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.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.boosting.convergence |
Package contains implementation of convergence checking algorithms for gradient boosting.
|
org.apache.ignite.ml.composition.boosting.convergence.mean |
Contains implementation of convergence checking computer by mean of absolute value of errors in dataset.
|
org.apache.ignite.ml.composition.boosting.convergence.median |
Contains implementation of convergence checking computer by median of medians of errors in dataset.
|
org.apache.ignite.ml.composition.boosting.convergence.simple |
Contains implementation of Stub for convergence checking.
|
org.apache.ignite.ml.composition.combinators.parallel |
Contains parallel combiners (ensembles) 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.dataset |
Base package for machine learning dataset classes.
|
org.apache.ignite.ml.dataset.impl.cache |
Base package for cache based implementation of machine learning dataset.
|
org.apache.ignite.ml.dataset.impl.local |
Base package for local implementation of machine learning dataset.
|
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.utils |
Contains util functionality for kNN algorithms.
|
org.apache.ignite.ml.math.isolve.lsqr |
Contains LSQR algorithm implementation.
|
org.apache.ignite.ml.multiclass |
Contains various multi-classifier models and trainers.
|
org.apache.ignite.ml.naivebayes.compound |
Contains Compound naive Bayes classifier.
|
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.preprocessing |
Base package for machine learning preprocessing classes.
|
org.apache.ignite.ml.preprocessing.binarization |
Contains binarization preprocessor.
|
org.apache.ignite.ml.preprocessing.encoding |
Contains encoding preprocessors.
|
org.apache.ignite.ml.preprocessing.finalization |
Contains vector finalization preprocessor.
|
org.apache.ignite.ml.preprocessing.imputing |
Contains Imputer preprocessor.
|
org.apache.ignite.ml.preprocessing.maxabsscaling |
Contains Max Abs Scaler preprocessor.
|
org.apache.ignite.ml.preprocessing.minmaxscaling |
Contains Min Max Scaler preprocessor.
|
org.apache.ignite.ml.preprocessing.normalization |
Contains Normalizer preprocessor.
|
org.apache.ignite.ml.preprocessing.standardscaling |
Contains Standard scaler preprocessor.
|
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.evaluator |
Package for model evaluator classes.
|
org.apache.ignite.ml.sql |
Contains util classes that help to work with machine learning models in SQL and train models on data from SQL tables.
|
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.
|
org.apache.ignite.ml.util.generators |
Contains utility classes for data streams generation.
|
Modifier and Type | Method and Description |
---|---|
<K,V> GmmModel |
GmmTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
|
protected <K,V> GmmModel |
GmmTrainer.updateModel(GmmModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
<K,V> KMeansModel |
KMeansTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
protected <K,V> KMeansModel |
KMeansTrainer.updateModel(KMeansModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
<K,V> BaggedModel |
BaggedTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
<K,V> BaggedModel |
BaggedTrainer.update(BaggedModel 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> BaggedModel |
BaggedTrainer.updateModel(BaggedModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
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. |
Modifier and Type | Method and Description |
---|---|
protected <V,K,C extends Serializable> |
GDBTrainer.computeInitialValue(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> builder,
Preprocessor<K,V> preprocessor)
Compute mean value of label as first approximation.
|
<K,V> GDBModel |
GDBTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
protected abstract <V,K> boolean |
GDBTrainer.learnLabels(DatasetBuilder<K,V> builder,
Preprocessor<K,V> preprocessor)
Defines unique labels in dataset if need (useful in case of classification).
|
protected <V,K> boolean |
GDBBinaryClassifierTrainer.learnLabels(DatasetBuilder<K,V> builder,
Preprocessor<K,V> preprocessor)
Defines unique labels in dataset if need (useful in case of classification).
|
protected <V,K> boolean |
GDBRegressionTrainer.learnLabels(DatasetBuilder<K,V> builder,
Preprocessor<K,V> preprocessor)
Defines unique labels in dataset if need (useful in case of classification).
|
<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(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.
|
protected <K,V> GDBModel |
GDBTrainer.updateModel(GDBModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
abstract <K,V> ConvergenceChecker<K,V> |
ConvergenceCheckerFactory.create(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> vectorizer)
Create an instance of ConvergenceChecker.
|
boolean |
ConvergenceChecker.isConverged(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
ModelsComposition currMdl)
Checks convergency on dataset.
|
Constructor and Description |
---|
ConvergenceChecker(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor,
double precision)
Constructs an instance of ConvergenceChecker.
|
Modifier and Type | Method and Description |
---|---|
<K,V> ConvergenceChecker<K,V> |
MeanAbsValueConvergenceCheckerFactory.create(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Create an instance of ConvergenceChecker.
