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Change Parameter Type datasetMapping : IgniteFunction<? super IgniteModel<I,O1>,DatasetMapping<L,L>> to datasetMapping : IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> in method public TrainersSequentialComposition(tr1 DatasetTrainer<? extends IgniteModel<I,O1>,L>, tr2 DatasetTrainer<? extends IgniteModel<O1,O2>,L>, datasetMapping IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>>) in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition |
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Change Parameter Type datasetMappingProducer : IgniteFunction<AdaptableDatasetModel<I,O,IW,OW,M>,DatasetMapping<L,L>> to datasetMappingProducer : IgniteFunction<AdaptableDatasetModel<I,O,IW,OW,M>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> in method public andThen(tr DatasetTrainer<M1,L>, datasetMappingProducer IgniteFunction<AdaptableDatasetModel<I,O,IW,OW,M>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>>) : TrainersSequentialComposition<I,O,O1,L> in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer |
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Change Parameter Type afterFeatureExtractor : IgniteFunction<Vector,Vector> to afterExtractor : IgniteFunction<LabeledVector<L>,LabeledVector<L>> in method private AdaptableDatasetTrainer(before IgniteFunction<I,IW>, wrapped DatasetTrainer<M,L>, after IgniteFunction<OW,O>, afterExtractor IgniteFunction<LabeledVector<L>,LabeledVector<L>>, builder UpstreamTransformerBuilder) in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer |
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Change Parameter Type vector : V to vector : Vector in method public LabeledVector(vector Vector, lb L) in class org.apache.ignite.ml.structures.LabeledVector |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : LinearRegressionModel in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : LinearRegressionModel in class org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to extractor : FeatureLabelExtractor<K,V,L> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,L>) : BaggedModel in class org.apache.ignite.ml.composition.bagging.BaggedTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,double[]>] to extractor : FeatureLabelExtractor<K,V,double[]> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,double[]>) : MultilayerPerceptron in class org.apache.ignite.ml.nn.MLPTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to extractor : FeatureLabelExtractor<K,V,L> in method public update(mdl ModelsSequentialComposition<I,O1,O2>, datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,L>) : ModelsSequentialComposition<I,O1,O2> in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : KNNClassificationModel in class org.apache.ignite.ml.knn.classification.KNNClassificationTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : MultiClassModel<M> in class org.apache.ignite.ml.multiclass.OneVsRestTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,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 extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : LogisticRegressionModel in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : ANNClassificationModel 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 extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : DiscreteNaiveBayesModel in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method protected updateModel(mdl IgniteModel<Vector,Double>, datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : IgniteModel<Vector,Double> in class org.apache.ignite.ml.composition.BaggingTest.CountTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : KMeansModel in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : KNNRegressionModel in class org.apache.ignite.ml.knn.regression.KNNRegressionTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method protected updateModel(mdl ModelsComposition, datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : ModelsComposition in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to extractor : FeatureLabelExtractor<K,V,L> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,L>) : ModelsSequentialComposition<I,O1,O2> in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,L>] to extractor : FeatureLabelExtractor<K,V,L> in method public update(mdl BaggedModel, datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,L>) : BaggedModel in class org.apache.ignite.ml.composition.bagging.BaggedTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : GaussianNaiveBayesModel in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : SVMLinearClassificationModel in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer |
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Merge Parameter [featureExtractor : IgniteBiFunction<K,V,Vector>, lbExtractor : IgniteBiFunction<K,V,Double>] to extractor : FeatureLabelExtractor<K,V,Double> in method protected updateModel(mdl DecisionTreeNode, datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,Double>) : DecisionTreeNode in class org.apache.ignite.ml.tree.DecisionTree |
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Change Variable Type coercedMapping : IgniteFunction<IgniteModel<I,O>,DatasetMapping<L,L>> to coercedMapping : IgniteFunction<IgniteModel<I,O>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> in method public andThen(tr DatasetTrainer<M1,L>, datasetMappingProducer IgniteFunction<AdaptableDatasetModel<I,O,IW,OW,M>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>>) : TrainersSequentialComposition<I,O,O1,L> in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer |
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Change Variable Type mapping : DatasetMapping<L,L> to mapping : IgniteFunction<LabeledVector<L>,LabeledVector<L>> in method public update(mdl ModelsSequentialComposition<I,O1,O2>, datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,L>) : ModelsSequentialComposition<I,O1,O2> in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition |
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Change Variable Type labeledVector : LabeledVector<Vector,Double> to labeledVector : LabeledVector<Double> in method public setLabel(idx int, lb double) : void in class org.apache.ignite.ml.structures.LabeledVectorSet |
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Change Variable Type row : LabeledVector<Vector,Double> to row : LabeledVector<Double> in method public testAccessMethods() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type neighbor : LabeledVector<Vector,Double> to neighbor : LabeledVector<Double> in method private simpleRegression(neighbors List<LabeledVector>) : double in class org.apache.ignite.ml.knn.regression.KNNRegressionModel |
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Change Variable Type neighbor : LabeledVector<Vector,Double> to neighbor : LabeledVector<Double> in method private weightedRegression(neighbors List<LabeledVector>, v Vector) : double in class org.apache.ignite.ml.knn.regression.KNNRegressionModel |
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Change Variable Type featureExtractor : IgniteBiFunction<K,V,Vector> to featureExtractor : FeatureLabelExtractor<K,V,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 mapping : DatasetMapping<L,L> to mapping : IgniteFunction<LabeledVector<L>,LabeledVector<L>> in method public fit(datasetBuilder DatasetBuilder<K,V>, extractor FeatureLabelExtractor<K,V,L>) : ModelsSequentialComposition<I,O1,O2> in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition |
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Change Variable Type lbExtractor : IgniteBiFunction<K,V,L1> to extractor : FeatureLabelExtractor<K,V,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 lbExtractor : IgniteBiFunction<K,V,L> to extractor : FeatureLabelExtractor<K,V,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 arr : LabeledVector<Vector,ProbableLabel>[] to arr : LabeledVector<ProbableLabel>[] in method private buildLabelsForCandidates(centers List<Vector>, centroidStat CentroidStat) : LabeledVectorSet<ProbableLabel,LabeledVector> in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer |
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