mvpa2.clfs.blr.BLR

Inheritance diagram of BLR

class mvpa2.clfs.blr.BLR(sigma_p=None, sigma_noise=1.0, **kwargs)

Bayesian Linear Regression (BLR).

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • estimates+: Internal classifier estimates the most recent predictions are based on
  • 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
  • raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
  • trained_dataset: The dataset it has been trained on
  • trained_nsamples+: Number of samples it has been trained on
  • trained_targets+: Set of unique targets it has been trained on
  • training_stats: Confusion matrix of learning performance
  • training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Methods

clone() Create full copy of the classifier.
compute_log_marginal_likelihood() Compute log marginal likelihood using self.train_fv and self.targets.
generate(ds) Yield processing results.
get_postproc() Returns the post-processing node or None.
get_sensitivity_analyzer(**kwargs) Factory method to return an appropriate sensitivity analyzer for
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
set_hyperparameters(*args) Set hyperparameters’ values.
set_postproc(node) Assigns a post-processing node
set_space(name) Set the processing space name of this node.
summary() Providing summary over the 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 a BLR regression analysis.

Parameters :

sigma_noise : float

the standard deviation of the gaussian noise. (Defaults to 0.1)

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

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.
compute_log_marginal_likelihood() Compute log marginal likelihood using self.train_fv and self.targets.
generate(ds) Yield processing results.
get_postproc() Returns the post-processing node or None.
get_sensitivity_analyzer(**kwargs) Factory method to return an appropriate sensitivity analyzer for
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
set_hyperparameters(*args) Set hyperparameters’ values.
set_postproc(node) Assigns a post-processing node
set_space(name) Set the processing space name of this node.
summary() Providing summary over the 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
compute_log_marginal_likelihood()

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

set_hyperparameters(*args)

Set hyperparameters’ values.

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

NeuroDebian

NITRC-listed