All Classes
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All Classes Interface Summary Class Summary Enum Summary Class Description Address Employee address.AlgorithmSpecificDatasetExample Example that shows how to implement your own algorithm (gradient descent trainer for linear regression) which uses dataset as an underlying infrastructure.ANNClassificationExample Run ANN multi-class classification trainer (ANNClassificationTrainer) over distributed dataset.ANNClassificationExportImportExample Run ANN multi-class classification trainer (ANNClassificationTrainer) over distributed dataset.BaggedLogisticRegressionSGDTrainerExample This example shows how bagging technique may be applied to arbitrary trainer.BinarizationExample Example that shows how to use binarization preprocessor to binarize data.BostonHousePricesPredictionExample Example of using Linear Regression model in Apache Ignite for house prices prediction.CacheAffinityExample This example demonstrates the simplest code that populates the distributed cache and co-locates simple closure execution with each key.CacheApiExample This example demonstrates some of the cache rich API capabilities.CacheAsyncApiExample This example demonstrates some of the cache rich API capabilities.CacheAutoStoreExample Demonstrates usage of cache with underlying persistent store configured.CacheBasedDatasetExample Example that shows how to create dataset based on an existing Ignite Cache and then use it to calculatemeanandstdvalues as well ascovarianceandcorrelationmatrices.CacheClientBinaryPutGetExample This example demonstrates use of binary objects with Ignite cache.CacheClientBinaryQueryExample This example demonstrates use of binary objects with cache queries.CacheConfig Configuration for the streaming cache to store the stream of words.CacheContinuousAsyncQueryExample This examples demonstrates asynchronous continuous query API.CacheContinuousQueryExample This examples demonstrates continuous query API.CacheContinuousQueryWithTransformerExample This example demonstrates how to use continuous queries together with the transformer APIs.CacheDataStreamerExample Demonstrates how cache can be populated with data utilizingIgniteDataStreamerAPI.CacheEntryProcessorExample This example demonstrates the simplest code that populates the distributed cache and co-locates simple closure execution with each key.CacheEventsExample This examples demonstrates events API.CacheJdbcPersonStore Example ofCacheStoreimplementation that uses JDBC transaction with cache transactions and mapsLongtoPerson.CacheJdbcStoreExample Demonstrates usage of cache with underlying persistent store configured.CacheLoadOnlyStoreExample Example of how to load data from CSV file usingCacheLoadOnlyStoreAdapter.CachePutGetExample This example demonstrates very basic operations on cache, such as 'put' and 'get'.CacheQueryExample Cache queries example.CacheStarSchemaExample CacheTransactionExample Demonstrates how to use cache transactions.CatboostClassificationModelParserExample This example demonstrates how to import Catboost model and use imported model for distributed inference in Apache Ignite.CatboostRegressionModelParserExample This example demonstrates how to import Catboost model and use imported model for distributed inference in Apache Ignite.ClientKubernetesPutGetExample Demonstrates how to use Ignite thin client within the Kubernetes cluster using KubernetesConnectionConfiguration.ClientPutGetExample Demonstrates how to use Ignite thin client for basic put/get cache operations.ClusterGroupExample Demonstrates new functional APIs.CompoundNaiveBayesExample Run naive Compound Bayes classification model based on Nnaive Bayes classifier algorithm (GaussianNaiveBayesTrainer)and Discrete naive Bayes classifier algorithm (DiscreteNaiveBayesTrainer) over distributed cache.CompoundNaiveBayesExportImportExample Run naive Compound Bayes classification model based on Nnaive Bayes classifier algorithm (GaussianNaiveBayesTrainer)and Discrete naive Bayes classifier algorithm (DiscreteNaiveBayesTrainer) over distributed cache.ComputeAsyncExample Demonstrates a simple use ofIgniteRunnable.ComputeBroadcastExample Demonstrates broadcasting computations within cluster.ComputeCallableExample Demonstrates using ofIgniteCallablejob execution on the cluster.ComputeClientBinaryTaskExecutionExample This example demonstrates