Multivariate Pattern Analysis in Python

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# mvpa2.clfs.transerror

Utility class to compute the transfer error of classifiers.

Functions

`auc_error`(predicted, target) | Computes the area under the ROC for the given the target and predicted to make the prediction.
`auto_null_dist`(dist) | Cheater for human beings – wraps `dist` if needed with some
`ceil`(x) | Return the ceiling of x as a float.
`chisquare`(obs[, exp]) | Compute the chisquare value of a contingency table with arbitrary dimensions.
`corr_error`(predicted, target) | Computes the correlation between the target and the predicted values.
`corr_error_prob`(predicted, target) | Computes p-value of correlation between the target and the predicted values.
`enhanced_doc_string`(item, *args, **kwargs) | Generate enhanced doc strings for various items.
`friedmanchisquare`(*args) | Computes the Friedman test for repeated measurements The Friedman test tests the null hypothesis that repeated measurements of the same individuals have the same distribution.
`linregress`(x[, y]) | Calculate a regression line This computes a least-squares regression for two sets of measurements.
`log10`(x) | Return the base 10 logarithm of x.
`mean_mismatch_error`(predicted, target) | Computes the percentage of mismatches between some target and some predicted values.
`mean_power_fx`(data) | Returns mean power
`nanmean`(x[, axis]) | Compute the mean over the given axis ignoring nans.
`relative_rms_error`(predicted, target) | Ratio between RMSE and root mean power of target output.
`rms_error`(predicted, target) | Computes the root mean squared error of some target and some predicted values.
`root_mean_power_fx`(data) | Returns root mean power
`table2string`(table[, out]) | Given list of lists figure out their common widths and print to out :Parameters: table : list of lists of strings What is aimed to be printed out : None or stream Where to print.

Classes

`BayesConfusionHypothesis`([alpha, ...]) | Bayesian hypothesis testing on confusion matrices.
`ClassWithCollections`([descr]) | Base class for objects which contain any known collection Classes inherited from this class gain ability to access collections and their items as simple attributes.
`ClassifierError`(clf[, labels, train]) | Compute (or return) some error of a (trained) classifier on a dataset.
`Collectable`([value, name, doc]) | Collection element.
`ConditionalAttribute`([enabled]) | Simple container intended to conditionally store the value ..
`Confusion`([attr, labels, add_confusion_obj]) | Compute a confusion matrix from predictions and targets (Node interface) This class is very similar to `ConfusionMatrix` and `ConfusionMatrixError`.
`ConfusionBasedError`(clf[, labels, ...]) | For a given classifier report an error based on internally computed error measure (given by some `ConfusionMatrix` stored in some conditional attribute of `Classifier`).
`ConfusionMatrix`([labels, labels_map]) | Class to contain information and display confusion matrix.
`ConfusionMatrixError`([labels]) | Compute confusion matrix as an “error function” This class can be used to compute confusion matrices from classifier output inside cross-validation fold without the `stats` conditional attribute.
`Dataset`(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes.
`Node`([space, postproc]) | Common processing object.
`ROCCurve`(labels[, sets]) | Generic class for ROC curve computation and plotting ..
`RegressionStatistics`(**kwargs) | Class to contain information and display on regression results.
`StringIO`([buf]) | class StringIO([buffer])
`SummaryStatistics`([targets, predictions, ...]) | Basic class to collect targets/predictions and report summary statistics It takes care about collecting the sets, which are just tuples (targets, predictions, estimates).

Exceptions

`BayesConfusionHypothesis`([alpha, ...]) | Bayesian hypothesis testing on confusion matrices.
`ClassWithCollections`([descr]) | Base class for objects which contain any known collection Classes inherited from this class gain ability to access collections and their items as simple attributes.
`ClassifierError`(clf[, labels, train]) | Compute (or return) some error of a (trained) classifier on a dataset.
`Collectable`([value, name, doc]) | Collection element.
`ConditionalAttribute`([enabled]) | Simple container intended to conditionally store the value ..
`Confusion`([attr, labels, add_confusion_obj]) | Compute a confusion matrix from predictions and targets (Node interface) This class is very similar to `ConfusionMatrix` and `ConfusionMatrixError`.
`ConfusionBasedError`(clf[, labels, ...]) | For a given classifier report an error based on internally computed error measure (given by some `ConfusionMatrix` stored in some conditional attribute of `Classifier`).
`ConfusionMatrix`([labels, labels_map]) | Class to contain information and display confusion matrix.
`ConfusionMatrixError`([labels]) | Compute confusion matrix as an “error function” This class can be used to compute confusion matrices from classifier output inside cross-validation fold without the `stats` conditional attribute.
`Dataset`(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes.
`Node`([space, postproc]) | Common processing object.
`ROCCurve`(labels[, sets]) | Generic class for ROC curve computation and plotting ..
`RegressionStatistics`(**kwargs) | Class to contain information and display on regression results.
`StringIO`([buf]) | class StringIO([buffer])
`SummaryStatistics`([targets, predictions, ...]) | Basic class to collect targets/predictions and report summary statistics It takes care about collecting the sets, which are just tuples (targets, predictions, estimates).

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