def test_random(self):
def random_triangle_matrix(n, lower=True):
A = scipy.sparse.random(n, n, density=0.1, format='coo')
if lower:
A = scipy.sparse.tril(A)
else:
A = scipy.sparse.triu(A)
A = A.tocsr(copy=False)
for i in range(n):
A[i, i] = np.random.rand() + 1
return A
np.random.seed(1234)
for lower in (True, False):
for n in (10, 10**2, 10**3):
A = random_triangle_matrix(n, lower=lower)
for m in (1, 10):
for b in (np.random.rand(n, m),
np.random.randint(-9, 9, (n, m)),
np.random.randint(-9, 9, (n, m)) +
np.random.randint(-9, 9, (n, m)) * 1j):
x = spsolve_triangular(A, b, lower=lower)
assert_array_almost_equal(A.dot(x), b)
x = spsolve_triangular(A,
b,
lower=lower,
unit_diagonal=True)
A.setdiag(1)
assert_array_almost_equal(A.dot(x), b)
def test_zero_diagonal(self):
n = 5
rng = np.random.default_rng(43876432987)
A = rng.standard_normal((n, n))
b = np.arange(n)
A = scipy.sparse.tril(A, k=0, format='csr')
x = spsolve_triangular(A, b, unit_diagonal=True, lower=True)
A.setdiag(1)
assert_allclose(A.dot(x), b)
# Regression test from gh-15199
A = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0]], dtype=np.float64)
b = np.array([1., 2., 3.])
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning, "CSR matrix format is")
spsolve_triangular(A, b, unit_diagonal=True)
def test_input_types(self):
A = array([[1., 0.], [1., 2.]])
b = array([[2., 0.], [2., 2.]])
for matrix_type in (array, csc_matrix, csr_matrix):
x = spsolve_triangular(matrix_type(A), b, lower=True)
assert_array_almost_equal(A.dot(x), b)