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mlpack_perceptron - perceptron


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


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 --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 (-m) or training a perceptron given
training data (-t), or both those things at once. In addition, this program allows
classification on a test dataset (-T) and will save the classification results to the
given output file (-o). The perceptron model itself may be saved with a file specified
using the -M option.

The training data given with the -t option should have class labels as its last dimension
(so, if the training data is in CSV format, labels should be the last column).
Alternately, the -l (--labels_file) option may be used to specify a separate file of

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' (and
'training_labels.csv)' and saves the model to ’perceptron.xml'.

$ perceptron -t training_data.csv -l training_labels.csv -m perceptron.csv

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

$ perceptron -i perceptron.xml -T test_data.csv -o 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 -m (--input_model_file). 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.


--help (-h)
Default help info.

--info [string]
Get help on a specific module or option. Default value ''. --input_model_file
(-m) [string] File containing input perceptron model. Default value ''.

--labels_file (-l) [string]
A file containing labels for the training set. Default value ''.

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

--output_file (-o) [string]
The file in which the predicted labels for the test set will be written. Default
value ’output.csv'. --output_model_file (-M) [string] File to save trained
perceptron model to. Default value ''.

--test_file (-T) [string]
A file containing the test set. Default value ’'. --training_file (-t) [string] A
file containing the training set. Default value ''.

--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.



For further information, including relevant papers, citations, and theory, For further
information, including relevant papers, citations, and theory, consult the documentation
found at http://www.mlpack.org or included with your consult the documentation found at
http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK. DISTRIBUTION OF


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