mvpa.clfs.blr.BLR¶
- class mvpa.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
- 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 +)
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
- 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.
