bicg1 cg1 coo_csr csr_coo csr_dense csr_dusmm csr_dusmv csr_dussm csr_dussv csr_ssr csr_trans dense_csr ssr_csr ssr_dusmv

csr_coo() def csr_coo ( m  , n  , val  , rowptr  , colind  , base  , val2  , rowind2  , colind2  , base2    ) CSR -> COO Purpose This routine converts the sparse matrix in CSR format to the sparse matrix in COO format. The order of elements of output matrix will be same as val array of input matrix. Arrys val and val2, and, colind and colind2 may be identical arrays. Returns info (int) = 0: Successful exit. = i < 0: The (-i)-th argument is invalid. Parameters [in] m Number of rows of the matrix. (m >= 0) (if m = 0, returns without computation) [in] n Number of columns of the matrix. (n >= 0) (if n = 0, returns without computation) [in] val Numpy ndarray (1-dimensional, float, nnz) Values of nonzero elements of input matrix (nnz is the number of nonzero elements of input matrix). [in] rowptr Numpy ndarray (1-dimensional, int32, m + 1) Row pointers of input matrix. [in] colind Numpy ndarray (1-dimensional, int32, nnz) Column indices of input matrix (nnz is the number of nonzero elements of input matrix). [in] base Indexing of rowptr and colind. = 0: Zero-based (C style) indexing: Starting index is 0. = 1: One-based (Fortran style) indexing: Starting index is 1. [out] val2 Numpy ndarray (1-dimensional, float, nnz) Values of nonzero elements of output matrix (nnz is the number of nonzero elements of input matrix). [out] rowind2 Numpy ndarray (1-dimensional, int32, nnz) Row indices of output matrix (nnz is the number of nonzero elements of input matrix). [out] colind2 Numpy ndarray (1-dimensional, int32, nnz) Column indices of output matrix (nnz is the number of nonzero elements of input matrix). [in] base2 Indexing of rowind2 and colind2. = 0: Zero-based (C style) indexing: Starting index is 0. = 1: One-based (Fortran style) indexing: Starting index is 1.