48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
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"""
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===============================
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Nearest Centroid Classification
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===============================
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Sample usage of Nearest Centroid classification.
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It will plot the decision boundaries for each class.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.colors import ListedColormap
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from sklearn import datasets
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.neighbors import NearestCentroid
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# import some data to play with
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iris = datasets.load_iris()
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# we only take the first two features. We could avoid this ugly
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# slicing by using a two-dim dataset
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X = iris.data[:, :2]
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y = iris.target
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# Create color maps
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cmap_light = ListedColormap(["orange", "cyan", "cornflowerblue"])
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cmap_bold = ListedColormap(["darkorange", "c", "darkblue"])
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for shrinkage in [None, 0.2]:
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# we create an instance of Nearest Centroid Classifier and fit the data.
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clf = NearestCentroid(shrink_threshold=shrinkage)
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clf.fit(X, y)
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y_pred = clf.predict(X)
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print(shrinkage, np.mean(y == y_pred))
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_, ax = plt.subplots()
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DecisionBoundaryDisplay.from_estimator(
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clf, X, cmap=cmap_light, ax=ax, response_method="predict"
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)
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor="k", s=20)
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plt.title("3-Class classification (shrink_threshold=%r)" % shrinkage)
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plt.axis("tight")
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plt.show()
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