| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# 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.

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