def totensor(self):
"""returns a new tensor object that contains the same values"""
temp = numpy.ndarray(self.shape);
temp.fill(0);
for i in range(0, len(self.vals)):
temp.put(tools.sub2ind(self.shape, self.subs[i])[0], self.vals[i][0]);
return tensor.tensor(temp, self.shape);
def totensor(self):
"""returns a new tensor object that contains the same values"""
temp = numpy.ndarray(self.shape);
temp.fill(0);
for i in range(0, len(self.vals)):
temp.put(tools.sub2ind(self.shape, self.subs[i])[0], self.vals[i][0]);
return tensor.tensor(temp, self.shape);
def tendiag(vals, shape=None):
"""special constructor, construc a tensor with the values in the diagonal"""
#if shape is None or
#number of dimensions of shape is less than the number of values given
if (shape == None or len(shape) < len(vals)):
shape = [len(vals)] * len(vals)
else:
shape = list(shape)
for i in range(0, len(vals)):
if (shape[i] < len(vals)):
shape[i] = len(vals)
data = numpy.ndarray(shape)
data.fill(0)
# put the values in the ndarray
for i in range(0, len(vals)):
data.put(tools.sub2ind(shape, [i] * len(shape)), vals[i])
return tensor(data, shape)
def tendiag(vals, shape=None):
"""special constructor, construc a tensor with the values in the diagonal"""
#if shape is None or
#number of dimensions of shape is less than the number of values given
if (shape == None or len(shape) < len(vals)):
shape = [len(vals)]*len(vals);
else:
shape = list(shape);
for i in range(0, len(vals)):
if(shape[i] < len(vals)):
shape[i] = len(vals);
data = numpy.ndarray(shape);
data.fill(0);
# put the values in the ndarray
for i in range(0, len(vals)):
data.put(tools.sub2ind(shape,[i]*len(shape)), vals[i]);
return tensor(data, shape);
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 __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);
