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Cognitive Foundry 4.0.0 released

March 24th, 2017

This version contains many enhancements, new algorithms, adds a Graph package, and a new matrix package implementation. You can download it directly here or from Maven Central through dependency management tools like Ivy and Maven.

* General:
 * 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.
* Common:
 * 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.
* Learning:
 * 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.
* Text:
 * Added valance spreading implementation.
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