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 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_dimscheck(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 totensor(self):
sz = self.tsize;
order = numpy.concatenate((self.rindices, self.cindices));
order = order.tolist();
data = self.data.reshape(tools.getelts(sz,order));
data = tensor.tensor(data).ipermute(order).data;
return tensor.tensor(data, sz);
def totensor(self):
sz = self.tsize
order = numpy.concatenate((self.rindices, self.cindices))
order = order.tolist()
data = self.data.reshape(tools.getelts(sz, order))
data = tensor.tensor(data).ipermute(order).data
return tensor.tensor(data, sz)
def __init__(self, T, rdim=None, cdim=None, tsiz=None, option=None):
"""Create a sptenmat object from a given ndarray or sptensor T"""
if (rdim != None and rdim.__class__ == list):
rdim = numpy.array(rdim)
if (cdim != None and cdim.__class__ == list):
cdim = numpy.array(cdim)
if (tsiz != None and tsiz.__class__ == list):
tsiz = numpy.array(tsiz)
#subs, vals, rdims, cdims, tsize are all given
#When I is a (2-D) ndarray or sptensor, and rdim, cdim, tsiz are given
if (rdim != None and cdim != None and tsiz != None):
B = T.flatten().reshape(len(T), T.size / len(T))
subs = []
vals = []
maxrowind = 0
maxcolind = 0
for i in range(0, len(B)):
for j in range(0, len(B[0])):
if (B[i][j] != 0):
subs.extend([[i, j]])
vals.extend([B[i][j]])
if (i > maxrowind): maxrowind = i
if (j > maxcolind): maxcolind = j
self.subs = numpy.array(subs)
self.vals = numpy.array(vals)
self.rdims = rdim.copy()
self.cdims = cdim.copy()
self.tsize = tsiz
n = len(self.tsize)
temp = numpy.concatenate((self.rdims, self.cdims))
temp.sort()
if not ((numpy.arange(n) == temp).all()):
raise ValueError("Incorrect specification of dimensions")
if (tools.getelts(self.tsize, self.rdims).prod() < maxrowind):
raise ValueError("error, invalid row index")
if (tools.getelts(self.tsize, self.cdims).prod() < maxcolind):
raise ValueError("error, invalid column index")
return
# T is a sptensor
T = T.copy()
self.tsize = T.shape
self.subs = T.subs
self.vals = T.vals
n = T.ndims()
if (rdim != None):
if (cdim != None):
self.rdims = rdim
self.cdims = cdim
elif (option != None):
if (option == 'fc'):
self.rdims = rdim
if (self.rdims.size != 1):
raise ValueError(
"only one row dimension for 'fc' option")
self.cdims = []
for i in range(self.rdim[0] + 1, n):
self.cdims.append(i)
for i in range(0, self.rdim[0]):
self.cdims.append(i)
self.cdims = numpy.array(self.cdims)
elif (option == 'bc'):
self.rdims = rdim
if (self.rdims.size != 1):
raise ValueError(
"only one row dimension for 'bc' option")
self.cdims = []
for i in range(0, self.rdim[0])[::-1]:
self.cdims.append(i)
for i in range(self.rdim[0] + 1, n)[::-1]:
self.cdims.append(i)
self.cdims = numpy.array(self.cdims)
else:
raise ValueError("unknown option: {0}".format(option))
else:
self.rdims = rdim
self.cdims = tools.notin(n, self.rdims)
elif (cdim != None):
self.cdims = cdim
if (option == 't'):
self.rdims = tools.notin(n, self.cdims)
else:
raise ValueError("unknown option: {0}".format(option))
else:
raise ValueError("Both rdims and cdims are None")
#error check
temp = numpy.concatenate((self.rdims, self.cdims))
temp.sort()
if not ((numpy.arange(n) == temp).all()):
raise ValueError("Incorrect specification of dimensions")
rsize = tools.getelts(self.tsize, self.rdims)
csize = tools.getelts(self.tsize, self.cdims)
if (len(rsize) == 0):
ridx = numpy.ndarray([T.nnz()])
