Package | Description |
---|---|
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 convergency checking algorithms for gradient boosting.
|
org.apache.ignite.ml.composition.boosting.convergence.simple |
Contains implementation of Stub for convergence checking.
|
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.cache.util |
Contains util classes used in cache based implementation of dataset.
|
org.apache.ignite.ml.dataset.impl.local |
Base package for local implementation of machine learning dataset.
|
org.apache.ignite.ml.environment |
Package contains environment utils for ML 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.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.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.selection.cv |
Root package for cross-validation 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 | Method and Description |
---|---|
KMeansTrainer |
KMeansTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Method and Description |
---|---|
BaggedTrainer<L> |
BaggedTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Field and Description |
---|---|
protected LearningEnvironmentBuilder |
GDBLearningStrategy.envBuilder
Learning environment builder.
|
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.
|
GDBLearningStrategy |
GDBLearningStrategy.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Sets learning environment.
|
GDBBinaryClassifierOnTreesTrainer |
GDBBinaryClassifierTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
GDBRegressionTrainer |
GDBRegressionTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
GDBTrainer |
GDBTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Method and Description |
---|---|
boolean |
ConvergenceChecker.isConverged(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
ModelsComposition currMdl)
Checks convergency on dataset.
|
Modifier and Type | Method and Description |
---|---|
boolean |
ConvergenceCheckerStub.isConverged(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
ModelsComposition currMdl)
Checks convergency on dataset.
|
Modifier and Type | Method and Description |
---|---|
StackedVectorDatasetTrainer<O,AM,L> |
StackedVectorDatasetTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
StackedDatasetTrainer<IS,IA,O,AM,L> |
StackedDatasetTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
SimpleStackedDatasetTrainer<I,O,AM,L> |
SimpleStackedDatasetTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Method and Description |
---|---|
<C extends Serializable,D extends AutoCloseable> |
DatasetBuilder.build(LearningEnvironmentBuilder envBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
PartitionDataBuilder<K,V,C,D> partDataBuilder,
LearningEnvironment localLearningEnv)
Constructs a new instance of
Dataset that includes allocation required data structures and
initialization of context part of partitions. |
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(Ignite ignite,
IgniteCache<K,V> upstreamCache,
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(Map<K,V> upstreamMap,
LearningEnvironmentBuilder envBuilder,
int partitions,
PartitionContextBuilder<K,V,C> partCtxBuilder,
PartitionDataBuilder<K,V,C,D> partDataBuilder,
LearningEnvironment environment)
Creates a new instance of local 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.createSimpleDataset(Ignite ignite,
IgniteCache<K,V> upstreamCache,
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(Ignite ignite,
IgniteCache<K,V> upstreamCache,
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.createSimpleDataset(Map<K,V> upstreamMap,
int partitions,
LearningEnvironmentBuilder envBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
Preprocessor<K,V> featureExtractor)
Creates a new instance of local
SimpleDataset using the specified partCtxBuilder and featureExtractor . |
static <K,V,CO extends Serializable> |
DatasetFactory.createSimpleDataset(Map<K,V> upstreamMap,
int partitions,
LearningEnvironmentBuilder envBuilder,
Preprocessor<K,V> featureExtractor)
Creates a new instance of local
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 . |
static <K,V,C extends Serializable,CO extends Serializable> |
DatasetFactory.createSimpleLabeledDataset(Ignite ignite,
IgniteCache<K,V> upstreamCache,
