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:

• Common Core:
  • Added NumericMap interface, which provides a mapping of keys to numeric values.
  • Added ScalarMap interface, which extends NumericMap to provide a mapping of objects to scalar values represented as doubled.
  • Added AbstractScalarMap and AbstractMutableDoubleMap to provide abstract, partial implementations of the ScalarMap interface.
  • Added VectorSpace interface, where a VectorSpace is a type of Ring that you can perform Vector-like operations on such as norm, distances, etc.
  • Added AbstractVectorSpace, which provides an abstract, partial implementation of the VectorSpace interface.
  • Updated Vector, AbstractVector, VectorEntry to build on new VectorSpace interface and AbstractVectorSpace class.
  • Added InfiniteVector interface, which has a potentially infinite number of indices, but contains only a countable number in any given instance.
  • Added DefaultInfiniteVector, an implementation of the InfiniteVector interface backed by a LinkedHashMap.
  • Rewrote FiniteCapacityBuffer from the ground up, now with backing from a fixed-size array to minimize memory allocation.
  • Renamed IntegerCollection to IntegerSpan.
• Learning Core:
  • Updated ReceiverOperatingCharacteristic to improve calculation
  • Added PriorWeightedNodeLearner interface, which provides for configuring the prior weights on the learning algorithm that searches for a decision function inside a decision tree.
  • Updated AbstractDecisionTreeNode to fix off by one error in computing node's depth.
  • Updated CategorizationTreeLearner to add ability to specify class priors for decision tree algorithm.
  • Updated VectorThresholdInformationGainLearner to add class priors to information gain calculation.
  • Updated SequentialMinimalOptimization to improve speed.
  • Added LinearBasisRegression, which uses a basis function to generate vectors before performing a LinearRegression.
  • Added MultivariateLinearRegression, which performs multivariate regression; does not explicitly estimate a bias term or perform regularization.
  • Added LinearDiscriminantWithBias, which provides a LinearDiscriminant with an additional bias term that gets added to the output of the dot product.
  • Updated LinearRegression and LogisticRegression to provide for bias term estimation and use of L2 regularization.
  • Renamed SquashedMatrixMultiplyVectorFunction to GeneralizedLinearModel.
  • Renamed DifferentiableSquashedMatrixMultiplyVectorFunction to DifferentiableGeneralizedLinearModel.
  • Renamed MatrixMultiplyVectorFunction to MultivariateDiscriminant.
  • Added MultivariateDiscriminantWithBias, which provides a multivariate discriminant with a bias term.
  • Renamed DataHistogram to DataDistribution.
  • Renamed AbstractDataHistogram to AbstractDataDistribution.
  • Added DefaultDataDistribution, a default implementation of the DataDistribution interface that uses a backing map.
  • Added LogisticDistribution, an implementation of the scalar logistic distribution.
  • Updated MultivariateGaussian to provide for incremental estimation of covariance-matrix inverse without a single matrix inversion.
  • Removed DecoupledVectorFunction.
  • Removed DecoupledVectorLinearRegression.
  • Removed PointMassDistribution.
  • Removed MapBasedDataHistogram.
  • Removed MapBasedPointDistribution.
  • Removed MapBasedSortedDataHistogram.
  • Removed AbstractBayseianRegression.
  • Additional general reworking and clean up of distribution code, impacting classes in gov.sandia.cognition.statistics.distribution package.
• Text Core:
  • Renamed LatentDirichetAllocationVectorGibbsSampler to LatentDirichletAllocationVectorGibbsSampler to fix misspelling.
  • Added ParallelLatentDirichletAllocationVectorGibbsSampler, a parallelized version of Latent Dirichlet Allocation.

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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