Inherited from base.Classifier (private): _getFeatureIds, _postpredict, _posttrain, _prepredict, _pretrain, _regressionIsBogus, _setRetrainable

Inherited from misc.state.ClassWithCollections: __getattribute__, __new__, __setattr__, reset

Inherited from object: __delattr__, __format__, __hash__, __reduce__, __reduce_ex__, __sizeof__, __subclasshook__

Class Methods

[hide private]

Inherited from _svmbase._SVM (private): _customizeDoc

Class Variables

[hide private]

probabilities = StateVariable(enabled= False, doc= "Estimates ...

_KERNELS = {"linear":(svm.svmc.LINEAR, None, LinearSVMWeights)...

_KNOWN_PARAMS = ['epsilon', 'probability', 'shrinking', 'weigh...

_KNOWN_KERNEL_PARAMS = ['cache_size']

_KNOWN_IMPLEMENTATIONS = {'C_SVC':(svm.svmc.C_SVC, ('C',), ('b...

_clf_internals = _SVM._clf_internals+ ['libsvm']
Describes some specifics about the classifier -- is that it is doing regression for instance....

model = property(fget= lambda self: self.__model)
Access to the SVM model.

Inherited from _svmbase._SVM (private): _ATTRIBUTE_COLLECTIONS, _SVM_PARAMS

Inherited from base.Classifier: _DEV__doc__, feature_ids, predicting_time, predictions, regression, retrainable, trained_dataset, trained_labels, trained_nsamples, training_confusion, training_time, values

Inherited from misc.state.ClassWithCollections: descr

Instance Variables

[hide private]

__model
Holds the trained SVM.

Properties

[hide private]

Inherited from object: __class__

Method Details

[hide private]

init(self, kernel_type='linear', **kwargs)
(Constructor)

source code

Interface class to LIBSVM classifiers and regressions.

Default implementation (C/nu/epsilon SVM) is chosen depending on the given parameters (C/nu/tube_epsilon).

Parameters:

kernel_type - String must be a valid key for cls._KERNELS

Overrides: object.__init__

_train(self, dataset)

source code

Train SVM

Overrides: base.Classifier._train

_predict(self, data)

source code

Predict values for the data

Overrides: base.Classifier._predict

summary(self)

source code

Provide quick summary over the SVM classifier

Overrides: base.Classifier.summary

untrain(self)

source code

Untrain libsvm's SVM: forget the model

Overrides: base.Classifier.untrain

Class Variable Details

[hide private]

probabilities

Value:

StateVariable(enabled= False, doc= "Estimates of samples probabilities
 as provided by LibSVM")

_KERNELS

Value:

{"linear":(svm.svmc.LINEAR, None, LinearSVMWeights), "rbf":(svm.svmc.R
BF, ('gamma',), None), "poly":(svm.svmc.POLY, ('gamma', 'degree', 'coe
f0'), None), "sigmoid":(svm.svmc.SIGMOID, ('gamma', 'coef0'), None),}

_KNOWN_PARAMS

Value:

['epsilon', 'probability', 'shrinking', 'weight_label', 'weight']

_KNOWN_IMPLEMENTATIONS

Value:

{'C_SVC':(svm.svmc.C_SVC, ('C',), ('binary', 'multiclass'), 'C-SVM cla
ssification'), 'NU_SVC':(svm.svmc.NU_SVC, ('nu',), ('binary', 'multicl
ass'), 'nu-SVM classification'), 'ONE_CLASS':(svm.svmc.ONE_CLASS, (), 
('oneclass',), 'one-class-SVM'), 'EPSILON_SVR':(svm.svmc.EPSILON_SVR, 
('C', 'tube_epsilon'), ('regression',), 'epsilon-SVM regression'), 'NU
_SVR':(svm.svmc.NU_SVR, ('nu', 'tube_epsilon'), ('regression',), 'nu-S
VM regression')}

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Class SVM

__init__(self, kernel_type='linear', **kwargs) (Constructor)

_train(self, dataset)

_predict(self, data)

summary(self)

untrain(self)

probabilities

_KERNELS

_KNOWN_PARAMS

_KNOWN_IMPLEMENTATIONS

init(self, kernel_type='linear', **kwargs)
(Constructor)