ChangeLog.txt

This file contains the change log for the Cognitive Foundry.

Changes since last release:

Release 4.0.1 (2019-03-05):
  * Common:
    * Performance improvement to SparseVector set method to avoid a double lookup.

Release 4.0.0 (2017-03-24):
  * General:
    * Added new custom matrix package implementation. MTJ-based implementation is still default and the two should interoperate, though sticking to one implementation is generally more efficient.
    * Added new Graph package containing several graph algorithms.
  * Common:
    * New custom matrix package implementation in gov.sandia.cognition.math.matrix.custom. Contains both sparse and dense implementations of Vector and Matrix. It is optimized for certain use-cases around sparse matrices and dynamically switching between sparse and dense.
    * Added default implementations to scalar function interfaces. Makes them easier to use as lambdas.
    * Improved interoperability between matrix/vector implementations through abstract class implementations.
    * Added method to get vector and matrix factories from those objects.
    * Added methods create uniform or Gaussian random vectors and matrices.
    * Added method to check for multiplication dimensions matching for matrices.
    * Added method to count non-zeros in a vector.
    * Added methods to get max and min value from a VectorSpace, which includes implementations on vectors.
    * Added primitive ArrayList implementations: DoubleArrayList, IntArrayList.
    * CollectionUtil: Added collection equality checkers.
    * Added equals and hashCode implementations to DefaultKeyValuePair.
    * Indexer and DefaultIndexer: Added a clear method.
    * KDTree: Added method to find within a given radius.
  * Learning:
    * Changed implementation of Gamma distribution sampling algorithm to greatly improve performance. Also improves performance of Beta and Dirichlet distribution sampling.
    * Added DBSCAN clustering implementation.
    * Added mini-batch k-means clustering implementation.
    * Improvements to K-means and partitional cluster performance.
    * Added normalized centroid cluster creator, within-cluster divergence, and random cluster initializer.
    * Added implementation of Burrows Delta algorithm.
    * Added out-of-bag stopping criteria for bagging and refactored it for IVoting.
    * Improved memory use of IVoting by removing redundant allocation.
    * Added several conjugate gradient matrix solvers and matrix-vector solvers, also with preconditioning.
    * Added multi-partite valance algorithm.
    * Added hard sigmoid and hard tanh activation functions.
  * Text:
    * Added valance spreading implementation.

Release 3.4.3 (2016-03-21):
  * General:
    * Updated to Java 1.8.
  * Common:
    * Added callback-based transform methods to Vector that pass index and value.
  * Learning:
    * Fixed issue in RandomSubVectorThresholdLearner where feature selection was
      ignored if no sampling was done.
    * Fixed mean and variance computation in StudentTConfidence.
    * Added hyperbolic tangent function.

Release 3.4.2 (2015-10-30):
  * 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.

Release 3.4.1 (2015-05-11):
  * General:
    * Updated MTJ to version 1.0.2 and netlib-java to 1.1.2.
    * Updated XStream to version 1.4.8.
  * Common:
    * Fixed issue in VectorUnionIterator.
  * Learning:
    * Added Alternating Least Squares (ALS) Factorization Machine training
      implementation.
    * Fixed performance issue in Factorization Machine where linear component
      was not making use of sparsity.
    * Added utility function to sigmoid unit.

Release 3.4.0 (2015-04-02):
  * 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.

