Uses of Interface
org.apache.ignite.ml.IgniteModel

Packages that use IgniteModel

Package	Description
org.apache.ignite.ml	Root ML package.
org.apache.ignite.ml.clustering.gmm	Contains Gauss Mixture Model clustering algorithm (see GmmModel).
org.apache.ignite.ml.clustering.kmeans	Contains kMeans clustering algorithm.
org.apache.ignite.ml.composition	Contains classes for ensemble of models implementation.
org.apache.ignite.ml.composition.bagging	Contains bootstrap aggregation (bagging) trainer allowing to combine some other trainers and return a bagged version of them.
org.apache.ignite.ml.composition.boosting	Contains Gradient Boosting regression and classification abstract classes allowing regressor type selecting in child classes.
org.apache.ignite.ml.composition.combinators.parallel	Contains parallel combinators of trainers and models.
org.apache.ignite.ml.composition.combinators.sequential	Contains sequential combinators of trainers and models.
org.apache.ignite.ml.composition.stacking	Contains classes used for training with stacking technique.
org.apache.ignite.ml.environment.logging	Package contains several logging strategy realisations.
org.apache.ignite.ml.inference	Root package for model inference functionality.
org.apache.ignite.ml.inference.parser	Root package for model inference parsers.
org.apache.ignite.ml.knn	Contains main APIs for kNN algorithms.
org.apache.ignite.ml.knn.ann	Contains main APIs for ANN classification algorithms.
org.apache.ignite.ml.knn.classification	Contains main APIs for kNN classification algorithms.
org.apache.ignite.ml.knn.regression	Contains helper classes for kNN regression algorithms.
org.apache.ignite.ml.multiclass	Contains various multi-classifier models and trainers.
org.apache.ignite.ml.naivebayes.discrete	Contains Bernoulli naive Bayes classifier.
org.apache.ignite.ml.naivebayes.gaussian	Contains Gaussian naive Bayes classifier.
org.apache.ignite.ml.nn	Contains neural networks and related classes.
org.apache.ignite.ml.optimization	Contains implementations of optimization algorithms and related classes.
org.apache.ignite.ml.pipeline	Contains Pipeline API.
org.apache.ignite.ml.recommendation	Contains recommendation system framework.
org.apache.ignite.ml.regressions.linear	Contains various linear regressions.
org.apache.ignite.ml.regressions.logistic	Contains various logistic regressions.
org.apache.ignite.ml.selection.cv	Root package for cross-validation algorithms.
org.apache.ignite.ml.selection.scoring.cursor	Util classes used for score calculation.
org.apache.ignite.ml.selection.scoring.evaluator	Package for model evaluator classes.
org.apache.ignite.ml.svm	Contains main APIs for SVM(support vector machines) algorithms.
org.apache.ignite.ml.trainers	Contains model trainers.
org.apache.ignite.ml.tree	Root package for decision trees.
org.apache.ignite.ml.tree.boosting	Contains implementation of gradient boosting on trees.
org.apache.ignite.ml.tree.randomforest.data	Package contains helper data structures for random forest implementation.
org.apache.ignite.ml.xgboost	Base package for XGBoost model parser, correspondent DTOs and util classes.

Uses of IgniteModel in org.apache.ignite.ml

Methods in org.apache.ignite.ml that return IgniteModel

Modifier and Type	Method and Description
default <V1> IgniteModel<V1,V>	IgniteModel.andBefore(IgniteFunction<V1,T> before) Get a composition model of the form x -> mdl(before(x)).
default <V1> IgniteModel<T,V1>	IgniteModel.andThen(IgniteFunction<V,V1> after) Get a composition model of the form x -> after(mdl(x)).
default <V1> IgniteModel<T,V1>	IgniteModel.andThen(IgniteModel<V,V1> after) Get a composition model of the form x -> after(mdl(x)).
default <X,W> IgniteModel<T,X>	IgniteModel.combine(IgniteModel<T,W> other, BiFunction<V,W,X> combiner) Combines this model with other model via specified combiner

Methods in org.apache.ignite.ml with parameters of type IgniteModel

Modifier and Type	Method and Description
default <V1> IgniteModel<T,V1>	IgniteModel.andThen(IgniteModel<V,V1> after) Get a composition model of the form x -> after(mdl(x)).
default <X,W> IgniteModel<T,X>	IgniteModel.combine(IgniteModel<T,W> other, BiFunction<V,W,X> combiner) Combines this model with other model via specified combiner

Uses of IgniteModel in org.apache.ignite.ml.clustering.gmm

Classes in org.apache.ignite.ml.clustering.gmm that implement IgniteModel

Modifier and Type	Class and Description
class	GmmModel Gaussian Mixture Model.

