Package | Description |
---|---|
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.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.pipeline |
Contains Pipeline API.
|
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.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 |
Contains random forest implementation classes.
|
Modifier and Type | Class and Description |
---|---|
class |
GmmTrainer
Traner for GMM model.
|
Modifier and Type | Class and Description |
---|---|
class |
KMeansTrainer
The trainer for KMeans algorithm.
|
Modifier and Type | Method and Description |
---|---|
static <I,O,M extends 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. |
Modifier and Type | Method and Description |
---|---|
static <I,O,M extends 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. |
Modifier and Type | Class and Description |
---|---|
class |
BaggedTrainer<L>
Trainer encapsulating logic of bootstrap aggregating (bagging).
|
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.
|
Modifier and Type | Class and Description |
---|---|
class |
GDBBinaryClassifierTrainer
Trainer for binary classifier using Gradient Boosting.
|
class |
GDBRegressionTrainer
Trainer for regressor using Gradient Boosting.
|
class |
GDBTrainer
Abstract Gradient Boosting trainer.
|
Modifier and Type | Field and Description |
---|---|
protected IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>> |
GDBLearningStrategy.baseMdlTrainerBuilder
Base model trainer builder.
|
Modifier and Type | Method and Description |
---|---|
protected abstract @NotNull DatasetTrainer<? extends IgniteModel<Vector,Double>,Double> |
GDBTrainer.buildBaseModelTrainer()
Returns regressor model trainer for one step of GDB.
|
Modifier and Type | Method and Description |
---|---|
GDBLearningStrategy |
GDBLearningStrategy.withBaseModelTrainerBuilder(IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>> buildBaseMdlTrainer)
Sets base model builder.
|
Modifier and Type | Class and Description |
---|---|
class |
TrainersParallelComposition<I,O,L>
This class represents a parallel composition of trainers.
|
Modifier and Type | Method and Description |
---|---|
static <I,O,M extends IgniteModel<I,O>,T extends DatasetTrainer<M,L>,L> |
TrainersParallelComposition.of(List<T> trainers)
Create parallel composition of trainers contained in a given list.
|
Modifier and Type | Class and Description |
---|---|
class |
TrainersSequentialComposition<I,O1,O2,L>
Sequential composition of trainers.
|
Modifier and Type | Method and Description |
---|---|
DatasetTrainer<IgniteModel<I,O2>,L> |
TrainersSequentialComposition.unsafeSimplyTyped()
Performs coersion of this trainer to
DatasetTrainer<IgniteModel<I, O2>, L> . |
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.
|
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 |
StackedVectorDatasetTrainer<O,AM extends IgniteModel<Vector,O>,L>
StackedDatasetTrainer with Vector as submodels input and output. |
Modifier and Type | Method and Description |
---|---|
<M1 extends IgniteModel<Matrix,Matrix>> |
StackedVectorDatasetTrainer.addMatrix2MatrixTrainer(DatasetTrainer<M1,L> trainer)
|
<M1 extends IgniteModel<I,I>> |
SimpleStackedDatasetTrainer.addTrainer(DatasetTrainer<M1,L> trainer)
Adds submodel trainer along with converters needed on training and inference stages.
|
<M1 extends IgniteModel<IS,IA>> |
StackedDatasetTrainer.addTrainer(DatasetTrainer<M1,L> trainer)
Adds submodel trainer along with converters needed on training and inference stages.
|
<M1 extends IgniteModel<Vector,Vector>> |
StackedVectorDatasetTrainer.addTrainer(DatasetTrainer<M1,L> trainer)
Adds submodel trainer along with converters needed on training and inference stages.
|
<M1 extends IgniteModel<Vector,Double>> |
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. |
StackedVectorDatasetTrainer<O,AM,L> |
StackedVectorDatasetTrainer.withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)
Specify aggregator trainer.
|
StackedDatasetTrainer<IS,IA,O,AM,L> |
StackedDatasetTrainer.withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)
Specify aggregator trainer.
|
SimpleStackedDatasetTrainer<I,O,AM,L> |
SimpleStackedDatasetTrainer.withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)
Specify aggregator trainer.
|
Constructor and Description |
---|
SimpleStackedDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer,
IgniteBinaryOperator<I> aggregatingInputMerger)
Construct instance of this class.
|
SimpleStackedDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer,
IgniteBinaryOperator<I> aggregatingInputMerger,
IgniteFunction<I,I> submodelInput2AggregatingInputConverter,
IgniteFunction<Vector,I> vector2SubmodelInputConverter,
IgniteFunction<I,Vector> submodelOutput2VectorConverter)
Construct instance of this class.
|
StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer,
IgniteBinaryOperator<IA> aggregatingInputMerger,
IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter)
Constructs instance of this class.
|
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.
|
StackedVectorDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer)
Constructs instance of this class.
|
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.
|
Modifier and Type | Class and Description |
---|---|
class |
KNNTrainer<M extends KNNModel<Double>,Self extends KNNTrainer<M,Self>>
KNN model trader that trains model on top of distribtued spatial indices.
