oracular (1) mlpack_bayesian_linear_regression.1.gz

NAME

       mlpack_bayesian_linear_regression - bayesianlinearregression

SYNOPSIS

        mlpack_bayesian_linear_regression [-c bool] [-i unknown] [-m unknown] [-r unknown] [-s bool] [-t unknown] [-V bool] [-M unknown] [-o unknown] [-u unknown] [-h -v]

DESCRIPTION

       An  implementation  of  the  bayesian  linear  regression.   This  model  is  a  probabilistic  view  and
       implementation of the linear regression.  The  final  solution  is  obtained  by  computing  a  posterior
       distribution  from  gaussian  likelihood  and  a  zero  mean gaussian isotropic prior distribution on the
       solution.  Optimization is AUTOMATIC and does not require cross validation. The optimization is performed
       by  maximization  of  the evidence function. Parameters are tuned during the maximization of the marginal
       likelihood. This procedure includes the Ockham's razor that penalizes over complex solutions.

       This program is able to train a Bayesian linear regression model  or  load  a  model  from  file,  output
       regression predictions for a test set, and save the trained model to a file.

       To  train a BayesianLinearRegression model, the '--input_file (-i)' and ’--responses_file (-r)'parameters
       must be given. The '--center (-c)'and ’--scale (-s)' parameters control the centering and the normalizing
       options.  A  trained model can be saved with the '--output_model_file (-M)'. If no training is desired at
       all, a model can be passed via the '--input_model_file (-m)' parameter.

       The program can also provide predictions for test data using either the trained model or the given  input
       model.  Test  points  can  be specified with the ’--test_file (-t)' parameter. Predicted responses to the
       test points can be saved with the '--predictions_file (-o)' output parameter. The corresponding  standard
       deviation can be save by precising the '--stds_file (-u)' parameter.

       For   example,   the   following   command   trains   a  model  on  the  data  'data.csv'  and  responses
       'responses.csv'with center set to true and scale set to false (so, Bayesian linear  regression  is  being
       solved, and then the model is saved to ’blr_model.bin':

       $ mlpack_bayesian_linear_regression --input_file data.csv --responses_file responses.csv --center --scale
       --output_model_file blr_model.bin

       The following command uses the 'blr_model.bin' to provide predicted responses for the data 'test.csv' and
       save those responses to 'test_predictions.csv':

       $     mlpack_bayesian_linear_regression    --input_model_file    blr_model.bin    --test_file    test.csv
       --predictions_file test_predictions.csv

       Because the estimator computes a  predictive  distribution  instead  of  a  simple  point  estimate,  the
       '--stds_file (-u)' parameter allows one to save the prediction uncertainties:

       $     mlpack_bayesian_linear_regression    --input_model_file    blr_model.bin    --test_file    test.csv
       --predictions_file test_predictions.csv --stds_file stds.csv

OPTIONAL INPUT OPTIONS

       --center (-c) [bool]
              Center the data and fit the intercept if enabled.

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --input_file (-i) [unknown]
              Matrix of covariates (X).

       --input_model_file (-m) [unknown]
              Trained BayesianLinearRegression model to use.

       --responses_file (-r) [unknown]
              Matrix of responses/observations (y).

       --scale (-s) [bool]
              Scale each feature by their standard deviations if enabled.

       --test_file (-t) [unknown]
              Matrix containing points to regress on (test points).

       --verbose (-v) [bool]
              Display informational messages and the full list of parameters and timers at the end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --output_model_file (-M) [unknown]
              Output BayesianLinearRegression model.

       --predictions_file (-o) [unknown]
              If --test_file is specified, this file is where the predicted responses will be saved.

       --stds_file (-u) [unknown]
              If specified, this is where the standard deviations of the predictive distribution will be saved.

ADDITIONAL INFORMATION

       For further information, including relevant papers, citations,  and  theory,  consult  the  documentation
       found at http://www.mlpack.org or included with your distribution of mlpack.