ridx.fill(0)
else:
temp1 = []
for i in range(0, len(self.subs)):
temp2 = []
for j in range(0, len(self.rdims)):
temp2.extend([self.subs[i][self.rdims[j]]])
temp1.extend([temp2])
temp1 = numpy.array(temp1)
ridx = tools.sub2ind(rsize, temp1)
if (len(csize) == 0):
cidx = numpy.ndarray([T.nnz()])
cidx.fill(0)
else:
temp1 = []
for i in range(0, len(self.subs)):
temp2 = []
for j in range(0, len(self.cdims)):
temp2.extend([self.subs[i][self.cdims[j]]])
temp1.extend([temp2])
temp1 = numpy.array(temp1)
cidx = tools.sub2ind(csize, temp1)
self.subs = []
for i in range(0, len(ridx)):
self.subs.extend([[ridx[i][0], cidx[i][0]]])
self.subs = numpy.array(self.subs)
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 __init__(self, T, rdim = None, cdim = None, tsiz = None, option = None):
if(rdim is not None and rdim.__class__ == list):
rdim = numpy.array(rdim);
if(cdim is not None and cdim.__class__ == list):
cdim = numpy.array(cdim);
if(tsiz is not None and tsiz.__class__ == list):
tsiz = numpy.array(tsiz);
#constructor for the call tenmat(A, RDIMS, CDIMS, TSIZE)
if(rdim is not None and cdim is not None and tsiz is not None):
if(T.__class__ == numpy.ndarray):
# self.data = T.copy();
self.data = T
if(T.__class__ == tensor.tensor):
# self.data = T.data.copy();
self.data = T.data
self.rindices = rdim;
self.cindices = cdim;
# self.tsize = tuple(tsiz);
self.tsize = tsiz
n = len(self.tsize);
temp = numpy.concatenate((self.rindices,self.cindices));
temp.sort();
if not ((numpy.arange(n) == temp).all()):
raise ValueError("Incorrect specification of dimensions");
elif (tools.getelts(self.tsize, self.rindices).prod()
!= len(self.data)):
raise ValueError("size(T,0) does not match size specified");
elif (tools.getelts(self.tsize, self.cindices).prod()
!= len(self.data[0])):
raise ValueError("size(T,1) does not match size specified");
return;
#convert tensor to a tenmat
if(rdim is None and cdim is None):
self.data = None;
self.rindices = None;
self.cindices = None;
self.tsize = None;
return
# raise ValueError("Both of rdim and cdim are not given");
# T = T.copy(); #copy the tensor
self.tsize = numpy.array(T.shape);
n = T.ndims();
if (rdim is not None):
if(cdim is not None):
rdims = rdim;
cdims = cdim;
elif(option is not None):
if(option == 'fc'):
rdims = rdim;
if(rdims.size != 1):
raise ValueError("only one row dimension for 'fc' option");
cdims = [];
for i in range(rdim[0]+1,n):
cdims.append(i);
for i in range(0, rdim[0]):
cdims.append(i);
cdims = numpy.array(cdims);
elif(option == 'bc'):
rdims = rdim;
if(rdims.size != 1):
raise ValueError("only one row dimension for 'bc' option");
cdims = [];
for i in range(0, rdim[0])[::-1]:
cdims.append(i);
for i in range(rdim[0]+1,n)[::-1]:
cdims.append(i);
cdims = numpy.array(cdims);
elif(option == 't'):
cdims = rdim
rdims = tools.notin(n, cdims)
else:
raise ValueError("unknown option {0}".format(option));
#一般只指定rdims的执行这里
else:
rdims = rdim;
cdims = tools.notin(n, rdims);
else:
if(option == 't'):
cdims = cdim;
rdims = tools.notin(n, cdims);
else:
raise ValueError("unknown option {0}".format(option));
#error check
temp = numpy.concatenate((rdims,cdims));
temp.sort();
if not ((numpy.arange(n) == temp).all()):
raise ValueError("error, Incorrect specification of dimensions");
#permute T so that the dimensions specified by RDIMS come first
#!!!! order of data in ndarray is different from that in Matlab!
#this is (kind of odd process) needed to conform the result with Matlab!
