feature_normalisation_base.py 文件源码

def compute_std(self, file_list, mean_vector, start_index, end_index):
        local_feature_dimension = end_index - start_index

        std_vector = numpy.zeros((1, self.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_matrix = numpy.tile(mean_vector, (current_frame_number, 1))

            std_vector += numpy.reshape(numpy.sum((features[:, start_index:end_index] - mean_matrix) ** 2, axis=0), (1, local_feature_dimension))
            all_frame_number += current_frame_number

        std_vector /= float(all_frame_number)

        std_vector = std_vector ** 0.5

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

        return  std_vector