Uses of IgniteModel in org.apache.ignite.ml.clustering.kmeans

Classes in org.apache.ignite.ml.clustering.kmeans with type parameters of type IgniteModel

Modifier and Type	Interface and Description
interface	Clusterer<P,M extends IgniteModel> Base interface for clusterers.

Subinterfaces of IgniteModel in org.apache.ignite.ml.clustering.kmeans

Modifier and Type	Interface and Description
interface	ClusterizationModel<P,V> Base interface for all clusterization models.

Classes in org.apache.ignite.ml.clustering.kmeans that implement IgniteModel

Modifier and Type	Class and Description
class	KMeansModel This class encapsulates result of clusterization by KMeans algorithm.

Uses of IgniteModel in org.apache.ignite.ml.composition

Classes in org.apache.ignite.ml.composition that implement IgniteModel

Modifier and Type	Class and Description
class	ModelOnFeaturesSubspace Model trained on a features subspace with mapping from original features space to subspace.
class	ModelsComposition Model consisting of several models and prediction aggregation strategy.

Methods in org.apache.ignite.ml.composition with type parameters of type IgniteModel

Modifier and Type	Method and Description
static <I,O,M extends IgniteModel<I,O>,L> DatasetTrainer<IgniteModel<I,O>,L>	CompositionUtils.unsafeCoerce(DatasetTrainer<? extends M,L> trainer) Perform blurring of model type of given trainer to IgniteModel<I, O>, where I, O are input and output types of original model.

Methods in org.apache.ignite.ml.composition that return IgniteModel

Modifier and Type	Method and Description
IgniteModel<Vector,Double>	ModelOnFeaturesSubspace.getMdl() Returns model.

Methods in org.apache.ignite.ml.composition that return types with arguments of type IgniteModel

Modifier and Type	Method and Description
List<IgniteModel<Vector,Double>>	ModelsComposition.getModels() Returns containing models.
List<IgniteModel<Vector,Double>>	ModelsCompositionFormat.models()
static <I,O,M extends IgniteModel<I,O>,L> DatasetTrainer<IgniteModel<I,O>,L>	CompositionUtils.unsafeCoerce(DatasetTrainer<? extends M,L> trainer) Perform blurring of model type of given trainer to IgniteModel<I, O>, where I, O are input and output types of original model.

Constructor parameters in org.apache.ignite.ml.composition with type arguments of type IgniteModel

Constructor and Description
ModelsComposition(List<? extends IgniteModel<Vector,Double>> models, PredictionsAggregator predictionsAggregator) Constructs a new instance of composition of models.
ModelsCompositionFormat(List<IgniteModel<Vector,Double>> models, PredictionsAggregator predictionsAggregator) Creates an instance of ModelsCompositionFormat.

Uses of IgniteModel in org.apache.ignite.ml.composition.bagging

Classes in org.apache.ignite.ml.composition.bagging that implement IgniteModel

Modifier and Type	Class and Description
class	BaggedModel This class represents model produced by BaggedTrainer.

Constructor parameters in org.apache.ignite.ml.composition.bagging with type arguments of type IgniteModel

Constructor and Description
BaggedTrainer(DatasetTrainer<? extends IgniteModel,L> tr, PredictionsAggregator aggregator, int ensembleSize, double subsampleRatio, int featuresVectorSize, int featureSubspaceDim) Construct instance of this class with given parameters.

Uses of IgniteModel in org.apache.ignite.ml.composition.boosting

Classes in org.apache.ignite.ml.composition.boosting that implement IgniteModel

Modifier and Type	Class and Description
static class	GDBTrainer.GDBModel GDB model.