#lis = list(T.shape);
#temp = lis[T.ndims()-1];
#lis[T.ndims()-1] = lis[T.ndims()-2];
#lis[T.ndims()-2] = temp;
#T.data = T.data.reshape(lis).swapaxes(T.ndims()-1, T.ndims()-2);
#print T;
#T = T.permute([T.ndims()-1, T.ndims()-2]+(range(0,T.ndims()-2)));
#print T;
# ------原始是permute------------
# T = T.permute(numpy.concatenate((rdims,cdims)));
#convert T to a matrix;
# -------这里我改成了without_copy
T = T.permute_without_copy(numpy.concatenate((rdims,cdims)));
row = tools.getelts(self.tsize, rdims).prod()
col = tools.getelts(self.tsize, cdims).prod()
self.data = T.data.reshape([row, col]);
self.rindices = rdims;
self.cindices = cdims;
def __init__(self, T, rdim=None, cdim=None, tsiz=None, option=None):
"""
A matricized tensor object.
Converting a tensor to a matrix requires an ordered mapping of the tensor indices for the rows and columns
"""
if (rdim != None and rdim.__class__ == list):
rdim = numpy.array(rdim)
if (cdim != None and cdim.__class__ == list):
cdim = numpy.array(cdim)
if (tsiz != None and tsiz.__class__ == list):
tsiz = numpy.array(tsiz)
#constructor for the call tenmat(A, RDIMS, CDIMS, TSIZE)
if (rdim != None and cdim != None and tsiz != None):
if (T.__class__ == numpy.ndarray):
self.data = T.copy()
if (T.__class__ == tensor.tensor):
self.data = T.data.copy()
self.rindices = rdim
self.cindices = cdim
self.tsize = tuple(tsiz)
n = len(self.tsize)
temp = numpy.concatenate((self.rindices, self.cindices))
temp.sort()
if not ((numpy.arange(n) == temp).all()):
raise ValueError("Incorrect specification of dimensions")
elif (tools.getelts(self.tsize, self.rindices).prod() != len(
self.data)):
raise ValueError("size(T,0) does not match size specified")
elif (tools.getelts(self.tsize, self.cindices).prod() != len(
self.data[0])):
raise ValueError("size(T,1) does not match size specified")
return
#convert tensor to a tenmat
if (rdim == None and cdim == None):
raise ValueError("Both of rdim and cdim are not given")
T = T.copy()
#copy the tensor
self.tsize = T.shape
n = T.ndims()
if (rdim != None):
if (cdim != None):
rdims = rdim
cdims = cdim
elif (option != None):
if (option == 'fc'):
rdims = rdim
if (rdims.size != 1):
raise ValueError(
"only one row dimension for 'fc' option")
cdims = []
for i in range(rdim[0] + 1, n):
cdims.append(i)
for i in range(0, rdim[0]):
cdims.append(i)
cdims = numpy.array(cdims)
elif (option == 'bc'):
rdims = rdim
if (rdims.size != 1):
raise ValueError(
"only one row dimension for 'bc' option")
cdims = []
for i in range(0, rdim[0])[::-1]:
cdims.append(i)
for i in range(rdim[0] + 1, n)[::-1]:
cdims.append(i)
cdims = numpy.array(cdims)
else:
raise ValueError("unknown option {0}".format(option))
else:
rdims = rdim
cdims = tools.notin(n, rdims)
else:
if (option == 't'):
cdims = cdim
rdims = tools.notin(n, cdims)
else:
raise ValueError("unknown option {0}".format(option))
#error check
temp = numpy.concatenate((rdims, cdims))
temp.sort()
if not ((numpy.arange(n) == temp).all()):
raise ValueError("error, Incorrect specification of dimensions")
#permute T so that the dimensions specified by RDIMS come first
#!!!! order of data in ndarray is different from that in Matlab!
#this is (kind of odd process) needed to conform the result with Matlab!
