Self-organizing maps (SOMs), sometimes called Kohonen networks, are a special kind of neural networks. Unlike neuron-based backpropagation networks, which need supervised learning (you need to feed your features plus the correspondent outcome to allow the net to "learn" something), SOMs learn in an unsupervised manner, which means that you do not need to feed to the network the outcome that will want it to learn. In fact, you may not even know the outcome -- the network will "discover" the possibilities itself.
For more information about SOMs, see the following resources:
Using this library is straightforward. There are basically two classes that you need to care about:
-
SOM(in filesom.php) - Objects of this class represent self-organizing maps. This is the only file you need to include in your application to actually use a SOM. -
SOMTrainer(trainer.php) - This class is used to create trainer objects. Trainer objects are used to train a SOM so that it can do classification later.
The basic workflow is:
require_once('som.php');
require_once('trainer.php');
// Create a SOM with a 20x20 surface for feature vectors that hold 5
// elements
$som = new SOM(20, 20, 5);
$trainer = new SOMTrainer($som);
// Let our feature vectors elements be numbers between 0 and 1
// (inclusive). We can also use an array the size of our feature to
// set the minimum or maximum value for each element of input
// vectors individually.
$trainer->setLimits(0, 1);
// Let's train our network
foreach ($mydata_vectors as $vector)
// NB: $vector is a 5-elements feature vector array that holds
// the data we want to classify
$trainer->train($vector);
// Now save the SOM
file_put_contents('som.dat', serialize($som));
require_once('som.php');
$data = file_get_contents('som.dat');
$som = unserialize($data);
// Get best matching unit (BMU) -- the vector that best match the
// input vector, that's it
$vec = $som->getBMU($input_vector);
print_r($vec);
// Get coordinates of the BMU
list ($x, $y) = $som->getBMUCoords($input_vector)
print "Best match is at ($x, $y)\n";
You can customize the SOM and the trainer by simply subclassing the
classes above. Subclasses may override many functions and parameters
used to generate the network, such as the initialization algorithm,
the learning rate decay, the neighborhood function, etc. Please see
the files som.php and trainer.php for more information.
Ses the examples in the examples/ folder.
Check the issue queue on the PHP-SOM page on GitHub to see if it was not fixed yet. If not, please open a new issue.
Copyright (C) 2007-2015 Flávio Veloso
For licensing details, see the file LICENSE.