parallel_distance_old.py 文件源码

def _sparse_distance(lock, input_list, global_idx, rows, cols, data, dist_function):
    """Parallelize a general computation of a sparse distance matrix.

    Parameters
    ----------
    lock : multiprocessing.synchronize.Lock
        Value returned from multiprocessing.Lock().
    input_list : list
        List of values to compare to input_list[idx] (from 'idx' on).
    shared_arr : array_like
        Numpy array created as a shared object. Iteratively updated with the result.
        Example:
            shared_array = np.frombuffer(mp.Array('d', n*n).get_obj()).reshape((n,n))

    Returns
    -------

    """
    list_len = len(input_list)
    # PID = os.getpid()
    # print("PID {} takes index {}".format(PID, index_i))
    while global_idx.value < list_len:
        with lock:
            if not global_idx.value < list_len: return
            idx = global_idx.value
            global_idx.value += 1
            if (idx) % 100 == 0: progressbar(idx, list_len)

        elem_1 = input_list[idx]
        for idx_j in range(idx+1, list_len):
             _res = dist_function(elem_1, input_list[idx_j])
             if _res > 0:
                 i, j, d = idx, idx_j, list_len
                 c_idx = d*(d-1)/2 - (d-i)*(d-i-1)/2 + j - i - 1
                 data[c_idx] = _res
                 rows[c_idx] = i
                 cols[c_idx] = j