dANN
| General | |||||
|---|---|---|---|---|---|
| Description | An Artificial Intelligence Library written in Java. | ||||
| Last Activity | Today | ||||
| License | OSCL Type C | ||||
| Homepage | dANN | ||||
| Download | |||||
| Distributions | Binary ZIP w/JavaDoc Binary Tarball w/JavaDoc Source ZIP Source Tarball | ||||
| Documentation | Javadoc repository Javadoc for GIT master Javadoc for stable release | ||||
| Development |
TRAC Bug Tracking Hudson Continuous Integration | ||||
| Support | |||||
| IRC Room | #dANN on irc.freenode.org | ||||
| Mailing Lists | dANN Announcements dANN Development Syncleus Announcements | ||||
dANN is an Artificial Intelligence and Artificial Genetics library targeted at employing conventional techniques as well as acting as a platform for research & development of novel techniques. As new techniques are developed and proven to be effective they will be integrated into the core library. It is currently written in Java, C++, and C#. However only the java version is currently in active development. If you want to obtain a version other than the java version you will need to get it directly from GIT.
Our intentions are two fold. First, to provide a powerful interface for programs to include conventional artificial neural network technology into their code. Second, To act as a testing ground for research and development of new AI concepts. We provide new AI technology we have developed, and the latest algorithms already on the market. In the spirit of modular programming the library also provides access to the primitive components giving you greater control over implementing your own unique AI algorithms. You can either let our library do all the work, or you can override any step along the way.
dANN currently implements several conventional as well as new algorithms inspired by its biological counterparts. The following is an incomplete list of some of the libraries features:
- Graph Theory
- Search
- Path Finding
- A*
- Dijkstra
- Bellman-Ford
- Johnson's
- Floyd-Warshall
- Optimization
- Hill Climbing Local Search
- Path Finding
- Graph Drawing
- Hyperassociative Map
- 3D Hyperassociative Map Visualization
- Hyperassociative Map
- Cycle Detection
- Colored Depth-first Search
- Exhaustive Depth First Search
- Minimal Spanning Tree Detection (MST)
- Kruskal
- Prim
- Topological Sort Algorithm
- Search
- Evolutionary Algorithms
- Genetic Algorithms
- Genetic Wavelets
- Naive Classifier
- Naive Bayes Classifier
- Naive Fisher Classifier
- Data Processing
- Signal Processing
- Language Processing
- Word Parsing
- Word Stemming
- Porter Stemming Algorithm
- Data Interrelational Graph
- Graphical Models
- Markov Random Fields
- Dynamic Markov Random Field
- Bayesian Networks
- Dynamic Bayesian Networks
- Dynamic Graphical Models
- Hidden Markov Models
- Baum–Welch Algorithm
- Layered Hidden Markov Models
- Hierarchical Hidden Markov Models
- Hidden Markov Models
- Markov Random Fields
- Artificial Neural Networks
- Activation Function Collection
- Backpropagation Networks
- Self Organizing Maps
- Realtime Neural Networks
- Spiking Neural Networks
- Izhikevich Algorithm
- Spiking Neural Networks
- 3D Network Visualization
- Mathematics
- Statistics
- Markov Chains
- Markov Chain Monte Carlo (Parameter Estimation)
- Markov Chains
- Counting
- Combinations
- Permutations
- Lexicographic
- Johnson-Trotter Algorithm
- Complex Numbers
- N-Dimensional Vectors
- Greatest Common Denominator
- Binary Algorithm
- Euclidean Algorithm
- Extended Euclidean Algorithm
- Linear Algebra
- Cholesky Decomposition
- Hessenberg Decomposition
- Eigenvalue Decomposition
- LU Decomposition
- QR Decomposition
- Singular Value Decomposition
- Statistics
We've included a package of examples. Some examples included are:
- 3-input XOR using Neural Network
- 8 layer Hyperassociative Map
- Neural Image Compression using Neural Network
- 3D Color Maping to 2D/1D Space using Self Organized Map
- Traveling Salesman Problem (TSP) using Genetic Algorithm
- Wavelet Genetics
- Microphone Spectrum Analyzer using FFT
- Path Finding Editable Grid using A*
dANN is provided under the OSCL Type-C license.
Some documents you should take a look at if your new to dANN:
Using the Library
Using the Examples
The GIT is the best place for obtaining the latest source. However if you use the GIT for obtaining and using the source please consider providing feedback so we can take your suggestions into consideration.
If you need any help installing, compiling, or patching this software in any way at all please feel free to contact us. We cant promise support, but we do encourage you to try. We will do our best to offer our help.
[edit] Resources
Latest Distribution:
Zip Binary Release w/JavaDoc
Tarball Binary Release w/JavaDoc
Zip Source Release w/JavaDoc
Tarball Source Release w/JavaDoc
Screenshots:
Collection of Screenshots
8 Layer Hyperassociative Map
Neural Image Compression
All Versions:
Release Repository
Public GIT:
git://git.syncleus.com/dANN.git
For more information check out the Repository
Bug Reporting & Development Status:
TRAC reporting
Hudson Continuous Integration reporting
API Documentation:
Javadoc repository for all versions
Javadoc for current GIT trunk
Javadoc for current stable release
IRC Support:
Visit our IRC channel on freenode in the #dANN channel. For more information see: Syncleus:IRC
#dANN logs on freenode
