iux2ShrwTjEm4d29Bs8w84t27KzZUbOfPwSLKvxtg6LCGE\nEEKYoSgKLYK7ckNbhexjh8m9mWDrkIQQQgibur8HJRyr4F67Mai1kJkIiRchJ+Of7YoKfeOO5D40\nmhyfOlRNPEmd3//L1aMRZGTbLuzbtP5VabxqCT7DHybj1DlOh4SSsveQrcMSQgghhBkdmlTjtF8r\nFIOBGxs32TocIYQQwqbu70EJQOvsBp51wdETdDmQfNlk1oQh+Bmy2w1Erc+lwZmfyNn+CzduZNp8\nEUyVvZban06h9uy30aXf4tzw0cT+3zJZZ0IIIYSopDRqFXX6BpGicSJt/x506em2DkkIIYSwmft+\nUAIARQWu1cCj9j9ZEzcvQM6tgu9p3JG8/4wmyys/a6Lqjs+4Hn6GvDzbDgAoioJv6GAa/boYOx9P\nrr7/GRdeeQddRqZN4xJCCCGEad3bBBDu3RxVXi4J27fZOhwhhBDCZmRQ4k52TvlZE05eoM+F5ChI\niwV9wawJpf8zZLR9ELU+l9on/0fm1l9IS75V9H4riGu7FjTdvAyXdi1IXLOVMw89S9blaFuHJYQQ\nQoi7OGg1ePfsSaZKS+L2beizK8G8UCGEEMIGZFDibooKXKr+nTVhD5lJkHgBctILvEfd5AFyHxpN\nhmcdvG+ewm3TPG6ePI3Bxqtgaqt602jll/g+PYTMiEhO9w8ledcfNo1JCCGEEIX16liX4x6NUGdl\nkLxvj63DEUIIIWzivh6UMBgMRU+9sHMCzzrg5P131sQVSI0Bvc74FsXNE/WAZ0ht9RBqfS7+x38m\nY8tyslNtOzdUpbWj9oeTqDPnXfSZWZx/aiwx87+VdSaEEEKISsTNWYu2SxC5ipq4DRsx6HSWCwkh\nhBD/Mvf1oMTOI7m8/GEcf5zINf2DXVGBiy941MnPmshKzs+ayC6YNWHfvANZD44mzaMungmncdww\nn/SIk9h6FUyf4Q/T+LclaKv5Ev3hAiJHvYUu3fbTTIQQQoh/g8jkS+y6WLpsxN7dG3PSrR7q1CTS\nwg6XUWRCCCHEveO+HpSo669Go4Zfd2fz9bos0jL0pt9o5/j3WhPeoM+DlMJZExp3T+wGPE1C8/+g\n6PPwCvuFrK3L0d1Kq6CjMc2ldTOabvkB105tSNqwkzMPPkvmhSibxiSEEEL8G+y9doBFh3/gSqr1\n6zf5VnEku2139CjErF0rWY1CCCHuO/f1oEQdfzUzR/vQoIaaiMs6Pv0xk1MX80y/WVH+zpqoCxqH\nO7Im/hl0UFQqXFu1J73/aFKq1MP9xmns1s4n5/wJm2ZN2Hl70vDnhVR9fjiZ5y9yZsAIkrbttVk8\nQgghxL9BR792AGyN2lWq/QT1ask5l5qo4mLIOHO6LEITQggh7hn39aAEgKebmhcGOfBwdy1ZOQa+\nXZ/Fih1ZZOcUtdaEQ/50Dmefv7MmrkLqtQJZE46eHmgHjiCmySAUvQ7XgyvI3fYzhgzbZU2o7DTU\nen8Cdee/jz43j7+efp1rc5dg0BeRHSKEEEIIsxp51KeORw2Ox58iLiPe6v3UqubKjSadAIhZs7as\nwhNCCCHuCff9oASASlHo3krLuGGO+Hur+PN0HnP/l0HU9SIWnFKU/EEJz9tZEymFsiZUKhVebdty\ns+8Yktzr4RJ3Bs2a+egjw22aNeE9eABNVn+NNsCPa59+yV/PvUGejRfmFEIIIe5FiqIwqHEwBgxs\nj/q9VPvq3K8Dlx2rYbh4nqzLl8smQCGEEOIeIIMSd/DzUjN2qCNBbey4mWLgixWZbD2Yg66ox3xq\nbmdN+OZnSqRchZTo/AyKv1XxdUfTfwSXGw4CvQ7HAyvR7/gf2DBrwrlFI5pu+gG3rh1I3rqHMwNG\nkPnXJZvFI4QQQtyrHqjeGl9Hbw5eP0JydorV+2lSy4OLdfKng8Suk2wJIYQQ9w8ZlLiLRqPwUFd7\nXnrUATdnhS0Hc1iwMpOE5CKmOSgKOHv/nTXhCNmpcPMCZKUa36K1U1GtfVuie44l0S0Qx9gI1Gvn\no1y0XdaEnVcVGv40j2ovh5J18QqnBzxN4qbSzYkVQggh7jcqlYo+tXqgM+jYedX69ZoURaFNSFeu\n23uSE36MnBs3yjBKIYQQovK6rwclLkZl8N3yKDIzC0/TCAzQ8MaTTrRuqCHqup45/8vg4OkiHh0K\noLEHj9r5i2Ea9JAaXSBrQlHAz98NffDT/FX/UdDp0O5fibLzJ5tlTSgaDTXfHUu9hTNBrydy5JtE\nz14oz0kXQgghSqBDtba4a13Zd+1PMnIzrN5P20a+nPFvhYKBuA0byjBCIYQQovK6rwclDoen8NWy\ny0x4/ywXogpfRDjaKzwV7MCTwfaoFPhlRzbfbcgiPaOIgQlFyX9sqGfd/MeIGrMmUowZEa4OUL1D\na/7q8jo33QLRxpxFs3Y+yoXjNsua8BoUTJN132Jfqzoxn3/D+adfJy851XJBIYQQQmCn0tCrZney\ndTn8Hn3A6v2oVSoaBPcgWePCrT/3k5cq38VCCCH+/e7rQYlHB1TlicE1iI3LZtLMc6zbdsNkJkSb\nhna88aQT9aqrOXVRx6c/ZRBxuYhHh0J+1kSV2uBS9e+siWv5mRO6/DJqFdSt7UJGr2c4W28wBp0O\n7R+/ot5lu6wJpyb1abpxKe5BnUjZ+QenB4wgIyLSJrEIIYQQ95ou/g/gqHFkd/Q+cnQ5Vu+na8sA\nwn2bo9LlkbB1axlGKIQQQlRO6mnTpk2zdRDFkZFh/Rd8UdQqhaAu1Qjws+PoyVT+PJLMhagMWjV1\nw96+4HiNo71C28Ya7LUKEZd0HDmbx61MA/Wqq1GrlcI7VxSwcwJ7d8jLgpxbkJUMajtQ24Oi4GwP\njn5+nHNphzb1Oi4J51FFHgUnVwxVqubvowKpHB3wGhSMQacjecseElasx75ODbzbNC6X9v83cXa2\nlzayQNrIMmkjy6SNLCuvNnJ2ti/zfVak8jpvbre3nUpDdl42ZxLP42bvSm23mlbtT61WEaNyw+nU\nIXKvXMardx8UjaaMo/73kc8G25M+sD3pg8pB+sE0c9cR9/WgBOQ3jpuLQo9Only+msmxU2ns+TOR\nerWc8PUu2HCKolDHT03TOmouxeiJuKzj5IU8avupcXMuIulEpQYHd1BpIDc9f0pHXlb+gIVKjUYF\n3h52xPq25YauCp5JZ7G7cgolMRZ91TpgV7EXgYpKhXvXDjg2DiRpy+8k/rYZXWYWju1boaju68Qa\ns+TDxzJpI8ukjSyTNrJMBiVMK+9BCQB/l2r8Hv0H19Jj6VG9MyrFuu/N6lXd2HnoErXSolGcXXAK\nDCzLkP+V5LPB9qQPbE/6oHKQfjBNBiXMuH3SODqo6d7RE3utirDwFHbuT0SnN9CkgQsqVcGMBVdn\nFR2aaMjOhYjLOg6dyUOtQG0/FYqp7AZFyV9jwuHvrIncv7MmVBrQ2KMoClUcDah9/Tnt3B7H9Os4\nx59HFXkEnNxskjXhWL8OHiE9SNlzkIQNu0gPO4F77y6oHR0qNI57hXz4WCZtZJm0kWXSRpbJoIRp\nFTEoYa/WkpqTxtnEv/Bx9CbA1d+qfWrt1JzLdsDrXBiZUVF49+krNwYskM8G25M+sD3pg8pB+sE0\nc9cR8g13B5VK4dEB1Zg5qSE+XlpWrLvOux+fJyGx8Ellp1EY1N2eUYMccHFU2Hggh4WrMklMLeLR\noQBqLVSpBa5++X+nxUDKVdDlAuDuoKd5oCNXOozkdN0h6HUG7Pb/imb3jzZZa8KxQV2ablyK78Ce\npO49xOn+I7h16lyFxyGEEELcK3rX6I5KUbHtym70BjPXBBb06dqIk+71UaenkHLoYBlGKIQQQlQu\nkilhYiTL21NLry6eXL+RzbFTaezaf5Pqfg4E+BXOEvB2V9GhiR03U/Sci9Jz6HQu7i4Kft6Wsibc\nIC/7jqwJNWgcUKkUvJ315HoEcMapHc4Z+VkT6gtHMNgga0JlryXw2Ue4lZFD8pbfubliA9oa/jg1\nrl9hMdwLZETUMmkjy6SNLJM2skwyJUyrqOsIJztHbmQkcC4pklpuAVR18rFqv04OGo4lqfG/eJT0\n6Gt49e5t+rpCAPLZUBlIH9ie9EHlIP1gmkzfMKOok0Zrp6Jzuyp4emgJO57C7weSSEvPo3lj10IL\nW9ppFFoGavByVxFxWcfxv3TEJRqoX0ONnaaIC4jba02o7fIXwcxOg9xM0DqhqNS42OvxqGLHWdcO\npKrc8Uw6h13USZSbMeir1q7QtSacXRzQtGqOU7OGJG/9ncTVW8hLTcOtawcUtSTbgHz4FIe0kWXS\nRpZJG1kmgxKmVeR1hK+TN3uvHeBmZhKd/NpbPZjg5evB8YMRVE2OxqFOXbRVq5VFyP9K8tlge9IH\ntid9UDlIP5gm0zespCgK/Xp488l7jahR3YGNO+J5a8Y5omOzTL63XWM7JjzhRG0/FeGReXzyYwbn\nr5h5dKiigKMHeNYDrUt+1kTiRchIBIMBJzsDrQOyURq1Y3/LN7jpXh/1tXNo181HdfE4mHh8aXny\nCO5Bkw3f41C/DnFL/se5Ya+Qm5BYoTEIIYQQlZ2/SzWaezfmUmoUF1IuW72fAF8Xkpp3ASBmzdoy\nik4IIYSoXGRQohhqVnfkk3cbERzkzeWrmbwx/Sw79t7EYGJQwMtdxauDHenfSUt6poEvV2exZk82\nuXlmBhDUduBeA24viJV+HZKjQJeDSoF6Xrk0rOPEieajCq41setHyEgtp6M2zTGwNk03fIdH/56k\nHTjK6eBQ0sPPVGgMQggh7k2zZs3i8ccfZ9iwYZw4caLAtl69evHEE08QGhpKaGgocXFxrFixwvh3\naGgorVu3BiA0NJTBgwcbXz916pQtDsesfrV6ArAlamep9tO1b1suOPlD1AUyL0SWRWhCCCFEpSIP\nvi4me62Kl0bUpGUTV7749gpffBtF+JlUXhpREydHdYH3qlQKfdpraVhLzY9bsthzPJfzV3U82c8e\nfx+16QoUBRyrgNYZ0mIhJx1uXgCXquDogYeTnnY1szjn0IF9VRrR/MJyPK+dQ7VuPnntBqCv26rC\n1ppQuzgTuGQ2sV98R/TsRUQMep7aH72Nz+MPVUj9Qggh7j2HDh0iKiqK5cuXc+HCBSZPnszy5csL\nvGfJkiU4Ozsb/37sscd47LHHjOU3bdpk3Pbhhx/SoEGDigneCnXda1PPvQ5nbp4jOi3G6idxNKhR\nhd2BHah3YjWxa9dR9/XXyzhSIYQQwrYkU6KEOrXz4L/TG9GwnjN7DyYxYfpZ/rp0y+R7a/iqGT/M\nic7N7bh+U89nyzPZdTQHvblpF7ezJtyqg6L6J2siLwetGppVy6ZGgDNHmr74d9aEHrs/VlV41oSi\nUuE/5jka/PAZKgd7Lr0+nctvz0afk1thMQghhLh3HDhwgD59+gBQr149UlJSSE9PL3b5BQsW8Mor\nr5RXeOWiX60gALZd2W31PhRFoV2/zsTYe5F7Opyc67FlE5wQQghRScighBV8ve2Z8VYDBg+sSlx8\nNm/POsfqzXHo9YUHG7R2CoN72vP8fxxwclBYvy+HL3/LIinNzGPCFCV/EUzPemDvCrkZkHgBMm6i\nYKC6ex5tArJIqtmRfS0nklTljrUmLhyr0LUmqvTqQtNNP+DYqB43vl/B2cdeIudGQoXVL4QQ4t6Q\nkJCAh4eH8W9PT0/i4+MLvGfq1KkMHz6cTz/9tMAUyRMnTuDn54ePzz9Pspg3bx5PPvkk7733HllZ\nhdd6qgyaejXC37kaR+LCSci8afV+Wjfw4WyNtijA9fUbyi5AIYQQohKQ6RtW0mgUnhpcnRaNXfls\nyWW+/+UaJ86kMeb5WlRxsyv0/sa1NbzxhJpfdmZx+qKOOT9lMLinPa0bFH6vkVoDbgGQnQpp1yE9\nDrJSwc0fF3t72lTP5IKDK4fsX6LGjQM0urwWuz9WoYs6RV7Hh8HJrRxb4B8OtQNosu5bLo3/gMR1\n2zgdEkr9JR/j0rZ5hdQvhBDi3nP3ukxjxoyhW7duuLu78+qrr7JlyxZCQkIAWLlyJY888ojxvSNG\njKBhw4bUrFmTqVOn8uOPPzJy5Eiz9Xl4OKHRFDGFspR8fFyL3DakeX/m/fkt+28c4Pl2w62uo8Pg\nfiTOPUCVQwdwf/EZtJ4elgvdZ8z1g6gY0ge2J31QOUg/lIwMSpRSiyZuzJ3emPlfR3H0ZCrjp0Yw\n7oXatGhSeEDAxUnh2YEOHDydx5q92SzbnM2ZSzoeDbLH0b6I9SBuZ01onfMHJrJT85/Q4eyD2smL\nBj45eDrpOKvuRLx7I1pfXo7btfMVvtaE2tmJev83C+eWjbk66wsiHn2BWjMn4vvUo+VetxBCiMrP\n19eXhIR/Mulu3LhRIPNh0KBBxv/v3r0758+fNw5KHDx4kClTphi39+3b1/j/vXr1YuPGjRbrT0rK\nKFX8RfHxcSU+Pq3I7YEODfBy8GTnpT/o6dcDN611F6rN63jwbdWWBEXv4+xPK/F7fJi1If8rWeoH\nUf6kD2xP+qBykH4wzdxAjUzfKANV3Ox4Z2w9nhlandT0PKbNiWTZr9fIM/HEDUVR6NjMjgnDnahZ\nVcXRc3l8+mMGkdFmHuXLjsEAACAASURBVB0KoNKAe0B+5oSigls3IOkS5GXj7ayjfY1MHDzcOdDw\nJc4GDsGgv73WxLIKW2tCURT8XhlBw5/mo3Jx5vLEWVx6cyb6bHlOrxBC3O+6dOnCli1bADh9+jS+\nvr64uLgAkJaWxsiRI8nJyf++OHz4MPXr1wcgLi4OZ2dntFotkJ9h8cwzz5Camv/ddvDgQeN7KyO1\nSk2fmj3I0+ex6+o+q/djp1FTo08Q6WoHknfvQpeZWYZRCiGEELajnjZt2jRbB1EcGRnl88PW2dm+\nTPatKAqNAl1o3dyNE2fSOHw8lRNn0mjRxBVnp8IJKU4OCu2baFAUhYjLOsIi8sjJM1DXX41KZSaz\nQWOf/5QOXS7k3oLMZEBBY+9IVVcdahVEqWpxzast3jkxOMT9hTryKAZHVwwe1azKmihpGznUCsDr\nP31JO3CElB37Sdl7kCo9O6N2dbZc+B5VVufRv5m0kWXSRpZJG1lWXm3k7GxfqvJ+fn5ERkYyb948\n9u7dy9SpU9mzZw/R0dE0btyY5ORkZsyYwerVq6lZsyYjR45EURQuX75MeHg4//nPf4D871utVst7\n773H2rVrARg3bhx2dmamQ2Lb6wg/52r8EXOISylX6B7QETuV+ViLUt3XjR2Ho6iVdhUcnXCuX3mf\nPlLR5LPB9qQPbE/6oHKQfjDN3HWEYrh7UmcZmjVrFuHh4SiKwuTJk2nRooVx248//sjatWtRqVQ0\na9aMd955x+y+yisFpjzSazIydXz5wxX2/JmEk6OaV5+tSed2Rc/9jLqu46ctWSSkGPD3VvFksD3V\nvIox7zU7Lf/xofo80DiAmz9oHEjNUhFxw57MHIV6iX9Q78I6lLwcdNUbWLXWhLVtpMvI4vLEmdxc\ntQk7Hy8CF8/G9YFWJd7PvUDStCyTNrJM2sgyaSPLyquN7vX5sba+jthyeSdrL27m4Xr96ff/7L1n\neJzXeef9e8o80zs6QBC9EgR7A7sk25IdW5atOOVNsmmOY3v97tobX3E2jhUnVjaxk+wmchy/ju3I\neV1iVVuWLFKFvQMkQHQQIApJsKBNQceU/QCKakM8ADjgDMTzuy5+4EXizJn//zwHZ+65z32v3LPo\n13vm5UZKnvk/qBYLpd/4e2SdYMy9gtgbEo/wIPEID5ID4UNsEnJ94639yL/2ta/xta997da/jY6O\n8t3vfpcf/vCH/PjHP6arq4v6+vqlmspdx2JW+G9/mMd//b2VhMNRvv4v3XzrB31MTcfuuLEyQ+Hz\nv25hS6VK/2CEf/zJBEfqdVqHwmxnDk/hbM2J0ORsrYmxARzGMOtzJshwhOjy1nC0+k+YSC1CudKB\n9vO716FDsZgo+OevkvvVLzAz7KPt0T/i+vd/+q7iZgKBQCAQvNfZkb0Vk2Lk9UtHmAkvvn323poS\nzrtKUMYCBE4cj+MMBQKBQCBIDEsWlJirH7nBYMBgMDA+Pk4oFGJiYgKn07lUU0kIkiSxd7uXb3yl\njLwcM/sPDvLFv2qj70rsO6BGTeLR+0z87gdNGA3w/OFpvvOzSfyjc7QOBZAVcGSDc8Vs3YmxARi5\niBqepCxtmvK0SabMHg4X/TG9FR+DaPSu1pqQJImMP/h1yv7zmyhOB73/8+/o/u9/SWQiOdu3CQQC\ngUCwFFgMZnZkbyU4PcrJa3WLHsdtNxLdvIswMld/8SLRiM45QSAQCASCJGfJghJz9SM3Go185jOf\n4f7772fPnj1UV1eTn5+/VFNJKDmZJv72y6U8dF8qfVcm+ZO/amP/ocHbZgusKlT5H79poTxPoaMv\nzDd+NM75Tp0imPCWrAkXhKZg5CKM3iDdFmJDzgQOU4Q2Vw0n1/0PptPemjVx9q5kTTi2baDy5f/A\nWl3B4E9/QctH/5Cpy9eW/HUFAoFAIEgW9qzYjiqrvNp7kHAkvOhx7tuzihZ7PsrwDUYbzsVxhgKB\nQCAQ3H3uWkvQt34IHx0d5dvf/jYvv/wyNpuN3/md36GtrY2ysrLb/nyi+ovHiz/7b062bx7kb/6p\nnW892Udb5wRf/GwJdtu7LUhNhT/9vSivnxnnxy8HePKlSXasNfP/POTAbNKJI6W7mB71EezvJjI+\niBIeIzO7gJxMldbLUVqueDlQ+MdsWnEKd+PPMBx/DvVqO6b7fxXZ7rrtsHHRKNVO5tGf0PTZx7j8\n5LO0PvRbrP3x/yZl95Y7HzsJWO73re8GQiN9hEb6CI30ERolJ06jgy0Z6znaf4pzA41sSF9cnaVM\nrxV/9XY42kX/z16gdO36OM9UIBAIBIK7x5IFJebqR97V1cWKFSvweDwAbNiwgaampjmDEonqLx5P\nyouM/MNjZfzj/9fDgWMDNLf7+fwf5VNaGLsrxep8SP+EhR/tn+TIuQmauyb5jfeZyM/SC84o4MyH\n0RuEJ0fwXWwGi5c0aypalkrrDSOnpS2kbiiiqus/obuF4L//L0IbHyRSsPZdHTrirVHm419CKSuh\n78tf59QHfo/cL3+O9D/8DaRFdAZJFkRBG32ERvoIjfQRGukjCl0mN/fl7uJY/2n29x5gfVr1on/3\n7XxgHe1nX6P4cg8TFzowi04cAoFAIFimLNn1jbn6kWdnZ9PV1cXk5GxdgaamJvLy8pZqKklFikfj\nq39SzK9+OIOBoWn+7G/aefala0Qisa9QpHtk/uujZu7faGAkGOWbz0zwyxNThMM6Vy5kBRyZ4MoF\n2QDjQzB8EZc6yoacCVKtIQZI4VDRHzO85qNAdDZr4i7UmpAkifTf+ThlT30bg8dF32P/yMXPfpnw\nuKgzIRAIBIL3NmmWFNalrebK6FVahjsWPU5RtpP+ks0A9P/s5/GankAgEAgEdx3lsccee2wpBp6r\nH3lVVRWapvH444/z7LPPUlVVxaOPPjrneInsLx5vZFmiqsxOZamN+qYgp876ae8ao7rSgdn07iwI\nWZYoXqFStEKh83KY5u4wbb1hCrIVrGadb1gUDUxuiIZhegwmfShShFSXhskAQ+MqVwwrmV65Gs/0\nNZT+CyidZ4mabETdGSBJS6aRMScD78PvJ1h3Hv/rx/G/ehTn7i2ozoW1LE0GRD9ifYRG+giN9BEa\n6bNUGs3VX3w5kEzniBRzCkf7T+Kb8rMlc8OiX1vxeLleew77tR5s6zeiOpbf7894IfaGxCM8SDzC\ng+RA+BCbuc4RUnSZ9GdMdH/xpSIQDPHP3+uhtiGA06Hy//5BHmtX3f5QMTkV5bnDU9S2hjCo8OHt\nRrZWqfNL/5weg2A/hGdmgxWOLMaiVlqvGxmdVjCrYdaOH8PW8EukmSnCWcWEtnyE1LycJdUoMjVN\n7198g4H/eBbF7aToX76Gc9fyqjOR6HW0HBAa6SM00kdopI+4vhGbZDtHfLP+u7QMt/OF9Z+hwLly\nUa8diUb5ztf/kz0dL2PcsIWVn/rUosZ5LyD2hsQjPEg8woPkQPgQm7nOEUuWKRFvkukbjnhiNMrs\n2OzGZlU50+DnwLFhpqbCVJbaUOR3BxpUVaKqUCXDI9PWG+J8V5hLNyIUr1AwGuaRNWF2QzQC06Mw\n6UOTwmR4NcJRmeEJlctKLmpRFfbJ6yhXO1E6zyJb7UxZU95VayJeSKqC+4EdGDLS8L18kMGnX0I2\natg2Lv6u7d0m0etoOSA00kdopI/QSB+RKRGbZDtHuI1OTl6rY3RmbNEFLyVJYsruZuJsHYYr3Ti3\n70Axmxc11nJH7A2JR3iQeIQHyYHwITZznSNEUCIJFo0kSZQUWlm/2kljW5DahgD1TQGqyu3YrLFr\nkWZ4ZdaXqVwdjNDeF6a2NUSqWybNrVMmRJLAaAODFWbGYXoUacqPx2HAYVUYnlC4MW1nJHMNnlQb\n6rULhC/UIw1eJpKeB5op/gLcxLq6DOfOLfheO8rILw8weaEb595tyJphyV4zXiTDOkp2hEb6CI30\nERrpI4ISsUm2c4TH5KZ1uIP2kU7WplZh12yLev2sFBv7zl0j39/DTCiCY/XqRY2z3BF7Q+IRHiQe\n4UFyIHyIjQhKzEEyLRqPy8DeGi9DvhnONgY4cGyI9FSN3OzY33qYNIl1ZSoWk0RrT5iz7SECoxGK\nchRURS9rwgBmFxC9mTXhx6zMkOHRGJtRGJkw0K/mYilbhXNmCC53oHTWETVZibozlyxrQstMw/vR\nDzB6tgn/geP4XjmMc+cWVLdzSV4vXiTTOkpWhEb6CI30ERrpI4ISsUm2c4QkSVgNVupuNDAdnqY6\nddWiXl+WJYY0J8r500T6uvHs2YusaYsaazkj9obEIzxIPMKD5ED4EBsRlJiDZFs0BoPMlnUuMlI1\nahsCHDk1wtDwNKsr7Kjqu7MgJEliZYZCVaFCz9UIrb1hGi6EyM1QcNnmkTWh2UB7I2tiDGXaT5pL\nxWBQGRpXuDZlh+J1WJyW2escvc3IS5w1oVgteB95kJA/iP/Voww+8xKW8iJMBblL8nrxINnWUTIi\nNNJHaKSP0EgfEZSITTKeI9IsKZwbaOSCr4vNGeuxGBZ39SIrzc4rZy6RF7xEVDNhLS1d1DjLGbE3\nJB7hQeIRHiQHwofYiKDEHCTroslbYWHbRhdtnaPUnQ9w8qyPihIbLmfsqww2i8ymcpVQGFp7wpxp\nCc2OkyUj62U1vCNrQpry49CmSXEZ8U2qXA/IDJhycFZWoo3eQO7vXPKsCUlRcN1Xg7Yii5GXDzL0\n9EugyNg3rUnKOhPJuo6SCaGRPkIjfYRG+oigRGyS8RwhSRImxUj9QBNRolR6yxY1jkGV6YvasLec\nZqq3B+8DDyAp7+7m9V5G7A2JR3iQeIQHyYHwITYiKDEHybxo7DaVvTVeJqci1DYEeP3oEDarSlGe\nJeYHc1mWKM1VKciWuXBptnVoR1+YwmwFi0nng/ytrAkbzEzA9CjajJ8Mt4xqtnEjIHN10oZSuBqb\nx458l7ImrJUluPZuw//6MXwvH2Ki5QKuvduQjcmVmprM6yhZEBrpIzTSR2ikjwhKxCZZzxGZ1nRO\nXq2jy99DTdZmjMrifr9lpTs5dOoiK4KXkV1uzPn5i57TckTsDYlHeJB4hAfJgfAhNiIoMQfJvmgU\nRWJtlYPClRbONvo5Ueuj5/IE1RUOjFrs6xkeh8ymCgMjo1HaesOcbpnBbpHITpX1swxuZU0A06PI\nU37SbTOYTAaGJgwMjBsI2rJxVqxCDb4la8JoJepZmqwJLT0V78ceYqyhBf+BE4y8fBDHjk0YPK64\nv9ZiSfZ1lAwIjfQRGukjNNJHBCVik6znCFma/d3cNNSKQVYpcRctahyjptAyZiSto5axS5dIuf+B\npMwsXCrE3pB4hAeJR3iQHAgfYiOCEnOwXBZNdoaJnVs8XOwb51xjkCOnhinKs5Lqjf2NikGVWF2k\nkuqSaOsJ09AZpn8wQnGOiqbXOlSSZutMaDYITRAaD2CJjJDhlgmEzIxMqFybsmIpW4XR6ZjNmuhb\n2qwJxWIm5ZEHiYxP4HvlCINPvYi5OB9zUV7cX2sxLJd1lEiERvoIjfQRGukjghKxSeZzRJYtg6P9\nJ+kJXGJn9lZUOXbnLT3S0l2cOdVOlv8yWnY2xqzsO5rXckLsDYlHeJB4hAfJgfAhNiIoMQfLadFY\nzAq7tnpQFYkz9X4OHBtClqGs2HbbuhGZKQrrSlWuDMy2Dq1rC5HukUl16RTBhNmsCZMbi9XIzJgf\nZdpPhmUc1WhmcNzA9VEDYXcW9ooq5MAAcv+FJc2akGQZ5+6tGPNz8b18kKFnfkk0HMG+dV3Cvw1a\nTusoUQiN9BEa6SM00kcEJWKTzOcIVVaYiczQMtyOzWClwLlyUePYzAbODEBOzzmC/dfw7t6T8N+P\ndwuxNyQe4UHiER4kB8KH2IigxG0YGw/Tf2Maq3n5/MKWJYnKUjtV5XYaWgKcOuenpWOUNRV2zObY\nRa3MRon15SpGTaK1ezYwMTYRpTBbQdFrHSpJuNJSGQ8bYWYCaWYMJyOkOmWGpywMjasMhyw4KipR\nHM7Z6xx9zciDl4ik5y9J1oSlvAjnfdvxHzyJb/8hxs634tpbg2xK3IFZbD76CI30ERrpIzTSRwQl\nYpPMQQmALFsmh64c51LwCjtztqFI8/jyIAauNDetp5pJHb6EpbQMQ0rqHc9tOSD2hsQjPEg8woPk\nQPgQGxGUuA0/ef4qX/9mJzcGp1hdYccQo+VmspLq1dizzUv/tUnONQU5eHyYFVkmsjJiBwEkSSI/\nU6EyX6G7P0JrT5jGrhB5mQoO69zv22o1Mj4ZAZNrNvthegwt5CfLOs60YmVoQuNa0ICWloG5bDWy\nf2DJa01oaSl4P/Yg403tBA6cYPiXB3DUbMCQ4onr68wXsfnoIzTSR2ikj9BIHxGUiE2yByU0xcDY\nzBhtIxfwmtyssC/u6oXLZuRI7yQr+5sJDgzj3b79jue2HBB7Q+IRHiQe4UFyIHyIjQhK3IbsTBMX\nLk5Q2+DXbbmZjBg1mZpNbhx2A7UNfg6eGGZ8IsyqUtttMyDsVplNFSpTM7OtQ0+3hFAkyMu8fRHM\nWw/WG7UmjPZbWRMpyhBOm8KNCSsDYwYmMOOoqESyuWavcyxh1oRiNuH96AeITs/g23+YwadexFSQ\ni7mkIK6vMx/E5qOP0EgfoZE+QiN9RFAiNskelIDZThyHLh/n6vh1dmZvXfTVC83r4cqZBpw3erCt\nXYfqdMZlfsmM2BsSj/Ag8QgPkgPhQ2xEUOI2WMwKH/+VXIZHJm+13LTbVApv03IzGZEkieICKxuq\nnTS1B6ltCFDX6Keq3I7dFrtQliJLlK1UycuU6egL03QxTOflMEU5Cmbju9/3ux4sWb2ZNSHD9Bjm\nsI9s6yjBiI3BCY0boyr2rHTU4ndmTVjinjUhyTLOnZsxlxQwsu8QQ8++TGRqGse29Ujy3ct8EZuP\nPkIjfYRG+giN9BFBidgsh6CEWTUxNDlC+0gn2bZMMq3pixon1WXm1TYf+YMXGA+M4t60KS7zS2bE\n3pB4hAeJR3iQHAgfYiOCEnNgt5soLTBRuNJC3fnZlpt9/ZOsqbSjGZbPdQ6308De7V58/hBnG2cD\nLCleA3krLLf9mRTnbOvQIX+E9t4Ip5tncNokMlPenjUR88GSJNAsYHTAzCRyaIx0dQiLUeHahIVr\nQQOSZsJWtoroW7MmBi4tSYcOc2kBrvftxH/oJL79hxk924Trvhpkc/xrWsRCbD76CI30ERrpIzTS\nRwQlYrMcghIA6ZZUDl85weDEEDVZmxb1JYkkSYScHoJn6zD1d+PcVoNiscZtjsmI2BsSj/Ag8QgP\nkgPhQ2xEUGIO3lg0b7Tc7OoZ51xjgKOnRygttOJ1x265mYyoqsymtS6y043Unvdz9LRPt16GQZWo\nLlLxOmVae8LUXwhzfThK8QoFgzp7EJrzwXoja0JWkKZHseEjyzLG8IydG+NG/JMKrpw0pMJqJP8A\nytVOlM6zS5I1YUjxkPKxhxhv65ytM/GL13BsXY8hzRu317gdYvPRR2ikj9BIH6GRPiIoEZvlEpSw\naVaujF6jfaSTQlc+KebF/Q7LTLGyv/4G+b5upqfDOKqr4zbHZETsDYlHeJB4hAfJgfAhNiIoMQdv\nXTQWs8LurR6iQG2Dn9ePDaFpMiUF1mVznQNgZY6ZbRvdtHeNcfZ8gBO1PsqLbbhdsetlSJJEVqrC\nmhKVSzfCt1qHZqXIeJ2y/oMlSWC4mTURmkIJjZKlDaKoClcnbFwLGjBbNcwlVUStrjc7dCxB1oRs\nMuJ9+P0QieLbd4jBp36BMTcbS3lR3F4jFmLz0UdopI/QSB+hkT4iKBGb5RKUAEgxezjWf5rAVJDN\nmesXNYYsSfhMbmg4g3SpG/fuvcja8vmiZaGIvSHxCA8Sj/AgORA+xEYEJebgnYtGliWqyu2Ul9g4\n1xjg5Fk/nd3jrKm0YzLGbrmZjNitKntqPEzPRGbrZRwbwmJSKC64fb0Mi0liY7mKqki09oSpbQ0x\nORVlVbGZqcl56C+rYHKCrCJNj+KUfKSbRxmYtnM1aGIqLOHKTiNasLRZE5Ik4ajZgKWihJH9hxl+\nfh/h0XEc2zcsWZ0JsfnoIzTSR2ikj9BIHxGUiM1yCkq4jE66fN20j3SyyluGy7i4QpXZaXb2114i\nL9BHRFGxlZfHdZ7JhNgbEo/wIPEID5ID4UNsRFBiDm63aNJTjeza5qH30gTnmoIcPjlC4UoLaSnL\n51CmyBJrKh2UFFg42xjgZJ2Pi30TVFc4MBpjfziXJImCbIXyPIWuK2Fae8LUtU6yMl3CrtM69OYA\nYDDPBidCkxjCo2QbB4hKBq6M2RgcU3E6VNSi1TdrTSxd1oS5OA/3B3YTOHIa3ytHGD3TgHPvdhRL\n/OtMiM1HH6GRPkIjfYRG+oigRGyWU1ACwGl0cPraWcZmJlifvrirF6oic0VyYG0+zXRfD9777kdS\nYxfBXu6IvSHxCA8Sj/AgORA+xEYEJeZgrkVjMirs3OLBqMmcqfdz8NgwAOUlNuRldJ0jM93Erq0e\nevomONcU4MipYQry5g6wOK2zRTAnpqClO8TplhCqCiszbt869G3IyptZEzNjuOURUo2jXJt0cjlo\nRJXBlplOpHANUmAQpf/CkmRNGLxuUh79IBMXuvEfOM7wz/dj37IOLT0lLuO/gdh89BEa6SM00kdo\npI8ISsRmuQUlvCYPTUOtdIx0sT6tGpu2uEKV2RlODp7uJjdwGcnhxFJQGOeZJgdib0g8woPEIzxI\nDoQPsRFBiTnQWzSSJFFebKO6wk5DS5DT5/y0dIyypsKO2bx8rnOYTQq7tnrQDG8GWKLRKOXFNmQ5\ndgBAUSQq8lWqSqycvzBFU1eY7v7IbVuHvotbWROztSa0yCjZ2gChqErvqIPglIzbqSIVvDNroi+u\nWROyUcPz4QeQFGW2zsTTL2LMSsdSWRKX8UFsPvNBaKSP0EgfoZE+IigRm+UWlJAkCavBytkbDcxE\nZlidWrmocTSDQvuECW9HLeN9faTc/8BdbZl9txB7Q+IRHiQe4UFyIHyIjQhK3IbXjw7x9X/pxGpW\nWJFlmjMDIMWjsafGQ//1Sc41Bjl4fJjcbBNZ6Xen5WQ8kCSJipK3BFjq/TS1j1JdYccyR4ClINdK\nRW6EAV+E9t4wZ1pncDskMr3zDMq8kTWhGJBmxvAqPryGIFcmnPQHjViNUUzpGYQL3siaiH+tCUmS\ncGxdh7W6At++Qwz/bD+hET+OHZuRlDs/nInNRx+hkT5CI32ERvqIoERslltQAmbbg9bdqOeCr5ut\nWRsxqYs7c6RnuDh5soPswGW0jEyMOSviPNPEI/aGxCM8SDzCg+RA+BAbEZS4DdcGpth3YICjp0do\nuTBGUZ4FpyN2hwoATZOp2ejGYTdwpsHPwePDTE6FqSy1odwm2yAZSfFo7N3u4eqNKc41BjhwbIic\nTBPZmbEPO1arkdDMNGuKVZw2mdbeMOc6wgz4Im9rHTonb601EZ7CeDNrYjJsoNPvIBwBl8NANH81\nUZv77VkTaXlxy5owFeTieWgvgaNn8L16lODJs7j2bkOxWu5oXLH56CM00kdopI/QSB8RlIjNcgxK\nSJKEJhtoGGwGoMJbuqhxLCYDZ0dksi6eI3ilH+/evcuqq9h8EHtD4hEeJB7hQXIgfIiNCErchhVZ\nZj78gRV09wWpbw6y//AgY+NhSgutGAy3LwRZXGBlw2onjW1BahsC1DcFWF1hx2ZdPsWjNIPMtg0u\nPC6N2gY/h06MMDoaYlW5HUV5+0HljQdLkiRy0hTWFKv0XQvT3hvmbHuI7FQZj2Oe2QayAkYnKBrS\nzChe1YdbDdI75uL6mIbLHEFNfUfWRNdZopqZqCcrLlkTqttJyqMfZLK7D//rxxn62X7sm6rRMtMW\nPabYfPQRGukjNNJHaKSPCErEZjkGJQAyrOmc6D/DxUAvO7K3oCm3//JkLjxpHhpPtZA2cglzUTFa\n2uJ/5yUjYm9IPMKDxCM8SA6ED7ERQYk5yMywsmG1jcKVZto7xzh7M3PA6VRZmWO+7TcJbpeBvTVe\nBkdmONsY4PVjw2SmG1mRZV6SeS4FkiRRmGdh01oXzR2j1DYEqG3wU1Vmx2F/M8DyzgfLYpLYWKEi\nSW+2Dp0ORSnIUm5bn+IdLwwG082siWlM0VGytAHGQiodPieaAjZrjKyJG/GrNSFrBjwfuh/ZZMT3\n8kEGn3oRLS0Fa1XZosYTm48+QiN9hEb6CI30EUGJ2CzXoIQiyUSJ0jzUhlHRKHYXLGocp1Xj6OVp\nVl5uJHhjEO+OHXGeaWIRe0PiER4kHuFBciB8iI0ISszBG4smO8PE+3anYFAlGlqCHD/j43xrkIKV\nFtzO2N9KGAwym9c5SUsxUtvg5/DJEfyBGVZXvDvbIJlxOWYDLIHREHXnA7x+bAiPy0DeitmgTKwH\nS5YkinIUSlcqdF0O09I9+6cgS8ZmWUjWhANUDWl6NmvCqQTpCrgITGu4LWFkb+abWRNX45s1IUkS\n9k1rsK6vwrf/MMM/f4WZgSEcO7cgKQsrYio2H32ERvoIjfQRGukjghKxWa5BCYAsawZHr5ykJ3iJ\nnTnbUOXFFdq2eD30nDmPe6AX6+o1qC5XnGeaOMTekHiEB4lHeJAcCB9iI4ISc/DWRaMoEpWldnZt\n9TAwNE19c5BXDg3iD4YoLbSiae/+sC1JEvm5Fjavc9LSMUrd+QC19X5WvSPbINlRVYmNa5ysyDJR\n2xDg2JkRrt6YorrCgctpuq3+Ltts69CxiSitvWFOt4QwabAifZ6tQyUJVBOYXG9mTRgGCEyrdAac\n2I0RTBaNSN7SZU2Y8lbg+dB9BE7U4X/1KIGjZ3Du2YZim3/7NbH56CM00kdopI/QSB8RlIjNcg5K\nqLLKdHialuEOHJqdPGfuosbxOk283hEk70Y7Y74A7s2b4zzTxCH2hsQjPEg8woPkQPgQGxGUmINY\ni8ZqUdm+yUNpuY/eeQAAIABJREFUoZWOi2Ocawrw2pEh7FblVvbAO3HaDeyp8RJ8I9vg6BAet4H8\n3DsroHi3yc02s2Ozm46L45xtDHC81sfqCieWOT77q4pEZYFKdopMe2+Ixq4wvVdni2CatHlmM8jy\nzawJI8yM4VV9OKQg7T4XM1EVpzkCnndkTXTW3ezQcedZE6rLQcrHP8hUX/9snYnnXsa2rgpjdsa8\nfl5sPvoIjfQRGukjNNJHBCVis5yDEgCZ1gwOXT7O5dF+duVsQ5YW3jlKkiSiTg8jZ89i7u/GsWUb\ninX+AfhkRuwNiUd4kHiEB8mB8CE2IigxB3Mtmsw0I+/bnYLZJHO+NciJOh/nGgPk5ZrxurV3/X9V\nkdhQPZttUHfez7HTPq7fmKK60o5BXT49wa0WlT3bvEQiUWob/Lz02jWMmkxJgXXO7Ic0j8z6MpUb\nIxHa+2Zbh6Y4ZdI983zvN7MmJLMLwjOYokGyDAOMTKj0jjlwmyOoJuObWRNX45s1IRsMuB/ai2K3\nMvLyIYaefhGD14Vldblu1ofYfPQRGukjNNJHaKSPCErEZrkHJYyKRmB6lLaRC6SaU8ixZy1qnAyv\nlf3nB8gfucjU5DTOtWvjPNPEIPaGxCM8SDzCg+RA+BAbEZSYA71Fo8gS5cU2dm/zMOKbob45yKtH\nhhgamaa00IbR+O4P3LnZZmo2umnvmi2ceaLWR0WJ7ba1KZIRWZZYXeGgrMhKQ8soJ2p9dHaPU11p\nx2S8/V1WoyaxtkTFbpFp7ZntzjESiFCUo6DOp3UogCSDyQHKbNaER/VjJUjriAtVVbAao0SXKGtC\nkiTsG1Zj37QG3ytHGH7hVab7r+PctQVJvf11HLH56CM00kdopI/QSB8RlIjNcg9KAGRY0jl05TjX\nxgfYkb1lUW09JUkiaHUTPncG+XI3rl27kY3L21sQe0MyIDxIPMKD5ED4EBsRlJiD+S4ai1lh2wY3\nq0ptXOgZ51xTkFePDGI2KRSstCC/42Bgs6rsqfEwPR2htmH2OofNqlKUZ1lWvcEz0ow88qEVtF0I\ncK4pwOETI+SvtJCeevtFJUkSK9IVqgpVeq+FaesNU38hRE6agtu+gIwR1YhkdhENz2CKjJKhDnJj\nzMD1KRsucwRZi5U10UskPf+OsyaMudl4PvwAwZPn8L9+DP+hkzh3b0V12GL+f7H56CM00kdopI/Q\nSJ9kDko8/vjjPPHEEzzzzDOUlJSQnp5+69/27t3Lvn37eP7553nuueeoqamhubmZj3/84xw6dIjn\nnnuO5uZmdu3axdWrV/n0pz/N008/zeHDh7nvvvtQdAoUJ/ocEQ8sBjMDE4O0j3SSa88m3bq4tp7Z\nqTb21fWT7+8lJCnYKyriPNO7j9gbEo/wIPEID5ID4UNsRFBiDha6aNJSjDywMwW7VaWxLcjJs37O\n1PtZmWMmxfP2Kx2KLLFmlYOiPAt1jX5O1ProvTLJ2lV2NMPyuc7h9VpYX2XFbFI40+DjwLFhZkIR\nKkvtc7YAtZklNpbPZhe09IQ50xoiHFlA61AASUYy3aw1MT2OR/GhhYNc8LuwmGSMKm/Jmhh6S9bE\nnXfoUB12Uj7+EFNXr8/WmXj2l1jXVmJc8e6UWbH56CM00kdopI/QSJ9kDUqcPn2aAwcO8OSTT7J2\n7Voee+wxHn300Vv//uSTT/LjH/+YT3ziEzzyyCPYbDauXLnC8PAw3/nOd3jkkUfYtWsXMBvc+NCH\nPsSf/umf0traSl9fH1VVVXO+frKcI+6UNHMKR66cYHhyhK2ZGxf1RYciy1xX7ZgaTxPq68F73/1z\nZgMuB8TekHiEB4lHeJAcCB9iM9c5Yvl8Mk4iVFXiV96XxhOPV7KnxkN33wRferyD//NvPYz4Z971\n/zdUO/nHvyynosTGyTof//0rbbR3jSVg5otHliUe/kA6j3+plLQUjWdevM6f/20HNwan5vw5VZF4\ncKuRT3/MjNsu8eqZGf75qQlujEQWNgGjA8lbSNToxKGMs0prZfDGMH0jCtEoYHEQ2vObzNR8DGQZ\nw6kXMLz67zA6suj3DCCbTRT878dY+dd/QtgfoO1XP821f/sJ0Wj0jsYVCASCe40TJ05w//33A1BY\nWIjf72d0dHRRY506dYr77rsPgD179nDixIm4zTPZybJlUJVSQXegj05f96LH2b0xn3pvBcrUBMMH\nD8ZvggKBQCAQLJDlHRZPMG6ngc/9fh4P7EzhOz+8xMHjw5w+5+PXPpLFg3tT31ZDwevW+OoXi3nq\n51f56QvX+J//q53ffCSLj7w/ff5ZA0lASYGVv/9KOf/6gz6Onh7h84+18ZnfzWXrevecP1eQpfCF\nX7fw3OEpaltD/MOPx/nwdiNbq9T5f8sjK0jObJhyQOAq+cZ+gpM+Oq7mkpemYFQhUrCG6YwC1JM/\nR7nSjvbCE4TWv59I8cZFZ01IkkT6730Cc0UxnZ/8U/r+4huMnW8h/2//DNl85y1JBQKB4F5gcHCQ\nysrKW3/3eDwMDAxgs715Le4rX/kKV65cYf369XzhC18AoLOzk0996lP4/X4++9nPUlNTw8TEBJo2\nm53o9XoZGBjQfX2324Kqzn3FY7GkptqXZNzb8YnqD9L4WgsHrx5mW0n1osdJff/7mfphI0MvvkDR\nRz6AwXF330e8uds+CN6N8CDxCA+SA+HDwhBBiThQXmzj639RxiuHBvnhs/187yeXeeXIIJ/8zRWs\nKntzQSqyxK89nMWqMjv/8O0efvBUP42to3zuD1biciyfIphWi8Ln/yiP6ko7//bDy/zdN7t5/+4g\nv/trORi12yffmIwSv/6AiYq8EE+9PskzB6do6QnxifuN2C0LSNox2pG9FsKB69infRRH27l8LQOr\ny4PXFr2VNRHpbkA98yKGUy8Q6W1mZuvDYJs7eDIXji3rWPXyf3DhD7/I0NMvMdHWRfF3vx7zOodA\nIBAI5uadGWef+9zn2LFjB06nk8985jPs27ePtWvX8tnPfpYHH3yQS5cu8du//dvs379/znFux8jI\neNzm/lZSU+0MDASXZOzb4SaVIlc+9ddaOHuxnRWL7MSxbX0+T+1bw+6BWlq+/X2y/svvxnmmd49E\n+CB4O8KDxCM8SA6ED7GZK1Ajrm/ECUWW+MCeVL75eCUP7PRyuX+SL//dBf7+X7sZHH77naJVZXb+\n8S/LWFfl4FxTgM9/pY3zrctr4UqSxP07Uvj6X5SSl2Nm38FBvvhXbVy6MqH7s9XFKn/ymxZKVii0\n9oT5xg8naLoYWtgEZAXFlUXUmUtEUsnVrmIMXqRvIEQ4AkjSbNbEr3yOcE4p8rWLaC88gdxxGqIL\nvDryFrSsdMqf/Q6pv/4Rxpvaaf7Ab+E/cnrR4wkEAsG9QlpaGoODg7f+fuPGDVJTU2/9/eGHH8br\n9aKqKjt37qSjo4P09HQeeughJEkiNzeXlJQUrl+/jsViYXJyEoDr16+Tlra4go/Lmfet3APAK70H\nFj2G227E+773MaC5CB49xMTFi/GankAgEAgE80YUuoxzIRKjUWbjGhfrVjvouTRBfXOQVw4NIstQ\nlG9BuXlVw2RU2LHZ/bbikeFIlIoSW9Jd55hLI6fdwN7tXsbGw9SdD/DasSFcDgMFueY5r2WYNIl1\nZSoWk3SrdWhg9GbrUGX+719SNWSzi5lQBHM0iCM6xLWggqyZ0FQJDEYieVVE7Z6bHTpakG/0EUnP\nA828UCluvqaC63070dJTGHn5IINPvYhqs6BVVSyrzip3G1H0Rx+hkT5CI32StdClwWDgRz/6EQ8/\n/DDNzc3U1dXxG7/xGwAEg0E+/elP8+CDD6IoCt/73vfYtGkTra2tHD16lHXr1jEwMMAPfvADPvWp\nT9Hd3c3ExARlZWV8//vfZ926dW+7GhKL5XKOmC+pZi8Ng810jHSxMX0dVoNlUeMUZrt4tnWc0uEO\nRi9exL1r97L8XSb2hsQjPEg8woPkQPgQG9F94zacqffzvZ/0kZttwmGL700Wr1vjvh1eUj0aze2j\nnKn3c+z0CNkZJjLSZg2RJImyIhtrKh00tAQ5U++nqX2U6go7FvPS3HtdDHoPlqJIrF/tJC/HzNnG\nAMfO+Lh8dZLqSsecXUYkSWJlhkJVoULP1QitvWEaLoTIzVBw2RaQxCPJKCY7YcVMeHoclxxgZnwU\nX8iKxaQgSRJRdwbhgrVIwUGU/psdOjQzUW/mompNSJKEtboCx45N+F87yvXnX2Gyqxfn3m3IhuVz\nFeduIjZofYRG+giN9EnWoERmZiadnZ380z/9E0eOHOErX/kKhw8f5vLly5SXl+Pz+fjrv/5rnn/+\neXJzc/n93/99cnJy+Pd//3d+9KMf8cILL/DFL36RwsJCVq1axRNPPMFPf/pTDAYDn/3sZ++JlqBv\nRZIkzKqZcwONhKJhqlLKFzWOqsiY0lLpON9F6mAvisOBOb8gzrNdesTekHiEB4lHeJAcCB9iM9c5\nQooukzYCS3EvZ9/BAf71B5cwGWX+6LdWsHubN+6vATA6FuLHz1/l5dcHiERh8zonv/drOaSlvGnM\n2HiIb36/jxN1Puw2hc/9fh4bqp1LMp+FspB7UQND0/zDt7tp6xwjPUXj83+UT0mhVffnQqEovzw5\nzaGzM0gSPLBJ476NhluZJfMmEmbCN4A5NEw0CoOk4fJ6MKg3gxzRKHJ3A+qZl5CmJ4hkFDCz5WGw\nL77WxPT1QXo//SVGTpzDXF5E8Xe/gSkvZ9HjvVcR9+v0ERrpIzTSZ6k0Wu5Fu5Zq3SRyTYYjYb56\n8uv4pgN8deuXcBoX51E0GuVb//8Jth/5PkaDSuHf/C2qwxHn2S4tYm9IPMKDxCM8SA6ED7GZ6xxx\nT2dKFOVZqSh1cfzMEEdP+7g+MEV1hf3ND7BxQtNk1q92smmtk74rs1c69h8cJBKd7WahKBKaQWbb\nRhcup4Ez9X4OnhhmYiLMqjLbwj+Yx5mFRPusFoU927xEgTMNfl4/NoSqSpQWWudMB5VlidJclYJs\nmQuXwjR3h+noC1OYrWAxLeD9SzIGs41p2Up4ahyHFGBqLMgEFoyaCreyJtbELWtCsVko+eSjBK4M\n4H/1KIPPvISlsgRT3ooFj/VeRkSN9REa6SM00idZMyUSzXstUwJAlmRUWeH8YAuyJFPmKV7UOLM1\nO7zsr79OQaCX6UAAx7r1cZ7t0iL2hsQjPEg8woPkQPgQG3F9Yw5WlXtYU2Glo2uMs40Bjtf6KC+2\n4XbFPwXf7Zytv5CRbqS1Y5TahgBHTg2TnqqRnWFCkiSK8q1sqHbS1BaktiHAuaYAq8vt2KyJa5Sy\n0AdLliWqyu1UlNiobwpy6qyf9q4x1lQ6MJnmTq/1OGQ2VRgYGY3S1hvmdMsMNrNEdqq8oDuuisGA\nYnERnARrNIhxZgT/BGjmm7Uu3lZrogulrxn5ei+RtDwwLrzWhM1hQdu6CS0rnZGXDzL01ItImgHb\npjXL8m7uUiA2aH2ERvoIjfQRQYnYvBeDEgBZ1gyOXT1Nt7+PnTlbMMiLO7/YzAYuK26ibY1ovR1Y\nyisxeJcmg3QpSLQPAuFBMiA8SA6ED7ERQYk5sFqNyFKY3TUeZmYi1DYEeP3YEBaTQnGBJe4fKCVJ\nIm+FhQd2pRAOR6lvDnD45Aid3WMU51uw21TcTgN7arwM+2Y4ez7AgWNDZKQayc1eXGHGO2WxD1Z6\nqpHdWz1c6p/kXFOQQyeGycsx36qpcTsMqsTqIpVUl0RbT5jznWGuDEYozlHRDAsogilJGC1WxrER\nnp7AfjNrIiybUA2Gd2RNDKFcfSNrwkTUm7WgrIk3NLJWleHctQX/6ycY+eUBJtq7cO3dhqxp8x7r\nvYrYoPURGukjNNJHBCVi814NSiiyQiQaoXmoDbNiptCVv+ixCrKdPNs+QelQO6NdN4teysujUVui\nfRAID5IB4UFyIHyIjQhKzMEbi0aRJdZUOigttFJ3PsDJOh8X+yaornRg1OL/C9lgkFmzysHW9S4u\nX5uivjnIvkODzMxEKSmwYDYpbF7nIj1Fo7YhwOFTI4z4Z1hdbl9Qd4p4cCcP1htdRqwWlTP1fg4c\nH2Z6OkJlqV23y0hmisK6UpUrAxHa+8LUtYVI98ikuhbmh6YZkMwuRsYl7ARQpn2MT0XQzJbZwMOt\nrAnvmx06Fpg18VaNtMw0vB97kLH6ZvyvH8e37zCOHZsweFwLmvd7DbFB6yM00kdopI8ISsTmvRqU\nAMiyZXD48kn6Ri+zM3sbiry4YtmqImNJS6X1fBdpw70oNhvmgsI4z3ZpSAYf7nWEB4lHeJAcCB9i\nI4ISc/DORZOZZmTXVg89fROcawpw5OQwhXmWtxWljCdOh4Hd2zysyDbTemGUuvMBDp0YJtWjkZNp\nIj/Xwtb1Llo6Zv/tTL2PylIbTvvd6/Bwpw+WJM3WlFi/2klja5AzDX4amud3LcVslFhfrmLUJFq7\nZwMTYxNRCrMVlAUEZ2RZwmKzMhyyQ2gCK0GmxwKgGpFV7Y6zJt6pkWIx433kQcKjY/heOcLQ0y9i\nLivCXLhy3nN+ryE2aH2ERvoIjfQRQYnYvJeDEgbZwGR4ktbhDlxGJysdi69plOG1cGhII6uvkan2\nVlw1O5BNpjjOdmlIBh/udYQHiUd4kBwIH2IjghJzEGvRmE0Ku7Z60Azy7Lf7x4YBKC+xIS9BfQBJ\nksjNNvPArhQAGpqDHDk9QlvnGIV5FnKyzOzd7mV0LETd+QCvHx3G7TSQn2u+K/UK4vVgeVwG9tZ4\nGRqZoa5x9ppMeqqmey1FkiTyMxUq8xW6+yO09oRp7AqRl6ngsC4sa8JiUglpLobGFBxSAHnaz9R0\nGNVoBkledNZELI0kRca1ZxvGvBxGXj7E0NMvgiRh37z2nqwzITZofYRG+giN9BFBidi8l4MSAJnW\nDA5dOU7/6DV2Zm9Flhaf5bkyN4VfnrtBYaCXqREfjg0b4jjTpSFZfLiXER4kHuFBciB8iI0ISszB\n7RaNJElUlNiorrDT0BLk9Dk/ze2jrK6wYzEvLi1SD4Mqs7rCQc0mN1evz17p2H9okPHJMBXFNras\nd7My20RtQ4BjZ0a4emOKNRUODIalve8ZzwfLYJDZsv7NaylHTo0wNDJNdbkDVZ37g7rdKrOpQmVq\nBlp7wpxuCaFIkJe5sCKYBkXCYrNwddKFEhrHHA0SGg8gqUakt2ZNFK5BCswva2IujSwVxbj21OA/\neALfywcZb2rHubcG2Xhv1ZkQG7Q+QiN9hEb6iKBEbN7rQQmTasQ35ad95AIZljSybJmLHstqMnDV\n4CbU1oyp7wLm0jIMKSlxnG38SRYf7mWEB4lHeJAcCB9iI4ISc6C3aFI8GntqPFy7McXZxgAHjw+R\nk2kiO2PpUhkdNpWdW9zkr7TQ3jnG2fMBXj82jNtloGajmx2b3Te7hQQ5Xrd03ULeYCkerPxcC1s3\nuGi7eWXl1DkfFSU2XI6534ciS5StVMnLlOnoC9N0MUzn5TBFOQpm40KKYILDojCmuBkcU3DJAaQp\nP6FQCFmzLDhrQk8jLT0F7yMPMd7Yhv/AcUZeen22zoTXPe85L3fEBq2P0EgfoZE+IigRm/d6UAIg\nw5LGocvHuTExyPasLXeUlVeY7eSZjklKB9sY7erCk+RFL5PJh3sV4UHiER4kB8KH2IigxBzMZ9Fo\nmsy2jS5cTgNn6v0cOjHC2FiIqjL7guoaLARJksjJNPG+XSmoisT5lgDHzvhobBtldbmdjz6YQSgU\n5Uy9n9ePDWE1KxTnx79bCCzdg+Wwqeyt8TIxGZ7tenJ0CLtNpTBP/32kOGdbhw75I7T3RjjdPIPT\nJpGZsrCsCbMBLFYz3WMetMg4pkiQ8EQAWTXCArIm5qORYjHh/ej7iUxM4XvlCINPvYipOA9z8eIr\npS8nxAatj9BIH6GRPiIoEZt7IShhNVi4Pj5A+0gnec5c0iyLz25QZBlbWgpN57vJGO5FNpsxFxXH\ncbbxJZl8uFcRHiQe4UFyIHyIjQhKzMF8F40kSRTlW9m01kVT2yi15wPUnfezqsyOwzZ3scY7QVUk\nVpXZ2bnFw42haeqbg7xyeJCx8TC/+uEMKkpsnD0f4ESdj55LE6xZ5UCLc7eQpXywFEViXZWTwpVm\n6hoDHK/10XtlkjWVdt33YVAlqotUvE6Z1p4w9RfCXB+OUrxCwaBzFeRtc5DBbZUZCnsZGldvZU1E\nQjNI78yacHiR+9+aNbESjOb5ryNZxrlrC6bClfj2HWLomV8SDYWwb12X1N9AxQOxQesjNNJHaKSP\nCErEJtHniLtFqtnL0f6TjEz52Jq58Y7GyvBYODpsJL23kemOVpw121HMiWlPrkey+XAvIjxIPMKD\n5ED4EBsRlJiDhS4al8PA3u0e/ME3ik4OkeIxkLfCsiTzewObVWXHZg/F+RY6Lo5ztjHAq0eGKC20\n8duPZtF9aYJzTUGOnh6huMBCiid+9QruxoOVnWFi5xYPXb3jnGsMcPT0CEX5FlK9c78PSZLISlVY\nU6Jy6Ub4VuvQzBSZFOf8P+RLEthNUQxmMxeCHszMZk1EJvyzdSZU422yJs4S1UyYV+QxPj4z79ez\nlBXhemAH/kMn8e07zFhDC669Ncim5X3onwuxQesjNNJHaKSPCErEJlnOEUuNw2inJ9BH+0gn5Z5i\n3KY7a0edt8LLSw0DFAV6mBoaxrlxU5xmGl+SzYd7EeFB4hEeJAfCh9iIoMQcLGbRqKrMpjUucjKN\n1J33c/S0j+sDU1RX2DGoS/ttd1b67JUOo1HmfEuQE3U+2i+O818+kUOKR6P25nUOVZEoK7LG5TrH\n3XqwLGaFXds8yLJ0633IEpQV63c9sZgkNparqIpEa0+Y2tYQk1M3W4fK89dAU8Brk7g0lUpg6s2s\niWhoGkmzviNrIuVW1kT4ciczKblzduh4J4ZULykfe4jxlgv4D5xg+MXXcNRswJDimfcYywmxQesj\nNNJHaKSPCErEJpnOEUuN2+ji5LVaRmdG2ZC+9o7GspgMXNfcTLU2Y77cibm4BENqapxmGj+S0Yd7\nDeFB4hEeJAfCh9iIoMQc3Mmiyc02U7PxjaKTs1cPlrroJMxeeagosbF7m4fhkWnqm4K8dmSIrAwT\nj344g+a2UU6d89PRNcaaSgcm0511C7mbD5YsSawqtVNVbqe+KcDpc35aL4xRXWHHrNP1RJIkCrIV\nyvMUuq6Eae0J09QVJj9Txr6A1qGyBF5rhIhqpcOfglUewxQZJTrhe0fWRPps1kRwGC53oFyoI2ow\n3rZDR8zXMpvwPvx+ouEwvn2HGfzpLzDmr8BSWjjv+S4XxAatj9BIH6GRPiIoEZtkPEcsFR6Ti9bh\nC7SPdLI2tQq7Zruj8QqynDx7YYqSgTZGL1zAvWs3krI0ncgWSzL6cK8hPEg8woPkQPgQGxGUmIM7\nXTQ2q8ruGg8zM5HZYo3HhrCYFIoLlqbo5FuxmBW2bXRTUWKjs3v22kN9U4CHP5CGosC5piCHTw6T\nn2smPXXxh8lEPFipXo3dNV76r01yrjHAwePD5GabyErX73ritM4WwZyYgtbe2dahqgorMxZWBNOi\nRfHYJDrH0hibeSNrIgChKXhrrYmVq7Dn5DDT04ZyqQX5ek/MDh23Q5JlnNs3YS4vYmTfIYafe5nI\nxCSObevfU3UmxAatj9BIH6GRPiIoEZtkPUcsBZIkYdds1F6vZyo8xZrUVXc0niJLONJTOH++h8yR\nPiSjEUtxSZxmGx+S0Yd7DeFB4hEeJAfCh9iIoMQcxGPRKLLEmkoHpYVW6s4HOFnn42LfBNWVDoxx\nLjoZi/RUIw/sTMFqUWhsDVJ7PoDZpLB1g4uWjlEOHB8mFI5SWWJDXsBVhjdI1INl1GS2b3LjsBs4\n0+Dn4PFhJibCrCqz6V7JUBSJinyV3PSbrUO7wnT3RxbcOlSVId0WZixq5UIwBZsy/mbWhPJm1oR9\nZR7BjAqk4DBKf+eisibMxfm4P7AL/+FT+PYfZrT2PM77alDMS9d+9m4iNmh9hEb6CI30EUGJ2CTz\nOWIpSDV7qR9o5ILvIpsz1mMx3FmBynS3heM+E2m9jcx0tOLcWoNiWdp6WgshWX24lxAeJB7hQXIg\nfIiNCErMQTwXTWaakV1bPfT0TXCuKcCRk8MU5llIS1n6g5wsS5QV2dhT48UfCFHfHORC9zjrqhxM\n38ziaGwLUl3pwKJzDeKdJPLBkiSJ4gIrG6udNLUFqW0IcK4xQFWZDfs8up6kumQ2lBkY8EVo7w1z\numUGj0Mi0zt/DSQJnKYIDotMayCdyZCKW/EjTQcgNAmaFavNzPg0RFaumq01cbVzUVkTBq+blI9/\nkIm2LvwHTzD8wqvYt65DS1t8W7dkQWzQ+giN9BEa6SOCErFZDueIeCJJEkbFSP1AExEiVHrL7njM\n/BVeftEwSHGgh6mBQZybN8dhpvEhWX24lxAeJB7hQXIgfIiNCErMQbwXjdmksGurB80gc6bez4Fj\nwwCUl+gXa4zX629Z72J1uZ2LPeO0XhgDICfTxIXucQ4cG2JFlonsjPl/+54MD5bbaWDvdi8j/tmu\nJ68dHSLVq5G3Qv/DvmaQWFOs4rLLtPaGOdcRZsAXWXDrUKMaJcMeYnDazsWxVOxvyZpQNI3pkPKW\nWhNrF501IRs1PB95H8Bs29CnXkRbkYWlPHn7w8+HZFhHyY7QSB+hkT4iKBGb5XKOiCeZ1nROXq2j\ny99NTdZmjMqddeaymFQGTW7GW1uxXOnEVFCAlpYep9neGcnsw72C8CDxCA+SA+FDbBIWlHj88cd5\n4okneOaZZygpKSE9/c1fXFevXuWTn/wkP/nJT2hqamLPnj1zjrWcDhOSNFuIsrrCTkNLkNP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6JFR2WfHmtzLcGmRsyXzEMyjU8r2Ymow2RD0SD2KBrEB4oOw6MkJUYgnhfNSbNGq0VFxa6QWWPf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R5b\nq+OxNQopybYZY+QfEzneq2ciJGIzhClLmoipmgAYP9vJ6c/+T8ZOncW6fhUF//J/ozjNELgIoXEQ\nZbCmgF495AqXzqJUvYEw4iNiTyK06Vmi7sRzgV/ovfYgkKhFie9///t4vV4+8YlPALBz507efPNN\nLBYL7777Lt/73vewWCxMTk7S2dnJxz72Mf76r/8agCeeeIK33nrrplaPqxw4cIB33nmHv//7v5/x\n/bVzxOJmrvLQPXyJf278D3wTfrakb+C5og/P6chQgKPHe/jPd08ijI3w6fFanJfakZ0uUr/4JYz5\nBXP63veDthcWHi0HiYGWh9jMdI7Q2jc0eU1cHqYYOWwKOze70enEKa+JQXp6Va+JmcZeLnSMZFlk\n7QoH6Slq28Hhah89VyZYXmZFkefXJ8Dl1LFjk5vevknqW1RvhrRkA8UF9nmNkdEgsW6VgxXlVs6d\nV1s63j/Yh04Ryc823dF0DUkSWL3MTm6mkdqmAJXHfHRdGmd5ue2+JraIokBxlkxeusjpC2Faz4Vp\nOx+mPN+AEA1N+zqDEiXFGmI8qLZzXBqSMSoRzLrbCxOy047nuacZP9eJf28l/W/uwrp+NbrcIhDE\nKa+JAIQmQHej18Q40sVTU14TIaJJ2QnlNbHQe+1BIFHbNxRF4ec//znPPPMMra2t1NbW8sILLwBQ\nUFDAJz/5SZ577jk2b95MU1MT//iP/whAT08Pu3btuvaz0WiUP/7jP2bHjh3o9Xp+9atfkZGRwapV\nq2Z8f+0csbiZqzzYdFZWJy+nbfAMLf0n6B6+xDJPGZI4d6qbDK+F1UVeWruG2TeZjNVmIqnnDIHK\nCkSjEUNuXkK2c2h7YeHRcpAYaHmIjeYpMQPaoonPwxYjUVSnXKxb6eDMuVHqWlQzydRkPempsdsi\nEiVG2RlGNq1x0tY+Ql1zgMoaH6WFFpyOO1cHzAaKIrJhtQO3S3fNm8E/FKSkwDTvnhcel47Htnhw\n2hVaTg5ztN7PkTofmWkGkjx39iErI9XAo+tcnOlQJ7Ycrh6kKM+Mx3XnUz5i4bKJrC1TGByO0nY+\nzMG6McwGgXTv9KNDJRG85jAGOUr/qETvsMJ4SMBhDN/WziHqFFx/8BiiQY/v3f30/fptdMkezKtX\nqQqJ4Lg6oWPMp3pN6M1TXhM5170mOhPLayJR9loik6hFidTUVM6cOcP3vvc9Dh06xLe//W0OHjxI\nV1cX+fn5134uEAjc5CnR0dFBY2MjH/rQhwC151+n0/Gtb32L3/72twB85StfQVFmfs5p54jFzVzm\nwSAbeCR5JZ2BLloHTtI2eIZlnjL00v39DZgJq0nHpqWp+IYn2Nunp9eWRsnERUbra5m82I1pyVLE\nOGt+vtH2wsKj5SAx0PIQG81TYgY0eU18HuYYhcOq18Qv3rxEKBRly3onf/JCJlbLzf32iRajYCjC\nz1+/yG/e7UWWBT79XDpPP+ZdkG9WOrtVz4sLF8fJzTLytS/mzvukkKsEhkL87PWLvH+wj2gUNq91\n8uk/Sr/j4kI4HOVXb13i129dRhThhY+k8cwHk+9IdRGPurYgbxyYZHQ8SnmexB/tMGAxzXzd0UmB\n4716hickjEqE0qQJbIZIzJ/17a+i/Ut/Q9gXIOnFj5L1t/8TUZFhrB+GrwBR0FvBmqq2diSo10Si\n7bVEJFHbNxYa7RyxuJmPPIQiIX5+8jWOXq7FY3Tz5eWfJcnkndP3BDjUdJGf7jqFfnyYz4wcxXLl\nAkpyMmlffAl9Zuacv/+dou2FhUfLQWKg5SE2WvvGDGiVrPg8zDG6qprYsMpxbarDvgpVNZFxg2oi\n0WIkiQIrym0U55upbQpwpNbH2c4xlpfbZmxDmQvsNoUdm9xMBOFonY+9h/vxuBVyMk3zeh8Aer3I\nmhV2Vi2z0XFhjIbWId4/0IcoQkGuCSlOcUEUBZaWWCkvtlDfMsTROj9t7dOPQb0bUj0SO9c7ONM5\nTltnmNqTIZJdIl7H9PlSJEixhohEoX9U5vKQjCiAzRDh1vqTIScT1x/sJFBVi3/3YQKHj2HfsQnJ\n6VVVE6FxmLyqmlBumNBxo2riuOozsYCqiUTba4lIoiolFhrtHLG4mY88iILIMk85UaI09bVS29NI\ngSMXpyG+99T9kJ1sZUWBh8YLw+yPpOGxyLguniFQeRjZ6cSQlT2n73+naHth4dFykBhoeYiN1r4x\nA9qiiY8WI/WD9c7NbvR61Wvi4JFBLvWMU15sRa8TEzZGqUl6tm5w0dE5Rn1LgENHBsjPMd1x28Js\nIcsCH9iehsMqUNOkel5c6ZtgebkVeZ49LwDcTh07H3XjdelobRvmWIOfiupB0lMMpCTFj02SR8/2\njW66Lo2po2SrBsjJMN7Ra2fC6zZQmhVGrxM4cU4tTAyPRinIkKZtexEEcJki2AxhBsck+kZlAuMS\nTmOYW0MrO2x4PvY0E50XVZ+JN97Fsmop+swMMDjUaRw3ek0o5lu8Jk4jttcvqNdEou61REIrSsRG\nO0csbuYrD4IgUOQswKG3UX+lmerLdaSak0kxJ83p+9rNOjYtSaHXN85enxmf1UvhSBcjNccIDvRj\nKluCIC3sdBltLyw8Wg4SAy0PsdGKEjOgLZr4aDFSEUWB0kJVNXFmSjWxv6Kf1CQ9RfNs4ng3GA0S\nWze40Ckixxr816ZRlBZZEOexncNs1uNxSWx8xMGJM8PUNQ9xpM5HWZEFx11MxJgtBEEgL9vE41vc\nTExGaGgJsL9qgI4LoxTlmTGbZm5T0OtFHl3nxGyWqWlU4zoxEaa82BJXcTEdZrOesdFJclMlynMl\nzl2McOJ8mKb2ENmpEnbz9EUAoxIl2RpiZFI1wewZkjHpIphuMcEUFQXnUzuQrGYG3z1A/6tvo7gd\nmJaXIehMoLerqongCIwPqq0cuimvieQcpMsLq5rQnkfx0YoSsdHOEYub+c5DljWDbFsG9VeaOXa5\nHpNiIseWNafvqcgijxR7sZp07O8M0mzMolzyE2lrZaSpAVNpOZLl7sZfzybaXlh4tBwkBloeYqMV\nJWZAWzTx0WJ0M3abwo7NbgyG66qJzu4xSgpM894acacIgtqGsrzMSuPxIarr/bS2DbOszIrJOD/f\nrFxdR1aLzI5NbsbGw9Q2BdhX0Y/NKpOfbVoQzwudTmT1MjtrV9rp7FZbOnbtVz0nCvPMMxpzCoJA\ncb6Z1cvsNJ8coqYxQENLgKWlVizmu/deuHGvWc0ia8tkJoNwoiNM9fEQogA5qTObYCZZwijSdRPM\nYETAYbjZBFMQBKyPLMO6dgW+9w8x8NZuJi/2YN+6HkGnB4N9yldiGCaG1CKFYgKbZ8FVE9rzKD5a\nUSI22jlicbMQeUgyeShzFdHY10p9bzMT4QmKnQVz+rdMEATy0mwszXNT1znCQTJIM0Wxdp0hUFWB\nkpyCPi1tzt5/JrS9sPBoOUgMtDzERitKzIC2aOKjxeh2rqom1q920N4xyrEGH/sq+knx6slIWxgT\nxzvB49KxfZOLy70T1DWrU0UyUo3zYjx54zqSJIFVS+3kZU2N2qzxceHiOCvuc9Tm/eC0q8WmlGQ9\nJ04Nc6wxwKGjAyR7dXHj43Kovhn9g0HqmtVCS7JXR1a68a7u4da9JokCJdkyOakipzrDtJ4Nc/pC\nmIIMCZNh+nYOmyGCxxTCNyYxMCrTPyrjMIbR3VJ/0mel4/rQ4wwdqce/twL/gSPYt21AtllBMarF\niWuqCV9CqCa051F8tKJEbLRzxOJmofJg19tY6V3K8YFTNPcdp2e0l6Xu0jkdGQrgtOrZuDSF7v4x\n9gzZGbc4yA10Mny0isjYGKaSUoR5brHT9sLCo+UgMdDyEButKDED2qKJjxaj6bFb1Q+yHreJo7UD\nHDw6SPelcZYUW9HrE1M1odOJbFzjwGFXONbg50DVACMjIZaWWOd0XGesdZSeamDLehenz45Q3zJE\nRfUgRflm3M65G7M2E4IgkJNp4vGtHsLhKA2tqhLmzLkRCnNNt01duRFFEVm/2kGyR0dNY4BDRwfp\nH5xkeakNWb6zuE631zx2dXRovz9CW2eE6uNBbBaBNM/0qgmdrJpghiICA1MmmLIYxaq/2QRTtlnx\nfOwpJi71XPOZMK8sR5+RqnpMGOyq8eXkyJTXxNiU18SUaiI4gdR9CrG9DkLBOVdNaM+j+GhFidho\n54jFzULmwaQYWZO8gnOB87T2t3Had45l3nJ00ty2JupkibWlSeh1Enu7opw0ZrCEfoLHmxk9cRxT\n+RIk490Vx+8HbS8sPFoOEgMtD7HRihIzoC2a+GgxmhlREFj/iJflZSbaz49S3xJgX6X6TXlm2vwd\nBu4GQRAoyDWzdqWDlpPD1DQFqG3ys7TUOuMH7/thunVkMkps3+gmEoWaRj97K/rR60SK8swL0s4B\naoFhxRIbG1Y76Lo8QUPrEO8d6CMYjFKUZ5rRnDM3y8SGRxycPD2sTj6p81FebMFhi384nWmvKbLA\n8gIZt13kREeYxtNhLg9EKMyQ0Smx4yQK4DaHsegj9I/K9I3IDE+IOE1hpBt+BUGRcX5wG7LTju+9\n/fT/+m0kqxXzynI1B1dVE+EJtTgx7lMLFjeqJno65kU1oT2P4qMVJWKjnSMWNwudB0VSeCRpBb1j\nfRwfaKOp7zhL3CWYlLk9BwiCQGGGg5JsJ9WdI1RImWTrgxgvnGaoqhJ9Zha6pLk14bzKQudAQ8tB\noqDlITZaUWIGtEUTHy1G8TGb9chihB2b3ZgMEvVTXhOJrppw2BR2bHbhHwpR2xSY03GdM60jURRY\nVmqlpMBMfXOAI3V+2jtGWVFuW9DY2W0K2za6yEw3cmKqyHCgagCvS0dGqmHaooltyjdjfCJCTaOf\nPYf6sVpk8nNm9s2It9cEQSDNK7GiSOZCb5i2zgi1bSFSPSIe+/RxMulUE8zhiesmmBZdBKMSvena\nllVLsG5Yje/9wwy+vYeJzm4c2zYgKLJahNDfqJoYguAY6K56TayKoZrIUl83i2jPo/hoRYnYaOeI\nxU0i5EESJVZ4lzAZmaS57zi1vY0UOfOx6+e+tc1tN7ChPIVzV0bZPeJCsFjJGDzHUFUlCALGwqI5\nL/QnQg4edrQcJAZaHmKjFSVmQFs08dFiFJ+rMRIFgZICCxsfcV5TTeyt6CclgVUTsiyydoWD9BQ9\nNY3quM6eKxMsL7OizOK4zjtZRylJerZtcNHRNUZ9yxAHF2iE6Y0IgkBWupEPbPMA0Ng6xKHqQU6e\nGSE/x4TdGlsBIUkCK5fayM82Udfsp7LGR0fXmOqbMY0h6p3uNZNBYE2pjCIJHO8IU3MixNhElPx0\nadrJH7IIyVbVLHNgVOLysEwkCnbjze0c+oxU3B/+AEPHGvHvrcS3t0L1mbBbVcOKa6qJyeteE4II\nesuUaiJ3TlUT2vMoPlpRIjbaOWJxkyh5EASBUlcRZsVEQ28z1T31ZFrTSDJ55vy99TqJ9WUpCKLA\n+xcFzpnSKA/3MtHcwPjZdsxLliLq524fJ0oOHma0HCQGWh5ioxUlZkBbNPHRYhSfW2Nks8psv6qa\naAlw8OggXRfHKC+2YNAv7Bzx6cjOMLJpjZO29hHqmlXzydJCC07H7PTE3uk6MhgktqyfGmHa6Gd/\nxQAIUFo4vyNMb0WWRZaV2di01smlHrWlY9eBPkbHwxTnmVGmMehMT1F9M9Qi1RCHqwcpyDXhdd/u\nm3E3e00QBPLSJUpzJNq7w5zoCNPSHiY3VcQ6zehQQQCHMYLLFGZwTKJ/VGZgVMJpDKPcsCwlqwXP\nx54m2NuHf28F/a+9g2lZKYbsdPUHRAn0NpB0MDkMk0MQHJ0X1YT2PIqPVpSIjXaOWNwkWh5ybFmk\nWVJp6G3iWE89dr2VLGvGnL+vIAiUZDkpSLdT2TnGETmTAt0oSscphqqPYsjPR3G55uS9Ey0HDyNa\nDhIDLQ+xWbCixMsvv8wPfvADXnvtNYqKikhOTr72744cOcJXv/pVXnvtNWpra9mxY8eMsjLtMLFw\naDGKT6wY3aiaONupfiDdWzGQ0F4TFrPM9k0ugsEINY2qysNkkCjMu/9xOh+ODQAAIABJREFUnXf7\ngbusyMKyUisNrQGq6/0cPzXMijIrxnkaYTodNovMlvVO8rJNnDwzQl1TgL0VAzgdClnpsVs6TEaJ\nrRtdSKLAsQbVN0MUoOSWQsu97DW7WTXBHJ+8PjpUliE7ZXoTTL0cJcUWYiIkMDAmczkgo5ejmHXX\nVROCLOH4wBZ0yR4G391P36/fRjQZsaxeql5XEEAx3KyaGBsEQZpT1YT2PIqPVpSIjXaOWNwkYh5S\nzEkUOQtovNJCXW8T0WiEQkf+vPglJTmNrC9L4fTlUXaPJ2E06Um5cpZAZQWi0YghN2/W7yMRc/Cw\noeUgMdDyEJsFKUpUV1ezb98+fvzjH7Ny5Uq+853v8Nxzz13795/97Gf513/9Vz7zmc/w29/+FrPZ\nTE5OzrTX0w4TC4cWo/jMFCObVWb7Jjdmk+o1cejoIBe6E1c1IYkCK8ptFOWZqGsOcKTWx9nOMZaX\n29BP03ZwJ9zLOvK6dWzf6Kb78jj1LUPsrxogO8NAavLCjl0VBIH0VAMf2OpBlgSajgeoOOaj+eQw\n+dlGHPbb1SWiIFBebGVpqZWGltiFlnvda5IkUJojk5Wsjg5taQ9ztjtCQaaEUT+9CabXEsaoqCaY\nV0ZkxoIiTmP42vAMQRAwLy/D9uha/HsOM/jOXsbbz2PfsRFRmfodr6omZL3qNXFVNaEY50Q1oT2P\n4qMVJWKjnSMWN4maB6fBwXJvOa39bTT1tdI/Pki5uwRRmHu/JKNeZkN5CqFwlPd6dFw0p1AycZGx\nhjomL3ZjWrL0+rN8FkjUHDxMaDlIDLQ8xGZBihKvvvoqy5cvp7y8HJfLxSuvvMIzzzyDTqdKlj/y\nkY/gmpKPHT16FIfDQUlJybTX0w4TC4cWo/jEi5EoCBTnW9i4xsnZKRn/vsMDJHl0ZKUnpmoiNdnA\n1g0uOjrHqG8JcOg+/R3udR3pdSKb1zqxWmS1naNygImJMEuKrYjT+CfMF7IksKTEypb1Lq70T9LQ\nOsT7B/sYGg5RnG9GF6Olw+vWsX2Tm4tXCy2VA2SmGUhLMdz3XvM6RB4pUbjii9DWGab6eBCnVSDV\nM30BwKKPkmQJERhXTTB7h2WshggG+boJpj4tGfdHPshwbRP+fZX43j+Efet6ZMeU6kEQQDaA0aGq\nJiZHYOwWr4mUW1QTrjQw2+/6d9SeR/HRihKx0c4Ri5tEzoNZMfNI8grO+M7R2n+SDn8ny7zlKOLc\nTLu6EVEUKM91kZVs4dD5CWp1WRRLAcT2kwzX1WAqKkG23/2zOBaJnIOHBS0HiYGWh9gsSFHi7bff\npqSkhLy8PADeeecd1q9fj9PpBLhWnOjt7eWf/umf+Iu/+AuMM8xS1g4TC4cWo/jcaYxsFlU1YTHJ\n1LX4OXR0kM4EVk0YDRJbN0z5OzT42VcxAEBp0d37O9zPOhIEgaI8M6uX2Wk+MURNY4DG1gDLy6yY\nTXN/qIuHxSyzeZ2LojwTp86OUtccYPehfmwWmZxM420SWb1OZNNaJzarQk2jn/1VA4yNhVm7ys34\nePC+7kWnCKwolHFYRU6cD9NwKsyVwQiFmRKKHDtnigQp1hDRKPSPSlwekhEEsBuut3NIFhPujz5F\naNCPf/dh+l57B1N5EYaczOsXEsTbVROTI6Bc9ZpYfYtqYpJoUvZdqSa051F8tKJEbLRzxOIm0fOg\nl3SsSV7BpZEeWgdO0tp/kqWeMgzy/Cj/Ut1m1pYm0XpxlD2hVBwGEc/ldgKVh5GdTgxZ2ff9Home\ng4cBLQeJgZaH2Mx0jhCi0Wh02n97H3zzm99k69atPPbYYwA8//zzvPzyy+Tm5l77mf7+fv70T/+U\nr371q2zevHnG64VCYWQ58T60aWjcKxcujvLyP7XRfCKAw6bw518sYMdm77z0mt4LzSf8fPd/n+By\n7wQrltj51tdKF2QqxuhoiP/1z6d5/0AvFrPEX/33YrZt8s77fUzHZDDCL3/TxY9/eZ7xiQjlxVb+\n/IuFlBRYY/786XPDfPvvj9PZPUZxgYXv/kUZGbPkOdLTH+L/fdVHe1cQl13kCx91UJo7c86uBKIc\nPRNlbBK8VlhbIGC6pQXkwo9epeWl7xAJhij+v/6c/K9//rZ1GwkFGb7UwURgAAQBc1IGRncqgiAQ\nunCGsV3/RdTfj+hKxvDE88ipObPyO2toTMeVK0Nzcl2v1zpn19a4cx6UPESiEX596rcc7K7Eobfz\npeWfJd2SOm/vHwxF+NXeM+yp66J0vJs/vFKBODGObfMWkl74FKLudhPmO+VBycFiRstBYqDlITZe\nb+yzMMxhUeL73/8+Xq+XT3ziEwDs3LmTN998E4vFAsDw8DAvvvgiX/nKV9iyZUvc62mHiYVDi1F8\n7jVG4UiUd/Zc4aevdTM5GWXDagef/1RmTE+CRGB4JMQ//2cnVbU+rBaJ//7ZHNasuDPZ52yuo2g0\nyp7D/fzbzy4wORnlg9s9/PEnMmK2SywUfQOT/Ocvu6g45kMQ4PGtHj75bBo2y+3KjvGJMP/+sy72\nHO7HoBf5wouZbNvgnpX7CEei7D4WZHf1JNEobF2l8OR6HfI0qgmAYBjarujpG5GRxSjF3gm8lvBN\nPzNc38KZP/lLJi/14Hx6B3n/+G0ki/n2i40HYOgSRMNqi4ctTf3P4CRS/fvIbUeICgLhsk2El+8A\naea1rz2P4jNXMZrpMPEgoJ0jFjcPUh6i0Si7Ow/wm/Z3MEgGPr/0RYpdBfN6D9UnevjR709iGPHx\nor8Ks+8y+sxMUr/4ErobjOnvhgcpB4sVLQeJgZaH2Mx0jpiz9g1FUfj5z3/OM888Q2trK7W1tbzw\nwgvX/v3f/u3f8tRTT/HEE0/c0fU02eXCocUoPvcaI9VrwsymNU7OXZvQ0Y/HpZt2ksNCotOJbFzj\nwGFXONbg50DVACMjIZaWWJGkme91NteRIAjkZZtYv9JB66lhapsC1DT6WVpixWZd+HYOUCdubFzj\npKzIwplzo9Q3B9h9sA+zSSI3y3RT+4ssi6xd6aCk0EHlsX4OV/vo7ZtgWZkVRb6/QosoCBRkSBRn\nS7R3hTneEeb4uTC5aSJWU+xrSyJ4zWH0cpT+UYneYYWJsKCaYE7dti41CfdHn2SkvlX1mXjvIPYt\n65CdtxSpZP2U10RoakKHT/3/9VaiGUU3eE2cQjzfqk7omMFrQnsexUdr34iNdo5Y3DxIeRAEgXxH\nDskmLw29TVT31OM2uuZVMZHutfBIcRJN3aPsi6STrAtjv9hOoKoCJSUVfWraXV/zQcrBYkXLQWKg\n5SE2C+IpkZqaypkzZ/je977HoUOH+Pa3v83Bgwfp6uoiLS2Nr33tawwODvLGG2/wxhtvEAwGWbJk\nybTX0w4TC4cWo/jcb4ysU14TVrNMXUuAw9U+znePs6TYgsGQWG1LgiBQkGtm7UoHLSeHqWkKUNvk\nZ2mpFWsMFcBV5mId2W0KOza7CQyFqG1SR5i6nQq5WaZZfZ/7Idmr5/EtHswmieYTQxyp9VPT6Cc7\nw4jHdbNMdmmZixXlZtrOjFDXHKCqxkdZkQXnLChnHBZ1dOjIWJQT59XRoXodZCbHHh0qCGDVR/CY\nQ/jHRQZGZfpGZGyGCPopE0zJZMT97JOEh0fwvX+IvlffxlicjzH/lt5kQQSDbUohMQKTw+o/ihFs\nXsIFqyE0eUdeE9rzKD5aUSI22jlicfMg5iHNkkKBI5eGKy3U9DQwGhylyFmANA+TOQAsRoWNS1MZ\nHA3yrs/GiNFO3lAnw0eriIyNYSopRRDv/F4exBwsNrQcJAZaHmKzIJ4Ss40mu1w4tBjFZzZjdKln\nnB/8qJPjp4axmCU+/8lMNq9zJpxqAtS2g1f+q4vdB+O3Hcz1OjpcPcAPf9zJ6FiEbRtdfP5TmRgT\nrKAz4Avyk193s79KNQzdsdnNf/tYGg6bWnS4GqNgKMLPXr/Im+/2IssCn34unacfmz2/kZb2EL/a\nM87IOBRlSnzicT12y/QHz3AEzg3o6PIrCETJc0+SYQ9x4+30vfYO5/7i74iOT5D+P79A2lc+F/sw\nGwnD8GUY96v/2+wFkwcEAaGnA6XydYThQSI2D6GNzxL1Zt70cu15FB+tfSM22jlicfMg5+HySC//\n3vITLo30kGXN4HNLPonHODstfHfK4aZL/HRXG7aRfj7lr8QQ6MdQUEjqF76EMmVSH48HOQeLBS0H\niYGWh9gsSPvGbKN9w7FwaDGKz2zGyGqR2b7Rhc0iU98yxOHqQTq6xigvtibch2xZFlm7wkF6ip6a\nRj+Hq330XJlgeYy2g7leR1npRjatcV5TGRyp9VFaODsqg9nCaJBYv9rBslIrZztGqW8J8P6BfvQ6\nkfwcExaLGiNJFFhRbqMw13TtdznbOcbycht63f1/g5bkElldItM7eH10qNsukuKOfW1RAJcpjFUf\nURUTozJDEyJOYxhp6iWmskIc2zfh31+F7939jLa0Yd+xCVF/i2natQkdBgiOTqkmhkCeRjURnCSa\nfF01oT2P4qMpJWKjnSMWNw9yHiw6M+tTHyEwOURr/0mOXq4lyeghxXxv3g73QlaylRUFHmq7xzlI\nBhnyBOau0wwdqUSflY3OmxT3Gg9yDhYLWg4SAy0PsVmQ9o3ZRjtMLBxajOIz2zG6OgJz01on5zrH\naGgZYs/hxPWayM6YKgi0qwWBypqpgoDjekFgPtaRxSyzfZOL8YkINY0B9h7ux2KWKcgxJVTMvG4d\nj2/xYLcptJwc4mi9n6P1PvJzLNhuUCykJRvYusFFR+cY9S0BDh0ZIC/HNCtTT/Q6gZVFMlaTOjq0\n/lSIgUCEwgxpWhNMky5KsjXEyKTA4JjM5WEZiy6KUVEFd7pkD+5nn2K0+ST+fZUMvrMX26NrUdwx\nvmWT9WBwqMqJyREY90E0CgYL0fRiIil5SD3nbvOa0J5H8dGKErHRzhGLmwc9D5Ioscxbjsfgornv\nOMd66ue9ncNm1rFxSQo9gUneDTgJG81kDnYwVFUBgoCxsGjGv6UPeg4WA1oOEgMtD7HRihIzoC2a\n+Ggxis9cxchqVlUTdqtMffOUauLCGOUliaeauFoQCAanCgIV/ZgMEoV5akFgvtaRJAqsXGIjP9tE\nbZOfqlofnd3jrFhiTajpHKIoUJhnZudmN8MjYepbhnhnTw+XesYpzjNjNKr5NRoktm5woVNEjjX4\n2V8xQDQapbTIcpNZ5r0gCAKZyRLLCmTOXw5z8nyYhtMhMpIknNbYsZJFSLaEkcUo/SMyPcMKoYiA\nwxhGEEAyGXB/5AkiYxOqz8Sv38ZQmIOxMPf2iwki6K2qSuKqamJiaHqvieAkhpxCRsdD9/V7L3a0\nokRstHPE4max5CHDmsZy7xJO+c7S0n+C4/1tlLgKMCnz45WkyCKri73YzTre6xZoN6RSGu5lsrmB\n8bPtmJcsRdTHfhYslhw8yGg5SAy0PMRGK0rMgLZo4qPFKD5zGSNBUD+8bl7rpOPCGPVTqgm3U0d2\nRmKpJq62HRTl3d524HIa53UdpacY2LLeRftUm8Th6kGK88y4Xfc+g30uMOgl1q50sGqpja6Lk9Q2\n+dl1oA9JEijINSGJAoIgUFZkYXmZlcbjQ1Q3+Gk5OczyMism4/0Xp8xGgTWlqknp8Y4wx06ECIWj\n5KZJiGJsE0y7IYLbHMY3JjEwKtM/ImE3htFJIIgi9q3rMeRn43vvAP2v/Z5oKIR1w6rYPhOyTlVN\nRCNqYWLcp/53g/W6aqK3A6m7jdDpRsLOtBkndDzsaEWJ2GjniMXNYsqDVWdhfeoj+CcDC9LOIQgC\nuak2luS5Odo1ToWYQa40jP78aYaqj2LIz0dxuW573WLKwYOKloPEQMtDbLSixAxoiyY+WoziM1+t\nCds2urBbFRqmPmSf7UxMr4nUGG0HpYXWm1oT5gOTUWLbBhdRoKbRz96KfhRFpDjfnFDFHAC3U8fH\nn8nGoI/S2jbEsQY/lTWDpKcYSElSH+Iel44dm11c6p2gvjnAvop+MlINpKca7vv9RVGgMFOmIFPi\nTJc6NvTk+TB5aRIWY+xY6eUoqdYQwbDAwJjM5SEZRYpi0UUQBDCVFOB4/FH8B47ge+8gI43HcezY\nhGiI8UfpqmpCMcHkVdVEABSDqprIV1UTwoW2KdXExLQTOh52tKJEbLRzxOJmseVBFiWWe8txG5wL\n1s7htOrZuDSFrsFJfj/iRdErpPafI1BZgWg0YsjNu+lv6WLLwYOIloPEQMtDbLSixAxoiyY+Wozi\nM18xulE1cb5L9ZrYe7gfl1MhO8OYUB+0b207+P3eHogyK20Hd4MoCiwttVJaZKG+OcDROj9nzo2y\notyKQZ9YH2gtFj2pXpnHHvUwNh6msWWIfZUDdHaNUZRvxmyS0CkiGx9x4HLoqGn0c6BqkOHhEEtK\nrUjS/cfVaRVZW6oQGI1y8rxqgmnUC2QmxR4dKgrgMYcx6VQTzCsjMqOT100wFa8bz0efYvT4afz7\nqhh4ew+2TY+geG7/lg0ASQcGJ0Rv9Jq4rpqwlSwh2HlabenovO41oXEdrSgRG+0csbhZrHm4tZ3j\nRP8pil2FmBTjvLy/TpZYW5qEQSfz+0sKFwxeSicuMd5Qx+TFbkxLliIqqn/UYs3Bg4SWg8RAy0Ns\ntKLEDGiLJj5ajOIz3zGymGW2bXDhsCnUtwSoqPYlpGrixraDlpPDHKnz0do2zLJZaju4G5K9erZu\ndHF+qgXmQNUg+bNkGjlbXF1Hep3II8vtrFlhp7N7jIbWId47cAWiUJhnRpbUaR1rVzpobRumpilA\nbZOfJcVWbFb5vu9DlgWW5sukuETaOkM0nQnT2ROhMFNCr4td+DDroiRZQgxNiAyMyfQOy1j1EQxK\nFNFowP3ME0TDYXzvHaTvV79Dn5uJqTg/9g0IwnXVRPAG1YRswJqRyVDaEggFEbtPI2mqidvQihKx\n0c4Ri5vFnIdr7RwTAVoHTnL0cg1JJi8p5vgTMWYDQRAoyLBTmuOkoitItZxBAT6kc20M19VgKi5B\ntmlGxImAloPEQMtDbLSixAxoiyY+WozisxAxEgSBwlwzj65z0jGlmthzqB+XI/FUEx6Xjo/+YRZn\nO4aoaw6wv7KfjFQj6Sn333ZwNxj0ElvWu9DrRI41+tlXMUAkGqVsntUb03HrOnI6FHZudpPs1XP8\n1DDHGtW2nRSvnrQUAw6bwo5NbgLDIWqbVHNRl1MhJ3N28p/iFlldLHOpTx0dWnMiiNchkuSaxgRT\nghRrCAHoH5W4PCQTRfWfEEUR++a1GEsLGHzvAANvvEtkbBzbxtWxfSZAVU0YnTd5TUTDYUKSiWh6\nEZGU3CmviauqCc1rArSixHRo54jFzWLPQ6x2jrHgGEXOfMR5audw2wxsWJJCe98EuyaSsSjg7T1L\noLIC2eHEVVK4qHPwILDY98GDgpaH2GhFiRnQFk18tBjFZyFjdFU14bRPqSaO+Wg/P8qSYsu1CQ6J\ngMtpZEWZCYdd4ViDnwNVA4yMhFhaMjttB3eKIAiUFl43jTzW4Ke1bZjl5fOv3riVWOtIEARys0w8\nvsVDMBiloTXAwSODnD0/SkGuGYddYc0KO5lpBmoaA1RUD3Kpd4IVZTaUWZg2YtAJrCqRMRkETnSE\nqWsL4R+OUJAhIcfImyCAwxjBaQozOCbRPyozOCrhMIZRJDAW5uL84Fb8B4/i23WQ4Zom7Ds3IRmn\nKVAJAugtoJghOEpo1A8TfpANYE+amtChqSZuRCtKxEY7RyxuHpY8LHQ7h16RWF+WjCCKvN1j4IrB\nRdFoN6O11Yx0nEew2pFdroT6YuRh4mHZB4mOlofYaEWJGdAWTXy0GMVnoWMkCAIFuWa2rHdyvmtc\nVU0c7sdpn71vze+XqzEqyDWzdqWDlpPX2w6WllqxWu6/7eBu8Lh0bN/k4mLPOPUtQ+yvHCAr3UBa\n8vyqN25kpnWkU0RWLrWxfpWDrktqjnft7yMYilKUZyY3y8Sj65ycOjtKXXOAihofJflmXM77nzYi\nCALZKRJL8yU6LkU4eT5M4+kQWckSjmlGhxrkKCnWEBMh8ZoJpkGOYNFHUdxOPB99irG2s/j3VzHw\n1m6sG1ahS/JMfxOSAkYHRqNCaMQP436IhEBvI5qhqiZETTUBaEWJ6dDOEYubhykPidDOUZzlpDDD\nzoELIWqVDIoEP9EzJwlUHGKkoR5BltClpCJID2+BeCF4mPZBIqPlITZaUWIGtEUTHy1G8UmUGJlN\n6oQOp0OhvllVTZzpSAzVxI0xctgUdmx24R+aajs43I/HrZCTOT9z2K+i04lsWuPEZlU41uhnf+UA\n4+NhykssSDFGYc41d7KOHDaF7RtdZKQZOHF6hJrGAAeODOB16yjKN7N9o5twOKpOGzk8gF4vUpQ3\nO9NGLCaRtaUyoTCcmBodGo1OjQ6NcX1JVE0wjXKU/lGJKyMKYyEBpzGMbNTj+vAHANSxob9+G11m\nGqbSwulvQBBwJicxGlKmvCZG1OKErAd7EpEbVRNn6iD0cKomtKJEbLRzxOLmYcvD1XYO1wK2c3gd\nRtaVpXCyZ5z3gmlccWaQapVQus8xUl+H78A+wsPD6JJTkEzz+/f9YeVh2weJipaH2GhFiRnQFk18\ntBjFJ5FiJAgCBTmqaqKza5yGVlU14Vhg1cStMZJlkbUrHKSn6Klp9HO42kfPlQmWl1lR5PkbHXp1\noskjy+w0nxyipjFAQ0uApaVWLOb5VW/c6ToSBIGsdCOPb/UQiUBj6xCHjg7S1j5Ccb6ZzetcFBeY\nqW8OcKTOT3vHKMvLZmfaiCgKFGfJ5KWLnL4QpvVcmLbzYQoyJEyG2O0cFn0ErzlEYEK8NqHDpo9g\nUMC28RFMS4rx7TrAwG/eIxQYwrZ5LYIUew2YzXpGx6NgdADRKa8JP4RDoLcSzSgmkpp3g2qihajr\n4VJNaEWJ2GjniMXNw5qHTGsayzzlnF6gdg6jXmbDkhRkWaKpP0pFJIVmawEetxXHUC/jJ47j2/M+\nExcuINlsyG5PQqg3FysP6z5INLQ8xEYrSsyAtmjio8UoPokYo1tVE5VTqonyYsuCeCdMF6PsDCOb\n1jhpax+hrjlAZY2P0kILTocyr/fndKimkX2DQeqaA+yr6CclSU9W+vwc7ODu15Eiiywvt7FpjZOL\nPWoBateBPsYnImxZ7+KxRz10do9T1xzgQNUgudkmkr2z88HSZRNZW6YwOBylbWp0qMUokO6NPTpU\nmTLBjEShf1Rt5xAFsBkiGAtycD65ncChavy7DzNUXY9jxyYk0+2xvxYjQQCdRf0nOAbBqeKEFEM1\n0f5wqSa0okRstHPE4uZhzsPVdg7fhH+qnaOW5Hls5xAFgeJMB3/0RAlmnUj3UJjKURtV5mJEt5cU\neZJwexuBygqG62pBFNClpiHI81v4fxh4mPdBIqHlITZaUWIGtEUTHy1G8UnUGN2omrjQrXon7D60\nMF4TM8XIYpbZvslFMBihplGdImEySBTmmeb1HhVFZN0qO0kePTWNAQ4dHWTQH2RZqTWmqeNsc6/r\nyGaV2brBRU6miZNnRqhtCrC/coDUZD2ffDYVo1HiWKOPfRUDhMJRyossiLPQnqLIAssKZLwOgZMd\nYZrOhOnui1CYIaNTYqsmXKYINoNqgtk3KuMfl3Aawxg8Djwfe4rx9vP491Uy8OYuLOtWokvx3nSN\n22I05TUBTI0O9UM4+FCrJrSiRGy0c8Ti5mHPg9rOsWRB2zlsVgMeq55tK9Ioy3ExFoxwZECmUpfL\nZWcWaXZFbe1obMC3fy/hQAAlORnJbJ6X+3sYeNj3QaKg5SE2WlFiBrRFEx8tRvFJ9BiZTeqHVpdT\nR8PUhI7T5+ZXNREvRpIosKLcRlGeibrmAEdqfZztHGN5uQ29bn7bOXKzTKxbZef4qWHqmgIca/BR\nXmzBbp1b9cb9rCNBEMhIM/CBrR5ESW3pqDjm4/jpYZ7emcS2jW6aTqjTRppODLGs1IrZNDvfUqV6\nJFYVy3RfUUeH1p4MkewS8Tpi582oREm2hhidFBmcMsE06SJYLAquP3wMUacw+O4B+n79NrrUJMxL\niq+9NmaMBAF0ZtBZIXSD14Sku66aCIdumNAxTjQpZ9GqJrSiRGy0c8TiRsuDyrV2jsH2eW/nuJoD\nQRBw2w2sKU1m89JUZFmktT9CVSSFJnshHq8dx1Af4yeP49u7m/GOc0gWK4rXq7V23CfaPkgMtDzE\nRitKzIC2aOKjxSg+D0KMBEEgP8fElvUuOi+OqRM6DvVht8nkZs29auJOY5SabGDrBhcdnWPUtwQ4\ndGSA/BwTSZ75/UBktyps3+RmaPiqGecALsfcKkxmYx3JssDSEiuPrnPR2zepTuk40IdBJ/HFF7MY\n9F9tT1GVFJlps3NQNeoFVpfK6HUCJ86phYnh0SgFGVLMka+SCEmWMDpJNcHsHVaYDAs4TRHs61di\nXlmOb9dBBt7cRbBvENuWdQiSNHOMJBkMTrVIMTkMEwEIT6oTOtKLplQT529QTaSC2TErv38ioRUl\nYqOdIxY3Wh6us1DtHLFyYDLIlOe42Lk6A6/DSFcgRNWIlUpLEYI3mRQlSLj9FENHKhk6dhQAfWoq\ngjy/LZyLBW0fJAZaHmKjFSVmQFs08dFiFJ8HKUZmk8TWDS7cLh31LaqHw6mzc6+auJsYGQ3qPeoU\nkWMNfvZVDABQWmSJOeVhrpAlgUeW28lMM1Db5KfimI/LVyZVM05l9tUbs7mOLGaZR9e5KMgxXRsT\neujoAE9s87B6mZ2aJj8HjwziDwRZOkvtKYIgkJsqUZ4rce5ihBPnwzS1h8hOlbCbb4+XMOUp4TGF\n8I1JDIzJ9I/K2A1hbIWZuJ7eSaCyBv+ewwQqarDv3IQtyTlzjK6qJvRW1WviNtXEqhtUE/VTqonF\n5TWhFSVio50jFjdaHm4mZjtHaG7bOWbKgSyJZKdY2b4yneIsJ6NzVET+AAAgAElEQVQTYaoHJLW1\nw5VNmkOP7uJ5Rpsa8O3dQ8jnQ/EmIVksc3KvixVtHyQGWh5ioxUlZkBbNPHRYhSfBy1GgiCQn62q\nJroujVPfEphz1cTdxkgQBMqKLCwvs9J4fIjqej+tbcMsK7POu1FnVvp1M8765gCVtT7Kiiw47bP7\nTc5crKO0FLWlQ68TaTweoKrGz/BIiE8/l073pQlqrranFFmw22bn97GaRdaWyUwG1dGh1cdDiALk\npMY2wdTJqglmKCIwMCpzaUhGEaM4k614nnuaiY4u/Psq6f/Ne7g2riLicsW/CVEGg2NKNTGiek2E\nJsBgJZpefLNq4nzrolJNaEWJ2GjniMWNlofYxGrnKJmjdo47yYEgCFOjRJPZuCQFWRRp6QtTFUmi\n0V6IN9mJY6Sf8bYT+PbuZqz9DKLZhOJN0lo77gBtHyQGWh5ioxUlZkBbNPHRYhSfBzVGZpPElvVO\nPC4dDa3qhI65Uk3ca4w8Lh3bN7m43DtBXXOA/ZX9ZKQaSU8xzOr9xeOqGefkpGrGuedwPxaTREHu\n7JlxztU6kiS1wLNtg5u+QbWlo6rGxyMr7ORmmqhvGWJPRT8Om0LeLBWlJFGgJFsmJ1XkVGeY1rNh\nTl+YfnSoKIDbHMaijzAwKtM3IjM8IeKyi3j+cCeSycjguwfo+slvULwuzMtK49/EraqJ4AiM+aZU\nE8mLVjWRyEWJl19+mR/84Ae89tprFBUVkZycfNvP/MM//AM//OEPefbZZzl69Cgf+9jHOHDgAG+8\n8Qatra1s3bqVS5cu8aUvfYlXX32VgwcPsnPnTiRp5rxp54jFjZaH6bm1nePIHLVz3G0OzAaF8ly1\ntcNtM9AdCHFk2EKluQghJZUUXZjI2VMMHT3C0JEqiETQpaYiKrpZve/FhLYPEgMtD7HRihIzoC2a\n+Ggxis+DHCNBEMjLNrF1g4uui6pqYvfBPmxWedY+oML9xUinE9m4xoHDrnCswc+BqgFGRkIsLbHG\n9CuYKyRRYMUSGwU5Juqa/VTV+unoGmNFuQ3dLJhxzvU6MpskNq1xUlpg5tS5ERpah+gbmGT7Rjfd\nlyaoPOaj+/IEy8ts6GapPcVjV0eH9vsjtHVGqD4exGYRSPPEVk2YdKoJ5vCUCWbPkIxVH8G7cRmW\nR5bh332Igd++z+TlK9i3rkeQ76CAcE01IU6pJgKLWjWRqEWJ6upq9u3bx49//GNWrlzJd77zHZ57\n7rmbfubMmTP89Kc/RZIknn32Wbq7uxkYGODf/u3fePbZZ9m6dSugFjf+4A/+gL/6q7/ixIkTdHZ2\nsnTp0hnfXztHLG60PMzMfLRz3GsOZEkkJ9XG9pXpFGY61NaOPpFKJZtL7lzS3QZ0lzsZbW5UWzsG\nB1E8HmSrbVbuezGh7YPEQMtDbLSixAxoiyY+WozisxhiZDJOqSbcU6qJGh9t7SOUF1sxm+7/m+P7\njZEgCBTkmlm70kHLyWFqmgLUNvlZWmrFapnfWedpKQa2rHdxpmOUhpYhDlcPUphnwuO6v29v5msd\npSTpeXyrB5NRoun4EG3tI6Ql63HaFJpODFFRPUhRnhn3ff4+V1FkgeUFMm67yImOMI2nw1wemH50\nqCxCsiWEJED/qMTlYZlwFJJLUin89Ifo3XsE/54K/IeO4ti+Ecl6B+PkBAF0JtDbIDh+g2pCAVsS\nkcLVi0Y1kahFiVdffZXly5dTXl6Oy+XilVde4ZlnnkGnu77Ovv71r/PlL3+Z6urqa0WJ5uZmnnzy\nyZuu9fLLL/Otb30LSZIwGAy89dZbPPXUUzO+v3aOWNxoebgzrrZznJqDdo7Z+DufNNXasaE8GVEU\naO0LcSSURIOjCE+KB+fYABNtJ/Dv28vY6VOIRiNKcrLW2jGFtg8SAy0PsZnpHCFEo9HoPN7LPXPl\nytCcXNfrtc7ZtRcLWozis9hi1DcwyT//Zyf1LQGMBpHPfDyDx7e47+uP/mzGaHwizCv/1cXug/0Y\n9CJfeDGTbRvcs3LtuyEcifLr317iV29dRhDgk8+m8cwH1YPUvbAQ62hgcJIf/7qbg0cGAcjNNNLR\nNYYoqr/Ph5+4998nFv3+CD/fNU7HpQg2s8AnHtdTnDV9USkwLnKiV89YUMSqD7O5VGa4p4+Or/8d\n/a//HsXrpuBf/x7ruhV3fhPRKIwNwHAvEFXbO6ypIMoIvZ3IVa8jBvqJWN2ENn5ELU48QMzVOvJ6\nrff1+m9+85ts3bqVxx57DIAXXniBv/u7vyM3NxeA119/nb6+Pp566im+8Y1v8JOf/ISjR4/y3e9+\nl6ysLPx+Py+99BKbNm1iw4YNVFVVAdDZ2cnXv/51fvGLX8z4/qFQGPlOlDUaGg8B48Fx/r32Fxw8\nfxSzYuTP1r7I2oy7eI7OE6PjQfbVXOCtw+fovjKMEI2w3exjTaANoeM0APqkJFKefILkx3eiWO/v\nOaWhobEwaEWJRfZhci7QYhSfxRijaDTK3sMD/McvuhgdC7O83MqXP5ON131v357PRYwOHR3ghz/u\nZGw8wraNLj7/qUyMhvn/0NFycoj/5186GPQHWbnExv/xJ9k47sE0ciHXUWvbEP/2swuc7xrHoBcR\nRRgdi7Ci3Mr/+JMcHLNo6hmJRNlXG+Tdo5NEIvDoCoWnN+pQ5NjFj1AETvfp6BlSkEUocE+QZAnS\n+x+/oPO7/4QgQNZ3v0bSZ567u8JZaAKGLkFwFAQJrCmqkiIcQmrYjXRC/dAbLllPeOVjID8YfcwP\nSlHi+eef5+WXXyY3Nxefz8dLL73Ej370I3p6eq4VJXp6eqitreXJJ5/kwoULvPjii+zatYutW7de\nK0qcP3+ev/zLv4xblNDOEYsbLQ93TzQa5cilGn556jcEI0F2ZD7Kh/OfRBbvTX04lzmIRKMcPzfA\n7toumtr7AciVhnmC8zjPNROdnETQ6bCuW49zx+PoMzPn5D4SHW0fJAZaHmIz0zlCa9/Q5DVx0WIU\nn8UYoxu9Ji5eHqe+ZYjdh/qwmmXysu/ea2IuYpSdcX0qRl1zgKoaH6WFFpyO+Z1vnuTRs22ji85u\n1ZPjYNUAudkmkr13J3dfyHWU5NHz+BYPNqtMS9swo2MRjAaRrksTHKgaICfTSErS7ExfEASBvHSJ\n0hyJ9u4wJzrCtLSHyU0VscYYHSoK4DWHMSoRBsZkeodlxkIimZtKsW9Yie/9wwz+bg+TXZexb9uA\nIN/hgVqUwWBX/3NyeMprYhz0VqIZxURS8xGvdDxwXhOJ2r7R2tqKIAgsWbIEgB/+8Id87nOfQ6fT\nsX//fvbv38/vfvc73nrrLc6cOUN/fz9PPvkkhYWFCIKA3W7nd7/7HZs3b+btt9/m4x//OLIsc+rU\nKdrb229r8bgV7RyxuNHycPcIgkCmNf3mdo6BU5Q4762dYy5zIAgCSU4T68tTWFeWjAAcvxLkSMhD\nvb2YpDQvznEfE20n8R/Yx+jJE4h6A7rkFARxbkagJiLaPkgMtDzERvOUmAFt0cRHi1F8FnOMTEaJ\nR9c5SfLoqW8ZoqrWx8kzI5QVWTCb7vzblLmK0dWpGMFghGONAfZW9GMySBTmzd5UjDvBoFfjZDRI\nHGv0s69igHA4SlmR5Y7bHxZ6HYni/8/efcfHVZ2J///ce6cXlVGvlizJlhGuYIML2BhjEgihhY2D\nMcnCNwnJwgbC7oavN4H8Nt/AQkgDJ7QYkkAKxBDSCMW4914kV7lKstXLzGg0/f7+GFmWrdGMVWck\nn7dfeml0p515zp3rO8885xyJcWPN3DgnBUd7gKPHXQB4vEHWbGrG6w1SNt46aMM5Es2hSTDd3vNL\nh2oUGNPL0qEWvUppnp66lkBXciK1JJPsu2/CuW0Pbas20rZmC4nzZqJJuMS17SUJtMZQcsLfOdeE\nuzWUqEhMJ1h8NQT9yDVHQnNNeDtQM+J7rol4TUpotVp+//vfc8cdd1BRUcHOnTu59957ASguLmbx\n4sXcc889zJkzh3379vHTn/6Uv/71r2zYsIFp06bR0NDAb3/7Wx566CFOnDhBR0cHpaWlvPHGG0yb\nNo2ysrKIzy/OI0Y30Q/9Z9VZuCbzKlo9bRxoOty5Okd6n1fnGK4+sBi1TCxKYf60XBItOqpbPGxz\nmthoLEbOySfLqBI8fgTnju3YN24g6POGVu3Qj+xljS+FeB/EB9EP4YmkRARip4lOxCi60R4jSZIo\nzDcxb5aNmnNVE+uasFoUisZc2of/oYyRIktMKUtg3FgTu/bb2bKzleOnO5hcloB+EFbFuFSSJFFa\nbGFKWQJ7DzjYvqeN8sNOJl9hvaQlVuNlPzIYFK6ZmsTUKxM4ftpFS6sfSYKDR9vZU25n0hVWLObB\nmVxUUSQmFGjIzwgtHVp+PMDxmiDFeQpGfc/9KilRj1UJJUuaXAq1Dg0aq4Wi+z6Dr66Btk830vTu\nB5gnX4E+P/vSGyIrocSEoum2Qkdn1UTOxVUT5ai27LitmojXpERWVhaVlZW88MILrF+/nqeeeop1\n69ZRXV1NUVFR1+3sdjuffvopd911Fzk5Ofz617/m97//PX/729/4r//6L4qKirjyyitZtmwZ77zz\nDlqtlocfflgsCXqZE/0wMBpZw+TUsm6rc+zC7Xf3aXWO4e4DrUamKDuR+VflMjY7AUeHnx2NsEnJ\noyZzHLlpFvT1VbjK99P66Sf46uvQJKegSYrPY/dgEO+D+CD6ITwx0WUEYsxPdCJG0V1OMVJVldWb\nmln++9BcE5MmWPm3f80nPTXyB5bhilFzq4+fv3aSfQcdpCRreexrBZSNH/6Jr9pdfn7xxmk272zF\nalF45IECpk9JjHifeNyPAkGVlesaeXNFDe2uIAB6ncwjD4xh9ozkQX0up0vlnVVuKo4HMOjg7hv0\nTBt/4VCc7jFq7ZA5WKfHE5BJNAQoTXNj/8MKTn/vR6gq5H/v38n46r19r5gJeMF+NlQ1IclgyQgt\nKRrwo+z9FOXAptDN4nSuiXidUyLWxHnE6Cb6YfCccdbyq/K3qHPVMyYhjwfLFpNitEW9Xzz0wZnG\ndj7dVc2m/bV4fAHMkp9bTXUUVe9DbWoAwFBUTNL8BVivuvrSh/uNEPHQB4Loh96IOSUiEJms6ESM\norucYnRx1cSeilDVhMWsMDZC1cRwxchoUJg704ZOK7N9T2gYBcCEcRbkYRzOodPKzJqeRFKilu17\n2li7uRlXR4ArSy0ovQx/iMf9SJYkigvMLLg+lY6OAMdOuggEVDbtaOVMrZtpExPR9DJBZV/ptBJT\nSjQkWWUOngqw50iAhpYgJXlK1ySY3WNk0KpkWv10+GRaOjTUObWkXV1K5ryraFu5gZYPVuE5UUXi\nDbOQtX048eyqmtB2Vk04wN8xYqom4rVSItbEecToJvph8JwbztHibuNA86UP54iHPrCadEwuSmX+\ntBwSTDqqmt1sd5jYYChGzi8gyywRPHEU584dtK1fi+rxoMvMRDYYYtruwRIPfSCIfuiNGL4Rgdhp\nohMxiu5yjNG5uSYy0/XsqQjNNXHwaDtl48PPNTGcMZIkiSvGWZh8hZW9Bxxs291GxWEnky5xGMVg\ntqO40MzVkxMpP+Rgx147u/fbmTjBijXM8Id43o/0OpmrJycyfUoiR46302r3c7rGzSfrGpk4wYIt\naXCqBSRJIjddYUqJhtO1AQ6fDrDrsJ+cVBlbotwjRoocmgRTr1FpcinUO7WQkUXRlxbQvmsfbas2\n0fbpBhLnXYsmMaEvDTk/10TAG0pOuFtBliExo9tcE0fjbq4JkZQIT5xHjG6iHwaXRtYwOa2MZEMy\n+xsr2F63O+pwjnjqA61GoSgnkRuvyqUgMwG7y8fOBtgk5VCTWUpeRgL6hhpcFeW0fPoJ3tqzaJKS\n0CQnD+t8VIMtnvrgcib6ITyRlIhA7DTRiRhFd7nGSJIkCvJMzJtp40ydmz3lvVdNxCJGqTYdN8y2\nUVvvYdd+O2s2NZGbZSQnc3i/EUlO1HLD7BSaW33s2m9n1YYmMtJ05OdcOLv5SNiPbElaFs5NJTVZ\nx94DdlwdQT5e24TD4WPaxIRBO5kzGSSmX6FBkiQOngyw46Afj0/lyhIjbveFMZIksOqDpJr92N0y\nzR0amqVExi6+GbmtmbaVG2h895+YrxyPoSC3bw2RldAyoYquc4UOR2gJUUNn1UR2EXJ9qGpCOVVO\nMDkLLLGtmhBJifDEecToJvph8J1bnWNi6hUcaTkedXWOeOwDSZLITDExe2IWV49PQ1VVDtR52Oq1\nsTu5lPT8LGx+J54jh7BvWEfbmlV0HKvE39IMkoRitSJFma8mnsRjH1yORD+EJ5ISEYidJjoRo+gu\n9xgZw1RNHDjipGycpWtCxFjFSKfrOYyivd3PxFIrijJ834ZoNTLXTEsiI1XHzn121m9tobnFy6QJ\nCV3DH0bKfiRJEkUFJm65MY2qmg7O1Hk4esLFx+samVhqHbQlWWVJojhXYfwYhWPVAQ6cDLDnsJsx\nmRJWU89v6nQKZCb4CQQlml0a6lx6UhbOIXVMCq0fraFxxQfIeh2W6ZP7ljyRJNAazldN+NrB3QKS\ncmHVxJn4qJoQSYnwxHnE6Cb6Yegk6KyXNJwj3vsgwaxjcnEq86bmYDXpqGp0s91hYINuLHJhEVnJ\nRjQuB54Tx3EdqMC+YR0tH/0TV0U53rpaVJ8PxWJB1sXXPELdxXsfXC5EP4QnkhIRiJ0mOhGj6ESM\nulVNzErhbJ0nVDWxvgmzKbRCh8USuxidG0YxY2oS5Yec7NhnZ+e+ttAwCsvwTnJVmG9i5lVJHDji\nZNd+O1v3tHLleAuJCdoRtx/ptDLXXWujtMjE9j1tOJwBPl7bSGOTh8llCWgGKemTZAktHdreoVJx\nws+2A370OsjL6Ll0qCSBzRTAagjS3KGhsV1DYNwVFN58FY7VG2n552rcR0+QOH8Wsq6PyZNzVRMa\nXWg4h7ezakJvRc3tnGui/lTMqyZEUiI8cR4xuol+GFrnh3Mkda7O0XM4x0jpA51WoTg3kRuvyiE/\n3YrD5WVnvcqmQAabraW0lExBP6YQc5oNHUG8p07iPnoEx7YttHz4AY7t23BXnSLQ7kTW65HN5rgZ\n8jFS+mC0E/0QnkhKRCB2muhEjKITMTrPaFCYMyOZrAwDeyrsXVUTUyclo8jBmLYtKUHL/Dk22hx+\ndu4LDaNITdFSkGca1nYkWDXMn5OCs93Prn12Pt3YRHKilitLk0bkfpSZbuD2z6RTfcZN9Vk3J053\n8M9VDWRl6MnNMgzKyZpGkSgbq2FCkZl9R9zsPxbg1NnQJJgGXc/HN2lVMi0+2r2dwzmsOYxZtJDA\n/v20rd5E6yfrSLz+WjTJkVdE6UGSQGMIrcYR7JxroqMltEpHYibB4qsuqppwoaYXwDCW/4qkRHji\nPGJ0E/0w9C4cznGM8qaDHGo+2jWcY6T1gSxJZKeamT0xi2nj0tAoEj5/kKONHvY79Wz22FinLeR0\n4VXIRSWYMzMxGnUE6s7iOX6c9j27aV21krY1q3Efq8TXOeRDk5CAJA/fcuTdjbQ+GK1EP4QnkhIR\niJ0mOhGj6ESMLhSqmjBeUDXxj0/OYjQoFBX0vkLHcNBoZGZMSSInU8+OvW1s2NZKXYOHyVdY0WqG\n7yRCUSSunpzImBwDO/ba2bS9leozHZSNN6PVxuZkZiBkWWL2jGTGFYaqJlwdQTZub6XisJPSEvOg\nVaQUjzEzIT9IfUuQw6cDbDvgIyVRJjOlZ8wUGdItATSySlO7QkMwkaQ7byHB19o5z8QHmCYUYxib\n3/eGyHJn1YS+W9VEO+gtqLmlMa2aEEmJ8MR5xOgm+mH4hIZzXN05nOMQW2p3kmlKZ2xa7ojtg0Sz\njoljU5g3NYeF0/OYUJBMepIRRZaoavFwoE1hW0cC68llf+Zk/CVXYszNxZxkQWq3hx3y4auvI+jz\noliswzbkQ7wP4oPoh/BEUiICsdNEJ2IUnYhReOeqJrIzDew94GDzjtCH1Cu6zTURK2Nyjcyenszh\nY+3s2m9n845WJpRYBm0+hEuVl23kumuSOXKsne17Wtm8o5XSEgu2YW7HYMnKMHDT3FSOnXRR3+il\nvsnLh6sb8PmCjCsyoxlg4sds1hPw+Zg6ToPVFFo6dPcRP832ICW5So/lSSUJEg1BUswB2joUmj06\nfNdcT05pOvaP19C04gMkjYJlxpS+J8vOVU0YkyDg66yaaAWkblUTAeQzR4a1akIkJcIT5xGjm+iH\n4XXxcI5tdbupcdRBAGyGZJQ4WImov7QamfQkI6X5ycy6MovPXpPPtJI0clLNGPUaWpw+DjUH2eUy\nscGfwZaECbSVTEVfUIA51YaeAJ5TJ+k4egTH1s4hHzu24ak6TbDdhaQ3IJuG5gsa8T6ID6IfwhNJ\niQjEThOdiFF0Ika9kySJMblG7r4tn+MnHV0rdJiMcsyrJixmDTfMtuHzBdm+186qjU2YDAolY4e3\nXWaThhtmpaDVadm8ozlm7Rgsep3MvFk2rBYNeyvsBINw8Gg7qzc2kZqsIy+7/0M6zr3XJEkiL0Nh\nUrGGU7UBDp0KsOeon9x0hWRrz8SHXqOSafXj65wEszWvjOyF1+DbsIGWD9fQceAoSfNnIev78W2W\nJIMhAZRzVRPO0G+9pXOuieJhrZoQSYnwxHnE6Cb6Yfh1H85R2XqCg41H2Vm/l9XVGzjTXovEyE9Q\nQGiYR5JFz9jsRK4uTefmGfnMmZhFYZaVBIsOry/I0YbQkI9NnUM+qsdejTJ2HOasDIxGHf7as7iP\nH8O5exetn35C29rQkA9/S0toyId1cIZ8iPdBfBD9EJ5ISkQgdproRIyiEzGKLjXFxNQyEzmZobkm\ntuxso/xQqGrCGsOqCUWWmFKWwLixJnbtt7NlZyvHT3cwuSwBvW74hlHIssS82ZlkZ2jYtc/Oll2t\nnKjqYMowt2OwSJLEuLFmpk9JZP9BB472AB3uIJt2tHLgaDvFBSYSE/peDXLxe81slJg+IbT/HDgZ\nYPtBP/6ASmG2gixfmPiQJUg1BzDrgjS7NDSbszHffguGo/uxr9lMy4drSLhuBlpbPxMGGv35qglf\n71UTmmO7Qit0DFHVhEhKhCfOI0Y30Q+xk6CzMifnWuYUT0PyKzS7WzjWdpJd9ftYXbWeaudZVFXF\nZkhGI8e2SnKwmAwactMtTC5K5YapOdx0dR6l+UmkJhqRJTjd7OaAXWFrRwLryKU8ewqBkrLQkI9E\nC5KjDc+JE7gqyrGvX0fLxx+GhnzU1RH0+fo95EO8D+KD6IfwRFIiArHTRCdiFJ2IUXTnYjQm18gN\ns1OorfewpyJUNWHQyxQXxrYqICvDwNyZNk6e7mB3uZ31W5opKjCRnjp8H8TMZj2JVom51yZz/LSL\n3eUO1m9tpqTQTFpK/C5BFklyopb5c1JoafVxoqoDWYa6Bi8fr2uk3RVgfFHf5tAI916TZYmSPA0l\neQqV1QEOnAhVTozNVrAYe+5TZp1KutWPwyPTolrxL/gcKbIT56fraPzTPzCWFGIsLujfCz5XNaEx\nhBITXkeocqJrrolipCGumhBJifDEecToJvohtiRJIi81gzGGAublzmFi6hWYtSZaPK0cazvJ7ob9\nrK5az2lHDUE1iM2QjHaUJCigc8hHsokJY5KZPTGLz1yTz5SSVLJTzRh0Ck0Ob2jIR3toyMfWhFIc\nJVPRFxRiTrWhU/1hhnxsx1NVRbC9HclwaUM+xPsgPoh+CE8kJSIQO010IkbRiRhF1z1GRoPC7BnJ\n5GQZ2HvAzpZdnVUTgzghYn8YDQpzZ9rQaWW272lj9cZmACaMsyAPQ8LkXIyMxlA7ZFlix542Vm1s\nQqNIlBbHz7JjfaHVyFwzLYnsDD17yu34/Sp6nUzFESerNzaRmKhhTK7xkl5bpPdaslVmxgQtDpfK\noVOhSTCNeom89J5Lh2pkyLT6kSRocmtxTLkOW2kuvlWraHr3n6hBFevMaf2Pt0bfuUKH//wKHRCq\nmijprJqo6aya8Axu1YRISoQnziNGN9EPsdd9eF2iPoFSWwlzc2czJX0iFq2ZVo+DY20n2NNQzqrT\n6zjlqCIQ7ExQKCNzHqXeyLJEslVPUU4i0ydkcPP0PGZPzKIg00qCSYfbF+RIg5d9nUM+1msLOVN8\nNcrYkvNDPs7WXDjkY90a3MePdQ75kNFYrT2GfIj3QXwQ/RBepPMISVVVdRjb0m8NDY4hedy0NOuQ\nPfZoIWIUnYhRdL3FqLXNxytvVbFlZys6ncR9d+dw641pPUrvh9uhSic/eeUkDU1eysZbePSrBaTa\nhrZaIVyMyg87+OkrJ2lu9TG5zMqj/6eApMSRe/J2ts7Nj18+ybFTLqwWhQ53AL8fJpSY+eriPArz\nIy/Peqnvtb1H/axY7cblhtIxCl9coCfBHL4io80tc7BOj9svYz1VgfzEv+OrPkPSgusY++L/oEm0\n9uu1dvE4wHE2lKDQGMCaDVoDUkMVmk3vIdsbUS3J+GbdiZpROLDnYuiOR2lpA4xDjInziNFN9EPs\nXUofnG2vY3f9PnbX7+dMey0AiqQw3lbM1LRJTEq7AovWPBzNjbl2t49jNW0crQ79nDhrx+c/v3R6\nikXLFKuH4mAzKW1nkapOEGhr7bpe0ukwFI7FWFyCsbgEQ1ERmWMyxfsgDojjUXiRziNEUkLsNFGJ\nGEUnYhRdpBipqsrG7S28+lYVDmeAK8ZZePhf88nKMAxzKy/kbPfzy1+fZvPOVqwWhUceKGD6lMQh\ne77eYtRm9/Hi66fYuc9OUoKGR79awOSyhCFrx1Dz+YP87t0z/OWjejSKRG62gZNVHcgS3HxDGvfe\nmdXr6ix9ea+1OYP88RMPR6oCmA3wLzcauLIo/OP6A3CkUU+9U4PiaCHxh9/GvXEL+rH5lCz/Eabx\nRf1+vQAEA+CsA3fnCaUpFcxpEPSj7F2FcmAjkqriH38tgfmRtEUAACAASURBVKk3gbb/CTCRlAhP\nnEeMbqIfYq+vfVDXXs/uhnL21O+jynkGAFmSGZdUxNT0iUxOuxKrzjJUzY07/kCQU3UOjla1UVnT\nRmV1K3aXr+t6vVZmog0myG1kttdhqD2F7+wZOPdRTpIwZGWCwYRsMqEYjcgmE7LRhGIyIXf9bUQx\nms5fNpmQ9P2fgFroSRyPwhNJiQjEThOdiFF0IkbRXUqMWu0+Xn2zis3nqibuyuHWBbGtmlBVlY/W\nNPL6H6rx+VU+tyCN++/J6dM8CJcqUoyCQZW/fVLPmytqCAbhrlsy+NId2SjKyD2J2LmvjReWn8Lu\n8DO+yEybw0dtvZcEi4YlX8hm/pyUHn3f1/daUFXZsNfHPzZ68QfgmjINt1+nR6/rGTdVhTqnhqMN\nOgL+IEm//gneX7+ObDIy9mdPYfvcggG/ZjxOcJwJVU0oekjIBq1xUKsmRFIiPHEeMbqJfoi9gfRB\ng6uJPQ372V2/n1OOKgAkJEqSxnYlKBL1IzcZ3x+qqlLf2tGZpGjlaHUbZ5tcXdfLksRYm5aJegdj\nPA1Ym6pR62vxt7dDINC3J5MkZKMJ2dSZsOhMYFycvJB7SXQoRhOSZvTMETJQ4ngUnkhKRCB2muhE\njKITMYruUmOkqiqbtrfy6ltV2J1+JpSYefiBMWTHuGriVHUHz790guqzbsbmG3n8G4WD3qZLidGR\n4+385OUT1DV6KS028/hDhUM+rGQoNbf6+NlrJ9l/0EFKsparJyewdnMLbk+QkkITX70vj5LC86W8\n/X2v1TYF+N1HHs40BklJlFi80MCYrPBzOLh8Egfr9Dg8CoY1H6A8811UVwdZD3+F3O98A2mgcz/0\nqJpI6ayaCFxQNREYfw3+qQv7XDUhkhLhifOI0U30Q+wNVh80dTSzp6Gc3fX7OWE/BYQSFGMTC5ia\nPpEpaVeSbBi6ZZXjmbPDR2V1G0c7kxQnz9rxB85/lEu26jHpFRK0kKAEscgBLJIfk+TDqPoxBL0Y\ngl60fi8avwfF60b2ecDdQbCjg6DLRbDDRdDt7nPbJJ2uWxWGMZTk6JG86K16w4Ss1w/KsqjxQByP\nwhNJiQjEThOdiFF0IkbR9TVGrXYfr75VxeYdrei0EovvzubWBekoMayacHsCLP9DddeKIV+/P495\nM1MG7fEvNUbtrgC//PUpNu1oxWJWeOSBMcyYOnJP0AJBlT9/UMcf3j8DKnzupnSaW71s2NaKJMGC\n61K47+4cEqyaAb3X/H6VD7d6WbPTF3rc6VoWzNCF3aeCKpxs1nK6VYt8/Cjm7z5MoKqKxHkzKfrF\n/0OTPAjDeLxOsJ+FoK9n1cTmPyO3NfSrakIkJcIT5xGjm+iH2BuKPmhxt3YlKI63nUQl9LGlMCGf\nKekTmZo2kRSjbVCfcyTx+YOcqnWEkhRVbZxtduFo99Lh8dOXD3iSBCa9BrNBi9GgwaSTSdQEscp+\nLFIAEz5M+NEHvRiCPrR+T2dSw4PsdYO7g0C3pEbA5epntYYR2WDs/G3o9mPs5bIh7G0lvT6mw1HE\n8Sg8kZSIQOw00YkYRSdiFF1/Y7RxewuvvhmqmigtDlVN5GTGtmpi/dZmXvrNaTrcQebNsvG1+/Iw\nGga+ckJfYqSqKh+vDQ0r8fqGdljJcLl4ctHPzk/l7b/WUlXjxmJWWHxXNvfeXUhzs3NAz1NZ7ecP\nH3todarkZ8gsvtlAalL4uLW4ZA7W6/G2OjD/8D9RN65Hn58TmmeibNyA2gGEqiba68+vznFB1cRq\nlAMbulVN3ATa6CtgiKREeOI8YnQT/RB7Q90HbR47ezsTFEdbj3clKPKtuUxNn8jUtEmkmQbvi4KR\n6FwfBFUVtyeAy+3D5fHT7vbjcvu7/nad+9vjC13nufB6ry8Y/cm60WlkjIZQUsOk12DSK1h0YJU6\nKzXwY8KHQfWiD/jQBrxofW4UnwfJ40Z1dxBwuULVGh0dBN0doWqNYN/a0UWSkPX6UMJCbwgtqXpx\nAkN/cULjosvGbkmOPlZwiONReCIpEYHYaaITMYpOxCi6gcSoze7jtd9VsXF7qGri3ruy+dxNsa2a\nqK338ONXTlB5wkV2hp7HHypk7JjIK0dE058Ynaru4EcvHafmrIexY4z8x0OFMZ8gdCAunlz0m18e\nQ0OTlz/+5QyujiAlYy08sCib0uKBTX7W4VF5d42H3Yf96LRw+3V6rinThP1mxRuAw/V6mpwy+jeW\nofz6JWSDnsKfPEnKHTcPqB3nn6Qd7Gc6qyZ0nVUTpp5VEzPvRM2MXDUhkhLhifOI0U30Q+wNZx84\nvE72NVSwu2E/h1sqCaqhD6+5luzOBMVEMszpw9KWeDJYfeDzB+nw+GnvkcToTFy4/d2SGb5u14V+\ngn34eCkBRr0GkyH0Y9RpMOo1GLQyRo2KWQ5ixI8RPwbVj171owt60QX8aAJeFL8Hxe9F9nnB4ybQ\n4Ub1uLsSG8GO0GXV7+93PCSdLnyVxrm/jZ2X9aHtCTYLDpcPSVZCQz6V0O9zPyjKRdfJnZc1SHLo\nsqQ5fzvknkubj0QiKRGB+E8sOhGj6ESMohuMGG3a0cIrb1Zhd3RWTfzrGHKyYvcB3OcP8vv3zvD+\nh/VoNBJfvic0MWd//+Pob4zcngCvvVXFqo3NGA0y37g/n+uuHbnlrOcmF33jj6EqkFsXpPH5m9P5\n4/tnWb2xGYB5s2zcf08OyQNcHnXXYR/vrvbg9kJZocK/3GjAYgo/CeYZu4ZjTTqktZ+if/oJaG8n\n8+v3kfffDw/OBF9qEJz10BF6jRhtYEnvc9WESEqEJ84jRjfRD7EXqz5w+trZ33CA3Q37OdR8lIAa\nGjaQbc7sGuKRZc4YFR/qoomH94Gqqri9gc6kRs/qjF4THZ2393j7OOyjG40iY9QrGHQKBp0Go07B\noNdg0CkYNRImKYBJ8mOUAhhUH3rVjz7oQxf0oQn40PhDSY5zCY6g++KfUKJD9XgGMWKX6OKkxiUk\nNvp0H0UJVYQoCpqkJBKvmzvoc3yIpEQE8fDmjXciRtGJGEU3WDHqUTVxZzafWxjbqold+9v4+a9C\nq0hMn5LIww+MIcHS9w+pA43Rms1NvPLbKtyeIAuuS+H/3JuHXj9yh3NcPLnotx8qRNHo+NGywxw/\n3YHJKLPo9mw+Oz8Njab//d/iCPKHjz0cqwlgNUl8cYGeCQXh+8/pkThYb8B15ATG7z4Cp06QMGc6\nRS89gzZlkOb18LpCK3QEvKGqCWsW6MxIjdWhFTqiVE2IpER44jxidBP9EHvx0AcuXwf7G0MJioPN\nR/AHQ9+OZ5jSuyoocixZozZBEQ99MFDBYCip4fb6Q8mNzt9uT7dtHn/XbTq6bT//O3QbjzfQp7k1\nulNk6XxyQx/6bTiX5NBKmOQgZgIYJT+Gc1UcQR/agA+LUaHD6UYiiBQMIgUDSGoQAkEkNYAUDELn\ndgJBUM9dDoSGrAQDoZ9zfwcCqIEAajAIAX/ocqDb9oC/87pzfwf6PqfHRQqefg5d+uBWG4mkRASj\n4c071ESMohMxim6wY9S9amJckZlHHhhDbgyrJppbffz8tZPs61xF4rGvFVA2vm8f4gYjRjW1bn78\n8glOnO4gL9vA4w8VMibXOKDHjCWPJ8jyP1TxSefkoo9/s4SpZSY+WdvI7947g7M9QF6Oga8tzuPK\n0v5/aA6qKmt3+/jnJi+BIMyaqOW2OTp02p4nroEgHG/SUXPWg/6H30FZvwpdTiYly5/HPKl0IC/3\nvAFUTYikRHjiPGJ0E/0Qe/HWBx1+NxWNB9ndsJ+KpkP4OhMUacYUpqZPYmraRPKsOaMqQRFvfRBr\nQVXF6wtcmLjwdEtceHtJblx8G0/ocl+GpQw2WZKQ5dBvSZZCf0sgn7ssh/6Wzl0GFBkUgmhQUc79\nSEE06rnLKooaQJFU5HPbCGJIsPL5u2ej1Qx8vrTuRFIiAvHmjU7EKDoRo+iGIkZ2h5/XflfFhm0t\naDWhuSZui2HVRDCo8ud/1vH7P4dWkfiXz2fxhdsyL7k9gxUjry/Ib96p4YNPG9BpJR68N4+brk8Z\n0SdeG7e18MvfnMLVEWTuTBtfvy8Pn1/ld++d4ZN1jagqzJmRzJf/JWdAS6SeaQgtHVrbHCQtWWLx\nzQby0sP/p9zYrnCoVgu/fgXd68uQdDoKf7SU1C/c2u/n78HnCs01EfCCrA3NNRGlakIkJcIT5xGj\nm+iH2IvnPnD7PRxoPszu+n2UNx7EG/QBkGJI7hziMYmChLwR/f8kxHcfjHSqquL1B89XZHQmMTq6\nJTHOJTD0Bi3Odg/BoEpQVVGDoQRJUFW7tgWDocc8v43z16kq6kXbVJUo9w9t6+3+F9wnqEasINFq\nZJ752rXYEgb3yz6RlIhAvHmjEzGKTsQouqGM0eadoaqJNnt8VE1cvIrEo18tuKQPyoMdoy07W1n2\nxinaXQHmzEjmG1/Ox2Qc3Kz3cKpr8PDC66c5cNhBVnpoctGiAhNHT7Tz2ltVHD3hwqCXuee2TG5b\nmI5W07+hKz6/ygebvKzb40OW4eZrdMy/SoscJrnk8UscqtfT9ul69D/4LySng4wHF5H35KPI2kGY\nZwJCVRPtDeBqCv1tTAZzOqjBsFUTadmpIikRhjiPGN1EP8TeSOkDb8DLgeYjXQkKdyA0P0CSPpGC\nhHyyzRlkWTLJNmeQZkxFkUfO/5sjpQ9Gu5HQD6p6USIjqHZuCyUl9NrB3+8jnUco3//+978/6M84\nBFwu75A8rtmsH7LHHi1EjKITMYpuKGOUl21k/uwUmlq87N5vZ+W6RjQamXFFZuQYfOuRatNxw2wb\ntfUedu23s2ZTE7lZxqhLmQ52jHKzDcyZkczR4y52l9vZuL2F0mIztuT+VxLEksWs4Qu35dNm97B9\nbxurNjRhMMjMvCqJBdelkpaio+KQk+172ti4rYWcTAOZ6dGX0LyYIkuUjtFQkCVz5HSAiuMBjlYF\nKM5VMBku3J80MmRY/Mj5BTTNuBl55xZcq9dj37KLpPmzUMwDW5EFCC0gr7OEfnwd4HWC2w46I2ru\nBILZJUgNp1FqjqCc2IcmIwe3ZmCrk4RjNvc9lvFEnEeMbqIfYm+k9IEiK2SaQ3NMzM+7joLEfBRJ\noba9jipnDUdbj7O7fh/rajbzyak17Krfx7G2E9S5Gmj3uVAkBaPGEJdVFSOlD0a7kdAPkhQa9qHI\nEhpFRquR0WoUdFoFjTI085FFOo8QSYkRsNPEmohRdCJG0Q11jPR6mZlXJ1OQa2TvQQfbdrexp9xO\nabGZROvAVmjoD51OZtb0JJIStWzf08bazc20t/uZWGpFUcKfyAxFjMwmDfNmpRAIqOzYa2f1xmaM\nBoVxY01xeUIVjdVqoKRAT2mRmV3ldrbsbKPypIspZQlcMc7KTden4PEG2VNuZ83mZk5WuRg31ozZ\n1PeqhdREmRlXaGlqC3L4dJBtB3wkWCSyUy9cmkuSINEYJDnTSuP1dxE4dQr/xg00vv8x1hmT0WUN\n0kRRihaMnZNpep3gboOADxIzCZZcDcEg8pmj+A/vJlA2B6TBPakQSYnwxPE/Poh+iL2R2AeKrJBh\nSmNy2pUsyJ/LrOwZTLCNI9eaTZI+EQmZelcDVc4zHGmpZGf9XtZUb2Tl6bXsbSjneNsp6l2NuANu\ntLIGg6KP6f+tI7EPRiPRD+GJpEQEYqeJTsQoOhGj6IYrRrnZBubPSaG5xcuu/Q5WrmtCo5EYN9Yc\ntvx+KEmSRHGhmRlTkyg/5GTHPjs797UxcYIVa5jVOYYqRrIsMfmKBMYXmdm5386WXa0cP93B5LIE\n9LqRtTrHuRhlpuuZN9PGqeoOdpc7WLu5hbFjTORlG7lqUiIzpiZyuqaDPRUOPl4TmnOiZKy514RQ\nb7QaicnFGlISZQ6eDLD3aIDa5iAluZoek2DqNSqZKRKu6z+DExOs+5TGd/6BNiMV88RBmgBTkkBn\nDlVN+DvA2x5KTmiNqHlXEMwdj6VkAm5TyuA8XzciKRGeOP7HB9EPsTfS+0CSJIwaA2mmVMYmFjA5\nrYzZOdewcMwNXJt1NaW2YnLMWSTorAQJUuuqp8pRw6GWo2yv282qqvWsqtrA/sYDnGg7TZO7GU/A\ng1bWoVd0w5KsGOl9MFqIfghPJCUiEDtNdCJG0YkYRTecMdLrZGZelUxhnpF9Bxxs3d3G7nNVEwnD\nXzWRlKBl/hwbbQ4/O/fZWbWhidQULQV5F5b2D3WMstL1zJ1p48TpDnaX21m/pZniQjNpKSNnOEf3\nGBkMCtdfa8Ogl9mxt43VG5sJBFSuGGfBlqxj/pwUMjP0HDziZPteO+u3NpORpos6jOZikiSRnaYw\nZZyGqvoAh08H2XnYT1aKTGrShUkdWYI0SxD9VVNoKJyGvGENbX//BE9dE4lzrw2tBz4YFC0YkkNJ\nCq8TPPbQZJgJmVjHFAzJfiSSEuGJ4398EP0Qe6O1DyRJwqQ1kmFKoyipkCnpE7kuZyYL8+cxPWMq\n45KLyDJnYNFZ8Kt+zrSf5bSjmgPNh9lWu4tPq9axtmYT5U0HOW2vptndijfgQ6/RoVMG9//f0doH\nI43oh/BilpR4+umnWbZsGe+++y7jxo0jIyOj67pNmzbx2GOP8e6771JfX8+MGTMiPpY4mRgYjy9A\ns92NRiP3eZxQf2PkcHk5XtOGQaf0abKUfZUN/GHlUUx6hQybuU/PebbRycZ9ZzEbNFhNfTvQ9zdG\nldUt/GXDCQwaCVtC35Ze7G+MDp5s4t01xzAbNKQlDcKY9Uv0943H+OX75fj9fsbl2y75fmcbnWws\nr0Mn0+d+eX/dUX7553I8Xh+lYy7921+Hy4sn6OGW+ek4nQF2lzv4ZH0Tiiwxvihy1cSGvTX86h8H\n0CoS+ZkJfWpvZXULf9t4EotRc8H+oNHIzJiSRE6mnh1729iwrZW6Bg+Tr7Ci1cjsq2zgzY8OoddI\nfd7v+8JoULh+pg2NIrF9Txufbmikut7BNVNT+jT/xodbTvDS++WowSDFucl9asPOQ3W8+dFhjDqF\n7NS+zX1w8fFIkiQmlFiYUpbA3gMOtu9pY99BB5OvSMBs0lCQZ+KmuanUNrk4fLSD9VtbqDzRTkmh\nKWy1ysW6HxesJoXpEzRoFYkDJwPsOOSnw6NSlKP0WGHFolep0+hovPazGCr20rF2Pc3rdpA8fxaK\nZZD6t6tqwtpVNeF1NvPH1SdweaU+xzYakZQI73I5j4h3oh9i73LrA1mSsejMZJozKEkuYlr6JObm\nzmLhmHlclT6Z4qRCMk1pmLUm3AEPNc6znHJUUdF0iC21O1h5ei0barZwsOkIVc4aWjytBIIBDBoD\nWrl/EyVfbn0Qr0Q/hBfpPGLIVt/Ytm0by5cv55VXXuHYsWMsXbqUt99+u+v6W265heXLl5ORkcF9\n993H//zP/1BcXNzr44lZs/snEAzy9qpKdh9poNnuwZagZ+q4NL44vxhFvrQP3n2Nkdfv54e/3UVN\ng5OgGvrmMCfNwn/fPw2dpveDbG2ri6Uvb+mx/emHriUzygdvp9vLt1/ciD9wfnfWKBI/eWQ2FkPk\nD8H9jVGz081/LNvUY/vzD8/CZon8TWx/Y9Rg7+A7v9zcY/uz35xJWh8TIn1x8HQLP/r97h7b//Pe\nqUzI7/0D6UD6peJkMz/+454e2x9fNIWygt4TIr3FduGkIpb/roaWNj/FhSb+/YEx5OVcGLMT9Q5+\n8Pr2Ho/5vQemU5geeeWBvuwPtfUefvzKCSpPuEhP09FhbEJjCFxwm0vZ7/vr3Ov0uRTaa82ofhmN\nycd3Hx3H5OLICYbD1a08+9auHtu/c980xucmRbxvTXM733t1a4/tP/jaNeRcYiIm0vGo3RXg5d+e\nZsO2FixmhX/7yhgKinRdx5WAR8ZVb8TfoUWjSNzx2QzuvjUDg75nQjDacaGqPsDvPnLT0KKSaZNZ\nfLOe7DSlx+uUJIkpY/OY9rc30a78B3JqKuN+9SwJMyZf0uu9VDVNTrZuq+DzUyyoKnzzzToCat9i\nG41YfSO80X4eMVKIfog90QeReQNeal31nHXWcba9jjPttZxtr6PZ3dLjtsn6JLItmWSZM8g2h35n\nmtOjVlaIPogPoh/Ci8nqGytWrGDy5MmUlZVhs9lYvnw5d9xxBzqdjqqqKtavX89XvvIVJEmisbGR\nuro6Jk/u/SRNfMPRP3/89Cgrd1TT4Ql94OnwBDh+xk6Hx8/EsZf2rXNfY/Q/v95BVb2za/1bFbC3\ne9lb2cQNU3N6vd+//2x92O2f7qjm9jmFEZ/z4Z+uu+CDL0BQhU+2V3Hb7Mj37W+M/u0n68Ju/3hb\nVdT29jdGj/w0fIxWbo8eo4H4zks9EyEAm/bXRnzegfTLEy+Hf87N5ZGfs7fYNjrb+cEjk2hu9bF7\nvz1s1cTjL24M+5jrdp+JGt++7A8Ws4YbZtvw+YLs3GfHa9chySqKIcC5YoVL2e/769zrVLQqugQv\nQa+C36Vl7aZmxo+1RFyx4r9+2TPxArBx39mo7X305xvCbl+9s+aSX2uk45FOG1qJI9WmY/veNtZt\naWHllho0Jj+SBLJGRZfgQ9EH8LoUDhxqZ+3mZtJsOnKzLpxJPdpxIdEcmgTT7YWDJwNsO+BHo8CY\nLJnHXrjwdda2tFFTOpW8cSVoNq6m6U//gKRkrFMmDNoY40df2MCROh/bT7gpr/FQZw+1uy+xjUZU\nSoQ32s8jRgrRD7En+iAyRVZI1CeQa82m1FbC9MypzM+7jvl51zEp9QoKE8eQarRhUPQ4vA6qnGc4\n3naKvY0VbDyzlY9PrWZb7S4OtxzjrLMWh8+JiopJY0TunNhY9EF8EP0QXqTziEFaRL2nxsZGysrK\nuv622Ww0NDRgsVhoaGjAZrNdcF1VVdVQNeWy5fEF2H2kIex1u480cvfcokFfg9bh8lLT4Ax7XU2D\nE4fLG7Z8f19l+HZ2v35ScVrY6842Ont88D3HH1A52+gkq5cy5v7GqLK6Z1b74ut7K2nvb4wOnmyK\n+JwHTzYxoWDwJ7f7+8ZjUa//3OyiHtsH0i/vrzsa8TnfX3eUO64v6bE9WmyRgzz61QJmXZ3Ey789\nzVvvnqHisJMnv13Mhr01EZ9zw94a5kwOnzDqz/6g1chMnaZn5X4n7bUmOhpMBH0KpvSOrttE2u/7\n6+LXKSsq5ux2PK16OhoM/H8/qeTbXyvgumt7VqN8uOVExMf+cMsJPnNt+A/AOw/VRbzvzkN1XFWa\nEfE2l0KSJBZcn8r4IjP/74VK6hvA71Gw5jmRpM4RD1YfWrOPiel5bN7q5LlfnmDh3FS+8eV84NKP\nCzqtxF3z9EwoUHh7pYe/b/Sy/5gr7P3qm1p4x5rHkh8vQ/P9/6bmv5/Bb3cw5ltfGfBr7h7bWnuA\nWnugx/WDEVtBEARh8Bk1BgoTx1CYOOaC7e0+V6iiwhmqqDjbXsuZ9lr2NVawr7Gi63ayJJNuTCXL\nkkm+LZOODh/nCuGDBEEFtfNf98vqBdvPbwOVYNjbBgFQ1a57dT7P+cudlzovd9928f1CjxW86P6g\nIkkSiqSEfuTO35Lc7fK57RdtkxQ0ndvlzu2a7re94PG6XZbli+6vIHdu13S7rTzIK1oJFxqypMTF\nBjpKJDnZhEYzuB+gzxnpJam9OdvYTrPDE/a6FocbRaclLfXSS6YvxZmjDQR76eqgCg5vkLFjej7W\nhr+UR3zcDRV13DhzbNjr1pdH/rBTWdvOpAlZYa/rb4zeXhP5g/rOymZmTs0Pe11/Y/TGh4ciPueW\nQw1cP70g4m3649PdZ6Je/693TOmxfSD9smrP2Yj3XbXnLF+9e1qP7Zca21sXWrluZiYv/eYEXm+Q\ntDQrn+6JnJT4dE8Ndy4Iv4JCf/eHDX8pR2v2kzDGgaveiCRf2PhI+31/hXudkgSGZA8ao59gm4XU\nVHPY9/zK3ZFjtHJ3DUtumxT2urV/6jkU54Lr95/lM9f1PoSvu0s5HqWlWbnymko2bnAT9MtcXJAg\nyWBI8/DbX1zNq2+eIMVm6Hrcvh4X0tJgyhUBfv9PB3uP9r7ven0+tspWvvLxCo5/+/skZNgG5f+f\nwYytIAiCEB/MWhPFSYUUJ51P9quqisPnvDBR0TkcpNZVz+76fTFscf9ISF1VgxISKirBzqRFPJGQ\nIiQ4ZDSSpjNRosFsMCAHFfSKvvNHh17RY9Ccv9x9u17RdV4Xuqzp55wiI9mQveL09HQaGxu7/q6v\nryctLS3sdXV1daSnR17HvaUl/LdPAzWax/wEfAFsVj1N9p4n18lWAwGv75Jee19iZNXJyBJhPxjK\nUuj6cI81pyyDHQfre33cOWUZvbahODNyYqU409zrffsbo6uKbXy05VSvz3lVsa3X5+xvjK4tTWP9\nnt4TBNeWpg3Jvnzj1GzeW9/7a71xanbY5x1Iv8yfksVfN/VePTV/SlbY+/Y1tg98MZQUaWhwcOOU\nHF4/e7jX57xxSk6v7e3v/nBuv5c1Kpbsnse4SPt9f0V6nRpDgAfuzGRCsT7s8y6YmsM7a3qvllgw\ntfcYzZ2YRfmx5l7vO3di+D69WF+OR3MnZfZa8QCh+Bp1Ab71YChhdO5x+3tcuHuuQkGGll+8H6FN\nE7Ow5SVi+9NPL3jOgRis2EYzWhP4giAII4UkSSTorCTYrJTazleMqqpKq6eNoMFLW5uLzo/6oQrB\nzn90uyxJnds6H7Prr67L524DIHVNhH3+ft22dbttKLlw7rLcdf+u6y9KQPQ2hDGoBgmqQQJqkEDQ\nH/qtBggEA/g7fwfUIEE1gD8YCF3XtT3Qeb/QbYPdtvm7PVbw3GOpAQLB4EX3D1z43N1uG+x8br/q\n77qf1+89f99gAP9FFYt9pZFCCQ2dokOv0WNQLkpmaHTnEx0Rkhvdkx+KPDRf7g+WIUtKzJ49mxdf\nfJFFixZRUVFBeno6FkuoVDs3Nxen00l1dTWZmZmsZ0CX7QAAFEhJREFUXr2a559/fqiactnSaxWm\njktj5Y7qHtdNHZc66EM3ILSyQk6ahar6niX0OWmWXldeiFaiHun6rFQLGkUKO1RAo0i9DhGA/sco\n2moDka7vb4yiDc0YiqEbAJ+bXRQxKRFu6AYMrF/uuL4kYlIi3NAN6H9sAeZMzuH1f/aelOht6Ab0\nf38YyH7fXwN5nZ+5tjBiUqK3oRtA5/CBiijXD67+xncgx85YvM5YPKcgCIIQPyRJItmQFErcM/K/\nbJUlGVmSQx9UB3nZ1OFgSzFRU9eE2+/BE/DiCXg6f7x4Ore5u21zBzx4/F68gZ7XOTwOGgON+NUB\nJjpkTc/kRmfSQ6/oOq8LXbYZkpmeOXVYh6wM2eobAM8//zw7duxAkiSeeuopDhw4gNVq5aabbmL7\n9u1diYiFCxfy4IMPRnwsMWt2/5yfQb6RFoebZKuBqeNSxeob3fQ3RmL1jZG1+ka02MLwrb7R3UD2\n+/4ayOuM59U3wulvfAdy7ByM19lXw/GcI71SQpxHjG6iH2JP9EHsiT6ID0PRD/6gH29XwiKU6Lgw\n6dH5O0zS43wy5MLrIg2T+f613yHNNLhfeEY6jxjSpMRgEicTA+PxBWhzeki06PtcIdHfGDlcXqrr\nneSmR/6G+mL7KhtYtauG+dNy+vxN8dlGJ3srm5hcnBLxm/hw+hujyuoWdlY2c1WxLeo35hfrb4wO\nnmxi7Z4zzJ2SPWQVEuH8feMxVu6sYcFVOb1WSIRzttFJZW07xZnmPvfL++uOsmrXGeZPy+61QiKc\n/sYWQpNBfryjioVX50WsHAinsrqFjftrmT0xs0/7w77KBjZU1DGnLGNIKiTCGcjr/HDLCT7ZUc1N\nV+dGrJAIZ+ehOlburGbBVbl9/ha/v8ej/h5XBnLsHMjr7K+dh+pYu/8scydmDfpzDkZS4umnn2bv\n3r1IksTSpUuZNKnnHCQ//vGP2bNnD2+++SYAzz33HDt37sTv9/P1r3+dhQsX8sQTT1BRUUFSUigR\n9uCDDzJv3ryIzy3OI0Y30Q+xJ/og9kQfxIeR0A+qquJXAxckMs4lLXSKjrEXTbw6GERSIoKRsNPE\nmohRdCJG0YkYRSdiFJ2IUXRDFaOBJiW2bdvG8uXLeeWVVzh27BhLly7l7bffvuA2lZWVfPe730Wr\n1fLmm2+yZcsWli9fzmuvvUZLSwt33nkna9as4YknnuDmm2/mhhtuuOTnF+cRo5voh9gTfRB7og/i\ng+iH8CKdR4i1TQRBEARBGHKbN29mwYIFABQVFdHW1obTeeH8L//7v//LY4891vX39OnT+fnPfw5A\nQkICHR0dBAIDG1crCIIgCEJ8ufzWGxEEQRAEYdg1NjZSVlbW9bfNZqOhoaFrEuz33nuPGTNmkJNz\nfiiRoiiYTKG5P1asWMH111+PooSG0bz11lu88cYbpKSk8L3vfQ+brfe5ZkAsLX45EP0Qe6IPYk/0\nQXwQ/dA3IikhCIIgCMKw6z56tLW1lffee4833niDurq6HrdduXIlK1as4PXXXwfg9ttvJykpiQkT\nJvDqq6+ybNkynnzyyYjPJ5YWH91EP8Se6IPYE30QH0