Fields in org.apache.ignite.ml.composition.boosting with type parameters of type IgniteModel

Modifier and Type	Field and Description
protected IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>>	GDBLearningStrategy.baseMdlTrainerBuilder Base model trainer builder.

Methods in org.apache.ignite.ml.composition.boosting that return types with arguments of type IgniteModel

Modifier and Type	Method and Description
<K,V> List<IgniteModel<Vector,Double>>	GDBLearningStrategy.learnModels(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Implementation of gradient boosting iterations.
<K,V> List<IgniteModel<Vector,Double>>	GDBLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

Method parameters in org.apache.ignite.ml.composition.boosting with type arguments of type IgniteModel

Modifier and Type	Method and Description
GDBLearningStrategy	GDBLearningStrategy.withBaseModelTrainerBuilder(IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>> buildBaseMdlTrainer) Sets base model builder.

Constructor parameters in org.apache.ignite.ml.composition.boosting with type arguments of type IgniteModel

Constructor and Description
GDBModel(List<? extends IgniteModel<Vector,Double>> models, WeightedPredictionsAggregator predictionsAggregator, IgniteFunction<Double,Double> internalToExternalLblMapping) Creates an instance of GDBModel.

Uses of IgniteModel in org.apache.ignite.ml.composition.combinators.parallel

Classes in org.apache.ignite.ml.composition.combinators.parallel that implement IgniteModel

Modifier and Type	Class and Description
class	ModelsParallelComposition<I,O> Parallel composition of models.

Methods in org.apache.ignite.ml.composition.combinators.parallel with type parameters of type IgniteModel

Modifier and Type	Method and Description
static <I,O,M extends IgniteModel<I,O>,T extends DatasetTrainer<M,L>,L> TrainersParallelComposition<I,O,L>	TrainersParallelComposition.of(List<T> trainers) Create parallel composition of trainers contained in a given list.

Methods in org.apache.ignite.ml.composition.combinators.parallel that return IgniteModel

Modifier and Type	Method and Description
<K,V> IgniteModel<I,List<O>>	TrainersParallelComposition.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> IgniteModel<I,List<O>>	TrainersParallelComposition.update(IgniteModel<I,List<O>> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected <K,V> IgniteModel<I,List<O>>	TrainersParallelComposition.updateModel(IgniteModel<I,List<O>> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

Methods in org.apache.ignite.ml.composition.combinators.parallel that return types with arguments of type IgniteModel

Modifier and Type	Method and Description
List<IgniteModel<I,O>>	ModelsParallelComposition.submodels() List of submodels constituting this model.

Methods in org.apache.ignite.ml.composition.combinators.parallel with parameters of type IgniteModel

Modifier and Type	Method and Description
boolean	TrainersParallelComposition.isUpdateable(IgniteModel<I,List<O>> mdl) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(M) and DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>) in this class we explicitly override update method.
<K,V> IgniteModel<I,List<O>>	TrainersParallelComposition.update(IgniteModel<I,List<O>> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected <K,V> IgniteModel<I,List<O>>	TrainersParallelComposition.updateModel(IgniteModel<I,List<O>> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

Constructor parameters in org.apache.ignite.ml.composition.combinators.parallel with type arguments of type IgniteModel

Constructor and Description
ModelsParallelComposition(List<IgniteModel<I,O>> submodels) Construc an instance of this class from list of submodels.

Uses of IgniteModel in org.apache.ignite.ml.composition.combinators.sequential

Classes in org.apache.ignite.ml.composition.combinators.sequential that implement IgniteModel

Modifier and Type	Class and Description
class	ModelsSequentialComposition<I,O1,O2> Sequential composition of models.

Fields in org.apache.ignite.ml.composition.combinators.sequential with type parameters of type IgniteModel

Modifier and Type	Field and Description
protected IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>>	TrainersSequentialComposition.datasetMapping Dataset mapping.

Methods in org.apache.ignite.ml.composition.combinators.sequential that return IgniteModel

Modifier and Type	Method and Description
IgniteModel<I,O1>	ModelsSequentialComposition.firstModel() Get first model.
IgniteModel<O1,O2>	ModelsSequentialComposition.secondModel() Get second model.

