Multivariate Pattern Analysis in Python

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# mvpa2.measures.irelief

FeaturewiseMeasure performing multivariate Iterative RELIEF (I-RELIEF) algorithm. See : Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 6, pp. 1035-1051, June 2007.

Functions

`pnorm_w`(data1[, data2, weight, p]) | Weighted p-norm between two datasets (scipy.weave implementation)

Classes

`Dataset`(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes.
`ExponentialKernel`(*args, **kwargs) | The Exponential kernel class.
`FeaturewiseMeasure`([null_dist]) | A per-feature-measure computed from a `Dataset` (base class).
`IterativeRelief`([threshold, kernel_width, ...]) | `FeaturewiseMeasure` that performs multivariate I-RELIEF
`IterativeReliefOnline`([a, permute, max_iter]) | `FeaturewiseMeasure` that performs multivariate I-RELIEF
`IterativeReliefOnline_Devel`([a, permute, ...]) | `FeaturewiseMeasure` that performs multivariate I-RELIEF
`IterativeRelief_Devel`([threshold, kernel, ...]) | `FeaturewiseMeasure` that performs multivariate I-RELIEF

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