def _binopt(self, other, op):
"""
Do the binary operation fn to two sparse matrices using
fast_csr_matrix only when other is also a fast_csr_matrix.
"""
# e.g. csr_plus_csr, csr_minus_csr, etc.
if not isinstance(other, fast_csr_matrix):
other = csr_matrix(other)
# e.g. csr_plus_csr, csr_minus_csr, etc.
fn = getattr(_sparsetools, self.format + op + self.format)
maxnnz = self.nnz + other.nnz
idx_dtype = get_index_dtype((self.indptr, self.indices,
other.indptr, other.indices),
maxval=maxnnz)
indptr = np.empty(self.indptr.shape, dtype=idx_dtype)
indices = np.empty(maxnnz, dtype=idx_dtype)
bool_ops = ['_ne_', '_lt_', '_gt_', '_le_', '_ge_']
if op in bool_ops:
data = np.empty(maxnnz, dtype=np.bool_)
else:
data = np.empty(maxnnz, dtype=upcast(self.dtype, other.dtype))
fn(self.shape[0], self.shape[1],
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)
actual_nnz = indptr[-1]
indices = indices[:actual_nnz]
data = data[:actual_nnz]
if actual_nnz < maxnnz // 2:
# too much waste, trim arrays
indices = indices.copy()
data = data.copy()
if isinstance(other, fast_csr_matrix) and (not op in bool_ops):
A = fast_csr_matrix((data, indices, indptr), dtype=data.dtype, shape=self.shape)
else:
A = csr_matrix((data, indices, indptr), dtype=data.dtype, shape=self.shape)
return A
def test_upcast(self):
assert_equal(sputils.upcast('intc'), np.intc)
assert_equal(sputils.upcast('int32', 'float32'), np.float64)
assert_equal(sputils.upcast('bool', complex, float), np.complex128)
assert_equal(sputils.upcast('i', 'd'), np.float64)
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