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 1.7.

Changes to previous version:

• General:
  • Upgraded MTJ to 1.0.3.
• Common:
  • 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.
• Learning:
  • 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).
• Text:
  • Improvements to LDA Gibbs sampler.

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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