Multivariate Pattern Analysis in Python

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# mvpa2.clfs.warehouse

Collection of classifiers to ease the exploration.

Functions

`absolute_features`() | Returns a mapper that converts features into absolute values.
`is_sequence_type` | isSequenceType(a) – Return True if a has a sequence type, False otherwise.
`maxofabs_sample`() | Returns a mapper that finds max of absolute values of all samples.

Classes

`BLR`([sigma_p, sigma_noise]) | Bayesian Linear Regression (BLR).
`FeatureSelectionClassifier`(clf, mapper, **kwargs) | This is nothing but a `MappedClassifier`.
`FixedNElementTailSelector`(nelements, **kwargs) | Given a sequence, provide set of IDs for a fixed number of to be selected elements.
`FractionTailSelector`(felements, **kwargs) | Given a sequence, provide Ids for a fraction of elements ..
`GNB`(**kwargs) | Gaussian Naive Bayes `Classifier`.
`GPR`([kernel]) | Gaussian Process Regression (GPR).
`GeneralizedLinearKernel`(*args, **kwargs) | The linear kernel class.
`LDA`(**kwargs) | Linear Discriminant Analysis.
`LinearCSVMC`([C]) | C-SVM classifier using linear kernel.
`LinearKernel`(*args, **kwargs) | Simple linear kernel: K(a,b) = a*b.T ..
`LinearLSKernel`(*args, **kwargs) | A simple Linear kernel: K(a,b) = a*b.T ..
`LinearNuSVMC`([nu]) | Nu-SVM classifier using linear kernel.
`LinearSVMKernel` | A simple Linear kernel: K(a,b) = a*b.T ..
`MulticlassClassifier`(clf[, bclf_type]) | `CombinedClassifier` to perform multiclass using a list of
`OneWayAnova`([space]) | `FeaturewiseMeasure` that performs a univariate ANOVA.
`PLR`([lm, criterion, reduced, maxiter]) | Penalized logistic regression `Classifier`.
`PolyLSKernel`(**kwargs) | Polynomial kernel: K(a,b) = (gamma*a*b.T + coef0)**degree ..
`QDA`(**kwargs) | Quadratic Discriminant Analysis.
`RangeElementSelector`([lower, upper, ...]) | Select elements based on specified range of values ..
`RbfCSVMC`([C]) | C-SVM classifier using a radial basis function kernel See documentation of `AttributesCollector` for more information ..
`RbfLSKernel`(**kwargs) | Radial Basis Function kernel (aka Gaussian): K(a,b) = exp(-gamma*||a-b||**2) ..
`RbfNuSVMC`([nu]) | Nu-SVM classifier using a radial basis function kernel See documentation of `AttributesCollector` for more information ..
`RbfSVMKernel` | Radial Basis Function kernel (aka Gaussian): K(a,b) = exp(-gamma*||a-b||**2) ..
`RegressionAsClassifier`(clf[, centroids, ...]) | Allows to use arbitrary regression for classification.
`SMLR`(**kwargs) | Sparse Multinomial Logistic Regression `Classifier`.
`SMLRWeights`(clf[, force_train]) | `SensitivityAnalyzer` that reports the weights SMLR trained
`SVM`(**kwargs) | Support Vector Machine Classifier.
`SensitivityBasedFeatureSelection`(...[, ...]) | Feature elimination.
`SigmoidLSKernel`(**kwargs) | Sigmoid kernel: K(a,b) = tanh(gamma*a*b.T + coef0) ..
`SplitClassifier`(clf[, partitioner, splitter]) | `BoostedClassifier` to work on splits of the data
`SquaredExponentialKernel`([length_scale, sigma_f]) | The Squared Exponential kernel class.
`Warehouse`([known_tags, matches]) | Class to keep known instantiated classifiers Should provide easy ways to select classifiers of needed kind: clfswh[‘linear’, ‘svm’] should return all linear SVMs clfswh[‘linear’, ‘multiclass’] should return all linear classifiers capable of doing multiclass classification ..
`kNN`([k, dfx, voting]) | k-Nearest-Neighbour classifier.

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