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.TH mlpack_perceptron 1 "11 January 2024" "mlpack-4.3.0" "User Commands"
.SH NAME
\fBmlpack_perceptron \fP- perceptron
.SH SYNOPSIS
.nf
.fam C
 \fBmlpack_perceptron\fP [\fB-m\fP \fIunknown\fP] [\fB-l\fP \fIunknown\fP] [\fB-n\fP \fIint\fP] [\fB-T\fP \fIunknown\fP] [\fB-t\fP \fIunknown\fP] [\fB-V\fP \fIbool\fP] [\fB-o\fP \fIunknown\fP] [\fB-M\fP \fIunknown\fP] [\fB-P\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--predictions_file\fP (\fB-P\fP)' output parameter. The
perceptron model may be saved with the '\fB--output_model_file\fP (\fB-M\fP)' output
parameter.
.PP
Note: the following parameter is deprecated and will be removed in mlpack
4.0.0: '\fB--output_file\fP (\fB-o\fP)'.
Use '\fB--predictions_file\fP (\fB-P\fP)' instead of '\fB--output_file\fP (\fB-o\fP)'.
.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
$ \fBmlpack_perceptron\fP \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
$ \fBmlpack_perceptron\fP \fB--input_model_file\fP perceptron_model.bin \fB--test_file\fP
test_data.csv \fB--predictions_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 [string]
Print help on a specific option. Default value ''. 
.TP
.B
\fB--input_model_file\fP (\fB-m\fP) [\fIunknown\fP]
Input perceptron model. 
\fB--labels_file\fP (\fB-l\fP) [\fIunknown\fP] A matrix containing labels for the training set. 
.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) [\fIunknown\fP]
A matrix containing the test set. 
.TP
.B
\fB--training_file\fP (\fB-t\fP) [\fIunknown\fP]
A matrix containing the training set. 
.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 

\fB--output_file\fP (\fB-o\fP) [\fIunknown\fP] The matrix in which the predicted labels for the test set will be written. 
.TP
.B
\fB--output_model_file\fP (\fB-M\fP) [\fIunknown\fP]
Output for trained perceptron model. 
.TP
.B
\fB--predictions_file\fP (\fB-P\fP) [\fIunknown\fP]
The matrix in which the predicted labels for the test set will be written.
.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.