Q/hCeGbwiCIAiCEFPp6ek0NjZ2/V1fX09a\nWmjS0S1bttDc3MzixYt5+OGHqaio4OmnnwZg/fr1vPzyy7z22mtYraETmpkzZzJhwgQA5s+fz5Ej\nR4b51QiCIAiCMFhEUkIQBEEQhCE3e/ZsPvroIwAqKipIT0/vGrrxmc98hg8++IB33nmHZcuWUVZW\nxtKlS3E4HDz33HO88sorXSttADzyyCNUVVUBsHXrVkpKLn1lHkEQBEEQ4osYviEIgiAIwpCbNm0a\nZWVlLFq0CEmSeOqpp3jvvfewWq3cdNNNYe/zwQcf0NLSwqOPPtq17dlnn2Xx4sU8+uijGI1GTCYT\nzzzzzHC9DEEQBEEQBplYElSM+YlKxCg6EaPoRIyiEzGKTsQounhdEjTWxHnE6Cb6IfZEH8Se6IP4\nIPohPDGnhCAIgiAIgiAIgiAIcUckJQRBEARBEARBEARBiAmRlBAEQRAEQRAEQRAEISZEUkIQBEEQ\nBEEQBEEQhJgQSQlBEARBEARBEARBEGJCJCUEQRAEQRAEQRAEQYgJkZQQBEEQBEEQBEEQBCEmJFVV\n1Vg3QhAEQRAEQRAEQRCEy4+olBAEQRAEQRAEQRAEISZEUkIQBEEQBEEQBEEQhJgQSQlBEARBEARB\nEARBEGJCJCUEQRAEQRAEQRAEQYgJkZQQBEEQBEEQBEEQBCEmRFJCEARBEARBEARBEISYuGyTEtu2\nbWPmzJmsXr26a9uSJUu4++67WbJkCUuWLKG8vDyGLYy9cDE6dOgQixYtYtGiRTz11FMxbF18ee+9\n95g7d27XvvPSSy/Fuklx5emnn+aLX/wiixYtYt++fbFuTtzZunUr1157bdf+84Mf/CDWTYobR44c\nYcGCBbz11lsAnD17liVLlnDvvffyrW99C6/XG+MWxt7FMXriiSe47bbbuvanNWvWxLaBo5g4tsXe\nc889xxe/+EXuvvtuPv7441g357LldrtZsGAB7733Xqybctn661//yuc//3nuuusucdyPgfb2dh5+\n+GGWLFnCokWLWL9+faybNKJoYt2AWDh9+jRvvPEG06ZN63HdM888w7hx42LQqvjSW4x++MMfsnTp\nUiZNmsTjjz/O2rVrmTt3boxaGV9uueUWvvOd78S6GXFn27ZtnDp1irfffptjx46xdOlS3n777Vg3\nK+7MmDGDF154IdbNiCsul4sf/OAHzJw5s2vbCy+8wL333stnP/tZfvKTn7BixQruvffeGLYytsLF\nCODb3/42N9xwQ4xadXkQx7bY27JlC0ePHuXtt9+mpaWFO++8k4ULF8a6WZell156icTExFg347LV\n0tLCL37xC959911cLhcvvvgi8+bNi3WzLit//vOfKSws5PHHH6euro4vf/nLfPjhh7Fu1ohxWVZK\npKWlsWzZMqxWa6ybErfCxcjr9VJTU8OkSZMAuOGGG9i8eXOsmiiMEJs3b2bBggUAFBUV0dbWhtPp\njHGrhJFAp9Px2muvkZ6e3rVt69at3HjjjYA4BkH4GAnDQxzbYm/69On8/Oc/ByAhIYGOjg4CgUCM\nW3X5OXbsGJWVleJDcAxt3ryZmTNnYrFYSE9PFxWXMZCcnExraysAdrud5OTkGLdoZLkskxJGoxFF\nUcJe98ILL7B48WKefPJJ3G73MLcsfoSLUUtLCwkJCV1/p6Sk0NDQMNxNi1vbtm3jwQcf5Mtf/jIH\nDhyIdXPiRmNj4wUHZpvNJvabMCorK3nooYf40pe+xMaNG2PdnLig0WgwGAwXbOvo6ECn0wHiGATh\nYwTw1ltvcf/99/PYY4/R3Nwcg5aNfuLYFnuKomAymQBYsWIF119/fa/nd8LQefbZZ3niiSdi3YzL\nWnV1NW63m4ceeoh77733sk/Yx8Ktt97KmTNnuOmmm7jvvvtE9XQfjfrhG3/605/405/+dMG2Rx55\nhOuuu67Hbe+//37Gjx9Pfn4+Tz31FL/73e948MEHh6upMdOXGHWnqupQNituhYvXrbfeyiOPPMK8\nefPYvXs33/nOd/jb3/4WoxbGt8t1v4mkoKCAhx9+mM9+9rNUVVVx//338/HHH3d9+BbCE/tSeLff\nfjtJSUlMmDCBV199lWXLlvHkk0/GulmjntgfY2flypWsWLGC119/PdZNuey8//77TJkyhby8vFg3\n5bLX2trKsmXLOHPmDPfffz+rV69GkqRYN+uy8Ze//IXs7GyWL1/OoUOHWLp0qZhjpQ9GfVLinnvu\n4Z577rmk2950001dl+fPn88HH3wwVM2KK5caI5vN1lWWBFBXV3dZlgxHi9fUqVNpbm4mEAiIb2yA\n9PR0Ghsbu/6ur68nLS0thi2KPxkZGdxyyy0A5Ofnk5qaSl1dnTjJC8NkMuF2uzEYDJftMSia7vNL\nzJ8/n+9///uxa8woJo5t8WH9+vW8/PLL/OpXvxLDcmNgzZo1VFVVsWbNGmpra9HpdGRmZjJr1qxY\nN+2ykpKSwtSpU9FoNOTn52M2m2lubiYlJSXWTbts7Nq1izlz5gBQWlpKfX29+CzQB5fl8I1wVFXl\nK1/5Cna7HQiNWy4pKYlxq+KLVqtl7Nix7NixA4CPP/44ajXF5eK1117j73//OxCaCd9ms4mDUKfZ\ns2fz0UcfAVBRUUF6ejoWiyXGrYovf/3rX1m+fDkADQ0NNDU1kZGREeNWxadZs2Z17U/iGBTeI488\nQlVVFSD+LxtK4tgWew6Hg+eee45XXnmFpKSkWDfnsvSzn/2Md999l3feeYd77rmHb37zmyIhEQNz\n5sxhy5YtBINBWlpacLlcYk6DYTZmzBj27t0LQE1NDWazWXwW6ANJvQzrDdesWcPy5cs5fvw4NpuN\ntLQ0Xn/9dT744AN+9atfYTQaycjI4Ic//CFGozHWzY2J3mJUWVnJk08+STAYZPLkyfzf//t/Y93U\nuFBbW8t//ud/oqoqfr+/a4USIeT5559nx44dSJLEU089RWlpaaybFFecTif/8R//gd1ux+fz8fDD\nD4tVbYDy8nKeffZZampq0Gg0ZGRk8Pzzz/PEE0/g8XjIzs7mmWeeQavVxrqpMRMuRvfddx+vvvoq\nRqMRk8nEM888I74tGyLi2BZbb7/9Ni+++CKFhYVd25599lmys7Nj2KrL14svvkhOTg533XVXrJty\nWfrjH//IihUrAPjGN77RNSm0MDza29tZunQpTU1N+P1+vvWtb/VYGUvo3WWZlBAEQRAEQRAEQRAE\nIfbE8A1BEARBEARBEARBEGJCJCUEQRAEQRAEQRAEQYgJkZQQBEEQBEEQBEEQBCEmRFJCEARBEARB\nEARBEISYEEkJQRAEQRAEQRAEQRBiQiQlBEHo1dq1a1m8eDFLlizhC1/4Ao8++ih2u31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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f44415b61d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fWm6RstfhUKx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Train linear classifier"
      ]
    },
    {
      "metadata": {
        "id": "U66Rv60R2HzC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_linear_classifier_model(\n",
        "    learning_rate,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets,\n",
        "    test_examples,\n",
        "    test_targets):\n",
        "  \"\"\"Trains a neural network classification model.\"\"\"\n",
        "\n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "  \n",
        "  # Create a linear classifier object.\n",
        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)  \n",
        "  linear_classifier = tf.estimator.LinearClassifier(\n",
        "      feature_columns=construct_feature_columns(training_examples),\n",
        "      optimizer=my_optimizer,\n",
        "  )\n",
        "  \n",
        "  # Create input functions.\n",
        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
        "                                          training_targets[\"Result\"], \n",
        "                                          batch_size=batch_size)\n",
        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
        "                                                  training_targets[\"Result\"], \n",
        "                                                  num_epochs=1, \n",
        "                                                  shuffle=False)\n",
        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
        "                                                    validation_targets[\"Result\"], \n",
        "                                                    num_epochs=1, \n",
        "                                                    shuffle=False)\n",
        "  predict_test_input_fn = lambda: my_input_fn(test_examples, \n",
        "                                                    test_targets[\"Result\"], \n",
        "                                                    num_epochs=1, \n",
        "                                                    shuffle=False)\n",
        "  \n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training nn classifier model...\")\n",
        "  print(\"LogLoss (on training data):\")\n",
        "  training_log_losses = []\n",
        "  validation_log_losses = []\n",
        "  test_log_losses = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    linear_classifier.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "    # Take a break and compute predictions.    \n",
        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
        "    \n",
        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
        "    \n",
        "    test_probabilities = linear_classifier.predict(input_fn=predict_test_input_fn)\n",
        "    test_probabilities = np.array([item['probabilities'] for item in test_probabilities])\n",
        "    \n",
        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
        "    test_log_loss = metrics.log_loss(test_targets, test_probabilities)\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_log_losses.append(training_log_loss)\n",
        "    validation_log_losses.append(validation_log_loss)\n",
        "    test_log_losses.append(test_log_loss)\n",
        "  print(\"Model training finished.\")\n",
        "  \n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.ylabel(\"LogLoss\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"LogLoss vs. Periods\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(training_log_losses, label=\"training\")\n",
        "  plt.plot(validation_log_losses, label=\"validation\")\n",
        "  plt.plot(test_log_losses, label=\"test\")\n",
        "  plt.legend()\n",
        "\n",
        "  return linear_classifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5TpqisuLxaND",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "outputId": "f753a6eb-2269-4a3e-ec3d-2d167342eab7"
      },
      "cell_type": "code",
      "source": [
        "linear_classifier = train_linear_classifier_model(\n",
        "    learning_rate=0.0001,\n",
        "    steps=1000,\n",
        "    batch_size=20,\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets,\n",
        "    test_examples=test_examples,\n",
        "    test_targets=test_targets)\n",
        "\n",
        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
        "\n",
        "print(\"AUC on the validation set: %0.3f\" % evaluation_metrics['auc'])\n",
        "print(\"Accuracy on the validation set: %0.3f\" % evaluation_metrics['accuracy'])"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training nn classifier model...\n",
            "LogLoss (on training data):\n",
            "  period 00 : 0.63\n",
            "  period 01 : 0.58\n",
            "  period 02 : 0.57\n",
            "  period 03 : 0.56\n",
            "  period 04 : 0.55\n",
            "  period 05 : 0.55\n",
            "  period 06 : 0.55\n",
            "  period 07 : 0.55\n",
            "  period 08 : 0.55\n",
            "  period 09 : 0.55\n",
            "Model training finished.\n",
            "AUC on the validation set: 0.799\n",
            "Accuracy on the validation set: 0.724\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Avik24xEVRc13W7DV17u5ShERkZ5BYcZNjsxq2lycTniQDwm9g9iRV4lH6sU4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\nERERxTWGGSIiIoprDDNEREQU1/4/zgFA3EtRTMsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4442081eb8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "LMUbqPNOxhKw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "d4e105f3-7606-46e3-eac1-2627c3251920"
      },
      "cell_type": "code",
      "source": [
        "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
        "# Get just the probabilities for the positive class.\n",
        "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n",
        "\n",
        "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n",
        "    validation_targets, validation_probabilities)\n",
        "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n",
        "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n",
        "_ = plt.legend(loc=2)"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hZo4JvU6rcoVCXF+DpYktxdup620gzBjKhtzV5Edd/8oTMT5JCAuv43S5OV7U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\n3e4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4445878208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_E1qntd5bt-2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Neural Network Compact Results Model"
      ]
    },
    {
      "metadata": {
        "id": "2IMNIXI4hucK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Load data and visualize"
      ]
    },
    {
      "metadata": {
        "id": "AnkM5oC9e_Jy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Load datasets\n",
        "\n",
        "reg_compact = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/RegularSeasonCompactResults.csv\", sep=\",\")\n",
        "t_compact = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/NCAATourneyCompactResults.csv\", sep=\",\")\n",
        "t_seeds = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/NCAATourneySeeds.csv\", sep=\",\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3YxyMIL_h41P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "524a860a-5f8a-4c8d-f013-8cdddb065e14"
      },
      "cell_type": "code",
      "source": [
        "display.display(reg_compact.head())\n",
        "display.display(t_compact.head())\n",
        "display.display(t_seeds.head())"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Season</th>\n",
              "      <th>DayNum</th>\n",
              "      <th>WTeamID</th>\n",
              "      <th>WScore</th>\n",
              "      <th>LTeamID</th>\n",
              "      <th>LScore</th>\n",
              "      <th>WLoc</th>\n",
              "      <th>NumOT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1985</td>\n",
              "      <td>20</td>\n",
              "      <td>1228</td>\n",
              "      <td>81</td>\n",
              "      <td>1328</td>\n",
              "      <td>64</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1106</td>\n",
              "      <td>77</td>\n",
              "      <td>1354</td>\n",
              "      <td>70</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1112</td>\n",
              "      <td>63</td>\n",
              "      <td>1223</td>\n",
              "      <td>56</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1165</td>\n",
              "      <td>70</td>\n",
              "      <td>1432</td>\n",
              "      <td>54</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1192</td>\n",
              "      <td>86</td>\n",
              "      <td>1447</td>\n",
              "      <td>74</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Season  DayNum  WTeamID  WScore  LTeamID  LScore WLoc  NumOT\n",
              "0    1985      20     1228      81     1328      64    N      0\n",
              "1    1985      25     1106      77     1354      70    H      0\n",
              "2    1985      25     1112      63     1223      56    H      0\n",
              "3    1985      25     1165      70     1432      54    H      0\n",
              "4    1985      25     1192      86     1447      74    H      0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Season</th>\n",
              "      <th>DayNum</th>\n",
              "      <th>WTeamID</th>\n",
              "      <th>WScore</th>\n",
              "      <th>LTeamID</th>\n",
              "      <th>LScore</th>\n",
              "      <th>WLoc</th>\n",
              "      <th>NumOT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1985</td>\n",
              "      <td>136</td>\n",
              "      <td>1116</td>\n",
              "      <td>63</td>\n",
              "      <td>1234</td>\n",
              "      <td>54</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1985</td>\n",
              "      <td>136</td>\n",
              "      <td>1120</td>\n",
              "      <td>59</td>\n",
              "      <td>1345</td>\n",
              "      <td>58</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1985</td>\n",
              "      <td>136</td>\n",
              "      <td>1207</td>\n",
              "      <td>68</td>\n",
              "      <td>1250</td>\n",
              "      <td>43</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1985</td>\n",
              "      <td>136</td>\n",
              "      <td>1229</td>\n",
              "      <td>58</td>\n",
              "      <td>1425</td>\n",
              "      <td>55</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1985</td>\n",
              "      <td>136</td>\n",
              "      <td>1242</td>\n",
              "      <td>49</td>\n",
              "      <td>1325</td>\n",
              "      <td>38</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Season  DayNum  WTeamID  WScore  LTeamID  LScore WLoc  NumOT\n",
              "0    1985     136     1116      63     1234      54    N      0\n",
              "1    1985     136     1120      59     1345      58    N      0\n",
              "2    1985     136     1207      68     1250      43    N      0\n",
              "3    1985     136     1229      58     1425      55    N      0\n",
              "4    1985     136     1242      49     1325      38    N      0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Season</th>\n",
              "      <th>Seed</th>\n",
              "      <th>TeamID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1985</td>\n",
              "      <td>W01</td>\n",
              "      <td>1207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1985</td>\n",
              "      <td>W02</td>\n",
              "      <td>1210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1985</td>\n",
              "      <td>W03</td>\n",
              "      <td>1228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1985</td>\n",
              "      <td>W04</td>\n",
              "      <td>1260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1985</td>\n",
              "      <td>W05</td>\n",
              "      <td>1374</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Season Seed  TeamID\n",
              "0    1985  W01    1207\n",
              "1    1985  W02    1210\n",
              "2    1985  W03    1228\n",
              "3    1985  W04    1260\n",
              "4    1985  W05    1374"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wrQPr7Km5y-m",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Essential functions"
      ]
    },
    {
      "metadata": {
        "id": "ok2DgIoOfOAv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def make_reg_season_data(reg_compact_data):\n",
        "  # For each season with regular season statistics, aggregate all of the game statistics for the winning and losing team,\n",
        "  # and their opponents stats for each game played.\n",
        "  dataframe = reg_compact_data.copy()\n",
        "  dataframe = dataframe.drop(labels=[\"NumOT\", \"DayNum\"], axis=1)\n",
        "  dataframe_w_team = dataframe.groupby(['WTeamID', 'Season']).agg({'WScore': [\"mean\", \"count\"]})\n",
        "  dataframe_l_team = dataframe.groupby(['LTeamID', 'Season']).agg({'LScore': [\"mean\", \"count\"]})\n",
        "  dataframe_w_opp = dataframe.groupby(['LTeamID', 'Season']).agg({'WScore': [\"mean\", \"count\"]})\n",
        "  dataframe_w_opp = dataframe_w_opp.rename(columns={'WScore':'OppWScore'})\n",
        "  dataframe_l_opp = dataframe.groupby(['WTeamID', 'Season']).agg({'LScore': [\"mean\", \"count\"]})\n",
        "  dataframe_l_opp = dataframe_l_opp.rename(columns={'LScore':'OppLScore'})\n",
        "\n",
        "  # Rename the index names so that they can be merged\n",
        "  dataframe_w_team.index.names = ['TeamID', 'Season']\n",
        "  dataframe_l_team.index.names = ['TeamID', 'Season']\n",
        "  dataframe_w_opp.index.names = ['TeamID', 'Season']\n",
        "  dataframe_l_opp.index.names = ['TeamID', 'Season']\n",
        "  \n",
        "  # Merge teams\n",
        "  dataframe_w = dataframe_w_team.join(dataframe_w_opp, how = 'outer')\n",
        "  dataframe_l = dataframe_l_team.join(dataframe_l_opp, how = 'outer')\n",
        "  dataframe = dataframe_w.join(dataframe_l, how = 'outer')\n",
        "  \n",
        "  # Include calculated win percentage. 'GamesPlayed' will be used to compute the weighted averages of the winning/losing game statistics for each team for each season.\n",
        "  dataframe['GamesPlayed'] = dataframe['WScore']['count'] + dataframe['LScore']['count']\n",
        "  dataframe['WinPercent'] = dataframe['WScore']['count']/dataframe['GamesPlayed']\n",
        "  \n",
        "  # Game statistics names for the combined list.\n",
        "  stats = ['Score']\n",
        "  # Combine winning/losing game stats into season stats for each team.\n",
        "  for stat in stats:\n",
        "      dataframe['Team' + stat] = (dataframe['W' + stat]['mean']*dataframe['W' + stat]['count'] + dataframe['L' + stat]['mean']*dataframe['L' + stat]['count'])/dataframe['GamesPlayed']\n",
        "      dataframe['Opp' + stat] = (dataframe['OppW' + stat]['mean']*dataframe['OppW' + stat]['count'] + dataframe['OppL' + stat]['mean']*dataframe['OppL' + stat]['count'])/dataframe['GamesPlayed']\n",
        "      del dataframe['W' + stat]\n",
        "      del dataframe['L' + stat]\n",
        "      del dataframe['OppW' + stat]\n",
        "      del dataframe['OppL' + stat]\n",
        "      \n",
        "  dataframe['PointDiff'] = dataframe['TeamScore'] - dataframe['OppScore']\n",
        "  \n",
        "  return dataframe"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xnRxDnQk6_Af",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def make_tourney_data(t_seeds, t_compact):\n",
        "  # Clean Data\n",
        "  local_t_seeds = t_seeds.copy()\n",
        "  local_t_seeds['Seed'] = local_t_seeds['Seed'].apply(lambda seed: int(seed[1:3]))\n",
        "  t_wseeds = local_t_seeds.rename(columns={'TeamID':'WTeamID', 'Seed':'WSeed'})\n",
        "  t_lseeds = local_t_seeds.rename(columns={'TeamID':'LTeamID', 'Seed':'LSeed'})\n",
        "  t_compact = t_compact.drop(labels=[\"DayNum\", \"WScore\", \"LScore\", \"WLoc\", \"NumOT\"], axis=1)\n",
        "  t_compact = t_compact.rename(columns={'WTeam': 'WTeamID', 'LTeam': 'LTeamID'})\n",
        "\n",
        "  # Merge Seeds with Team ID\n",
        "  t_dummy = pd.merge(left=t_compact, right=t_wseeds, how='left', on=['Season', 'WTeamID'])\n",
        "  t_concat = pd.merge(left=t_dummy, right=t_lseeds, on=['Season', 'LTeamID'])\n",
        "  t_concat['SeedDiff'] = t_concat['WSeed'] - t_concat['LSeed']\n",
        "\n",
        "  # make DataFrame that summarizes SeedDiff and Result\n",
        "  t_concat = t_concat.drop(labels=[\"WSeed\", \"LSeed\"], axis=1)\n",
        "  t_wins = t_concat.copy()\n",
        "  t_wins['Result'] = 1\n",
        "\n",
        "  t_losses = t_concat.copy()\n",
        "  t_losses['SeedDiff'] = -t_concat['SeedDiff']\n",
        "  t_losses['Result'] = 0\n",
        "\n",
        "  # concat seediff with results into DataFrame to use for predictions\n",
        "  full_results = pd.concat((t_wins, t_losses))\n",
        "  \n",
        "  stats = ['GamesPlayed','WinPercent','TeamScore', 'OppScore','PointDiff']\n",
        "  for stat in stats:\n",
        "    full_results['W' + stat] = 0\n",
        "  for stat in stats:\n",
        "    full_results['L' + stat] = 0\n",
        "  full_results = full_results.apply(lambda ser: ser.astype('float64'))\n",
        "  \n",
        "  full_results = full_results.reset_index(drop=True)\n",
        "  full_results = full_results.reindex(\n",
        "      np.random.permutation(full_results.index))\n",
        "  \n",
        "  return full_results"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lHk7G4iC7BAg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def lookup_reg_season(tourney_series):\n",
        "  '''Used as lambda function to add reg_season results to t_results series'''\n",
        "  stats = ['GamesPlayed','WinPercent','TeamScore', 'OppScore','PointDiff']\n",
        "  season = tourney_series['Season']\n",
        "  wteam = tourney_series['WTeamID']\n",
        "  lteam = tourney_series['LTeamID']\n",
        "  w_reg_season_stats = reg_season.xs((wteam, season), level=('TeamID', 'Season'))\n",
        "  l_reg_season_stats = reg_season.xs((lteam, season), level=('TeamID', 'Season'))\n",
        "  for stat in stats:\n",
        "    tourney_series['W' + stat] = w_reg_season_stats[stat]\n",
        "  for stat in stats:\n",
        "    tourney_series['L' + stat] = l_reg_season_stats[stat]\n",
        "  return tourney_series"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jOHUzsI36py_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Run data through pipeline to make it usable"
      ]
    },
    {
      "metadata": {
        "id": "SAZFbz5Z5Vjk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "reg_season = make_reg_season_data(reg_compact)\n",
        "t_results = make_tourney_data(t_seeds, t_compact)\n",
        "prediction_results = (t_results.apply(lookup_reg_season, axis=1)).dropna(axis=0, how='any')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O84F8_o3i2CT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "11e2b9fd-b81b-40d8-ba7f-56f141c9fac0"
      },
      "cell_type": "code",
      "source": [
        "display.display(reg_season.head())\n",
        "display.display(prediction_results.head())"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
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              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>GamesPlayed</th>\n",
