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 checkedAdd and checkedMultiply functions to MathUtil, providing a means for conducting Integer addition and multiplication with explicit checking for overflow and underflow, and throwing an ArithmeticException if they occur. Java fails silently in integer over(under)flow situations.
  • Added explicit integer overflow checks to DenseMatrix. The underlying MTJ library stores dense matrices as a single dimensional arrays of integers, which in Java are 32-bit. When creating a matrix with numRows rows and numColumns columns, if numRows * numColumns is more than 2^31 - 1, a silent integer overflow would occur, resulting in later ArrayIndexOutOfBoundsExceptions when attempting to access matrix elements that didn't get allocated.
  • Added new methods to DiagonalMatrix interface for multiplying diagonal matrices together and for inverting a DiagonalMatrix.
  • Optimized operations on diagonal matrices in DiagonalMatrixMTJ.
  • Added checks to norm method in AbstractVectorSpace and DefaultInfiniteVector for power set to NaN, throwing an ArithmeticException if encountered.
• Learning Core:
  • Optimized matrix multiplies in LogisticRegression to avoid creating dense matrices unnecessarily and to reduce computation time using improved DiagonalMatrix interfaces.
  • Added regularization and explicit bias estimation to MultivariateLinearRegression.
  • Added ConvexReceiverOperatingCharacteristic, which computes the convex hull of the ROC.
  • Fixed rare corner-case bug in ReceiverOperatingCharacteristic and added optional trapezoidal AUC computation.
  • Cleaned up constant in MultivariateCumulativeDistributionFunction and added publication references.

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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