Estimator for classifier error distributions.

Inheritance diagram of mvpa2.clfs.stats


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


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, ...]) 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