Release 3.3.3 (2013-05-20):
  * General:
    * Made code able to compile under both Java 1.6 and 1.7. This required
      removing some potentially unsafe methods that used varargs with generics.
    * Upgraded XStream dependency to 1.4.4.
    * Improved support for regression algorithms in learning.
    * Added general-purpose adapters to make it easier to compose learning
      algorithms and adapt their input or output.
  * Common Core:
    * Added isSparse, toArray, dotDivide, and dotDivideEquals methods for 
      Vector and Matrix.
    * Added scaledPlus, scaledPlusEquals, scaledMinus, and scaledMinusEquals to
      Ring (and thus Vector and Matrix) for potentially faster such operations.
    * Fixed issue where matrix and dense vector equals was not checking for 
      equal dimensionality.
    * Added transform, transformEquals, tranformNonZeros, and 
      transformNonZerosEquals to Vector.
    * Made LogNumber into a signed version of a log number and moved the prior
      unsigned implementation into UnsignedLogNumber.
    * Added EuclideanRing interface that provides methods for times, 
      timesEquals, divide, and divideEquals. Also added Field interface that 
      provides methods for inverse and inverseEquals. These interfaces are now 
      implemented by the appropriate number classes such as ComplexNumber, 
      MutableInteger, MutableLong, MutableDouble, LogNumber, and 
      UnsignedLogNumber.
    * Added interface for Indexer and DefaultIndexer implementation for
      creating a zero-based indexing of values.
    * Added interfaces for MatrixFactoryContainer and 
      DivergenceFunctionContainer.
    * Added ReversibleEvaluator, which various identity functions implement as 
      well as a new utility class ForwardReverseEvaluatorPair to create a 
      reversible evaluator from a pair of other evaluators.
    * Added method to create an ArrayList from a pair of values in
      CollectionUtil.
    * ArgumentChecker now properly throws assertion errors for NaN values.
      Also added checks for long types.
    * Fixed handling of Infinity in subtraction for LogMath.
    * Fixed issue with angle method that would cause a NaN if cosine had a
      rounding error.
    * Added new createMatrix methods to MatrixFactory that initializes the 
      Matrix with the given value.
    * Added copy, reverse, and isEmpty methods for several array types to 
      ArrayUtil.
    * Added utility methods for creating a HashMap, LinkedHashMap, HashSet, or
      LinkedHashSet with an expected size to CollectionUtil.
    * Added getFirst and getLast methods for List types to CollectionUtil.
    * Removed some calls to System.out and Exception.printStackTrace.
  * Common Data:
    * Added create method for IdentityDataConverter.
    * ReversibleDataConverter now is an extension of ReversibleEvaluator.
  * Learning Core:
    * Added general learner transformation capability to make it easier to adapt
      and compose algorithms. InputOutputTransformedBatchLearner provides this
      capability for supervised learning algorithms by composing together a
      triplet. CompositeBatchLearnerPair does it for a pair of algorithms.
    * Added a constant and identity learners.
    * Added Chebyshev, Identity, and Minkowski distance metrics.
    * Added methods to DatasetUtil to get the output values for a dataset and
      to compute the sum of weights.
    * Made generics more permissive for supervised cost functions.
    * Added ClusterDistanceEvaluator for taking a clustering that encodes the 
      distance from an input value to all clusters and returns the result as a
      vector.
    * Fixed potential round-off issue in decision tree splitter.
    * Added random subspace technique, implemented in RandomSubspace.
    * Separated functionality from LinearFunction into IdentityScalarFunction.
      LinearFunction by default is the same, but has parameters that can change
      the slope and offset of the function.
    * Default squashing function for GeneralizedLinearModel and 
      DifferentiableGeneralizedLinearModel is now a linear function instead of 
      an atan function.
    * Added a weighted estimator for the Poisson distribution.
    * Added Regressor interface for evaluators that are the output of 
      (single-output) regression learning algorithms. Existing such evaluators
      have been updated to implement this interface.
    * Added support for regression ensembles including additive and averaging
      ensembles with and without weights. Added a learner for regression bagging
      in BaggingRegressionLearner.
    * Added a simple univariate regression class in UnivariateLinearRegression.
    * MultivariateDecorrelator now is a VectorInputEvaluator and
      VectorOutputEvaluator.
    * Added bias term to PrimalEstimatedSubGradient.
  * Text Core:
    * Fixed issue with the start position for tokens from LetterNumberTokenizer
      being off by one except for the first one.

Release 3.3.2 (2011-11-07):
  * 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.

Release 3.3.1 (2011-10-06):
  * 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 doubles.
    * 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.
      
