Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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99 lines
3.2 KiB
99 lines
3.2 KiB
4 years ago
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from numpy import array, kron, diag
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from numpy.testing import assert_, assert_equal
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from scipy.sparse import spfuncs
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from scipy.sparse import csr_matrix, csc_matrix, bsr_matrix
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from scipy.sparse._sparsetools import (csr_scale_rows, csr_scale_columns,
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bsr_scale_rows, bsr_scale_columns)
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from scipy.sparse.sputils import matrix
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class TestSparseFunctions(object):
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def test_scale_rows_and_cols(self):
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D = matrix([[1,0,0,2,3],
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[0,4,0,5,0],
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[0,0,6,7,0]])
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#TODO expose through function
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S = csr_matrix(D)
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v = array([1,2,3])
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csr_scale_rows(3,5,S.indptr,S.indices,S.data,v)
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assert_equal(S.todense(), diag(v)*D)
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S = csr_matrix(D)
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v = array([1,2,3,4,5])
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csr_scale_columns(3,5,S.indptr,S.indices,S.data,v)
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assert_equal(S.todense(), D@diag(v))
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# blocks
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E = kron(D,[[1,2],[3,4]])
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S = bsr_matrix(E,blocksize=(2,2))
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v = array([1,2,3,4,5,6])
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bsr_scale_rows(3,5,2,2,S.indptr,S.indices,S.data,v)
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assert_equal(S.todense(), diag(v)@E)
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S = bsr_matrix(E,blocksize=(2,2))
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v = array([1,2,3,4,5,6,7,8,9,10])
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bsr_scale_columns(3,5,2,2,S.indptr,S.indices,S.data,v)
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assert_equal(S.todense(), E@diag(v))
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E = kron(D,[[1,2,3],[4,5,6]])
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S = bsr_matrix(E,blocksize=(2,3))
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v = array([1,2,3,4,5,6])
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bsr_scale_rows(3,5,2,3,S.indptr,S.indices,S.data,v)
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assert_equal(S.todense(), diag(v)@E)
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S = bsr_matrix(E,blocksize=(2,3))
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v = array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15])
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bsr_scale_columns(3,5,2,3,S.indptr,S.indices,S.data,v)
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assert_equal(S.todense(), E@diag(v))
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def test_estimate_blocksize(self):
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mats = []
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mats.append([[0,1],[1,0]])
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mats.append([[1,1,0],[0,0,1],[1,0,1]])
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mats.append([[0],[0],[1]])
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mats = [array(x) for x in mats]
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blks = []
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blks.append([[1]])
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blks.append([[1,1],[1,1]])
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blks.append([[1,1],[0,1]])
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blks.append([[1,1,0],[1,0,1],[1,1,1]])
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blks = [array(x) for x in blks]
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for A in mats:
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for B in blks:
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X = kron(A,B)
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r,c = spfuncs.estimate_blocksize(X)
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assert_(r >= B.shape[0])
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assert_(c >= B.shape[1])
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def test_count_blocks(self):
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def gold(A,bs):
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R,C = bs
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I,J = A.nonzero()
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return len(set(zip(I//R,J//C)))
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mats = []
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mats.append([[0]])
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mats.append([[1]])
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mats.append([[1,0]])
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mats.append([[1,1]])
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mats.append([[0,1],[1,0]])
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mats.append([[1,1,0],[0,0,1],[1,0,1]])
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mats.append([[0],[0],[1]])
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for A in mats:
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for B in mats:
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X = kron(A,B)
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Y = csr_matrix(X)
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for R in range(1,6):
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for C in range(1,6):
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assert_equal(spfuncs.count_blocks(Y, (R, C)), gold(X, (R, C)))
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X = kron([[1,1,0],[0,0,1],[1,0,1]],[[1,1]])
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Y = csc_matrix(X)
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assert_equal(spfuncs.count_blocks(X, (1, 2)), gold(X, (1, 2)))
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assert_equal(spfuncs.count_blocks(Y, (1, 2)), gold(X, (1, 2)))
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