Fixed database typo and removed unnecessary class identifier.
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00ad49a143
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5098 changed files with 952558 additions and 85 deletions
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venv/Lib/site-packages/scipy/cluster/tests/__init__.py
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venv/Lib/site-packages/scipy/cluster/tests/__init__.py
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from numpy import array
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Q_X = array([[5.26563660e-01, 3.14160190e-01, 8.00656370e-02],
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[7.50205180e-01, 4.60299830e-01, 8.98696460e-01],
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[6.65461230e-01, 6.94011420e-01, 9.10465700e-01],
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[9.64047590e-01, 1.43082200e-03, 7.39874220e-01],
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[1.08159060e-01, 5.53028790e-01, 6.63804780e-02],
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[9.31359130e-01, 8.25424910e-01, 9.52315440e-01],
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[6.78086960e-01, 3.41903970e-01, 5.61481950e-01],
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[9.82730940e-01, 7.04605210e-01, 8.70978630e-02],
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[6.14691610e-01, 4.69989230e-02, 6.02406450e-01],
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[5.80161260e-01, 9.17354970e-01, 5.88163850e-01],
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[1.38246310e+00, 1.96358160e+00, 1.94437880e+00],
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[2.10675860e+00, 1.67148730e+00, 1.34854480e+00],
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[1.39880070e+00, 1.66142050e+00, 1.32224550e+00],
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[1.71410460e+00, 1.49176380e+00, 1.45432170e+00],
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[1.54102340e+00, 1.84374950e+00, 1.64658950e+00],
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[2.08512480e+00, 1.84524350e+00, 2.17340850e+00],
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[1.30748740e+00, 1.53801650e+00, 2.16007740e+00],
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[1.41447700e+00, 1.99329070e+00, 1.99107420e+00],
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[1.61943490e+00, 1.47703280e+00, 1.89788160e+00],
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[1.59880600e+00, 1.54988980e+00, 1.57563350e+00],
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[3.37247380e+00, 2.69635310e+00, 3.39981700e+00],
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[3.13705120e+00, 3.36528090e+00, 3.06089070e+00],
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[3.29413250e+00, 3.19619500e+00, 2.90700170e+00],
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[2.65510510e+00, 3.06785900e+00, 2.97198540e+00],
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[3.30941040e+00, 2.59283970e+00, 2.57714110e+00],
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[2.59557220e+00, 3.33477370e+00, 3.08793190e+00],
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[2.58206180e+00, 3.41615670e+00, 3.26441990e+00],
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[2.71127000e+00, 2.77032450e+00, 2.63466500e+00],
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[2.79617850e+00, 3.25473720e+00, 3.41801560e+00],
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[2.64741750e+00, 2.54538040e+00, 3.25354110e+00]])
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ytdist = array([662., 877., 255., 412., 996., 295., 468., 268., 400., 754.,
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564., 138., 219., 869., 669.])
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linkage_ytdist_single = array([[2., 5., 138., 2.],
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[3., 4., 219., 2.],
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[0., 7., 255., 3.],
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[1., 8., 268., 4.],
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[6., 9., 295., 6.]])
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linkage_ytdist_complete = array([[2., 5., 138., 2.],
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[3., 4., 219., 2.],
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[1., 6., 400., 3.],
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[0., 7., 412., 3.],
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[8., 9., 996., 6.]])
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linkage_ytdist_average = array([[2., 5., 138., 2.],
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[3., 4., 219., 2.],
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[0., 7., 333.5, 3.],
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[1., 6., 347.5, 3.],
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[8., 9., 680.77777778, 6.]])
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linkage_ytdist_weighted = array([[2., 5., 138., 2.],
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[3., 4., 219., 2.],
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[0., 7., 333.5, 3.],
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[1., 6., 347.5, 3.],
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[8., 9., 670.125, 6.]])
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# the optimal leaf ordering of linkage_ytdist_single
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linkage_ytdist_single_olo = array([[5., 2., 138., 2.],
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[4., 3., 219., 2.],
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[7., 0., 255., 3.],
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[1., 8., 268., 4.],
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[6., 9., 295., 6.]])
