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Pull Up Method private buildLabeledDatasetOnListOfVectors(neighborsFromPartitions List<LabeledVector>) : LabeledDataset<Double,LabeledVector> from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to protected buildLabeledDatasetOnListOfVectors(neighborsFromPartitions List<LabeledVector>) : LabeledVectorSet<Double,LabeledVector> from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method public withK(k int) : KNNClassificationModel from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to public withK(k int) : NNClassificationModel from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method public equals(obj Object) : boolean from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to public equals(obj Object) : boolean from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method private getClassVoteForVector(stgy KNNStrategy, distance double) : double from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to protected getClassVoteForVector(stgy NNStrategy, distance double) : double from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method public hashCode() : int from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to public hashCode() : int from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method private getDistances(v Vector, trainingData LabeledDataset<Double,LabeledVector>) : TreeMap<Double,Set<Integer>> from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to protected getDistances(v Vector, trainingData LabeledVectorSet<Double,LabeledVector>) : TreeMap<Double,Set<Integer>> from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method private putDistanceIdxPair(distanceIdxPairs Map<Double,Set<Integer>>, i int, distance double) : void from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to protected putDistanceIdxPair(distanceIdxPairs Map<Double,Set<Integer>>, i int, distance double) : void from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method private getKClosestVectors(trainingData LabeledDataset<Double,LabeledVector>, distanceIdxPairs TreeMap<Double,Set<Integer>>) : LabeledVector[] from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to protected getKClosestVectors(trainingData LabeledVectorSet<Double,LabeledVector>, distanceIdxPairs TreeMap<Double,Set<Integer>>) : LabeledVector[] from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method private getClassWithMaxVotes(clsVotes Map<Double,Double>) : double from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to protected getClassWithMaxVotes(clsVotes Map<Double,Double>) : double from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method public toString(pretty boolean) : String from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to public toString(pretty boolean) : String from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method public withStrategy(stgy KNNStrategy) : KNNClassificationModel from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to public withStrategy(stgy NNStrategy) : NNClassificationModel from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method public toString() : String from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to public toString() : String from class org.apache.ignite.ml.knn.NNClassificationModel |
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Pull Up Method public withDistanceMeasure(distanceMeasure DistanceMeasure) : KNNClassificationModel from class org.apache.ignite.ml.knn.classification.KNNClassificationModel to public withDistanceMeasure(distanceMeasure DistanceMeasure) : NNClassificationModel from class org.apache.ignite.ml.knn.NNClassificationModel |
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Change Parameter Type dataset : Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> to dataset : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method private calcDataForNewCentroids(centers Vector[], dataset Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>>, cols int) : TotalCostAndCounts in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer |
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Change Parameter Type stgy : KNNStrategy to stgy : NNStrategy in method public withStrategy(stgy NNStrategy) : NNClassificationModel in class org.apache.ignite.ml.knn.NNClassificationModel |
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Change Parameter Type dataset : Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> to dataset : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method public KNNClassificationModel(dataset Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>>) in class org.apache.ignite.ml.knn.classification.KNNClassificationModel |
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Change Parameter Type stgy : KNNStrategy to stgy : NNStrategy in method public KNNModelFormat(k int, measure DistanceMeasure, stgy NNStrategy) in class org.apache.ignite.ml.knn.classification.KNNModelFormat |
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Change Parameter Type trainingData : LabeledDataset<Double,LabeledVector> to trainingData : LabeledVectorSet<Double,LabeledVector> in method protected getKClosestVectors(trainingData LabeledVectorSet<Double,LabeledVector>, distanceIdxPairs TreeMap<Double,Set<Integer>>) : LabeledVector[] in class org.apache.ignite.ml.knn.NNClassificationModel |
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Change Parameter Type trainingData : LabeledDataset<Double,LabeledVector> to trainingData : LabeledVectorSet<Double,LabeledVector> in method protected getDistances(v Vector, trainingData LabeledVectorSet<Double,LabeledVector>) : TreeMap<Double,Set<Integer>> in class org.apache.ignite.ml.knn.NNClassificationModel |
