def test_MLSVD_shape(self):
"""Given a tensor of dimension D compute multilinear_SVD and
check the size of each part of the result.
"""
D = (2,3,4)
A = np.arange(np.prod(D)).reshape(*D)
U_s_Vh_list = tensorial_kernel.multilinear_SVD(A)
assert(len(U_s_Vh_list) == len(D)) # check number of unfoldings
for i in range(len(D)):
assert(len(U_s_Vh_list[i]) == 3) # check number of results of each SVD
U, s, Vh = U_s_Vh_list[i]
assert(U.shape == (D[i], D[i])) # check shape of U
assert(s.shape == (D[i],)) # check shape of s
assert(Vh.shape == (D[i], np.prod(D[:i]+D[i+1:]))) # check shape of Vh
def test_MLSVD_shape(self):
"""Given a tensor of dimension D compute multilinear_SVD and
check the size of each part of the result.
"""
D = (2, 3, 4)
A = np.arange(np.prod(D)).reshape(*D)
U_s_Vh_list = tensorial_kernel.multilinear_SVD(A)
assert (len(U_s_Vh_list) == len(D)) # check number of unfoldings
for i in range(len(D)):
assert (len(U_s_Vh_list[i]) == 3
) # check number of results of each SVD
U, s, Vh = U_s_Vh_list[i]
assert (U.shape == (D[i], D[i])) # check shape of U
assert (s.shape == (D[i], )) # check shape of s
assert (Vh.shape == (D[i], np.prod(D[:i] + D[i + 1:]))
) # check shape of Vh
import tensorial_kernel
if __name__=='__main__':
import numpy as np
D = [64, 64, 34, 4]
# D = [276,10,10]
D_X = [5] + D
X = np.arange(np.prod(D_X)).reshape(*D_X)
D_Y = [6] + D
Y = np.arange(np.prod(D_Y)).reshape(*D_Y) + 1
mlSVD = tensorial_kernel.multilinear_SVD(X[0])
print "Projection Frobenius norm of ||X[0] - X[0]|| =",
print tensorial_kernel.compute_projection_frobenius_norm(mlSVD, mlSVD)
print
mlSVD1 = tensorial_kernel.multilinear_SVD(X[1])
print "Projection Frobenius norm of ||X[0] - X[1]|| =",
print tensorial_kernel.compute_projection_frobenius_norm(mlSVD1, mlSVD)
print