Release 3.3.0 (2011-06-22):
  * Common Core:
    * Added LogMath and LogNumber as utilities for computation involving numbers
      represented in log-space.
    * Added MutableInteger and MutableLong, which are like Integer and Long but
      with a mutable value, similar to MutableDouble.
    * UnivariateSummaryStatistics added.
    * Added DefaultIndentifiedValue.
  * Learning Core:
    * VectorNaiveBayesCategorizer: evaluateWithDiscriminant now properly
      normalizes the discriminant, which improves results for things like AUC.
    * PartitionalClusterer: Fixed corner-case bug.
    * Added confidence-weighted online learners based on variance, standard
      deviation, and AROW. They produce ConfidenceWeightedBinaryCategorizer
      objects with either diagonal or full covariance matrices.
    * Added balanced versions of bagging and IVoting in
      CategoryBalancedBaggingLearner and CategoryBalancedIVotingLearner.
    * Added online learners based on ROMMA, AROMMA, Ballseptron, Ramp-loss 
      Passive Aggressive Perceptron, Shifting Perceptron, Forgetron,
      Projectron, Randomized Budget Perceptron, and Stoptron.
    * Added KernelizableBinaryCategorizerOnlineLearner interface for an online
      linear binary categorizer that can also be used with a kernel. Also
      provided abstract class AbstractKernelizableBinaryCategorizerOnlineLearner
      and AbstractLinearCombinationOnlineLearner for common functionality.
    * Moved KernelPerceptron and KernelAdatron to the new
      learning.perceptron.kernel package.
    * Added KernelUtil utility class for dealing with kernels and kernel
      binary categorizers.
    * Refactored statistics to remove getMean() from Distribution due to
      some distributions not having a meaningful mean and confusion for what
      the mean meant in some classes.
    * Classes and interfaces interfaces named *Scalar* renamed to *Univariate*
      for clarity.
    * Added getTestStatistic() method to ConfidenceStatistic interface and
      implemented in existing classes.
    * Added support for multiple comparisons tests, including Bonferroni,
      Holm, Nemenyi, Shaffer, Sidak, and Tukey-Kramer and updated
      AnalysisOfVarianceOneWay (ANOVA) and FriedmanConfidence.
    * Added multiple-comparisons experiment classes BlockExperimentComparison
      and MultipleComparisonExperiment
   * Text Core:
     * Renamed ProbabilisticLatentSemanticAnalysis.Transform to
       ProbabilisticLatentSemanticAnalysis.Result for consistency.

