mvpa2.clfs.warehouseΒΆ

Collection of classifiers to ease the exploration.

Inheritance diagram of mvpa2.clfs.warehouse

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.

NeuroDebian

NITRC-listed