Multivariate Pattern Analysis in Python

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# 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

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