def __init__()

in torchnet/dataset/splitdataset.py [0:0]

• 25 lines of code
• 8 McCabe index (conditional complexity)

    def __init__(self, dataset, partitions, initial_partition=None):
        super(SplitDataset, self).__init__()

        self.dataset = dataset
        self.partitions = partitions

        # A few assertions
        assert isinstance(partitions, dict), 'partitions must be a dict'
        assert len(partitions) >= 2, \
            'SplitDataset should have at least two partitions'
        assert min(partitions.values()) >= 0, \
            'partition sizes cannot be negative'
        assert max(partitions.values()) > 0, 'all partitions cannot be empty'

        self.partition_names = sorted(list(self.partitions.keys()))
        self.partition_index = {partition: i for i, partition in
                                enumerate(self.partition_names)}

        self.partition_sizes = [self.partitions[parition] for parition in
                                self.partition_names]
        # if partition sizes are fractions, convert to sizes:
        if sum(self.partition_sizes) <= 1:
            self.partition_sizes = [round(x * len(dataset)) for x in
                                    self.partition_sizes]
        else:
            for x in self.partition_sizes:
                assert x == int(x), ('partition sizes should be integer'
                                     ' numbers, or sum up to <= 1 ')

        self.partition_cum_sizes = np.cumsum(self.partition_sizes)

        if initial_partition is not None:
            self.select(initial_partition)