use of binary objects with task execution.ComputeClientTask Task that is used forComputeClientBinaryTaskExecutionExampleand similar examples in .NET and C++.ComputeClosureExample Demonstrates a simple use of Ignite with reduce closure.ComputeContinuousMapperExample Demonstrates usage of continuous mapper.ComputeFailoverExample Demonstrates the usage of checkpoints in Ignite.ComputeFailoverNodeStartup Starts up an empty node with checkpoint-enabled configuration.ComputeFibonacciContinuationExample This example demonstrates how to use continuation feature of Ignite by performing the distributed recursive calculation of'Fibonacci'numbers on the cluster.ComputeReducerExample Demonstrates a simple use of Ignite with reduce closure.ComputeRunnableExample Demonstrates a simple use ofIgniteRunnable.ComputeTaskMapExample Demonstrates a simple use of Ignite withComputeTaskAdapter.ComputeTaskSplitExample Demonstrates a simple use of Ignite withComputeTaskSplitAdapter.Credit This class provides a simple model for a credit contract (or a loan).CreditRiskExample Monte-Carlo example.CreditRiskManager This class abstracts out the calculation of risk for a credit portfolio.CrossValidationExample CustomersClusterizationExample Example of using KMeans clusterization to determine the optimal count of clusters in data.DataRegionsExample This example demonstrates how to tweak particular settings of Apache Ignite page memory usingDataStorageConfigurationand set up several data regions for different caches withDataRegionConfiguration.DatasetHelper Common utility code used in some ML examples to report some statistic metrics of the dataset.DbH2ServerStartup Start H2 database TCP server in order to access sample in-memory database from other processes.DecisionTreeClassificationExportImportExample Example of using distributedDecisionTreeClassificationTrainer.DecisionTreeClassificationTrainerExample Example of using distributedDecisionTreeClassificationTrainer.DecisionTreeClassificationTrainerSQLInferenceExample Example of using distributedDecisionTreeClassificationTraineron a data stored in SQL table and inference made as SQL select query.DecisionTreeClassificationTrainerSQLTableExample Example of using distributedDecisionTreeClassificationTraineron a data stored in SQL table.DecisionTreeFromSparkExample Run Decision Tree model loaded from snappy.parquet file.DecisionTreeRegressionExportImportExample Example of using distributedDecisionTreeRegressionTrainer.DecisionTreeRegressionFromSparkExample Run Decision tree regression model loaded from snappy.parquet file.DecisionTreeRegressionTrainerExample Example of using distributedDecisionTreeRegressionTrainer.DeploymentExample Demonstrates how to explicitly deploy a task.DeploymentExample.ExampleTask Example task used to demonstrate direct task deployment through API.DimProduct Represents a product available for purchase.DimStore Represents a physical store location.DiscreteNaiveBayesExportImportExample Run naive Bayes classification model based on naive Bayes classifier algorithm (DiscreteNaiveBayesTrainer) over distributed cache.DiscreteNaiveBayesTrainerExample Run naive Bayes classification model based on naive Bayes classifier algorithm (DiscreteNaiveBayesTrainer) over distributed cache.Employee This class represents employee object.EmployeeKey This class represents key for employee object.EncoderExample Example that shows how to use String Encoder preprocessor to encode features presented as a strings.EncoderExampleWithNormalization Example that shows how to combine together two preprocessors: String Encoder preprocessor to encode features presented as a strings and Normalizer to normalize data presented as doubles.EncryptedCacheExample This example demonstrates the usage of Apache Ignite Persistent Store.EvaluatorExample Run SVM classification trainer (SVMLinearClassificationTrainer) over distributed dataset.EventsExample Demonstrates event consume API that allows to register event listeners on remote nodes.ExampleNodeStartup Starts up an empty node with example compute configuration.ExamplesUtils FactPurchase Represents a purchase record.FraudDetectionExample Example of using classification algorithms for fraud detection problem.GaussianNaiveBayesExportImportExample Run naive Bayes classification model based on naive Bayes classifier algorithm (GaussianNaiveBayesTrainer) over