focal (1) mlpack_softmax_regression.1.gz

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.