Java Kohonen Neural Network Library 2022

09/12 0 By bronell

Java Kohonen Neural Network Library is a handy set of classes and functions specially developed to help you with the design, train and use of Kohonen network (self organizing map).

 

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Kohonen network is very easy to understand and implement. It is self organizing (or unsupervised), it does not require any kind of pre-training. It can work with sets of data, and it can be used to map any kind of data to any kind of feature space. There are multiple types of Kohonen network which are very easy to implement. This library has classes for vectors (one dimensional), matrices (2D) and hyper-matrices (3D), and there are special classes for codebook vectors, Kohonen network hyper-matrices and Kohonen network hyper-matrices with codebooks.
This library has a simple design, using only two libraries: Java TreeMap for the structure, which is very efficient, and Lame library for the primitives, which is freely available. It can be combined with other libraries, and you can find examples for combining this library with the Android libraries (java.awt, java.awt.image, java.awt.image.BufferedImage, android.graphics and android.graphics.Bitmap).
This library works with large amount of data, and it is good for big datasets, which can be found in the web (like google) and in the databases.
This library was developed by Juan Manuel Camacho del Hoyo and his group at Universidad de Cantabria, Spain.
This library will be released under the LGPL v2.1 license, so you can use it in your applications.
Permissions
* License to use this library
* Export a JavaDoc
* Export a source code

Kohonen is a self-organizing neural network library
Kohonen is a library for constructing Kohonen networks, Kohonen neural networks, self organizing maps or Kohonen neural networks.
Kohonen is a type of neural network, that does not need any kind of pre-training. You can use it to classify data, and it is very easy to understand and implement. There are two versions of Kohonen networks, the simple version, which does not support vectors, and the one with codebook, which supports vectors.
Java Kohonen Neural Network Library Description:
Kohonen networks are one of the most popular networks. The Kohonen network is a self organizing network that does not need any kind of pre-training. It can map any kind of data to any kind of feature space. It is ideal to classify or to map any kind of data.

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—————————————————————————————-
=====================================
DESCRIPTION
=====================================
The following is a description of what’s included in the version 1.3.1 of
The Nokia Symbian OS base classes Library v1.3.1

This library contains the base classes for the Nokia Symbian OS Platform
All development tools must include this library, which are:
* C#
* Objective-C
* BlackBerry
* Webtop
* Java
* XNA
* Windows Mobile SDK
* MATLAB
* C++
* Java in J2ME
* HTML5
* Android
* SWIFT
** When developing the Nokia Common SDK, the developer must always include
the Nokia Symbian SDK for the Nokia Symbian OS, otherwise, the Nokia Symbian
SDK won’t work.
When adding/submitting pull requests, always mention that you are using
the Nokia Symbian OS base classes Library,
a86638bb04

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The library is basically a set of Kohonen’s Map Algorithm implementation. It’s more popular then Kohonen’s other algorithms (the K-means and MDS).
The library can be used as a stand-alone library.

Limitations:
1) No support for clustering with the means other than the centroids.
2) Only (x,y) input/output data format is supported, so you need to convert your input data to the format supported.
3) Support for negative values in the input data is not supported.
4) No support for multi-dimensional data.

Credits:
Used here are Kohonen’s book: “Self-Organization and Associative Memory” by Heinz Kohonen.
And the paper: “Kohonen’s SOM, a Self-Organizing Map” by Lyudmila M. Lyapunova and Heinz-Otto Peitgen.

End of credits.

Also, some more details about Kohonen neural networks can be found here:

The usage of the library is quite easy. The Kohonen implementation has two input parameters – grid and training set.
Step 1: Create input data grid (use the kohonen/data.h file)
Step 2: Create training set
Step 3: Create output data grid (can be used to visualize data or to train the model)
Step 4: Train your model
Step 5: Test the model (you can see the results in kohonen/output.h file)

Best Regards,
Vladimir Bortnik
c-opt.com

## Build

What’s New in the?

Kohonen neural network is a learning tool, developed by Teuvo Kohonen, where trained “self organizing map” could be used to approximate complex non-linear functions, as it is developed on a set of trained vectors. If you need to study functions, which are not linearly separable, then you should probably look at Kohonen neural network.

Kohonen neural network is one of the most advanced algorithms for neural networks.

We use a training set of vectors to construct the Kohonen network. We start with an initial map. This map is randomly constructed, so we can choose which vector should be which coordinate.

This process repeats itself. At each iteration, we “train” the next map. A part of the map is replaced by the average of the vectors in this area. Each vector in this new map is trained to be as similar as possible to all the others. In this process, the Kohonen network gets better. The more it is used, the more it improves.

To improve the learning speed, the network is not updated by all the vectors, but only by the most similar vectors.

Kohonen neural network is suitable for linear and non-linear classification, function approximation, clustering, and similar tasks.

Java Kohonen Neural Network Class Description:

The classes provided by Kohonen neural network library are:

Computational Engine

Kohonen.KohonenMap.KohonenMap()

Kohonen.KohonenMap.KohonenMap(Map vectorMap, Map outputMap)

Kohonen.KohonenMap.KohonenMap(Map vectorMap, Map outputMap, Map cellToVectorMap)

Kohonen.KohonenMap.KohonenMap(Map vectorMap, Map outputMap, Map cellToVectorMap, Map vectorToCellMap)

Kohonen.KohonenMap.KohonenMap(Kohonen.KohonenMap map)

Kohonen.KohonenMap.KohonenMap(Kohonen.KohonenMap map, Map vectorMap, Map outputMap)

Kohonen.KohonenMap.KohonenMap(Kohonen.KohonenMap map, Map vectorMap, Map outputMap, Map cellToVectorMap)

Kohonen.KohonenMap.KohonenMap(Kohonen.KohonenMap map, Map vector

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