def ttm(self, mat, dims = None, option = None):
""" computes the tensor times the given matrix.
arrs is a single 2-D matrix/array or a list of those matrices/arrays."""
if(dims == None):
dims = range(0,self.ndims());
#Handle when arrs is a list of arrays
if(mat.__class__ == list):
if(len(mat) == 0):
raise ValueError("the given list of arrays is empty!");
(dims,vidx) = tools.tt_dimscehck(dims, self.ndims(), len(mat));
Y = self.ttm(mat[vidx[0]],dims[0],option);
for i in range(1, len(dims)):
Y = Y.ttm(mat[vidx[i]],dims[i],option);
return Y;
if(mat.ndim != 2):
raise ValueError ("matrix in 2nd armuent must be a matrix!");
if(dims.__class__ == list):
if(len(dims) != 1):
raise ValueError("Error in number of elements in dims");
else:
dims = dims[0];
if(dims < 0 or dims > self.ndims()):
raise ValueError ("Dimension N must be between 1 and num of dimensions");
#Compute the product
N = self.ndims();
shp = self.shape;
order = []
order.extend([dims]);
order.extend(range(0,dims));
order.extend(range(dims+1,N));
newdata = self.permute(order).data;
newdata = newdata.reshape(shp[dims], tools.prod(shp)/shp[dims]);
if(option == None):
newdata = numpy.dot(mat, newdata);
p = mat.shape[0];
elif(option == 't'):
newdata = numpy.dot(mat.transpose(), newdata);
p = mat.shape[1];
else:
raise ValueError("Unknown option");
newshp = [p];
newshp.extend(tools.getelts(shp,range(0,dims)));
newshp.extend(tools.getelts(shp,range(dims+1,N)));
Y = tensor(newdata, newshp);
Y = Y.ipermute(order);
return Y;
def ttm(self, mat, dims = None, option = None):
""" computes the sptensor times the given matrix.
arrs is a single 2-D matrix/array or a list of those matrices/arrays."""
if(dims == None):
dims = range(0,self.ndims());
#Handle when arrs is a list of arrays
if(mat.__class__ == list):
if(len(mat) == 0):
raise ValueError("the given list of arrays is empty!");
(dims,vidx) = tools.tt_dimscehck(dims, self.ndims(), len(mat));
Y = self.ttm(mat[vidx[0]],dims[0],option);
for i in range(1, len(dims)):
Y = Y.ttm(mat[vidx[i]],dims[i],option);
return Y;
if(mat.ndim != 2):
raise ValueError ("matrix in 2nd armuent must be a matrix!");
if(option != None):
if (option == 't'):
mat = mat.transpose();
else:
raise ValueError ("unknown option.");
if(dims.__class__ == list):
if(len(dims) != 1):
raise ValueError("Error in number of elements in dims");
else:
dims = dims[0];
if(dims < 0 or dims > self.ndims()):
raise ValueError ("Dimension N must be between 1 and num of dimensions");
#Check that sizes match
if(self.shape[dims] != mat.shape[1]):
raise ValueError ("size mismatch on V");
#Compute the new size
newsiz = list(self.shape);
newsiz[dims] = mat.shape[0];
#Compute Xn
Xnt = sptenmat.sptenmat(self,None,[dims],None,'t');
rdims = Xnt.rdims;
cdims = Xnt.cdims;
I = [];
J = [];
for i in range(0, len(Xnt.subs)):
I.extend([Xnt.subs[i][0]]);
J.extend([Xnt.subs[i][1]]);
Z = (sparse.coo_matrix((Xnt.vals.flatten(),(I,J)),
shape = (tools.getelts(Xnt.tsize, Xnt.rdims).prod(),
tools.getelts(Xnt.tsize, Xnt.cdims).prod()))
* mat.transpose());
Z = tensor.tensor(Z,newsiz).tosptensor();
if(Z.nnz() <= 0.5 * numpy.array(newsiz).prod()):
Ynt = sptenmat.sptenmat(Z, rdims, cdims);
return Ynt.tosptensor();
else:
Ynt = tenmat.tenmat(Z.totensor(), rdims, cdims);
return Ynt.totensor();
def ttm(self, mat, dims = None, option = None):
""" computes the sptensor times the given matrix.
