.\" Text automatically generated by txt2man
.TH mlpack_perceptron 1 "18 November 2018" "mlpack-3.0.4" "User Commands"
.SH NAME
\fBmlpack_perceptron \fP- perceptron
.SH SYNOPSIS
.nf
.fam C
 \fBmlpack_perceptron\fP [\fB-m\fP \fIunknown\fP] [\fB-l\fP \fIstring\fP] [\fB-n\fP \fIint\fP] [\fB-T\fP \fIstring\fP] [\fB-t\fP \fIstring\fP] [\fB-V\fP \fIbool\fP] [\fB-o\fP \fIstring\fP] [\fB-M\fP \fIunknown\fP] [\fB-h\fP \fB-v\fP] 
.fam T
.fi
.fam T
.fi
.SH DESCRIPTION


This program implements a perceptron, which is a single level neural network.
The perceptron makes its predictions based on a linear predictor function
combining a set of weights with the feature vector. The perceptron learning
rule is able to converge, given enough iterations (specified using the
\(cq\fB--max_iterations\fP (\fB-n\fP)' parameter), if the data supplied is linearly
separable. The perceptron is parameterized by a matrix of weight vectors that
denote the numerical weights of the neural network.
.PP
This program allows loading a perceptron from a model (via the
\(cq\fB--input_model_file\fP (\fB-m\fP)' parameter) or training a perceptron given training
data (via the '\fB--training_file\fP (\fB-t\fP)' parameter), or both those things at once.
In addition, this program allows classification on a test dataset (via the
\(cq\fB--test_file\fP (\fB-T\fP)' parameter) and the classification results on the test set
may be saved with the '\fB--output_file\fP (\fB-o\fP)'output parameter. The perceptron
model may be saved with the '\fB--output_model_file\fP (\fB-M\fP)' output parameter.
.PP
The training data given with the '\fB--training_file\fP (\fB-t\fP)' option may have class
labels as its last dimension (so, if the training data is in CSV format,
labels should be the last column). Alternately, the '\fB--labels_file\fP (\fB-l\fP)'
parameter may be used to specify a separate matrix of labels.
.PP
All these options make it easy to train a perceptron, and then re-use that
perceptron for later classification. The invocation below trains a perceptron
on 'training_data.csv' with labels 'training_labels.csv', and saves the model
to 'perceptron_model.bin'.
.PP
$ perceptron \fB--training_file\fP training_data.csv \fB--labels_file\fP
training_labels.csv \fB--output_model_file\fP perceptron_model.bin
.PP
Then, this model can be re-used for classification on the test data
\(cqtest_data.csv'. The example below does precisely that, saving the predicted
classes to 'predictions.csv'.
.PP
$ perceptron \fB--input_model_file\fP perceptron_model.bin \fB--test_file\fP test_data.csv
\fB--output_file\fP predictions.csv
.PP
Note that all of the options may be specified at once: predictions may be
calculated right after training a model, and model training can occur even if
an existing perceptron model is passed with the '\fB--input_model_file\fP (\fB-m\fP)'
parameter. However, note that the number of classes and the dimensionality of
all data must match. So you cannot pass a perceptron model trained on 2
classes and then re-train with a 4-class dataset. Similarly, attempting
classification on a 3-dimensional dataset with a perceptron that has been
trained on 8 dimensions will cause an error.
.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]
Get help on a specific module or option.  Default value ''. 
.TP
.B
\fB--input_model_file\fP (\fB-m\fP) [\fIunknown\fP]
Input perceptron model. Default value ''. 
.TP
.B
\fB--labels_file\fP (\fB-l\fP) [\fIstring\fP]
A matrix containing labels for the training set. Default value ''. 
.TP
.B
\fB--max_iterations\fP (\fB-n\fP) [\fIint\fP]
The maximum number of iterations the perceptron is to be run Default value 1000. 
.TP
.B
\fB--test_file\fP (\fB-T\fP) [\fIstring\fP]
A matrix containing the test set. Default value ''. 
.TP
.B
\fB--training_file\fP (\fB-t\fP) [\fIstring\fP]
A matrix containing the training set. Default value ''. 
.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_file\fP (\fB-o\fP) [\fIstring\fP]
The matrix in which the predicted labels for the test set will be written. Default value ''. 
.TP
.B
\fB--output_model_file\fP (\fB-M\fP) [\fIunknown\fP]
Output for trained perceptron model. Default value ''.
.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.