def _mul_sparse_matrix(self, other):
M, K1 = self.shape
K2, N = other.shape
indptr = np.empty_like(self.indptr)
R, n = self.blocksize
# convert to this format
if isspmatrix_bsr(other):
C = other.blocksize[1]
else:
C = 1
from csr import isspmatrix_csr
if isspmatrix_csr(other) and n == 1:
other = other.tobsr(blocksize=(n, C), copy=False) # lightweight conversion
else:
other = other.tobsr(blocksize=(n, C))
csr_matmat_pass1(M // R, N // C, self.indptr, self.indices, other.indptr, other.indices, indptr)
bnnz = indptr[-1]
indices = np.empty(bnnz, dtype=np.intc)
data = np.empty(R * C * bnnz, dtype=upcast(self.dtype, other.dtype))
bsr_matmat_pass2(
M // R,
N // C,
R,
C,
n,
self.indptr,
self.indices,
np.ravel(self.data),
other.indptr,
other.indices,
np.ravel(other.data),
indptr,
indices,
data,
)
data = data.reshape(-1, R, C)
# TODO eliminate zeros
return bsr_matrix((data, indices, indptr), shape=(M, N), blocksize=(R, C))
def _mul_sparse_matrix(self, other):
M, K1 = self.shape
K2, N = other.shape
indptr = np.empty_like(self.indptr)
R, n = self.blocksize
#convert to this format
if isspmatrix_bsr(other):
C = other.blocksize[1]
else:
C = 1
from csr import isspmatrix_csr
if isspmatrix_csr(other) and n == 1:
other = other.tobsr(blocksize=(n, C),
copy=False) #lightweight conversion
else:
other = other.tobsr(blocksize=(n, C))
csr_matmat_pass1( M/R, N/C, \
self.indptr, self.indices, \
other.indptr, other.indices, \
indptr)
bnnz = indptr[-1]
indices = np.empty(bnnz, dtype=np.intc)
data = np.empty(R * C * bnnz, dtype=upcast(self.dtype, other.dtype))
bsr_matmat_pass2( M/R, N/C, R, C, n, \
self.indptr, self.indices, np.ravel(self.data), \
other.indptr, other.indices, np.ravel(other.data), \
indptr, indices, data)
data = data.reshape(-1, R, C)
#TODO eliminate zeros
return bsr_matrix((data, indices, indptr),
shape=(M, N),
blocksize=(R, C))