def __mul__(self, other):
if cupy.isscalar(other):
self.sum_duplicates()
return self._with_data(self.data * other)
elif isspmatrix_csr(other):
self.sum_duplicates()
other.sum_duplicates()
return cusparse.csrgemm(self, other)
elif csc.isspmatrix_csc(other):
self.sum_duplicates()
other.sum_duplicates()
return cusparse.csrgemm(self, other.T, transb=True)
elif base.isspmatrix(other):
return self * other.tocsr()
elif base.isdense(other):
if other.ndim == 0:
self.sum_duplicates()
return self._with_data(self.data * other)
elif other.ndim == 1:
self.sum_duplicates()
return cusparse.csrmv(self, cupy.asfortranarray(other))
elif other.ndim == 2:
self.sum_duplicates()
return cusparse.csrmm2(self, cupy.asfortranarray(other))
else:
raise ValueError('could not interpret dimensions')
else:
return NotImplemented
def __init__(self, arg1, shape=None, dtype=None, copy=False):
if shape is not None and len(shape) != 2:
raise ValueError(
'Only two-dimensional sparse arrays are supported.')
if base.issparse(arg1):
x = arg1.asformat(self.format)
data = x.data
row = x.row
col = x.col
if arg1.format != self.format:
# When formats are differnent, all arrays are already copied
copy = False
if shape is None:
shape = arg1.shape
has_canonical_format = x.has_canonical_format
elif util.isshape(arg1):
m, n = arg1
m, n = int(m), int(n)
data = cupy.zeros(0, dtype if dtype else 'd')
row = cupy.zeros(0, dtype='i')
col = cupy.zeros(0, dtype='i')
# shape and copy argument is ignored
shape = (m, n)
copy = False
has_canonical_format = True
elif isinstance(arg1, tuple) and len(arg1) == 2:
try:
data, (row, col) = arg1
except (TypeError, ValueError):
raise TypeError('invalid input format')
if not (base.isdense(data) and data.ndim == 1 and
base.isdense(row) and row.ndim == 1 and
base.isdense(col) and col.ndim == 1):
raise ValueError('row, column, and data arrays must be 1-D')
if not (len(data) == len(row) == len(col)):
raise ValueError(
'row, column, and data array must all be the same length')
has_canonical_format = False
else:
raise ValueError(
'Only (data, (row, col)) format is supported')
if dtype is None:
dtype = data.dtype
else:
dtype = numpy.dtype(dtype)
if dtype != 'f' and dtype != 'd':
raise ValueError('Only float32 and float64 are supported')
data = data.astype(dtype, copy=copy)
row = row.astype('i', copy=copy)
col = col.astype('i', copy=copy)
if shape is None:
if len(row) == 0 or len(col) == 0:
raise ValueError(
'cannot infer dimensions from zero sized index arrays')
shape = (int(row.max()) + 1, int(col.max()) + 1)
if len(data) > 0:
if row.max() >= shape[0]:
raise ValueError('row index exceeds matrix dimensions')
if col.max() >= shape[1]:
raise ValueError('column index exceeds matrix dimensions')
if row.min() < 0:
raise ValueError('negative row index found')
if col.min() < 0:
raise ValueError('negative column index found')
sparse_data._data_matrix.__init__(self, data)
self.row = row
self.col = col
self._shape = shape
self._has_canonical_format = has_canonical_format