| 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.composition.boosting.convergence |
Package contains implementation of convergency 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.predictionsaggregator |
Contains classes for several predictions aggregation strategies
working with predictions vector from models ensemble.
|
| org.apache.ignite.ml.dataset |
Base package for machine learning dataset classes.
|
| org.apache.ignite.ml.dataset.feature |
Package for helper classes over features such as
ObjectHistogram or
FeatureMeta. |
| org.apache.ignite.ml.dataset.impl.cache.util |
Contains util classes used in cache based implementation of 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.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.math.functions |
Contains serializable functions for distributed code algebra.
|
| org.apache.ignite.ml.math.primitives.matrix |
Contains matrix related classes.
|
| 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.optimization.updatecalculators |
Contains update calculators.
|
| 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.svm |
Contains main APIs for SVM(support vector machines) algorithms.
|
| org.apache.ignite.ml.tree |
Root package for decision trees.
|
| org.apache.ignite.ml.tree.randomforest.data |
Package contains helper data structures for random forest implementation.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Model<T,V>
Basic interface for all models.
|
| 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 | Class and Description |
|---|---|
static class |
GDBTrainer.GDBModel
GDB model.
|
| Modifier and Type | Field and Description |
|---|---|
protected IgniteFunction<Double,Double> |
GDBLearningStrategy.externalLbToInternalMapping
External label to internal mapping.
|
| Modifier and Type | Method and Description |
|---|---|
GDBLearningStrategy |
GDBLearningStrategy.withExternalLabelToInternal(IgniteFunction<Double,Double> externalLbToInternal)
Sets external to internal label representation mapping.
|
| 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 |
|---|---|
abstract <K,V> ConvergenceChecker<K,V> |
ConvergenceCheckerFactory.create(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor)
Create an instance of ConvergenceChecker.
|
| Constructor and Description |
|---|
ConvergenceChecker(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor,
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,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor)
Create an instance of ConvergenceChecker.
|
| Constructor and Description |
|---|
MeanAbsValueConvergenceChecker(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor,
double precision)
Creates an intance 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,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor)
Create an instance of ConvergenceChecker.
|
| Constructor and Description |
|---|
MedianOfMedianConvergenceChecker(long sampleSize,
IgniteFunction<Double,Double> lblMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
IgniteBiFunction<K,V,Vector> fExtr,
IgniteBiFunction<K,V,Double> lbExtr,
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,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor)
Create an instance of ConvergenceChecker.
|
| Constructor and Description |
|---|
ConvergenceCheckerStub(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
IgniteBiFunction<K,V,Vector> featureExtractor,
IgniteBiFunction<K,V,Double> lbExtractor)
Creates an intance of ConvergenceCheckerStub.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
PredictionsAggregator
Predictions aggregator interface.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MeanValuePredictionsAggregator
Predictions aggregator returning the mean value of predictions.
|
class |
OnMajorityPredictionsAggregator
Predictions aggregator returning the most frequently prediction.
|
class |
WeightedPredictionsAggregator
Predictions aggregator returning weighted plus of predictions.
|
| Modifier and Type | Method and Description |
|---|---|
default <C2 extends Serializable> |
PartitionContextBuilder.andThen(IgniteFunction<C,C2> fun)
Makes a composed partition
context builder that first builds a context and then applies the
specified function on the result. |
default <R> R |
Dataset.compute(IgniteFunction<D,R> map,
IgniteBinaryOperator<R> reduce)
Applies the specified
map function to every partition data in the dataset and then reduces
map results to final result by using the reduce function. |
default <R> R |
Dataset.compute(IgniteFunction<D,R> map,
IgniteBinaryOperator<R> reduce,
R identity)
Applies the specified
map function to every partition data in the dataset and then reduces
map results to final result by using the reduce function. |
default <I extends Dataset<C,D>> |
Dataset.wrap(IgniteFunction<Dataset<C,D>,I> wrapper)
Wraps this dataset into the specified wrapper to introduce new functionality based on
compute and
computeWithCtx methods. |
| Constructor and Description |
|---|
ObjectHistogram(IgniteFunction<T,Integer> bucketMapping,
IgniteFunction<T,Double> mappingToCntr)
Create an instance of ObjectHistogram.
