|Description||An Artificial Intelligence Library written in Java.|
|License||OSCL Type C|
JIRA Bug Tracking
Sonar Technical Debt
|IRC Room||#dANN on irc.freenode.org|
|Mailing Lists||Directory of Mailing Lists|
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:
We've included a package of examples. Some examples included are:
dANN is provided under the OSCL Type-C license.
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.
|Implements Algorithm||A* Algorithm +, Dijkstra Algorithm +, Bellman-Ford Algorithm +, Johnson's Algorithm +, Floyd-Warshall Algorithm +, Hill Climbing Algorithm +, Hyperassociative Map +, Colored Depth-first Search +, Kruskal Algorithm +, Prim Algorithm +, Genetic Algorithm +, Genetic Wavelets Algorithm +, Naive Bayes Classifier +, Fisher's Linear Discriminant +, Cooley Tukey Fast Fourier Transform +, Porter Stemming Algorithm +, Data Interrelational Graph +, Baum-Welch Algorithm +, Backpropagation +, Izhikevich Algorithm +, Johnson-Trotter Algorithm +, Binary Greatest Common Divisor Algorithm +, Euclidean Algorithm + and Extended Euclidean Algorithm +|
|Implements Software Feature||Graph Theory +, Path Finding (Graph Theory) +, Graph Drawing +, 3D Visualizations +, Cycle Detection (Graph Theory) +, Minimal Spanning Tree Detection +, Topological Sort +, Evolutionary Algorithms +, Naive Classifier +, Signal Processing +, Fast Fourier Transform +, Language Processing +, Word Parsing +, Word Stemming +, Graphical Models +, Markov Random Field +, Dynamic Markov Random Field +, Bayesian Network +, Dynamic Bayesian Netowrk +, Dynamic Graphical Models +, Hidden Markov Models +, Layered Hidden Markov Models +, Hierarchical Hidden Markov Model +, Artificial Neural Network +, Feedforward Neural Network +, Self Organizing Map +, Realtime Neural Networks +, Spiking Neural Networks +, Statistics +, Markov Chains +, Markov Chain Monte Carlo +, Counting (Math) +, Combinations Counting +, Permutation Counting +, Lexicographic Permutation Counting +, Complex Numbers +, N-Dimensional Vectors +, Greatest Common Denominator +, Linear Algebra +, Cholesky Decomposition +, Hessenberg Decomposition +, Eigenvalue Decomposition +, LU Decomposition +, QR Decomposition + and Singular Value Decomposition +|
|Source Repository||Documentation Repository::dANN:Source Repository +|