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## Uses of Interface
calhoun.analysis.crf.ModelManager

Packages that use ModelManager
calhoun.analysis.crf | the interface, main `Conrad` class, and solver for the Conrad engine.
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.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.solver | training and inference algorithms used in the CRF engine
calhoun.analysis.crf.solver.semimarkov |
calhoun.analysis.crf.test |

Uses of ModelManager in calhoun.analysis.crf

Classes in calhoun.analysis.crf that implement ModelManager
`class` | `BeanModel` a useful base class for creating model beans.

Methods in calhoun.analysis.crf that return ModelManager
`ModelManager` | `Conrad.getModel()` returns the configured ModelManager object.

Methods in calhoun.analysis.crf with parameters of type ModelManager
`double[]` | `CRFTraining.optimize(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data)` find the set of weights which maximizes the value of the objective function.
`CRFInference.InferenceResult` | `CRFInference.predict(ModelManager mm, InputSequence<?> data, double[] weights)` Return the labelling that maximizes the conditional probability P(y|x).
`void` | `Conrad.setModel(ModelManager model)` sets the model.
`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` | `ConstrainedFeatureManager.train(int startingIndex, ModelManager modelInfo, java.util.List data)`
`void` | `CompositeFeatureManager.train(int startingIndex, ModelManager modelInfo, java.util.List data)`
`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.

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

Methods in calhoun.analysis.crf.features.generic with parameters of type ModelManager
`void` | `StartFeatures.train(int startingIndex, ModelManager modelInfo, java.util.List data)` Start features don't train based on the data.
`void` | `IndicatorEdges.train(int startingIndex, ModelManager modelInfo, java.util.List data)` Edge features don't train based on the data.
`void` | `EndFeatures.train(int startingIndex, ModelManager modelInfo, java.util.List data)` Edge features don't train based on the data.
`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 ModelManager in calhoun.analysis.crf.features.interval13

Classes in calhoun.analysis.crf.features.interval13 that implement ModelManager
`class` | `Interval13Model`

Methods in calhoun.analysis.crf.features.interval13 with parameters of type ModelManager
`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 ModelManager in calhoun.analysis.crf.features.interval29

Classes in calhoun.analysis.crf.features.interval29 that implement ModelManager
`class` | `Interval29Model`

Methods in calhoun.analysis.crf.features.interval29 with parameters of type ModelManager
`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 ModelManager in calhoun.analysis.crf.features.supporting.phylogenetic

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

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

Methods in calhoun.analysis.crf.features.tricycle13 with parameters of type ModelManager
`void` | `IntronLengthFeature.train(int startingIndex, ModelManager modelInfo, java.util.List data)`
`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 ModelManager in calhoun.analysis.crf.io

Methods in calhoun.analysis.crf.io that return ModelManager
`ModelManager` | `OutputHandlerGeneCallStats.getManager()` gets the model used to generate results
`ModelManager` | `OutputHandlerGeneCallPredict.getManager()` gets the model used to generate results

Methods in calhoun.analysis.crf.io with parameters of type ModelManager
`void` | `OutputHandlerGeneCallStats.setManager(ModelManager manager)` sets the model used to generate results
`void` | `OutputHandlerGeneCallPredict.setManager(ModelManager manager)` sets the model used to generate results

Constructors in calhoun.analysis.crf.io with parameters of type ModelManager
`OutputHandlerGeneCallPredict(ModelManager manager, InputHandler inputHandler)` creates an output handler using this model and input handler
`OutputHandlerGeneCallStats(ModelManager manager, InputHandler inputHandler)` creates an output handler using this model and input handler

Uses of ModelManager in calhoun.analysis.crf.solver

Methods in calhoun.analysis.crf.solver with parameters of type ModelManager
`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)`
`CRFInference.InferenceResult` | `Viterbi.predict(ModelManager fm, InputSequence<?> seq, double[] lambda)`
`CRFInference.InferenceResult` | `SemiMarkovViterbiNoCache.predict(ModelManager fm, InputSequence<?> seq, double[] lambda)`
`CRFInference.InferenceResult` | `SemiMarkovViterbi.predict(ModelManager fm, InputSequence<?> seq, double[] lambda)`
`void` | `CacheProcessorBasic.setInputData(ModelManager fm, InputSequence<?> seq)`
`void` | `CacheProcessor.setInputData(ModelManager fm, InputSequence<?> 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 ModelManager in calhoun.analysis.crf.solver.semimarkov

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

Uses of ModelManager in calhoun.analysis.crf.test

Classes in calhoun.analysis.crf.test that implement ModelManager
`class` | `ZeroOrderManager`
`class` | `ZeroOrderModel`

Methods in calhoun.analysis.crf.test with parameters of type ModelManager
`void` | `ConstraintTest.FixedEdges.train(int startingIndex, ModelManager modelInfo, java.util.List data)`
`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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