mvpa2.measures.ireliefΒΆ
Multivariate Iterative RELIEF
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 |
