| Package | Description |
|---|---|
| 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.boosting |
Contains Gradient Boosting regression and classification abstract classes
allowing regressor type selecting in child classes.
|
| 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.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.binomial |
Contains binomial logistic regression.
|
| org.apache.ignite.ml.regressions.logistic.multiclass |
Contains multi-class logistic regression.
|
| 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 |
KMeansTrainer
The trainer for KMeans algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BaggingModelTrainer
Abstract trainer implementing bagging logic.
|
| Modifier and Type | Method and Description |
|---|---|
protected abstract DatasetTrainer<? extends Model<Vector,Double>,Double> |
BaggingModelTrainer.buildDatasetTrainerForModel()
Creates trainer specific to ensemble.
|
| 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 Model<Vector,Double>,Double>> |
GDBLearningStrategy.baseMdlTrainerBuilder
Base model trainer builder.
|
| Modifier and Type | Method and Description |
|---|---|
protected abstract @NotNull DatasetTrainer<? extends Model<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 Model<Vector,Double>,Double>> buildBaseMdlTrainer)
Sets base model builder.
|
| 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 algorithm trainer to solve multi-class classification task.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KNNRegressionTrainer
kNN algorithm trainer to solve regression task.
|
| 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 |
|---|---|
Pipeline<K,V,R> |
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<P extends Serializable>
Trainer of the logistic regression model based on stochastic gradient descent algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LogRegressionMultiClassTrainer<P extends Serializable>
All common parameters are shared with bunch of binary classification trainers.
|
| Modifier and Type | Method and Description |
|---|---|
double[] |
CrossValidation.score(DatasetTrainer<M,L> trainer,
Metric<L> scoreCalculator,
Ignite ignite,
IgniteCache<K,V> upstreamCache,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
int cv)
Computes cross-validated metrics.
|
double[] |
CrossValidation.score(DatasetTrainer<M,L> trainer,
Metric<L> scoreCalculator,
Ignite ignite,
IgniteCache<K,V> upstreamCache,
IgniteBiPredicate<K,V> filter,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
int cv)
Computes cross-validated metrics.
|
CrossValidationResult |
CrossValidation.score(DatasetTrainer<M,L> trainer,
Metric<L> scoreCalculator,
Ignite ignite,
IgniteCache<K,V> upstreamCache,
IgniteBiPredicate<K,V> filter,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
int cv,
ParamGrid paramGrid)
Computes cross-validated metrics with a passed parameter grid.
|
double[] |
CrossValidation.score(DatasetTrainer<M,L> trainer,
Metric<L> scoreCalculator,
Ignite ignite,
IgniteCache<K,V> upstreamCache,
IgniteBiPredicate<K,V> filter,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
UniformMapper<K,V> mapper,
int cv)
Computes cross-validated metrics.
|
double[] |
CrossValidation.score(DatasetTrainer<M,L> trainer,
Metric<L> scoreCalculator,
Map<K,V> upstreamMap,
IgniteBiPredicate<K,V> filter,
int parts,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
int cv)
Computes cross-validated metrics.
|
double[] |
CrossValidation.score(DatasetTrainer<M,L> trainer,
Metric<L> scoreCalculator,
Map<K,V> upstreamMap,
IgniteBiPredicate<K,V> filter,
int parts,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
UniformMapper<K,V> mapper,
int cv)
Computes cross-validated metrics.
|
double[] |
CrossValidation.score(DatasetTrainer<M,L> trainer,
Metric<L> scoreCalculator,
Map<K,V> upstreamMap,
int parts,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
int cv)
Computes cross-validated metrics.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SVMLinearBinaryClassificationTrainer
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.
|
class |
SVMLinearMultiClassClassificationTrainer
Base class for a soft-margin SVM linear multiclass-classification trainer based on the communication-efficient
distributed dual coordinate ascent algorithm (CoCoA) with hinge-loss function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultiLabelDatasetTrainer<M extends Model>
Interface for trainers that trains on dataset with multiple label per object.
|
class |
SingleLabelDatasetTrainer<M extends Model>
Interface for trainers that trains on dataset with singe label per object.
|
| 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.
|
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Ignite Database and Caching Platform : ver. 2.7.2 Release Date : February 6 2019