Release 3.2.0 (2011-05-19):
 * Upgraded to mtj-9.9.14 and added the netlib-java-0.9.3 library, which MTJ
   now depends on.
 * Common Core:
   * ParallelUtil: Added executeInParallel(tasks, algorithm)
   * CollectionUtil: Added removeElement.
   * Added MutableDouble class, which is like Double but with a mutable value.
     It also implements Ring and Vectorizable.
   * UnivariateStatisticsUtil: Improved stability of computeMean with large
     values.
   * VectorReader: Changed to use a Collection of tokens instead of requiring
     an ArrayList.
   * AbstractSingularValueDecomposition: Fixed a bug for certain types of
     rectangular matrices.
   * DiagonalMatrixMTJ: Made inverse faster.
   * ArgumentChecker: Added assertIsInRangeExclusive and fixed a formatting
     issue with some exception messages.
   * Added Identified interface.
 * Learning Core:
   * Added BatchAndIncrementalLearner interface, which is for algorithms that
     can be used in batch and incremental modes. Note that it defines that the
     BatchLearner interface uses a Collection of data for consistency with
     existing batch (non-incremental) algorithms. However, it does also include
     another learn method that takes an Iterable.
     AbstractBatchAndIncrementalLearner now implements it.
   * Added SupervisedIncrementalLearner interface, supervised version,
     SupervisedBatchAndIncrementalLearner, and abstract class
     AbstractSupervisedBatchAndIncrementalLearner. Many incremental learning
     algorithms now implement these interfaces and extend this abstract class.
   * VectorNaiveBayesCategorizer: Now has generic for the distribution type
     representing each feature, including OnlingBaggingCategorizerLearner,
     AbstractOnlineLinearBinaryCategorizerLearner,
     OnlinePassiveAggressivePerceptron, OnlinePerceptron, OnlineVotedPerceptron,
     and Winnow.
   * BaggingCategorizerLearner: Added a protected fillBag method, which
     can be overridden to implement a different sampling approach.
   * Added BatchMultiPerceptron, which is an implementation of a multi-class
     Perceptron that keeps one Perceptron per class.
   * Added MultiCategoryAdaBoost, which is an implementation of the AdaBoost.M1
     algorithm.
   * DecisionTree: Added findTerminalNode methods.
   * CrossFoldCreator: Added constructor that takes just a number of folds.
   * KernelBinaryCategorizer is now an interface with a default implementation
     in DefaultKernelBinaryCategorizer. KernelPerceptron, KernelAdatron,
     SequentialMinimalOptimization, and SuccessiveOverrelaxation now all
     use the new interface or default class.
   * Added LinearMultiCategorizer class, which keeps a LinearBinaryCategorizer
     for each class and
   * Removed GeneralizedScalarRadialBasisKernel class.
   * GaussianContextRecognizer: Now requires MixtureOfGaussians.PDF.
   * DefaultConfusionMatrix: Added copy constructor.
   * DiscreteDistribution: Added getDomainSize and implemented in subclasses.
   * DiscreteSamplingUtil: Added sampleWithReplacementInto method.
   * ProbabilityMassFunctionUtil: Added sampleMultiple and sampleSingle.
   * ScalarProbabilityDensityFunction: Added logEvaluate method and implemented
     in subclasses.
   * BayesianLinearRegression: Added incremental learner.
   * BayesianRobustLinearRegression: Added incremental learner.
   * Refactored LinearMixtureModel, MixtureOfGaussians, and
     ScalarMixtureDensityModel to be more consistent with other Foundry
     statistics classes.
   * MultivariateGaussian: Added incremental estimator.
   * Added MultivariateMixtureDensityModel class.
   * UnivaraiteGaussian: Added incremental estimator.
   * Added FriedmanConfidence, NemenyiConfidence, and TukeyRangeConfidence,
     which implement methods for computing confidence intervals.
 * Text Core:
   * LatentSemanticAnalysis: Added better handling of low-rank matrices.


Release 3.1.1 (2011-03-23):
 * All projects now depend on JUnit 4.8.2 instead of 3.8.2 or 4.6.
 * Upgraded to XStream to 1.3.1 (and its dependency xpp3_min to 1.1.4c).
 * Common Core:
   * KDTree: Fixed two bugs that caused the KDTree to not always return the
     nearest points in rare cases.
 * Learning Core:
   * PartitionalClusterer: Added an implementation of a partitional clustering
     algorithm.
   * IncrementalClusterCreator: New interface for a cluster creator that can
     incrementally update clusters. DefaultIncrementalClusterCreator is a
     default implementation of the interface that just updates the cluster
     memberships.
   * VectorMeanCentroidClusterCreator: Modified to implement the new
     IncrementalClusterCreator interface.
   * BinaryBaggingLearner: Generalized generics in constructor.
   * OnlineBaggingCategorizerLearner: Added an implementation of an online
     version of the bagging algorithm for building ensembles.
   * VotingCategorizerEnsemble: Added an unweighted voting categorization
     ensemble as a counterpart to WeightedVotingCategorizerEnsemble.
   * OnlinePassiveAggressivePerceptron: Added an implementation of the
     Passive-Aggressive algorithm for binary classification. Also contains
     PA-I (LinearSoftMargin) and PA-II (QuadraticSoftMargin) variants.

