29 lines
842 B
Python
29 lines
842 B
Python
import numpy as np
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import scipy.sparse as sp
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from numpy.testing import assert_array_almost_equal
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from sklearn.datasets import make_classification
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from sklearn.utils.sparsefuncs import mean_variance_axis0
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def test_mean_variance_axis0():
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X, _ = make_classification(5, 4, random_state=0)
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# Sparsify the array a little bit
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X[0, 0] = 0
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X[2, 1] = 0
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X[4, 3] = 0
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X_lil = sp.lil_matrix(X)
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X_lil[1, 0] = 0
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X[1, 0] = 0
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X_csr = sp.csr_matrix(X_lil)
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X_means, X_vars = mean_variance_axis0(X_csr)
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assert_array_almost_equal(X_means, np.mean(X, axis=0))
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assert_array_almost_equal(X_vars, np.var(X, axis=0))
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X_csc = sp.csc_matrix(X_lil)
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X_means, X_vars = mean_variance_axis0(X_csc)
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assert_array_almost_equal(X_means, np.mean(X, axis=0))
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assert_array_almost_equal(X_vars, np.var(X, axis=0))
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