distributed cache.GaussianNaiveBayesTrainerExample Run naive Bayes classification model based on naive Bayes classifier algorithm (GaussianNaiveBayesTrainer) over distributed cache.GBTFromSparkExample Run Gradient Boosted trees model loaded from snappy.parquet file.GBTRegressionFromSparkExample Run GBT Regression model loaded from snappy.parquet file.GDBOnTreesClassificationExportImportExample Example represents a solution for the task of classification learning based on Gradient Boosting on trees implementation.GDBOnTreesClassificationTrainerExample Example represents a solution for the task of classification learning based on Gradient Boosting on trees implementation.GDBOnTreesRegressionExportImportExample Example represents a solution for the task of regression learning based on Gradient Boosting on trees implementation.GDBOnTreesRegressionTrainerExample Example represents a solution for the task of regression learning based on Gradient Boosting on trees implementation.GmmClusterizationExample Example of using GMM clusterization algorithm.H2OMojoModelParserExample This example demonstrates how to import H2O MOJO model and use imported model for distributed inference in Apache Ignite.IgniteAtomicLongExample Demonstrates a simple usage of distributed atomic long.IgniteAtomicReferenceExample Demonstrates a simple usage of distributed atomic reference.IgniteAtomicSequenceExample Demonstrates a simple usage of distributed atomic sequence.IgniteAtomicStampedExample Demonstrates a simple usage of distributed atomic stamped.IgniteCountDownLatchExample Demonstrates a simple usage of distributed count down latch.IgniteExecutorServiceExample Simple example to demonstrate usage of distributed executor service provided by Ignite.IgniteLockExample This example demonstrates cache based reentrant lock.IgniteModelDistributedInferenceExample This example is based onLinearRegressionLSQRTrainerExample, but to perform inference it uses an approach implemented inorg.apache.ignite.ml.inferencepackage.IgniteQueueExample Ignite cache distributed queue example.IgniteSemaphoreExample This example demonstrates cache based semaphore.IgniteSetExample Ignite cache distributed set example.ImputingExample Example that shows how to use Imputing preprocessor to impute the missing value in the given data.IrisClassificationExample Example of using Knn model in Apache Ignite for iris class predicion.KMeansClusterizationExample Run KMeans clustering algorithm (KMeansTrainer) over distributed dataset.KMeansClusterizationExportImportExample Run KMeans clustering algorithm (KMeansTrainer) over distributed dataset.KMeansFromSparkExample Run KMeans model loaded from snappy.parquet file.KNNClassificationExample Run kNN multi-class classification trainer (KNNClassificationTrainer) over distributed dataset.KNNRegressionExample Run kNN regression trainer (KNNRegressionTrainer) over distributed dataset.LabelEncoderExample Example that shows how to use Label Encoder preprocessor to encode labels presented as a strings.LifecycleExample This example shows how to provide your ownLifecycleBeanimplementation to be able to hook into Ignite lifecycle.LifecycleExample.LifecycleExampleBean SimpleLifecycleBeanimplementation that outputs event type when it is occurred.LinearRegressionExportImportExample Run linear regression model based on LSQR algorithm (LinearRegressionLSQRTrainer) over cached dataset.LinearRegressionFromSparkExample Run linear regression model loaded from snappy.parquet file.LinearRegressionLSQRTrainerExample Run linear regression model based on LSQR algorithm (LinearRegressionLSQRTrainer) over cached dataset.LinearRegressionLSQRTrainerWithMinMaxScalerExample Run linear regression model based on LSQR algorithm (LinearRegressionLSQRTrainer) over cached dataset that was created using a minmaxscaling preprocessor (MinMaxScalerTrainer,MinMaxScalerPreprocessor).LinearRegressionSGDTrainerExample Run linear regression model based on based on stochastic gradient descent algorithm (LinearRegressionSGDTrainer) over cached dataset.LogisticRegressionExportImportExample Run logistic regression model based on stochastic gradient descent algorithm (LogisticRegressionSGDTrainer) over distributed cache.LogisticRegressionSGDTrainerExample Run logistic regression model based on stochastic gradient descent algorithm (LogisticRegressionSGDTrainer) over distributed