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(Ignite ignite,
LearningEnvironmentBuilder envBuilder,
IgniteCache<K,V> upstreamCache,
Preprocessor<K,V> vectorizer)
Creates a new instance of distributed
SimpleLabeledDataset using the specified featureExtractor
and lbExtractor . |
static <K,V,C extends Serializable,CO extends Serializable> |
DatasetFactory.createSimpleLabeledDataset(Map<K,V> upstreamMap,
int partitions,
LearningEnvironmentBuilder envBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
Preprocessor<K,V> vectorizer)
Creates a new instance of local
SimpleLabeledDataset using the specified partCtxBuilder , featureExtractor and lbExtractor . |
static <K,V,CO extends Serializable> |
DatasetFactory.createSimpleLabeledDataset(Map<K,V> upstreamMap,
LearningEnvironmentBuilder envBuilder,
int partitions,
Preprocessor<K,V> vectorizer)
Creates a new instance of local
SimpleLabeledDataset using the specified featureExtractor and
lbExtractor . |
Modifier and Type | Method and Description |
---|---|
<C extends Serializable,D extends AutoCloseable> |
CacheBasedDatasetBuilder.build(LearningEnvironmentBuilder envBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
PartitionDataBuilder<K,V,C,D> partDataBuilder,
LearningEnvironment localLearningEnv)
Constructs a new instance of
Dataset that includes allocation required data structures and
initialization of context part of partitions. |
Constructor and Description |
---|
CacheBasedDataset(Ignite ignite,
IgniteCache<K,V> upstreamCache,
IgniteBiPredicate<K,V> filter,
UpstreamTransformerBuilder upstreamTransformerBuilder,
IgniteCache<Integer,C> datasetCache,
LearningEnvironmentBuilder envBuilder,
PartitionDataBuilder<K,V,C,D> partDataBuilder,
UUID datasetId,
boolean upstreamKeepBinary,
LearningEnvironment localLearningEnv,
int retriesCnt)
Constructs a new instance of dataset based on Ignite Cache, which is used as
upstream and as reliable storage for
partition context as well. |
Modifier and Type | Method and Description |
---|---|
static LearningEnvironment |
ComputeUtils.getLearningEnvironment(Ignite ignite,
UUID datasetId,
int part,
LearningEnvironmentBuilder envBuilder)
Gets learning environment for given partition.
|
static <K,V,C extends Serializable> |
ComputeUtils.initContext(Ignite ignite,
String upstreamCacheName,
UpstreamTransformerBuilder transformerBuilder,
IgniteBiPredicate<K,V> filter,
String datasetCacheName,
PartitionContextBuilder<K,V,C> ctxBuilder,
LearningEnvironmentBuilder envBuilder,
int retries,
int interval,
boolean isKeepBinary,
DeployingContext deployingContext)
Initializes partition
context by loading it from a partition upstream . |
Modifier and Type | Method and Description |
---|---|
<C extends Serializable,D extends AutoCloseable> |
LocalDatasetBuilder.build(LearningEnvironmentBuilder envBuilder,
PartitionContextBuilder<K,V,C> partCtxBuilder,
PartitionDataBuilder<K,V,C,D> partDataBuilder,
LearningEnvironment learningEnvironment)
Constructs a new instance of
Dataset that includes allocation required data structures and
initialization of context part of partitions. |
Modifier and Type | Class and Description |
---|---|
class |
DefaultLearningEnvironmentBuilder
Builder for
LearningEnvironment . |
Modifier and Type | Method and Description |
---|---|
static LearningEnvironmentBuilder |
LearningEnvironmentBuilder.defaultBuilder()
Get default
LearningEnvironmentBuilder . |
LearningEnvironmentBuilder |
DefaultLearningEnvironmentBuilder.withDataTtl(long dataTtl)
Specify partition data time-to-live in seconds (-1 for an infinite lifetime).
|
LearningEnvironmentBuilder |
LearningEnvironmentBuilder.withDataTtl(long dataTtl)
Specify partition data time-to-live in seconds (-1 for an infinite lifetime).
|
default <T extends MLLogger.Factory & Serializable> |
LearningEnvironmentBuilder.withLoggingFactory(T loggingFactory)
Specify logging factory.
|
LearningEnvironmentBuilder |
LearningEnvironmentBuilder.withLoggingFactoryDependency(IgniteFunction<Integer,MLLogger.Factory> loggingFactory)
Specify dependency (partition -> logging factory).
|
default <T extends ParallelismStrategy & Serializable> |
LearningEnvironmentBuilder.withParallelismStrategy(T stgy)
Specifies Parallelism Strategy for LearningEnvironment.