Methods in org.apache.ignite.ml.composition.combinators.sequential that return types with arguments of type IgniteModel

Modifier and Type	Method and Description
DatasetTrainer<IgniteModel<I,O2>,L>	TrainersSequentialComposition.unsafeSimplyTyped() Performs coersion of this trainer to DatasetTrainer<IgniteModel<I, O2>, L>.

Method parameters in org.apache.ignite.ml.composition.combinators.sequential with type arguments of type IgniteModel

Modifier and Type	Method and Description
static <I,O,L> TrainersSequentialComposition<I,O,O,L>	TrainersSequentialComposition.ofSame(DatasetTrainer<? extends IgniteModel<I,O>,L> tr, IgniteBiFunction<Integer,? super IgniteModel<I,O>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping, IgniteBiPredicate<Integer,IgniteModel<I,O>> shouldStop, IgniteFunction<O,I> out2In) Construct sequential composition of same trainers.
static <I,O,L> TrainersSequentialComposition<I,O,O,L>	TrainersSequentialComposition.ofSame(DatasetTrainer<? extends IgniteModel<I,O>,L> tr, IgniteBiFunction<Integer,? super IgniteModel<I,O>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping, IgniteBiPredicate<Integer,IgniteModel<I,O>> shouldStop, IgniteFunction<O,I> out2In) Construct sequential composition of same trainers.
static <I,O,L> TrainersSequentialComposition<I,O,O,L>	TrainersSequentialComposition.ofSame(DatasetTrainer<? extends IgniteModel<I,O>,L> tr, IgniteBiFunction<Integer,? super IgniteModel<I,O>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping, IgniteBiPredicate<Integer,IgniteModel<I,O>> shouldStop, IgniteFunction<O,I> out2In) Construct sequential composition of same trainers.
static <I,O> ModelsSequentialComposition<I,O,O>	ModelsSequentialComposition.ofSame(List<? extends IgniteModel<I,O>> lst, IgniteFunction<O,I> output2Input) Get sequential composition of submodels with same type.

Constructors in org.apache.ignite.ml.composition.combinators.sequential with parameters of type IgniteModel

Constructor and Description
ModelsSequentialComposition(IgniteModel<I,O1> mdl1, IgniteModel<O1,O2> mdl2) Construct instance of this class from two given models.
ModelsSequentialComposition(IgniteModel<I,O1> mdl1, IgniteModel<O1,O2> mdl2) Construct instance of this class from two given models.

Constructor parameters in org.apache.ignite.ml.composition.combinators.sequential with type arguments of type IgniteModel

Constructor and Description
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Create sequential composition of two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Create sequential composition of two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Create sequential composition of two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteFunction<? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Construct sequential composition of given two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteFunction<? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Construct sequential composition of given two trainers.
TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteFunction<? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping) Construct sequential composition of given two trainers.

Uses of IgniteModel in org.apache.ignite.ml.composition.stacking

Classes in org.apache.ignite.ml.composition.stacking with type parameters of type IgniteModel

Modifier and Type	Class and Description
class	SimpleStackedDatasetTrainer<I,O,AM extends IgniteModel<I,O>,L> DatasetTrainer with same type of input and output of submodels.
class	StackedDatasetTrainer<IS,IA,O,AM extends IgniteModel<IA,O>,L> DatasetTrainer encapsulating stacking technique for model training.
class	StackedModel<IS,IA,O,AM extends IgniteModel<IA,O>> This is a wrapper for model produced by StackedDatasetTrainer.
class	StackedVectorDatasetTrainer<O,AM extends IgniteModel<Vector,O>,L> StackedDatasetTrainer with Vector as submodels input and output.

Classes in org.apache.ignite.ml.composition.stacking that implement IgniteModel

Modifier and Type	Class and Description
class	StackedModel<IS,IA,O,AM extends IgniteModel<IA,O>> This is a wrapper for model produced by StackedDatasetTrainer.

