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Multivariate Pattern Analysis in Python |
Mapped dataset
The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.datasets.mapped (for developers).
Bases: mvpa.datasets.base.Dataset
A Dataset which is created by applying a Mapper to the data.
Upon contruction MappedDataset uses a Mapper to transform the samples from their original into the two-dimensional matrix representation that is required by the Dataset class.
This class enhanced the Dataset interface with two additional methods: mapForward() and mapReverse(). Both take arbitrary data arrays (with matching shape) and transform them using the embedded mapper from the original dataspace into a one- or two-dimensional representation (for arrays corresponding to the shape of a single or multiple samples respectively) or vice versa.
Most likely, this class will not be used directly, but rather indirectly through one of its subclasses (e.g. MaskedDataset).
If samples and mapper arguments are not None the mapper is used to forward-map the samples array and the result is passed to the Dataset constructor.
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Select features given their ids.
The methods behaves similar to Dataset.selectFeatures(), but additionally takes care of adjusting the embedded mapper appropriately.
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See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the MappedDataset documentation.
Full API documentation of MappedDataset in module mvpa.datasets.mapped.