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.
maxofabs_sample() Returns a mapper that finds max of absolute values of all samples.

Classes

BLR(**kwargs[, 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
FractionTailSelector(felements, **kwargs) Given a sequence, provide Ids for a fraction of elements
GNB(**kwargs) Gaussian Naive Bayes Classifier.
GPR(**kwargs[, kernel]) Gaussian Process Regression (GPR).
GeneralizedLinearKernel(*args, **kwargs) The linear kernel class.
LDA(**kwargs) Linear Discriminant Analysis.
LinearCSVMC(**kwargs[, 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(**kwargs[, nu]) Nu-SVM classifier using linear kernel.
LinearSVMKernel A simple Linear kernel: K(a,b) = a*b.T
MulticlassClassifier(clf, **kwargs[, bclf_type]) CombinedClassifier to perform multiclass using a list of
OneWayAnova(**kwargs[, space]) FeaturewiseMeasure that performs a univariate ANOVA.
PLR(**kwargs[, 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(**kwargs[, lower, ...]) Select elements based on specified range of values ..
RbfCSVMC(**kwargs[, C]) C-SVM classifier using a radial basis function kernel
RbfLSKernel(**kwargs) Radial Basis Function kernel (aka Gaussian):
RbfNuSVMC(**kwargs[, nu]) Nu-SVM classifier using a radial basis function kernel
RbfSVMKernel Radial Basis Function kernel (aka Gaussian):
RegressionAsClassifier(clf, **kwargs[, ...]) Allows to use arbitrary regression for classification.
SMLR(**kwargs) Sparse Multinomial Logistic Regression Classifier.
SMLRWeights(clf, **kwargs[, 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, **kwargs[, ...]) BoostedClassifier to work on splits of the data
SquaredExponentialKernel(**kwargs[, ...]) The Squared Exponential kernel class.
Warehouse([known_tags, matches]) Class to keep known instantiated classifiers
kNN(**kwargs[, k, dfx, voting]) k-Nearest-Neighbour classifier.

NeuroDebian

NITRC-listed