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X = array([[1.43054825, -7.5693489],
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[6.95887839, 6.82293382],
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[2.87137846, -9.68248579],
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[7.87974764, -6.05485803],
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[8.24018364, -6.09495602],
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[7.39020262, 8.54004355]])
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linkage_X_centroid = array([[3., 4., 0.36265956, 2.],
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[1., 5., 1.77045373, 2.],
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[0., 2., 2.55760419, 2.],
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[6., 8., 6.43614494, 4.],
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[7., 9., 15.17363237, 6.]])
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linkage_X_median = array([[3., 4., 0.36265956, 2.],
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[1., 5., 1.77045373, 2.],
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[0., 2., 2.55760419, 2.],
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[6., 8., 6.43614494, 4.],
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[7., 9., 15.17363237, 6.]])
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linkage_X_ward = array([[3., 4., 0.36265956, 2.],
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[1., 5., 1.77045373, 2.],
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[0., 2., 2.55760419, 2.],
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[6., 8., 9.10208346, 4.],
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[7., 9., 24.7784379, 6.]])
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# the optimal leaf ordering of linkage_X_ward
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linkage_X_ward_olo = array([[4., 3., 0.36265956, 2.],
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[5., 1., 1.77045373, 2.],
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[2., 0., 2.55760419, 2.],
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[6., 8., 9.10208346, 4.],
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[7., 9., 24.7784379, 6.]])
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inconsistent_ytdist = {
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1: array([[138., 0., 1., 0.],
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[219., 0., 1., 0.],
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[255., 0., 1., 0.],
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[268., 0., 1., 0.],
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[295., 0., 1., 0.]]),
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2: array([[138., 0., 1., 0.],
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[219., 0., 1., 0.],
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[237., 25.45584412, 2., 0.70710678],
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[261.5, 9.19238816, 2., 0.70710678],
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[233.66666667, 83.9424406, 3., 0.7306594]]),
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3: array([[138., 0., 1., 0.],
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[219., 0., 1., 0.],
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[237., 25.45584412, 2., 0.70710678],
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[247.33333333, 25.38372182, 3., 0.81417007],
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[239., 69.36377537, 4., 0.80733783]]),
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4: array([[138., 0., 1., 0.],
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[219., 0., 1., 0.],
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[237., 25.45584412, 2., 0.70710678],
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[247.33333333, 25.38372182, 3., 0.81417007],
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[235., 60.73302232, 5., 0.98793042]])}
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fcluster_inconsistent = {
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0.8: array([6, 2, 2, 4, 6, 2, 3, 7, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1]),
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1.0: array([6, 2, 2, 4, 6, 2, 3, 7, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1]),
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2.0: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1])}
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fcluster_distance = {
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0.6: array([4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 6, 6, 6, 6, 6, 7, 6, 6, 6, 6, 3,
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1, 1, 1, 2, 1, 1, 1, 1, 1]),
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1.0: array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1]),
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2.0: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1])}
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fcluster_maxclust = {
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8.0: array([5, 5, 5, 5, 5, 5, 5, 6, 5, 5, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 4,
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1, 1, 1, 3, 1, 1, 1, 1, 2]),
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4.0: array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2,
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1, 1, 1, 1, 1, 1, 1, 1, 1]),
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1.0: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1])}
