NAME

       mlpack_linear_regression - simple linear regression and prediction

SYNOPSIS

        mlpack_linear_regression [-m unknown] [-l double] [-T string] [-t string] [-r string] [-V bool] [-M unknown] [-o string] [-h -v]

DESCRIPTION

       An  implementation  of simple linear regression and simple ridge regression using ordinary
       least squares. This solves the problem

         y = X * b + e

       where X (specified by '--training_file (-t)') and y (specified either as the  last  column
       of  the  input  matrix  '--training_file (-t)' or via the ’--training_responses_file (-r)'
       parameter) are known and b is the desired variable. If the covariance matrix (X'X) is  not
       invertible,  or  if the solution is overdetermined, then specify a Tikhonov regularization
       constant (with '--lambda (-l)') greater than  0,  which  will  regularize  the  covariance
       matrix   to   make   it   invertible.   The   calculated   b   may   be   saved  with  the
       ’--output_predictions_file (-o)' output parameter.

       Optionally, the calculated value of b is used to predict the responses for another  matrix
       X' (specified by the '--test_file (-T)' parameter):

          y' = X' * b

       and  the  predicted  responses  y'  may be saved with the ’--output_predictions_file (-o)'
       output parameter. This type of regression is  related  to  least-angle  regression,  which
       mlpack implements as the 'lars' program.

       For  example,  to  run  a linear regression on the dataset 'X.csv' with responses ’y.csv',
       saving the trained model to 'lr_model.bin', the following command could be used:

       $   mlpack_linear_regression   --training_file   X.csv   --training_responses_file   y.csv
       --output_model_file lr_model.bin

       Then,  to  use 'lr_model.bin' to predict responses for a test set 'X_test.csv', saving the
       predictions to 'X_test_responses.csv', the following command could be used:

       $  mlpack_linear_regression   --input_model_file   lr_model.bin   --test_file   X_test.csv
       --output_predictions_file X_test_responses.csv

OPTIONAL INPUT OPTIONS

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

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

       --input_model_file (-m) [unknown]
              Existing LinearRegression model to use.

       --lambda (-l) [double]
              Tikhonov  regularization  for ridge regression.  If 0, the method reduces to linear
              regression.  Default value 0.

       --test_file (-T) [string]
              Matrix containing X' (test regressors).

       --training_file (-t) [string]
              Matrix containing training set X (regressors).

       --training_responses_file (-r) [string]
              Optional vector containing y (responses). If not given, the responses  are  assumed
              to be the last row of the input file.

       --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 LinearRegression model.

       --output_predictions_file (-o) [string]
              If --test_file is specified, this matrix is where the predicted responses  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.