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31 lines
1013 B
Python
31 lines
1013 B
Python
#
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# Generate sample data for the DBSCAN test
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#
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# Lifted from http://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py
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#
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import numpy as np
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from sklearn.cluster import DBSCAN
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from sklearn import metrics
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from sklearn.datasets.samples_generator import make_blobs
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from sklearn.preprocessing import StandardScaler
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centers = [[1, 1], [-1, -1], [1, -1]]
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X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
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random_state=0)
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X = StandardScaler().fit_transform(X)
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X = X.astype(np.float64)
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db = DBSCAN(eps=0.3, min_samples=10, metric='l2', algorithm='brute').fit(X)
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core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
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core_samples_mask[db.core_sample_indices_] = True
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labels = db.labels_
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with open('dbscan.csv', 'w') as fscanout:
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with open('dbscan_labels.csv', 'w') as fscanlabout:
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for i in range(750):
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fscanout.write(",".join([str(x) for x in X[i,:]]) + "\n")
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fscanlabout.write(str(labels[i]) + "\n")
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