def _check_non_singular(self):
# Make a diagonal dominant, to make sure it is not singular
n = 5
a = csc_matrix(random.rand(n, n))
b = ones(n)
expected = splu(a).solve(b)
assert_array_almost_equal(factorized(a)(b), expected)
def test_leftright_precond(self):
"""Check that QMR works with left and right preconditioners"""
from scipy.sparse.linalg._dsolve import splu
from scipy.sparse.linalg._interface import LinearOperator
n = 100
dat = ones(n)
A = spdiags([-2 * dat, 4 * dat, -dat], [-1, 0, 1], n, n)
b = arange(n, dtype='d')
L = spdiags([-dat / 2, dat], [-1, 0], n, n)
U = spdiags([4 * dat, -dat], [0, 1], n, n)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"splu requires CSC matrix format")
L_solver = splu(L)
U_solver = splu(U)
def L_solve(b):
return L_solver.solve(b)
def U_solve(b):
return U_solver.solve(b)
def LT_solve(b):
return L_solver.solve(b, 'T')
def UT_solve(b):
return U_solver.solve(b, 'T')
M1 = LinearOperator((n, n), matvec=L_solve, rmatvec=LT_solve)
M2 = LinearOperator((n, n), matvec=U_solve, rmatvec=UT_solve)
with suppress_warnings() as sup:
sup.filter(DeprecationWarning, ".*called without specifying.*")
x, info = qmr(A, b, tol=1e-8, maxiter=15, M1=M1, M2=M2)
assert_equal(info, 0)
assert_normclose(A @ x, b, tol=1e-8)
def test_splu_perm(self):
# Test the permutation vectors exposed by splu.
n = 30
a = random.random((n, n))
a[a < 0.95] = 0
# Make a diagonal dominant, to make sure it is not singular
a += 4 * eye(n)
a_ = csc_matrix(a)
lu = splu(a_)
# Check that the permutation indices do belong to [0, n-1].
for perm in (lu.perm_r, lu.perm_c):
assert_(all(perm > -1))
assert_(all(perm < n))
assert_equal(len(unique(perm)), len(perm))
# Now make a symmetric, and test that the two permutation vectors are
# the same
# Note: a += a.T relies on undefined behavior.
a = a + a.T
a_ = csc_matrix(a)
lu = splu(a_)
assert_array_equal(lu.perm_r, lu.perm_c)
def test_bad_inputs(self):
A = self.A.tocsc()
assert_raises(ValueError, splu, A[:, :4])
assert_raises(ValueError, spilu, A[:, :4])
for lu in [splu(A), spilu(A)]:
b = random.rand(42)
B = random.rand(42, 3)
BB = random.rand(self.n, 3, 9)
assert_raises(ValueError, lu.solve, b)
assert_raises(ValueError, lu.solve, B)
assert_raises(ValueError, lu.solve, BB)
assert_raises(TypeError, lu.solve, b.astype(np.complex64))
assert_raises(TypeError, lu.solve, b.astype(np.complex128))
def test_lu_refcount(self):
# Test that we are keeping track of the reference count with splu.
n = 30
a = random.random((n, n))
a[a < 0.95] = 0
# Make a diagonal dominant, to make sure it is not singular
a += 4 * eye(n)
a_ = csc_matrix(a)
lu = splu(a_)
# And now test that we don't have a refcount bug
rc = sys.getrefcount(lu)
for attr in ('perm_r', 'perm_c'):
perm = getattr(lu, attr)
assert_equal(sys.getrefcount(lu), rc + 1)
del perm
assert_equal(sys.getrefcount(lu), rc)
def test_splu_basic(self):
# Test basic splu functionality.
n = 30
rng = random.RandomState(12)
a = rng.rand(n, n)
a[a < 0.95] = 0
# First test with a singular matrix
a[:, 0] = 0
a_ = csc_matrix(a)
# Matrix is exactly singular
assert_raises(RuntimeError, splu, a_)
# Make a diagonal dominant, to make sure it is not singular
a += 4 * eye(n)
a_ = csc_matrix(a)
lu = splu(a_)
b = ones(n)
x = lu.solve(b)
assert_almost_equal(dot(a, x), b)
def test_assume_sorted_indices_flag(self):
# a sparse matrix with unsorted indices
unsorted_inds = np.array([2, 0, 1, 0])
data = np.array([10, 16, 5, 0.4])
indptr = np.array([0, 1, 2, 4])
A = csc_matrix((data, unsorted_inds, indptr), (3, 3))
b = ones(3)
# should raise when incorrectly assuming indices are sorted
use_solver(useUmfpack=True, assumeSortedIndices=True)
with assert_raises(RuntimeError, match="UMFPACK_ERROR_invalid_matrix"):
factorized(A)
# should sort indices and succeed when not assuming indices are sorted
use_solver(useUmfpack=True, assumeSortedIndices=False)
expected = splu(A.copy()).solve(b)
assert_equal(A.has_sorted_indices, 0)
assert_array_almost_equal(factorized(A)(b), expected)
def check(dtype, complex_2=False):
A = self.A.astype(dtype)
if complex_2:
A = A + 1j * A.T
n = A.shape[0]
lu = splu(A)
# Check that the decomposition is as advertized
Pc = np.zeros((n, n))
Pc[np.arange(n), lu.perm_c] = 1
Pr = np.zeros((n, n))
Pr[lu.perm_r, np.arange(n)] = 1
Ad = A.toarray()
lhs = Pr.dot(Ad).dot(Pc)
rhs = (lu.L @ lu.U).toarray()
eps = np.finfo(dtype).eps
assert_allclose(lhs, rhs, atol=100 * eps)