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1085
venv/Lib/site-packages/scipy/cluster/tests/test_hierarchy.py
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1085
venv/Lib/site-packages/scipy/cluster/tests/test_hierarchy.py
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venv/Lib/site-packages/scipy/cluster/tests/test_vq.py
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venv/Lib/site-packages/scipy/cluster/tests/test_vq.py
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import warnings
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import sys
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import numpy as np
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from numpy.testing import (assert_array_equal, assert_array_almost_equal,
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assert_allclose, assert_equal, assert_,
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suppress_warnings)
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import pytest
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from pytest import raises as assert_raises
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from scipy.cluster.vq import (kmeans, kmeans2, py_vq, vq, whiten,
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ClusterError, _krandinit)
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from scipy.cluster import _vq
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from scipy.sparse.sputils import matrix
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TESTDATA_2D = np.array([
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-2.2, 1.17, -1.63, 1.69, -2.04, 4.38, -3.09, 0.95, -1.7, 4.79, -1.68, 0.68,
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-2.26, 3.34, -2.29, 2.55, -1.72, -0.72, -1.99, 2.34, -2.75, 3.43, -2.45,
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2.41, -4.26, 3.65, -1.57, 1.87, -1.96, 4.03, -3.01, 3.86, -2.53, 1.28,
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-4.0, 3.95, -1.62, 1.25, -3.42, 3.17, -1.17, 0.12, -3.03, -0.27, -2.07,
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-0.55, -1.17, 1.34, -2.82, 3.08, -2.44, 0.24, -1.71, 2.48, -5.23, 4.29,
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-2.08, 3.69, -1.89, 3.62, -2.09, 0.26, -0.92, 1.07, -2.25, 0.88, -2.25,
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2.02, -4.31, 3.86, -2.03, 3.42, -2.76, 0.3, -2.48, -0.29, -3.42, 3.21,
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-2.3, 1.73, -2.84, 0.69, -1.81, 2.48, -5.24, 4.52, -2.8, 1.31, -1.67,
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-2.34, -1.18, 2.17, -2.17, 2.82, -1.85, 2.25, -2.45, 1.86, -6.79, 3.94,
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-2.33, 1.89, -1.55, 2.08, -1.36, 0.93, -2.51, 2.74, -2.39, 3.92, -3.33,
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2.99, -2.06, -0.9, -2.83, 3.35, -2.59, 3.05, -2.36, 1.85, -1.69, 1.8,
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-1.39, 0.66, -2.06, 0.38, -1.47, 0.44, -4.68, 3.77, -5.58, 3.44, -2.29,
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2.24, -1.04, -0.38, -1.85, 4.23, -2.88, 0.73, -2.59, 1.39, -1.34, 1.75,
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-1.95, 1.3, -2.45, 3.09, -1.99, 3.41, -5.55, 5.21, -1.73, 2.52, -2.17,
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0.85, -2.06, 0.49, -2.54, 2.07, -2.03, 1.3, -3.23, 3.09, -1.55, 1.44,
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-0.81, 1.1, -2.99, 2.92, -1.59, 2.18, -2.45, -0.73, -3.12, -1.3, -2.83,
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0.2, -2.77, 3.24, -1.98, 1.6, -4.59, 3.39, -4.85, 3.75, -2.25, 1.71, -3.28,
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3.38, -1.74, 0.88, -2.41, 1.92, -2.24, 1.19, -2.48, 1.06, -1.68, -0.62,
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-1.3, 0.39, -1.78, 2.35, -3.54, 2.44, -1.32, 0.66, -2.38, 2.76, -2.35,
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3.95, -1.86, 4.32, -2.01, -1.23, -1.79, 2.76, -2.13, -0.13, -5.25, 3.84,
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-2.24, 1.59, -4.85, 2.96, -2.41, 0.01, -0.43, 0.13, -3.92, 2.91, -1.75,
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-0.53, -1.69, 1.69, -1.09, 0.15, -2.11, 2.17, -1.53, 1.22, -2.1, -0.86,
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-2.56, 2.28, -3.02, 3.33, -1.12, 3.86, -2.18, -1.19, -3.03, 0.79, -0.83,
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0.97, -3.19, 1.45, -1.34, 1.28, -2.52, 4.22, -4.53, 3.22, -1.97, 1.75,
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-2.36, 3.19, -0.83, 1.53, -1.59, 1.86, -2.17, 2.3, -1.63, 2.71, -2.03,
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3.75, -2.57, -0.6, -1.47, 1.33, -1.95, 0.7, -1.65, 1.27, -1.42, 1.09, -3.0,
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3.87, -2.51, 3.06, -2.6, 0.74, -1.08, -0.03, -2.44, 1.31, -2.65, 2.99,
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-1.84, 1.65, -4.76, 3.75, -2.07, 3.98, -2.4, 2.67, -2.21, 1.49, -1.21,
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1.22, -5.29, 2.38, -2.85, 2.28, -5.6, 3.78, -2.7, 0.8, -1.81, 3.5, -3.75,
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4.17, -1.29, 2.99, -5.92, 3.43, -1.83, 1.23, -1.24, -1.04, -2.56, 2.37,
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-3.26, 0.39, -4.63, 2.51, -4.52, 3.04, -1.7, 0.36, -1.41, 0.04, -2.1, 1.0,
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-1.87, 3.78, -4.32, 3.59, -2.24, 1.38, -1.99, -0.22, -1.87, 1.95, -0.84,