Methods in org.apache.ignite.ml.composition.stacking with type parameters of type IgniteModel

Modifier and Type	Method and Description
<M1 extends IgniteModel<Matrix,Matrix>> StackedVectorDatasetTrainer<O,AM,L>	StackedVectorDatasetTrainer.addMatrix2MatrixTrainer(DatasetTrainer<M1,L> trainer) Shortcut for adding trainer Matrix -> Matrix where this trainer is treated as Vector -> Vector, where input Vector is turned into 1 x cols Matrix and output is a first row of output Matrix.
<M1 extends IgniteModel<I,I>> SimpleStackedDatasetTrainer<I,O,AM,L>	SimpleStackedDatasetTrainer.addTrainer(DatasetTrainer<M1,L> trainer) Adds submodel trainer along with converters needed on training and inference stages.
<M1 extends IgniteModel<IS,IA>> StackedDatasetTrainer<IS,IA,O,AM,L>	StackedDatasetTrainer.addTrainer(DatasetTrainer<M1,L> trainer) Adds submodel trainer along with converters needed on training and inference stages.
<M1 extends IgniteModel<Vector,Vector>> StackedVectorDatasetTrainer<O,AM,L>	StackedVectorDatasetTrainer.addTrainer(DatasetTrainer<M1,L> trainer) Adds submodel trainer along with converters needed on training and inference stages.
<M1 extends IgniteModel<Vector,Double>> StackedVectorDatasetTrainer<O,AM,L>	StackedVectorDatasetTrainer.addTrainerWithDoubleOutput(DatasetTrainer<M1,L> trainer) Shortcut for adding trainer Vector -> Double where this trainer is treated as Vector -> Vector, where output Vector is constructed by wrapping double value.

Constructor parameters in org.apache.ignite.ml.composition.stacking with type arguments of type IgniteModel

Constructor and Description
StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter, List<DatasetTrainer<IgniteModel<IS,IA>,L>> submodelsTrainers, IgniteFunction<Vector,IS> vector2SubmodelInputConverter, IgniteFunction<IA,Vector> submodelOutput2VectorConverter) Create instance of this class.

Uses of IgniteModel in org.apache.ignite.ml.environment.logging

Methods in org.apache.ignite.ml.environment.logging that return IgniteModel

Modifier and Type	Method and Description
<K,V> IgniteModel<K,V>	CustomMLLogger.log(MLLogger.VerboseLevel verboseLevel, IgniteModel<K,V> mdl) Log model according to toString method.
<K,V> IgniteModel<K,V>	ConsoleLogger.log(MLLogger.VerboseLevel verboseLevel, IgniteModel<K,V> mdl) Log model according to toString method.
<K,V> IgniteModel<K,V>	NoOpLogger.log(MLLogger.VerboseLevel verboseLevel, IgniteModel<K,V> mdl) Log model according to toString method.
<K,V> IgniteModel<K,V>	MLLogger.log(MLLogger.VerboseLevel verboseLevel, IgniteModel<K,V> mdl) Log model according to toString method.

Methods in org.apache.ignite.ml.environment.logging with parameters of type IgniteModel

Modifier and Type	Method and Description
<K,V> IgniteModel<K,V>	CustomMLLogger.log(MLLogger.VerboseLevel verboseLevel, IgniteModel<K,V> mdl) Log model according to toString method.
<K,V> IgniteModel<K,V>	ConsoleLogger.log(MLLogger.VerboseLevel verboseLevel, IgniteModel<K,V> mdl) Log model according to toString method.
<K,V> IgniteModel<K,V>	NoOpLogger.log(MLLogger.VerboseLevel verboseLevel, IgniteModel<K,V> mdl) Log model according to toString method.
<K,V> IgniteModel<K,V>	MLLogger.log(MLLogger.VerboseLevel verboseLevel, IgniteModel<K,V> mdl) Log model according to toString method.

Uses of IgniteModel in org.apache.ignite.ml.inference

Methods in org.apache.ignite.ml.inference with parameters of type IgniteModel

Modifier and Type	Method and Description
static <I extends Serializable,O extends Serializable> void	IgniteModelStorageUtil.saveModel(Ignite ignite, IgniteModel<I,O> mdl, String name) Saved specified model with specified name.

