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"""
Testing for Clustering methods
"""
import numpy as np
from numpy.testing import assert_equal, assert_array_equal
from ..affinity_propagation_ import AffinityPropagation, \
affinity_propagation
from ...datasets.samples_generator import make_blobs
n_clusters = 3
centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
X, _ = make_blobs(n_samples=60, n_features=2, centers=centers,
cluster_std=0.4, shuffle=True, random_state=0)
def test_affinity_propagation():
"""Affinity Propagation algorithm
"""
# Compute similarities
X_norms = np.sum(X ** 2, axis=1)
S = - X_norms[:, np.newaxis] - X_norms[np.newaxis, :] + 2 * np.dot(X, X.T)
p = 10 * np.median(S)
# Compute Affinity Propagation
cluster_centers_indices, labels = affinity_propagation(S, p)
n_clusters_ = len(cluster_centers_indices)
assert_equal(n_clusters, n_clusters_)
af = AffinityPropagation()
labels = af.fit(S, p).labels_
cluster_centers_indices = af.cluster_centers_indices_
n_clusters_ = len(cluster_centers_indices)
assert_equal(np.unique(labels).size, n_clusters_)
assert_equal(n_clusters, n_clusters_)
# Test also with no copy
_, labels_no_copy = affinity_propagation(S, p, copy=False)
assert_array_equal(labels, labels_no_copy)
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