|
Constructor and Description |
---|
MeanAbsValueConvergenceChecker(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor,
double precision)
Creates an instance of MeanAbsValueConvergenceChecker.
|
Modifier and Type | Method and Description |
---|---|
<K,V> ConvergenceChecker<K,V> |
MedianOfMedianConvergenceCheckerFactory.create(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Create an instance of ConvergenceChecker.
|
Constructor and Description |
---|
MedianOfMedianConvergenceChecker(long sampleSize,
IgniteFunction<Double,Double> lblMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor,
double precision)
Creates an instance of MedianOfMedianConvergenceChecker.
|
Modifier and Type | Method and Description |
---|---|
<K,V> ConvergenceChecker<K,V> |
ConvergenceCheckerStubFactory.create(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Create an instance of ConvergenceChecker.
|
boolean |
ConvergenceCheckerStub.isConverged(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
ModelsComposition currMdl)
Checks convergency on dataset.
|
Constructor and Description |
---|
ConvergenceCheckerStub(long sampleSize,
IgniteFunction externalLbToInternalMapping,
Loss loss,
DatasetBuilder datasetBuilder,
Preprocessor<K,V> preprocessor,
double precision)
Creates an instance of ConvergenceCheckerStub.
|
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. |
Modifier and Type | Method and Description |
---|---|
<K,V> ModelsSequentialComposition<I,O1,O2> |
TrainersSequentialComposition.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
<K,V> ModelsSequentialComposition<I,O1,O2> |
TrainersSequentialComposition.update(ModelsSequentialComposition<I,O1,O2> 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> ModelsSequentialComposition<I,O1,O2> |
TrainersSequentialComposition.updateModel(ModelsSequentialComposition<I,O1,O2> 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. |
Modifier and Type | Method and Description |
---|---|
<K,V> StackedModel<IS,IA,O,AM> |
StackedDatasetTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
<K,V> StackedModel<IS,IA,O,AM> |
StackedDatasetTrainer.update(StackedModel<IS,IA,O,AM> 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> StackedModel<IS,IA,O,AM> |
StackedDatasetTrainer.updateModel(StackedModel<IS,IA,O,AM> 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. |
Modifier and Type | Method and Description |
---|---|
DatasetBuilder<K,V> |
DatasetBuilder.withFilter(IgniteBiPredicate<K,V> filterToAdd)
Returns new instance of DatasetBuilder using conjunction of internal filter and
filterToAdd . |
DatasetBuilder<K,V> |
DatasetBuilder.withUpstreamTransformer(UpstreamTransformerBuilder builder)
Returns new instance of
DatasetBuilder with new UpstreamTransformerBuilder added
to chain of upstream transformer builders. |
Modifier and Type | Method and Description |
---|---|
static <K,V,C extends Serializable,D extends AutoCloseable> |
DatasetFactory.create(DatasetBuilder<K,V> datasetBuilder,
LearningEnvironmentBuilder envBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
PartitionDataBuilder<K,V,C,D> partDataBuilder,
LearningEnvironment environment)
Creates a new instance of distributed dataset using the specified
partCtxBuilder and partDataBuilder . |
static <K,V,C extends Serializable,D extends AutoCloseable> |
DatasetFactory.create(DatasetBuilder<K,V> datasetBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
PartitionDataBuilder<K,V,C,D> partDataBuilder)
Creates a new instance of distributed dataset using the specified
partCtxBuilder and partDataBuilder . |
static <K,V,C extends Serializable,CO extends Serializable> |
DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder,
LearningEnvironmentBuilder envBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
Preprocessor<K,V> featureExtractor)
Creates a new instance of distributed
SimpleDataset using the specified partCtxBuilder and featureExtractor . |
static <K,V,CO extends Serializable> |
DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder,
LearningEnvironmentBuilder envBuilder,
Preprocessor<K,V> featureExtractor)
Creates a new instance of distributed
SimpleDataset using the specified featureExtractor . |
static <K,V,C extends Serializable,CO extends Serializable> |
DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder,
LearningEnvironmentBuilder envBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
Preprocessor<K,V> vectorizer)
Creates a new instance of distributed
SimpleLabeledDataset using the specified partCtxBuilder ,
featureExtractor and lbExtractor . |
static <K,V,CO extends Serializable> |
DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder,
LearningEnvironmentBuilder envBuilder,
Preprocessor<K,V> vectorizer)
Creates a new instance of distributed
SimpleLabeledDataset using the specified featureExtractor
and lbExtractor . |
Modifier and Type | Class and Description |
---|---|
class |
CacheBasedDatasetBuilder<K,V>
A dataset builder that makes
CacheBasedDataset . |
Modifier and Type | Method and Description |
---|---|
DatasetBuilder<K,V> |
CacheBasedDatasetBuilder.withFilter(IgniteBiPredicate<K,V> filterToAdd)
Returns new instance of DatasetBuilder using conjunction of internal filter and
