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## Uses of Class
de.jstacs.classifiers.ClassDimensionException

Packages that use ClassDimensionException
de.jstacs.classifiers | This package provides the framework for any classifier.
de.jstacs.classifiers.assessment | This package allows to assess classifiers.
de.jstacs.classifiers.trainSMBased | Provides the classes for `Classifier`s that are based on `TrainableStatisticalModel`s
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |

Uses of ClassDimensionException in de.jstacs.classifiers

Methods in de.jstacs.classifiers that throw ClassDimensionException
`void` | `AbstractScoreBasedClassifier.setClassWeights(boolean add, double... weights)` Sets new class weights.

Uses of ClassDimensionException in de.jstacs.classifiers.assessment

Constructors in de.jstacs.classifiers.assessment that throw ClassDimensionException
`ClassifierAssessment(AbstractClassifier... aCs)` Creates a new `ClassifierAssessment` from a set of `AbstractClassifier`s.
`ClassifierAssessment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and, in addition, classifiers that will be constructed using the given `TrainableStatisticalModel`s.
`ClassifierAssessment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `ClassifierAssessment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`ClassifierAssessment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `ClassifierAssessment` from a set of `TrainableStatisticalModel`s.
`KFoldCrossValidation(AbstractClassifier... aCs)` Creates a new `KFoldCrossValidation` from a set of `AbstractClassifier`s.
`KFoldCrossValidation(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `KFoldCrossValidation` .
`KFoldCrossValidation(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `KFoldCrossValidation` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`KFoldCrossValidation(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `KFoldCrossValidation` from a set of `TrainableStatisticalModel`s.
`RepeatedHoldOutExperiment(AbstractClassifier... aCs)` Creates a new `RepeatedHoldOutExperiment` from a set of `AbstractClassifier`s.
`RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `RepeatedHoldOutExperiment`.
`RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `RepeatedHoldOutExperiment` from a set of `TrainableStatisticalModel`s.
`RepeatedSubSamplingExperiment(AbstractClassifier... aCs)` Creates a new `RepeatedSubSamplingExperiment` from a set of `AbstractClassifier`s.
`RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `RepeatedSubSamplingExperiment`.
`RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `RepeatedSubSamplingExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`RepeatedSubSamplingExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `RepeatedSubSamplingExperiment` from a set of `TrainableStatisticalModel`s.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier... aCs)` Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `AbstractClassifier`s.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `Sampled_RepeatedHoldOutExperiment`.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `Sampled_RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`Sampled_RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `TrainableStatisticalModel`s.

Uses of ClassDimensionException in de.jstacs.classifiers.trainSMBased

Constructors in de.jstacs.classifiers.trainSMBased that throw ClassDimensionException
`TrainSMBasedClassifier(boolean cloneModels, TrainableStatisticalModel... models)` This constructor creates a new instance with the given `TrainableStatisticalModel`s and clones these if necessary.
`TrainSMBasedClassifier(TrainableStatisticalModel... models)` The default constructor that creates a new instance with the given `TrainableStatisticalModel`s.

Uses of ClassDimensionException in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared that throw ClassDimensionException
`SharedStructureClassifier(int length, StructureLearner.ModelType model, byte order, StructureLearner.LearningType method, FSDAGTrainSM... models)` Creates a new `SharedStructureClassifier` from given `FSDAGTrainSM`s.

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