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2.17, -5.38, 3.56, -1.27, 2.9, -1.79, 3.31, -5.47, 3.85, -1.44, 3.69,
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-2.02, 0.37, -1.29, 0.33, -2.34, 2.56, -1.74, -1.27, -1.97, 1.22, -2.51,
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-0.16, -1.64, -0.96, -2.99, 1.4, -1.53, 3.31, -2.24, 0.45, -2.46, 1.71,
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-2.88, 1.56, -1.63, 1.46, -1.41, 0.68, -1.96, 2.76, -1.61,
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2.11]).reshape((200, 2))
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# Global data
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X = np.array([[3.0, 3], [4, 3], [4, 2],
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[9, 2], [5, 1], [6, 2], [9, 4],
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[5, 2], [5, 4], [7, 4], [6, 5]])
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CODET1 = np.array([[3.0000, 3.0000],
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[6.2000, 4.0000],
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[5.8000, 1.8000]])
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CODET2 = np.array([[11.0/3, 8.0/3],
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[6.7500, 4.2500],
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[6.2500, 1.7500]])
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LABEL1 = np.array([0, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1])
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class TestWhiten(object):
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def test_whiten(self):
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desired = np.array([[5.08738849, 2.97091878],
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[3.19909255, 0.69660580],
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[4.51041982, 0.02640918],
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[4.38567074, 0.95120889],
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[2.32191480, 1.63195503]])
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for tp in np.array, matrix:
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obs = tp([[0.98744510, 0.82766775],
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[0.62093317, 0.19406729],
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[0.87545741, 0.00735733],
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[0.85124403, 0.26499712],
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[0.45067590, 0.45464607]])
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assert_allclose(whiten(obs), desired, rtol=1e-5)
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def test_whiten_zero_std(self):
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desired = np.array([[0., 1.0, 2.86666544],
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[0., 1.0, 1.32460034],
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[0., 1.0, 3.74382172]])
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for tp in np.array, matrix:
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obs = tp([[0., 1., 0.74109533],
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[0., 1., 0.34243798],
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[0., 1., 0.96785929]])
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter('always')
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assert_allclose(whiten(obs), desired, rtol=1e-5)
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assert_equal(len(w), 1)
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assert_(issubclass(w[-1].category, RuntimeWarning))
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def test_whiten_not_finite(self):
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for tp in np.array, matrix:
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for bad_value in np.nan, np.inf, -np.inf:
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obs = tp([[0.98744510, bad_value],
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[0.62093317, 0.19406729],
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[0.87545741, 0.00735733],
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[0.85124403, 0.26499712],
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[0.45067590, 0.45464607]])
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assert_raises(ValueError, whiten, obs)
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class TestVq(object):
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def test_py_vq(self):
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initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
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for tp in np.array, matrix:
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label1 = py_vq(tp(X), tp(initc))[0]
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assert_array_equal(label1, LABEL1)
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def test_vq(self):
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initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
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for tp in np.array, matrix:
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label1, dist = _vq.vq(tp(X), tp(initc))
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assert_array_equal(label1, LABEL1)
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tlabel1, tdist = vq(tp(X), tp(initc))
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def test_vq_1d(self):
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# Test special rank 1 vq algo, python implementation.
