mvpa2.clfs.warehouse.RbfCSVMC

class mvpa2.clfs.warehouse.RbfCSVMC(C=-1.0, **kwargs)

C-SVM classifier using a radial basis function kernel

See documentation of AttributesCollector for more information

Methods

clone()	Create full copy of the classifier.
generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_sensitivity_analyzer(**kwargs)	Returns an appropriate SensitivityAnalyzer.
get_space()	Query the processing space name of this node.
is_trained([dataset])	Either classifier was already trained.
predict(obj, data, *args, **kwargs)
repredict(obj, data, *args, **kwargs)
reset()
retrain(dataset, **kwargs)	Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
set_postproc(node)	Assigns a post-processing node Set to None to disable postprocessing.
set_space(name)	Set the processing space name of this node.
summary()	Provide quick summary over the SVM classifier
train(ds)	The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain()	Reverts changes in the state of this node caused by previous training

Initialize instance of RbfCSVMC

Parameters:

kernel :

Kernel object. (Default: None)

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

tube_epsilon :

Epsilon in epsilon-insensitive loss function of epsilon-SVM regression (SVR). (Default: 0.01)

C :

Trade-off parameter between width of the margin and number of support vectors. Higher C – more rigid margin SVM. In linear kernel, negative values provide automatic scaling of their value according to the norm of the data. (Default: -1.0)

weight :

Custom weights per label. (Default: [])

probability :

Flag to signal either probability estimate is obtained within LIBSVM. (Default: 0)

epsilon :

Tolerance of termination criteria. (For nu-SVM default is 0.001). (Default: 5e-05)

weight_label :

To be used in conjunction with weight for custom per-label weight. (Default: [])

shrinking :

Either shrinking is to be conducted. (Default: 1)

nu :

Fraction of datapoints within the margin. (Default: 0.5)

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space: str, optional :

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

Methods

clone()	Create full copy of the classifier.
generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_sensitivity_analyzer(**kwargs)	Returns an appropriate SensitivityAnalyzer.
get_space()	Query the processing space name of this node.
is_trained([dataset])	Either classifier was already trained.
predict(obj, data, *args, **kwargs)
repredict(obj, data, *args, **kwargs)
reset()
retrain(dataset, **kwargs)	Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
set_postproc(node)	Assigns a post-processing node Set to None to disable postprocessing.
set_space(name)	Set the processing space name of this node.
summary()	Provide quick summary over the SVM classifier
train(ds)	The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain()	Reverts changes in the state of this node caused by previous training