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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method protected updateModel(mdl ModelsSequentialComposition<I,O1,O2>, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : ModelsSequentialComposition<I,O1,O2> in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,double[]> to extractor : Vectorizer<K,V,C,double[]> in method protected updateModel(lastLearnedMdl MultilayerPerceptron, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,double[]>) : MultilayerPerceptron in class org.apache.ignite.ml.nn.MLPTrainer |
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Change Parameter Type yExtractor : IgniteBiFunction<K,V,Double> to vectorizer : Vectorizer<K,V,CO,Double> in method public LabelPartitionDataBuilderOnHeap(vectorizer Vectorizer<K,V,CO,Double>) in class org.apache.ignite.ml.structures.partition.LabelPartitionDataBuilderOnHeap |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : ModelsComposition in class org.apache.ignite.ml.composition.boosting.GDBTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : LogisticRegressionModel in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : ANNClassificationModel in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer |
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Change Parameter Type featureExtractor : IgniteBiFunction<K,V,Vector> to featureExtractor : Vectorizer<K,V,CO,?> in method public createSimpleDataset(upstreamMap Map<K,V>, partitions int, envBuilder LearningEnvironmentBuilder, featureExtractor Vectorizer<K,V,CO,?>) : SimpleDataset<EmptyContext> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public update(mdl ModelsSequentialComposition<I,O1,O2>, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : ModelsSequentialComposition<I,O1,O2> in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl ModelsComposition, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : ModelsComposition in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : ModelsSequentialComposition<I,O1,O2> in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : AdaptableDatasetModel<I,O,IW,OW,M> in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : StackedModel<IS,IA,O,AM> in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public update(mdl M, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : M in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : IgniteModel<Vector,Double> in class org.apache.ignite.ml.composition.bagging.BaggingTest.CountTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : GaussianNaiveBayesModel in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl LinearRegressionModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : LinearRegressionModel in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl IgniteModel<Vector,Double>, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : IgniteModel<Vector,Double> in class org.apache.ignite.ml.composition.bagging.BaggingTest.CountTrainer |
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Change Parameter Type featureExtractor : IgniteBiFunction<K,V,Vector> to featureExtractor : Vectorizer<K,V,CO,?> in method public SimpleDatasetDataBuilder(featureExtractor Vectorizer<K,V,CO,?>) in class org.apache.ignite.ml.dataset.primitive.builder.data.SimpleDatasetDataBuilder |
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Change Parameter Type featureExtractor : IgniteBiFunction<K,V,Vector> to featureExtractor : Vectorizer<K,V,CO,?> in method public createSimpleDataset(ignite Ignite, upstreamCache IgniteCache<K,V>, envBuilder LearningEnvironmentBuilder, partCtxBuilder PartitionContextBuilder<K,V,C>, featureExtractor Vectorizer<K,V,CO,?>) : SimpleDataset<C> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : DecisionTreeNode in class org.apache.ignite.ml.tree.DecisionTree |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,CO,Double> in method public DecisionTreeDataBuilder(extractor Vectorizer<K,V,CO,Double>, buildIdx boolean) in class org.apache.ignite.ml.tree.data.DecisionTreeDataBuilder |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(newMdl MultiClassModel<M>, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : MultiClassModel<M> in class org.apache.ignite.ml.multiclass.OneVsRestTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public BootstrappedDatasetBuilder(extractor Vectorizer<K,V,C,Double>, samplesCnt int, subsampleSize double) in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetBuilder |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,double[]> to extractor : Vectorizer<K,V,C,double[]> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,double[]>) : MultilayerPerceptron in class org.apache.ignite.ml.nn.MLPTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : LinearRegressionModel in class org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method protected updateModel(mdl StackedModel<IS,IA,O,AM>, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : StackedModel<IS,IA,O,AM> in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : GmmModel in class org.apache.ignite.ml.clustering.gmm.GmmTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public updateModel(mdl KNNRegressionModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : KNNRegressionModel in class org.apache.ignite.ml.knn.regression.KNNRegressionTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl KMeansModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : KMeansModel in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public update(mdl BaggedModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : BaggedModel in class org.apache.ignite.ml.composition.bagging.BaggedTrainer |