#lis = list(T.shape);
#temp = lis[T.ndims()-1];
#lis[T.ndims()-1] = lis[T.ndims()-2];
#lis[T.ndims()-2] = temp;
#T.data = T.data.reshape(lis).swapaxes(T.ndims()-1, T.ndims()-2);
#print T;
#T = T.permute([T.ndims()-1, T.ndims()-2]+(range(0,T.ndims()-2)));
#print T;
T = T.permute(numpy.concatenate((rdims, cdims)))
#convert T to a matrix;
row = tools.getelts(self.tsize, rdims).prod()
col = tools.getelts(self.tsize, cdims).prod()
self.data = T.data.reshape([row, col], order='F')
self.rindices = rdims
self.cindices = cdims
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
def __init__(self, T, rdim=None, cdim=None, tsiz=None, option=None):
"""
A matricized tensor object.
Converting a tensor to a matrix requires an ordered mapping of the tensor indices for the rows and columns
"""
if rdim != None and rdim.__class__ == list:
rdim = numpy.array(rdim)
if cdim != None and cdim.__class__ == list:
cdim = numpy.array(cdim)
if tsiz != None and tsiz.__class__ == list:
tsiz = numpy.array(tsiz)
# constructor for the call tenmat(A, RDIMS, CDIMS, TSIZE)
if rdim != None and cdim != None and tsiz != None:
if T.__class__ == numpy.ndarray:
self.data = T.copy()
if T.__class__ == tensor.tensor:
self.data = T.data.copy()
self.rindices = rdim
self.cindices = cdim
self.tsize = tuple(tsiz)
n = len(self.tsize)
temp = numpy.concatenate((self.rindices, self.cindices))
temp.sort()
if not ((numpy.arange(n) == temp).all()):
raise ValueError("Incorrect specification of dimensions")
elif tools.getelts(self.tsize, self.rindices).prod() != len(self.data):
raise ValueError("size(T,0) does not match size specified")
elif tools.getelts(self.tsize, self.cindices).prod() != len(self.data[0]):
raise ValueError("size(T,1) does not match size specified")
return
# convert tensor to a tenmat
if rdim == None and cdim == None:
raise ValueError("Both of rdim and cdim are not given")
T = T.copy()
# copy the tensor
self.tsize = T.shape
n = T.ndims()
if rdim != None:
if cdim != None:
rdims = rdim
cdims = cdim
elif option != None:
if option == "fc":
rdims = rdim
if rdims.size != 1:
raise ValueError("only one row dimension for 'fc' option")
cdims = []
for i in range(rdim[0] + 1, n):
cdims.append(i)
for i in range(0, rdim[0]):
cdims.append(i)
cdims = numpy.array(cdims)
elif option == "bc":
rdims = rdim
if rdims.size != 1:
raise ValueError("only one row dimension for 'bc' option")
cdims = []
for i in range(0, rdim[0])[::-1]:
cdims.append(i)
for i in range(rdim[0] + 1, n)[::-1]:
cdims.append(i)
cdims = numpy.array(cdims)
else:
raise ValueError("unknown option {0}".format(option))
else:
rdims = rdim
cdims = tools.notin(n, rdims)
else:
if option == "t":
cdims = cdim
rdims = tools.notin(n, cdims)
else:
raise ValueError("unknown option {0}".format(option))
# error check
temp = numpy.concatenate((rdims, cdims))
temp.sort()
if not ((numpy.arange(n) == temp).all()):
raise ValueError("error, Incorrect specification of dimensions")
# permute T so that the dimensions specified by RDIMS come first
#!!!! order of data in ndarray is different from that in Matlab!
