def test_example(self):
pitts_py.initialize()
# do something interesting...
tt = pitts_py.TensorTrain_double([5,5,5])
pitts_py.randomize(tt)
pitts_py.finalize()
n = 10000000
m = 50
X = pitts_py.MultiVector_double(n, m)
pitts_py.randomize(X)
for j in range(1, m+1):
print("n: ", n, ", m: ", j)
X.resize(n,j)
pitts_qr(X)
numpy_qr(X)
n = n//10
m = 100
print("n: ", n, ", m: ", m)
X.resize(n, m)
pitts_py.randomize(X)
pitts_qr(X)
numpy_qr(X)
n = n//10
m = 200
print("n: ", n, ", m: ", m)
X.resize(n, m)
pitts_py.randomize(X)
pitts_qr(X)
numpy_qr(X)
pitts_py.finalize(True)
eye_i = TTOp_dummy.getSubTensor(iDim)
tridi_i = np.zeros((n_i,n_i))
for i in range(n_i):
for j in range(n_i):
if i == j:
tridi_i[i,j] = 2. / (n_i+1)
elif i+1 == j or i-1 == j:
tridi_i[i,j] = -1. / (n_i+1)
else:
tridi_i[i,j] = 0
TTOp_dummy.setSubTensor(iDim, tridi_i.reshape(1,n_i,n_i,1))
pitts_py.axpby(1, TTOp_dummy, 1, TTOp)
TTOp_dummy.setSubTensor(iDim, eye_i)
return TTOp
TTOp = LaplaceOperator([10,]*5)
x0 = pitts_py.TensorTrain_double(TTOp.row_dimensions())
x0.setOnes()
sigma, q = tt_jacobi_davidson(TTOp, x0, symmetric=True, eps=1.e-8)
r = pitts_py.TensorTrain_double(x0.dimensions())
pitts_py.apply(TTOp, q, r)
sigma_ref = pitts_py.dot(q, r)
r_nrm = pitts_py.axpby(-sigma, q, 1, r)
print("Residual norm: %g" % r_nrm)
print("Est. eigenvalue: %g, real Ritz value: %g, error: %g" % (sigma, sigma_ref, np.abs(sigma-sigma_ref)))
pitts_py.finalize()