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        "\n# Nearest Centroid Classification\n\nSample usage of Nearest Centroid classification.\nIt will plot the decision boundaries for each class.\n"
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        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.colors import ListedColormap\n\nfrom sklearn import datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.neighbors import NearestCentroid\n\n# import some data to play with\niris = datasets.load_iris()\n# we only take the first two features. We could avoid this ugly\n# slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\n\n# Create color maps\ncmap_light = ListedColormap([\"orange\", \"cyan\", \"cornflowerblue\"])\ncmap_bold = ListedColormap([\"darkorange\", \"c\", \"darkblue\"])\n\nfor shrinkage in [None, 0.2]:\n    # we create an instance of Nearest Centroid Classifier and fit the data.\n    clf = NearestCentroid(shrink_threshold=shrinkage)\n    clf.fit(X, y)\n    y_pred = clf.predict(X)\n    print(shrinkage, np.mean(y == y_pred))\n\n    _, ax = plt.subplots()\n    DecisionBoundaryDisplay.from_estimator(\n        clf, X, cmap=cmap_light, ax=ax, response_method=\"predict\"\n    )\n\n    # Plot also the training points\n    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor=\"k\", s=20)\n    plt.title(\"3-Class classification (shrink_threshold=%r)\" % shrinkage)\n    plt.axis(\"tight\")\n\nplt.show()"
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