def _add_dense(self, other):
if other.shape != self.shape:
raise ValueError('Incompatible shapes.')
dtype = upcast_char(self.dtype.char, other.dtype.char)
order = self._swap('CF')[0]
result = np.array(other, dtype=dtype, order=order, copy=True)
M, N = self._swap(self.shape)
y = result if result.flags.c_contiguous else result.T
csr_todense(M, N, self.indptr, self.indices, self.data, y)
return matrix(result, copy=False)
def toarray(self, order=None, out=None):
if out is None and order is None:
order = self._swap('cf')[0]
out = self._process_toarray_args(order, out)
if not (out.flags.c_contiguous or out.flags.f_contiguous):
raise ValueError('Output array must be C or F contiguous')
# align ideal order with output array order
if out.flags.c_contiguous:
x = self.tocsr()
y = out
else:
x = self.tocsc()
y = out.T
M, N = x._swap(x.shape)
csr_todense(M, N, x.indptr, x.indices, x.data, y)
return out
def fast_in_place_add(ndarray, matrix):
"""
Performs fast in-place addition of a sparse CSC matrix to an ndarray of the
same size
This is based on the code found in:
scipy/sparse/compressed.py:_cs_matrix._add_dense
"""
if ndarray.shape != matrix.shape:
raise ValueError("Shapes do not match: {} vs {}".format(
ndarray.shape, matrix.shape))
if not ndarray.flags.f_contiguous:
raise ValueError("ndarray must be in Fortran order")
# In order to use Compressed Sparse Row (csr) operations with Compressed
# Sparse Column (csc) matrices we need to transpose the matrix
# we're adding into. This is a fast operation which just returns a new view
# on the underlying data.
transposed = ndarray.transpose()
rows, columns = transposed.shape
_sparsetools.csr_todense(rows, columns, matrix.indptr, matrix.indices,
matrix.data, transposed)