def generate_training_pairs()

in ar-cnn/data_generator.py [0:0]

• 39 lines of code
• 7 McCabe index (conditional complexity)

    def generate_training_pairs(self):
        '''
        Generates Training Pairs till @training_input / @training_target have @batch_size files.
        '''

        # Create the training and target lists
        training_input = []
        training_target = []
        while len(training_input) <= self.batch_size:
            target_pianoroll = self.sample_list[self.sample_index]
            self.sample_index = (self.sample_index + 1) % len(self.sample_list)
            try:
                training_data_shape = (self.bars * self.beats_per_bar *
                                       self.beat_resolution,
                                       self.number_of_pitches,
                                       self.number_of_channels)

                # For each pianoroll section, add or remove certain percentage of notes
                add_remove_notes = AddAndRemoveAPercentageOfNotes(
                    sampling_lower_bound_remove=self.
                    sampling_lower_bound_remove,
                    sampling_upper_bound_remove=self.
                    sampling_upper_bound_remove,
                    sampling_lower_bound_add=self.sampling_lower_bound_add,
                    sampling_upper_bound_add=self.sampling_upper_bound_add)

                input_pianorolls = add_remove_notes.sample(
                    target_pianoroll, self.samples_per_data_item)

                for input_pianoroll in input_pianorolls:
                    training_input.append(
                        input_pianoroll.reshape(training_data_shape))
                    xor_target = np.logical_xor(input_pianoroll,
                                                target_pianoroll)
                    training_target.append(
                        xor_target.reshape(training_data_shape))

                if len(training_input) >= self.batch_size:
                    training_input = np.asarray(
                        training_input[:self.batch_size])
                    training_target = np.asarray(
                        training_target[:self.batch_size])
                    return training_input, training_target

            except Exception as e:
                print('Error generating input and target pair')
                traceback.print_exc()