.\" Text automatically generated by txt2man
.TH mlpack_softmax_regression 1 "12 December 2020" "mlpack-3.4.2" "User Commands"
.SH NAME
\fBmlpack_softmax_regression \fP- softmax regression
.SH SYNOPSIS
.nf
.fam C
 \fBmlpack_softmax_regression\fP [\fB-m\fP \fIunknown\fP] [\fB-l\fP \fIstring\fP] [\fB-r\fP \fIdouble\fP] [\fB-n\fP \fIint\fP] [\fB-N\fP \fIbool\fP] [\fB-c\fP \fIint\fP] [\fB-T\fP \fIstring\fP] [\fB-L\fP \fIstring\fP] [\fB-t\fP \fIstring\fP] [\fB-V\fP \fIbool\fP] [\fB-M\fP \fIunknown\fP] [\fB-p\fP \fIstring\fP] [\fB-h\fP \fB-v\fP] 
.fam T
.fi
.fam T
.fi
.SH 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.
.PP
Training a softmax regression model is done by giving a file of training
points with the '\fB--training_file\fP (\fB-t\fP)' parameter and their corresponding
labels with the '\fB--labels_file\fP (\fB-l\fP)' parameter. The number of classes can be
manually specified with the '\fB--number_of_classes\fP (\fB-c\fP)' parameter, and the
maximum number of iterations of the L-BFGS optimizer can be specified with the
\(cq\fB--max_iterations\fP (\fB-n\fP)' parameter. The L2 regularization constant can be
specified with the '\fB--lambda\fP (\fB-r\fP)' parameter and if an intercept term is not
desired in the model, the '\fB--no_intercept\fP (\fB-N\fP)' parameter can be specified.
.PP
The trained model can be saved with the '\fB--output_model_file\fP (\fB-M\fP)' output
parameter. If training is not desired, but only testing is, a model can be
loaded with the '\fB--input_model_file\fP (\fB-m\fP)' parameter. At the current time, a
loaded model cannot be trained further, so specifying both '\fB--input_model_file\fP
(\fB-m\fP)' and '\fB--training_file\fP (\fB-t\fP)' is not allowed.
.PP
The program is also able to evaluate a model on test data. A test dataset can
be specified with the '\fB--test_file\fP (\fB-T\fP)' parameter. Class predictions can be
saved with the '\fB--predictions_file\fP (\fB-p\fP)' output parameter. If labels are
specified for the test data with the '\fB--test_labels_file\fP (\fB-L\fP)' parameter, then
the program will print the accuracy of the predictions on the given test set
and its corresponding labels.
.PP
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:
.RE
.PP

.RS
$ \fBmlpack_softmax_regression\fP \fB--training_file\fP dataset.csv \fB--labels_file\fP
labels.csv \fB--output_model_file\fP sr_model.bin
.PP
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:
.PP
$ \fBmlpack_softmax_regression\fP \fB--input_model_file\fP sr_model.bin \fB--test_file\fP
test_points.csv \fB--predictions_file\fP predictions.csv
.RE
.PP

.SH OPTIONAL INPUT OPTIONS 

.TP
.B
\fB--help\fP (\fB-h\fP) [\fIbool\fP]
Default help info. 
.TP
.B
\fB--info\fP [\fIstring\fP]
Print help on a specific option. Default value ''. 
.TP
.B
\fB--input_model_file\fP (\fB-m\fP) [\fIunknown\fP]
File containing existing model (parameters). 
.TP
.B
\fB--labels_file\fP (\fB-l\fP) [\fIstring\fP]
A matrix containing labels (0 or 1) for the points in the training set (y). The labels must order as a row. 
.TP
.B
\fB--lambda\fP (\fB-r\fP) [\fIdouble\fP]
L2-regularization constant Default value 0.0001. 
.TP
.B
\fB--max_iterations\fP (\fB-n\fP) [\fIint\fP]
Maximum number of iterations before termination. Default value 400. 
.TP
.B
\fB--no_intercept\fP (\fB-N\fP) [\fIbool\fP]
Do not add the intercept term to the model. 
.TP
.B
\fB--number_of_classes\fP (\fB-c\fP) [\fIint\fP]
Number of classes for classification; if unspecified (or 0), the number of classes found in the labels will be used. Default value 0. 
.TP
.B
\fB--test_file\fP (\fB-T\fP) [\fIstring\fP]
Matrix containing test dataset. 
.TP
.B
\fB--test_labels_file\fP (\fB-L\fP) [\fIstring\fP]
Matrix containing test labels. 
.TP
.B
\fB--training_file\fP (\fB-t\fP) [\fIstring\fP]
A matrix containing the training set (the matrix of predictors, X). 
.TP
.B
\fB--verbose\fP (\fB-v\fP) [\fIbool\fP]
Display informational messages and the full list of parameters and timers at the end of execution. 
.TP
.B
\fB--version\fP (\fB-V\fP) [\fIbool\fP]
Display the version of mlpack.  
.SH OPTIONAL OUTPUT OPTIONS 

.TP
.B
\fB--output_model_file\fP (\fB-M\fP) [\fIunknown\fP]
File to save trained softmax regression model to. 
.TP
.B
\fB--predictions_file\fP (\fB-p\fP) [\fIstring\fP]
Matrix to save predictions for test dataset into.
.SH 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.