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## Uses of Class
calhoun.analysis.crf.io.TrainingSequence

Packages that use TrainingSequence
calhoun.analysis.crf | the interface, main `Conrad` class, and solver for the Conrad engine.
calhoun.analysis.crf.executables.viewer | prototype of a viewing utility for examiining the results of inference.
calhoun.analysis.crf.features.generic | features useful across different models
calhoun.analysis.crf.features.interval13 | mdoel definition and setup for the interval13 model
calhoun.analysis.crf.features.interval29 |
calhoun.analysis.crf.features.supporting | utility classes used by feature managers in gene calling models
calhoun.analysis.crf.features.supporting.phylogenetic | utility classes for phylogenetic analysis
calhoun.analysis.crf.features.tricycle13 | basic features and model for the tricycle13 gene calling model
calhoun.analysis.crf.io | handles input and output of gene calling formats
calhoun.analysis.crf.scoring | local similarity functions used in gene calling models
calhoun.analysis.crf.solver | training and inference algorithms used in the CRF engine
calhoun.analysis.crf.solver.semimarkov |
calhoun.analysis.crf.test |

Uses of TrainingSequence in calhoun.analysis.crf

Methods in calhoun.analysis.crf with parameters of type TrainingSequence
`double` | `LocalPathSimilarityScore.evaluate(int yprev, int y, TrainingSequence<?> seq, int pos)` compute a real-valued score between a given path and the true path at a given position.

Method parameters in calhoun.analysis.crf with type arguments of type TrainingSequence
`double[]` | `CRFTraining.optimize(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)` find the set of weights which maximizes the value of the objective function.
`void` | `CRFObjectiveFunctionGradient.setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)` sets the training data that will be used for evaluation of the objective function.
`void` | `Conrad.test(java.util.List<? extends TrainingSequence<?>> data)`
`void` | `Conrad.test(java.util.List<? extends TrainingSequence<?>> data, java.lang.String location)` runs a trained model against a set of input data with known results and evaluates the performance.
`void` | `FeatureManager.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends InputType>> data)` Provides access to the entire training set so that FeatureManager can compute global properties and assign feature indices.
`void` | `Conrad.train(java.util.List<? extends TrainingSequence<?>> data)` fully trains this Conrad engine with this training data.
`void` | `Conrad.trainFeatures(java.util.List<? extends TrainingSequence<?>> data)` trains only the features in the current model with this training data.
`void` | `Conrad.trainWeights(java.util.List<? extends TrainingSequence<?>> data)` optimizes the feature weights for the current model with this training data.

Uses of TrainingSequence in calhoun.analysis.crf.executables.viewer

Constructors in calhoun.analysis.crf.executables.viewer with parameters of type TrainingSequence
`ViterbiTableModel(Conrad crfModel, TrainingSequence seq)`

Uses of TrainingSequence in calhoun.analysis.crf.features.generic

Method parameters in calhoun.analysis.crf.features.generic with type arguments of type TrainingSequence
`void` | `WeightedStateChanges.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `WeightedEdges.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<?>> data)`

Uses of TrainingSequence in calhoun.analysis.crf.features.interval13

Method parameters in calhoun.analysis.crf.features.interval13 with type arguments of type TrainingSequence
`void` | `StateTransitionsInterval13.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `StateLengthLogprobInterval13.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `ReferenceBasePredictorInterval13Base.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `PWMInterval13.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `GeneConstraintsInterval13.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `ESTInterval13.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data)`
`void` | `BlastInterval13.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data)`
`void` | `PhylogeneticLogprobInterval13.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends MultipleAlignmentInputSequence.MultipleAlignmentColumn>> data)`
`void` | `GapFeaturesInterval13.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends MultipleAlignmentInputSequence.MultipleAlignmentColumn>> data)`
`void` | `FootprintsInterval13.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends MultipleAlignmentInputSequence.MultipleAlignmentColumn>> data)`

Uses of TrainingSequence in calhoun.analysis.crf.features.interval29

Method parameters in calhoun.analysis.crf.features.interval29 with type arguments of type TrainingSequence
`void` | `StateTransitionsInterval29.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `StateLengthLogprobInterval29.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `ReferenceBasePredictorInterval29Base.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `PWMInterval29.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `GeneConstraintsInterval29.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `ESTInterval29.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data)`
`void` | `GapFeaturesInterval29.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends MultipleAlignmentInputSequence.MultipleAlignmentColumn>> data)`
`void` | `FootprintsInterval29.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends MultipleAlignmentInputSequence.MultipleAlignmentColumn>> data)`

Uses of TrainingSequence in calhoun.analysis.crf.features.supporting

Method parameters in calhoun.analysis.crf.features.supporting with type arguments of type TrainingSequence
`void` | `MarkovPredictorLogprob.train(java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`

Uses of TrainingSequence in calhoun.analysis.crf.features.supporting.phylogenetic

