Be notified of new releases
Create your free GitHub account today to subscribe to this repository for new releases and build software alongside 28 million developers.Sign up
Released March 24, 2017
- Added new custom matrix package implementation. MTJ-based implementation is still default and the two should interoperate, though sticking to one implementation is generally more efficient.
- Added new Graph package containing several graph algorithms.
- New custom matrix package implementation in gov.sandia.cognition.math.matrix.custom. Contains both sparse and dense implementations of Vector and Matrix. It is optimized for certain use-cases around sparse matrices and dynamically switching between sparse and dense.
- Added default implementations to scalar function interfaces. Makes them easier to use as lambdas.
- Improved interoperability between matrix/vector implementations through abstract class implementations.
- Added method to get vector and matrix factories from those objects.
- Added methods create uniform or Gaussian random vectors and matrices.
- Added method to check for multiplication dimensions matching for matrices.
- Added method to count non-zeros in a vector.
- Added methods to get max and min value from a VectorSpace, which includes implementations on vectors.
- Added primitive ArrayList implementations: DoubleArrayList, IntArrayList.
- CollectionUtil: Added collection equality checkers.
- Added equals and hashCode implementations to DefaultKeyValuePair.
- Indexer and DefaultIndexer: Added a clear method.
- KDTree: Added method to find within a given radius.
- Changed implementation of Gamma distribution sampling algorithm to greatly improve performance. Also improves performance of Beta and Dirichlet distribution sampling.
- Added DBSCAN clustering implementation.
- Added mini-batch k-means clustering implementation.
- Improvements to K-means and partitional cluster performance.
- Added normalized centroid cluster creator, within-cluster divergence, and random cluster initializer.
- Added implementation of Burrows Delta algorithm.
- Added out-of-bag stopping criteria for bagging and refactored it for IVoting.
- Improved memory use of IVoting by removing redundant allocation.
- Added several conjugate gradient matrix solvers and matrix-vector solvers, also with preconditioning.
- Added multi-partite valance algorithm.
- Added hard sigmoid and hard tanh activation functions.
- Added valance spreading implementation.