Multivariate Pattern Analysis in Python

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# 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`([descr]) | Base class for objects which contain any known collection Classes inherited from this class gain ability to access collections and their items as simple attributes.
`ConditionalAttribute`([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[, ro, index, value, name, doc]) | This class shall serve as a representation of a parameter.
`ProcrusteanMapper`([space]) | Mapper to project from one space to another using Procrustean transformation (shift + scaling + rotation).
`StaticProjectionMapper`(proj, **kwargs) | Mapper to project data onto arbitrary space using transformation given as input.
`ZScoreMapper`([params, param_est, ...]) | Mapper to normalize features (Z-scoring).

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