def mttkrp(self, U, n):
""" Matricized tensor times Khatri-Rao product for tensor.
Calculates the matrix product of the n-mode matricization of X with
the Khatri-Rao product of all entries in U except the nth.
Parameters
----------
U - factorization
Returns
-------
out : Khatri-Rao product as a numpy array
"""
N = self.ndims()
if len(U) != N:
raise ValueError("U has the wrong length")
Xn = self.permute(
numpy.concatenate(([n], numpy.arange(0, n), numpy.arange(n + 1,
N))))
## use the Fortran ordering system to maintain consistent with Matlab code
Xn = Xn.data.reshape(self.dimsize(n),
numpy.prod(self.shape) / self.dimsize(n),
order='F')
Z = khatrirao.khatrirao_array([U[i] for i in range(len(U)) if i != n],
reverse=True)
V = numpy.dot(Xn, Z)
return V
def __calculatePi(X, M, R, n, N):
"""
Calculate the product of all matrices but the n-th (Eq 3.6 in Chi + Kolda ArXiv paper)
"""
Pi = None
if X.__class__ == sptensor.sptensor:
Pi = np.ones((X.nnz(), R))
for nn in np.concatenate((np.arange(0, n), np.arange(n+1, N))):
Pi = np.multiply(M.U[nn][X.subs[:, nn],:], Pi)
else:
Pi = khatrirao.khatrirao_array([M.U[i] for i in range(len(M.U)) if i != n], reverse=True)
return Pi
def calculatePi(X, M, n):
"""
Calculate the product of all matrices but the n-th (Eq 3.6 in Chi + Kolda ArXiv paper)
"""
Pi = None
if X.__class__ == sptensor.sptensor:
Pi = np.ones((X.nnz(), M.R))
for nn in np.concatenate((np.arange(0, n), np.arange(n + 1,
X.ndims()))):
Pi = np.multiply(M.U[nn][X.subs[:, nn], :], Pi)
else:
Pi = khatrirao.khatrirao_array(
[M.U[i] for i in range(len(M.U)) if i != n], reverse=True)
return Pi
def mttkrp(self, U, n):
""" Matricized tensor times Khatri-Rao product for tensor.
Calculates the matrix product of the n-mode matricization of X with
the Khatri-Rao product of all entries in U except the nth.
Parameters
----------
U - factorization
Returns
-------
out : Khatri-Rao product as a numpy array
"""
N = self.ndims()
if len(U) != N:
raise ValueError("U has the wrong length");
Xn = self.permute(numpy.concatenate(([n], numpy.arange(0, n), numpy.arange(n+1, N))))
## use the Fortran ordering system to maintain consistent with Matlab code
Xn = Xn.data.reshape(self.dimsize(n), numpy.prod(self.shape)/self.dimsize(n), order='F');
Z = khatrirao.khatrirao_array([U[i] for i in range(len(U)) if i != n], reverse=True);
V = numpy.dot(Xn,Z);
return V;
def toTensor(self):
"""Convert this to a dense tensor"""
tmp = khatrirao.khatrirao_array(self.U, True)
data = np.inner(self.lmbda, tmp);
return tensor.tensor(data, self.shape);
def toTensor(self):
"""Convert this to a dense tensor"""
tmp = khatrirao.khatrirao_array(self.U, True)
data = np.inner(self.lmbda, tmp);
return tensor.tensor(data, self.shape);
