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mvpa2.clfs.statsΒΆ
Estimator for classifier error distributions.
Functions
| auto_null_dist(dist) | Cheater for human beings – wraps dist if needed with some |
| is_datasetlike(obj) | Check if an object looks like a Dataset. |
| kstest(rvs, cdf[, args, N, alternative, mode]) | Perform the Kolmogorov-Smirnov test for goodness of fit |
| match_distribution(data[, nsamples, loc, ...]) | 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) |
| AttributePermutator(attr[, count, limit, assure]) | Node to permute one a more attributes in a dataset. |
| ClassWithCollections([descr]) | Base class for objects which contain any known collection |
| ConditionalAttribute([enabled]) | Simple container intended to conditionally store the value |
| Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
| FixedNullDist(dist, **kwargs) | Proxy/Adaptor class for SciPy distributions. |
| MCNullDist(permutator[, dist_class, measure]) | 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([tail]) | Base class for null-hypothesis testing. |
| rv_semifrozen(dist[, loc, scale, args]) | Helper proxy-class to fit distribution when some parameters are known |
