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Jstacs Changelog

What's new in Jstacs 2.1

Jun 18, 2013
  • New classes:
  • MultipleIterationsCondition: Requires another TerminationCondition to fail a contiguous, specified number of times
  • ClassifierFactory: Allows for creating standard classifiers
  • SeqLogoPlotter: Plot PNG sequence logos from within Jstacs
  • MultivariateGaussianEmission: Multivariate Gaussian emission density for a Hidden Markov Model
  • MEManager: Maximum entropy model
  • New features and improvements:
  • Alignment: Added free shift alignment
  • PerformanceMeasure and sub-classes: Extension to weighted test data
  • DNAAlphabet: Parser speed-up
  • PFMComparator: Extension to PFM from other sources/databases
  • ToolBox: New convenience methods for computing several statistics (e.g., median, correlation)
  • SignificantMotifOccurrencesFinder: New methods for computing PWMs and statistics from predictions
  • SequenceScore and sub-classes: New method toString(NumberFormat)
  • AbstractClassifier, ClassifierAssessment and sub-classes: Adaption to weighted PerformanceMeasures
  • DataSet: Adaption to weighted data, e.g., partitioning
  • REnvironment: Changed several methods from String to CharSequence
  • Restructuring:
  • changed MultiDimensionalSequenceWrapperDiffSM to MultiDimensionalSequenceWrapperDiffSS
  • Several minor new features, bug fixes, and code cleanups

New in Jstacs 2.0 (Jun 18, 2013)

  • Restructuring and Renaming:
  • former ScoringFunction, NormalizableScoringFunction, Model
  • new base-interface SequenceScore
  • new sub-interface StatisticalModel of SequenceScore for all statistical models with sub-iterfaces DifferentiableStatisticalModel and TrainableStatisticalModel
  • new interface DifferentiableSequenceScore replaces ScoringFunction
  • new interface DifferentiableStatisticalModel replaces NormalizableScoringFunction
  • new interface TrainableStatisticalModel replaces Model
  • new abstract class AbstractDifferentiableSequenceScore
  • new abstract class AbstractDifferentiableStatisticalModel replaces AbstractNormalizableScoringFunction
  • new abstract class AbstractTrainableStatisticalModel replaces AbstractModel
  • former *Models renamed to *TrainSM
  • former *ScoringFunction renamed to *DiffSS or *DiffSM
  • getProbFor removed from TrainableStatisticalModel (former Model) and conceptually replaced by getLogProbFor
  • getLogScore(Sequence,int,int) with changed meaning of arguments: getLogScore(Sequence,start,end) instead of getLogScore(Sequence,start,length)
  • isTrained() replaced by common method isInitialized()
  • Sample renamed to DataSet
  • Parameters and Results
  • new super-class of Parameters and Results: AnnotatedEntity
  • common list-type for Parameters and Results: AnnotatedEntityList
  • Renaming: CollectionParameter -> SelectionParameter, MultiSelectionCollectionParameter -> MultiSelectionParameter, new super-class AbstractSelectionParameter
  • major refactoring due to common hierarchy and code-cleanup
  • lazy evaluation of Parameter/ParameterSet hierarchies moved from ParameterSet (loadParameters()) to ParameterSetContainer (constructor on class)
  • SubclassFinder adapted to lazy evaluation
  • performance measures
  • new abstract super-class AbstractPerformanceMeasure of all performance measures
  • new interface NumericalPerformanceMeasure for all performance measures that return a single number (as opposed, e.g., to curves)
  • new class PerformanceMeasureParameterSet for a collection of general performance measures
  • new class NumericalPerformanceMeasureParameterSet for a collection of NumericalPerformanceMeasures
  • used in evaluate-method of AbstractClassifier and in ClassifierAssessments
  • evaluate and evaluateAll in AbstractClassifier joined
  • new class IndependentProductDiffSS as super-class of IndepedentProductDiffSM (former IndependentProductScoringFunction)
  • new class UniformDiffSS as super-class of UniformDiffSM (former UniformScoringFunction)
  • New functionality:
  • multi-threaded implementation of Baum-Welch and Viterbi training of hidden Markov models
  • new Interface Singleton that can be used for singleton instances to save memory, current examples: DNAAlphabet, DNAAlphabetContainer, ProteinAlphabet
  • added possibility to use NaN-values with ContinuousAlphabets
  • added ProteinAlphabet
  • added ArbitraryFloatSequence including static methods for DataSet creation for cases where double-precision is not needed
  • new performance measure MaximumFMeasure
  • access to Parameters in ParameterSets and Results in ResultSets by name
  • emitDataSet in BayesianNetworkDiffSM
  • new static method Time.getTimeInstance that returns UserTime or RealTime depending on availability of shared lib
  • SubclassFinder allows for adding own base packages
  • new method overlaps() in LocatedSequenceAnnotationWithLength
  • AbstractTerminationCondition used in ScoreClassifier and sub-classes
  • public method propagateESS in HMMFactory
  • new method generateLog in DirichletMRG for drawing log-values
  • added DifferentiableStatisticalModelFactory
  • Bugfixes/Improvements:
  • bugfix in propagation of equivalent sample size in HMMFactory
  • bugfix in random initialization of BasicHigherOrderTransition
  • improved Alignment implementation
  • SafeOutputStream with new static factory method getSafeOutputStream, write methods now work on Objects
  • Documentation:
  • improved Javadocs in many classes and packages
  • new Cookbook with extensive documentation and explanation
  • Misc:
  • output of NonParsableException more verbose
  • Exceptions in multi-threaded code now lead to exit of program instead of only stopping the thread
  • update of RServe/RClient

New in Jstacs 1.5 (Jun 18, 2013)

  • new package de.jstacs.algorithms.alignment for sequence alignment algorithms
  • new class de.jstacs.models.ModelFactory with static classes to construct many standard models
  • de.jstacs.utils.galaxy.GalaxyAdaptor, an adaptor to Galaxy, which allows for creating Galaxy applications using Jstacs ParameterSets, also requires new interface GalaxyConvertible
  • new package de.jstacs.models.hmm for a variety of hidden Markov models, which can be learned by different learning principles including generative and discriminative learning principles, maximization and sampling methods
  • new package de.jstacs.sampling that contains general infrastructure for parameter sampling
  • new class de.jstacs.scoringFunctions.MappingScoringFunction that allows for internal mapping of symbols from the alphabet
  • new package de.jstacs.classifier.scoringFunctionBases.sampling containing classifiers that sample their parameters by the Metropolis-Hastings algorithm
  • new interface de.jstacs.scoringFunctions.SamplingScoringFunction for NormalizableScoringFunctions that can be used in Metropolis-Hastings sampling of parameters
  • bugfix in XMLParser for cases, where the tag of interest also occurrs within other, nested tags

New in Jstacs 1.4 (Jun 18, 2013)

  • added DincleotideProperty for computing properties like melting temperature, twist, or G/C content
  • support for multidimensional sequence data
  • more widespread use of TerminationConditions
  • completely rewritten XMLParser
  • extension of motif discovery to weighted data
  • OneSampleLogGenDisMixFunction for using the same Sample with different weights for the different classes
  • Jstacs requires Java 1.6 now

New in Jstacs 1.3.1 (Jun 18, 2013)

  • Partitioning of Samples including weights
  • Release of Dispom (de-novo discovery of differentially abundant transcription factor binding sites including their positional preference)
  • Several bugfixes