Release 3.1.0 (2011-02-23):

 * Text: New package in this release for doing statistical text analysis and
   information retrieval. Especially useful for doing machine learning over
   text data.
 * Common Core:
   * CollectionUtil: Added toStringDelimited method.
   * VectorFactoryContainer is now an interface, existing implementation moved
     to DefaultVectorFactoryContainer.
   * ObjectUtil: Added getBytes method.
 * Learning Core:
   * OnlineLearner interface renamed to IncrementalLearner, which is a more
     appropriate name. Other similar classes also renamed:
     AbstractBatchAndIncrementalLearner
   * Added BatchLearnerContainer interface and renamed
     AbstractBatchLearnerWrapper to AbstractBatchLearnerContainer
   * Added DiscriminantCategorizer and BinaryDiscriminantCategorizer interfaces.
     Almost all Categorizers now implement one of these interfaces; some
     existing methods were renamed to match these standard interfaces.
   * Added DistanceSamplingClusterInitializer, which is an implementation of
     the k-means++ initialization algorithm.
   * HiddenMarkovModel and BaumWelchAlgorithm had some performance improvements.
   * Added KernelPrincipalComponentsAnalysis, which is an implementation of
     kernel PCA using eigen-decomposition.
   * DatasetUtil: Added countOutputValues.
   * TargetEstimatePair and WeightedTargetEstimatePair are now interfaces;
     existing implementations moved to DefaultTargetEstimatePair and
     DefaultWeightedTargetEstimatePair.
   * ConstantEvaluator: Now extends AbstractCloneableSerializable.
   * ThresholdBinaryCategorizer is now an interface. Existing implementation
     moved to ScalarThresholdBinaryCategorizer.
   * AbstractThresholdBinaryCategorizer now longer contains the threshold
     directly, instead it assumes the threshold is applied in evaluateAsDouble
     and categorizes based on zero. This is to remove the confusion of having
     both a bias and a threshold in some classes and to clarify the meaning of
     evaluateAsDouble so that it matches the new BinaryDiscriminantCategorizer
     interface.
   * DataHistogram: Added isEmpty method and implemented in
     AbstractDataHistogram.
   * Made new ConfusionMatrix and BinaryConfusionMatrix interfaces and put them
     in the gov.sandia.cognition.learning.performance.categorization. Refactored
     existing confusion matrix code to implement these interfaces; the existing
     class ending up in DefaultBinaryConfusionMatrix. New confusion matrix
     interface uses generics to be useful for more than just binary
     categorization.
   * Now uses JUnit 4.6 for tests.

Release 3.0.3 (2010-12-22):
* Common Core:
  * UnivariateStatisticsUtil: Weighted computations handle negative weights by
    using absolute values to fix bug.

Release 3.0.2 (2010-12-15):
* Common Core:
  * ValueMapper: Added create() method.
  * UnivariateStatisticsUtil: Addressed numerical instability in one-pass
    variance calculations.
  * DefaultNamedValue: Added create() methods.
  * Added ArgumentChecker utility class.
  * Added KeyValuePair interface and DefaultKeyValuePair implementation.
* Learning Core:
  * AdaBoost: Generalized generics.
  * BaggingCategorizerLearner: Will create a new Random if null.
  * WeightedBinaryEnsemble: Added generic for the member type.
  * IVotingCategorizerLearner: Performance improvements, added vote histogram
    factory.
  * HiddenMarkovModel: Made computeObservationsLikelihoods public.
  * ParallelHiddenMarkovModel: Added copy constructor.
  * Added online perceptron and voted perceptron implementations.
  * Added primal estimated sub-gradient SVM learner.
  * AbstractDecisionTreeLearner: Performance tweaks.
  * AbstractDecisionTreeNode: Added parent field.
  * AbstractVectorThresholdMaximumGainLearner: Fixed corner-case bug.
  * CategorizationTreeLearner: Added maximum depth parameter and cleaned up
    implementation.
  * CategorizationTreeNode: Support of parent field.
  * DecisionTreeNode: Added getParent(), getDepth(), and getTreeSize() methods.
  * RegressionTreeLearner: Added maximum depth parameter.
  * RegressionTreeNode: Support of parent field.
  * DatasetUtil: Added splitOnOutput method.
  * ConstantEvaluator: Added create method.
  * LinearBinaryCategorizer: Now allows null weight vector (evaluates to 0.0).
  * VectorElementThresholdCategorizer: Added toString().
  * AbstractDataHistogram: Now contains default implementations of getFraction()
    and addAll().
  * DataHistogram: Minor refactoring to be more consistent with other
    statistics distributions. In particular, getDomain() is to be used instead
    of getValues(), which was removed. Moved many of the methods that were in
    the implementation class into the interface.
  * DiscreteSamplingUtil: New utility class for sampling discrete objects.
  * ScalarSummaryStatistics: Bug fix.
  * MapBasedDataHistogram: Reworked with changes to DataHistogram. New PMF,
    learner, and factory implementations.
  * UnivariateGaussian: Learner classes generic updated to be a
    UnivariateGaussian.PDF, which it was already returning.
  * Added YuleSimonDistribution distribution class.
  * KolmogorovSmirnovConfidence: Fixed infinite loop with given NaNs.

