38 lines
880 B
Python
38 lines
880 B
Python
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"""
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Common utilities for testing clustering.
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"""
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import numpy as np
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###############################################################################
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# Generate sample data
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def generate_clustered_data(
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seed=0, n_clusters=3, n_features=2, n_samples_per_cluster=20, std=0.4
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):
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prng = np.random.RandomState(seed)
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# the data is voluntary shifted away from zero to check clustering
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# algorithm robustness with regards to non centered data
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means = (
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np.array(
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[
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[1, 1, 1, 0],
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[-1, -1, 0, 1],
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[1, -1, 1, 1],
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[-1, 1, 1, 0],
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]
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)
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+ 10
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)
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X = np.empty((0, n_features))
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for i in range(n_clusters):
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X = np.r_[
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X,
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means[i][:n_features] + std * prng.randn(n_samples_per_cluster, n_features),
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]
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return X
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