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clfs.blr

Module: clfs.blr

Inheritance diagram for mvpa.clfs.blr:

Bayesian Linear Regression (BLR).

BLR

class mvpa.clfs.blr.BLR(sigma_p=None, sigma_noise=1.0, **kwargs)

Bases: mvpa.clfs.base.Classifier

Bayesian Linear Regression (BLR).

Note

Available state variables:

• feature_ids: Feature IDS which were used for the actual training.
• log_marginal_likelihood: Log Marginal Likelihood
• predicted_variances: Variance per each predicted value
• predicting_time+: Time (in seconds) which took classifier to predict
• predictions+: Most recent set of predictions
• trained_dataset: The dataset it has been trained on
• trained_labels+: Set of unique labels it has been trained on
• trained_nsamples+: Number of samples it has been trained on
• training_confusion: Confusion matrix of learning performance
• training_time+: Time (in seconds) which took classifier to train
• values+: Internal classifier values the most recent predictions are based on

(States enabled by default are listed with +)

See also

Please refer to the documentation of the base class for more information:

Classifier

Initialize a BLR regression analysis.

Parameters:

• sigma_noise (float) – the standard deviation of the gaussian noise. (Defaults to 0.1)
• regression – Either to use ‘regression’ as regression. By default any Classifier- derived class serves as a classifier, so regression does binary classification. (Default: False)
• enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
• disable_states (None or list of basestring) – Names of the state variables which should be disabled

compute_log_marginal_likelihood()

Compute log marginal likelihood using self.train_fv and self.labels.

set_hyperparameters(*args)

Set hyperparameters’ values.

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