cache.LogRegFromSparkExample Run logistic regression model loaded from snappy.parquet file.LogRegFromSparkThroughPMMLExample Run logistic regression model loaded from PMML file.MaxAbsScalerExample Example that shows how to use MaxAbsScaler preprocessor to scale the given data.MemcacheRestExample This example shows how to use Memcache client for manipulating Ignite cache.MemcacheRestExampleNodeStartup Starts up an empty node with cache configuration that contains default cache.MessagingExample Example that demonstrates how to exchange messages between nodes.MessagingPingPongExample Demonstrates simple message exchange between local and remote nodes.MessagingPingPongListenActorExample Demonstrates messaging withMessagingListenActorconvenience adapter.MinMaxScalerExample Example that shows how to use MinMaxScaler preprocessor to scale the given data.MLExamplesCommonArgs Some common arguments for examples in ML module.MLPTrainerExample Example of using distributedMultilayerPerceptron.MLSandboxDatasets The names of popular datasets used in examples.ModelStorageExample This example demonstrates how to work withModelStorage.MovieLensExample Example of recommendation system based on MovieLens dataset (see https://grouplens.org/datasets/movielens/).MovieLensSQLExample Example of recommendation system based on MovieLens dataset (see https://grouplens.org/datasets/movielens/) and SQL.NormalizationExample Example that shows how to use normalization preprocessor to normalize each vector in the given data.OneVsRestClassificationExample Run One-vs-Rest multi-class classification trainer (OneVsRestTrainer) parametrized by binary SVM classifier (SVMLinearClassificationTrainer) over distributed dataset to build two models: one with min-max scaling and one without min-max scaling.OpenCensusMetricsExporterExample This example demonstrates usage of the `ignite-opencensus` integration module.Organization This class represents organization object.OrganizationType Organization type enum.ParametricVectorGeneratorExample Examples of usingParametricVectorGeneratorfor generating two dimensional data.PersistentStoreExample This example demonstrates the usage of Apache Ignite Persistent Store.PersistentStoreExampleNodeStartup Person Person model.Person Person class.QueryWords Periodically query popular numbers from the streaming cache.RandomForestClassificationExample Example represents a solution for the task of wine classification based on a Random Forest implementation for multi-classification.RandomForestClassificationExportImportExample Example represents a solution for the task of wine classification based on a Random Forest implementation for multi-classification.RandomForestFromSparkExample Run Random Forest model loaded from snappy.parquet file.RandomForestRegressionExample Example represents a solution for the task of price predictions for houses in Boston based on a Random Forest implementation for regression.RandomForestRegressionExportImportExample Example represents a solution for the task of price predictions for houses in Boston based on a Random Forest implementation for regression.RandomForestRegressionFromSparkExample Run Random Forest regression model loaded from snappy.parquet file.RegressionMetricExample Run kNN regression trainer (KNNRegressionTrainer) over distributed dataset.SandboxMLCache Common utility code used in some ML examples to set up test cache.ServiceMiddlewareExample The example shows how to add a middleware layer for distributed services in Ignite.ServicesExample Example that demonstrates how to deploy distributed services in Ignite.SimpleMapService<K,V> Simple map service.SimpleMapServiceImpl<K,V> Simple service which utilizes Ignite cache as a mechanism to provide distributedSimpleMapServicefunctionality.SpringBeanExample Demonstrates a simple use of Ignite configured with Spring.SqlDdlExample Example to showcase DDL capabilities of Ignite's SQL engine.SqlDmlExample Example to showcase DML capabilities of Ignite's SQL engine.SqlJdbcCopyExample This example demonstrates usage of COPY command via Ignite thin JDBC driver.SqlJdbcExample This example demonstrates usage of Ignite JDBC driver.SqlQueriesExample SQL queries example with the usage of Java SQL API.StandardGeneratorsExample Examples of using standard dataset generators.StandardScalerExample Example that shows how to use StandardScaler preprocessor to scale