NAME

       mlpack_softmax_regression - softmax regression

SYNOPSIS

        mlpack_softmax_regression [-m unknown] [-l string] [-r double] [-n int] [-N bool] [-c int] [-T string] [-L string] [-t string] [-V bool] [-M unknown] [-p string] [-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  the
       '--training_file  (-t)'  parameter  and their corresponding labels with the '--labels_file
       (-l)'  parameter.  The  number  of  classes   can   be   manually   specified   with   the
       '--number_of_classes  (-c)'  parameter, and the maximum number of iterations of the L-BFGS
       optimizer  can  be  specified  with  the  ’--max_iterations  (-n)'   parameter.   The   L2
       regularization  constant  can  be  specified  with the '--lambda (-r)' parameter and if an
       intercept term is not desired in the model, the '--no_intercept  (-N)'  parameter  can  be
       specified.

       The  trained  model  can be saved with the '--output_model_file (-M)' output parameter. If
       training  is  not  desired,  but  only  testing  is,  a  model  can  be  loaded  with  the
       '--input_model_file (-m)' parameter. At the current time, a loaded model cannot be trained
       further, so specifying both '--input_model_file (-m)' and '--training_file  (-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_file  (-T)'  parameter.  Class  predictions  can  be  saved  with   the
       '--predictions_file (-p)' output parameter. If labels are specified for the test data with
       the '--test_labels_file (-L)' parameter, then the program will print the accuracy  of  the
       predictions on the given test set and its corresponding labels.

       For  example,  to  train  a softmax regression model on the data 'dataset.csv' with labels
       'labels.csv' with a maximum of 1000 iterations for training, saving the trained  model  to
       'sr_model.bin', the following command can be used:

              $  mlpack_softmax_regression  --training_file  dataset.csv --labels_file labels.csv
              --output_model_file sr_model.bin

              Then, to use 'sr_model.bin' to  classify  the  test  points  in  'test_points.csv',
              saving  the  output  predictions to 'predictions.csv', the following command can be
              used:

              $    mlpack_softmax_regression    --input_model_file    sr_model.bin    --test_file
              test_points.csv --predictions_file predictions.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]
              File containing existing model (parameters).

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

       --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) [bool]
              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_file (-T) [string]
              Matrix containing test dataset.

       --test_labels_file (-L) [string]
              Matrix containing test labels.

       --training_file (-t) [string]
              A matrix containing the training set (the matrix of predictors, X).

       --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]
              File to save trained softmax regression model to.

       --predictions_file (-p) [string]
              Matrix to save predictions for test dataset into.

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.