filterToAdd . |
DatasetBuilder<K,V> |
CacheBasedDatasetBuilder.withUpstreamTransformer(UpstreamTransformerBuilder builder)
Returns new instance of
DatasetBuilder with new UpstreamTransformerBuilder added
to chain of upstream transformer builders. |
Modifier and Type | Class and Description |
---|---|
class |
LocalDatasetBuilder<K,V>
A dataset builder that makes
LocalDataset . |
Modifier and Type | Method and Description |
---|---|
DatasetBuilder<K,V> |
LocalDatasetBuilder.withFilter(IgniteBiPredicate<K,V> filterToAdd)
Returns new instance of DatasetBuilder using conjunction of internal filter and
filterToAdd . |
DatasetBuilder<K,V> |
LocalDatasetBuilder.withUpstreamTransformer(UpstreamTransformerBuilder builder)
Returns new instance of
DatasetBuilder with new UpstreamTransformerBuilder added
to chain of upstream transformer builders. |
Modifier and Type | Method and Description |
---|---|
protected <K,V> M |
KNNTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
protected <K,V> M |
KNNTrainer.updateModel(M mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
<K,V> ANNClassificationModel |
ANNClassificationTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
|
protected <K,V> ANNClassificationModel |
ANNClassificationTrainer.updateModel(ANNClassificationModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
static <K,V,C extends Serializable> |
KNNUtils.buildDataset(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> vectorizer)
Builds dataset.
|
Constructor and Description |
---|
LSQROnHeap(DatasetBuilder<K,V> datasetBuilder,
LearningEnvironmentBuilder envBuilder,
PartitionDataBuilder<K,V,LSQRPartitionContext,SimpleLabeledDatasetData> partDataBuilder,
LearningEnvironment localLearningEnv)
Constructs a new instance of OnHeap LSQR algorithm implementation.
|
Modifier and Type | Method and Description |
---|---|
<K,V> MultiClassModel<M> |
OneVsRestTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
|
protected <K,V> MultiClassModel<M> |
OneVsRestTrainer.updateModel(MultiClassModel<M> newMdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
<K,V> CompoundNaiveBayesModel |
CompoundNaiveBayesTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
|
protected <K,V> CompoundNaiveBayesModel |
CompoundNaiveBayesTrainer.updateModel(CompoundNaiveBayesModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
<K,V> DiscreteNaiveBayesModel |
DiscreteNaiveBayesTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
|
protected <K,V> DiscreteNaiveBayesModel |
DiscreteNaiveBayesTrainer.updateModel(DiscreteNaiveBayesModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
<K,V> GaussianNaiveBayesModel |
GaussianNaiveBayesTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
|
protected <K,V> GaussianNaiveBayesModel |
GaussianNaiveBayesTrainer.updateModel(GaussianNaiveBayesModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
<K,V> MultilayerPerceptron |
MLPTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
|
protected <K,V> MultilayerPerceptron |
MLPTrainer.updateModel(MultilayerPerceptron lastLearnedMdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
PipelineMdl<K,V> |
Pipeline.fit(DatasetBuilder datasetBuilder)
Fits the pipeline to the input dataset builder.
|
Modifier and Type | Method and Description |
---|---|
default Preprocessor<K,V> |
PreprocessingTrainer.fit(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Preprocessor<K,V> |
PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Modifier and Type | Method and Description |
---|---|
BinarizationPreprocessor<K,V> |
BinarizationTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Modifier and Type | Method and Description |
---|---|
EncoderPreprocessor<K,V> |
EncoderTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Modifier and Type | Method and Description |
---|---|
VectorFinalizationPreprocessor<K,V> |
VectorFinalizationTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Modifier and Type | Method and Description |
---|---|
ImputerPreprocessor<K,V> |
ImputerTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Modifier and Type | Method and Description |
---|---|
MaxAbsScalerPreprocessor<K,V> |
MaxAbsScalerTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Modifier and Type | Method and Description |
---|---|
MinMaxScalerPreprocessor<K,V> |
MinMaxScalerTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Modifier and Type | Method and Description |
---|---|
NormalizationPreprocessor<K,V> |
NormalizationTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Modifier and Type | Method and Description |
---|---|
StandardScalerPreprocessor<K,V> |
StandardScalerTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
Modifier and Type | Method and Description |
---|---|
<K,O extends Serializable,S extends Serializable> |
RecommendationTrainer.fit(DatasetBuilder<K,? extends ObjectSubjectRatingTriplet<O,S>> datasetBuilder)
Fits prediction model.
|
RecommendationModel<Serializable,Serializable> |
RecommendationTrainer.fit(DatasetBuilder<Object,BinaryObject> datasetBuilder,
String objFieldName,
String subjFieldName,
String ratingFieldName)
Fits prediction model on a data stored in binary format.
