<commit_before>"""Testing for K-means"""
from cStringIO import StringIO
import sys
import warnings

import numpy as np
from scipy import sparse as sp

from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true

from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_less
from sklearn.utils.fixes import unique
from sklearn.metrics.cluster import v_measure_score
from sklearn.cluster import KMeans, k_means
from sklearn.cluster import MiniBatchKMeans
from sklearn.cluster.k_means_ import _labels_inertia
from sklearn.cluster.k_means_ import _mini_batch_step
from sklearn.cluster._k_means import csr_row_norm_l2
from sklearn.datasets.samples_generator import make_blobs


# non centered, sparse centers to check the
centers = np.array([
    [0.0, 5.0, 0.0, 0.0, 0.0],
    [1.0, 1.0, 4.0, 0.0, 0.0],
    [1.0, 0.0, 0.0, 5.0, 1.0],
])
n_samples = 100
n_clusters, n_features = centers.shape
X, true_labels = make_blobs(n_samples=n_samples, centers=centers,
                            cluster_std=1., random_state=42)
X_csr = sp.csr_matrix(X)


def test_square_norms():
    x_squared_norms = (X ** 2).sum(axis=1)
    x_squared_norms_from_csr = csr_row_norm_l2(X_csr)<commit_msg>PY3: six.moves.cStringIO to fix StringIO import<commit_after>"""Testing for K-means"""
import sys
import warnings

import numpy as np
from scipy import sparse as sp

from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true

from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_less
from sklearn.utils.fixes import unique
from sklearn.metrics.cluster import v_measure_score
from sklearn.cluster import KMeans, k_means
from sklearn.cluster import MiniBatchKMeans
from sklearn.cluster.k_means_ import _labels_inertia
from sklearn.cluster.k_means_ import _mini_batch_step
from sklearn.cluster._k_means import csr_row_norm_l2
from sklearn.datasets.samples_generator import make_blobs
from sklearn.externals.six.moves import cStringIO as StringIO


# non centered, sparse centers to check the
centers = np.array([
    [0.0, 5.0, 0.0, 0.0, 0.0],
    [1.0, 1.0, 4.0, 0.0, 0.0],
    [1.0, 0.0, 0.0, 5.0, 1.0],
])
n_samples = 100
n_clusters, n_features = centers.shape
X, true_labels = make_blobs(n_samples=n_samples, centers=centers,
                            cluster_std=1., random_state=42)
X_csr = sp.csr_matrix(X)


def test_square_norms():
    x_squared_norms = (X ** 2).sum(axis=1)
    x_squared_norms_from_csr = csr_row_norm_l2(X_csr)
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{'dir': 'git-commits-cleaned', 'id': '7643796', 'n_tokens_mistral': 1112, 'n_tokens_neox': 1041, 'n_words': 274}