def test_sparse_symmetric_reverse_permute():
"Sparse: Symmetric Reverse Permute"
# CSR version
A = rand_dm(25, 0.5)
perm = np.random.permutation(25)
x = sp_permute(A.data, perm, perm)
B = sp_reverse_permute(x, perm, perm)
assert_equal((A.full() - B.toarray()).all(), 0)
# CSC version
B = A.data.tocsc()
perm = np.random.permutation(25)
x = sp_permute(B, perm, perm)
B = sp_reverse_permute(x, perm, perm)
assert_equal((A.full() - B.toarray()).all(), 0)
def test_sparse_symmetric_reverse_permute():
"Sparse: Symmetric Reverse Permute"
# CSR version
A = rand_dm(25, 0.5)
perm = np.random.permutation(25)
x = sp_permute(A.data, perm, perm)
B = sp_reverse_permute(x, perm, perm)
assert_equal((A.full() - B.toarray()).all(), 0)
# CSC version
B = A.data.tocsc()
perm = np.random.permutation(25)
x = sp_permute(B, perm, perm)
B = sp_reverse_permute(x, perm, perm)
assert_equal((A.full() - B.toarray()).all(), 0)
def test_sparse_nonsymmetric_reverse_permute():
"Sparse: Nonsymmetric Reverse Permute"
# CSR square array check
A = rand_dm(25, 0.5)
rperm = np.random.permutation(25)
cperm = np.random.permutation(25)
x = sp_permute(A.data, rperm, cperm)
B = sp_reverse_permute(x, rperm, cperm)
assert_equal((A.full() - B.toarray()).all(), 0)
# CSC square array check
A = rand_dm(25, 0.5)
rperm = np.random.permutation(25)
cperm = np.random.permutation(25)
B = A.data.tocsc()
x = sp_permute(B, rperm, cperm)
B = sp_reverse_permute(x, rperm, cperm)
assert_equal((A.full() - B.toarray()).all(), 0)
# CSR column vector check
A = coherent(25, 1)
rperm = np.random.permutation(25)
x = sp_permute(A.data, rperm, [])
B = sp_reverse_permute(x, rperm, [])
assert_equal((A.full() - B.toarray()).all(), 0)
# CSC column vector check
A = coherent(25, 1)
rperm = np.random.permutation(25)
B = A.data.tocsc()
x = sp_permute(B, rperm, [])
B = sp_reverse_permute(x, rperm, [])
assert_equal((A.full() - B.toarray()).all(), 0)
# CSR row vector check
A = coherent(25, 1).dag()
cperm = np.random.permutation(25)
x = sp_permute(A.data, [], cperm)
B = sp_reverse_permute(x, [], cperm)
assert_equal((A.full() - B.toarray()).all(), 0)
# CSC row vector check
A = coherent(25, 1).dag()
cperm = np.random.permutation(25)
B = A.data.tocsc()
x = sp_permute(B, [], cperm)
B = sp_reverse_permute(x, [], cperm)
assert_equal((A.full() - B.toarray()).all(), 0)
def test_sparse_nonsymmetric_reverse_permute():
"Sparse: Nonsymmetric Reverse Permute"
# CSR square array check
A = rand_dm(25, 0.5)
rperm = np.random.permutation(25)
cperm = np.random.permutation(25)
x = sp_permute(A.data, rperm, cperm)
B = sp_reverse_permute(x, rperm, cperm)
assert_equal((A.full() - B.toarray()).all(), 0)
# CSC square array check
A = rand_dm(25, 0.5)
rperm = np.random.permutation(25)
cperm = np.random.permutation(25)
B = A.data.tocsc()
x = sp_permute(B, rperm, cperm)
B = sp_reverse_permute(x, rperm, cperm)
assert_equal((A.full() - B.toarray()).all(), 0)
# CSR column vector check
A = coherent(25, 1)
rperm = np.random.permutation(25)
x = sp_permute(A.data, rperm, [])
B = sp_reverse_permute(x, rperm, [])
assert_equal((A.full() - B.toarray()).all(), 0)
# CSC column vector check
A = coherent(25, 1)
rperm = np.random.permutation(25)
B = A.data.tocsc()
x = sp_permute(B, rperm, [])
B = sp_reverse_permute(x, rperm, [])
assert_equal((A.full() - B.toarray()).all(), 0)
# CSR row vector check
A = coherent(25, 1).dag()
cperm = np.random.permutation(25)
x = sp_permute(A.data, [], cperm)
B = sp_reverse_permute(x, [], cperm)
assert_equal((A.full() - B.toarray()).all(), 0)
# CSC row vector check
A = coherent(25, 1).dag()
cperm = np.random.permutation(25)
B = A.data.tocsc()
x = sp_permute(B, [], cperm)
B = sp_reverse_permute(x, [], cperm)
assert_equal((A.full() - B.toarray()).all(), 0)