|
ObjectHistogram(IgniteFunction<T,Integer> bucketMapping,
IgniteFunction<T,Double> mappingToCntr)
Create an instance of ObjectHistogram.
|
| Modifier and Type | Method and Description |
|---|---|
static <R> Collection<R> |
ComputeUtils.affinityCallWithRetries(Ignite ignite,
Collection<String> cacheNames,
IgniteFunction<Integer,R> fun,
int retries)
Calls the specified
fun function on all partitions so that is't guaranteed that partitions with the same
index of all specified caches will be placed on the same node and will not be moved before computation is
finished. |
static <R> Collection<R> |
ComputeUtils.affinityCallWithRetries(Ignite ignite,
Collection<String> cacheNames,
IgniteFunction<Integer,R> fun,
int retries,
int interval)
Calls the specified
fun function on all partitions so that is't guaranteed that partitions with the same
index of all specified caches will be placed on the same node and will not be moved before computation is
finished. |
| 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 | Interface and Description |
|---|---|
interface |
IgniteCurriedBiFunction<A,B,T>
Serializable binary function.
|
interface |
IgniteCurriedTriFunction<A,B,C,D>
Serializable curried tri-function.
|
interface |
IgniteDifferentiableVectorToDoubleFunction
Interface for differentiable functions from vector to double.
|
| Modifier and Type | Method and Description |
|---|---|
static <A,B> IgniteFunction<A,IgniteSupplier<B>> |
Functions.outputSupplier(IgniteFunction<A,B> f)
Transform function of form a -> b into a -> (() -> b).
|
| Modifier and Type | Method and Description |
|---|---|
default <V> IgniteBiFunction<T,U,V> |
IgniteBiFunction.andThen(IgniteFunction<? super R,? extends V> after) |
static <A,B extends Comparable<B>> |
Functions.argmin(List<A> args,
IgniteFunction<A,B> f) |
static <A,B> IgniteFunction<A,IgniteSupplier<B>> |
Functions.outputSupplier(IgniteFunction<A,B> f)
Transform function of form a -> b into a -> (() -> b).
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
AbstractMatrix.foldColumns(IgniteFunction<Vector,Double> fun)
Collects the results of applying a given function to all columns in this matrix.
|
Vector |
Matrix.foldColumns(IgniteFunction<Vector,Double> fun)
Collects the results of applying a given function to all columns in this matrix.
|
Vector |
AbstractMatrix.foldRows(IgniteFunction<Vector,Double> fun)
Collects the results of applying a given function to all rows in this matrix.
|
Vector |
Matrix.foldRows(IgniteFunction<Vector,Double> fun)
Collects the results of applying a given function to all rows in this matrix.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultilayerPerceptron
Class encapsulating logic of multilayer perceptron.
|
| Modifier and Type | Method and Description |
|---|---|
IgniteFunction<List<U>,U> |
UpdatesStrategy.allUpdatesReducer()
Get function used to reduce updates from different trainings
(for example, averaging of gradients of all parallel trainings).
|
IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> |
MLPTrainer.getArchSupplier()
Get the multilayer perceptron architecture supplier that defines layers and activators.
|
IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> |
MLPTrainer.getLoss()
Get the loss function to be minimized during the training.
|
IgniteFunction<List<U>,U> |
UpdatesStrategy.locStepUpdatesReducer()
Get function used to reduce updates in one training
(for example, sum all sequential gradient updates to get one gradient update).