              "      <th>WinPercent</th>\n",
              "      <th>TeamScore</th>\n",
              "      <th>OppScore</th>\n",
              "      <th>PointDiff</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TeamID</th>\n",
              "      <th>Season</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">1101</th>\n",
              "      <th>2014</th>\n",
              "      <td>21.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>63.1</td>\n",
              "      <td>78.6</td>\n",
              "      <td>-15.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>28.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>61.0</td>\n",
              "      <td>71.9</td>\n",
              "      <td>-10.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>27.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>69.9</td>\n",
              "      <td>76.3</td>\n",
              "      <td>-6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>67.9</td>\n",
              "      <td>72.6</td>\n",
              "      <td>-4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1102</th>\n",
              "      <th>1985</th>\n",
              "      <td>24.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>63.1</td>\n",
              "      <td>68.9</td>\n",
              "      <td>-5.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              GamesPlayed WinPercent TeamScore OppScore PointDiff\n",
              "                                                                 \n",
              "TeamID Season                                                    \n",
              "1101   2014          21.0        0.1      63.1     78.6     -15.5\n",
              "       2015          28.0        0.2      61.0     71.9     -10.9\n",
              "       2016          27.0        0.3      69.9     76.3      -6.4\n",
              "       2017          25.0        0.4      67.9     72.6      -4.8\n",
              "1102   1985          24.0        0.2      63.1     68.9      -5.8"
            ]
          },
          "metadata": {
            "tags": []
          }
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Season</th>\n",
              "      <th>WTeamID</th>\n",
              "      <th>LTeamID</th>\n",
              "      <th>SeedDiff</th>\n",
              "      <th>Result</th>\n",
              "      <th>WGamesPlayed</th>\n",
              "      <th>WWinPercent</th>\n",
              "      <th>WTeamScore</th>\n",
              "      <th>WOppScore</th>\n",
              "      <th>WPointDiff</th>\n",
              "      <th>LGamesPlayed</th>\n",
              "      <th>LWinPercent</th>\n",
              "      <th>LTeamScore</th>\n",
              "      <th>LOppScore</th>\n",
              "      <th>LPointDiff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3277</th>\n",
              "      <td>2003.0</td>\n",
              "      <td>1277.0</td>\n",
              "      <td>1160.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>67.2</td>\n",
              "      <td>61.0</td>\n",
              "      <td>6.3</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>75.5</td>\n",
              "      <td>70.1</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4186</th>\n",
              "      <td>2017.0</td>\n",
              "      <td>1462.0</td>\n",
              "      <td>1268.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>74.6</td>\n",
              "      <td>71.4</td>\n",
              "      <td>3.3</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>73.5</td>\n",
              "      <td>68.5</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>781</th>\n",
              "      <td>1997.0</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>1266.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>74.7</td>\n",
              "      <td>69.2</td>\n",
              "      <td>5.5</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>68.3</td>\n",
              "      <td>57.0</td>\n",
              "      <td>11.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2758</th>\n",
              "      <td>1995.0</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>1332.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>93.9</td>\n",
              "      <td>81.1</td>\n",
              "      <td>12.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>84.9</td>\n",
              "      <td>80.0</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2479</th>\n",
              "      <td>1990.0</td>\n",
              "      <td>1462.0</td>\n",
              "      <td>1207.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>83.3</td>\n",
              "      <td>72.6</td>\n",
              "      <td>10.7</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>79.8</td>\n",
              "      <td>67.2</td>\n",
              "      <td>12.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Season  WTeamID  LTeamID  SeedDiff  Result  WGamesPlayed  WWinPercent  \\\n",
              "3277  2003.0   1277.0   1160.0       3.0     0.0          31.0          0.6   \n",
              "4186  2017.0   1462.0   1268.0      -5.0     0.0          34.0          0.6   \n",
              "781   1997.0   1344.0   1266.0       3.0     1.0          31.0          0.6   \n",
              "2758  1995.0   1400.0   1332.0      -5.0     0.0          28.0          0.8   \n",
              "2479  1990.0   1462.0   1207.0      -3.0     0.0          29.0          0.9   \n",
              "\n",
              "      WTeamScore  WOppScore  WPointDiff  LGamesPlayed  LWinPercent  \\\n",
              "3277        67.2       61.0         6.3          31.0          0.6   \n",
              "4186        74.6       71.4         3.3          31.0          0.7   \n",
              "781         74.7       69.2         5.5          30.0          0.7   \n",
              "2758        93.9       81.1        12.8          25.0          0.7   \n",
              "2479        83.3       72.6        10.7          25.0          0.8   \n",
              "\n",
              "      LTeamScore  LOppScore  LPointDiff  \n",
              "3277        75.5       70.1         5.5  \n",
              "4186        73.5       68.5         5.0  \n",
              "781         68.3       57.0        11.3  \n",
              "2758        84.9       80.0         4.8  \n",
              "2479        79.8       67.2        12.6  "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jZy_y1qK590L",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Partition data into training, validation, and test dataframes"
      ]
    },
    {
      "metadata": {
        "id": "BhhKKDQdLaiI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "STATS = ['SeedDiff', 'WSeason', 'LSeason', 'WGamesPlayed', 'LGamesPlayed','WWinPercent', 'LWinPercent', 'WTeamScore', 'LTeamScore', 'WOppScore', 'LOppScore', 'WPointDiff', 'LPointDiff']\n",
        "# STATS = ['SeedDiff', 'WPointDiff', 'LPointDiff', 'WWinPercent', 'LWinPercent', 'WGamesPlayed', 'LGamesPlayed']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8mqnV_6s6TGX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# concat seediff with results into DataFrame to use for predictions\n",
        "prediction_results = prediction_results.reindex(\n",
        "    np.random.permutation(prediction_results.index))\n",
        "\n",
        "# Calculate indices for training, validation, testing\n",
        "num_total_examples = len(prediction_results.index)\n",
        "num_training_examples = math.floor(num_total_examples*0.7)\n",
        "num_validation_examples = math.floor(num_total_examples*0.15)\n",
        "index_validation_examples = num_training_examples + num_validation_examples\n",
        "num_test_examples = math.floor(num_total_examples*0.15)\n",
        "index_test_examples = num_training_examples + num_validation_examples + num_test_examples\n",
        "\n",
        "# Choose the first 65% of examples for training  and normalize.\n",
        "training_examples = normalize_linear_scale(preprocess_features(prediction_results.iloc[0:num_training_examples]))\n",
        "training_targets = preprocess_targets(prediction_results.iloc[0:num_training_examples])\n",
        "\n",
        "# Choose 15% after training examples for validation  and normalize\n",
        "validation_examples = normalize_linear_scale(preprocess_features(prediction_results.iloc[num_training_examples:index_validation_examples]))\n",
        "validation_targets = preprocess_targets(prediction_results.iloc[num_training_examples:index_validation_examples])\n",
        "\n",
        "# Choose 15% after validation examples for testing  and normalize\n",
        "test_examples = normalize_linear_scale(preprocess_features(prediction_results.iloc[index_validation_examples:index_test_examples]))\n",
        "test_targets = preprocess_targets(prediction_results.iloc[index_validation_examples:index_test_examples])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rFJxoiqp2BTc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Double-check that we've done the right thing.\n",
        "\n",
        "print(\"Training examples summary:\")\n",
        "display.display(training_examples.describe())\n",
        "display.display(training_examples.isnull().values.any())\n",
        "print(\"Validation examples summary:\")\n",
        "display.display(validation_examples.describe())\n",
        "display.display(validation_examples.isnull().values.any())\n",
        "print(\"Test examples summary:\")\n",
        "display.display(test_examples.describe())\n",
        "display.display(test_examples.isnull().values.any())\n",
        "\n",
        "print(\"Training examples head:\")\n",
        "display.display(training_examples.head())\n",
        "print(\"Validation examples head:\")\n",
        "display.display(validation_examples.head())\n",
        "print(\"Test examples head:\")\n",
        "display.display(test_examples.head())\n",
        "\n",
        "# print(\"Training targets summary:\") \n",
        "# display.display(training_targets.describe())\n",
        "# display.display(training_targets.isnull().values.any())\n",
        "# print(\"Validation targets summary:\")\n",
        "# display.display(validation_targets.describe())\n",
        "# display.display(validation_targets.isnull().values.any())\n",
        "# print(\"Test targets summary:\")\n",
        "# display.display(test_targets.describe())\n",
        "# display.display(test_targets.isnull().values.any())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pWRyDBXN6GiT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Test out neural network regressor"
      ]
    },
    {
      "metadata": {
        "id": "tV5SvA7x2Nlx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "outputId": "bc219e29-e851-4d22-c739-c10f1f0c8afe"
      },
      "cell_type": "code",
      "source": [
        "_ = train_nn_regression_model(\n",
        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0001),\n",
        "    steps=9000,\n",
        "    batch_size=90,\n",
        "    hidden_units=[10, 10, 10, 5, 5],\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets,\n",
        "    test_examples=test_examples,\n",
        "    test_targets=test_targets)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training neural network model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 0.49\n",
            "  period 01 : 0.48\n",
            "  period 02 : 0.46\n",
            "  period 03 : 0.43\n",
            "  period 04 : 0.42\n",
            "  period 05 : 0.41\n",
            "  period 06 : 0.41\n",
            "  period 07 : 0.40\n",
            "  period 08 : 0.40\n",
            "  period 09 : 0.40\n",
            "Model training finished.\n",
            "Final RMSE (on training data):   0.40\n",
            "Final RMSE (on validation data): 0.40\n",
            "Final RMSE (on test data): 0.41\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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emb/fmjNp0iRat26Nvb29fhj7f9rmURwcrDAx0RRbzr8r7MMTxiVtUzpJu5Re0jalk7TL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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4440fdf080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "npVRrlLo8c7v",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Test out neural network classifier"
      ]
    },
    {
      "metadata": {
        "id": "wZ1T8DbO8fqe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "outputId": "93efa02b-ca9e-44c0-a7c1-c5502f653178"
      },
      "cell_type": "code",
      "source": [
        "nn_classifier = train_nn_classifier_model(\n",
        "    learning_rate=0.00015,\n",
        "    steps=9000,\n",
        "    batch_size=90,\n",
        "    hidden_units=[10, 10, 10, 5, 5],\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets,\n",
        "    test_examples=test_examples,\n",
        "    test_targets=test_targets)\n",
        "\n",
        "evaluation_metrics = nn_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
        "\n",
        "print(\"AUC on the validation set: %0.3f\" % evaluation_metrics['auc'])\n",
        "print(\"Accuracy on the validation set: %0.3f\" % evaluation_metrics['accuracy'])"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training nn classifier model...\n",
            "LogLoss (on training data):\n",
            "  period 00 : 0.63\n",
            "  period 01 : 0.56\n",
            "  period 02 : 0.52\n",
            "  period 03 : 0.50\n",
            "  period 04 : 0.48\n",
            "  period 05 : 0.47\n",
            "  period 06 : 0.46\n",
            "  period 07 : 0.46\n",
            "  period 08 : 0.45\n",
            "  period 09 : 0.45\n",
            "Model training finished.\n",
            "AUC on the validation set: 0.851\n",
            "Accuracy on the validation set: 0.735\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x4jdX2047YhSKlTXpP61El59fJ7EKIYQQDZFJC5w5c+Zw//33M3r0aI4cqbxi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\nYPsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f44325691d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "viz-hM8984EF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "93bab6da-fab4-4263-ca1e-4c19337600b2"
      },
      "cell_type": "code",
      "source": [
        "validation_probabilities = nn_classifier.predict(input_fn=predict_validation_input_fn)\n",
        "# Get just the probabilities for the positive class.\n",
        "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n",
        "\n",
        "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n",
        "    validation_targets, validation_probabilities)\n",
        "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n",
        "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n",
        "_ = plt.legend(loc=2)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cwf3T00xOI9L9qlqrWVOwlqK644TYg1mWsYgb48dh0XmupRMqYek114+IIX3I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\njbCIiIgjGmERERFHNMIiIiKO/AVxVkkuIBDCtAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f444183fb00>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gpjnpnDmbzxr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Neural Network Detailed Results Model"
      ]
    },
    {
      "metadata": {
        "id": "Y34aV9KQ9XqW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Load datasets\n",
        "\n",
        "# reg_compact = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/RegularSeasonCompactResults.csv\", sep=\",\")\n",
        "# seasons = pd.read_csv(\"https://storage.googleapis.com/march_madness_ml_datasets/Seasons.csv\", sep=\",\")\n",
        "# teams = pd.read_csv(\"https://storage.googleapis.com/march_madness_ml_datasets/Teams.csv\", sep=\",\")\n",
        "# t_slots = pd.read_csv(\"https://storage.googleapis.com/march_madness_ml_datasets/TourneySlots.csv\", sep=\",\")\n",
        "t_compact = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/NCAATourneyCompactResults.csv\", sep=\",\")\n",
        "t_detailed = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/NCAATourneyDetailedResults.csv\", sep=\",\")\n",
        "reg_detailed = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/RegularSeasonDetailedResults.csv\", sep=\",\")\n",
        "t_seeds = pd.read_csv(\"https://storage.googleapis.com/march-madness-ml-2018/NCAATourneySeeds.csv\", sep=\",\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hSr729uoAS4S",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "display.display(t_compact.head())\n",
        "display.display(t_detailed.head())\n",
        "display.display(reg_detailed.head())\n",
        "display.display(t_seeds.head())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IeavCNVSUIZf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Essential Functions"
      ]
    },
    {
      "metadata": {
        "id": "hhH5eDl2HWtH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def make_reg_season_data(reg_detailed_data):\n",
        "  # For each season with regular season statistics, aggregate all of the game statistics for the winning and losing team,\n",
        "  # and their opponents stats for each game played.\n",
        "  dataframe = reg_detailed_data.copy()\n",
        "  dataframe = dataframe.drop(labels=[\"NumOT\", \"DayNum\", 'WLoc'], axis=1)\n",
        "  \n",
        "  dataframe_w_team = dataframe.groupby(['WTeamID', 'Season']).agg({'WScore': [\"mean\", \"count\"], 'WFGM': [\"mean\", \"count\"], 'WFGA': [\"mean\", \"count\"], 'WFGM3': [\"mean\", \"count\"], 'WFGA3': [\"mean\", \"count\"], \n",
        "                                                         'WFTM': [\"mean\", \"count\"], 'WFTA': [\"mean\", \"count\"], 'WOR': [\"mean\", \"count\"], 'WDR': [\"mean\", \"count\"], 'WAst': [\"mean\", \"count\"],\n",
        "                                                        'WTO': [\"mean\", \"count\"], 'WStl': [\"mean\", \"count\"], 'WBlk': [\"mean\", \"count\"], 'WPF': [\"mean\", \"count\"]})\n",
        "  dataframe_l_team = dataframe.groupby(['LTeamID', 'Season']).agg({'LScore': [\"mean\", \"count\"], 'LFGM': [\"mean\", \"count\"], \n",
        "                                                        'LFGA': [\"mean\", \"count\"], 'LFGM3': [\"mean\", \"count\"], 'LFGA3': [\"mean\", \"count\"], \n",
        "                                                         'LFTM': [\"mean\", \"count\"], 'LFTA': [\"mean\", \"count\"], 'LOR': [\"mean\", \"count\"], 'LDR': [\"mean\", \"count\"], 'LAst': [\"mean\", \"count\"],\n",
        "                                                        'LTO': [\"mean\", \"count\"], 'LStl': [\"mean\", \"count\"], 'LBlk': [\"mean\", \"count\"], 'LPF': [\"mean\", \"count\"]})\n",
        "  dataframe_w_opp = dataframe.groupby(['LTeamID', 'Season']).agg({'WScore': [\"mean\", \"count\"], 'WFGM': [\"mean\", \"count\"], 'WFGA': [\"mean\", \"count\"], 'WFGM3': [\"mean\", \"count\"], 'WFGA3': [\"mean\", \"count\"], \n",
        "                                                         'WFTM': [\"mean\", \"count\"], 'WFTA': [\"mean\", \"count\"], 'WOR': [\"mean\", \"count\"], 'WDR': [\"mean\", \"count\"], 'WAst': [\"mean\", \"count\"],\n",
        "                                                        'WTO': [\"mean\", \"count\"], 'WStl': [\"mean\", \"count\"], 'WBlk': [\"mean\", \"count\"], 'WPF': [\"mean\", \"count\"]})\n",
        "  dataframe_w_opp = dataframe_w_opp.rename(columns={'WScore':'OppWScore', 'WFGM':'OppWFGM', 'WFGA':'OppWFGA', 'WFGM3':'OppWFGM3', 'WFGA3':'OppWFGA3', 'WFTM':'OppWFTM',\n",
        "                                                        'WFTA':'OppWFTA', 'WOR':'OppWOR', 'WDR':'OppWDR', 'WAst':'OppWAst', 'WTO':'OppWTO', 'WStl':'OppWStl', 'WBlk':'OppWBlk',\n",
        "                                                        'WPF':'OppWPF'})\n",
        "  dataframe_l_opp = dataframe.groupby(['WTeamID', 'Season']).agg({'LScore': [\"mean\", \"count\"], 'LFGM': [\"mean\", \"count\"], \n",
        "                                                        'LFGA': [\"mean\", \"count\"], 'LFGM3': [\"mean\", \"count\"], 'LFGA3': [\"mean\", \"count\"], \n",
        "                                                         'LFTM': [\"mean\", \"count\"], 'LFTA': [\"mean\", \"count\"], 'LOR': [\"mean\", \"count\"], 'LDR': [\"mean\", \"count\"], 'LAst': [\"mean\", \"count\"],\n",
        "                                                        'LTO': [\"mean\", \"count\"], 'LStl': [\"mean\", \"count\"], 'LBlk': [\"mean\", \"count\"], 'LPF': [\"mean\", \"count\"]})\n",
        "  dataframe_l_opp = dataframe_l_opp.rename(columns={'LScore':'OppLScore', 'LFGM':'OppLFGM', 'LFGA':'OppLFGA', 'LFGM3':'OppLFGM3', 'LFGA3':'OppLFGA3', 'LFTM':'OppLFTM',\n",
        "                                                        'LFTA':'OppLFTA', 'LOR':'OppLOR', 'LDR':'OppLDR', 'LAst':'OppLAst', 'LTO':'OppLTO', 'LStl':'OppLStl', 'LBlk':'OppLBlk',\n",
        "                                                        'LPF':'OppLPF'})\n",
        "\n",
        "  # Rename the index names so that they can be merged\n",
        "  dataframe_w_team.index.names = ['TeamID', 'Season']\n",
        "  dataframe_l_team.index.names = ['TeamID', 'Season']\n",
        "  dataframe_w_opp.index.names = ['TeamID', 'Season']\n",
        "  dataframe_l_opp.index.names = ['TeamID', 'Season']\n",
        "  \n",
        "  # Merge teams\n",
        "  dataframe_w = dataframe_w_team.join(dataframe_w_opp, how = 'outer')\n",
        "  dataframe_l = dataframe_l_team.join(dataframe_l_opp, how = 'outer')\n",
        "  dataframe = dataframe_w.join(dataframe_l, how = 'outer')\n",
        "  \n",
        "  # Include calculated win percentage. 'GamesPlayed' will be used to compute the weighted averages of the winning/losing game statistics for each team for each season.\n",
        "  dataframe['GamesPlayed'] = dataframe['WScore']['count'] + dataframe['LScore']['count']\n",
        "  dataframe['WinPercent'] = dataframe['WScore']['count']/dataframe['GamesPlayed']\n",
        "  \n",
        "  # Game statistics names for the combined list.\n",
        "  stats = ['Score', 'FGM', 'FGA', 'FGM3', 'FGA3', 'FTM', 'FTA', 'OR', 'DR', 'Ast', 'TO', 'Stl', 'Blk', 'PF']\n",
        "  # Combine winning/losing game stats into season stats for each team.\n",
        "  for stat in stats:\n",
        "      dataframe['Team' + stat] = (dataframe['W' + stat]['mean']*dataframe['W' + stat]['count'] + dataframe['L' + stat]['mean']*dataframe['L' + stat]['count'])/dataframe['GamesPlayed']\n",
        "      dataframe['Opp' + stat] = (dataframe['OppW' + stat]['mean']*dataframe['OppW' + stat]['count'] + dataframe['OppL' + stat]['mean']*dataframe['OppL' + stat]['count'])/dataframe['GamesPlayed']\n",
        "      del dataframe['W' + stat]\n",
        "      del dataframe['L' + stat]\n",
        "      del dataframe['OppW' + stat]\n",
        "      del dataframe['OppL' + stat]\n",
        "      \n",
        "  dataframe['PointDiff'] = dataframe['TeamScore'] - dataframe['OppScore']\n",
        "  \n",
        "  # Make percentages for TEAM fg, ft, to/ast, stl/blk\n",
        "  dataframe['TeamFG'] = dataframe['TeamFGM']/dataframe['TeamFGA']\n",
        "  del dataframe['TeamFGM']\n",
        "  del dataframe['TeamFGA']\n",
        "  dataframe['TeamFG3'] = dataframe['TeamFGM3']/dataframe['TeamFGA3']\n",
        "  del dataframe['TeamFGM3']\n",
        "  del dataframe['TeamFGA3']\n",
