mlpack_perceptron - perceptron

       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.

mlpack-4.5.1                                     29 January 2025                            mlpack_perceptron(1)

       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.

mlpack_perceptron - perceptron

--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.

--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.

mlpack_perceptron [-munknown] [-lunknown] [-nint] [-Tunknown] [-tunknown] [-Vbool] [-Munknown] [-Punknown] [-h-v]