xenial (1) mlpack_softmax_regression.1.gz

Provided by: mlpack-bin_2.0.1-1_amd64 bug

NAME

       mlpack_softmax_regression - softmax regression

SYNOPSIS

        mlpack_softmax_regression [-h] [-v] [-m string] [-l string] [-r double] [-n int] [-N] [-c int] [-M string] [-p string] [-T string] [-L string] [-t string] -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  (-m)  option.   If  training  is  not
       desired,  but  only  testing is, a model can be loaded with the --input_model (-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.

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.  --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 ''.

       --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.

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(1)