| Package | Description |
|---|---|
| org.apache.ignite.ml |
Root ML package.
|
| 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.environment.logging |
Package contains several logging strategy realisations.
|
| 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.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.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.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.
|
| Modifier and Type | Method and Description |
|---|---|
default <X,W> Model<T,X> |
Model.combine(Model<T,W> other,
BiFunction<V,W,X> combiner)
Combines this model with other model via specified combiner
|
| Modifier and Type | Method and Description |
|---|---|
default <X,W> Model<T,X> |
Model.combine(Model<T,W> other,
BiFunction<V,W,X> combiner)
Combines this model with other model via specified combiner
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Clusterer<P,M extends Model>
Base interface for clusterers.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ClusterizationModel<P,V>
Base interface for all clusterization models.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KMeansModel
This class encapsulates result of clusterization by KMeans algorithm.
|
| 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.
|
| Modifier and Type | Method and Description |
|---|---|
Model<Vector,Double> |
ModelOnFeaturesSubspace.getMdl()
Returns model.
|
| Modifier and Type | Method and Description |
|---|---|
protected abstract DatasetTrainer<? extends Model<Vector,Double>,Double> |
BaggingModelTrainer.buildDatasetTrainerForModel()
Creates trainer specific to ensemble.
|
List<Model<Vector,Double>> |
ModelsComposition.getModels()
Returns containing models.
|
List<Model<Vector,Double>> |
ModelsCompositionFormat.models() |
| Constructor and Description |
|---|
ModelsComposition(List<? extends Model<Vector,Double>> models,
PredictionsAggregator predictionsAggregator)
Constructs a new instance of composition of models.
|
ModelsCompositionFormat(List<Model<Vector,Double>> models,
PredictionsAggregator predictionsAggregator)
Creates an instance of ModelsCompositionFormat.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
GDBTrainer.GDBModel
GDB model.
|
| 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 |
|---|---|
<K,V> List<Model<Vector,Double>> |
GDBLearningStrategy.learnModels(DatasetBuilder<K,V> datasetBuilder,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor)
Implementation of gradient boosting iterations.
|
<K,V> List<Model<Vector,Double>> |
GDBLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate,
DatasetBuilder<K,V> datasetBuilder,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor)
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 |
|---|---|
GDBLearningStrategy |
GDBLearningStrategy.withBaseModelTrainerBuilder(IgniteSupplier<DatasetTrainer<? extends Model<Vector,Double>,Double>> buildBaseMdlTrainer)
Sets base model builder.
|
| Constructor and Description |
|---|
GDBModel(List<? extends Model<Vector,Double>> models,
WeightedPredictionsAggregator predictionsAggregator,
IgniteFunction<Double,Double> internalToExternalLblMapping)
Creates an instance of GDBModel.
|
| Modifier and Type | Method and Description |
|---|---|
<K,V> Model<K,V> |
NoOpLogger.log(MLLogger.VerboseLevel verboseLevel,
Model<K,V> mdl)
Log model according to toString method.
|
<K,V> Model<K,V> |
ConsoleLogger.log(MLLogger.VerboseLevel verboseLevel,
Model<K,V> mdl)
Log model according to toString method.
|
<K,V> Model<K,V> |
MLLogger.log(MLLogger.VerboseLevel verboseLevel,
Model<K,V> mdl)
Log model according to toString method.
|
<K,V> Model<K,V> |
CustomMLLogger.log(MLLogger.VerboseLevel verboseLevel,
Model<K,V> mdl)
Log model according to toString method.
|
| Modifier and Type | Method and Description |
|---|---|
<K,V> Model<K,V> |
NoOpLogger.log(MLLogger.VerboseLevel verboseLevel,
Model<K,V> mdl)
Log model according to toString method.
|
<K,V> Model<K,V> |
ConsoleLogger.log(MLLogger.VerboseLevel verboseLevel,
Model<K,V> mdl)
Log model according to toString method.
|
<K,V> Model<K,V> |
MLLogger.log(MLLogger.VerboseLevel verboseLevel,
Model<K,V> mdl)
Log model according to toString method.
|
<K,V> Model<K,V> |
CustomMLLogger.log(MLLogger.VerboseLevel verboseLevel,
Model<K,V> mdl)
Log model according to toString method.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NNClassificationModel
Common methods and fields for all kNN and aNN models
to predict label based on neighbours' labels.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ANNClassificationModel
ANN model to predict labels in multi-class classification task.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KNNClassificationModel
kNN algorithm model to solve multi-class classification task.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KNNRegressionModel
This class provides kNN Multiple Linear Regression or Locally [weighted] regression (Simple and Weighted versions).
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultilayerPerceptron
Class encapsulating logic of multilayer perceptron.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
SmoothParametrized<M extends Parametrized<M>>
Interface for models which are smooth functions of their parameters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
PipelineMdl<K,V>
Wraps the model produced by
Pipeline. |
| Modifier and Type | Method and Description |
|---|---|
Model<Vector,Double> |
PipelineMdl.getInternalMdl() |
| Modifier and Type | Method and Description |
|---|---|
PipelineMdl<K,V> |
PipelineMdl.withInternalMdl(Model<Vector,Double> internalMdl) |
| 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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LogisticRegressionModel
Logistic regression (logit model) is a generalized linear model used for binomial regression.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LogRegressionMultiClassModel
Base class for multi-classification model for set of Logistic Regression classifiers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CrossValidation<M extends Model<Vector,L>,L,K,V>
Cross validation score calculator.
|
| Constructor and Description |
|---|
CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
Model<Vector,L> mdl)
Constructs a new instance of cache based truth with prediction cursor.
|
CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache,
IgniteBiPredicate<K,V> filter,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
Model<Vector,L> mdl)
Constructs a new instance of cache based truth with prediction cursor.
|
LocalLabelPairCursor(Map<K,V> upstreamMap,
IgniteBiPredicate<K,V> filter,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
Model<Vector,L> mdl)
Constructs a new instance of local truth with prediction cursor.
|
| Modifier and Type | Method and Description |
|---|---|
static <L,K,V> double |
Evaluator.evaluate(IgniteCache<K,V> dataCache,
IgniteBiPredicate<K,V> filter,
Model<Vector,L> mdl,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
Accuracy<L> metric)
Computes the given metric on the given cache.
|
static <L,K,V> double |
Evaluator.evaluate(IgniteCache<K,V> dataCache,
Model<Vector,L> mdl,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,L> lbExtractor,
Accuracy<L> metric)
Computes the given metric on the given cache.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SVMLinearBinaryClassificationModel
Base class for SVM linear classification model.
|
class |
SVMLinearMultiClassClassificationModel
Base class for multi-classification model for set of SVM classifiers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DatasetTrainer<M extends Model,L>
Interface for trainers.
|
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 | Interface and Description |
|---|---|
interface |
DecisionTreeNode
Base interface for decision tree nodes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DecisionTreeConditionalNode
Decision tree conditional (non-leaf) node.
|
class |
DecisionTreeLeafNode
Decision tree leaf node which contains value.
|
| Modifier and Type | Method and Description |
|---|---|
<K,V> List<Model<Vector,Double>> |
GDBOnTreesLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate,
DatasetBuilder<K,V> datasetBuilder,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor)
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 | Class and Description |
|---|---|
class |
TreeNode
Decision tree node class.
|
class |
TreeRoot
Tree root class.
|
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Ignite Database and Caching Platform : ver. 2.7.2 Release Date : February 6 2019