Uses of IgniteModel in org.apache.ignite.ml.inference.parser

Methods in org.apache.ignite.ml.inference.parser that return IgniteModel

Modifier and Type	Method and Description
IgniteModel<I,O>	IgniteModelParser.parse(byte[] mdl) Accepts serialized model represented by byte array, parses it and returns Model.

Uses of IgniteModel in org.apache.ignite.ml.knn

Classes in org.apache.ignite.ml.knn that implement IgniteModel

Modifier and Type	Class and Description
class	KNNModel<L> KNN model build on top of distribtued spatial indices.
class	NNClassificationModel Common methods and fields for all kNN and aNN models to predict label based on neighbours' labels.

Uses of IgniteModel in org.apache.ignite.ml.knn.ann

Classes in org.apache.ignite.ml.knn.ann that implement IgniteModel

Modifier and Type	Class and Description
class	ANNClassificationModel ANN model to predict labels in multi-class classification task.

Uses of IgniteModel in org.apache.ignite.ml.knn.classification

Classes in org.apache.ignite.ml.knn.classification that implement IgniteModel

Modifier and Type	Class and Description
class	KNNClassificationModel KNN classification model.

Uses of IgniteModel in org.apache.ignite.ml.knn.regression

Classes in org.apache.ignite.ml.knn.regression that implement IgniteModel

Modifier and Type	Class and Description
class	KNNRegressionModel KNN regression model.

Uses of IgniteModel in org.apache.ignite.ml.multiclass

Classes in org.apache.ignite.ml.multiclass with type parameters of type IgniteModel

Modifier and Type	Class and Description
class	MultiClassModel<M extends IgniteModel<Vector,Double>> Base class for multi-classification model for set of classifiers.
class	OneVsRestTrainer<M extends IgniteModel<Vector,Double>> This is a common heuristic trainer for multi-class labeled models.

Classes in org.apache.ignite.ml.multiclass that implement IgniteModel

Modifier and Type	Class and Description
class	MultiClassModel<M extends IgniteModel<Vector,Double>> Base class for multi-classification model for set of classifiers.

Uses of IgniteModel in org.apache.ignite.ml.naivebayes.discrete

Classes in org.apache.ignite.ml.naivebayes.discrete that implement IgniteModel

Modifier and Type	Class and Description
class	DiscreteNaiveBayesModel Discrete naive Bayes model which predicts result value y belongs to a class C_k, k in [0..K] as {@code p(C_k,y) =x_1*p_k1^x *...

Uses of IgniteModel in org.apache.ignite.ml.naivebayes.gaussian

Classes in org.apache.ignite.ml.naivebayes.gaussian that implement IgniteModel

Modifier and Type	Class and Description
class	GaussianNaiveBayesModel Simple naive Bayes model which predicts result value y belongs to a class C_k, k in [0..K] as {@code p(C_k,y) = p(C_k)*p(y_1,C_k) *...

Uses of IgniteModel in org.apache.ignite.ml.nn

Classes in org.apache.ignite.ml.nn that implement IgniteModel

Modifier and Type	Class and Description
class	MultilayerPerceptron Class encapsulating logic of multilayer perceptron.

Uses of IgniteModel in org.apache.ignite.ml.optimization

Subinterfaces of IgniteModel in org.apache.ignite.ml.optimization

Modifier and Type	Interface and Description
interface	SmoothParametrized<M extends Parametrized<M>> Interface for models which are smooth functions of their parameters.

Uses of IgniteModel in org.apache.ignite.ml.pipeline

Classes in org.apache.ignite.ml.pipeline that implement IgniteModel

Modifier and Type	Class and Description
class	PipelineMdl<K,V> Wraps the model produced by Pipeline.

Methods in org.apache.ignite.ml.pipeline that return IgniteModel

Modifier and Type	Method and Description
IgniteModel<Vector,Double>	PipelineMdl.getInternalMdl()

Methods in org.apache.ignite.ml.pipeline with parameters of type IgniteModel

Modifier and Type	Method and Description
PipelineMdl<K,V>	PipelineMdl.withInternalMdl(IgniteModel<Vector,Double> internalMdl)

Uses of IgniteModel in org.apache.ignite.ml.recommendation

Classes in org.apache.ignite.ml.recommendation that implement IgniteModel

Modifier and Type	Class and Description
class	RecommendationModel<O extends Serializable,S extends Serializable> Recommendation model that predicts rating for ObjectSubjectPair.