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data = X[:, 0]
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initc = data[:3]
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a, b = _vq.vq(data, initc)
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ta, tb = py_vq(data[:, np.newaxis], initc[:, np.newaxis])
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assert_array_equal(a, ta)
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assert_array_equal(b, tb)
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|
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def test__vq_sametype(self):
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a = np.array([1.0, 2.0], dtype=np.float64)
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b = a.astype(np.float32)
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assert_raises(TypeError, _vq.vq, a, b)
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|
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def test__vq_invalid_type(self):
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a = np.array([1, 2], dtype=int)
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assert_raises(TypeError, _vq.vq, a, a)
|
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|
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def test_vq_large_nfeat(self):
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X = np.random.rand(20, 20)
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code_book = np.random.rand(3, 20)
|
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|
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codes0, dis0 = _vq.vq(X, code_book)
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codes1, dis1 = py_vq(X, code_book)
|
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assert_allclose(dis0, dis1, 1e-5)
|
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assert_array_equal(codes0, codes1)
|
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|
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X = X.astype(np.float32)
|
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code_book = code_book.astype(np.float32)
|
||||
|
||||
codes0, dis0 = _vq.vq(X, code_book)
|
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codes1, dis1 = py_vq(X, code_book)
|
||||
assert_allclose(dis0, dis1, 1e-5)
|
||||
assert_array_equal(codes0, codes1)
|
||||
|
||||
def test_vq_large_features(self):
|
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X = np.random.rand(10, 5) * 1000000
|
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code_book = np.random.rand(2, 5) * 1000000
|
||||
|
||||
codes0, dis0 = _vq.vq(X, code_book)
|
||||
codes1, dis1 = py_vq(X, code_book)
|
||||
assert_allclose(dis0, dis1, 1e-5)
|
||||
assert_array_equal(codes0, codes1)
|
||||
|
||||
|
||||
class TestKMean(object):
|
||||
def test_large_features(self):
|
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# Generate a data set with large values, and run kmeans on it to
|
||||
# (regression for 1077).
|
||||
d = 300
|
||||
n = 100
|
||||
|
||||
m1 = np.random.randn(d)
|
||||
m2 = np.random.randn(d)
|
||||
x = 10000 * np.random.randn(n, d) - 20000 * m1
|
||||
y = 10000 * np.random.randn(n, d) + 20000 * m2
|
||||
|
||||
data = np.empty((x.shape[0] + y.shape[0], d), np.double)
|
||||
data[:x.shape[0]] = x
|
||||
data[x.shape[0]:] = y
|
||||
|
||||
kmeans(data, 2)
|
||||
|
||||
def test_kmeans_simple(self):
|
||||
np.random.seed(54321)
|
||||
initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
|
||||
for tp in np.array, matrix:
|
||||
code1 = kmeans(tp(X), tp(initc), iter=1)[0]
|
||||
assert_array_almost_equal(code1, CODET2)
|
||||
|
||||
def test_kmeans_lost_cluster(self):
|
||||
# This will cause kmeans to have a cluster with no points.
|
||||
data = TESTDATA_2D
|
||||
initk = np.array([[-1.8127404, -0.67128041],
|
||||
[2.04621601, 0.07401111],
|
||||
[-2.31149087, -0.05160469]])
|
||||
|
||||
kmeans(data, initk)
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning,
|
||||
"One of the clusters is empty. Re-run kmeans with a "
|
||||
"different initialization")
|
||||
kmeans2(data, initk, missing='warn')
|
||||
|
||||
assert_raises(ClusterError, kmeans2, data, initk, missing='raise')
|
||||
|
||||
def test_kmeans2_simple(self):
|
||||
np.random.seed(12345678)
|
||||
initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
|
||||
for tp in np.array, matrix:
|
||||
code1 = kmeans2(tp(X), tp(initc), iter=1)[0]
|
||||
code2 = kmeans2(tp(X), tp(initc), iter=2)[0]
|
||||
|
||||
assert_array_almost_equal(code1, CODET1)
|
||||
assert_array_almost_equal(code2, CODET2)
|
||||
|
||||
def test_kmeans2_rank1(self):
|
||||
data = TESTDATA_2D
|
||||
data1 = data[:, 0]
|
||||
|
||||
initc = data1[:3]
|
||||
code = initc.copy()
|
||||
kmeans2(data1, code, iter=1)[0]
|
||||
kmeans2(data1, code, iter=2)[0]
|
||||
|
||||
def test_kmeans2_rank1_2(self):
|
||||
data = TESTDATA_2D
|
||||
data1 = data[:, 0]
|
||||
kmeans2(data1, 2, iter=1)
|
||||
|
||||
def test_kmeans2_high_dim(self):
|
||||
# test kmeans2 when the number of dimensions exceeds the number
|
||||
# of input points
|
||||
data = TESTDATA_2D
|
||||
data = data.reshape((20, 20))[:10]
|
||||
kmeans2(data, 2)
|
||||
|
||||
def test_kmeans2_init(self):
|
||||
np.random.seed(12345)
|
||||
data = TESTDATA_2D
|
||||
|
||||
kmeans2(data, 3, minit='points')
|
||||
kmeans2(data[:, :1], 3, minit='points') # special case (1-D)
|
||||
|
||||
kmeans2(data, 3, minit='++')
|
||||
kmeans2(data[:, :1], 3, minit='++') # special case (1-D)
|
||||
|
||||
# minit='random' can give warnings, filter those
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(message="One of the clusters is empty. Re-run.")
