NAME

       mlpack_lars - lars

SYNOPSIS

        mlpack_lars [-i unknown] [-m unknown] [-l double] [-L double] [-r unknown] [-t unknown] [-c bool] [-V bool] [-M unknown] [-o unknown] [-h -v]

DESCRIPTION

       An  implementation of LARS: Least Angle Regression (Stagewise/laSso). This is a stage-wise
       homotopy-based   algorithm   for   L1-regularized   linear    regression    (LASSO)    and
       L1+L2-regularized linear regression (Elastic Net).

       This  program  is  able to train a LARS/LASSO/Elastic Net model or load a model from file,
       output regression predictions for a test set, and save the trained model to  a  file.  The
       LARS algorithm is described in more detail below:

       Let X be a matrix where each row is a point and each column is a dimension, and let y be a
       vector of targets.

       The Elastic Net problem is to solve

         min_beta 0.5 || X * beta - y ||_2^2 + lambda_1 ||beta||_1 +
           0.5 lambda_2 ||beta||_2^2

       If lambda1 > 0 and lambda2 = 0, the problem is the LASSO.  If lambda1 > 0 and lambda2 > 0,
       the  problem  is  the  Elastic  Net.  If lambda1 = 0 and lambda2 > 0, the problem is ridge
       regression.  If lambda1 =  0  and  lambda2  =  0,  the  problem  is  unregularized  linear
       regression.

       For  efficiency reasons, it is not recommended to use this algorithm with ’--lambda1 (-l)'
       =  0.  In  that  case,  use  the  'linear_regression'  program,  which   implements   both
       unregularized linear regression and ridge regression.

       To  train  a  LARS/LASSO/Elastic  Net model, the '--input_file (-i)' and ’--responses_file
       (-r)'  parameters  must  be  given.  The   '--lambda1   (-l)',   ’--lambda2   (-L)',   and
       '--use_cholesky  (-c)'  parameters  control  the  training 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 '--output_predictions_file
       (-o)' output parameter.

       For example, the following command trains a model on the  data  'data.csv'  and  responses
       'responses.csv'  with lambda1 set to 0.4 and lambda2 set to 0 (so, LASSO is being solved),
       and then the model is saved to 'lasso_model.bin':

       $ mlpack_lars --input_file data.csv --responses_file responses.csv --lambda1 0.4 --lambda2
       0 --output_model_file lasso_model.bin

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

       $     mlpack_lars     --input_model_file     lasso_model.bin     --test_file      test.csv
       --output_predictions_file test_predictions.csv

OPTIONAL INPUT OPTIONS

       --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 LARS model to use.

       --lambda1 (-l) [double]
              Regularization parameter for l1-norm penalty.  Default value 0.

       --lambda2 (-L) [double]
              Regularization parameter for l2-norm penalty.  Default value 0.

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

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

       --use_cholesky (-c) [bool]
              Use  Cholesky decomposition during computation rather than explicitly computing the
              full Gram matrix.

       --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 LARS model.

       --output_predictions_file (-o) [unknown]
              If  --test_file  is  specified,  this file 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.