def test1():
A = ttensor.ttensor(tensor.tenrands([2, 3, 4]), [numpy.random.random([10, 2]), numpy.random.random([30, 3]), numpy.random.random([40, 4])]).totensor()
[a, b] = dta(A, [1, 2, 3])
print a
print b
Core = numpy.arange(24).reshape([2, 3, 4])
# Core = numpy.array([[1,3,5],[2,4,6]] , [[7,9,11],[8,10,12]])
u1 = numpy.array([[1, 2], [3, 4]])
u2 = numpy.array([[0, 1, 0], [1, 0, 1], [1, 1, 1]])
u3 = numpy.array([[1, 1, 1, 1], [1, 2, 3, 4], [1, 1, 1, 1]])
tt = ttensor.ttensor(tensor.tensor(Core), [u1, u2, u3])
print tt
[a, b] = dta(tt.totensor(), [1, 2, 3])
print a
print a.totensor()
print b
def test1():
A = ttensor.ttensor(tensor.tenrands([2, 3, 4]), [
numpy.random.random([10, 2]),
numpy.random.random([30, 3]),
numpy.random.random([40, 4])
]).totensor()
[a, b] = dta(A, [1, 2, 3])
print a
print b
Core = numpy.arange(24).reshape([2, 3, 4])
# Core = numpy.array([[1,3,5],[2,4,6]] , [[7,9,11],[8,10,12]])
u1 = numpy.array([[1, 2], [3, 4]])
u2 = numpy.array([[0, 1, 0], [1, 0, 1], [1, 1, 1]])
u3 = numpy.array([[1, 1, 1, 1], [1, 2, 3, 4], [1, 1, 1, 1]])
tt = ttensor.ttensor(tensor.tensor(Core), [u1, u2, u3])
print tt
[a, b] = dta(tt.totensor(), [1, 2, 3])
print a
print a.totensor()
print b
def dta(new_tensor, rank, variance_matrix_list=None, alpha=None):
"""Dynamic Tensor Analysis"""
# number of order of the input tensor.
order = new_tensor.ndims()
# If the co-variacne matrices are not given,
# initialize all of them to be 0
if variance_matrix_list is None:
variance_matrix_list = []
dv = new_tensor.shape
for i in range(0, order):
variance_matrix_list.extend(
[sparse.coo_matrix(([], ([], [])), [dv[i], dv[i]])])
# If the forgetting factor is not given, it is 1.
if alpha is None:
alpha = 1
u = []
new_variance_matrix_list = []
for i in range(0, order):
if new_tensor.__class__ == tensor.tensor:
new_tensor_matricize = tenmat.tenmat(new_tensor, [i]).tondarray()
elif new_tensor.__class__ == sptensor.sptensor:
new_tensor_matricize = sptenmat.sptenmat(new_tensor,
[i]).tosparsemat()
elif new_tensor.__class__ == ttensor.ttensor:
raise TypeError("It is not supported yet.")
return
else:
raise TypeError(
"1st argument must be tensor, sptensor, or ttensor")
return
new_variance_matrix_list.extend([
numpy.array(alpha * variance_matrix_list[i] + numpy.dot(
new_tensor_matricize, new_tensor_matricize.transpose()))
])
# print "new,", new_variance_matrix_list
(eigenvalue, eigenmatrix) = eigwrapper(new_variance_matrix_list[i],
rank[i])
u.extend([numpy.array(eigenmatrix)])
# print new_tensor
core = new_tensor.ttm(u, None, 't')
reconstruct_tensor = ttensor.ttensor(core, u)
print "core:", sparsity(core.tondarray().tolist())
return reconstruct_tensor, new_variance_matrix_list
def dta(new_tensor, rank, variance_matrix_list=None, alpha=None):
"""Dynamic Tensor Analysis"""
# number of order of the input tensor.
order = new_tensor.ndims()
# If the co-variacne matrices are not given,
# initialize all of them to be 0
if variance_matrix_list is None:
variance_matrix_list = []
dv = new_tensor.shape
for i in range(0, order):
variance_matrix_list.extend([sparse.coo_matrix(([], ([], [])), [dv[i], dv[i]])])
# If the forgetting factor is not given, it is 1.
if alpha is None:
alpha = 1
u = []
new_variance_matrix_list = []
for i in range(0, order):
if new_tensor.__class__ == tensor.tensor:
new_tensor_matricize = tenmat.tenmat(new_tensor, [i]).tondarray()
elif new_tensor.__class__ == sptensor.sptensor:
new_tensor_matricize = sptenmat.sptenmat(new_tensor, [i]).tosparsemat()
elif new_tensor.__class__ == ttensor.ttensor:
raise TypeError("It is not supported yet.")
return
else:
raise TypeError("1st argument must be tensor, sptensor, or ttensor")
return
new_variance_matrix_list.extend([numpy.array(alpha*variance_matrix_list[i] + numpy.dot(new_tensor_matricize, new_tensor_matricize.transpose()))])
# print "new,", new_variance_matrix_list
(eigenvalue, eigenmatrix) = eigwrapper(new_variance_matrix_list[i], rank[i])
u.extend([numpy.array(eigenmatrix)])
# print new_tensor
core = new_tensor.ttm(u, None, 't')
reconstruct_tensor = ttensor.ttensor(core, u)
print "core:", sparsity(core.tondarray().tolist())
return reconstruct_tensor, new_variance_matrix_list