|
Modifier and Type | Class and Description |
---|---|
class |
ANNClassificationTrainer
ANN algorithm trainer to solve multi-class classification task.
|
Modifier and Type | Class and Description |
---|---|
class |
KNNClassificationTrainer
KNN classification model trader that trains model on top of distribtued spatial indices.
|
Modifier and Type | Class and Description |
---|---|
class |
KNNRegressionTrainer
KNN regression model trader that trains model on top of distribtued spatial indices.
|
Modifier and Type | Class and Description |
---|---|
class |
OneVsRestTrainer<M extends IgniteModel<Vector,Double>>
This is a common heuristic trainer for multi-class labeled models.
|
Modifier and Type | Class and Description |
---|---|
class |
DiscreteNaiveBayesTrainer
Trainer for the Discrete naive Bayes classification model.
|
Modifier and Type | Class and Description |
---|---|
class |
GaussianNaiveBayesTrainer
Trainer for the naive Bayes classification model.
|
Modifier and Type | Class and Description |
---|---|
class |
MLPTrainer<P extends Serializable>
Multilayer perceptron trainer based on partition based
Dataset . |
Modifier and Type | Method and Description |
---|---|
DatasetTrainer |
Pipeline.getTrainer()
Returns trainer.
|
Modifier and Type | Method and Description |
---|---|
Pipeline<K,V,C,L> |
Pipeline.addTrainer(DatasetTrainer trainer)
Adds a trainer.
|
Modifier and Type | Class and Description |
---|---|
class |
LinearRegressionLSQRTrainer
Trainer of the linear regression model based on LSQR algorithm.
|
class |
LinearRegressionSGDTrainer<P extends Serializable>
Trainer of the linear regression model based on stochastic gradient descent algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
LogisticRegressionSGDTrainer
Trainer of the logistic regression model based on stochastic gradient descent algorithm.
|
Modifier and Type | Field and Description |
---|---|
protected DatasetTrainer<M,L> |
AbstractCrossValidation.trainer
Trainer.
|
Modifier and Type | Method and Description |
---|---|
AbstractCrossValidation<M,L,K,V> |
AbstractCrossValidation.withTrainer(DatasetTrainer<M,L> trainer) |
Modifier and Type | Class and Description |
---|---|
class |
SVMLinearClassificationTrainer
Base class for a soft-margin SVM linear classification trainer based on the communication-efficient distributed dual
coordinate ascent algorithm (CoCoA) with hinge-loss function.
|
Modifier and Type | Class and Description |
---|---|
class |
AdaptableDatasetTrainer<I,O,IW,OW,M extends IgniteModel<IW,OW>,L>
Type used to adapt input and output types of wrapped
DatasetTrainer . |
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.
|
Modifier and Type | Method and Description |
---|---|
static <I,L> DatasetTrainer<IgniteModel<I,I>,L> |
DatasetTrainer.identityTrainer()
Returns the trainer which returns identity model.
|
<L1> DatasetTrainer<M,L1> |
DatasetTrainer.withConvertedLabels(IgniteFunction<L1,L> new2Old)
Creates
DatasetTrainer with same training logic, but able to accept labels of given new type of labels. |
DatasetTrainer<M,L> |
DatasetTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Method and Description |
---|---|
<O1,M1 extends IgniteModel<O,O1>> |
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 <L> BaggedTrainer<L> |
TrainerTransformers.makeBagged(DatasetTrainer<? extends IgniteModel,L> trainer,
int ensembleSize,
double subsampleRatio,
PredictionsAggregator aggregator)
Add bagging logic to a given trainer.
|
static <M extends IgniteModel<Vector,Double>,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.of(DatasetTrainer<M,L> wrapped)
Construct instance of this class from a given
DatasetTrainer . |
Modifier and Type | Class and Description |
---|---|
class |
DecisionTree<T extends ImpurityMeasure<T>>
Distributed decision tree trainer that allows to fit trees using row-partitioned dataset.
|
class |
DecisionTreeClassificationTrainer
Decision tree classifier based on distributed decision tree trainer that allows to fit trees using row-partitioned
dataset.
|
class |
DecisionTreeRegressionTrainer
Decision tree regressor based on distributed decision tree trainer that allows to fit trees using row-partitioned
dataset.
|
Modifier and Type | Class and Description |
---|---|
class |
GDBBinaryClassifierOnTreesTrainer
Implementation of Gradient Boosting Classifier Trainer on trees.
|
class |
GDBRegressionOnTreesTrainer
Implementation of Gradient Boosting Regression Trainer on trees.
|
Modifier and Type | Class and Description |
---|---|
class |
RandomForestClassifierTrainer
Classifier trainer based on RandomForest algorithm.
|
class |
RandomForestRegressionTrainer
Regression trainer based on RandomForest algorithm.
|
class |
RandomForestTrainer<L,S extends ImpurityComputer<BootstrappedVector,S>,T extends RandomForestTrainer<L,S,T>>
Class represents a realization of Random Forest algorithm.
|
GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024