NAME

       mlpack_perceptron - perceptron

SYNOPSIS

        mlpack_perceptron [-m unknown] [-l string] [-n int] [-T string] [-t string] [-V bool] [-o string] [-M unknown] [-P string] [-h -v]

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
       ’--max_iterations (-n)' 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.

       This  program  allows  loading a perceptron from a model (via the ’--input_model_file (-m)' parameter) or
       training a perceptron given training data (via the  '--training_file  (-t)'  parameter),  or  both  those
       things  at once.  In addition, this program allows classification on a test dataset (via the ’--test_file
       (-T)' parameter) and the classification results on the test set may be saved with the '--predictions_file
       (-P)'  output  parameter.  The  perceptron  model may be saved with the '--output_model_file (-M)' output
       parameter.

       Note: the following parameter is deprecated and will be removed in mlpack  4.0.0:  '--output_file  (-o)'.
       Use '--predictions_file (-P)' instead of '--output_file (-o)'.

       The  training  data  given  with  the  '--training_file  (-t)'  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
       '--labels_file (-l)' parameter may be used to specify a separate matrix of labels.

       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'.

       $     mlpack_perceptron     --training_file     training_data.csv    --labels_file    training_labels.csv
       --output_model_file perceptron_model.bin

       Then, this model can be re-used for classification on the test data ’test_data.csv'.  The  example  below
       does precisely that, saving the predicted classes to 'predictions.csv'.

       $  mlpack_perceptron --input_model_file perceptron_model.bin --test_file test_data.csv --predictions_file
       predictions.csv

       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
       '--input_model_file (-m)' 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.

OPTIONAL INPUT OPTIONS

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --input_model_file (-m) [unknown]
              Input perceptron model.

       --labels_file (-l) [string]
              A matrix containing labels for the training set.

       --max_iterations (-n) [int]
              The maximum number of iterations the perceptron is to be run Default value 1000.

       --test_file (-T) [string]
              A matrix containing the test set.

       --training_file (-t) [string]
              A matrix containing the training set.

       --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_file (-o) [string]
              The matrix in which the predicted labels for the test set will be written.

       --output_model_file (-M) [unknown]
              Output for trained perceptron model.

       --predictions_file (-P) [string]
              The matrix in which the predicted labels for the test set will be written.

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.