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mvpa.clfs.stats

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, **kwds[, args, N, ...])	Return the D-value and the p-value for a Kolmogorov-Smirnov test
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)
AttributePermutator(attr, **kwargs[, count, ...])	Node to permute one a more attributes in a dataset.
ClassWithCollections(**kwargs[, descr])	Base class for objects which contain any known collection
ConditionalAttribute(*args, **kwargs[, 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, **kwargs[, ...])	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