the given data.Step_1_Read_and_Learn Usage ofDecisionTreeClassificationTrainerto predict death in the disaster.Step_10_Bagging MinMaxScalerTrainerandNormalizationTrainerare used in this example due to different values distribution in columns and rows.Step_11_Boosting MinMaxScalerTrainerandNormalizationTrainerare used in this example due to different values distribution in columns and rows.Step_12_Model_Update MinMaxScalerTrainerandNormalizationTrainerare used in this example due to different values distribution in columns and rows.Step_13_RandomSearch To choose the best hyper-parameters the cross-validation withParamGridwill be used in this example.Step_14_Parallel_Brute_Force_Search To choose the best hyper-parameters the cross-validation withParamGridwill be used in this example.Step_15_Parallel_Random_Search To choose the best hyper-parameters the cross-validation withParamGridwill be used in this example.Step_16_Genetic_Programming_Search To choose the best hyper-parameters the cross-validation withParamGridwill be used in this example.Step_17_Parallel_Genetic_Programming_Search To choose the best hyper-parameters the cross-validation withParamGridwill be used in this example.Step_2_Imputing Usage ofImputerTrainerto fill missed data (Double.NaN) values in the chosen columns.Step_3_Categorial Let's add two categorial features "sex", "embarked" to predict more precisely than inStep_1_Read_and_Learn.Step_3_Categorial_with_One_Hot_Encoder Let's add two categorial features "sex", "embarked" to predict more precisely than inStep_1_Read_and_Learn..Step_4_Add_age_fare Add yet two numerical features "age", "fare" to improve our model overStep_3_Categorial.Step_5_Scaling MinMaxScalerTrainerandNormalizationTrainerare used in this example due to different values distribution in columns and rows.Step_5_Scaling_with_Pipeline MinMaxScalerTrainerandNormalizationTrainerare used in this example due to different values distribution in columns and rows.Step_6_KNN Change classification algorithm that was used inStep_5_Scalingfrom decision tree to kNN (KNNClassificationTrainer) because sometimes this can be beneficial.Step_7_Split_train_test The highest accuracy in the previous example (Step_6_KNN) is the result of overfitting.Step_8_CV To choose the best hyper-parameters the cross-validation will be used in this example.Step_8_CV_with_Param_Grid To choose the best hyper-parameters the cross-validation withParamGridwill be used in this example.Step_8_CV_with_Param_Grid_and_pipeline To choose the best hyper-parameters the cross-validation withParamGridwill be used in this example.Step_9_Scaling_With_Stacking MinMaxScalerTrainerandNormalizationTrainerare used in this example due to different values distribution in columns and rows.StreamTransformerExample Stream random numbers into the streaming cache.StreamVisitorExample Stream random numbers into the streaming cache.StreamVisitorExample.Instrument Financial instrument.StreamWords Stream words into Ignite cache.SVMBinaryClassificationExample Run SVM binary-class classification model (SVMLinearClassificationModel) over distributed dataset.SVMExportImportExample Run SVM binary-class classification model (SVMLinearClassificationModel) over distributed dataset.SVMFromSparkExample Run SVM model loaded from snappy.parquet file.TargetEncoderExample Example that shows how to use Target Encoder preprocessor to encode labels presented as a mean target value.TitanicUtils The utility class.TrainingWithBinaryObjectExample Example of support model training with binary objects.TrainingWithCustomPreprocessorsExample This example demonstrates an ability of using custom client classes in cluster in case of absence of these classes on server nodes.TrainTestDatasetSplitterExample Run linear regression model over dataset split on train and test subsets (TrainTestDatasetSplitter).VectorGeneratorFamilyExample Example of using distribution families.VectorGeneratorPrimitivesExample Example of using primitive generators and combiners for generators.WordsSocketStreamerClient Example demonstrates streaming of data from external components into Ignite cache.WordsSocketStreamerServer Example demonstrates streaming of data from external components into Ignite cache.XGBoostModelParserExample This example demonstrates how to import XGBoost model and use imported model for distributed inference in Apache Ignite.