This content refers to an unreleased development version of PyMVPA
mvpa.clfs.statsΒΆ
Estimator for classifier error distributions.
Functions
| auto_null_dist(dist) | Cheater for human beings – wraps dist if needed with some |
| match_distribution(data, **kwargs[, ...]) | Determine best matching distribution. |
| nanmean(x[, axis]) | Compute the mean over the given axis ignoring NaNs. |
| plot_distribution_matches(data, matches[, ...]) | Plot best matching distributions |
Classes
| AdaptiveNormal(dist, **kwargs) | Adaptive Normal Distribution: params are (0, sqrt(1/nfeatures)) |
| AdaptiveNullDist(dist, **kwargs) | Adaptive distribution which adjusts parameters according to the data |
| AdaptiveRDist(dist, **kwargs) | Adaptive rdist: params are (nfeatures-1, 0, 1) |
| FixedNullDist(dist, **kwargs) | Proxy/Adaptor class for SciPy distributions. |
| MCNullDist(**kwargs[, dist_class, ...]) | Null-hypothesis distribution is estimated from randomly permuted data labels. |
| Nonparametric(dist_samples[, correction]) | Non-parametric 1d distribution – derives cdf based on stored values. |
| NullDist(**kwargs[, tail]) | Base class for null-hypothesis testing. |
| rv_semifrozen(dist[, loc, scale, args]) | Helper proxy-class to fit distribution when some parameters are known |