Uses of IgniteModel in org.apache.ignite.ml.regressions.linear

Classes in org.apache.ignite.ml.regressions.linear that implement IgniteModel

Modifier and Type	Class and Description
class	LinearRegressionModel Simple linear regression model which predicts result value Y as a linear combination of input variables: Y = weights * X + intercept.

Uses of IgniteModel in org.apache.ignite.ml.regressions.logistic

Classes in org.apache.ignite.ml.regressions.logistic that implement IgniteModel

Modifier and Type	Class and Description
class	LogisticRegressionModel Logistic regression (logit model) is a generalized linear model used for binomial regression.

Uses of IgniteModel in org.apache.ignite.ml.selection.cv

Classes in org.apache.ignite.ml.selection.cv with type parameters of type IgniteModel

Modifier and Type	Class and Description
class	AbstractCrossValidation<M extends IgniteModel<Vector,L>,L,K,V> Cross validation score calculator.
class	CrossValidation<M extends IgniteModel<Vector,L>,L,K,V> Cross validation score calculator.
class	DebugCrossValidation<M extends IgniteModel<Vector,L>,L,K,V> Cross validation score calculator.

Uses of IgniteModel in org.apache.ignite.ml.selection.scoring.cursor

Constructors in org.apache.ignite.ml.selection.scoring.cursor with parameters of type IgniteModel

Constructor and Description
CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl) Constructs a new instance of cache based truth with prediction cursor.
CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl) Constructs a new instance of cache based truth with prediction cursor.
LocalLabelPairCursor(Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl) Constructs a new instance of local truth with prediction cursor.

Uses of IgniteModel in org.apache.ignite.ml.selection.scoring.evaluator

Methods in org.apache.ignite.ml.selection.scoring.evaluator with parameters of type IgniteModel

Modifier and Type	Method and Description
static <K,V> BinaryClassificationMetricValues	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the given metrics on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,L> mdl, Preprocessor<K,V> preprocessor, Metric<L> metric) Computes the given metric on the given cache.
static <K,V> BinaryClassificationMetricValues	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the given metrics on the given cache.
static <L,K,V> double	Evaluator.evaluate(IgniteCache<K,V> dataCache, IgniteModel<Vector,L> mdl, Preprocessor<K,V> preprocessor, Metric<L> metric) Computes the given metric on the given cache.
static <K,V> BinaryClassificationMetricValues	Evaluator.evaluate(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the given metrics on the given cache.
static <L,K,V> double	Evaluator.evaluate(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,L> mdl, Preprocessor<K,V> preprocessor, Metric<L> metric) Computes the given metric on the given cache.
static <K,V> BinaryClassificationMetricValues	Evaluator.evaluate(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the given metrics on the given cache.
static <L,K,V> double	Evaluator.evaluate(Map<K,V> dataCache, IgniteModel<Vector,L> mdl, Preprocessor<K,V> preprocessor, Metric<L> metric) Computes the given metric on the given cache.
static <K,V> RegressionMetricValues	Evaluator.evaluateRegression(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor) Computes the regression metrics on the given cache.

Uses of IgniteModel in org.apache.ignite.ml.svm

Classes in org.apache.ignite.ml.svm that implement IgniteModel

Modifier and Type	Class and Description
class	SVMLinearClassificationModel Base class for SVM linear classification model.

Uses of IgniteModel in org.apache.ignite.ml.trainers

Classes in org.apache.ignite.ml.trainers with type parameters of type IgniteModel

Modifier and Type	Class and Description
class	AdaptableDatasetModel<I,O,IW,OW,M extends IgniteModel<IW,OW>> Model which is composition of form before `andThen` inner Mdl `andThen` after.
class	AdaptableDatasetTrainer<I,O,IW,OW,M extends IgniteModel<IW,OW>,L> Type used to adapt input and output types of wrapped DatasetTrainer.
class	DatasetTrainer<M extends IgniteModel,L> Interface for trainers.
class	MultiLabelDatasetTrainer<M extends IgniteModel> Interface for trainers that trains on dataset with multiple label per object.
class	SingleLabelDatasetTrainer<M extends IgniteModel> Interface for trainers that trains on dataset with singe label per object.

