29 lines
877 B
Python
29 lines
877 B
Python
from sklearn import datasets
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from sklearn.cluster import KMeans
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from sklearn import metrics
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iris = datasets.load_iris()
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# print 150 samples
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print(iris.target)
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# clusters=3, centroiden zufällig wählen, n_init=50
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kmeans = KMeans(n_clusters=3, init='random', n_init=50)
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# fit auf daten
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kmeans.fit(iris.data)
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# print alle daten
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#print(list(zip(iris.target, kmeans.labels_)))
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# gegenüberstellung
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print("gold standard vs. prediction")
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for target_label, predicted_label in zip(iris.target, kmeans.labels_):
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print(f'{target_label} vs. {predicted_label}')
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print(metrics.homogeneity_score(iris.target, kmeans.labels_))
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print(metrics.completeness_score(iris.target, kmeans.labels_))
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print(metrics.adjusted_rand_score(iris.target, kmeans.labels_))
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print(metrics.silhouette_score(iris.data, kmeans.labels_))
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# erkenntnis, der Algo ist nicht perfekt für diese Art von Daten!!
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