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Change Parameter Type featureExtractor : IgniteBiFunction<K,V,Vector> to featureExtractor : Vectorizer<K,V,CO,?> in method public createSimpleDataset(ignite Ignite, upstreamCache IgniteCache<K,V>, featureExtractor Vectorizer<K,V,CO,?>) : SimpleDataset<EmptyContext> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Change Parameter Type trainingSet : IgniteCache<Integer,Point> to trainingSet : IgniteCache<Integer,LabeledVector<Double>> in method private generatePoints(trainingSet IgniteCache<Integer,LabeledVector<Double>>) : void in class org.apache.ignite.examples.ml.tree.DecisionTreeRegressionTrainerExample |
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Change Parameter Type datasetBuilder : LocalDatasetBuilder<double[],Double> to datasetBuilder : LocalDatasetBuilder<Integer,LabeledVector<Double>> in method public createChecker(factory ConvergenceCheckerFactory, datasetBuilder LocalDatasetBuilder<Integer,LabeledVector<Double>>) : ConvergenceChecker<Integer,LabeledVector<Double>,Integer> in class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceCheckerTest |
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Change Parameter Type externalLbToInternalMapping : IgniteFunction<Double,Double> to externalLbToInternalMapping : IgniteFunction in method public ConvergenceCheckerStub(sampleSize long, externalLbToInternalMapping IgniteFunction, loss Loss, datasetBuilder DatasetBuilder, vectorizer Vectorizer<K,V,C,Double>, precision double) in class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStub |
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Change Parameter Type xExtractor : IgniteBiFunction<K,V,Vector> to vectorizer : Vectorizer<K,V,CO,Double> in method public LabeledDatasetPartitionDataBuilderOnHeap(vectorizer Vectorizer<K,V,CO,Double>) in class org.apache.ignite.ml.structures.partition.LabeledDatasetPartitionDataBuilderOnHeap |
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Change Parameter Type featureExtractor : IgniteBiFunction<K,V,Vector> to featureExtractor : Vectorizer<K,V,CO,?> in method public createSimpleDataset(ignite Ignite, upstreamCache IgniteCache<K,V>, envBuilder LearningEnvironmentBuilder, featureExtractor Vectorizer<K,V,CO,?>) : SimpleDataset<EmptyContext> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Change Parameter Type featureExtractor : IgniteBiFunction<K,V,Vector> to vectorizer : Vectorizer<K,V,C,Double> in method public create(sampleSize long, externalLbToInternalMapping IgniteFunction<Double,Double>, loss Loss, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : ConvergenceChecker<K,V,C> in class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStubFactory |
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Change Parameter Type featureExtractor : IgniteBiFunction<K,V,Vector> to featureExtractor : Vectorizer<K,V,CO,?> in method public createSimpleDataset(datasetBuilder DatasetBuilder<K,V>, envBuilder LearningEnvironmentBuilder, partCtxBuilder PartitionContextBuilder<K,V,C>, featureExtractor Vectorizer<K,V,CO,?>) : SimpleDataset<C> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl LinearRegressionModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : LinearRegressionModel in class org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public Builder(extractor Vectorizer<K,V,C,Double>, countOfComponents int) in class org.apache.ignite.ml.clustering.gmm.GmmPartitionData.Builder |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : KNNRegressionModel in class org.apache.ignite.ml.knn.regression.KNNRegressionTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method protected updateModel(mdl IgniteModel<I,List<O>>, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : IgniteModel<I,List<O>> in class org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to vectorizer : Vectorizer<K,V,C,L> in method public fit(ignite Ignite, cache IgniteCache<K,V>, vectorizer Vectorizer<K,V,C,L>) : M in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method protected updateModel(mdl BaggedModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : BaggedModel in class org.apache.ignite.ml.composition.bagging.BaggedTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : IgniteModel<I,List<O>> in class org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public update(mdl IgniteModel<I,List<O>>, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : IgniteModel<I,List<O>> in class org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl ANNClassificationModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : ANNClassificationModel in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : ModelsComposition in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : KMeansModel in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer |
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Change Parameter Type datasetBuilder : DatasetBuilder<K,V> to datasetBuilder : DatasetBuilder in method public ConvergenceCheckerStub(sampleSize long, externalLbToInternalMapping IgniteFunction, loss Loss, datasetBuilder DatasetBuilder, vectorizer Vectorizer<K,V,C,Double>, precision double) in class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStub |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : MultiClassModel<M> in class org.apache.ignite.ml.multiclass.OneVsRestTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method protected updateModel(mdl AdaptableDatasetModel<I,O,IW,OW,M>, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : AdaptableDatasetModel<I,O,IW,OW,M> in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : BaggedModel in class org.apache.ignite.ml.composition.bagging.BaggedTrainer |
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Change Parameter Type lbExtractor : IgniteBiFunction<K,V,Double> to vectorizer : Vectorizer<K,V,C,Double> in method private extractClassLabels(datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : List<Double> in class org.apache.ignite.ml.multiclass.OneVsRestTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : ModelsSequentialComposition<I,O,O> in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition.SameTrainersSequentialComposition |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : DiscreteNaiveBayesModel in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer |
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Change Parameter Type featureExtractor : IgniteBiFunction<K,V,Vector> to featureExtractor : Vectorizer<K,V,CO,?> in method public createSimpleDataset(upstreamMap Map<K,V>, partitions int, envBuilder LearningEnvironmentBuilder, partCtxBuilder PartitionContextBuilder<K,V,C>, featureExtractor Vectorizer<K,V,CO,?>) : SimpleDataset<C> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl DiscreteNaiveBayesModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : DiscreteNaiveBayesModel in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer |
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Change Parameter Type featureExtractor : IgniteBiFunction<K,V,Vector> to featureExtractor : Vectorizer<K,V,CO,?> in method public createSimpleDataset(datasetBuilder DatasetBuilder<K,V>, envBuilder LearningEnvironmentBuilder, featureExtractor Vectorizer<K,V,CO,?>) : SimpleDataset<EmptyContext> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl SVMLinearClassificationModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : SVMLinearClassificationModel in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public update(mdl StackedModel<IS,IA,O,AM>, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,L>) : StackedModel<IS,IA,O,AM> in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl GmmModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : GmmModel in class org.apache.ignite.ml.clustering.gmm.GmmTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl DecisionTreeNode, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : DecisionTreeNode in class org.apache.ignite.ml.tree.DecisionTree |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : KNNClassificationModel in class org.apache.ignite.ml.knn.classification.KNNClassificationTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : SVMLinearClassificationModel in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : LinearRegressionModel in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl LogisticRegressionModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : LogisticRegressionModel in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl GaussianNaiveBayesModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : GaussianNaiveBayesModel in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl KNNClassificationModel, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : KNNClassificationModel in class org.apache.ignite.ml.knn.classification.KNNClassificationTrainer |
