mvpa2.algorithms.hyperalignment

Transformation of individual feature spaces into a common space

The Hyperalignment class in this module implements an algorithm published in Haxby et al., Neuron (2011) A common, high-dimensional model of the representational space in human ventral temporal cortex.

Functions

deepcopy(x[, memo, _nil])	Deep copy operation on arbitrary Python objects.
zscore(ds, **kwargs)	In-place Z-scoring of a Dataset or ndarray.

Classes

ChainMapper(nodes, **kwargs)	Class that amends ChainNode with a mapper-like interface.
ClassWithCollections(**kwargs[, descr])	Base class for objects which contain any known collection
ConditionalAttribute(*args, **kwargs[, enabled])	Simple container intended to conditionally store the value
Dataset(samples[, sa, fa, a])	Generic storage class for datasets with multiple attributes.
Hyperalignment(**kwargs)	Align the features across multiple datasets into a common feature space.
Parameter(default, **kwargs[, ro, index, ...])	This class shall serve as a representation of a parameter.
ProcrusteanMapper(**kwargs[, space])	Mapper to project from one space to another using Procrustean
StaticProjectionMapper(proj, **kwargs)	Mapper to project data onto arbitrary space using transformation given as input.
ZScoreMapper(**kwargs[, params, param_est, ...])	Mapper to normalize features (Z-scoring).