def __init__(self,
subs,
vals,
shape = None,
func = sum.__call__):
"""Create a sptensor object"""
if(subs.__class__ == list):
subs = numpy.array(subs);
if(vals.__class__ == list):
vals = numpy.array(vals);
if(shape.__class__ == list):
shape = numpy.array(shape);
if not(tools.tt_subscheck(subs)):
raise ValueError("Error in subscripts");
if not(tools.tt_valscheck(vals)):
raise ValueError("Error in values");
if (shape != None and not tools.tt_sizecheck(shape)):
raise ValueError("Error in shape");
if(vals.size != 0 and vals.size != 1 and len(vals) != len(subs)):
raise ValueError("Number of subscripts and values must be equal");
if (shape == None):
self.shape = tuple(subs.max(0) + 1);
else:
self.shape = tuple(shape);
# if func is given by user
if(func != None):
self.func = func;
if(subs.size == 0):
nzsub = numpy.array([]);
nzval = numpy.array([]);
else:
(newsub, loc) = uniquerows(subs);
newval = numpy.ndarray([len(newsub), 1]);
newval.fill(0);
for i in range(0, len(loc)):
newval[(int)(loc[i])] = func(vals[i], newval[(int)(loc[i])]);
nzsub = newsub.tolist();
nzval = newval.tolist();
i = 0;
while (i < len(nzsub)):
if(nzval[i][0] == 0):
nzsub.remove(nzsub[i]);
nzval.remove(nzval[i]);
else:
i = i+1;
self.subs = numpy.array(nzsub);
self.vals = numpy.array(nzval);
def __init__(self,
subs,
vals,
shape = None,
func = sum.__call__):
"""Create a sptensor object"""
if(subs.__class__ == list):
subs = numpy.array(subs);
if(vals.__class__ == list):
vals = numpy.array(vals);
if(shape.__class__ == list):
shape = numpy.array(shape);
if not(tools.tt_subscheck(subs)):
raise ValueError("Error in subscripts");
if not(tools.tt_valscheck(vals)):
raise ValueError("Error in values");
if (shape != None and not tools.tt_sizecheck(shape)):
raise ValueError("Error in shape");
if(vals.size != 0 and vals.size != 1 and len(vals) != len(subs)):
raise ValueError("Number of subscripts and values must be equal");
if (shape == None):
self.shape = tuple(subs.max(0) + 1);
else:
self.shape = tuple(shape);
# if func is given by user
if(func != None):
self.func = func;
if(subs.size == 0):
nzsub = numpy.array([]);
nzval = numpy.array([]);
else:
(newsub, loc) = uniquerows(subs);
newval = numpy.ndarray([len(newsub), 1]);
newval.fill(0);
for i in range(0, len(loc)):
newval[(int)(loc[i])] = func(vals[i], newval[(int)(loc[i])]);
nzsub = newsub.tolist();
nzval = newval.tolist();
i = 0;
while (i < len(nzsub)):
if(nzval[i][0] == 0):
nzsub.remove(nzsub[i]);
nzval.remove(nzval[i]);
else:
i = i+1;
self.subs = numpy.array(nzsub);
self.vals = numpy.array(nzval);
def __init__(self,
subs,
vals,
shape = None,
func = sum.__call__):
"""
Create a sptensor object. The subs array specifies the nonzero entries
in the tensor, with the kth row of subs corresponding to the kth entry
in vals.
Parameters
----------
subs - p x n array specifying the subscripts of nonzero entries
vals - the corresponding value of the nonzero entries
shape - the shape of the tensor object.
func - accumulation function for repeated subscripts
Returns
-------
out : sparse tensor object
"""
if(subs.__class__ == list):
subs = numpy.array(subs);
if(vals.__class__ == list):
vals = numpy.array(vals);
if(shape.__class__ == list):
shape = numpy.array(shape);
if not(tools.tt_subscheck(subs)):
raise ValueError("Error in subscripts");
if not(tools.tt_valscheck(vals)):
raise ValueError("Error in values");
if (shape != None and not tools.tt_sizecheck(shape)):
raise ValueError("Error in shape");
if(vals.size != 0 and vals.size != 1 and len(vals) != len(subs)):
raise ValueError("Number of subscripts and values must be equal");
if (shape == None):
self.shape = tuple(subs.max(0) + 1);
else:
self.shape = tuple(shape);
# if func is given by user
if(func != None):
self.func = func;
if(subs.size == 0):
nzsub = numpy.array([]);
nzval = numpy.array([]);
else:
(newsub, loc) = uniquerows(subs);
newval = numpy.ndarray([len(newsub), 1]);
newval.fill(0);
for i in range(0, len(loc)):
newval[(int)(loc[i])] = func(vals[i], newval[(int)(loc[i])]);
nzsub = newsub.tolist();
nzval = newval.tolist();
i = 0;
while (i < len(nzsub)):
if(nzval[i][0] == 0):
nzsub.remove(nzsub[i]);
nzval.remove(nzval[i]);
else:
i = i+1;
self.subs = numpy.array(nzsub);
self.vals = numpy.array(nzval);