def do_test_arpack_classic(self, dtype):
if not pyublasext.has_arpack():
return
size = 10
#A = make_random_matrix(size, dtype)
A = tmd.aclassmat[dtype]
Aop = pyublasext.MatrixOperator.make(A)
results = pyublasext.operator_eigenvectors(Aop, 3)
for value,vector in results:
err = numpy.dot(A, vector) - value*vector
self.assert_(la.norm(err) < 1e-7)
def do_test_arpack_shift_invert(self, dtype):
if not pyublasext.has_arpack():
return
size = 100
sigma = 1
#A = make_random_matrix(size, dtype)
#M = make_random_spd_matrix(size, dtype)
A = tmd.arpsi_a[dtype]
M = tmd.arpsi_m[dtype]
Mop = pyublasext.MatrixOperator.make(M)
shifted_mat_invop = pyublasext.LUInverseOperator.make(A - sigma * M)
results = pyublasext.operator_eigenvectors(
shifted_mat_invop * Mop, 5, Mop, spectral_shift=sigma)
for value,vector in results:
self.assert_(la.norm(numpy.dot(A,vector) - value*numpy.dot(M, vector)) < 1e-10)
def do_test_arpack_generalized(self, dtype):
if not pyublasext.has_arpack():
return
size = 100
A = tmd.agen_a[dtype]
Aop = pyublasext.MatrixOperator.make(A)
M = tmd.agen_m[dtype]
Mop = pyublasext.MatrixOperator.make(M)
Minvop = pyublasext.LUInverseOperator.make(M)
results = pyublasext.operator_eigenvectors(Minvop*Aop, 5, Mop)
for value,vector in results:
if A.dtype == float:
A_vec = A*vector.real.copy() + 1j*(A*vector.imag.copy())
else:
A_vec = A*vector
err = A_vec - value * numpy.dot(M, vector)
self.assert_(la.norm(err) < 1e-7)