Release 3.0.1 (2010-10-15):
* Common Core:
  * Added ArrayUtil class.
  * MultivariateStatisticsUtil: computeMean changed to accept Iterable instead
    of Collection.
  * Permutation: Added reorder method.
* Learning Core:
  * DirichletProcessClustering: Performance improvements.
  * Cluster: Made CloneableSerializable.
  * DefaultCluster: Now extends AbstractCloneableSerializable.
  * AbstractClusterHierarchyNode:: Now extends AbstractCloneableSerializable.
  * gov.sandia.cognition.learning.function.cost: Added AbstractCostFunction,
    KolmogorovSmirnocDivergence, NegativeLogLikelihood, and
    ParallelNegativeLogLikelihood.
  * SequantialMinimalOptimization: Added an initial implementatio of the
    algorithm.
  * WeightedEuclideanDistance: Added a weighted Euclidean distance metric.
  * Added EstimableDistribution interface.
  * AbstractMarkovChainMonteCarlo: Now keeps track of previous parameter.
  * DirichletProcessMixtureModel: Performance improvements.
  * ParallelDirichletProcessMixtureModel: Performance improvements.
  * MultivariateGaussianMeanBayesianEstimator: Update now optimized for case of
    a list instead of a single update.
  * MultivariateGaussianMeanCovarianceBayesianEstimator: Optimizations for case
    of providing a list of data.
  * BetaBinomialDistribution: Now implements EstimableDistribution.
  * BetaDistribution: Now implements EstimableDistribution.
  * ChineseRestaurantProcess: Now keeps track of total customers.
  * ExponentialDistribution: Now implements EstimableDistribution.
  * GammaDistribution: Now implements EstimableDistribution.
  * GeometricDistribution: Now implements EstimableDistribution.
  * LaplaceDistribution: Now implements EstimableDistribution.
  * MultinomialDistribution: Added logSize method.
  * MultivariateGaussian: Now implements EstimableDistribution.
  * NegativeBinomialDistribution: Now implements EstimableDistribution.
  * NormalInverseWishartDistribution: Now implements EstimableDistribution.
  * PoissonDistribution: Now implements EstimableDistribution.
  * ScalarDataDistribution: getDomain now returns in sorted order.
  * StudentTDistribution: Now implements EstimableDistribution.
  * UniformDistribution: Now implements EstimableDistribution.
  * UnivariateGaussian: Now implements EstimableDistribution.
  * ConfidenceIntervalEvaluator: Added computeConfidenceInterval method that
    takes a mean, variance, number of samples, and confidence. All existing
    ConfidenceIntervalEvaluators updated; which mostly meant changing static
    methods to be member methods and adding INSTANCE variables for those without
    parameters.
  * ChebyshevInequality: Now extends AbstractCloneableSerializable.

Release 3.0.0 (2010-06-25):
First open source release.