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PyMVPA: Python MultiVariate Pattern Analysis
Package mvpa :: Package clfs :: Module blr :: Class BLR
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Class BLR

source code

Bayesian Linear Regression (BLR).
Nested Classes [hide private]

Inherited from misc.state.ClassWithCollections: __metaclass__

Instance Methods [hide private]
 
__init__(self, sigma_p=None, sigma_noise=1.0, **kwargs)
Initialize a BLR regression analysis.		 source code
 
__repr__(self)
String summary of the object		 source code
 
compute_log_marginal_likelihood(self)
Compute log marginal likelihood using self.train_fv and self.labels.		 source code
 
_train(self, data)
Train regression using data (Dataset).		 source code
 
_predict(self, data)
Predict the output for the provided data.		 source code
 
set_hyperparameters(self, *args)
Set hyperparameters' values.		 source code

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

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 Variables [hide private]
  predicted_variances = StateVariable(enabled= False, doc= "Vari...
  log_marginal_likelihood = StateVariable(enabled= False, doc= "...
  _clf_internals = ['blr', 'regression', 'linear']
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, sigma_p=None, sigma_noise=1.0, **kwargs)
(Constructor)

source code 
Initialize a BLR regression analysis.
Parameters:
  • sigma_noise (float) - the standard deviation of the gaussian noise. (Defaults to 0.1)
Overrides: object.__init__

__repr__(self)
(Representation operator)

source code 
String summary of 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__

_train(self, data)

source code 
Train regression using data (Dataset).
Overrides: base.Classifier._train

_predict(self, data)

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

set_hyperparameters(self, *args)

source code 

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")

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PyMVPA: Python MultiVariate Pattern Analysis
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