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, **kwds[, args, N, ...]) | Perform the Kolmogorov-Smirnov test for goodness of fit |
| match_distribution(data, **kwargs[, ...]) | Determine best matching distribution. |
| nanmean(x[, axis]) | Compute the mean over the given axis ignoring NaNs. |
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 |
