def _mul_sparse_matrix(self, other):
"""
Do the sparse matrix mult returning fast_csr_matrix only
when other is also fast_csr_matrix.
"""
M, _ = self.shape
_, N = other.shape
major_axis = self._swap((M, N))[0]
if isinstance(other, fast_csr_matrix):
A = zcsr_mult(self, other, sorted=1)
return A
other = csr_matrix(other) # convert to this format
idx_dtype = get_index_dtype(
(self.indptr, self.indices, other.indptr, other.indices),
maxval=M * N)
# scipy 1.5 renamed the older csr_matmat_pass1 to the much more
# descriptive csr_matmat_maxnnz, but also changed the call and logic
# structure of constructing the indices.
try:
fn = getattr(_sparsetools, self.format + '_matmat_maxnnz')
nnz = fn(M, N, np.asarray(self.indptr, dtype=idx_dtype),
np.asarray(self.indices, dtype=idx_dtype),
np.asarray(other.indptr, dtype=idx_dtype),
np.asarray(other.indices, dtype=idx_dtype))
idx_dtype = get_index_dtype(
(self.indptr, self.indices, other.indptr, other.indices),
maxval=nnz)
indptr = np.empty(major_axis + 1, dtype=idx_dtype)
except AttributeError:
indptr = np.empty(major_axis + 1, dtype=idx_dtype)
fn = getattr(_sparsetools, self.format + '_matmat_pass1')
fn(M, N, np.asarray(self.indptr, dtype=idx_dtype),
np.asarray(self.indices, dtype=idx_dtype),
np.asarray(other.indptr, dtype=idx_dtype),
np.asarray(other.indices, dtype=idx_dtype), indptr)
nnz = indptr[-1]
idx_dtype = get_index_dtype(
(self.indptr, self.indices, other.indptr, other.indices),
maxval=nnz)
indices = np.empty(nnz, dtype=idx_dtype)
data = np.empty(nnz, dtype=upcast(self.dtype, other.dtype))
try:
fn = getattr(_sparsetools, self.format + '_matmat')
except AttributeError:
fn = getattr(_sparsetools, self.format + '_matmat_pass2')
fn(M, N, np.asarray(self.indptr, dtype=idx_dtype),
np.asarray(self.indices, dtype=idx_dtype), self.data,
np.asarray(other.indptr, dtype=idx_dtype),
np.asarray(other.indices, dtype=idx_dtype), other.data, indptr,
indices, data)
A = csr_matrix((data, indices, indptr), shape=(M, N))
return A
def _mul_sparse_matrix(self, other):
"""
Do the sparse matrix mult returning fast_csr_matrix only
when other is also fast_csr_matrix.
"""
M, K1 = self.shape
K2, N = other.shape
major_axis = self._swap((M,N))[0]
if isinstance(other, fast_csr_matrix):
A = zcsr_mult(self, other)
A.sort_indices()
return A
other = csr_matrix(other) # convert to this format
idx_dtype = get_index_dtype((self.indptr, self.indices,
other.indptr, other.indices),
maxval=M*N)
indptr = np.empty(major_axis + 1, dtype=idx_dtype)
fn = getattr(_sparsetools, self.format + '_matmat_pass1')
fn(M, N,
np.asarray(self.indptr, dtype=idx_dtype),
np.asarray(self.indices, dtype=idx_dtype),
np.asarray(other.indptr, dtype=idx_dtype),
np.asarray(other.indices, dtype=idx_dtype),
indptr)
nnz = indptr[-1]
idx_dtype = get_index_dtype((self.indptr, self.indices,
other.indptr, other.indices),
maxval=nnz)
indptr = np.asarray(indptr, dtype=idx_dtype)
indices = np.empty(nnz, dtype=idx_dtype)
data = np.empty(nnz, dtype=upcast(self.dtype, other.dtype))
fn = getattr(_sparsetools, self.format + '_matmat_pass2')
fn(M, N, np.asarray(self.indptr, dtype=idx_dtype),
np.asarray(self.indices, dtype=idx_dtype),
self.data,
np.asarray(other.indptr, dtype=idx_dtype),
np.asarray(other.indices, dtype=idx_dtype),
other.data,
indptr, indices, data)
A = csr_matrix((data,indices,indptr),shape=(M,N))
return A
def _mul_sparse_matrix(self, other):
"""
Do the sparse matrix mult returning fast_csr_matrix only
when other is also fast_csr_matrix.
"""
M, K1 = self.shape
K2, N = other.shape
major_axis = self._swap((M, N))[0]
if isinstance(other, fast_csr_matrix):
A = zcsr_mult(self, other)
A.sort_indices()
return A
other = csr_matrix(other) # convert to this format
idx_dtype = get_index_dtype(
(self.indptr, self.indices, other.indptr, other.indices),
maxval=M * N)
indptr = np.empty(major_axis + 1, dtype=idx_dtype)
fn = getattr(_sparsetools, self.format + '_matmat_pass1')
fn(M, N, np.asarray(self.indptr, dtype=idx_dtype),
np.asarray(self.indices, dtype=idx_dtype),
np.asarray(other.indptr, dtype=idx_dtype),
np.asarray(other.indices, dtype=idx_dtype), indptr)
nnz = indptr[-1]
idx_dtype = get_index_dtype(
(self.indptr, self.indices, other.indptr, other.indices),
maxval=nnz)
indptr = np.asarray(indptr, dtype=idx_dtype)
indices = np.empty(nnz, dtype=idx_dtype)
data = np.empty(nnz, dtype=upcast(self.dtype, other.dtype))
fn = getattr(_sparsetools, self.format + '_matmat_pass2')
fn(M, N, np.asarray(self.indptr, dtype=idx_dtype),
np.asarray(self.indices, dtype=idx_dtype), self.data,
np.asarray(other.indptr, dtype=idx_dtype),
np.asarray(other.indices, dtype=idx_dtype), other.data, indptr,
indices, data)
A = csr_matrix((data, indices, indptr), shape=(M, N))
return A