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Change Parameter Type extractor : FeatureLabelExtractor<K,V,Double> to extractor : Vectorizer<K,V,C,Double> in method protected updateModel(mdl ModelsComposition, datasetBuilder DatasetBuilder<K,V>, extractor Vectorizer<K,V,C,Double>) : ModelsComposition in class org.apache.ignite.ml.composition.boosting.GDBTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method public create(sampleSize long, externalLbToInternalMapping IgniteFunction<Double,Double>, loss Loss, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : ConvergenceChecker<K,V,C> in class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceCheckerFactory |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method public create(sampleSize long, externalLbToInternalMapping IgniteFunction<Double,Double>, loss Loss, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : ConvergenceChecker<K,V,C> in class org.apache.ignite.ml.composition.boosting.convergence.median.MedianOfMedianConvergenceCheckerFactory |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method public update(mdlToUpdate GDBTrainer.GDBModel, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : List<IgniteModel<Vector,Double>> in class org.apache.ignite.ml.tree.boosting.GDBOnTreesLearningStrategy |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,double[]>] to vectorizer : Vectorizer<K,V,CO,double[]> in method public createSimpleLabeledDataset(ignite Ignite, envBuilder LearningEnvironmentBuilder, upstreamCache IgniteCache<K,V>, vectorizer Vectorizer<K,V,CO,double[]>) : SimpleLabeledDataset<EmptyContext> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method public buildDataset(envBuilder LearningEnvironmentBuilder, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in class org.apache.ignite.ml.knn.KNNUtils |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method private getCentroids(vectorizer Vectorizer<K,V,C,Double>, datasetBuilder DatasetBuilder<K,V>) : List<Vector> in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,double[]>] to vectorizer : Vectorizer<K,V,CO,double[]> in method public createSimpleLabeledDataset(upstreamMap Map<K,V>, partitions int, envBuilder LearningEnvironmentBuilder, partCtxBuilder PartitionContextBuilder<K,V,C>, vectorizer Vectorizer<K,V,CO,double[]>) : SimpleLabeledDataset<C> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to vectorizer : Vectorizer<K,V,C,L> in method public fit(data Map<K,V>, parts int, vectorizer Vectorizer<K,V,C,L>) : M in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,double[]>] to vectorizer : Vectorizer<K,V,CO,double[]> in method public createSimpleLabeledDataset(datasetBuilder DatasetBuilder<K,V>, envBuilder LearningEnvironmentBuilder, vectorizer Vectorizer<K,V,CO,double[]>) : SimpleLabeledDataset<EmptyContext> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to vectorizer : Vectorizer<K,V,C,L> in method public update(mdl M, data Map<K,V>, parts int, vectorizer Vectorizer<K,V,C,L>) : M in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method public ConvergenceCheckerStub(sampleSize long, externalLbToInternalMapping IgniteFunction, loss Loss, datasetBuilder DatasetBuilder, vectorizer Vectorizer<K,V,C,Double>, precision double) in class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStub |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,double[]>] to vectorizer : Vectorizer<K,V,CO,double[]> in method public createSimpleLabeledDataset(ignite Ignite, upstreamCache IgniteCache<K,V>, envBuilder LearningEnvironmentBuilder, partCtxBuilder PartitionContextBuilder<K,V,C>, vectorizer Vectorizer<K,V,CO,double[]>) : SimpleLabeledDataset<C> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Merge Parameter [fExtr : IgniteBiFunction<K,V,Vector>, lbExtr : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method public MedianOfMedianConvergenceChecker(sampleSize long, lblMapping IgniteFunction<Double,Double>, loss Loss, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>, precision double) in class org.apache.ignite.ml.composition.boosting.convergence.median.MedianOfMedianConvergenceChecker |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to vectorizer : Vectorizer<K,V,C,L> in method public update(mdl M, data Map<K,V>, filter IgniteBiPredicate<K,V>, parts int, vectorizer Vectorizer<K,V,C,L>) : M in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to vectorizer : Vectorizer<K,V,C,L> in method public fit(ignite Ignite, cache IgniteCache<K,V>, filter IgniteBiPredicate<K,V>, vectorizer Vectorizer<K,V,C,L>) : M in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,double[]>] to vectorizer : Vectorizer<K,V,CO,double[]> in method public createSimpleLabeledDataset(upstreamMap Map<K,V>, envBuilder LearningEnvironmentBuilder, partitions int, vectorizer Vectorizer<K,V,CO,double[]>) : SimpleLabeledDataset<EmptyContext> in class org.apache.ignite.ml.dataset.DatasetFactory |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method public MeanAbsValueConvergenceChecker(sampleSize long, externalLbToInternalMapping IgniteFunction<Double,Double>, loss Loss, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>, precision double) in class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceChecker |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method public update(mdlToUpdate GDBTrainer.GDBModel, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : List<IgniteModel<Vector,Double>> in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to vectorizer : Vectorizer<K,V,C,L> in method public update(mdl M, ignite Ignite, cache IgniteCache<K,V>, filter IgniteBiPredicate<K,V>, vectorizer Vectorizer<K,V,C,L>) : M in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to vectorizer : Vectorizer<K,V,C,L> in method public fit(data Map<K,V>, filter IgniteBiPredicate<K,V>, parts int, vectorizer Vectorizer<K,V,C,L>) : M in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method public learnModels(datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : List<IgniteModel<Vector,Double>> in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to vectorizer : Vectorizer<K,V,C,L> in method public update(mdl M, ignite Ignite, cache IgniteCache<K,V>, vectorizer Vectorizer<K,V,C,L>) : M in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to vectorizer : Vectorizer<K,V,C,Double> in method private getCentroidStat(datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>, centers List<Vector>) : CentroidStat in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer |
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Change Variable Type trainingSetCfg : CacheConfiguration<Integer,LabeledPoint> to trainingSetCfg : CacheConfiguration<Integer,LabeledVector<Double>> in method public main(args String...) : void in class org.apache.ignite.examples.ml.selection.cv.CrossValidationExample |
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Change Variable Type extractor : FeatureLabelExtractor<K,V,Void> to extractor : Vectorizer<K,V,C,Void> in method public testRandomNumbersGenerator() : void in class org.apache.ignite.ml.environment.LearningEnvironmentTest |
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Change Variable Type cacheMock : Map<Integer,Double[]> to cacheMock : Map<Integer,double[]> in method protected getCacheMock(vals double[][]) : Map<Integer,double[]> in class org.apache.ignite.ml.common.TrainerTest |
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Change Variable Type trainingSetCfg : CacheConfiguration<Integer,Point> to trainingSetCfg : CacheConfiguration<Integer,LabeledVector<Double>> in method public main(args String...) : void in class org.apache.ignite.examples.ml.tree.DecisionTreeRegressionTrainerExample |
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Change Variable Type trainingSetCfg : CacheConfiguration<Integer,LabeledPoint> to trainingSetCfg : CacheConfiguration<Integer,LabeledVector<Double>> in method public main(args String...) : void in class org.apache.ignite.examples.ml.tree.DecisionTreeClassificationTrainerExample |
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Change Variable Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public constantTrainer(ml M) : DatasetTrainer<M,L> in class org.apache.ignite.ml.TestUtils |
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Change Variable Type scoreCalculator : CrossValidation<DecisionTreeNode,Double,Integer,LabeledPoint> to scoreCalculator : CrossValidation<DecisionTreeNode,Double,Integer,LabeledVector<Double>> in method public main(args String...) : void in class org.apache.ignite.examples.ml.selection.cv.CrossValidationExample |
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Change Variable Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public unsafeCoerce(trainer DatasetTrainer<? extends M,L>) : DatasetTrainer<IgniteModel<I,O>,L> in class org.apache.ignite.ml.composition.CompositionUtils |
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Change Variable Type checker : ConvergenceChecker<double[],Double> to checker : ConvergenceChecker<Integer,LabeledVector<Double>,Integer> in method public testConvergenceChecking() : void in class org.apache.ignite.ml.composition.boosting.convergence.median.MedianOfMedianConvergenceCheckerTest |
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Change Variable Type extractor : FeatureLabelExtractor<K,V,L1> to extractor : Vectorizer<K,V,C,L1> in method public withConvertedLabels(new2Old IgniteFunction<L1,L>) : DatasetTrainer<M,L1> in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Change Variable Type convCheck : ConvergenceChecker<K,V> to convCheck : ConvergenceChecker<K,V,C> in method public update(mdlToUpdate GDBTrainer.GDBModel, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : List<IgniteModel<Vector,Double>> in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy |
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Change Variable Type cacheMock : Map<Integer,Double[]> to cacheMock : Map<Integer,double[]> in method public testNaiveBaggingLogRegression() : void in class org.apache.ignite.ml.composition.bagging.BaggingTest |
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Change Variable Type sample : Map<double[],Double> to sample : Map<Integer,LabeledVector<Double>> in method public testFit() : void in class org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainerTest |
