def sparseScalarProductOfDot(A, B, C, out=None):
"""
Returns A * np.dot(B, C), however it does so keeping in mind
the sparsity of A, calculating values only where required.
Params
A - a sparse CSR matrix
B - a dense matrix
C - a dense matrix
out - if specified, must be a sparse CSR matrix with identical
non-zero pattern to A (i.e. same indices and indptr)
Returns
out_data, though note that this is the same parameter passed in and overwitten.
"""
assert ssp.isspmatrix_csr(A), "A matrix is not a CSR matrix"
assert not np.isfortran(B), "B matrix is not stored in row-major form"
assert not np.isfortran(C), "C matrix is not stored in row-major form"
if out is None:
out = A.copy()
if A.dtype == np.float64:
compiled.sparseScalarQuotientOfDot_f8(A.data, A.indices, A.indptr, B, C, out.data)
elif A.dtype == np.float32:
compiled.sparseScalarQuotientOfDot_f4(A.data, A.indices, A.indptr, B, C, out.data)
else:
_sparseScalarQuotientOfDot_py(A, B, C, out)
return out
def sparseScalarQuotientOfDot (A, B, C, out=None, start=None, end=None):
'''
Returns A / np.dot(B, C), however it does so keeping in mind
the sparsity of A, calculating values only where required.
Params
A - a sparse CSR matrix
B - a dense matrix
C - a dense matrix
out - if specified, must be a sparse CSR matrix with identical
non-zero pattern to A (i.e. same indices and indptr)
start - where to start indexing A
end - where to stop indexing A
Returns
out_data, though note that this is the same parameter passed in and overwitten.
'''
assert ssp.isspmatrix_csr(A), "A matrix is not a CSR matrix"
assert not np.isfortran(B), "B matrix is not stored in row-major form"
assert not np.isfortran(C), "C matrix is not stored in row-major form"
if start is None:
start = 0
if end is None:
end = A.shape[0]
if out is None:
out = A[start:end,:].copy()
if A.dtype == np.float64:
compiled.sparseScalarQuotientOfDot_f8(A.data, A.indices, A.indptr, B, C, out.data, start, end)
elif A.dtype == np.float32:
compiled.sparseScalarQuotientOfDot_f4(A.data, A.indices, A.indptr, B, C, out.data, start, end)
else:
raise ValueError ("No native Cython implementation for dtype " + str(A.dtype))
_sparseScalarQuotientOfDot_py(A,B,C, out)
return out