Method parameters in calhoun.analysis.crf.features.supporting.phylogenetic with type arguments of type TrainingSequence
`void` | `ColumnConditionalLogProbability.train(ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends MultipleAlignmentInputSequence.MultipleAlignmentColumn>> data)`

Uses of TrainingSequence in calhoun.analysis.crf.features.tricycle13

Method parameters in calhoun.analysis.crf.features.tricycle13 with type arguments of type TrainingSequence
`void` | `PositionWeightMatrixFeatures.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `MaxentMotifFeatures.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `KmerFeatures.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)` Computes the P(label | kmer) for each kmer across all of the training data.
`void` | `GeneConstraints.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)` Set up the matrix Depends on states starting with the words 'intergenic, intron, and exon'.
`void` | `EmissionMarkovFeature.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `CodingStopFeature.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `PWM_evolution.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data)`
`void` | `PfamPhase.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data)`
`void` | `PfamGenic.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data)`
`void` | `ESTIntron.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data)`
`void` | `ESTExon.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data)`
`void` | `ESTEdge.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data)`
`void` | `IntervalPresenceFeatures.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends IntervalInputSequence.IntervalPosition>> data)`
`void` | `GapConjunctionFeatures.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends MultipleAlignmentInputSequence.MultipleAlignmentColumn>> data)`
`void` | `FelsensteinFeatures.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends MultipleAlignmentInputSequence.MultipleAlignmentColumn>> data)`

Uses of TrainingSequence in calhoun.analysis.crf.io

Methods in calhoun.analysis.crf.io that return TrainingSequence
`TrainingSequence` | `OutputHandlerGeneCallStats.getLabeled(int i)`
`TrainingSequence` | `OutputHandlerGeneCallPredict.getLabeled(int i)`
`TrainingSequence` | `TrainingSequence.getTrainingComponent(java.lang.String name)` returns a new TrainingSequence created by taking a single component of the input sequence and pairing it with the hidden states for this Training Sequence.
`TrainingSequence<A>` | `TrainingSequence.subSequence(int start, int end)`

Methods in calhoun.analysis.crf.io that return types with arguments of type TrainingSequence
`static java.util.List<? extends TrainingSequence<java.lang.Character>>` | `StringInput.prepareData(java.lang.String str)` Convenience function for creating training sequences in test data.
`static java.util.List<? extends TrainingSequence<?>>` | `IntInput.prepareData(java.lang.String str)` Convenience function for creating training sequences in test data.
`java.util.List<? extends TrainingSequence<?>>` | `InputHandlerInterleaved.readTrainingData(java.lang.String location)`
`java.util.List<? extends TrainingSequence<?>>` | `InputHandlerFile.readTrainingData(java.lang.String location)`
`java.util.List<? extends TrainingSequence<?>>` | `InputHandlerDirectory.readTrainingData(java.lang.String location)`
`java.util.List<? extends TrainingSequence<?>>` | `InputHandler.readTrainingData(java.lang.String location)`
`java.util.List<? extends TrainingSequence<?>>` | `CompositeInput.LegacyInputHandler.readTrainingData(java.lang.String location)`
`java.util.List<? extends TrainingSequence<?>>` | `InputHandlerInterleaved.readTrainingData(java.lang.String location, boolean predict)`
`java.util.List<? extends TrainingSequence<?>>` | `InputHandlerFile.readTrainingData(java.lang.String location, boolean predict)`
`java.util.List<? extends TrainingSequence<?>>` | `InputHandlerDirectory.readTrainingData(java.lang.String location, boolean predict)`
`java.util.List<? extends TrainingSequence<?>>` | `InputHandler.readTrainingData(java.lang.String location, boolean predict)` returns the training data read from the specified location.
`java.util.List<? extends TrainingSequence<?>>` | `CompositeInput.LegacyInputHandler.readTrainingData(java.lang.String inputLocation, boolean predict)`

Methods in calhoun.analysis.crf.io with parameters of type TrainingSequence
`void` | `OutputHandlerGeneCallStats.calcResultIncrement(TrainingSequence training, int[] predictedHiddenSequence)` calculates statstics and output for results on a given test sequence
`void` | `OutputHandlerGeneCallPredict.calcResultIncrement(TrainingSequence training, int[] predictedHiddenSequence)` calculates statstics and output for results on a given test sequence
`static void` | `SequenceConverter.convertSeqFrom13To39(TrainingSequence<java.lang.Character> seq)`
`static void` | `SequenceConverter.convertSeqFrom39To13(TrainingSequence<java.lang.Character> seq)`
`static void` | `SequenceConverter.convertSeqFromInterval13ToTricycle13(TrainingSequence<java.lang.Character> seq)`
`static void` | `SequenceConverter.convertSeqFromTricycle13ToInterval13(TrainingSequence<java.lang.Character> seq)`
`void` | `SequenceConverter.writeHiddenSequence39GFF(TrainingSequence<java.lang.Character> refStates, java.lang.String filename)`
`static void` | `SequenceConverter.writeHiddenSequenceGFF(TrainingSequence<java.lang.Character> refStates, java.lang.String filename)`

