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Package mvpa :: Package clfs :: Module warehouse

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Module warehouse

Collection of classifiers to ease the exploration.

Classes

Warehouse
Class to keep known instantiated classifiers

Variables

_KNOWN_INTERNALS = ['knn', 'binary', 'svm', 'linear', 'smlr', ...

clfswh = Warehouse(known_tags= _KNOWN_INTERNALS)

regrswh = Warehouse(known_tags= _KNOWN_INTERNALS)

bad_classifiers = ['mpd', 'gpbt', 'gmnp', 'svrlight', 'krr',]

_optional_regressions = []

linearSVMC = clfswh ['linear', 'svm', cfg.get('svm', 'backend'...

rfesvm_split = SplitClassifier(linearSVMC)

rfesvm = LinearCSVMC()

Imports: operator, FeatureSelectionClassifier, SplitClassifier, MulticlassClassifier, SMLR, kNN, GNB, KernelLinear, KernelSquaredExponential, OneWayAnova, Absolute, SMLRWeights, FractionTailSelector, FixedNElementTailSelector, RangeElementSelector, SensitivityBasedFeatureSelection, default_backend, SVM, RbfNuSVMC, RbfCSVMC, cfg, externals, libsvm, warning, debug, LinearNuSVMC, sg, LinearCSVMC, lars, LARS, GLMNET_C, GLMNET_R, GPR, BLR, PLR

Variables Details

_KNOWN_INTERNALS

Value:

['knn', 'binary', 'svm', 'linear', 'smlr', 'does_feature_selection', '
has_sensitivity', 'multiclass', 'non-linear', 'kernel-based', 'lars', 
'regression', 'libsvm', 'sg', 'meta', 'retrainable', 'gpr', 'notrain2p
redict', 'ridge', 'blr', 'gnpp', 'enet', 'glmnet', 'gnb', 'plr']

linearSVMC

Value:

clfswh ['linear', 'svm', cfg.get('svm', 'backend', default= 'libsvm').
lower()] [0]

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