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PyMVPA: Python MultiVariate Pattern Analysis
Package mvpa :: Package clfs :: Module _svmbase :: Class _SVM
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Class _SVM

source code

Support Vector Machine Classifier.

Base class for all external SVM implementations.

Nested Classes [hide private]

Inherited from misc.state.ClassWithCollections: __metaclass__

Instance Methods [hide private]
 
__init__(self, kernel_type='linear', **kwargs)
Init base class of SVMs. Not to be publicly used		 source code
 
__repr__(self)
Definition of the object summary over the object		 source code
 
_getDefaultC(self, data)
Compute default C		 source code
 
_getDefaultGamma(self, dataset)
Compute default Gamma		 source code
 
getSensitivityAnalyzer(self, **kwargs)
Returns an appropriate SensitivityAnalyzer.		 source code

Inherited from base.Classifier: __str__, clone, isTrained, predict, repredict, retrain, summary, train, trained, untrain

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

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

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

Class Methods [hide private]
 
_customizeDoc(cls) source code
Class Variables [hide private]
  _ATTRIBUTE_COLLECTIONS = ['params', 'kernel_params']
  _SVM_PARAMS = {'C': Parameter(-1.0, doc= 'Trade-off parameter ...
  _clf_internals = ['svm', 'kernel-based']
Describes some specifics about the classifier -- is that it is doing regression for instance....

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

Properties [hide private]

Inherited from object: __class__

Method Details [hide private]

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

source code 

Init base class of SVMs. Not to be publicly used

TODO: handling of parameters might migrate to be generic for all classifiers. SVMs are chosen to be testbase for that functionality to see how well it would fit.

Parameters:
  • kernel_type (basestr) - String must be a valid key for cls._KERNELS
Overrides: object.__init__

__repr__(self)
(Representation operator)

source code 
Definition of the object summary over the object
Parameters:
  • fullname - Either to include full name of the module
  • prefixes - What other prefixes to prepend to list of arguments
Overrides: object.__repr__

_getDefaultC(self, data)

source code 

Compute default C

TODO: for non-linear SVMs

_getDefaultGamma(self, dataset)

source code 

Compute default Gamma

TODO: unify bloody libsvm interface so it makes use of this function. Now it is computed within SVMModel.__init__

getSensitivityAnalyzer(self, **kwargs)

source code 
Returns an appropriate SensitivityAnalyzer.
Overrides: base.Classifier.getSensitivityAnalyzer

Class Variable Details [hide private]

_SVM_PARAMS

Value:
{'C': Parameter(-1.0, doc= 'Trade-off parameter between width of the '\
 'margin and number of support vectors. Higher C -- ' 'more rigid marg\
in SVM. In linear kernel, negative ' 'values provide automatic scaling\
 of their value ' 'according to the norm of the data'), 'nu': Paramete\
r(0.5, min= 0.0, max= 1.0, doc= 'Fraction of datapoints within the mar\
gin'), 'cache_size': Parameter(100, doc= 'Size of the kernel cache, sp\
ecified in megabytes'), 'coef0': Parameter(0.5, doc= 'Offset coefficie\
nt in polynomial and sigmoid kernels'), 'degree': Parameter(3, doc= 'D\
...

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PyMVPA: Python MultiVariate Pattern Analysis
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