def predict(self, X):
"""Predict the closest cluster each sample in X belongs to.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
New data to predict.
Returns
-------
labels : array, shape [n_samples,]
Index of the cluster each sample belongs to.
"""
self._check_fitted()
X = self._check_test_data(X)
return _compute_labels_and_score(X, self.cluster_centers_)[0]
def score(self, X):
"""Opposite of the value of X on the K-means objective.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
New data.
Returns
-------
score : float
Opposite of the value of X on the K-means objective.
"""
self._check_fitted()
X = self._check_test_data(X)
return -_compute_labels_and_score(X, self.cluster_centers_)[1]
def test_labels_assignment_and_score():
# pure numpy implementation as easily auditable reference gold
# implementation
rng = np.random.RandomState(42)
noisy_centers = centers + rng.normal(size=centers.shape)
labels_gold = - np.ones(n_samples, dtype=np.int)
mindist = np.empty(n_samples)
mindist.fill(np.infty)
for center_id in range(n_clusters):
dist = np.sqrt(np.sum((X - noisy_centers[center_id]) ** 2, axis=1))
labels_gold[dist < mindist] = center_id
mindist = np.minimum(dist, mindist)
score_gold = mindist.sum()
assert_true((mindist >= 0.0).all())
assert_true((labels_gold != -1).all())
# perform label assignment using the dense array input
labels_array, score_array = _compute_labels_and_score(X, noisy_centers)
assert_array_almost_equal(score_array, score_gold)
assert_array_equal(labels_array, labels_gold)