27 lines
1 KiB
Python
27 lines
1 KiB
Python
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# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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# License: BSD 3 clause
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import numpy as np
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from scipy import sparse
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from sklearn.utils.graph import graph_laplacian
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from sklearn.utils.testing import ignore_warnings
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@ignore_warnings(category=DeprecationWarning)
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def test_graph_laplacian():
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for mat in (np.arange(10) * np.arange(10)[:, np.newaxis],
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np.ones((7, 7)),
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np.eye(19),
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np.vander(np.arange(4)) + np.vander(np.arange(4)).T,):
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sp_mat = sparse.csr_matrix(mat)
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for normed in (True, False):
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laplacian = graph_laplacian(mat, normed=normed)
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n_nodes = mat.shape[0]
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if not normed:
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np.testing.assert_array_almost_equal(laplacian.sum(axis=0),
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np.zeros(n_nodes))
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np.testing.assert_array_almost_equal(laplacian.T, laplacian)
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np.testing.assert_array_almost_equal(
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laplacian, graph_laplacian(sp_mat, normed=normed).toarray())
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