def calculatePhi(X, B, Pi, n, epsilon=1e-4, C=None):
"""
Calculate the matrix for multiplicative update
Parameters
----------
X : the observed tensor
B : the factor matrix associated with mode n
Pi : the product of all matrices but the n-th from above
n : the mode that we are trying to solve the subproblem for
epsilon : the
C : the augmented / non-augmented tensor (\alpha u \Psi or B \Phi) in sparse form
"""
Phi = None
if X.__class__ == sptensor.sptensor:
Phi = -np.ones((X.shape[n], B.shape[1]))
xsubs = X.subs[:,n]
if C != None:
v = np.sum(np.multiply(B[xsubs,:], Pi) + C, axis=1)
else:
v = np.sum(np.multiply(B[xsubs,:], Pi), axis=1)
wvals = X.vals.flatten() / v
for r in range(B.shape[1]):
Phi[:,r] = accumarray.accum_np(xsubs, np.multiply(wvals, Pi[:,r]), size=X.shape[n])
else:
Xn = tenmat.tenmat(X,[n])
V = np.inner(B,Pi)
W = Xn.data / np.maximum(V, epsilon)
Phi = np.inner(W, Pi.transpose())
return Phi
def calculatePhi(X, B, Pi, n, epsilon=1e-4, C=None):
"""
Calculate the matrix for multiplicative update
Parameters
----------
X : the observed tensor
B : the factor matrix associated with mode n
Pi : the product of all matrices but the n-th from above
n : the mode that we are trying to solve the subproblem for
epsilon : the
C : the augmented / non-augmented tensor (\alpha u \Psi or B \Phi) in sparse form
"""
Phi = None
if X.__class__ == sptensor.sptensor:
Phi = -np.ones((X.shape[n], B.shape[1]))
xsubs = X.subs[:, n]
if C != None:
v = np.sum(np.multiply(B[xsubs, :], Pi) + C, axis=1)
else:
v = np.sum(np.multiply(B[xsubs, :], Pi), axis=1)
wvals = X.vals.flatten() / v
for r in range(B.shape[1]):
Phi[:, r] = accumarray.accum_np(xsubs,
np.multiply(wvals, Pi[:, r]),
size=X.shape[n])
else:
Xn = tenmat.tenmat(X, [n])
V = np.inner(B, Pi)
W = Xn.data / np.maximum(V, epsilon)
Phi = np.inner(W, Pi.transpose())
return Phi
def calculatePhi(X, B, Pi, n, epsilon=1e-4, C=None):
"""
Calculate the matrix for multiplicative update
Parameters
----------
X : the observed tensor
B : the factor matrix associated with mode n
Pi : the product of all matrices but the n-th from above
n : the mode that we are trying to solve the subproblem for
epsilon : the
C : the augmented / non-augmented tensor (\alpha u \Psi or B \Phi) in sparse form
"""
Phi = None
if X.__class__ == sptensor.sptensor:
Phi = -np.ones((X.shape[n], B.shape[1]))
#print 'n:',n
xsubs = X.subs[:, n]
#print 'xsub:',xsubs.shape
if C != None:
v = np.sum(np.multiply(B[xsubs, :], Pi) + C, axis=1)
#print 'v1'
else:
#print 'B[xsubs,:]',B[xsubs,:].shape
#print 'Pi',Pi.shape
#print 'np.multiply(B[xsubs,:], Pi)',np.multiply(B[xsubs,:], Pi).shape
#print np.multiply(B[xsubs,:], Pi)
v = np.sum(np.array(np.multiply(B[xsubs, :], Pi)), axis=1)
#print 'v2'
#print 'v size:',v.shape
#print v
#print '___v size',v.shape
for i in range(len(v)):
if v[i] == 0:
v[i] = 1.0
wvals = X.vals.flatten() / v
#print('wvals',wvals.shape)
for r in range(B.shape[1]):
#print 'r',r
#print 'np.array(xsubs)',np.array(xsubs).shape
Pi = np.array(Pi)
#print 'Pi[:,r]',Pi[:,r].shape
#print 'np.array(np.multiply(wvals, Pi[:,r]))',np.array(np.multiply(Pi[:,r],np.array(wvals) )).shape
Phi[:,
r] = accumarray.accum_np(np.array(xsubs),
np.array(np.multiply(wvals, Pi[:,
r])),
size=X.shape[n])
#print 'Phi[:,r]',Phi[:,r].shape
else:
Xn = tenmat.tenmat(X, [n])
V = np.inner(B, Pi)
W = Xn.data / np.maximum(V, epsilon)
Phi = np.inner(W, Pi.transpose())
return Phi
def __calculatePhi(X, M, R, n, Pi, epsilon):
"""
Calculate the matrix for multiplicative update
"""
Phi = None
if X.__class__ == sptensor.sptensor:
Phi = -np.ones((X.shape[n], R))
xsubs = X.subs[:,n]
v = np.sum(np.multiply(M.U[n][xsubs,:], Pi), axis=1)
wvals = X.vals.flatten() / np.maximum(v, epsilon)
for r in range(R):
#Phi[:,r] = tools.accum_np(xsubs, np.multiply(wvals, Pi[:,r]), X.shape[n])
Phi[:,r] = accumarray.accum_np(xsubs, np.multiply(wvals, Pi[:,r]), size=X.shape[n])
else:
Xn = tenmat.tenmat(X,[n])
V = np.inner(M.U[n],Pi)
W = Xn.data / np.maximum(V, epsilon)
Phi = np.inner(W, Pi.transpose())
return Phi
