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.