def setUp(self):
self.n = 50000
self.B = poisson.poisson1d(self.n).to_csr()
self.b = numpy.zeros(self.n, 'd')
self.x = numpy.zeros(self.n, 'd')
self.x_exact = numpy.ones(self.n, 'd')
self.x_exact /= math.sqrt(self.n)
self.B.matvec(self.x_exact, self.b)
lmbd_min = 4.0 * math.sin(math.pi/2.0/self.n) ** 2
lmbd_max = 4.0 * math.sin((self.n - 1)*math.pi/2.0/self.n) ** 2
cond = lmbd_max/lmbd_min
self.tol = cond * macheps()
def setUp(self):
import numpy
self.n = 30
self.P = poisson.poisson1d(self.n)
for i in range(self.n):
self.P[i,i] = 4.0
self.A = poisson.poisson2d(self.n)
self.S = poisson.poisson2d_sym(self.n)
self.I = spmatrix.ll_mat_sym(self.n)
for i in range(self.n):
self.I[i,i] = -1.0
self.mask = numpy.zeros(self.n**2, 'l')
self.mask[self.n/2*self.n:(self.n/2 + 1)*self.n] = 1
self.mask1 = numpy.zeros(self.n**2, 'l')
self.mask1[(self.n/2 + 1)*self.n:(self.n/2 + 2)*self.n] = 1
def setUp(self):
self.n = 50000
self.B = PysparseMatrix( matrix=poisson.poisson1d(self.n) )
self.x_exact = numpy.ones(self.n)/math.sqrt(self.n)
self.normx = 1.0/math.sqrt(self.n)
self.b = self.B * self.x_exact
lmbd_min = 4.0 * math.sin(math.pi/2.0/self.n) ** 2
lmbd_max = 4.0 * math.sin((self.n - 1)*math.pi/2.0/self.n) ** 2
cond = lmbd_max/lmbd_min
self.tol = cond * macheps()
self.relerr = 0.0
self.nnz = self.B.getNnz()
self.LU = None
self.fmt = '\t%8.2e %8.2e %8d %8d %8d %6.2f %6.2f\n'
def testSubmatrix(self):
n = self.n
Psym = poisson.poisson1d_sym(n)
P = poisson.poisson1d(n)
for i in range(n):
P[i,i] = 4.0
Psym[i,i] = 4.0
# read and test diagonal blocks
for i in range(n):
self.failUnless(llmat_isEqual(self.A[n*i:n*(i+1),n*i:n*(i+1)], P))
self.failUnless(llmat_isEqual(self.S[n*i:n*(i+1),n*i:n*(i+1)], P))
self.failUnless(llmat_isEqual(self.A[n*i:n*(i+1),n*i:n*(i+1)], Psym))
self.failUnless(llmat_isEqual(self.S[n*i:n*(i+1),n*i:n*(i+1)], Psym))
# store and get diagonal blocks
R = spmatrix_util.ll_mat_rand(n*n, n*n, 0.01) # random matrix
for i in range(n):
R[n*i:n*(i+1),n*i:n*(i+1)] = P
self.failUnless(llmat_isEqual(R[n*i:n*(i+1),n*i:n*(i+1)], P))
R[n*i:n*(i+1),n*i:n*(i+1)] = Psym
self.failUnless(llmat_isEqual(R[n*i:n*(i+1),n*i:n*(i+1)], Psym))
# store and get off-diagonal blocks
for i in range(n-1):
R[n*i:n*(i+1),n*(i+1):n*(i+2)] = P
self.failUnless(llmat_isEqual(R[n*i:n*(i+1),n*(i+1):n*(i+2)], P))
R[n*i:n*(i+1),n*(i+1):n*(i+2)] = Psym
self.failUnless(llmat_isEqual(R[n*i:n*(i+1),n*(i+1):n*(i+2)], Psym))
# store and get diagonal blocks in symmetric matrix
R = spmatrix.ll_mat_sym(n*n)
for i in range(n):
R[n*i:n*(i+1),n*i:n*(i+1)] = Psym
self.failUnless(llmat_isEqual(R[n*i:n*(i+1),n*i:n*(i+1)], Psym))
# store and get off-diagonal blocks in symmetric matrix
for i in range(n-1):
R[n*(i+1):n*(i+2),n*i:n*(i+1)] = P
self.failUnless(llmat_isEqual(R[n*(i+1):n*(i+2),n*i:n*(i+1)], P))
R[n*(i+1):n*(i+2),n*i:n*(i+1)] = Psym
self.failUnless(llmat_isEqual(R[n*(i+1):n*(i+2),n*i:n*(i+1)], Psym))
def testNormGeneral(self):
A = poisson.poisson2d(self.n)
self.failUnless(A.norm('1') == 8)
self.failUnless(A.norm('inf') == 8)
self.failUnless(poisson.poisson1d(3).norm('fro') == 4)