def random_sparse_binary_matrix()

in theseus/utils/sparse_matrix_utils.py [0:0]

• 14 lines of code
• 5 McCabe index (conditional complexity)

def random_sparse_binary_matrix(rows, cols, fill, min_entries_per_col) -> csr_matrix:
    retv = lil_matrix((rows, cols))

    if min_entries_per_col > 0:
        min_entries_per_col = min(rows, min_entries_per_col)
        rows_array = np.arange(rows)
        for c in range(cols):
            for r in np.random.choice(rows_array, min_entries_per_col):
                retv[r, c] = 1.0

    num_entries = int(fill * rows * cols)
    while retv.getnnz() < num_entries:
        col = np.random.randint(cols)
        row = np.random.randint(rows)
        retv[row, col] = 1.0

    return retv.tocsr()