NAME

       mlpack_softmax_regression - softmax regression

SYNOPSIS

        mlpack_softmax_regression [-h] [-v]

DESCRIPTION

       This  program  performs softmax regression, a generalization of logistic regression to the
       multiclass case, and has support for L2 regularization. The program is  able  to  train  a
       model,  load  an  existing model, and give predictions (and optionally their accuracy) for
       test data.

       Training a softmax regression model is done by giving  a  file  of  training  points  with
       --training_file (-t) and their corresponding labels with --labels_file (-l). The number of
       classes can be manually specified  with  the  --number_of_classes  (-n)  option,  and  the
       maximum  number  of  iterations  of  the  L-BFGS  optimizer  can  be  specified  with  the
       --max_iterations (-M) option.  The  L2  regularization  constant  can  be  specified  with
       --lambda  (-r),  and  if an intercept term is not desired in the model, the --no_intercept
       (-N) can be specified.

       The trained model can be saved to a file with  the  --output_model_file  (-m)  option.  If
       training  is  not  desired,  but  only  testing  is,  a  model  can  be  loaded  with  the
       --input_model_file (-i) option. At the current time, a  loaded  model  cannot  be  trained
       further, so specifying both -i and -t is not allowed.

       The program is also able to evaluate a model on test data. A test dataset can be specified
       with the --test_data (-T) option. Class predictions will be saved in  the  file  specified
       with  the  --predictions_file (-p) option. If labels are specified for the test data, with
       the --test_labels (-L) option, then the program will print the accuracy of the predictions
       on the given test set and its corresponding labels.

OPTIONAL INPUT OPTIONS

       --help (-h)
              Default help info.

       --info [string]
              Get  help  on  a  specific module or option.  Default value ''.  --input_model_file
              (-m) [string] File containing existing model (parameters).  Default value ''.

       --labels_file (-l) [string]
              A file containing labels (0 or 1) for the points  in  the  training  set  (y).  The
              labels must order as a row. Default value ''.

       --lambda (-r) [double]
              L2-regularization constant Default value 0.0001.

       --max_iterations (-n) [int]
              Maximum number of iterations before termination.  Default value 400.

       --no_intercept (-N)
              Do  not add the intercept term to the model.  --number_of_classes (-c) [int] Number
              of classes for classification; if unspecified (or 0), the number of  classes  found
              in the labels will be used. Default value 0.

       --test_data (-T) [string]
              File containing test dataset. Default value ’'.

       --test_labels (-L) [string]
              File  containing  test  labels.  Default value ''.  --training_file (-t) [string] A
              file containing the training set (the matrix of predictors, X). Default value ''.

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

       --version (-V)
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --output_model_file  (-M)  [string]  File  to  save  trained  softmax regression model to.
       Default value ''.  --predictions_file (-p) [string] File  to  save  predictions  for  test
       dataset into.  Default value ''.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

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

                                                      mlpack_softmax_regression(16 November 2017)