|
||||
kmeans2(data, 3, minit='random')
|
||||
kmeans2(data[:, :1], 3, minit='random') # special case (1-D)
|
||||
|
||||
@pytest.mark.skipif(sys.platform == 'win32',
|
||||
reason='Fails with MemoryError in Wine.')
|
||||
def test_krandinit(self):
|
||||
data = TESTDATA_2D
|
||||
datas = [data.reshape((200, 2)), data.reshape((20, 20))[:10]]
|
||||
k = int(1e6)
|
||||
for data in datas:
|
||||
np.random.seed(1234)
|
||||
init = _krandinit(data, k)
|
||||
orig_cov = np.cov(data, rowvar=0)
|
||||
init_cov = np.cov(init, rowvar=0)
|
||||
assert_allclose(orig_cov, init_cov, atol=1e-2)
|
||||
|
||||
def test_kmeans2_empty(self):
|
||||
# Regression test for gh-1032.
|
||||
assert_raises(ValueError, kmeans2, [], 2)
|
||||
|
||||
def test_kmeans_0k(self):
|
||||
# Regression test for gh-1073: fail when k arg is 0.
|
||||
assert_raises(ValueError, kmeans, X, 0)
|
||||
assert_raises(ValueError, kmeans2, X, 0)
|
||||
assert_raises(ValueError, kmeans2, X, np.array([]))
|
||||
|
||||
def test_kmeans_large_thres(self):
|
||||
# Regression test for gh-1774
|
||||
x = np.array([1, 2, 3, 4, 10], dtype=float)
|
||||
res = kmeans(x, 1, thresh=1e16)
|
||||
assert_allclose(res[0], np.array([4.]))
|
||||
assert_allclose(res[1], 2.3999999999999999)
|
||||
|
||||
def test_kmeans2_kpp_low_dim(self):
|
||||
# Regression test for gh-11462
|
||||
prev_res = np.array([[-1.95266667, 0.898],
|
||||
[-3.153375, 3.3945]])
|
||||
np.random.seed(42)
|
||||
res, _ = kmeans2(TESTDATA_2D, 2, minit='++')
|
||||
assert_allclose(res, prev_res)
|
||||
|
||||
def test_kmeans2_kpp_high_dim(self):
|
||||
# Regression test for gh-11462
|
||||
n_dim = 100
|
||||
size = 10
|
||||
centers = np.vstack([5 * np.ones(n_dim),
|
||||
-5 * np.ones(n_dim)])
|
||||
np.random.seed(42)
|
||||
data = np.vstack([
|
||||
np.random.multivariate_normal(centers[0], np.eye(n_dim), size=size),
|
||||
np.random.multivariate_normal(centers[1], np.eye(n_dim), size=size)
|
||||
])
|
||||
res, _ = kmeans2(data, 2, minit='++')
|
||||
assert_array_almost_equal(res, centers, decimal=0)
|
||||
Loading…
Add table
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Reference in a new issue