        "  dataframe['TeamFT'] = dataframe['TeamFTM']/dataframe['TeamFTA']\n",
        "  del dataframe['TeamFTM']\n",
        "  del dataframe['TeamFTA']\n",
        "  dataframe['TeamAst_TO_Ratio'] = dataframe['TeamAst']/dataframe['TeamTO']\n",
        "  del dataframe['TeamAst']\n",
        "  del dataframe['TeamTO']\n",
        "  \n",
        "  # Make percentages for OPP fg, ft, to/ast, stl/blk\n",
        "  dataframe['OppFG'] = dataframe['OppFGM']/dataframe['OppFGA']\n",
        "  del dataframe['OppFGM']\n",
        "  del dataframe['OppFGA']\n",
        "  dataframe['OppFG3'] = dataframe['OppFGM3']/dataframe['OppFGA3']\n",
        "  del dataframe['OppFGM3']\n",
        "  del dataframe['OppFGA3']\n",
        "  dataframe['OppFT'] = dataframe['OppFTM']/dataframe['OppFTA']\n",
        "  del dataframe['OppFTM']\n",
        "  del dataframe['OppFTA']\n",
        "  dataframe['OppAst_TO_Ratio'] = dataframe['OppAst']/dataframe['OppTO']\n",
        "  del dataframe['OppAst']\n",
        "  del dataframe['OppTO']\n",
        "      \n",
        "      \n",
        "  return dataframe"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y_QVBp0gJIVP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def make_tourney_data(t_seeds, t_compact):\n",
        "  # Clean Data\n",
        "  local_t_seeds = t_seeds.copy()\n",
        "  local_t_seeds['Seed'] = local_t_seeds['Seed'].apply(lambda seed: int(seed[1:3]))\n",
        "  t_wseeds = local_t_seeds.rename(columns={'TeamID':'WTeamID', 'Seed':'WSeed'})\n",
        "  t_lseeds = local_t_seeds.rename(columns={'TeamID':'LTeamID', 'Seed':'LSeed'})\n",
        "  t_compact = t_compact.drop(labels=[\"DayNum\", \"WScore\", \"LScore\", \"WLoc\", \"NumOT\"], axis=1)\n",
        "  t_compact = t_compact.rename(columns={'WTeam': 'WTeamID', 'LTeam': 'LTeamID'})\n",
        "\n",
        "  # Merge Seeds with Team ID\n",
        "  t_dummy = pd.merge(left=t_compact, right=t_wseeds, how='left', on=['Season', 'WTeamID'])\n",
        "  t_concat = pd.merge(left=t_dummy, right=t_lseeds, on=['Season', 'LTeamID'])\n",
        "  t_concat['SeedDiff'] = t_concat['WSeed'] - t_concat['LSeed']\n",
        "\n",
        "  # make DataFrame that summarizes SeedDiff and Result\n",
        "  t_concat = t_concat.drop(labels=[\"WSeed\", \"LSeed\"], axis=1)\n",
        "  t_wins = t_concat.copy()\n",
        "  t_wins['Result'] = 1\n",
        "#   t_wins = t_wins.drop(labels=[\"LTeamID\"], axis=1)\n",
        "#   t_wins = t_wins.rename(columns={'WTeamID':'TeamID'})\n",
        "\n",
        "  t_losses = t_concat.copy()\n",
        "  t_losses['SeedDiff'] = -t_concat['SeedDiff']\n",
        "  t_losses['Result'] = 0\n",
        "#   t_losses = t_losses.drop(labels=[\"WTeamID\"], axis=1)\n",
        "#   t_losses = t_losses.rename(columns={'LTeamID':'TeamID'})\n",
        "\n",
        "  # concat seediff with results into DataFrame to use for predictions\n",
        "  full_results = pd.concat((t_wins, t_losses))\n",
        "  \n",
        "  stats = ['GamesPlayed','WinPercent','PointDiff',\n",
        "           'TeamScore', 'TeamFG','TeamFG3','TeamFT', 'TeamOR', 'TeamDR', 'TeamAst_TO_Ratio', 'TeamStl', 'TeamBlk', 'TeamPF',\n",
        "           'OppScore', 'OppFG','OppFG3','OppFT', 'OppOR', 'OppDR', 'OppAst_TO_Ratio', 'OppStl', 'OppBlk', 'OppPF'\n",
        "          ]\n",
        "  for stat in stats:\n",
        "    full_results['W' + stat] = 0\n",
        "  for stat in stats:\n",
        "    full_results['L' + stat] = 0\n",
        "  full_results = full_results.apply(lambda ser: ser.astype('float64'))\n",
        "  \n",
        "  full_results = full_results.reset_index(drop=True)\n",
        "  full_results = full_results.reindex(\n",
        "      np.random.permutation(full_results.index))\n",
        "  \n",
        "  return full_results"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yJ-katQxJkZD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def lookup_reg_season(tourney_series):\n",
        "  '''Used as lambda function to add reg_season results to t_results series'''\n",
        "  stats = ['GamesPlayed','WinPercent','PointDiff',\n",
        "           'TeamScore', 'TeamFG','TeamFG3','TeamFT', 'TeamOR', 'TeamDR', 'TeamAst_TO_Ratio', 'TeamStl', 'TeamBlk', 'TeamPF',\n",
        "           'OppScore', 'OppFG','OppFG3','OppFT', 'OppOR', 'OppDR', 'OppAst_TO_Ratio', 'OppStl', 'OppBlk', 'OppPF'\n",
        "          ]\n",
        "  season = tourney_series['Season']\n",
        "  if season >= 2003:\n",
        "    wteam = tourney_series['WTeamID']\n",
        "    lteam = tourney_series['LTeamID']\n",
        "    w_reg_season_stats = reg_season.xs((wteam, season), level=('TeamID', 'Season'))\n",
        "    l_reg_season_stats = reg_season.xs((lteam, season), level=('TeamID', 'Season'))\n",
        "    for stat in stats:\n",
        "      tourney_series['W' + stat] = w_reg_season_stats[stat]\n",
        "    for stat in stats:\n",
        "      tourney_series['L' + stat] = l_reg_season_stats[stat]\n",
        "    return tourney_series\n",
        "  return None"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gxWVuVlwXIc7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Run data through pipeline to make useable"
      ]
    },
    {
      "metadata": {
        "id": "rZj2cPWCiRKL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "922cee6a-463f-424f-b7c8-15a46a599d97"
      },
      "cell_type": "code",
      "source": [
        "reg_season = make_reg_season_data(reg_detailed)\n",
        "t_results = make_tourney_data(t_seeds, t_compact)\n",
        "display.display(reg_season.head())\n",
        "display.display(t_results.head())"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>GamesPlayed</th>\n",
              "      <th>WinPercent</th>\n",
              "      <th>TeamScore</th>\n",
              "      <th>OppScore</th>\n",
              "      <th>TeamOR</th>\n",
              "      <th>OppOR</th>\n",
              "      <th>TeamDR</th>\n",
              "      <th>OppDR</th>\n",
              "      <th>TeamStl</th>\n",
              "      <th>OppStl</th>\n",
              "      <th>...</th>\n",
              "      <th>OppPF</th>\n",
              "      <th>PointDiff</th>\n",
              "      <th>TeamFG</th>\n",
              "      <th>TeamFG3</th>\n",
              "      <th>TeamFT</th>\n",
              "      <th>TeamAst_TO_Ratio</th>\n",
              "      <th>OppFG</th>\n",
              "      <th>OppFG3</th>\n",
              "      <th>OppFT</th>\n",
              "      <th>OppAst_TO_Ratio</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>...</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TeamID</th>\n",
              "      <th>Season</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">1101</th>\n",
              "      <th>2014</th>\n",
              "      <td>21.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>63.1</td>\n",
              "      <td>78.6</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>20.3</td>\n",
              "      <td>24.1</td>\n",
              "      <td>5.8</td>\n",
              "      <td>7.0</td>\n",
              "      <td>...</td>\n",
              "      <td>18.7</td>\n",
              "      <td>-15.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>28.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>61.0</td>\n",
              "      <td>71.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>10.0</td>\n",
              "      <td>19.6</td>\n",
              "      <td>25.9</td>\n",
              "      <td>5.9</td>\n",
              "      <td>5.9</td>\n",
              "      <td>...</td>\n",
              "      <td>16.5</td>\n",
              "      <td>-10.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>27.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>69.9</td>\n",
              "      <td>76.3</td>\n",
              "      <td>8.2</td>\n",
              "      <td>8.6</td>\n",
              "      <td>22.5</td>\n",
              "      <td>25.2</td>\n",
              "      <td>6.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>...</td>\n",
              "      <td>19.7</td>\n",
              "      <td>-6.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>67.9</td>\n",
              "      <td>72.6</td>\n",
              "      <td>7.6</td>\n",
              "      <td>10.6</td>\n",
              "      <td>22.9</td>\n",
              "      <td>25.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>17.2</td>\n",
              "      <td>-4.8</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1102</th>\n",
              "      <th>2003</th>\n",
              "      <td>28.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>57.2</td>\n",
              "      <td>57.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>9.6</td>\n",
              "      <td>16.8</td>\n",
              "      <td>20.1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>...</td>\n",
              "      <td>18.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              GamesPlayed WinPercent TeamScore OppScore TeamOR OppOR TeamDR  \\\n",
              "                                                                              \n",
              "TeamID Season                                                                 \n",
              "1101   2014          21.0        0.1      63.1     78.6    8.0  10.4   20.3   \n",
              "       2015          28.0        0.2      61.0     71.9    8.3  10.0   19.6   \n",
              "       2016          27.0        0.3      69.9     76.3    8.2   8.6   22.5   \n",
              "       2017          25.0        0.4      67.9     72.6    7.6  10.6   22.9   \n",
              "1102   2003          28.0        0.4      57.2     57.0    4.2   9.6   16.8   \n",
              "\n",
              "              OppDR TeamStl OppStl       ...       OppPF PointDiff TeamFG  \\\n",
              "                                         ...                                \n",
              "TeamID Season                            ...                                \n",
              "1101   2014    24.1     5.8    7.0       ...        18.7     -15.5    0.4   \n",
              "       2015    25.9     5.9    5.9       ...        16.5     -10.9    0.4   \n",
              "       2016    25.2     6.7    5.1       ...        19.7      -6.4    0.4   \n",
              "       2017    25.0     7.0    6.0       ...        17.2      -4.8    0.5   \n",
              "1102   2003    20.1     6.0    5.4       ...        18.4       0.2    0.5   \n",
              "\n",
              "              TeamFG3 TeamFT TeamAst_TO_Ratio OppFG OppFG3 OppFT  \\\n",
              "                                                                   \n",
              "TeamID Season                                                      \n",
              "1101   2014       0.4    0.7              0.7   0.5    0.4   0.7   \n",
              "       2015       0.4    0.7              0.9   0.5    0.4   0.7   \n",
              "       2016       0.4    0.7              1.0   0.5    0.4   0.7   \n",
              "       2017       0.4    0.6              0.9   0.5    0.4   0.7   \n",
              "1102   2003       0.4    0.7              1.1   0.5    0.4   0.7   \n",
              "\n",
              "              OppAst_TO_Ratio  \n",
              "                               \n",
              "TeamID Season                  \n",
              "1101   2014               1.3  \n",
              "       2015               1.0  \n",
              "       2016               1.0  \n",
              "       2017               0.9  \n",
              "1102   2003               0.7  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <th>Season</th>\n",
              "      <th>WTeamID</th>\n",
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              "      <th>3979</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>1338.0</td>\n",
              "      <td>1160.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2490</th>\n",
              "      <td>1990.0</td>\n",
              "      <td>1210.0</td>\n",
              "      <td>1278.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2597</th>\n",
              "      <td>1992.0</td>\n",
              "      <td>1371.0</td>\n",
              "      <td>1281.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>1992.0</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>1314.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3048</th>\n",
              "      <td>1999.0</td>\n",
              "      <td>1211.0</td>\n",
              "      <td>1196.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      Season  WTeamID  LTeamID  SeedDiff  Result  WGamesPlayed  WWinPercent  \\\n",
              "3979  2014.0   1338.0   1160.0      -1.0     0.0           0.0          0.0   \n",
              "2490  1990.0   1210.0   1278.0       2.0     0.0           0.0          0.0   \n",
              "2597  1992.0   1371.0   1281.0       1.0     0.0           0.0          0.0   \n",
              "496   1992.0   1326.0   1314.0      -3.0     1.0           0.0          0.0   \n",
              "3048  1999.0   1211.0   1196.0      -4.0     0.0           0.0          0.0   \n",
              "\n",
              "      WPointDiff  WTeamScore  WTeamFG   ...    LOppScore  LOppFG  LOppFG3  \\\n",
              "3979         0.0         0.0      0.0   ...          0.0     0.0      0.0   \n",
              "2490         0.0         0.0      0.0   ...          0.0     0.0      0.0   \n",
              "2597         0.0         0.0      0.0   ...          0.0     0.0      0.0   \n",
              "496          0.0         0.0      0.0   ...          0.0     0.0      0.0   \n",
              "3048         0.0         0.0      0.0   ...          0.0     0.0      0.0   \n",
              "\n",
              "      LOppFT  LOppOR  LOppDR  LOppAst_TO_Ratio  LOppStl  LOppBlk  LOppPF  \n",
              "3979     0.0     0.0     0.0               0.0      0.0      0.0     0.0  \n",
              "2490     0.0     0.0     0.0               0.0      0.0      0.0     0.0  \n",
              "2597     0.0     0.0     0.0               0.0      0.0      0.0     0.0  \n",
              "496      0.0     0.0     0.0               0.0      0.0      0.0     0.0  \n",
              "3048     0.0     0.0     0.0               0.0      0.0      0.0     0.0  \n",
              "\n",
              "[5 rows x 51 columns]"
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          "metadata": {
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      ]
    },
    {
      "metadata": {
        "id": "ilGtW7Sb1NHo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "prediction_results = (t_results.apply(lookup_reg_season, axis=1)).dropna(axis=0, how='any')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gi8IzWt6PXOc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 741
        },
        "outputId": "3fb4804a-0c20-4487-8384-7356200ca092"
      },
      "cell_type": "code",
      "source": [
        "display.display(reg_season.head())\n",
        "display.display(t_results.head())\n",
        "display.display(prediction_results.head())"
      ],
      "execution_count": 170,
      "outputs": [
        {
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              "      <th></th>\n",
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              "      <th rowspan=\"4\" valign=\"top\">1101</th>\n",
              "      <th>2014</th>\n",
              "      <td>21.0</td>\n",
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              "      <td>63.1</td>\n",
              "      <td>78.6</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>20.3</td>\n",
              "      <td>24.1</td>\n",
              "      <td>5.8</td>\n",
              "      <td>7.0</td>\n",
              "      <td>...</td>\n",
              "      <td>18.7</td>\n",
              "      <td>-15.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.3</td>\n",
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              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>28.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>61.0</td>\n",
              "      <td>71.9</td>\n",
              "      <td>8.3</td>\n",
              "      <td>10.0</td>\n",
              "      <td>19.6</td>\n",
              "      <td>25.9</td>\n",
              "      <td>5.9</td>\n",
              "      <td>5.9</td>\n",
              "      <td>...</td>\n",
              "      <td>16.5</td>\n",
              "      <td>-10.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>27.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>69.9</td>\n",
              "      <td>76.3</td>\n",
              "      <td>8.2</td>\n",
              "      <td>8.6</td>\n",
              "      <td>22.5</td>\n",
              "      <td>25.2</td>\n",
              "      <td>6.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>...</td>\n",
              "      <td>19.7</td>\n",
              "      <td>-6.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>67.9</td>\n",
              "      <td>72.6</td>\n",
              "      <td>7.6</td>\n",
              "      <td>10.6</td>\n",
              "      <td>22.9</td>\n",
              "      <td>25.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>17.2</td>\n",
              "      <td>-4.8</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.9</td>\n",
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              "    <tr>\n",
              "      <th>1102</th>\n",
              "      <th>2003</th>\n",
              "      <td>28.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>57.2</td>\n",
              "      <td>57.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>9.6</td>\n",
              "      <td>16.8</td>\n",
              "      <td>20.1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>...</td>\n",
              "      <td>18.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              GamesPlayed WinPercent TeamScore OppScore TeamOR OppOR TeamDR  \\\n",
              "                                                                              \n",
              "TeamID Season                                                                 \n",
              "1101   2014          21.0        0.1      63.1     78.6    8.0  10.4   20.3   \n",
              "       2015          28.0        0.2      61.0     71.9    8.3  10.0   19.6   \n",
              "       2016          27.0        0.3      69.9     76.3    8.2   8.6   22.5   \n",
              "       2017          25.0        0.4      67.9     72.6    7.6  10.6   22.9   \n",
              "1102   2003          28.0        0.4      57.2     57.0    4.2   9.6   16.8   \n",
              "\n",
              "              OppDR TeamStl OppStl       ...       OppPF PointDiff TeamFG  \\\n",
              "                                         ...                                \n",
              "TeamID Season                            ...                                \n",
              "1101   2014    24.1     5.8    7.0       ...        18.7     -15.5    0.4   \n",
              "       2015    25.9     5.9    5.9       ...        16.5     -10.9    0.4   \n",
              "       2016    25.2     6.7    5.1       ...        19.7      -6.4    0.4   \n",
              "       2017    25.0     7.0    6.0       ...        17.2      -4.8    0.5   \n",
              "1102   2003    20.1     6.0    5.4       ...        18.4       0.2    0.5   \n",
              "\n",
              "              TeamFG3 TeamFT TeamAst_TO_Ratio OppFG OppFG3 OppFT  \\\n",
              "                                                                   \n",
              "TeamID Season                                                      \n",
              "1101   2014       0.4    0.7              0.7   0.5    0.4   0.7   \n",
              "       2015       0.4    0.7              0.9   0.5    0.4   0.7   \n",
              "       2016       0.4    0.7              1.0   0.5    0.4   0.7   \n",
              "       2017       0.4    0.6              0.9   0.5    0.4   0.7   \n",
              "1102   2003       0.4    0.7              1.1   0.5    0.4   0.7   \n",
              "\n",
              "              OppAst_TO_Ratio  \n",
              "                               \n",
              "TeamID Season                  \n",
              "1101   2014               1.3  \n",
              "       2015               1.0  \n",
              "       2016               1.0  \n",
              "       2017               0.9  \n",
              "1102   2003               0.7  \n",
              "\n",
              "[5 rows x 23 columns]"
            ]
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              "      <th>Season</th>\n",
              "      <th>WTeamID</th>\n",
              "      <th>LTeamID</th>\n",
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              "      <th>WTeamFG</th>\n",
              "      <th>...</th>\n",
              "      <th>LOppScore</th>\n",
              "      <th>LOppFG</th>\n",
              "      <th>LOppFG3</th>\n",
              "      <th>LOppFT</th>\n",
              "      <th>LOppOR</th>\n",
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              "      <th>LOppAst_TO_Ratio</th>\n",
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              "      <th>3979</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>1338.0</td>\n",
              "      <td>1160.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>10.1</td>\n",
              "      <td>72.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>67.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>22.2</td>\n",
              "      <td>1.1</td>\n",
              "      <td>6.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>20.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2490</th>\n",
              "      <td>1990.0</td>\n",
              "      <td>1210.0</td>\n",
              "      <td>1278.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2597</th>\n",
              "      <td>1992.0</td>\n",
              "      <td>1371.0</td>\n",
              "      <td>1281.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>1992.0</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>1314.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3048</th>\n",
              "      <td>1999.0</td>\n",
              "      <td>1211.0</td>\n",
              "      <td>1196.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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            "text/plain": [
              "      Season  WTeamID  LTeamID  SeedDiff  Result  WGamesPlayed  WWinPercent  \\\n",
              "3979  2014.0   1338.0   1160.0      -1.0     0.0          34.0          0.7   \n",
              "2490  1990.0   1210.0   1278.0       2.0     0.0           0.0          0.0   \n",
              "2597  1992.0   1371.0   1281.0       1.0     0.0           0.0          0.0   \n",
              "496   1992.0   1326.0   1314.0      -3.0     1.0           0.0          0.0   \n",
              "3048  1999.0   1211.0   1196.0      -4.0     0.0           0.0          0.0   \n",
              "\n",