Classes in org.apache.ignite.ml.trainers that implement IgniteModel

Modifier and Type	Class and Description
class	AdaptableDatasetModel<I,O,IW,OW,M extends IgniteModel<IW,OW>> Model which is composition of form before `andThen` inner Mdl `andThen` after.

Methods in org.apache.ignite.ml.trainers with type parameters of type IgniteModel

Modifier and Type	Method and Description
<O1,M1 extends IgniteModel<O,O1>> TrainersSequentialComposition<I,O,O1,L>	AdaptableDatasetTrainer.andThen(DatasetTrainer<M1,L> tr, IgniteFunction<AdaptableDatasetModel<I,O,IW,OW,M>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMappingProducer) Create a TrainersSequentialComposition of whis trainer and specified trainer.
static <M extends IgniteModel<Vector,Double>,L> BaggedTrainer<L>	TrainerTransformers.makeBagged(DatasetTrainer<M,L> trainer, int ensembleSize, double subsampleRatio, int featureVectorSize, int featuresSubspaceDim, PredictionsAggregator aggregator) Add bagging logic to a given trainer.
static <I,O,M extends IgniteModel<I,O>,L> AdaptableDatasetTrainer<I,O,I,O,M,L>	AdaptableDatasetTrainer.of(DatasetTrainer<M,L> wrapped) Construct instance of this class from a given DatasetTrainer.
<M1 extends IgniteModel<IW,OW>> AdaptableDatasetModel<I,O,IW,OW,M1>	AdaptableDatasetModel.withInnerModel(M1 mdl) Create new instance of this class with changed inner model.

Methods in org.apache.ignite.ml.trainers that return types with arguments of type IgniteModel

Modifier and Type	Method and Description
static <I,L> DatasetTrainer<IgniteModel<I,I>,L>	DatasetTrainer.identityTrainer() Returns the trainer which returns identity model.

Methods in org.apache.ignite.ml.trainers with parameters of type IgniteModel

Modifier and Type	Method and Description
<O1> AdaptableDatasetModel<I,O1,IW,OW,M>	AdaptableDatasetModel.andThen(IgniteModel<O,O1> after) Get a composition model of the form x -> after(mdl(x)).

Method parameters in org.apache.ignite.ml.trainers with type arguments of type IgniteModel

Modifier and Type	Method and Description
static <L> BaggedTrainer<L>	TrainerTransformers.makeBagged(DatasetTrainer<? extends IgniteModel,L> trainer, int ensembleSize, double subsampleRatio, PredictionsAggregator aggregator) Add bagging logic to a given trainer.

Uses of IgniteModel in org.apache.ignite.ml.tree

Subinterfaces of IgniteModel in org.apache.ignite.ml.tree

Modifier and Type	Interface and Description
interface	DecisionTreeNode Base interface for decision tree nodes.

Classes in org.apache.ignite.ml.tree that implement IgniteModel

Modifier and Type	Class and Description
class	DecisionTreeConditionalNode Decision tree conditional (non-leaf) node.
class	DecisionTreeLeafNode Decision tree leaf node which contains value.

Uses of IgniteModel in org.apache.ignite.ml.tree.boosting

Methods in org.apache.ignite.ml.tree.boosting that return types with arguments of type IgniteModel

Modifier and Type	Method and Description
<K,V> List<IgniteModel<Vector,Double>>	GDBOnTreesLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

Uses of IgniteModel in org.apache.ignite.ml.tree.randomforest.data

Classes in org.apache.ignite.ml.tree.randomforest.data that implement IgniteModel

Modifier and Type	Class and Description
class	TreeNode Decision tree node class.
class	TreeRoot Tree root class.

Uses of IgniteModel in org.apache.ignite.ml.xgboost

Classes in org.apache.ignite.ml.xgboost that implement IgniteModel

Modifier and Type	Class and Description
class	XGModelComposition XGBoost model composition.