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Archive for May, 2013

Cognitive Foundry 3.3.3 Released

May 20th, 2013 Comments off

Version 3.3.3 of the Cognitive Foundry is now available for download.

Here are the release notes:

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.
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