              "      WPointDiff  WTeamScore  WTeamFG   ...    LOppScore  LOppFG  LOppFG3  \\\n",
              "3979        10.1        72.5      0.5   ...         67.9     0.4      0.4   \n",
              "2490         0.0         0.0      0.0   ...          0.0     0.0      0.0   \n",
              "2597         0.0         0.0      0.0   ...          0.0     0.0      0.0   \n",
              "496          0.0         0.0      0.0   ...          0.0     0.0      0.0   \n",
              "3048         0.0         0.0      0.0   ...          0.0     0.0      0.0   \n",
              "\n",
              "      LOppFT  LOppOR  LOppDR  LOppAst_TO_Ratio  LOppStl  LOppBlk  LOppPF  \n",
              "3979     0.7     9.1    22.2               1.1      6.4      3.4    20.5  \n",
              "2490     0.0     0.0     0.0               0.0      0.0      0.0     0.0  \n",
              "2597     0.0     0.0     0.0               0.0      0.0      0.0     0.0  \n",
              "496      0.0     0.0     0.0               0.0      0.0      0.0     0.0  \n",
              "3048     0.0     0.0     0.0               0.0      0.0      0.0     0.0  \n",
              "\n",
              "[5 rows x 51 columns]"
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              "      <th>WTeamID</th>\n",
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              "      <th>WTeamScore</th>\n",
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              "      <th>LOppFG</th>\n",
              "      <th>LOppFG3</th>\n",
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              "      <th>LOppAst_TO_Ratio</th>\n",
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              "      <th>3979</th>\n",
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              "      <td>67.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
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              "      <td>3.4</td>\n",
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              "    <tr>\n",
              "      <th>1857</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>1257.0</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>21.1</td>\n",
              "      <td>82.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.7</td>\n",
              "      <td>12.3</td>\n",
              "      <td>23.9</td>\n",
              "      <td>0.8</td>\n",
              "      <td>6.1</td>\n",
              "      <td>4.7</td>\n",
              "      <td>23.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3546</th>\n",
              "      <td>2007.0</td>\n",
              "      <td>1338.0</td>\n",
              "      <td>1433.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>8.7</td>\n",
              "      <td>71.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>64.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>9.7</td>\n",
              "      <td>21.8</td>\n",
              "      <td>0.7</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.2</td>\n",
              "      <td>18.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3681</th>\n",
              "      <td>2009.0</td>\n",
              "      <td>1277.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>9.0</td>\n",
              "      <td>71.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>63.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.7</td>\n",
              "      <td>10.7</td>\n",
              "      <td>19.6</td>\n",
              "      <td>0.9</td>\n",
              "      <td>6.5</td>\n",
              "      <td>2.1</td>\n",
              "      <td>18.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1180</th>\n",
              "      <td>2003.0</td>\n",
              "      <td>1268.0</td>\n",
              "      <td>1462.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>13.9</td>\n",
              "      <td>80.8</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.7</td>\n",
              "      <td>12.3</td>\n",
              "      <td>21.3</td>\n",
              "      <td>0.9</td>\n",
              "      <td>6.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>20.2</td>\n",
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            "text/plain": [
              "      Season  WTeamID  LTeamID  SeedDiff  Result  WGamesPlayed  WWinPercent  \\\n",
              "3979  2014.0   1338.0   1160.0      -1.0     0.0          34.0          0.7   \n",
              "1857  2014.0   1257.0   1264.0      -9.0     1.0          34.0          0.9   \n",
              "3546  2007.0   1338.0   1433.0       8.0     0.0          34.0          0.8   \n",
              "3681  2009.0   1277.0   1425.0       8.0     0.0          32.0          0.8   \n",
              "1180  2003.0   1268.0   1462.0       3.0     1.0          28.0          0.7   \n",
              "\n",
              "      WPointDiff  WTeamScore  WTeamFG   ...    LOppScore  LOppFG  LOppFG3  \\\n",
              "3979        10.1        72.5      0.5   ...         67.9     0.4      0.4   \n",
              "1857        21.1        82.1      0.5   ...         70.0     0.4      0.3   \n",
              "3546         8.7        71.1      0.5   ...         64.8     0.4      0.4   \n",
              "3681         9.0        71.9      0.5   ...         63.5     0.4      0.3   \n",
              "1180        13.9        80.8      0.5   ...         66.0     0.4      0.3   \n",
              "\n",
              "      LOppFT  LOppOR  LOppDR  LOppAst_TO_Ratio  LOppStl  LOppBlk  LOppPF  \n",
              "3979     0.7     9.1    22.2               1.1      6.4      3.4    20.5  \n",
              "1857     0.7    12.3    23.9               0.8      6.1      4.7    23.6  \n",
              "3546     0.7     9.7    21.8               0.7      5.2      3.2    18.8  \n",
              "3681     0.7    10.7    19.6               0.9      6.5      2.1    18.8  \n",
              "1180     0.7    12.3    21.3               0.9      6.6      2.8    20.2  \n",
              "\n",
              "[5 rows x 51 columns]"
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      ]
    },
    {
      "metadata": {
        "id": "RK0NdORyfuE9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Partition data into training, validation, and test dataframes"
      ]
    },
    {
      "metadata": {
        "id": "-9AKcxD40JL2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# STATS = ['SeedDiff', 'WGamesPlayed', 'LGamesPlayed', 'WWinPercent', 'LWinPercent', 'WTeamScore', 'LTeamScore', 'WOppScore', 'LOppScore', 'WPointDiff', 'LPointDiff', 'WFG', 'LFG', 'WFG3', 'LFG3', 'WFT', 'LFT', 'WOR', 'LOR', 'WDR', 'LDR', 'WAst_TO_Ratio', 'LAst_TO_Ratio', 'WStl', 'LStl', 'WBlk', 'LBlk', 'WPF', 'LPF']\n",
        "STATS = ['SeedDiff', 'WGamesPlayed', 'LGamesPlayed', 'WWinPercent', 'LWinPercent','WTeamScore', 'LTeamScore', \n",
        "         'WOppScore', 'LOppScore','WPointDiff', 'LPointDiff', 'WTeamFG', 'LTeamFG', 'WOppFG', 'LOppFG', \n",
        "         'WTeamFG3', 'LTeamFG3', 'WOppFG3', 'LOppFG3', 'WTeamFT', 'LTeamFT', 'WOppFT', 'LOppFT', \n",
        "         'WTeamOR', 'LTeamOR', 'WOppOR', 'LOppOR', 'WTeamDR', 'LTeamDR', 'WOppDR', 'LOppDR', \n",
        "         'WTeamAst_TO_Ratio', 'LTeamAst_TO_Ratio', 'WOppAst_TO_Ratio', 'LOppAst_TO_Ratio', 'WTeamStl', 'LTeamStl',\n",
        "         'WOppStl', 'LOppStl', 'WTeamBlk', 'LTeamBlk', 'WOppBlk', 'LOppBlk', 'WTeamPF', 'LTeamPF', 'WOppPF', 'LOppPF']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Q1pL910Nvp5I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# concat seediff with results into DataFrame to use for predictions\n",
        "prediction_results = prediction_results.reindex(\n",
        "    np.random.permutation(prediction_results.index))\n",
        "\n",
        "# Calculate indices for training, validation, testing\n",
        "num_total_examples = len(prediction_results.index)\n",
        "num_training_examples = math.floor(num_total_examples*0.7)\n",
        "num_validation_examples = math.floor(num_total_examples*0.15)\n",
        "index_validation_examples = num_training_examples + num_validation_examples\n",
        "num_test_examples = math.floor(num_total_examples*0.15)\n",
        "index_test_examples = num_training_examples + num_validation_examples + num_test_examples\n",
        "\n",
        "# Choose the first 65% of examples for training  and normalize.\n",
        "training_examples = normalize_linear_scale(preprocess_features(prediction_results.iloc[0:num_training_examples]))\n",
        "training_targets = preprocess_targets(prediction_results.iloc[0:num_training_examples])\n",
        "\n",
        "# Choose 15% after training examples for validation  and normalize\n",
        "validation_examples = normalize_linear_scale(preprocess_features(prediction_results.iloc[num_training_examples:index_validation_examples]))\n",
        "validation_targets = preprocess_targets(prediction_results.iloc[num_training_examples:index_validation_examples])\n",
        "\n",
        "# Choose 15% after validation examples for testing  and normalize\n",
        "test_examples = normalize_linear_scale(preprocess_features(prediction_results.iloc[index_validation_examples:index_test_examples]))\n",
        "test_targets = preprocess_targets(prediction_results.iloc[index_validation_examples:index_test_examples])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8gGe0XHnFwkF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_ = training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "f-kxwoC1vrOd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Double-check that we've done the right thing.\n",
        "print(\"Training examples summary:\")\n",
        "display.display(training_examples.describe())\n",
        "display.display(training_examples.isnull().values.any())\n",
        "print(\"Validation examples summary:\")\n",
        "display.display(validation_examples.describe())\n",
        "display.display(validation_examples.isnull().values.any())\n",
        "print(\"Test examples summary:\")\n",
        "display.display(test_examples.describe())\n",
        "display.display(test_examples.isnull().values.any())\n",
        "\n",
        "# print(\"Training targets summary:\") \n",
        "display.display(training_targets.describe())\n",
        "display.display(training_targets.isnull().values.any())\n",
        "print(\"Validation targets summary:\")\n",
        "display.display(validation_targets.describe())\n",
        "display.display(validation_targets.isnull().values.any())\n",
        "print(\"Test targets summary:\")\n",
        "display.display(test_targets.describe())\n",
        "display.display(test_targets.isnull().values.any())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9t8f-V-VfzI7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Test out neural network regressor"
      ]
    },
    {
      "metadata": {
        "id": "w1Ie4h8ZA7-x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "outputId": "126426af-3023-4ec1-d7a6-fab078021477"
      },
      "cell_type": "code",
      "source": [
        "_ = train_nn_regression_model(\n",
        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0003),\n",
        "    steps=20000,\n",
        "    batch_size=70,\n",
        "    hidden_units=[20, 20, 15],\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets,\n",
        "    test_examples=test_examples,\n",
        "    test_targets=test_targets,\n",
        "    dropout=0.25)"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training neural network model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 0.50\n",
            "  period 01 : 0.48\n",
            "  period 02 : 0.47\n",
            "  period 03 : 0.45\n",
            "  period 04 : 0.44\n",
            "  period 05 : 0.43\n",
            "  period 06 : 0.42\n",
            "  period 07 : 0.41\n",
            "  period 08 : 0.41\n",
            "  period 09 : 0.40\n",
            "Model training finished.\n",
            "Final RMSE (on training data):   0.40\n",
            "Final RMSE (on validation data): 0.44\n",
            "Final RMSE (on test data): 0.43\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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5+Nvd6RHg0fK6oii49QzHPiXu7Nu5LRbMoWGauJ27rWwsRneG+A9k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\nIYRX+/8Bem0LHPC8qMQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4424c32470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "A96AEJP9-aZU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_ = train_nn_regression_model(\n",
        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.001),\n",
        "    steps=20000,\n",
        "    batch_size=70,\n",
        "    hidden_units=[20, 20, 15],\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets,\n",
        "    test_examples=test_examples,\n",
        "    test_targets=test_targets,\n",
        "    dropout=0.25)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UoAxDLtAAWyH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "outputId": "410b20db-269f-4a67-f6c0-7f8c89b34f01"
      },
      "cell_type": "code",
      "source": [
        "_ = train_nn_regression_model(\n",
        "    my_optimizer=tf.train.AdamOptimizer(learning_rate=0.0001),\n",
        "    steps=20000,\n",
        "    batch_size=70,\n",
        "    hidden_units=[20, 20, 15],\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets,\n",
        "    test_examples=test_examples,\n",
        "    test_targets=test_targets,\n",
        "    dropout=0.25)"
      ],
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training neural network model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 0.44\n",
            "  period 01 : 0.41\n",
            "  period 02 : 0.39\n",
            "  period 03 : 0.38\n",
            "  period 04 : 0.36\n",
            "  period 05 : 0.35\n",
            "  period 06 : 0.33\n",
            "  period 07 : 0.32\n",
            "  period 08 : 0.31\n",
            "  period 09 : 0.30\n",
            "Model training finished.\n",
            "Final RMSE (on training data):   0.30\n",
            "Final RMSE (on validation data): 0.46\n",
            "Final RMSE (on test data): 0.44\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AbmrLcWmotqSiOlGpr2mnwmaq06TjjkworT8hEdYsS+94LhBivsijz31bjo0/\no7h4jjoAcxhfJILmicUQRURA9/1uZL3xOqKfWQppt+6NbjtUMxDqQCU2n9uG7zP3Iac8D0m95kAi\nbLy5ihBSV6WtErdKc3CzNAvZ5Xkos5SxyYnJaoKtibUlioBQxAQ1XFviTFScyzKRlDr1E9JMVDPT\ngDPXihGlCYZK7v4SBC3JePgXaLdvBY/Hg+aPiyAfdo9H25VZyvHhhe3IMGQiKlCDJf0WIkIa7uXS\n+hb9muEmf4qLg3Egv0KLm6VZuGnMws1SZ+dYBq6nRClbW3K7liTIJTG5M1nxvLakNflTbNoTiovn\nfNbMlJycjLNnz4LH42HFihXo16/u/Chvv/02zpw5g+3bt7OPVVZWYtq0aXjqqacwa9Yst/vw1kHw\n0qYjKCmtwt8eGYiuMSFe2Ud9KtIvIP/99XBUViLioTlQJE726KRod9ix89pu/JL7O2RCKZ7oMx89\nw7q1Qol9g04A3MTluBirypyJS3Xycqss22XSNzFfhI7BsegU3AGdgmPRMTgWwWJ5mxnNw+XYtGcU\nF8/5pJnp+PHjuHXrFlJSUnD9+nWsWLECKSkpLutkZGTgxIkTEIlELo+///77CAlpvQSiPvMmdMd/\ndp7DutRzWLlgCJShrTMEOjC+D2KXr0Du2n+j+OsdsBYXQfXofPAE7k+oAr4AD/eYgVh5FFKufIP1\nZz7EzK5TMTZ2FOd+IRLibVa7FdnlubhpzEJmqbPWRVepd1lHI1OhU0gHdArugM7BHRAZqG4ziQsh\n7Y3XkpkjR45g/PjxAIAuXbrAaDSivLwcQbUutLhmzRosW7YM69evZx+7fv06MjIycP/993uraB7p\nExeOxTP6YmPqOby78xxWzB8MmaR1uhgFxHZA7EsvI3ftv2H830HY9DpELv4T+BJJo9uOjBoGTaAa\nH57fhtSM75FdlodHez4EsUDU6LaE+COGYVBkLsbN0mzcLM1CpjELueX5LqOBgkSB6BPey5m4hHRA\nx+CYdjtHEyFtkde+nYuLixEfH88uh4WFoaioiE1mUlNTMWzYMERHR7ts9+abb+Lll1/Grl27vFU0\nj01N6IyMWzrsP5mDjd9dwNLZ/SDwYHK7liAKC0Ps8hXI3/geKs6dRfZbbyD6uWUQhoY2um1cSEf8\nbehz+OD8dpzQnoLWpMXivguhkDS+LSFcZ7KabicupVm4VZqNCquJfV7AEyBGHoXOwR3Y5CVcEkY1\nlIS0Ya02mql21xyDwYDU1FRs2bIFWq2WfXzXrl0YMGAAYmNjPX5dhUIGodB7VcPPPDwQ+goLTl4u\nxLe/38KSma15XSQ5VP94Gdf4hf1JAAAgAElEQVTf34zC/f9F7pur0PuVv0PWoUOjWyohx6rIv+DD\nk1/gUOYRrDr+Nnoru6G3qht6Kbuhs6IDhH5epe6u/ZT4TkvGxeawI8uQiwxdJq6V3MS1kkzklWld\n1lEFhmNAZG90C++MbuGd0Sk0BiKqiawXfWa4ieJy97yWzKhUKhQXF7PLhYWFUCqVAICjR49Cp9Nh\n3rx5sFgsyMrKQnJyMgoLC5GdnY1Dhw6hoKAAYrEYGo0GI0eObHA/er2pwefullIph05XgT9O7omC\nkgp8fzgTIVIRxg2O8do+6xPy8HzYg0JQsisVZ/+2AlFPPQtZL88myZvdaQY0Yg32Z/2CU/kXcCr/\nAgBALBAjLrgjuini0DU0Dh2DYyHyo8m0qNMcN91tXPSVhuo+Ls5OulllubA6rOzzEoEEPRXd0Ck4\nlu3vIhffbrqGAzDoKgF4PrdLe0GfGW6iuHjOJ6OZTp06hXXr1mHLli1IT0/HqlWr8MUXX9RZLycn\nBy+99JLLaCYAWLduHaKjo302mglwPciKDWas2paGMrMVf/5Df/SNa/2hz6VHf0fBlo8AAJrH/ojg\nEQlN2t5QZUSG/gauGTORob+BAlMh+5yQL0Tn4A7oGhqHrqGdERfSEWJB6w1Lbyo6AXBTU+JSZbcg\nqzTbpa+L0VLKPs8DD1FBmurRRc7mIrVMSXOxNBN9ZriJ4uI5n4xmGjRoEOLj4zF37lzweDy8+uqr\nSE1NhVwux4QJE7y1W6+JCJXimYf64a3PT2PjtxewYv5gRCuDGt+wBQUPHwlhqAJ5G9ah4KMPYC0u\nRti0Bz3uCxAaEIIhmoEYohkIwDk/TYYhExmGG7hmuIEMQyauGW4AcPY76CCPqa656Yy4kE6QChvv\ngExIfRyMA4WmImQas9i+LvkVWjgYB7tOiFiO/so+6BQci87BHRArj6EJIAkhHqFJ89yoL2M+erEA\nm7+7iIgQCVYuHIJgWevXXlTl5SL33XdgKylBcMIoqJMWgie8+7zUZDXhuvGmM7HRZyK7PJf9suGB\nh1h5VHXNjTPBCRTJ7nqfzUW/ZripJi7llgo2aamZ06X2tP4ivhCx8hhnJ90Q59Do0IAQ6qTrRfSZ\n4SZ/jovdZIK1UAuLtgCWggJYi4sQPCIBgb3jG9+4GejaTM3U0EG269cb+O63m+gaHYK/PjIAIi92\nQG6IzWhA7tr/oOrWTch6xSPyT09DIGvZ5KLSVokbxltsjc2t0myX4a5RgRq2z03X0M4IFrdeJzZ/\nPgG0JQ7GgWKzDlpTIQoqClFsLcLlwusortS5rKeSRaBzcEe2r0t0YCTN6dLK6DPDTVyPi8NqhbWo\nCFZtwe2kpfq+vbS0zvph0x5ExAz33UOai5KZZmroIGMYBpt3X8Sxi1oMj1dj0bTePvlF6aiqQv7m\n91Fx9gzE0TGIXroMojDv9eWx2K24WXoL1wzOPjeZpbdgddjY59UyJZvYdAuN8+pQcK6fANoai90K\nrakI2gotCkxFKDAVQltRiEJTUZ3rFcmEUrZzbs1sur6sxSNO9JnhJi7EhXE4YNPrYdEW1ElarMXF\nwJ1pAo8HUUQERGoNxGoNxGq1875GA2FYuNe+DymZaSZ3B5nVZsdbn5/G9bxSzBzVGQ8kdPZaOdxh\nHA4UfvEpjAcPQBAaiujnlkHSoWOr7NvmsOFWaQ7b5+aG8abL9PDhkjB0q0luFHEtOtcHF04AbVG5\ntQIFFYVsTYszaSmCrlJf55pFAQIxNDI11IFKaGQqaAJViI/tAoFZQs1FHESfGW5qzbjYy8vr1K5Y\ntFpYC7VgLJY66wuCgyFWa24nLRo1ROpIiJRK8EWtP/0BJTPN1NhBZqywYNXWNJSUVuLJ6fEY1kvt\ntbK4wzAM9D/9iOKvUsALkCDqT08jsE/fVi+H3WFHTnledWfiG8gw3ITZZmafDw0IQdfQzugaGodu\noXFQy5TN/tKjE3PzORgH9JVGZ8JSnbTUJC/l1oo66weL5VDLlNAEqtmkRS1T1tvHheLCXRQbbmrp\nuDgsFli11f1Y2JoW57KjvLzO+rwACcRqNcSamqRFzSYwLd114W5RMtNMnhxkOUXlSN5+EnYHg789\nOhBdonx3TamytOMo+HAzGIcD6vkLETL6Pp+VBXB+aeaVF7B9bjIMN1y+LOWioNvJjSIOkYFqj4fd\n0om5cTaHDUXmEmcNS03CUt08ZKk1dwvg7OAdLg1jkxWNTAV1oAoamRKyJjQRUVy4i2LDTc2JC+Nw\nwFpSDGtBwe2kpcCZsNh0JXU3EAggilBCrNHUqmlxJjCCkFC/qUmlZKaZPD3Izl0vxrs7z0EuE+Pl\nBUMQHuK7Iczma9eQ+967cJSXI2zKNITPfIgzByrDMNCaCp19bgw3cE1/w2VeEZlQii6hndk+NzFB\nUQ12EqUT821mWyVbs6I1FVU3D2lRbNa5DH0GnKOIVDJlrWTFmbyopBEtMmsuxYW7KDbc5K5vpr20\ntDpRqZW0aLWwFhWCsdnqbCNUKGr1Y9FApKmuZQmPaJERr75GyUwzNeXD/3NaNr7Yfw0xyiC8NH8Q\npAG+O3As2gLk/ucdWIsKIb9nONSPPeGT9s3GMAyDYrPOZZ6bklqjYCSCAMSFdGL73HSQx0BYPUtx\nezsxMwyDUkuZS1+WmuTFUGWss36gUMbWrNxOWtQIk4R6ddK59hYXf0Kx4SZFoBD5FzPuSFq0sGoL\n4DCb66zPl0oh1kRCpFZDrImsrmlRQ6xSe3QxYn9GyUwzNeXDzzAMPv3pKg6ezkW/LuF47qF+4PN9\nVyNiKytF3vq1qLyeAWn3Hoh6+jkIAgN9Vh5P6SsNbGKTYbgBramIfU7EF6FzSEd0CekIVagClSYb\nBDwB+HwBhDwBBHwBBDw+BOx955+QLwCfV/2cy7qCWus6t/N1LdadQ51rNw/VnqelhiIgtE6zkCZQ\njSBRoE/eC31hchfFxnecw5sLXfqvOPu1aGE3GuqszxMKIVKpbycqNUmLRg1BkNzn5ylfoWSmmZr6\n4bc7HPjPV+eQnqnDxKGxmDuum9fK5gmHxYKCjzaj/GQahOHhkA+9B7Le8ZB26wa+iLuXKqit1FJ2\ne5Zi/Q3kVRR4dX/8mmSIJ4CAz4eQV50INZIw3b5/O2Hi13pMyBc2sB0fxqoyt0Od+Tw+VNKIO5IW\nFVQyJedmyKUvTO6i2HgX24+lVs2K81YLa0n9w5uF4eEIiokGwqqHOWsiIVarncOb+XTZjjtRMtNM\nzfnwmyqtWL39JPJLTFgwqQfuHxjtpdJ5hnE4UJy6E4b9P7FtrDyRCNLuPRAY3wey3vEQR8f4TaZf\nYTUhqzQHkiABdIZy2Bk77A6785ZxwO6wOW+rH7cxzuccDofzPrtuzX3HHa9hh93hgI1xvo6j+nFb\nA/toSTVDnWtGC9UkLxHScL+ZYI6+MLmLYnP3GIaB3WiApaAAlsJaCUv17Lf19WMRhITcrmGpNSeL\nc3izmOLSBJTMNFNzD7JCgxmrtqbBVGnDsof7I75TmBdK1zSOqiqYr12BKT0dFRfTYcnNYZ8ThIQi\nsHc8ZL3jIevdG8IQ701211K4cAJgGAaOmmSodhLkcD7mcJME1SRaDocdgeJAaGSqNjGdPxfiQupH\nsfFczXwst6fqr05cCrVgqqrqrM+XyVwSltqJC18idbsviovnKJlpprs5yK5mG/CvL09DJBRg5YLB\niAznVn8Vm0EP08WLqEi/ANPFdNjLbo8qCoiNrU5s+kDarTv4Yu41SdEJgJsoLtxFsXHlqKqq1Xfl\n9q2lUFv/fCxicXU/ljsTFg34QUE0Z1YroGSmme72IPv9Qj4+/P4SVKFS/H3BYMh9cFFKTzAOByy5\nOc7EJj0d5mtXXJukunWHLL4PAnv3gTiGG01SdALgJooLd7XH2DA2G6xFhXd0uq2+rpChbsdbdj6W\nOxIWkVoDYWioV/qxtMe4NBclM83UEgdZ6i/X8f3vt9A9JgQvzB0IkZD7nbocFgvM167ClH4BFekX\n7miSCoGsV29nf5te8RCG+qZJik4A3ERx4a62GhvG4YBNV+LS6bZm5tuGriskVITdnodFpWbnZhFF\nRIAnaN3+aW01Lt7gLpnx/1l0OG7GqDgU6MxIu1yIbT9exh+n9uJEzYY7fLEYgfF9EBjfB0oANoMB\npkvpbJNU2dEjKDt6BAAgjo5xJjbx3G2SIoT4P3t5OSz5+bBo850dcGsSlsL6J5ATyIMh7dqNnYOl\n5kKIIqWKzlNtECUzXsbn8fDE1F4oMZrx24UCaMJlmDqik6+L1STC0FAEj0hA8IgEMAwDS04OKi46\nExvz1SvQ5+ZA/9OP4AmFkHbrAVl8PALj+zhHSdHwQkKIhxi7HdbiYlgK8tk/a0EBLPn5sJfXrb3g\nS6UQx8TWbRZSqTl3XSHiXdTM5EZLVv8Zyqvw+tY06Muq8PTMPhjcQ9Uir+trbJNUdXJTlZ3NPieQ\nB1d3JI5HYHw8hKGKFtsvVc1yE8WFu7gUG7vZDGtNwpJfk7g4Rw/VqWXh8SBSqpzXFYqMrJ791jkn\ni0Du/xPIcSkuXEfNTBwQGhSApbP74Y1PT+GD3RcRHiJBJ02wr4t112o3SQGAzWiA6eJFmC6mo+Li\nBZQdO4KyY7WapHrHQxYfD2m3HuAHcGvCN0JIy2EcDtj0+jsSFuf9+ma9ZWtZqhMWsSYS4shIZ7MQ\nBy/HQriFambc8EbGfOZaMdZ9fQ7BQc6LUoYFt91raTAMc3uUVHWTFGN1Xq3Z2STV3VlzE98HATGx\nTWqSol8z3ERx4S5vxYYd4lxQ4JKwWLQFYCyWOusLw8PZREWsjmSTF0GI/8+z1Bz0mfEcjWZqJm8d\nZPuOZyHlQAY6qILw4vxBkIjbRwWZw2qB+do1mC6mw5R+AVXZWexzArkcsl7OWhtZ7z4QKdw3SdEJ\ngJsoLtx1N7FxznxrrJWo3K5tsZWU1FmfJxY752CpVcsi0lTPyUI1si7oM+M5SmaayVsHGcMw2Prj\nFfxyNg8Du0Xg6Vl9wW+Hv0hsRiNMly9WDwFPd6l6FkdFQdbb2Xwl7V63SYpOANxEceEuT2JTc0HE\n2s1CNTUu9V3BWRAaertJSHO7lkWoUFDnfw/RZ8Zz1GeGY3g8HuZP7I4igxmnrxVj56HrmDOmq6+L\n1eqEISEIvmcEgu8Z4WySystzJjYXL8B89QoM+3+CYf9P4AmFkHTtVt3fpg8CYjv4uuiE+DV7WZlz\neHNBXq0+Lc7rC8HhcFmXvYJz70iX2haRJhICqfup+glpLVQz44a3M+aKSitWbTsJrc6Exyb3xOj+\nUV7bl79xWC2ozMhg+9tUZd1in+MHBUEe1xkIV0IcGVX9izDKOUNnO6zh4hL6lckNjspK2Ax62PR6\n9pZfqkfpzSxnLUs90/ULguTODrcajUttiy8mkmtP6DPjOWpmaqbWOMi0OhNWbUtDpcWOFx4egJ4d\nW274cltiKy2F6ZKzScp09TJsxcV11uEFSJy/Gmva6WsSHZUKPCFVQrYGOjF7F+NwwF5aWidRcd4a\nnLcGfb1NQgAAPh8ipbJOs5BYEwlBUFDrvhkCgD4zTUHJTDO11kF2JUuPf315BhKxACsXDIE6jCZ7\nakyYXIS8C9duV5Hn5zmrybUFdeepEAicJ/Ca5KYm0YmkavKWRifm5nNUVdWToOjZBMWmN8BmNNRp\nBqqNHxgIYagCQoXijttQqLt3RrkwkBJ7jqHPjOcomWmm1jzIfj2Xhy17LkOtkOLvC4YgSErzKrjT\nUGzYGUTz81yHiebnwWEy1Vmf7cBYndwEREZBpImkJqtmohNzXYzDAXtZ6e2ak3oTFTe1KQAgEEAY\nGtpAolJ9Gxrqdpp+ig03UVw8Rx2A/cCoflEo0Jmw92gWNnxzHs8/PABCAY0GaCqeQFA9tbkawED2\ncYZhYC8trU5uanV6zM+H+fIlmC9fcnkdvkQCUXU1fE2CE1A9gRf9siU1HFVVDTb1sI8ZjYDd3uBr\n8GWBEIaFO5OVBhIVQVAQjQ4ixA06K3PIQ/d1gVZnxqmrRdi+7woem9yTagdaCI/HgzAkBMKQEMh6\n9HR5zlFZCYu24HaiU53kWHKyUXUzEy6/mQQCiJUqiKr7GtQkOtRk1bY4rBY4TCbYDIa6TT21EpX6\navtYAgGEISGQdOxUq/bE2eQjVITdrk2heVcIuWteTWaSk5Nx9uxZ8Hg8rFixAv369auzzttvv40z\nZ85g+/btAIC33noLJ0+ehM1mw5IlSzBx4kRvFpFT+DweFk3rjTWfncKv5/IRGR6IxHtoGLK38SUS\nSDp2gqRjJ5fHnU1WRbfn3GBvnTU7FXe8jiA01CW5oSar1sc4HHBUmuEwV8JhNsFhroTdbILDbHY+\nbqq+NZtgN5udj9/5V2mu9yrMtfGlUmeC0qlzg31UBPJgqk0hpJV4LZk5fvw4bt26hZSUFFy/fh0r\nVqxASkqKyzoZGRk4ceIERNXX3Th69CiuXbuGlJQU6PV6zJw5s10lMwAQIBbgudn98PrWE/jqYAbU\nCikGdlf6uljtkrPJyjlrab1NVnX65eTDdOkicOmiy+vUNFkFVPfLYefpoCYrFsMwYKxWl4TCYTbD\nbjKx92v+7NVJisNsgqOyJmmp2a6yWfvnBQSAL5FCEBQEkUoJvkTqTFhC6m/6odoUQrjFa2fSI0eO\nYPz48QCALl26wGg0ory8HEG1hv+tWbMGy5Ytw/r16wEAQ4cOZWtvgoODYTabYbfbIWhncxwo5AFY\nOrs/3vjsJDbtTsdL8wajo6bhjk+kdbk0WfXs5fIc22R1R7+cmiYrF9VNVgK5HODxAD7f+Uu+9i2P\nD/B57PLtx2qvy7v9uEBQ/TgP4DX0ere3YZ+rsy6PfRx8Png1r+u2nHxUVEhhzit2TTiqb+21khSH\nqXYi4qw9cdevpEECAfhSKQQSqfOChFKpy59AKgNfIgFfKgNfJmWTFAG7jvN5mkeFEP/mtWSmuLgY\n8fHx7HJYWBiKiorYZCY1NRXDhg1DdHQ0u45AIIBM5hyWvHPnTowePbrdJTI1OmrkWPxAPN5LPY+1\nX5/DygVDoJDTr0Gu86jJyuUKwnmwaAsA/x5UyLrV+CosXoAEfKkEArkcIlWtRERSnWzIaiUiUuet\noHayIpGCJxZTEx4hpPU6ANceAW4wGJCamootW7ZAq9XWWXf//v3YuXMnPv7440ZfV6GQQSj0XsLj\nbiiYt01SylFeZccnP1zE+9+l442nEtrNRSk94cvYNIsmFOjTrd6nGIYBHA4wNX92h8vy7fv2+h+3\n112v4ddo+H6j29jtbl8DPB6EMhkE1X/CwOpbmQwCmbT6fiAEUqoN8QW/+8y0ExSXu+e1b0aVSoXi\nWrO0FhYWQql09v04evQodDod5s2bB4vFgqysLCQnJ2PFihX49ddfsXHjRnz44YeQyxsPsF7vZjTB\nXeLC+P9RfdTIyNLj8Pl8rPnkOP40o0+7vCjlnbgQm9bDr/4DwAMgqP5rAbw7bu9WfXGxV/+xzA7A\n7L3PLalf+/rM+A+Ki+fcJX1e62qfkJCAffv2AQDS09OhUqnYJqbExETs2bMHO3bswPr16xEfH48V\nK1agrKwMb731FjZt2oTQ0FBvFc2v8Hg8LEjsgR6xoTh5pQjf/HLD10UihBBCOMVrNTODBg1CfHw8\n5s6dCx6Ph1dffRWpqamQy+WYMGFCvdvs2bMHer0ef/7zn9nH3nzzTURFte8LMAoFfDw9qy9WbUvD\nD0duQRMmQ0LfSF8XixBCCOEEupyBG1yr/ssvqcDqbSdRZbXjr48MRPfY9lt7xbXYECeKC3dRbLiJ\n4uI5nzQzkZYXGR6Ip2f2AQCsTz2PQi/2FyKEEEL8BSUzfqZXpzAkTeqBcrMV7+48B1Ol1ddFIoQQ\nQnyKkhk/NLp/FCYNi0V+iQkbdl2Aze7wdZEIIYQQn6Fkxk/94f6uGNA1Ahdv6vH5z1fh512fCCGE\nkGajZMZP8fk8LH6wN2JVQTh0Jg/703J8XSRCCCHEJyiZ8WMSsRBLZ/dDSKAYXx64hrMZxY1vRAgh\nhLQxlMz4ubBgCZ6b3Q9CAR8bv0tHdmG5r4tECCGEtCpKZtqAzpHBWDStN6osdqzdeRbG8ipfF4kQ\nQghpNZTMtBFDeqowa3QcSkqrsC71PCxWe+MbEUIIIW0AJTNtyNQRHTEiXoMbeaX4eM8lOGiEEyGE\nkHaAkpk2hMfj4bHJPdEtJgTHLxXiu8OZvi4SIYQQ4nWUzLQxIiEfz8zqC2WoBN/9dhNH0gt8XSRC\nCCHEqyiZaYPkMjGWzu4PaYAQW/ZcQkaO0ddFIoQQQryGkpk2KioiEH+aEQ+HA1iXeg5FBrOvi0QI\nIYR4BSUzbVifzuGYN6EbykxW/HvHWVzPoxoaQgghbQ8lM23cmEExSLynAwp0JqzedhLv77qAQqql\nIYQQ0oYIfV0A4n1zxnRF/y7h2HEwAycuF+LU1SKMGxyDaSM7IUgq8nXxCCGEkLtCNTPtRI8OCvx9\nwRAseTAeCnkAfjqRjRc3HsGPx7JgtdEEe4QQQvwX1cy0I3weD/f0VmNQdyX+ezIH3/9+EzsOZuDA\nqRzMui8Ow3qpwefxfF1MQgghpEmoZqYdEgn5SLynA9Y8OQITh8ZCX1aFzd9dxKqtabiSpfd18Qgh\nhJAmoWSmHQuSijB3XDesXjwcw3qpcLOgDG9+fhprd55DfkmFr4tHCCGEeISamQhUoVI8Ob0PJgw1\nYseBDJzJKMa56yUYPSAK0+/tjJBAsa+LSAghhDSIkhnC6hIVghfnDcLpa8X46tB1HDqdiyPpBZhy\nTwdMHNYBASKBr4tICCGE1EHJDHHB4/EwqLsS/bqE45ezefj2cCa++TUTB0/nYuboOCT0iQSfT52E\nCSGEcAf1mSH1Egr4GDsoBmuWjMDUER1RUWnDlj2X8f+2HMeFGyW+Lh4hhBDComSGuCUNEOKh+7rg\njcXDkdBXg9yiCryz4yze/vI0srRlvi4eIYQQQs1MxDNhwRI8MbU3JgyJxY6DGUi/qcfFLScwsq8G\nM0fFISxY4usiEkIIaacomSFN0kEtxwsPD8CFTB12HMzAb+cLcOJSISYOi8XkezpCGkCHFCGEkNbl\n1Wam5ORkPPzww5g7dy7OnTtX7zpvv/02kpKSmrQN8S0ej4e+ceF47fFheHxyT8gkQnz/+y28uOkI\nDpzKgc3u8HURCSGEtCNeS2aOHz+OW7duISUlBatXr8bq1avrrJORkYETJ040aRvCHXw+D6P6R+GN\nxSMwY1RnWGwOfPrTVbzy0XGcvloEhmF8XURCCCHtgNeSmSNHjmD8+PEAgC5dusBoNKK8vNxlnTVr\n1mDZsmVN2oZwT4BYgAcTOmPNkhG4f2A0CvVmrEs9jzc/O4UbeaW+Lh4hhJA2zmsdHIqLixEfH88u\nh4WFoaioCEFBQQCA1NRUDBs2DNHR0R5vUx+FQgah0HuTuSmVcq+9dlujVAIvdArHnAll+OT7izh+\nsQCrtqVh9IBoJE3pBU14YAvvj2LDRRQX7qLYcBPF5e61Wm/N2k0OBoMBqamp2LJlC7RarUfbNESv\nN7VI+eqjVMpRVETDj5tKwgeefLA3xgyIRMqBDPxyJhe/n8/DuMExmDayEwIlorveB8WGmygu3EWx\n4SaKi+fcJX1eS2ZUKhWKi4vZ5cLCQiiVSgDA0aNHodPpMG/ePFgsFmRlZSE5OdntNsT/9OigwMqF\nQ3D8khZfH7qBfcezcfhcPqaN7ISxg2IgEtI0R4QQQu6e175NEhISsG/fPgBAeno6VCoV21yUmJiI\nPXv2YMeOHVi/fj3i4+OxYsUKt9sQ/8Tn8TC8twbJi+/BnDFd4WCAlAMZ+PsHR3H8kpY6CRNCCLlr\nXquZGTRoEOLj4zF37lzweDy8+uqrSE1NhVwux4QJEzzehrQNIqEAifd0wL39IrH7t5s4cCoHG79N\nx77j2Xh4bFd0jw31dREJIYT4KR7j5z+NvdnWSG2Z3lOoN+Hr/93AicuFAICB3SIw+/4uiPSwkzDF\nhpsoLtxFseEmiovnfNJnhhB3VAoZ/jSjDybmGpFyMAOnrxXjbEYJ7hsYhekJnREcKPZ1EQkhhPgJ\n6oFJfKpLdAhemjcIT8/sC2WoBAdP5eLFTUfw/e83UWW1+7p4hBBC/ADVzBCf4/F4GNxDif5dw/G/\nM3n49nAmUn+5gYOnczFzVBxG9tGAz+f5upiEEEI4impmCGcIBXyMGxyDNUtGYOqIjig3W/Hxnkt4\n7ZMTSM/U+bp4hBBCOIqSGcI5MokQD93XBW8sHo6RfTTIKSzH2yln8E7KGWQX0uUtCCGEuKJmJsJZ\nYcES/N+03pgwJBY7DmbgQqYO6ZnHkdAvEotm9vN18QghhHAEJTOE8zpq5PjL3AE4f0OHrw5l4PC5\nfJy6WoTpCZ0xdnA0BHyqYCSEkPaMkhniF3g8Hvp1CUefzmH439k8fPPLDXzx32v49Vwe5k/sQZPu\nEUJIO0Y/aYlf4fN5GDMwGhtfHIfR/SORU1SBNZ+dwge7L8JYXuXr4hFCCPEBqpkhfikkKACPTe6F\nUf2i8OlPV3EkvQBnMoowY1Qcxg6ipidCCGlP6IxP/FqX6BC8vHAIkiZ2Bw88fLH/Gl7bkoZrOQZf\nF40QQkgroWSG+D0+n4cxg2KQvGQ47u0XiZyicrzx6Sl89P1FGCssvi4eIYQQL6NkhrQZwTIx/jil\nF1YkDUYHVRB+u1CAFZuP4r8nc2B3OHxdPEIIIV5CyQxpc7pGh+CVx4Zi3oTu4AH47OereP2TNGTk\nGH1dNEIIIV7Q7GTm5s2bLVgMQloWn8/DuMExSF48HAl9NcgqLEfypyfx0Q8XUUpNT4QQ0qa4TWYe\nf/xxl+UNGzaw91955RXvlIiQFhQcKMYTU3vjpfmDEKsKwm/nbzc9ORyMr4tHCCGkBbhNZmw2m8vy\n0aNH2fsMQ18ExH90iysH0VIAACAASURBVAnFK48NwbwJ3cHA2fT0j60nkJFLTU+EEOLv3CYzPB7P\nZbl2AnPnc4RwnYDPv9301EeDLG05krefxMd7LqHURE1PhBDir5rUZ4YSGNIWhASK8cS03nhx3iDE\nKINw+Fw+Vmw6ioOnqOmJEEL8kdsZgI1GI44cOcIul5aW4ujRo2AYBqWlpV4vHCHe1D02FK8+PgQH\nTuVi1683sP2nq/jlbD7mT+qOLlEhvi4eIYQQD/EYN51fkpKS3G68ffv2Fi9QUxUVlXnttZVKuVdf\nnzRfS8fGWF6FHQev40h6AQBgdP9IPHRfF8hl4hbbR3tAnxnuothwE8XFc0qlvMHn3CYz/oCSmfbJ\nW7G5kqXHZz9fRU5RBQIlQjx0XxeM7h8FPp+aWD1BnxnuothwE8XFc+6SGbd9ZsrLy/HJJ5+wy19+\n+SWmT5+O5557DsXFxS1WQEK4okcHBV59fCgeGdcNdgeDbfuuYNW2NNzIo2ZVQgjhKrfJzCuvvIKS\nkhIAQGZmJt555x0sX74cI0eOxOrVq1ulgIS0NgGfjwlDY5G8eDhGxKtxs6AMq7el4ZO9l1FGo54I\nIYRz3CYz2dnZeOGFFwAA+/btQ2JiIkaOHIm5c+dSzQxp80KDArDogXgsf3QgoiIC8cvZPKzYfBSH\nzuTC4d+ts4QQ0qa4TWZkMhl7//jx4xg+fDi7TMO0SXtR0/Q0d2xXZ9PTj1ewelsaMvOp6YkQQrjA\nbTJjt9tRUlKCrKwsnD59GgkJCQCAiooKmM3mVikgIVwgFPAxcVgHrF40HMN7q5GZX4ZVW9Ow7cfL\nKDdbfV08Qghp19zOM7No0SJMmTIFlZWVeOaZZxASEoLKyko8+uijmDNnTmuVkRDOUMgDsPjBeIzu\nH4VPf76KQ2fykHalCLPv74J7+0WCTzWWhBDS6hodmm21WlFVVYWgoCD2scOHD+Pee+9t9MWTk5Nx\n9uxZ8Hg8rFixAv369WOf27FjB3bu3Ak+n4+ePXvi1VdfhclkwvLly2E0GmG1WvH0009j1KhRbvdB\nQ7PbJy7ExmZ34L8nc7DrcCaqLHZ0jgxG0qTu6KQJ9mm5fIkLcSH1o9hwE8XFc+6GZrutmcnLy2Pv\n157xNy4uDnl5eYiKimpw2+PHj+PWrVtISUnB9evXsWLFCqSkpAAAzGYzfvjhB3z22WcQiURYsGAB\nTp8+jYsXL6Jz58544YUXoNVqsXDhQvz4448ev1FCWpNQwMekYR0wrJcaOw5m4NhFLV7/JA33D4zG\nzNFxCJKKfF1EQghpF9wmM2PHjkXnzp2hVCoB1L3Q5LZt2xrc9siRIxg/fjwAoEuXLjAajSgvL0dQ\nUBCkUim2bt0KwJnYlJeXQ6lUQqFQ4MqVKwCcyZNCobi7d0dIK1DIA7Ckpunppys4eDoXJy4XUtMT\nIYS0ErfJzJtvvolvv/0WFRUVmDp1KqZNm4awsDCPXri4uBjx8fHsclhYGIqKilyaqzZv3oxt27Zh\nwYIFiI2NRWxsLFJTUzFhwgSUlpZi06ZNje5HoZBBKBR4VKbmcFetRXyLa7FRKuUYMSAGu3+9gS9+\nuoxP9l7GkXQtnnyoH7rGhPq6eK2Ga3Eht1FsuInicvfcJjPTp0/H9OnTkZ+fj2+++Qbz5s1DdHQ0\npk+fjgkTJkAikXi8o/q65ixevBgLFizAokWLMHjwYOTk5CAqKgofffQRLl++jBUrViA1NdXt6+r1\nJo/L0FTUlsldXI7NqD5qxHcIwY6DGTh+qRDP//t/uH9QNGaNjkOgpG03PXE5Lu0dxYabKC6ea/bl\nDGpERkbiqaeewt69ezFp0iSsWrWq0Q7AKpXKZWK9wsJCtrnKYDDgxIkTAACJRILRo0fj1KlTOHXq\nFPu6PXv2RGFhIex2uydFJIRTwoIleHJ6H/xl7gBowmU4eCoXL206il/P5dGEe4QQ0sI8SmZKS0vx\n6aefYtasWfj000+xZMkS7Nmzx+02CQkJ2LdvHwAgPT0dKpWKbWKy2f5/e/ceF3Wd9///McNB5KSc\nBhA5CSqKIGKkgBKllJ03LSU32e/udWl22K222jXb1tpN97LfbtumZXm1lZldYkZlWWqeUTFUFBDP\ngHgCAQUVwQOH3x8qq2ZE6jgz8Lzfbnu77TB8mNf0cvDp5/V5f971TJgwgZMnTwKQn59PaGgowcHB\n5ObmAnDw4EFcXFywszPfCEnE3HqHePLKb27moeQwztY38sE3O/jbx5soKdO/xERErpcWl2avWbOG\nzz77jK1bt3L77bdz//3306NHj1b/8L///e9s3LgRg8HApEmT2LZtG25ubqSkpJCRkcGcOXOwt7en\nZ8+evPLKK9TW1jJx4kSOHDlCfX09Tz31FPHx8S2+hpZmt0+22Jujx08xd/keNu4ox2CA2/p15YGk\nUJzb0OjJFvvSXqg31kl9ab2WxkwthpmIiAhCQkLo27cvRuMPT+L87W9/uz4VXgOFmfbJlntTsPco\nc5bsouxoLW7ODjyUHE5ClF+bWPVky31p69Qb66S+tN5V32fmwtLrqqqqHyyTPnDgwHUoTaT9iQzx\n5C//dTNLNuxnwdpi3v9mO6tzD/HI7T0I8tWqBhGRn6vFMGM0GnnmmWc4ffo0np6evPvuuwQHB/Px\nxx8zc+ZMhg8ffqPqFGlT7O2M3DUwmIG9fZm7bDcbd1bwyocb2uToSUTE3FoMM//85z/58MMPCQsL\nY9myZfz5z3+msbGRTp068emnn96oGkXaLE93Jx5/IIqC4qPM+W4Xy3IOkL3jcJsaPYmImFuLq5mM\nRiNhYWEADBkyhIMHD5KWlsb06dPx9fW9IQWKtAeRoedGTw8mh3H6bAPvf7Ndq55ERFqpxTBjuOxf\nhf7+/qSkpJi1IJH26sLoacrYgdwUYaLw4HH+MmsDHy/ZyclTZy1dnoiI1WrVfWYuuDzciMj15+nu\nxOO/6MOzqTH4ejizPOcgE2fqhnsiIj+mxaXZUVFReHl5NT8+cuQIXl5eNDU1YTAYWLly5Y2osUVa\nmt0+tZfe1Dc08t2G/SxYu5fTZxsIC3DnkZSeBPtZ56qn9tIXW6TeWCf1pfWuemn2okWLrnsxItJ6\n9nZG7hwYzIDevqQv38OGHeX8ZdYGbu0XwAPtYK8nEZHWaDHMBAQE3Kg6RKQFnu5OPPaLPiSdv+He\n8pyDbNhRzoPJYSRG+WvVk4i0az/rmhkRsawLN9x7KDmMM2fP7/U0W6ueRKR9U5gRsTEXRk+Txw4g\nLsJE4aFzq55ma9WTiLRTCjMiNurC6OnZ1Bj8PJ1ZkXOQF95dT2auVj2JSPuiMCNi4yJDPHnlN+dG\nT2frG/ngW42eRKR9UZgRaQOuOHr6UKMnEWkfFGZE2pALo6fnUmPw89LoSUTaB4UZkTao94XR063/\nGT1N0ehJRNoohRmRNsrezsidA86Nnm7uZaLowuhp8U5q6jR6EpG2Q2FGpI3zdHdi/P0XjZ42n9vr\nabVGTyLSRijMiLQTl4+ePjw/etpbdtzSpYmIXBOFGZF25Eqjp79+uJGPNHoSERumMCPSDl0YPT1/\nfvS0UqMnEbFhCjMi7Viv86OnkbeGa/QkIjZLYUaknbO3MzJsQBBTxg3U6ElEbJLCjIgA4OHW4dzo\n6eF+Gj2JiE1RmBGRS/QK9vjP6Knh3Ohp8kebKC7V6ElErJPCjIj8QPPoaexABvT2pbj0OK/O2shH\ni3Zo9CQiVkdhRkR+lIdbBx69L5LnH+6Hv7cLK7ccYuLM9azaclCjJxGxGmYNM1OmTGHUqFGkpqaS\nl5d3yXPz5s1j5MiRpKam8vLLL9N0/hfjggULuO+++xg+fDgrV640Z3ki0kq9gj14+ddxzaOnWYt2\navQkIlbDbGEmOzubkpIS0tPTmTx5MpMnT25+rq6ujoULFzJnzhzmzp1LUVERmzdvpqqqirfeeotP\nPvmEd955h2XLlpmrPBH5mTR6EhFrZbYwk5WVxdChQwEICwvj2LFj1NTUANCxY0dmzZqFg4MDdXV1\n1NTU4OPjQ1ZWFvHx8bi6umIymfjrX/9qrvJE5CpdGD394aLR0wvvZp0bPTVq9CQiN57ZwkxlZSUe\nHh7Njz09PamoqLjke2bOnElKSgrDhg0jMDCQAwcOcOrUKcaPH8/o0aPJysoyV3kico0izo+eRt0W\nTn1jE7MW7eTZN1ezbe9RS5cmIu2M/Y16oaYrXCw4btw40tLSGDt2LP379wegurqa6dOnc+jQIdLS\n0lixYgUGg+FHf66HhzP29nZmq9vHx81sP1uujXpjHR65uxN3DurGB19tY9XmA/x97haiw70Zc2cv\nIkI8LV2eXESfGeukvlw7s4UZk8lEZWVl8+Py8nJ8fHyAc4Fl9+7dxMXF4eTkRFJSEjk5OXh5edGv\nXz/s7e0JCgrCxcWFo0eP4uXl9aOvU1VVa663gI+PGxUVJ8z28+XqqTfW51d39OAXyWG8/+VW8vZU\n8vy0TGLCvXkgqRuBJldLl9fu6TNjndSX1msp9JltzJSYmMjixYsBKCgowGQy4ep67hdafX09EyZM\n4OTJkwDk5+cTGhrKoEGDWL9+PY2NjVRVVVFbW3vJqEpErFt41848M7IvE34ZS/eundiyp5JJ72fz\nzpdbKTtqvn94iEj7ZrYzM7GxsURGRpKamorBYGDSpElkZGTg5uZGSkoKTzzxBGlpadjb29OzZ0+G\nDBmCwWDgjjvuYOTIkQD86U9/wmjUrXBEbE2PwM5M+GUsW4uPkrGqiOzt5WzcUcGgaD/uSwzF093J\n0iWKSBtiaLrSxSw2xJyn53T6z3qpN9bpSn1pampi084KPs8sovRILfZ2BpL7BXBPfAjuLo4WqrT9\n0WfGOqkvrdfSmOmGXQAsIu2TwWDgpggTsT18yCoo48s1xSzdeIDM3FJS4roy7OYgnJ0cLF2miNgw\nhRkRuSGMRgOJUf4M6O3L6txDfLVuL1+vK2H5poPcOTCIof0D6eBovpWJItJ2KcyIyA1lb2fkttiu\nJEb5szznAN9klfDZqiK+23iAe+KDuSUmAAd7XSsnIq2n3xgiYhEdHOy4c0AwU8cncF9iCKfPNvDJ\n0t1MnJlFZu4hGhobLV2iiNgIhRkRsShnJ3t+MbgbU8fHc8fNgRw7eZYPvt3Bn97LJnv7Ye3OLSI/\nSWFGRKyCu7Mjo27rzv88OpDkmC5UVtfxzpcFvPLBBnL3VF7xLuIiIqBrZkTEyni6O5E2LIJhA4L4\nck0x6wsO86/5eYQHdGLELd3oGaQbaYrIpRRmRMQqmTycGXtvJHcODObz1UVs3l3J1E82ExniwfBb\nwgj1d7d0iSJiJRRmRMSqdfVx5bcjoik6dJzPVxdSsLeKgr0bie3hwwODQwnw0b5PIu2dwoyI2IRu\nXdx5NrUf20uqyFhdSM6uCjbvqmBgpC/3DwrF5OFs6RJFxEIUZkTEpvQK9mDiI/3JKzxCxuoisgoO\nk729nMF9u3BvQggebh0sXaKI3GAKMyJicwwGA33DvYkK82LjjnI+X13Eys0HWZtfym2xAdw1MBg3\nZ+37JNJeKMyIiM0yGgzc3MuX/j19WJdfxpdri1mcvZ9VWw5xe1wgd9wcRMcO+jUn0tbpUy4iNs/O\naGRw3y4MjPRj5ZaDLFy3lwVr97Js0wHuig/mttiudHDQvk8ibZVumicibYaDvZGUmwL5n/HxjLil\nG01N8OmKQia8m8WKnAPUN2iLBJG2SGFGRNocJ0d77o4PYepj8dwdH0zd6XpmL9nFxJnrWZtfSmOj\n7iYs0pYozIhIm+Xi5MCIW8KYOj6BoTd1pbrmNP9euJ0/v5/Npp3l2iJBpI3QNTMi0uZ1cnFk9NAe\n3BEXxIK1xazNL+Otz7cS7OfGiKRuRIZ6YjAYLF2miFwlhRkRaTe8Ojnx67t6cefAYL7ILCJ7ezmv\nz8ulR2Bnhid1o0dgZ0uXKCJXQWFGRNodP09nxt/fh7sGnuDz1UXkFh7hf+bkEB3mxQODuxHs52bp\nEkXkZ1CYEZF2K8jXjace6sueg8fIWFVIXuER8gqPcFOEiQcGh+Lv5WLpEkWkFRRmRKTdCw/oxPMP\n92NbSRUZq4rYuKOcTTvLGdDbl7vjQwjwVqgRsWYKMyIinNsiITLEk97BHmzZXcnnmcWsLzjM+oLD\n9O/pwz3xIRo/iVgphRkRkYsYDAb69fAhprs3uXuO8NW6YjbtrGDTzgqiw7y4JyGE8IBOli5TRC6i\nMCMicgUGg4GY7t70Dfdi294qvlq3t/maml7BHtyTEEJEUGct6RaxAgozIiItMBgMRIZ6Ehnqyc59\nVXydVUJB8VG2l1QRHtCJexJCiOqm+9SIWJLCjIhIK/UM8qBnkAdFh47z9bq9bNlTyRuf5hLs68Y9\nCSH06+GNUaFG5IZTmBER+Zm6dXHndw9Gs7+8hoVZe9mwvZy3Ps8nwNuFu+ODietlws6o3WJEbhSz\nftqmTJnCqFGjSE1NJS8v75Ln5s2bx8iRI0lNTeXll1++ZI+UU6dOMXToUDIyMsxZnojINQk0uTL+\n/j68OnYAiX38KD1Sy8yvtvHi/35PZu4h7dItcoOYLcxkZ2dTUlJCeno6kydPZvLkyc3P1dXVsXDh\nQubMmcPcuXMpKipi8+bNzc/PmDGDTp20WkBEbIO/lwv/dU9v/vboQJJjunD0+Ck++HYHL7ybxfKc\nA5ytb7B0iSJtmtnCTFZWFkOHDgUgLCyMY8eOUVNTA0DHjh2ZNWsWDg4O1NXVUVNTg4+PDwCFhYXs\n2bOH5ORkc5UmImIWPp07kjYsgv95NJ6hN3XlRO1ZPl6yiz+8k8Xi7H2cPqNQI2IOZgszlZWVeHh4\nND/29PSkoqLiku+ZOXMmKSkpDBs2jMDAQACmTp3KhAkTzFWWiIjZebo7MXpoD6Y+lsCdA4M4daaB\n9OV7eH7GOr5et5faU/WWLlGkTblhFwBffE3MBePGjSMtLY2xY8fSv39/9u/fT0xMTHOwaQ0PD2fs\n7e2uZ6mX8PHRHT+tlXpjndSX//DxgfAQL8bcHclXmUUsyCwiY3URi7P3cc/gbtw3OAx3F8cbWI96\nY43Ul2tntjBjMpmorKxsflxeXt48Sqqurmb37t3ExcXh5OREUlISOTk5FBQUsH//flauXElZWRmO\njo74+fmRkJDwo69TVVVrrreAj48bFRUnzPbz5eqpN9ZJfflxKbEBDIr0ZXnOAZZs2E/6d7v4YmUh\nt8YGcEdcIJ1cO5j19dUb66S+tF5Loc9sYSYxMZFp06aRmppKQUEBJpMJV1dXAOrr65kwYQILFizA\nxcWF/Px87rvvPsaOHdt8/LRp0wgICGgxyIiI2JKOHey5Oz6EoTcFsnrLIb79voRF3+9j2aYDJEV3\n4c6BQXi6O1m6TBGbY7YwExsbS2RkJKmpqRgMBiZNmkRGRgZubm6kpKTwxBNPkJaWhr29PT179mTI\nkCHmKkVExKp0cLAjJS6Q5H4BrM0v5Zv1JSzLOcDKLQdJjPLjroHBmDycLV2miM0wNF3pYhYbYs7T\nczr9Z73UG+ukvlyd+oZGvt92mK+zSjh8tBaDAQb29uWu+BACvF2uy2uoN9ZJfWk9i4yZRESkdezt\njCRG+RMf6cfGneV8vW4vWQWHWV9wmP49fbgnIYQgX10kKvJjFGZERKyE0Wjg5l6+3BRhIndPJV+t\n3cvGnRVs3FlBdJgX9yaEEBagG4qKXE5hRkTEyhgNBvp19yEm3JuCvUf5au1e8gqPkFd4hF7BHtyb\nEELPoM7aqVvkPIUZERErZTAY6BPqRZ9QL3buq+LrdXsp2FvF9pIqwrt24t6EEPqEeirUSLunMCMi\nYgN6BnnQM8iDwkPHWLiuhC17KvnnvFyC/dy4NyGEmO7eGBVqpJ1SmBERsSFhXTrxuwej2Xf4BF9n\nlbBpRznTM/IJ8Hbh7oRgbo7wxWhUqJH2RWFGRMQGBfm68fgv+nCo8iQLs0r4ftthZi7YxpeZxdwV\nH0x8pB/2dmbbfk/Equg+My3Q+n/rpd5YJ/XFcsqr6/h2fQlr8kppaGzCy92JuwYGMSjaHwd7O/XG\nSqkvrdfSfWYUZlqgP2TWS72xTuqL5R09fopF3+9jVe4hztY30snVkTtvDmL40J7UHK+zdHlyGX1m\nWk9h5irpD5n1Um+sk/piPY6dPMOS7H0s33yQ02ca6NjBjv49TQyK8qd7105aAWUl9JlpPYWZq6Q/\nZNZLvbFO6ov1qak7y/JNB1hbUEZF1bkzM74eHUmM8iehj582trQwfWZaT2HmKukPmfVSb6yT+mK9\nvLxcydy0jzX5pWzaWcHZ+kYMBogM9WRQlD/9unvjYG9n6TLbHX1mWk97M4mItHNGo4HeIZ70DvGk\nNqWe7B2HWZtXytaio2wtOoqLkz0DevsyKNqfYF83jaHEpijMiIi0M85O9iTHBJAcE8ChypOsyS8l\na2sZy3MOsjznIF19XBgU5c/APn64OztaulyRn6QxUwt0+s96qTfWSX2xXj/Vm4bGRvKLjrI2r5Qt\neyppaGzCzmggOsyLQdH+RHXz0n1rzECfmdbTmElERFpkZzQSE+5NTLg3J2rPsL7gMGvyS9m8u5LN\nuytxd3EkIdKPxGh/ArxdLF2uyCUUZkRE5BJuzo6kxAWSEhdISdkJ1uSXsr6gjEXZ+1iUvY9Qf3cG\nRfszoJcJZycHS5crojAjIiI/LtjPjWA/N0beGk7unkrW5JeSX3SE4tLjzF22m9gePgyK8qdXsIf2\nhBKLUZgREZGf5GBv5KYIEzdFmKg6cZqsgjIy80r5ftthvt92GE/3DiT08WdQlB8mD2dLlyvtjC4A\nboEuzLJe6o11Ul+slzl609TUROHB46zJP0T29nJOnWkAoEdgZwZF+XNThA9Ojvo3c0v0mWk9XQAs\nIiLXncFgILxrJ8K7duLhIT3YtKucNXml7NhXza791cxZuou4niYGRWsLBTEvhRkREblmHRztSOjj\nT0Iffyqq61ibX8ra/DLW5JeyJr9UWyiIWWnM1AKd/rNe6o11Ul+slyV609jUxM6SKtbkl7Lx4i0U\nQjwZFK0tFECfmZ9DYyYREbnhjAYDvUI86RXiyS8v3kKh+Chbi89toXBzb18GRfkT4qctFOTqKcyI\niIjZXb6Fwtr8UtZtLWNFzkFW5Bwk4PwWCvGRfri7aAsF+Xk0ZmqBTv9ZL/XGOqkv1ssae9PQ2MjW\noqOsyS9ly+72uYWCNfbFWmnMJCIiVsfOaKRvuDd9L2yhsO3cGKp5CwVnB+L7+DEoyp8AH1dLlytW\nTGFGREQszs3ZkZSbAkm5KZB9h0+wJq+U9dsOszh7P4uz9xPq78agKH8G9PbVFgryA2YdM02ZMoXc\n3FwMBgMTJ04kOjq6+bl58+Yxf/58jEYjERERTJo0CYPBwGuvvcamTZuor6/n0Ucf5fbbb2/xNTRm\nap/UG+ukvlgvW+zN2frGS7ZQaGoCezsjsT28Gdy3y7ktFGz8omFb7IulWGTMlJ2dTUlJCenp6RQW\nFjJx4kTS09MBqKurY+HChcyZMwcHBwfS0tLYvHkzZ86cYffu3aSnp1NVVcUDDzzwk2FGRETapitt\nobAmr5Ts7eVkby/Hy70DiVH+DIryx7tzR0uXKxZktjCTlZXF0KFDAQgLC+PYsWPU1NTg6upKx44d\nmTVrFnAu2NTU1ODj40OXLl2az964u7tTV1dHQ0MDdnbt+z4EIiLtnYdbB+4aGMydA4IoPHScNXmH\n+H57OQvW7mXB2r30CvZgcLQ/sT18cHTQ3xntjdnCTGVlJZGRkc2PPT09qaiowNX1PxdxzZw5k48+\n+oi0tDQCAwMBcHY+t0HZ/PnzSUpK+skg4+HhjL0Zb7rU0mmtlixevJg77rjjJ79v8uTJl7z/yz32\n2GPMmDHjqmpo6662N2Je6ov1aiu9MZnciY/pyqnT9azNO8R32fsoKDrC9pIqXJzsSerXlaE3B9E9\nsLNN3LumrfTFkm7YBcBXujRn3LhxpKWlMXbsWPr370///v0BWLp0KfPnz+f999//yZ9bVVV73Wu9\n4GpnmaWlh8jI+ILY2ISf/N5x434H/Pi1P3/5y2uap16B5szWSX2xXm21N9EhHkSHeHD4aC1r8ktZ\nm1/Kt1l7+TZrL119XBgU3YWBkb64O1vnvWvaal/MwSLXzJhMJiorK5sfl5eX4+PjA0B1dTW7d+8m\nLi4OJycnkpKSyMnJoX///mRmZvLOO+/w3nvv4eZmm2n19densn17AYMHx3H77XdSWnqIN954m7/9\n7S9UVJRTV1fHb34zjsTEwTz55Dh+//s/sGLFMk6erGHfvhIOHjzA7373LPHxidx99xAWLlzGk0+O\nIy5uADk5G6murmbq1H/i7e3NX/7yEmVlpURFRbN8+VI+//wbS799EZEbztfTmRG3hPHA4G5sLT7K\nmrxDbN5dydxlu/l0xR5iunszONqfyFBP7Ixt+9417ZHZwkxiYiLTpk0jNTWVgoICTCZT84ipvr6e\nCRMmsGDBAlxcXMjPz+e+++7jxIkTvPbaa3z44Yd07tz5utQxb/keNuwov6pj7ewMNDT88IxSXISJ\nkbeF/+hxDz88hoyMeYSGhrFv317efvs9qqqOcvPNA7nzzns4ePAAL700gcTEwZccV15+mL///U3W\nr1/Hl19+Rnx84iXPu7i48K9/zWDGjGmsXr2cLl26cubMaWbO/JC1azOZN+//rup9ioi0FcbzN92L\nDvM6d++agsNk5h1i084KNu2soLOrY/NFw76ezpYuV64Ts4WZ2NhYIiMjSU1NxWAwMGnSJDIyMnBz\ncyMlJYUnnniCtLQ07O3t6dmzJ0OGDGHevHlUVVXx9NNPN/+cqVOn0qVLF3OVaXa9ep27bsjNzZ3t\n2wtYsCADg8HI8ePHfvC90dExwLmzWjU1NT94vm/ffs3PHzt2jJKSYqKi+gIQH5+oC6VFRC7i5uxI\nSlwgQ2/qSsnhora2fwAAFCpJREFUE2TmlbK+4DALs0pYmFVC966dGBTtT1yECSdH3XbNlpm1e889\n99wljyMiIpr///Dhwxk+fPglz48aNYpRo0Zd1xpG3hbe4lmUllyPWaaDw7mbO3333SKOHz/OW2+9\nx/Hjx/nv/x7zg++9OIxc6Rqjy59vamrCaDz3NYPBYBMXuomI3GgGg4EQP3dC/NwZdWs4ObsrWJNX\nyra9Vew+cIxPvttNXC8TSdFdCAtw1+9SG6QoagZGo5GGhoZLvlZdXY2/fxeMRiOrVi3n7Nmz1/w6\nAQFdWblyGQDZ2et/8JoiInIpRwc7Bvb2Y2BvPyqr61i79dy9ay78z8/TmcHR/sT38aOzawdLlyut\npKugzCA4OJSdO3dw8uR/RkXJybexbl0mTz31GB07dsRkMvHBB/97Ta+TkDCYkydP8thj/0Vu7mbc\n3Ttda+kiIu2Gd+eO3D8olKmPxfNcagwDe/tSeewUn64s5Lm31vHm/DxydlVQ39Bo6VLlJ2jX7BZY\n+5K548ePkZOzkeTkIVRUlPPUU4/xySefWbqsG8Lae9NeqS/WS71pnZOnzpK97TCZeaXsLTv338vN\n2YH4SD8GR1//DS/Vl9bTrtltlLOzC8uXL+WTT2bT1NTIb3/7e0uXJCJi01ycHLg1tiu3xnZlf3kN\na/JKySooY8mG/SzZsJ9Qf3cG9/Xn5ghfnJ30V6i10JmZFigxWy/1xjqpL9ZLvbl69Q2NbNl96YaX\njvZG+vc0MTjanx5Bna96w0v1pfV0ZkZEROQq2dtduuHluq2lZJ4/Y5NVUIZPZycSo/xJ7OOPVycn\nS5fbLunMTAuUmK2XemOd1Bfrpd5cX01NTew+cIzMvENs2FHOmbONGIDeoZ4MjvanX3dvHFqxb6D6\n0no6MyMiInIdGQwGegR2pkdgZ0YP7cGGHeWsySuloPgoBcVHcXGyZ2BvPwb39SfI1za35rElCjMi\nIiLXoGMHe5L6diGpbxdKj5xkTV4pa7eWsSznAMtyDhDk68rg6C4M6O2La0cHS5fbJuk+Mxb04IP3\nUltby+zZH7J1a94lz9XW1vLgg/e2ePyFG+Z9881XrFq1wmx1iohI6/h7ufDQreH8/fEEfjcimn7d\nvTlYcZI53+3i99PXMOOLrWwtOkJjo01f4WF1dGbGCowZ8/9+9jGlpYdYunQxyclDuOuulkOPiIjc\nWPZ2RmK6exPT3ZtjJ8+QtbWs+fqaDTvK8XDrQGKUP/cnh6Nd9a6dwowZ/OY3v2TKlH/g5+dHWVkp\nL7zwLD4+Jurq6jh16hTPPPM8vXv3af7+yZNfJjl5CDEx/XjxxT9w5syZ5k0nAZYs+Zb589OxszMS\nEhLGH//4Iq+/PpXt2wv44IP/pbGxkc6dOzNixCjefvtf5OfnUl/fwIgRIxk27G6efHIccXEDyMnZ\nSHV1NVOn/hM/Pz9L/KcREWl3Ork4MmxAEHfcHEhR6XHW5JXy/bbDfL1uL1+v20uInxuxPXyI7eFD\nF28XS5drk9p8mMnY8zWby/Ov6lg7o4GGK5wK7GeKYnj4PT96XFLSraxdu5oRI0aSmbmKpKRbCQvr\nTlJSMps2bWDOnFlMnvz//eC4xYu/pVu3MH73u2dZtmwJS5cuBqCuro5//GMabm5uPPHEWAoL9/Dw\nw2PIyJjHr389ln//+10AtmzJoaiokBkz3qeuro5f/SqVpKRkAFxcXPjXv2YwY8Y0Vq9ezsiRo6/q\nv4mIiFwdg8FAWJdOhHXpROpt3dm0q5yNOyvJL6xkb9kJMlYX4efp3BxsQv3dtOllK7X5MGMJSUm3\nMn36G4wYMZI1a1bx5JPPMHfubP7v/2Zz9uxZnJyufB+CvXuLiInpD0C/fv2bv+7u7s4LLzwLQElJ\nMceOVV/x+B07thETEwtAx44dCQnpxv79+wHo27cfACaTiWPHjl2fNyoiIlelg6MdCX38uf/WHuzd\nf5TcPZXk7Kpka9ERvllfwjfrS/Bw60Bsdx9ie3jTI6gzdkZd5vpj2nyYGR5+T4tnUVpytev/u3UL\n48iRCg4fLuPEiRNkZq7E29vESy/9lR07tjF9+htXPK6pCYzGcyn8wsVhZ8+e5fXXX+PDDz/By8ub\nP/zh6R99XYPBwMV3DaqvP9v88+zs/jOVtfFbC4mItCkuTg4k9PEnoY8/p882UFB8lJxdFeTuqWxe\nEeXiZE9MuDexPXyIDPXE0UFX2lyszYcZS4mPH8TMmW8zePAtVFdXERbWHYBVq1ZQX19/xWOCgoLZ\nsWM7yclDyMnZCEBt7Uns7Ozw8vLm8OEyduzYTn19PY6OjjQ0NFxyfEREJLNm/ZsxY/4ftbW1HDx4\ngK5dg8z7RkVE5Lrp4GDXPGaqb2hk1/5qNu2qYPOuCtZuLWPt1jIcHYxEhXoR28OHvuFeODtpubfC\njJnccsutjB//Gz788P84daqOV1+dxIoVSxkxYiRLly5h4cIFPzhm2LC7mTjxOZ566jGio2MwGAx0\n6tSZuLgB/Pd/pxEe3p3Ro8fw5puvM23au+zcuYM33/wHLi7ndnHt2zeGnj0jeOKJsdTX1zN+/JN0\n7NjxRr91ERG5DuztjPQO8aR3iCe/TOlBcelxcnZVkLOrkk27Kti0qwI7o4GIYA9ie/jQr7s3nV07\nWLpsi9B2Bi3Qbaatl3pjndQX66XeWKer6UtTUxOHjtSeDzYVlJSdO94AdAtwbz6z4+vhbIaKLUfb\nGYiIiLQRBoOBAG8XArxduDchhMpjdWzeVUnOrgp2Haim8OBxPl1RSICPC7Hdfejf04dAk2ubXhml\nMzMt0L9krJd6Y53UF+ul3lin692X47VnyN19LtgU7K2ivqERAO9OTs1nbMIDOjUvDrElOjMjIiLS\nDrg7OzK4bxcG9+1C3el6tp5fGZVXWMmSDftZsmE/7s4OxHQ/tzKqV7AnDva2v+RbYUZERKQN6tjB\nnrgIE3ERJs7WN7K9pIqcXRVs2V3B6txSVueW4uRoR3TYuZVRUd286NjBNmOBbVYtIiIireZgbyQ6\nzIvoMC8a7+jJnoPHmi8gzt5eTvb28vOrp86tjIrp7o27s6Oly241hRkREZF2xGg00COwMz0COzPq\ntnD2l9c0L/nOKzxCXuERDIuge9fO56+z8ca7k3Xf5kNhxkxWrlxGcvKQVn//li05BAeH4OHhacaq\nRERE/sNgMBDk60aQrxu/GNyN8qpacs6vjNq9v5pd+6uZu2w3wb5uxPbwbt4M09pWRinMmEFp6SGW\nLl38s8LMwoULePjhRxRmRETEYkwezgwbEMSwAUEcqznN5vMro7aXVFFy+ASfZxbj69HxP5thdnHH\naAXBRmHGDF5/fSrbtxfw/vszKSraw4kTJ2hoaODpp58nPLw7H3/8IatWrcBoNJKYOJhevXqTmbmS\n4uIiXn31Nfz8/Cz9FkREpJ3r5NqB5H4BJPcLoPbUWfIKj7BpVwX5RUf49vt9fPv9Pjq7OtKvuw+x\nPX3oGdgZezvLrIxq82Gm4tO5nNi44aqOLbEz0nB+jf7F3G6Kw+eh1B897uGHx5CRMQ+j0ciAAQnc\ne+8vKC4u4l//+jtvvPE2c+d+zBdfLMLOzo4vvviMuLiBhIf34Pe//4OCjIiIWB1nJwcGRvoxMNKP\nM2cbKNh79PzKqEpWbD7Iis0HcXGy55HbezKgt+8Nr8+sYWbKlCnk5uZiMBiYOHEi0dHRzc/NmzeP\n+fPnYzQaiYiIYNKkSRgMhhaPsTX5+XlUV1exePE3AJw+fQqA5OQhPP3046SkDOP224dZskQREZGf\nxdHBjn7dfejX3YeGxkZ27T+3Mmpr8VGOnzxjkZrMFmays7MpKSkhPT2dwsJCJk6cSHp6OgB1dXUs\nXLiQOXPm4ODgQFpaGps3b6a+vv5Hj7laPg+ltngWpcVjr/HOjA4O9jzzzPP06XNpIHvuuRcoKdnL\n8uXf8dvfPsrMmbOu+jVEREQsxc5opFewB72CPSxah9mGW1lZWQwdOhSAsLAwjh07Rk1NDQAdO3Zk\n1qxZODg4UFdXR01NDT4+Pi0eY0uMRiMNDQ307t2H1atXAlBcXMTcuR9TU1PDBx/8L8HBIfz612Nx\nc+tEbe3J5mNERETk5zHbmZnKykoiIyObH3t6elJRUYGrq2vz12bOnMlHH31EWloagYGBrTrmch4e\nztjb25nnTdDyXhA/pn//KCZP3kW3biFUVpbx1FOP0tjYyIsvvkhoqD+nT5/kscd+jbOzM3FxsYSF\ndSUxMZ5Jk17g7bffpnv37mZ4J23P1fRGzE99sV7qjXVSX67dDbsA+Er7WY4bN460tDTGjh1L//79\nW3XM5aqqaq9LfVdy9WMmBz799KsrPlNRcYLx45/+wddGjfoVo0b9qvmxtEyb5lkn9cV6qTfWSX1p\nvZZCn9nGTCaTicrKyubH5eXl+Pj4AFBdXc2GDedWGDk5OZGUlEROTk6Lx4iIiIhcidnCTGJiIosX\nLwagoKAAk8nUPC6qr69nwoQJnDx5EoD8/HxCQ0NbPEZERETkSsw2ZoqNjSUyMpLU1FQMBgOTJk0i\nIyMDNzc3UlJSeOKJJ0hLS8Pe3p6ePXsyZMgQDAbDD44RERERaYmhqTUXplgxc84aNcu0XuqNdVJf\nrJd6Y53Ul9azyDUzIiIiIjeCwoyIiIjYNIUZERERsWkKMyIiImLTFGZERETEpinMiIiIiE2z+aXZ\nIiIi0r7pzIyIiIjYNIUZERERsWkKMyIiImLTFGZERETEpinMiIiIiE1TmBERERGbpjBzBVOmTGHU\nqFGkpqaSl5dn6XLkIq+99hqjRo1ixIgRLFmyxNLlyGVOnTrF0KFDycjIsHQpcpEFCxZw3333MXz4\ncFauXGnpcgQ4efIkTz75JGPGjCE1NZXMzExLl2TT7C1dgLXJzs6mpKSE9PR0CgsLmThxIunp6ZYu\nS4D169eze/du0tPTqaqq4oEHHuD222+3dFlykRkzZtCpUydLlyEXqaqq4q233uKzzz6jtraWadOm\nkZycbOmy2r3PP/+c0NBQnn32WQ4fPsyvfvUrFi1aZOmybJbCzGWysrIYOnQoAGFhYRw7doyamhpc\nXV0tXJnExcURHR0NgLu7O3V1dTQ0NGBnZ2fhygSgsLCQPXv26C9KK5OVlUV8fDyurq64urry17/+\n1dIlCeDh4cHOnTsBOH78OB4eHhauyLZpzHSZysrKS/5QeXp6UlFRYcGK5AI7OzucnZ0BmD9/PklJ\nSQoyVmTq1KlMmDDB0mXIZQ4cOMCpU6cYP348o0ePJisry9IlCXD33Xdz6NAhUlJSeOSRR/jjH/9o\n6ZJsms7M/ATt9mB9li5dyvz583n//fctXYqc98UXXxATE0NgYKClS5ErqK6uZvr06Rw6dIi0tDRW\nrFiBwWCwdFnt2pdffkmXLl3497//zY4dO5g4caKuNbsGCjOXMZlMVFZWNj8uLy/Hx8fHghXJxTIz\nM3nnnXd47733cHNzs3Q5ct7KlSvZv38/K1eupKysDEdHR/z8/EhISLB0ae2el5cX/fr1w97enqCg\nIFxcXDh69CheXl6WLq1dy8nJYdCgQQBERERQXl6usfk10JjpMomJiSxevBiAgoICTCaTrpexEidO\nnOC1117j3XffpXPnzpYuRy7yxhtv8NlnnzFv3jweeughHn/8cQUZKzFo0CDWr19PY2MjVVVV1NbW\n6voMKxAcHExubi4ABw8exMXFRUHmGujMzGViY2OJjIwkNTUVg8HApEmTLF2SnPfNN99QVVXF008/\n3fy1qVOn0qVLFwtWJWLdfH19ueOOOxg5ciQAf/rTnzAa9e9YSxs1ahQTJ07kkUceob6+npdfftnS\nJdk0Q5MuChEREREbpnguIiIiNk1hRkRERGyawoyIiIjYNIUZERERsWkKMyIiImLTFGZE5IY5cOAA\nffr0YcyYMc27BT/77LMcP3681T9jzJgxNDQ0tPr7H374Yb7//vurKVdEbITCjIjcUJ6ensyePZvZ\ns2czd+5cTCYTM2bMaPXxs2fP1s3FROQSummeiFhUXFwc6enp7Nixg6lTp1JfX8/Zs2f585//TO/e\nvRkzZgwRERFs376dWbNm0bt3bwoKCjhz5gwvvfQSZWVl1NfXc//99zN69Gjq6up45plnqKqqIjg4\nmNOnTwNw+PBhnnvuOQBOnTrFqFGjePDBBy351kXkOlGYERGLaWho4LvvvqN///48//zzvPXWWwQF\nBf1g4z1nZ2c+/vjjS46dPXs27u7u/OMf/+DUqVPcddddDB48mHXr1uHk5ER6ejrl5eUMGTIEgG+/\n/ZZu3brxyiuvcPr0aT799NMb/n5FxDwUZkTkhjp69ChjxowBoLGxkZtuuokRI0bw5ptv8uKLLzZ/\nX01NDY2NjcC5bUYul5uby/DhwwFwcnKiT58+FBQUsGvXLvr37w+c2zi2W7duAAwePJhPPvmECRMm\ncMsttzBq1Cizvk8RuXEUZkTkhrpwzczFTpw4gYODww++foGDg8MPvmYwGC553NTUhMFgoKmp6ZK9\nhy4EorCwMBYuXMiGDRtYtGgRs2bNYu7cudf6dkTECugCYBGxODc3N7p27cqqVasAKC4uZvr06S0e\n07dvXzIzMwGora2loKCAyMhIwsLC2Lx5MwClpaUUFxcD8NVXX5Gfn09CQgKTJk2itLSU+vp6M74r\nEblRdGZGRKzC1KlTefXVV5k5cyb19fVMmDChxe8fM2YML730Er/85S85c+YMjz/+OF27duX+++9n\n+fLljB49mq5duxIVFQVAeHg4kyZNwtHRkaamJsaOHYu9vX4FirQF2jVbREREbJrGTCIiImLTFGZE\nRETEpinMiIiIiE1TmBERERGbpjAjIiIiNk1hRkRERGyawoyIiIjYNIUZERERsWn/P22WOQRIkEyn\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f440b3793c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_nbSu-zwf40w",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Test out neural network classifier"
      ]
    },
    {
      "metadata": {
        "id": "mnLV4TqlgCHM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "nn_classifier = train_nn_classifier_model(\n",
        "    learning_rate=0.00025,\n",
        "    steps=6000,\n",
        "    batch_size=90,\n",
        "    hidden_units=[10, 10, 5, 5],\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets,\n",
        "    test_examples=test_examples,\n",
        "    test_targets=test_targets)\n",
        "\n",
        "evaluation_metrics = nn_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
        "\n",
        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "F9EhQP2UWISD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "d3ce166d-c163-411e-b2a2-51a3e0453b0b"
      },
      "cell_type": "code",
      "source": [
        "validation_probabilities = nn_classifier.predict(input_fn=predict_validation_input_fn)\n",
        "# Get just the probabilities for the positive class.\n",
        "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n",
        "\n",
        "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n",
        "    validation_targets, validation_probabilities)\n",
        "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n",
        "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n",
        "_ = plt.legend(loc=2)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2VLHs0NvsKdqHn9WPOUPvZ0LMLZ0y/ZpFJSwibdaW6fNC374M39XQNOubdhV8Pf1WOatJ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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd9d8e1b278>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "h7dmgQlhWi9I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}