Method parameters in calhoun.analysis.crf.io with type arguments of type TrainingSequence
`void` | `TrainingSequenceIO.readTrainingSequences(java.lang.Object location, java.util.List<TrainingSequence<java.util.Map<java.lang.String,java.lang.Object>>> seqs)` reads training sequences from the specified location.
`void` | `IntInput.readTrainingSequences(java.lang.Object location, java.util.List<TrainingSequence<java.util.Map<java.lang.String,java.lang.Object>>> seqs)`
`void` | `GTFInputInterval29.readTrainingSequences(java.lang.Object location, java.util.List<TrainingSequence<java.util.Map<java.lang.String,java.lang.Object>>> seqs)`
`void` | `GTFInputInterval13.readTrainingSequences(java.lang.Object location, java.util.List<TrainingSequence<java.util.Map<java.lang.String,java.lang.Object>>> seqs)`
`void` | `AllIntergenicHiddenStateReader.readTrainingSequences(java.lang.Object location, java.util.List<TrainingSequence<java.util.Map<java.lang.String,java.lang.Object>>> seqs)`
`static java.util.ArrayList<java.util.ArrayList<java.lang.Integer>>` | `SequenceConverter.stateVector2StateLengths(java.util.List<? extends TrainingSequence<?>> data, int nStates)`
`void` | `OutputHandlerGeneCallStats.writeGTF(java.util.List<? extends TrainingSequence<?>> refStates, java.lang.String filename)`
`void` | `OutputHandlerGeneCallPredict.writeGTF(java.util.List<? extends TrainingSequence<?>> refStates, java.lang.String filename)`
`void` | `InputHandlerInterleaved.writeTrainingData(java.lang.String location, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `InputHandlerFile.writeTrainingData(java.lang.String location, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `InputHandlerDirectory.writeTrainingData(java.lang.String location, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `InputHandler.writeTrainingData(java.lang.String location, java.util.List<? extends TrainingSequence<?>> data)` writes training data to the specified location.

Constructor parameters in calhoun.analysis.crf.io with type arguments of type TrainingSequence
`IteratorAdapterTrainingSequenceInput(java.util.Iterator<? extends TrainingSequence<T>> trainingIterator)` constructs a new iterator which will extract the input sequence from the training sequences

Uses of TrainingSequence in calhoun.analysis.crf.scoring

Methods in calhoun.analysis.crf.scoring with parameters of type TrainingSequence
`double` | `SimScoreStateAndExonBoundariesInt13.evaluate(int yprev, int y, TrainingSequence<?> seq, int pos)`
`double` | `SimScoreMinExonBoundaryMiscallsSV13.evaluate(int yprev, int y, TrainingSequence<?> seq, int pos)`
`double` | `SimScoreMinCodingMiscallsSV13.evaluate(int yprev, int y, TrainingSequence<?> seq, int pos)`
`double` | `SimScoreMaxStateAgreement.evaluate(int yprev, int y, TrainingSequence<?> seq, int pos)`

Uses of TrainingSequence in calhoun.analysis.crf.solver

Methods in calhoun.analysis.crf.solver that return types with arguments of type TrainingSequence
`java.util.List<? extends TrainingSequence<?>>` | `CacheProcessorBasic.getData()`
`java.util.List<? extends TrainingSequence<?>>` | `CacheProcessor.getData()`

Method parameters in calhoun.analysis.crf.solver with type arguments of type TrainingSequence
`double[]` | `TwoPassOptimizer.optimize(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`double[]` | `StandardOptimizer.optimize(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`double[]` | `SimplexOptimizer.optimize(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`double[]` | `SeededOptimizer.optimize(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`double[]` | `FixedWeightOptimizer.optimize(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `NoCachingCacheProcessor.setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `MaximumLikelihoodSemiMarkovGradient.setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `MaximumLikelihoodGradient.setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `CacheProcessorDeluxe.setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `CacheProcessorBasic.setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `CacheProcessor.setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `CacheProcessor.SolverSetup.setup(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data, boolean allPaths, short[] maxStateLengths2, boolean ignoreSemiMarkovSelfTransitions)`

Uses of TrainingSequence in calhoun.analysis.crf.solver.semimarkov

Method parameters in calhoun.analysis.crf.solver.semimarkov with type arguments of type TrainingSequence
`void` | `CleanMaximumLikelihoodSemiMarkovGradient.setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`
`void` | `CleanLocalScoreSemiMarkovGradient.setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)`

Uses of TrainingSequence in calhoun.analysis.crf.test

Method parameters in calhoun.analysis.crf.test with type arguments of type TrainingSequence
`void` | `TestFeatures.TestFeature.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)`
`void` | `GeneConstraintsToy.train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)` Set up the matrix Depends on states starting with the words 'intergenic, intron, and exon'.

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