mvpa2.generators.partition.HalfPartitioner

Inheritance diagram of HalfPartitioner

class mvpa2.generators.partition.HalfPartitioner(count=None, selection_strategy='equidistant', attr='chunks', space='partitions', **kwargs)

Partition a dataset into two halves of the sample attribute.

The partitioner yields two datasets. In the first set second half of chunks are labeled ‘1’ and the first half labeled ‘2’. In the second set the assignment is reversed (1st half: ‘1’, 2nd half: ‘2’).

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • raw_results: Computed results before invoking postproc. Stored only if postproc is not None.

(Conditional attributes enabled by default suffixed with +)

Parameters :

count : None or int

Desired number of splits to be output. It is limited by the number of splits possible for a given splitter (e.g. OddEvenSplitter can have only up to 2 splits). If None, all splits are output (default).

selection_strategy : str

If count is not None, possible strategies are possible: ‘first’: First count splits are chosen; ‘random’: Random (without replacement) count splits are chosen; ‘equidistant’: Splits which are equidistant from each other.

attr : str

Sample attribute used to determine splits.

space : str

Name of the to be created sample attribute defining the partitions. In addition, a dataset attribute named ‘space_set’ will be added to each output dataset, indicating the number of the partition set it corresponds to.

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

NeuroDebian

NITRC-listed