main.py 文件源码

def make_forecast(model: Sequential, look_back_buffer: numpy.ndarray, timesteps: int=1, batch_size: int=1):
    forecast_predict = numpy.empty((0, 1), dtype=numpy.float32)
    for _ in trange(timesteps, desc='predicting data\t', mininterval=1.0):
        # make prediction with current lookback buffer
        cur_predict = model.predict(look_back_buffer, batch_size)
        # add prediction to result
        forecast_predict = numpy.concatenate([forecast_predict, cur_predict], axis=0)
        # add new axis to prediction to make it suitable as input
        cur_predict = numpy.reshape(cur_predict, (cur_predict.shape[1], cur_predict.shape[0], 1))
        # remove oldest prediction from buffer
        look_back_buffer = numpy.delete(look_back_buffer, 0, axis=1)
        # concat buffer with newest prediction
        look_back_buffer = numpy.concatenate([look_back_buffer, cur_predict], axis=1)
    return forecast_predict