Package mvpa :: Package clfs :: Module gpr :: Class GPR
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Class GPR

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Gaussian Process Regression (GPR).
Nested Classes [hide private]

Inherited from misc.state.Stateful: __metaclass__

Instance Methods [hide private]
 
__init__(self, kernel=KernelSquaredExponential(), sigma_noise=0.001, **kwargs)
Initialize a GPR regression analysis.
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__repr__(self)
String summary of the object
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compute_log_marginal_likelihood(self)
Compute log marginal likelihood using self.train_fv and self.labels.
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_train(self, data)
Train the classifier using data (Dataset).
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_predict(self, data)
Predict the output for the provided data.
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set_hyperparameters(self, *args)
Set hyperparameters' values.
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Inherited from base.Classifier: __str__, getSensitivityAnalyzer, isTrained, predict, regression, train, train2predict, trained, untrain

Inherited from misc.state.Stateful: __getattribute__, __setattr__, reset

Inherited from object: __delattr__, __hash__, __new__, __reduce__, __reduce_ex__

Class Variables [hide private]
  predicted_variances = StateVariable(enabled= False, doc= "Vari...
  log_marginal_likelihood = StateVariable(enabled= False, doc= "...
  _clf_internals = ['gpr', 'regression', 'non-linear']
Describes some specifics about the classifier -- is that it is doing regression for instance....

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

Instance Variables [hide private]

Inherited from base.Classifier (private): _regression, _train2predict

Properties [hide private]

Inherited from misc.state.Stateful: descr

Inherited from object: __class__

Method Details [hide private]

__init__(self, kernel=KernelSquaredExponential(), sigma_noise=0.001, **kwargs)
(Constructor)

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Initialize a GPR regression analysis.
Parameters:
  • kernel (Kernel) - a kernel object defining the covariance between instances. (Defaults to KernelSquaredExponential())
  • sigma_noise (float) - the standard deviation of the gaussian noise. (Defaults to 0.001)
Overrides: object.__init__

__repr__(self)
(Representation operator)

source code 
String summary of the object
Overrides: object.__repr__

_train(self, data)

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Train the classifier using data (Dataset).
Overrides: base.Classifier._train

_predict(self, data)

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Predict the output for the provided data.
Overrides: base.Classifier._predict

set_hyperparameters(self, *args)

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Set hyperparameters' values.

Note that this is a list so the order of the values is important.


Class Variable Details [hide private]

predicted_variances

Value:
StateVariable(enabled= False, doc= "Variance per each predicted value"\
)

log_marginal_likelihood

Value:
StateVariable(enabled= False, doc= "Log Marginal Likelihood")