Cognitive Foundry version 3.4.0 released
This version contains an implementation of factorization machines, improvements to statistical distributions, improvements to tree learners, convenience methods, performance improvements, and bug fixes. It also upgrades to Java 1.7. You can download it directly here or from Maven Central through dependency management tools like Ivy and Maven.
* Now requires Java 1.7 or higher.
* Improved compatibility with Java 1.8 functions by removing
ClonableSerializable requirement from many function-style interfaces.
* Common Core:
* Improved iteration speed over sparse MTJ vectors.
* Added utility methods for more stable log(1+x), exp(1-x), log(1 – exp(x)),
and log(1 + exp(x)) to LogMath.
* Added method for creating a partial permutations to Permutation.
* Added methods for computing standard deviation to UnivariateStatisticsUtil.
* Added increment, decrement, and list view methods to Vector and Matrix.
* Added shorter versions of get and set for Vector and Matrix getElement and
* Added aliases of dot for dotProduct in VectorSpace.
* Added utility methods for divideByNorm2 to VectorUtil.
* Added a learner for a Factorization Machine using SGD.
* Added a iterative reporter for validation set performance.
* Added new methods to statistical distribution classes to allow for faster
sampling without boxing, in batches, or without creating extra memory.
* Made generics for performance evaluators more permissive.
* ParameterGradientEvaluator changed to not require input, output, and
gradient types to be the same. This allows more sane gradient definitions
for scalar functions.
* Added parameter to enforce a minimum size in a leaf node for decision
tree learning. It is configured through the splitting function.
* Added ability to filter which dimensions to use in the random subspace
and variance tree node splitter.
* Added ReLU, leaky ReLU, and soft plus activation functions for neural
* Added IntegerDistribution interface for distributions over natural numbers.
* Added a method to get the mean of a numeric distribution without boxing.
* Fixed an issue in DefaultDataDistribution that caused the total to be off
when a value was set to less than or equal to 0.
* Added property for rate to GammaDistribution.
* Added method to get standard deviation from a UnivariateGaussian.
* Added clone operations for decision tree classes.
* Fixed issue TukeyKramerConfidence interval computation.
* Fixed serialization issue with SMO output.