arrs is a single 2-D matrix/array or a list of those matrices/arrays."""
if(dims == None):
dims = range(0,self.ndims());
#Handle when arrs is a list of arrays
if(mat.__class__ == list):
if(len(mat) == 0):
raise ValueError("the given list of arrays is empty!");
(dims,vidx) = tools.tt_dimscehck(dims, self.ndims(), len(mat));
Y = self.ttm(mat[vidx[0]],dims[0],option);
for i in range(1, len(dims)):
Y = Y.ttm(mat[vidx[i]],dims[i],option);
return Y;
if(mat.ndim != 2):
raise ValueError ("matrix in 2nd armuent must be a matrix!");
if(option != None):
if (option == 't'):
mat = mat.transpose();
else:
raise ValueError ("unknown option.");
if(dims.__class__ == list):
if(len(dims) != 1):
raise ValueError("Error in number of elements in dims");
else:
dims = dims[0];
if(dims < 0 or dims > self.ndims()):
raise ValueError ("Dimension N must be between 1 and num of dimensions");
#Check that sizes match
if(self.shape[dims] != mat.shape[1]):
raise ValueError ("size mismatch on V");
#Compute the new size
newsiz = list(self.shape);
newsiz[dims] = mat.shape[0];
#Compute Xn
Xnt = sptenmat.sptenmat(self,None,[dims],None,'t');
rdims = Xnt.rdims;
cdims = Xnt.cdims;
I = [];
J = [];
for i in range(0, len(Xnt.subs)):
I.extend([Xnt.subs[i][0]]);
J.extend([Xnt.subs[i][1]]);
Z = (sparse.coo_matrix((Xnt.vals.flatten(),(I,J)),
shape = (tools.getelts(Xnt.tsize, Xnt.rdims).prod(),
tools.getelts(Xnt.tsize, Xnt.cdims).prod()))
* mat.transpose());
Z = tensor.tensor(Z,newsiz).tosptensor();
if(Z.nnz() <= 0.5 * numpy.array(newsiz).prod()):
Ynt = sptenmat.sptenmat(Z, rdims, cdims);
return Ynt.tosptensor();
else:
Ynt = tenmat.tenmat(Z.totensor(), rdims, cdims);
return Ynt.totensor();
def ttm(self, mat, dims=None, option=None):
""" computes the tensor times the given matrix.
arrs is a single 2-D matrix/array or a list of those matrices/arrays."""
if (dims == None):
dims = range(0, self.ndims())
#Handle when arrs is a list of arrays
if (mat.__class__ == list):
if (len(mat) == 0):
raise ValueError("the given list of arrays is empty!")
(dims, vidx) = tools.tt_dimscehck(dims, self.ndims(), len(mat))
Y = self.ttm(mat[vidx[0]], dims[0], option)
for i in range(1, len(dims)):
Y = Y.ttm(mat[vidx[i]], dims[i], option)
return Y
if (mat.ndim != 2):
raise ValueError("matrix in 2nd armuent must be a matrix!")
if (dims.__class__ == list):
if (len(dims) != 1):
raise ValueError("Error in number of elements in dims")
else:
dims = dims[0]
if (dims < 0 or dims > self.ndims()):
raise ValueError(
"Dimension N must be between 1 and num of dimensions")
#Compute the product
N = self.ndims()
shp = self.shape
order = []
order.extend([dims])
order.extend(range(0, dims))
order.extend(range(dims + 1, N))
# 第一步把对应模I Permute到第一个,然后reshape成(I,I~)
# 第二步matrix * 上面得到的矩阵
newdata = self.permute(order).data
newdata = newdata.reshape(shp[dims],
tools.prod(shp) / shp[dims])
if (option == None):
newdata = numpy.dot(mat, newdata)
p = mat.shape[0]
elif (option == 't'):
newdata = numpy.dot(mat.transpose(), newdata)
p = mat.shape[1]
else:
raise ValueError("Unknown option")
newshp = [p]
newshp.extend(tools.getelts(shp, range(0, dims)))
newshp.extend(tools.getelts(shp, range(dims + 1, N)))
Y = tensor(newdata, newshp)
# 这里的ipermute很关键,按照相反permute,理解的不是很直观,有时间可以多想想
Y = Y.ipermute(order)
return Y