|
LearningEnvironmentBuilder |
LearningEnvironmentBuilder.withParallelismStrategyDependency(IgniteFunction<Integer,ParallelismStrategy> stgy)
Specifies dependency (partition -> Parallelism Strategy for LearningEnvironment).
|
default LearningEnvironmentBuilder |
LearningEnvironmentBuilder.withParallelismStrategyType(ParallelismStrategy.Type stgyType)
Specifies Parallelism Strategy Type for LearningEnvironment.
|
LearningEnvironmentBuilder |
LearningEnvironmentBuilder.withParallelismStrategyTypeDependency(IgniteFunction<Integer,ParallelismStrategy.Type> stgyType)
Specifies dependency (partition -> Parallelism Strategy Type for LearningEnvironment).
|
default LearningEnvironmentBuilder |
LearningEnvironmentBuilder.withRandom(Random random)
Specify random numbers generator for learning environment.
|
LearningEnvironmentBuilder |
DefaultLearningEnvironmentBuilder.withRandomDependency(IgniteFunction<Integer,Random> rngSupplier)
Specify dependency (partition -> random numbers generator).
|
LearningEnvironmentBuilder |
LearningEnvironmentBuilder.withRandomDependency(IgniteFunction<Integer,Random> rngSupplier)
Specify dependency (partition -> random numbers generator).
|
default LearningEnvironmentBuilder |
LearningEnvironmentBuilder.withRNGSeed(long seed)
Specify seed for random number generator.
|
LearningEnvironmentBuilder |
DefaultLearningEnvironmentBuilder.withRNGSeedDependency(IgniteFunction<Integer,Long> seed)
Specify dependency (partition -> seed for random number generator).
|
LearningEnvironmentBuilder |
LearningEnvironmentBuilder.withRNGSeedDependency(IgniteFunction<Integer,Long> seed)
Specify dependency (partition -> seed for random number generator).
|
Modifier and Type | Method and Description |
---|---|
ANNClassificationTrainer |
ANNClassificationTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
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 |
---|---|
GaussianNaiveBayesTrainer |
GaussianNaiveBayesTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Method and Description |
---|---|
MLPTrainer<P> |
MLPTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Method and Description |
---|---|
void |
Pipeline.setEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Set learning environment builder.
|
Modifier and Type | Method and Description |
---|---|
Preprocessor<K,V> |
PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
default Preprocessor<K,V> |
PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder,
Ignite ignite,
IgniteCache<K,V> cache,
Preprocessor<K,V> basePreprocessor)
Fits preprocessor.
|
default Preprocessor<K,V> |
PreprocessingTrainer.fit(LearningEnvironmentBuilder envBuilder,
Map<K,V> data,
int parts,
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 |
---|---|
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 |
---|---|
RecommendationTrainer |
RecommendationTrainer.withLearningEnvironmentBuilder(LearningEnvironmentBuilder environmentBuilder)
Set up learning environment builder.
|
Modifier and Type | Field and Description |
---|---|
protected LearningEnvironmentBuilder |
AbstractCrossValidation.envBuilder
Learning environment builder.
|
Modifier and Type | Method and Description |
---|---|
AbstractCrossValidation<M,L,K,V> |
AbstractCrossValidation.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Field and Description |
---|---|
protected LearningEnvironmentBuilder |
DatasetTrainer.envBuilder
Learning environment builder.
|
Modifier and Type | Method and Description |
---|---|
DatasetTrainer<M,L> |
DatasetTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Method and Description |
---|---|
DecisionTree<T> |
DecisionTree.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
DecisionTreeClassificationTrainer |
DecisionTreeClassificationTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
DecisionTreeRegressionTrainer |
DecisionTreeRegressionTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Method and Description |
---|---|
GDBRegressionOnTreesTrainer |
GDBRegressionOnTreesTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
GDBBinaryClassifierOnTreesTrainer |
GDBBinaryClassifierOnTreesTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
Modifier and Type | Method and Description |
---|---|
RandomForestClassifierTrainer |
RandomForestClassifierTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
Changes learning Environment.
|
GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024