Displaying 1 to 6 from 6 results

jlearn - Machine Learning Library, written in J

  •    J

WIP Machine learning library, written in J. Various algorithm implementations, including MLPClassifiers, MLPRegressors, Mixture Models, K-Means, KNN, RBF-Network, Self-organizing Maps. Models can be serialized to text files, with a mixture of text and binary packing. The size of the serialized file depends on the size of the model, but will probably range from 10 MB and upwards for NN models (including convnets and rec-nets).

pyERA - Python implementation of the Epigenetic Robotic Architecture (ERA)

  •    Python

Because the different modules are standalone you can use pyERA for building SOM using only the som.py class. Feel free to fork the project and add your own stuff. Any feedback is appreciated. The Epigenetic Robotic Architecture (ERA) is a hybrid behavior-based robotics and neural architecture purposely built to implement embodied principles in cognitive development. This architecture has been already tested in a variety of cognitive and developmental tasks directly modeling child psychology data. The ERA architecture uses a behaviour-based subsumption mechanism to handle the integration of competing sensorimotor input. The learning system is based on an ensemble of pre-trained SOMs connected via Hebbian weights. The basic unit of the ERA architecture is formed by the structured association of multiple self-organizing maps. Each SOM receives a subset of the input available to that unit and is typically partially prestabilized using random input distributed across the appropriate ranges for those inputs. In the simplest case, the ERA architecture comprises of multiple SOMs, each receiving input from a different sensory modality, and each with a single winning unit. Each of these winning units is then associated to the winning unit of a special “hub” SOM using a bidirectional connection weighted with positive Hebbian learning.

kohonen4j - Kohonen Self-Organizing Maps in Java

  •    Java

For a more detailed description of self-organizing maps and the program design of kohonen4j, consider reading the vignette. The kohonen4j fits a self-organizing map, a type of artificial neural network, to an input csv data file. The input csv must be rectangular and nonjagged with only numeric values. As output, the program plots a heatmap that displays a 2D representation of the data. Observations are maped to their closest nodes, and the output plot displays the most frequently mapped nodes in the brightest shade, while nodes that are not maped to any observations are black.




ANNetGPGPU - A GPU (CUDA) based Artificial Neural Network library

  •    C++

Here is an example input image with the related output image. CUDA image generator example. The picture illustrates a some houses in vienna. CUDA image generator example. This image was calculated on a GTX 1080. It took apprx. 20 min and 500 MB VRAM. GUI-example: Designer for back propagation networks. The layout of the underlying library is 1:1 represented as a QSceneGraph. After definition of the network topology, the in- and output can be defined by the user and the network trained accordingly. At the end, the error of each test-training cycle is plotted, which gives a handy representation of the network performance.

som - self-organizing map (SOM) / Kohonen network

  •    Javascript

Creates a new SOM instance with x * y dimensions. Train the SOM with the provided trainingSet.