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
MultilayerPerceptron.differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss,
Matrix inputsBatch,
Matrix truthBatch)
Compose function in the following way: feed output of this model as input to second argument to loss function.
|
MLPTrainer<P> |
MLPTrainer.withArchSupplier(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier)
Set up the multilayer perceptron architecture supplier that defines layers and activators.
|
MLPTrainer<P> |
MLPTrainer.withLoss(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)
Set up the loss function to be minimized during the training.
|
| Constructor and Description |
|---|
MLPTrainer(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier,
IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss,
UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy,
int maxIterations,
int batchSize,
int locIterations,
long seed)
Constructs a new instance of multilayer perceptron trainer.
|
MLPTrainer(IgniteFunction<Dataset<EmptyContext,SimpleLabeledDatasetData>,MLPArchitecture> archSupplier,
IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss,
UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy,
int maxIterations,
int batchSize,
int locIterations,
long seed)
Constructs a new instance of multilayer perceptron trainer.
|
MLPTrainer(MLPArchitecture arch,
IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss,
UpdatesStrategy<? super MultilayerPerceptron,P> updatesStgy,
int maxIterations,
int batchSize,
int locIterations,
long seed)
Constructs a new instance of multilayer perceptron trainer.
|
UpdatesStrategy(ParameterUpdateCalculator<M,U> updatesCalculator,
IgniteFunction<List<U>,U> locStepUpdatesReducer,
IgniteFunction<List<U>,U> allUpdatesReducer)
Construct instance of this class with given parameters.
|
UpdatesStrategy(ParameterUpdateCalculator<M,U> updatesCalculator,
IgniteFunction<List<U>,U> locStepUpdatesReducer,
IgniteFunction<List<U>,U> allUpdatesReducer)
Construct instance of this class with given parameters.
|
| 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 | Field and Description |
|---|---|
static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> |
LossFunctions.HINGE
Hinge loss function.
|
static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> |
LossFunctions.L1
L1 loss function.
|
static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> |
LossFunctions.L2
L2 loss function.
|
static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> |
LossFunctions.LOG
Log loss function.
|
static IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> |
LossFunctions.MSE
Mean squared error loss function.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
SmoothParametrized.differentiateByParameters(IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss,
Matrix inputsBatch,
Matrix truthBatch)
Compose function in the following way: feed output of this model as input to second argument to loss function.
|
| Modifier and Type | Field and Description |
|---|---|
protected IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> |
SimpleGDUpdateCalculator.loss
Loss function.
|
protected IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> |
RPropUpdateCalculator.loss
Loss function.
|
| Modifier and Type | Method and Description |
|---|---|
P |
ParameterUpdateCalculator.init(M mdl,
IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)
Initializes the update calculator.
|
NesterovParameterUpdate |
NesterovUpdateCalculator.init(M mdl,
IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)
Initializes the update calculator.
|
SimpleGDParameterUpdate |
SimpleGDUpdateCalculator.init(SmoothParametrized mdl,
IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)
Initializes the update calculator.
|
RPropParameterUpdate |
RPropUpdateCalculator.init(SmoothParametrized mdl,
IgniteFunction<Vector,IgniteDifferentiableVectorToDoubleFunction> loss)
Initializes the update calculator.
|
| Modifier and Type | Class and Description |
|---|---|
class |
PipelineMdl<K,V>
Wraps the model produced by
Pipeline. |
| 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 |
SVMLinearBinaryClassificationModel
Base class for SVM linear classification model.
|
class |
SVMLinearMultiClassClassificationModel
Base class for multi-classification model for set of SVM classifiers.
|
| 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 | Class and Description |
|---|---|
class |
TreeNode
Decision tree node class.
|
class |
TreeRoot
Tree root class.
|
| Modifier and Type | Field and Description |
|---|---|
static IgniteFunction<List<FeatureMeta>,Integer> |
FeaturesCountSelectionStrategies.ALL |
static IgniteFunction<List<FeatureMeta>,Integer> |
FeaturesCountSelectionStrategies.LOG2 |
static IgniteFunction<List<FeatureMeta>,Integer> |
FeaturesCountSelectionStrategies.ONE_THIRD |
static IgniteFunction<List<FeatureMeta>,Integer> |
FeaturesCountSelectionStrategies.SQRT |
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Ignite Database and Caching Platform : ver. 2.7.2 Release Date : February 6 2019