ABAGAIL

This library is the result of close to a year of research and class work in artificial intelligence (AI). It contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves.

Fixed a bug in the SVM code, expanded the reinforcement learning package.

Features

Hidden Markov Models

• Baum-Welch reestimation algorithm, scaled forward-backward algorithm, Viterbi algorithm
• Support for Input-Output Hidden Markov Models
• Write your own output or transition probability distribution or use the provided distributions, including neural network based conditional probability distributions

Neural Networks

• Feed-forward backpropagation neural networks of arbitrary topology
• Configurable error functions with sum of squares, weighted sum of squares
• Multiple activation functions with logistic sigmoid, linear, tanh, and soft max
• Choose your weight update rule with standard update rule, standard update rule with momentum, Quickprop, RPROP
• Online and batch training

Support Vector Machines

• Fast training with the sequential minimal optimization algorithm
• Support for linear, polynomial, tanh, radial basis function kernels

Decision Trees

• Information gain or GINI index split criteria
• Binary or all attribute value splitting
• Chi-square signifigance test pruning with configurable confidence levels
• Boosted decision stumps with AdaBoost

K Nearest Neighbors

• Fast kd-tree implementation for instance based algorithms of all kinds
• KNN Classifier with weighted or non-weighted classification, customizable distance function

Linear Algebra Algorithms

• Basic matrix and vector math, a variety of matrix decompositions based on the standard algorithms
• Solve square systems, upper triangular systems, lower triangular systems, least squares
• Singular Value Decomposition, QR Decomposition, LU Decomposition, Schur Decomposition, Symmetric Eigenvalue Decomposition, Cholesky Factorization
• Make your own matrix decomposition with the easy to use Householder Reflection and Givens Rotation classes

Optimization Algorithms

• Randomized hill climbing, simulated annealing, genetic algorithms, and discrete dependency tree MIMIC
• Make your own crossover functions, mutation functions, neighbor functions, probability distributions, or use the provided ones.
• Optimize the weights of neural networks and solve travelling salesman problems

Graph Algorithms

• Kruskals MST and DFS

Clustering Algorithms

• EM with gaussian mixtures, K-means

Data Preprocessing

• PCA, ICA, LDA, Randomized Projections
• Convert from continuous to discrete, discrete to binary

Reinforcement Learning

• Value and policy iteration for Markov decision processes