Features¶
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