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Change Variable Type xorData : Map<Integer,double[][]> to xorData : Map<Integer,LabeledVector<double[]>> in method public testUpdate() : void in class org.apache.ignite.ml.nn.MLPTrainerTest.ComponentParamTests |
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Change Variable Type sample : Map<double[],Double> to sample : Map<Integer,LabeledVector<Double>> in method public testUpdate() : void in class org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainerTest |
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Change Variable Type cacheMock : Map<Integer,Double[]> to cacheMock : Map<Integer,double[]> in method protected count(cntr IgniteTriFunction<Long,CountData,LearningEnvironment,Long>) : void in class org.apache.ignite.ml.composition.bagging.BaggingTest |
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Change Variable Type xorCacheCfg : CacheConfiguration<Integer,LabeledPoint> to xorCacheCfg : CacheConfiguration<Integer,LabeledVector<double[]>> in method private xorTest(updatesStgy UpdatesStrategy<? super MultilayerPerceptron,P>) : void in class org.apache.ignite.ml.nn.MLPTrainerIntegrationTest |
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Change Variable Type trainingSetCfg : CacheConfiguration<Integer,LabeledPoint> to trainingSetCfg : CacheConfiguration<Integer,LabeledVector<double[]>> in method public main(args String[]) : void in class org.apache.ignite.examples.ml.nn.MLPTrainerExample |
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Change Variable Type xorData : Map<Integer,double[][]> to xorData : Map<Integer,LabeledVector<double[]>> in method private xorTest(updatesStgy UpdatesStrategy<? super MultilayerPerceptron,P>) : void in class org.apache.ignite.ml.nn.MLPTrainerTest.ComponentParamTests |
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Change Variable Type pnt : LabeledPoint to pnt : LabeledVector<Double> in method public main(args String...) : void in class org.apache.ignite.examples.ml.tree.DecisionTreeClassificationTrainerExample |
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Change Variable Type xorCache : IgniteCache<Integer,LabeledPoint> to xorCache : IgniteCache<Integer,LabeledVector<double[]>> in method private xorTest(updatesStgy UpdatesStrategy<? super MultilayerPerceptron,P>) : void in class org.apache.ignite.ml.nn.MLPTrainerIntegrationTest |
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Change Variable Type convCheck : ConvergenceChecker<K,V> to convCheck : ConvergenceChecker<K,V,C> in method public update(mdlToUpdate GDBTrainer.GDBModel, datasetBuilder DatasetBuilder<K,V>, vectorizer Vectorizer<K,V,C,Double>) : List<IgniteModel<Vector,Double>> in class org.apache.ignite.ml.tree.boosting.GDBOnTreesLearningStrategy |
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Change Variable Type datasetBuilder : LocalDatasetBuilder<double[],Double> to datasetBuilder : LocalDatasetBuilder<Integer,LabeledVector<Double>> in method public testConvergenceChecking() : void in class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceCheckerTest |
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Change Variable Type checker : ConvergenceChecker<double[],Double> to checker : ConvergenceChecker<Integer,LabeledVector<Double>,Integer> in method public testConvergenceChecking() : void in class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceCheckerTest |
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Change Variable Type extractor : FeatureLabelExtractor<K,V,L> to extractor : Vectorizer<K,V,C,L> in method public identityTrainer() : DatasetTrainer<IgniteModel<I,I>,L> in class org.apache.ignite.ml.trainers.DatasetTrainer |
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Change Variable Type checker : ConvergenceChecker<double[],Double> to checker : ConvergenceChecker<Integer,LabeledVector<Double>,Integer> in method public testConvergenceCheckingWithAnomaliesInData() : void in class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceCheckerTest |
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Change Variable Type datasetBuilder : LocalDatasetBuilder<double[],Double> to datasetBuilder : LocalDatasetBuilder<Integer,LabeledVector<Double>> in method public testConvergenceChecking() : void in class org.apache.ignite.ml.composition.boosting.convergence.median.MedianOfMedianConvergenceCheckerTest |
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Change Variable Type datasetBuilder : LocalDatasetBuilder<double[],Double> to datasetBuilder : LocalDatasetBuilder<Integer,LabeledVector<Double>> in method public testConvergenceCheckingWithAnomaliesInData() : void in class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceCheckerTest |
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Change Variable Type pnt : LabeledPoint to pnt : LabeledVector<double[]> in method public main(args String[]) : void in class org.apache.ignite.examples.ml.nn.MLPTrainerExample |
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