mvpa2.measures.ireliefΒΆ
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 |
