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datasets.meta

Module: datasets.meta

Inheritance diagram for mvpa.datasets.meta:

Dataset container

MetaDataset

class mvpa.datasets.meta.MetaDataset(datasets)

Bases: object

Dataset container

The class is useful to combine several Datasets with different origin and type and bind them together. Such a combined dataset can then by used to e.g. pass it to a classifier.

MetaDataset does not permanently duplicate data stored in the dataset it contains. The combined samples matrix is build on demand and samples attribute access is redirected to the first dataset in the container.

Currently operations other than samples or feature selection are not fully supported, e.g. passing a MetaDataset to detrend() will initially result in a detrended MetaDataset, but the combined and detrended samples matrix will be lost after the next call to selectSamples() or selectFeatures(), which freshly pulls samples from all datasets in the container.

Initialize dataset instance

Parameters:datasets (list) –

applyMapper(*args, **kwargs)

Apply a mapper on all underlying datasets.

datasets

getNFeatures()

Number of features per sample.

getNSamples()

Currently available number of samples.

getRandomSamples(nperlabel)

Return a MetaDataset with a random subset of samples.

mapReverse(val)

Perform reverse mapping

Return :List of results per each used mapper and the corresponding part of the provided val.

nfeatures

Number of features per sample.

nsamples

Currently available number of samples.

permuteLabels(*args, **kwargs)

Toggle label permutation.

rebuildSamples()

Update the combined samples matrix from all underlying datasets.

selectFeatures(ids, sort=True)

Do feature selection on all underlying datasets at once.

selectSamples(*args, **kwargs)

Select samples from all underlying datasets at once.

setSamplesDType(dtype)

Set the data type of the samples array.