random_projection.py 文件源码

def find_nearest_instances(training_data_instances, training_data_labels, test_data_instances, test_data_labels):
    start_time = time.time()
    # speed using multiple processes
    NUMBER_OF_PROCESSES = 4
    processes = []
    # shared by different processes, to be mentioned is that
    # global variable is only read within processes
    # the update of global variable within a process will not be submitted 
    classified_results = multiprocessing.Array('i', len(test_data_instances), lock = False)
    test_data_subdivisions = range(0, len(test_data_instances) + 1,\
                                    int(len(test_data_instances) / NUMBER_OF_PROCESSES))
    test_data_subdivisions[-1] = len(test_data_instances)
    for process_index in range(NUMBER_OF_PROCESSES):
        process = multiprocessing.Process(target = find_nearest_instances_subprocess,
                                          args = (training_data_instances,
                                                  training_data_labels,
                                                  test_data_instances,
                                                  test_data_subdivisions[process_index],
                                                  test_data_subdivisions[process_index + 1],
                                                  classified_results))
        process.start()
        processes.append(process)

    print "Waiting..."
    # wait until all processes are finished
    for process in processes:
        process.join()
    print "Complete."
    print "--- %s seconds ---" % (time.time() - start_time)

    error_count = 0
    confusion_matrix = np.zeros((10, 10), dtype=np.int)
    for test_instance_index, classified_label in zip(range(len(test_data_instances)),\
                                                      classified_results):        
        if test_data_labels[test_instance_index] != classified_label:
            error_count += 1
        confusion_matrix[test_data_labels[test_instance_index]][classified_label] += 1        

    error_rate = 100.0 * error_count / len(test_data_instances)

    return classified_results, error_rate, confusion_matrix