mvpa2.algorithms.hyperalignmentΒΆ
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). |
