NAME

       mlpack_perceptron - perceptron

SYNOPSIS

        mlpack_perceptron [-m unknown] [-l unknown] [-n int] [-T unknown] [-t unknown] [-V bool] [-M unknown] [-P unknown] [-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.

       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) [unknown] 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) [unknown]
              A matrix containing the test set.

       --training_file (-t) [unknown]
              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_model_file (-M) [unknown]
              Output for trained perceptron model.

       --predictions_file (-P) [unknown]
              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.