def _compute_projections(tensor_slices, factors, svd_fun, out=None):
A, B, C = factors
if out is None:
out = [
T.zeros((tensor_slice.shape[0], C.shape[1]),
**T.context(tensor_slice))
for tensor_slice in tensor_slices
]
slice_idxes = range(T.shape(A)[0])
for projection, i, tensor_slice in zip(out, slice_idxes, tensor_slices):
a_i = A[i]
lhs = T.dot(B, T.transpose(a_i * C))
rhs = T.transpose(tensor_slice)
U, S, Vh = svd_fun(T.dot(lhs, rhs), n_eigenvecs=A.shape[1])
out[i] = tl.index_update(projection, tl.index[:],
T.transpose(T.dot(U, Vh)))
return out
def _project_tensor_slices(tensor_slices, projections, out=None):
if out is None:
rank = projections[0].shape[1]
num_slices = len(tensor_slices)
num_cols = tensor_slices[0].shape[1]
out = T.zeros((num_slices, rank, num_cols),
**T.context(tensor_slices[0]))
for i, (tensor_slice,
projection) in enumerate(zip(tensor_slices, projections)):
slice_ = T.dot(T.transpose(projection), tensor_slice)
out = tl.index_update(out, tl.index[i, :], slice_)
return out