def _ttv_compute(self, v, dims, vidx, remdims):
nvals = self.vals
nsubs = self.subs
for i in range(len(dims)):
idx = nsubs[dims[i]]
w = v[vidx[i]]
nvals = nvals * w[idx]
# Case 1: all dimensions used -> return sum
if len(remdims) == 0:
return nvals.sum()
nsubs = tuple(self.subs[i] for i in remdims)
nshp = tuple(self.shape[i] for i in remdims)
# Case 2: result is a vector
if len(remdims) == 1:
c, nsubs = accum(nsubs, nvals, shape=nshp, with_subs=True)
nshp = nshp[0]
if len(np.nonzero(c)[0]) <= 0.5 * nshp:
return sptensor(nsubs, c, (nshp,))
else:
return c
# Case 3: result is an array
return sptensor(nsubs, nvals, shape=nshp, accumfun=np.sum)
def __init__(self,
subs,
vals,
shape=None,
dtype=None,
accumfun=None,
issorted=False):
if not isinstance(subs, tuple):
raise ValueError('Subscripts must be a tuple of array-likes')
if len(subs[0]) != len(vals):
raise ValueError('Subscripts and values must be of equal length')
if dtype is None:
dtype = np.array(vals).dtype
for i in range(len(subs)):
if np.array(subs[i]).dtype.kind != 'i':
raise ValueError('Subscripts must be integers')
vals = np.array(vals, dtype=dtype)
if accumfun is not None:
vals, subs = accum(subs,
vals,
issorted=False,
with_subs=True,
func=accumfun)
self.subs = subs
self.vals = vals
self.dtype = dtype
self.issorted = issorted
self.accumfun = accumfun
if shape is None:
self.shape = tuple(np.array(subs).max(axis=1).flatten() + 1)
else:
self.shape = tuple(int(d) for d in shape)
self.ndim = len(subs)
def __init__(self, subs, vals, shape=None, dtype=None, accumfun=None):
if not isinstance(subs, tuple):
raise ValueError('Subscripts must be a tuple of array-likes')
if len(subs[0]) != len(vals):
raise ValueError('Subscripts and values must be of equal length')
if dtype is None:
dtype = array(vals).dtype
for i in range(len(subs)):
if array(subs[i]).dtype.kind != 'i':
raise ValueError('Subscripts must be integers')
vals = array(vals)
if accumfun is not None:
vals, subs = accum(
subs, vals,
sorted=False, with_subs=True, fun=accumfun
)
self.subs = subs
self.vals = vals
self.dtype = dtype
if shape is None:
self.shape = tuple(array(subs).max(axis=1).flatten())
else:
self.shape = tuple(int(d) for d in shape)
self.ndim = len(subs)
