feature_normalisation_base.py 文件源码

def compute_mean(self, file_list, start_index, end_index):

        local_feature_dimension = end_index - start_index

        mean_vector = numpy.zeros((1, local_feature_dimension))
        all_frame_number = 0

        io_funcs = BinaryIOCollection()
        for file_name in file_list:
            features, current_frame_number = io_funcs.load_binary_file_frame(file_name, self.feature_dimension)

            mean_vector += numpy.reshape(numpy.sum(features[:, start_index:end_index], axis=0), (1, local_feature_dimension))
            all_frame_number += current_frame_number

        mean_vector /= float(all_frame_number)

        # po=numpy.get_printoptions()
        # numpy.set_printoptions(precision=2, threshold=20, linewidth=1000, edgeitems=4)
        self.logger.info('computed mean vector of length %d :' % mean_vector.shape[1] )
        self.logger.info(' mean: %s' % mean_vector)
        # restore the print options
        # numpy.set_printoptions(po)

        return  mean_vector