# this is (kind of odd process) needed to conform the result with Matlab!
# lis = list(T.shape);
# temp = lis[T.ndims()-1];
# lis[T.ndims()-1] = lis[T.ndims()-2];
# lis[T.ndims()-2] = temp;
# T.data = T.data.reshape(lis).swapaxes(T.ndims()-1, T.ndims()-2);
# print T;
# T = T.permute([T.ndims()-1, T.ndims()-2]+(range(0,T.ndims()-2)));
# print T;
T = T.permute(numpy.concatenate((rdims, cdims)))
# convert T to a matrix;
row = tools.getelts(self.tsize, rdims).prod()
col = tools.getelts(self.tsize, cdims).prod()
self.data = T.data.reshape([row, col], order="F")
self.rindices = rdims
self.cindices = cdims
def __init__(self, T, rdim = None, cdim = None, tsiz = None, option = None):
if(rdim != None and rdim.__class__ == list):
rdim = numpy.array(rdim);
if(cdim != None and cdim.__class__ == list):
cdim = numpy.array(cdim);
if(tsiz != None and tsiz.__class__ == list):
tsiz = numpy.array(tsiz);
#constructor for the call tenmat(A, RDIMS, CDIMS, TSIZE)
if(rdim != None and cdim != None and tsiz != None):
if(T.__class__ == numpy.ndarray):
self.data = T.copy();
if(T.__class__ == tensor.tensor):
self.data = T.data.copy();
self.rindices = rdim;
self.cindices = cdim;
self.tsize = tuple(tsiz);
n = len(self.tsize);
temp = numpy.concatenate((self.rindices,self.cindices));
temp.sort();
if not ((numpy.arange(n) == temp).all()):
raise ValueError("Incorrect specification of dimensions");
elif (tools.getelts(self.tsize, self.rindices).prod()
!= len(self.data)):
raise ValueError("size(T,0) does not match size specified");
elif (tools.getelts(self.tsize, self.cindices).prod()
!= len(self.data[0])):
raise ValueError("size(T,1) does not match size specified");
return;
#convert tensor to a tenmat
if(rdim == None and cdim == None):
raise ValueError("Both of rdim and cdim are not given");
T = T.copy(); #copy the tensor
self.tsize = T.shape;
n = T.ndims();
if (rdim != None):
if(cdim != None):
rdims = rdim;
cdims = cdim;
elif(option != None):
if(option == 'fc'):
rdims = rdim;
if(rdims.size != 1):
raise ValueError("only one row dimension for 'fc' option");
cdims = [];
for i in range(rdim[0]+1,n):
cdims.append(i);
for i in range(0, rdim[0]):
cdims.append(i);
cdims = numpy.array(cdims);
elif(option == 'bc'):
rdims = rdim;
if(rdims.size != 1):
raise ValueError("only one row dimension for 'bc' option");
cdims = [];
for i in range(0, rdim[0])[::-1]:
cdims.append(i);
for i in range(rdim[0]+1,n)[::-1]:
cdims.append(i);
cdims = numpy.array(cdims);
else:
raise ValueError("unknown option {0}".format(option));
else:
rdims = rdim;
cdims = tools.notin(n, rdims);
else:
if(option == 't'):
cdims = cdim;
rdims = tools.notin(n, cdims);
else:
raise ValueError("unknown option {0}".format(option));
#error check
temp = numpy.concatenate((rdims,cdims));
temp.sort();
if not ((numpy.arange(n) == temp).all()):
raise ValueError("error, Incorrect specification of dimensions");
#permute T so that the dimensions specified by RDIMS come first
#!!!! order of data in ndarray is different from that in Matlab!
#this is (kind of odd process) needed to conform the result with Matlab!
