Released October 30, 2015
This version contains several new components, performance enhancements, and an upgraded version of MTJ.
- Upgraded MTJ to 1.0.3.
- Added package for hash function computation including Eva, FNV-1a, MD5, Murmur2, Prime, SHA1, SHA2
- Added callback-based forEach implementations to Vector and InfiniteVector, which can be faster for iterating through some vector types.
- Optimized DenseVector by removing a layer of indirection.
- Added method to compute set of percentiles in UnivariateStatisticsUtil and fixed issue with percentile interpolation.
- Added utility class for enumerating combinations.
- Adjusted ScalarMap implementation hierarchy.
- Added method for copying a map to VectorFactory and moved createVectorCapacity up from SparseVectorFactory.
- Added method for creating square identity matrix to MatrixFactory.
- Added Random implementation that uses a cached set of values.
- Implemented feature hashing.
- Added factory for random forests.
- Implemented uniform distribution over integer values.
- Added Chi-squared similarity.
- Added KL divergence.
- Added general conditional probability distribution.
- Added interfaces for Regression, UnivariateRegression, and MultivariateRegression.
- Fixed null pointer exception that can happen in K-means with an empty cluster.
- Fixed name of maxClusters property on AgglomerativeClusterer (was called maxMinDistance).
- Improvements to LDA Gibbs sampler.