Description:

The Cognitive Foundry is a modular Java software library for the research and development of cognitive systems. It is primarily designed for research and development to be easy to plug into applications to provide adaptive behaviors.

The main part of the Foundry is the Machine Learning package, which contains reusable components and algorithms for machine learning and statistics. It contains many algorithms for supervised and unsupervised learning as well as statistical modeling. It is interface-centric and uses generics to make it easy to customize to the needs of individual applications.

The Cognitive Foundry's development is led by Sandia National Laboratories and is released under the open source BSD License. It requires Java 1.6.

Changes to previous version:

• General:
  • 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 setElement.
  • Added aliases of dot for dotProduct in VectorSpace.
  • Added utility methods for divideByNorm2 to VectorUtil.
• Learning:
  • 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 networks.
  • 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.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic, Platform Independent

Data Formats: Matlab, Csv, Xml, Xstream

Tags: Classification, Clustering, Adaboost, Decision Tree Learning, Algorithms, Gaussian Mixture Models, Bagging, Ensemble Methods, Gaussian Processes, Affinity Propagation, Bfgs, Generics, Genetic Algorith

Archive: download here

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