#lis = list(T.shape);
#temp = lis[T.ndims()-1];
#lis[T.ndims()-1] = lis[T.ndims()-2];
#lis[T.ndims()-2] = temp;
#T.data = T.data.reshape(lis).swapaxes(T.ndims()-1, T.ndims()-2);
#print T;
#T = T.permute([T.ndims()-1, T.ndims()-2]+(range(0,T.ndims()-2)));
#print T;
T = T.permute(numpy.concatenate((rdims,cdims)));
#convert T to a matrix;
row = tools.getelts(self.tsize, rdims).prod()
col = tools.getelts(self.tsize, cdims).prod()
self.data = T.data.reshape([row, col]);
self.rindices = rdims;
self.cindices = cdims;
def __init__(self, T, rdim = None, cdim = None, tsiz = None, option = None):
"""Create a sptenmat object from a given ndarray or sptensor T"""
if(rdim != None and rdim.__class__ == list):
rdim = numpy.array(rdim);
if(cdim != None and cdim.__class__ == list):
cdim = numpy.array(cdim);
if(tsiz != None and tsiz.__class__ == list):
tsiz = numpy.array(tsiz);
#subs, vals, rdims, cdims, tsize are all given
#When I is a (2-D) ndarray or sptensor, and rdim, cdim, tsiz are given
if(rdim != None and cdim != None and tsiz != None):
B = T.flatten().reshape(len(T), T.size / len(T));
subs = [];
vals = [];
maxrowind = 0;
maxcolind = 0;
for i in range(0,len(B)):
for j in range(0, len(B[0])):
if(B[i][j] != 0):
subs.extend([[i,j]]);
vals.extend([B[i][j]]);
if(i > maxrowind): maxrowind = i;
if(j > maxcolind): maxcolind = j;
self.subs = numpy.array(subs);
self.vals = numpy.array(vals);
self.rdims = rdim.copy();
self.cdims = cdim.copy();
self.tsize = tsiz;
n = len(self.tsize);
temp = numpy.concatenate((self.rdims,self.cdims));
temp.sort();
if not ((numpy.arange(n) == temp).all()):
raise ValueError ("Incorrect specification of dimensions");
if (tools.getelts(self.tsize, self.rdims).prod() < maxrowind):
raise ValueError ("error, invalid row index");
if (tools.getelts(self.tsize, self.cdims).prod() < maxcolind):
raise ValueError ("error, invalid column index");
return;
# T is a sptensor
T = T.copy();
self.tsize = T.shape;
self.subs = T.subs;
self.vals = T.vals;
n = T.ndims();
if (rdim != None):
if(cdim != None):
self.rdims = rdim;
self.cdims = cdim;
elif(option != None):
if(option == 'fc'):
self.rdims = rdim;
if(self.rdims.size != 1):
raise ValueError ("only one row dimension for 'fc' option");
self.cdims = [];
for i in range(self.rdim[0]+1,n):
self.cdims.append(i);
for i in range(0, self.rdim[0]):
self.cdims.append(i);
self.cdims = numpy.array(self.cdims);
elif(option == 'bc'):
self.rdims = rdim;
if(self.rdims.size != 1):
raise ValueError ("only one row dimension for 'bc' option");
self.cdims = [];
for i in range(0, self.rdim[0])[::-1]:
self.cdims.append(i);
for i in range(self.rdim[0]+1,n)[::-1]:
self.cdims.append(i);
self.cdims = numpy.array(self.cdims);
else:
raise ValueError ("unknown option: {0}".format(option));
else:
self.rdims = rdim;
self.cdims = tools.notin(n, self.rdims);
elif(cdim != None):
self.cdims = cdim;
if(option == 't'):
self.rdims = tools.notin(n, self.cdims);
else:
raise ValueError ("unknown option: {0}".format(option));
else:
raise ValueError("Both rdims and cdims are None");
#error check
temp = numpy.concatenate((self.rdims,self.cdims));
temp.sort();
if not ((numpy.arange(n) == temp).all()):
raise ValueError ("Incorrect specification of dimensions");
rsize = tools.getelts(self.tsize, self.rdims);
csize = tools.getelts(self.tsize, self.cdims);
if (len(rsize) == 0):
ridx = numpy.ndarray([T.nnz()]);
ridx.fill(0);
else:
temp1 = [];
for i in range (0, len(self.subs)):
temp2 = [];
for j in range(0, len(self.rdims)):
temp2.extend([self.subs[i][self.rdims[j]]]);
temp1.extend([temp2]);
temp1 = numpy.array(temp1);
ridx = tools.sub2ind(rsize, temp1);
if (len(csize) == 0):
cidx = numpy.ndarray([T.nnz()]);
cidx.fill(0);
else:
temp1 = [];
for i in range (0, len(self.subs)):
temp2 = [];
for j in range(0, len(self.cdims)):
temp2.extend([self.subs[i][self.cdims[j]]]);
temp1.extend([temp2]);
temp1 = numpy.array(temp1);
cidx = tools.sub2ind(csize, temp1);
self.subs = [];
for i in range(0,len(ridx)):
self.subs.extend([[ridx[i][0], cidx[i][0]]]);
self.subs = numpy.array(self.subs);
