Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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42 lines
1.3 KiB
42 lines
1.3 KiB
"""test sparse matrix construction functions"""
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from numpy.testing import assert_equal
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from scipy.sparse import csr_matrix
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import numpy as np
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from scipy.sparse import extract
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class TestExtract(object):
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def setup_method(self):
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self.cases = [
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csr_matrix([[1,2]]),
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csr_matrix([[1,0]]),
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csr_matrix([[0,0]]),
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csr_matrix([[1],[2]]),
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csr_matrix([[1],[0]]),
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csr_matrix([[0],[0]]),
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csr_matrix([[1,2],[3,4]]),
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csr_matrix([[0,1],[0,0]]),
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csr_matrix([[0,0],[1,0]]),
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csr_matrix([[0,0],[0,0]]),
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csr_matrix([[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]]),
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csr_matrix([[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]]).T,
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]
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def find(self):
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for A in self.cases:
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I,J,V = extract.find(A)
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assert_equal(A.toarray(), csr_matrix(((I,J),V), shape=A.shape))
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def test_tril(self):
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for A in self.cases:
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B = A.toarray()
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for k in [-3,-2,-1,0,1,2,3]:
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assert_equal(extract.tril(A,k=k).toarray(), np.tril(B,k=k))
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def test_triu(self):
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for A in self.cases:
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B = A.toarray()
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for k in [-3,-2,-1,0,1,2,3]:
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assert_equal(extract.triu(A,k=k).toarray(), np.triu(B,k=k))
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