feature_normalisation_base.py 文件源码

def find_min_max_values(self, in_file_list, start_index, end_index):

        local_feature_dimension = end_index - start_index

        file_number = len(in_file_list)
        min_value_matrix = numpy.zeros((file_number, local_feature_dimension))
        max_value_matrix = numpy.zeros((file_number, local_feature_dimension))
        io_funcs = BinaryIOCollection()
        for i in xrange(file_number):
            features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension)

            temp_min = numpy.amin(features[:, start_index:end_index], axis = 0)
            temp_max = numpy.amax(features[:, start_index:end_index], axis = 0)

            min_value_matrix[i, ] = temp_min;
            max_value_matrix[i, ] = temp_max;

        self.min_vector = numpy.amin(min_value_matrix, axis = 0)
        self.max_vector = numpy.amax(max_value_matrix, axis = 0)
        self.min_vector = numpy.reshape(self.min_vector, (1, local_feature_dimension))
        self.max_vector = numpy.reshape(self.max_vector, (1, local_feature_dimension))

        # po=numpy.get_printoptions()
        # numpy.set_printoptions(precision=2, threshold=20, linewidth=1000, edgeitems=4)
        self.logger.info('found min/max values of length %d:' % local_feature_dimension)
        self.logger.info('  min: %s' % self.min_vector)
        self.logger.info('  max: %s' % self.max_vector)
        # restore the print options
        # numpy.set_printoptions(po)