SYNOPSIS

perceptron [-h] [-v] -T string -t string [-i int] [-l string] [-o string] -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 using the --iterations (-i) parameter, if the data supplied is linearly separable. The Perceptron is parameterized by a matrix of weight vectors which denotes the numerical weights of the Neural Network. This program allows training of a perceptron, and then application of the learned perceptron to a test dataset. To train a perceptron, a training dataset must be passed to --train_file (-t). Labels can either be present as the last dimension of the training dataset, or given explicitly with the --labels_file (-l) parameter. A test file is given through the --test_file (-T) parameter. The predicted labels for the test set will be stored in the file specified by the --output_file (-o) parameter.

REQUIRED OPTIONS

--test_file (-T) [string]

A file containing the test set.

--train_file (-t) [string]

A file containing the training set.

OPTIONS

--help (-h)

Default help info.

--info [string]

Get help on a specific module or option. Default value ''.

--iterations (-i) [int]

The maximum number of iterations the perceptron is to be run Default value 1000.

--labels_file (-l) [string]

A file containing labels for the training set. Default value ''.

--output (-o) [string]

The file in which the predicted labels for the test set will be written. Default value 'output.csv'.

--verbose (-v)

Display informational messages and the full list of parameters and timers at the end of execution.

--version (-V)

Display the version of mlpack.

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.