|
<K,O extends Serializable,S extends Serializable> |
RecommendationTrainer.update(DatasetBuilder<K,? extends ObjectSubjectRatingTriplet<O,S>> datasetBuilder,
RecommendationModel<O,S> mdl)
Fits prediction model.
|
RecommendationModel<Serializable,Serializable> |
RecommendationTrainer.update(DatasetBuilder<Object,BinaryObject> datasetBuilder,
String objFieldName,
String subjFieldName,
String ratingFieldName,
RecommendationModel<Serializable,Serializable> mdl)
Updates prediction model on a data stored in binary format.
|
Modifier and Type | Method and Description |
---|---|
<K,V> LinearRegressionModel |
LinearRegressionLSQRTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
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<K,V> LinearRegressionModel |
LinearRegressionSGDTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
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protected <K,V> LinearRegressionModel |
LinearRegressionLSQRTrainer.updateModel(LinearRegressionModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
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protected <K,V> LinearRegressionModel |
LinearRegressionSGDTrainer.updateModel(LinearRegressionModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
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Modifier and Type | Method and Description |
---|---|
<K,V> LogisticRegressionModel |
LogisticRegressionSGDTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
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protected <K,V> LogisticRegressionModel |
LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
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Modifier and Type | Method and Description |
---|---|
protected double[] |
AbstractCrossValidation.score(Function<IgniteBiPredicate<K,V>,DatasetBuilder<K,V>> datasetBuilderSupplier)
Computes cross-validated metrics.
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protected double[] |
AbstractCrossValidation.scorePipeline(Function<IgniteBiPredicate<K,V>,DatasetBuilder<K,V>> datasetBuilderSupplier)
Computes cross-validated metrics.
|
Modifier and Type | Method and Description |
---|---|
static <K,V> EvaluationResult |
Evaluator.evaluate(DatasetBuilder<K,V> datasetBuilder,
IgniteModel<Vector,Double> mdl,
Preprocessor<K,V> preprocessor,
Metric... metrics)
Evaluate model.
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Modifier and Type | Class and Description |
---|---|
class |
SqlDatasetBuilder
Subclass of
CacheBasedDatasetBuilder that should be used to work with SQL tables. |
Modifier and Type | Method and Description |
---|---|
<K,V> SVMLinearClassificationModel |
SVMLinearClassificationTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
protected <K,V> SVMLinearClassificationModel |
SVMLinearClassificationTrainer.updateModel(SVMLinearClassificationModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Gets state of model in arguments, update in according to new data and return new model.
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Modifier and Type | Method and Description |
---|---|
<K,V> M |
DatasetTrainer.fit(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
<K,V> M |
DatasetTrainer.fit(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor,
LearningEnvironment learningEnvironment)
Trains model based on the specified data.
|
<K,V> AdaptableDatasetModel<I,O,IW,OW,M> |
AdaptableDatasetTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
|
protected abstract <K,V> M |
DatasetTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
<K,V> M |
DatasetTrainer.update(M 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> AdaptableDatasetModel<I,O,IW,OW,M> |
AdaptableDatasetTrainer.updateModel(AdaptableDatasetModel<I,O,IW,OW,M> mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
|
protected abstract <K,V> M |
DatasetTrainer.updateModel(M mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
<K,V> DecisionTreeModel |
DecisionTreeTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
protected <K,V> DecisionTreeModel |
DecisionTreeTrainer.updateModel(DecisionTreeModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains new model based on dataset because there is no valid approach to update decision trees.
|
Modifier and Type | Method and Description |
---|---|
<K,V> List<IgniteModel<Vector,Double>> |
GDBOnTreesLearningStrategy.update(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.
|
Modifier and Type | Method and Description |
---|---|
<K,V> RandomForestModel |
RandomForestTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
protected <K,V> RandomForestModel |
RandomForestTrainer.updateModel(RandomForestModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
default DatasetBuilder<Vector,Double> |
DataStreamGenerator.asDatasetBuilder(int datasetSize,
IgniteBiPredicate<Vector,Double> filter,
int partitions)
Convert first N values from stream to
DatasetBuilder . |
default DatasetBuilder<Vector,Double> |
DataStreamGenerator.asDatasetBuilder(int datasetSize,
IgniteBiPredicate<Vector,Double> filter,
int partitions,
UpstreamTransformerBuilder upstreamTransformerBuilder)
Convert first N values from stream to
DatasetBuilder . |
default DatasetBuilder<Vector,Double> |
DataStreamGenerator.asDatasetBuilder(int datasetSize,
int partitions)
Convert first N values from stream to
DatasetBuilder . |
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Ignite Database and Caching Platform : ver. 2.12.0 Release Date : January 10 2022