Provided by: mlpack-bin_3.0.4-1_amd64 bug


       mlpack_lars - lars


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


       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

       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':

       $ 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':

       $ lars --input_model_file lasso_model.bin --test_file  test.csv  --output_predictions_file


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

       --info [string]
              Get help on a specific module or option.  Default value ''.

       --input_file (-i) [string]
              Matrix of covariates (X). Default value ''.

       --input_model_file (-m) [unknown]
              Trained LARS model to use. Default value ''.

       --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) [string]
              Matrix of responses/observations (y). Default value ''.

       --test_file (-t) [string]
              Matrix containing points to regress on (test points). Default value ''.

       --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.


       --output_model_file (-M) [unknown]
              Output LARS model. Default value ''.

       --output_predictions_file (-o) [string]
              If  --test_file  is  specified,  this file is where the predicted responses will be
              saved. Default value ''.


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