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Change Parameter Type dataset : Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> to dataset : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method private initClusterCentersRandomly(dataset Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>>, k int) : Vector[] in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer |
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Change Parameter Type dataset : Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> to dataset : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method public KNNRegressionModel(dataset Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>>) in class org.apache.ignite.ml.knn.regression.KNNRegressionModel |
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Change Parameter Type data : LabeledDataset to data : LabeledVectorSet in method private getDeltas(data LabeledVectorSet, copiedWeights Vector, amountOfObservation int, tmpAlphas Vector, randomIdx int) : Deltas in class org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer |
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Change Parameter Type stgy : KNNStrategy to stgy : NNStrategy in method private classify(neighbors List<LabeledVector>, v Vector, stgy NNStrategy) : double in class org.apache.ignite.ml.knn.classification.KNNClassificationModel |
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Change Parameter Type stgy : KNNStrategy to stgy : NNStrategy in method private testLongly(stgy NNStrategy) : void in class org.apache.ignite.ml.knn.KNNRegressionTest |
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Change Parameter Type dataset : LabeledDataset to dataset : LabeledVectorSet in method public LabeledVectorSetTestTrainPair(dataset LabeledVectorSet, testPercentage double) in class org.apache.ignite.ml.structures.LabeledVectorSetTestTrainPair |
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Change Parameter Type dataset : Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> to dataset : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method private calculateUpdates(weights Vector, dataset Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>>) : Vector in class org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer |
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Change Parameter Type stgy : KNNStrategy to stgy : NNStrategy in method protected getClassVoteForVector(stgy NNStrategy, distance double) : double in class org.apache.ignite.ml.knn.NNClassificationModel |
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Change Return Type LabeledDataset<Double,LabeledVector> to LabeledVectorSet<Double,LabeledVector> in method protected buildLabeledDatasetOnListOfVectors(neighborsFromPartitions List<LabeledVector>) : LabeledVectorSet<Double,LabeledVector> in class org.apache.ignite.ml.knn.NNClassificationModel |
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Change Return Type KNNClassificationModel to NNClassificationModel in method public withDistanceMeasure(distanceMeasure DistanceMeasure) : NNClassificationModel in class org.apache.ignite.ml.knn.NNClassificationModel |
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Change Return Type Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> to Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method public buildDataset(datasetBuilder DatasetBuilder<K,V>, featureExtractor IgniteBiFunction<K,V,Vector>, lbExtractor IgniteBiFunction<K,V,Double>) : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in class org.apache.ignite.ml.knn.KNNUtils |
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Change Return Type LabeledDataset to LabeledVectorSet in method public train() : LabeledVectorSet in class org.apache.ignite.ml.structures.LabeledVectorSetTestTrainPair |
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Change Return Type LabeledDataset<Double,LabeledVector> to LabeledVectorSet<Double,LabeledVector> in method public build(upstreamData Iterator<UpstreamEntry<K,V>>, upstreamDataSize long, ctx C) : LabeledVectorSet<Double,LabeledVector> in class org.apache.ignite.ml.structures.partition.LabeledDatasetPartitionDataBuilderOnHeap |
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Change Return Type LabeledDataset to LabeledVectorSet in method public loadFromTxtFile(pathToFile Path, separator String, isDistributed boolean, isFallOnBadData boolean) : LabeledVectorSet in class org.apache.ignite.ml.structures.preprocessing.LabeledDatasetLoader |
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Change Return Type KNNStrategy to NNStrategy in method public getStgy() : NNStrategy in class org.apache.ignite.ml.knn.classification.KNNModelFormat |
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Change Return Type LabeledDataset to LabeledVectorSet in method public loadDatasetFromTxt(rsrcPath String, isFallOnBadData boolean) : LabeledVectorSet in class org.apache.ignite.ml.knn.LabeledDatasetHelper |
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Change Return Type LabeledDataset to LabeledVectorSet in method public copy() : LabeledVectorSet in class org.apache.ignite.ml.structures.LabeledVectorSet |
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Change Return Type LabeledDataset to LabeledVectorSet in method public test() : LabeledVectorSet in class org.apache.ignite.ml.structures.LabeledVectorSetTestTrainPair |
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Change Return Type KNNClassificationModel to NNClassificationModel in method public withStrategy(stgy NNStrategy) : NNClassificationModel in class org.apache.ignite.ml.knn.NNClassificationModel |
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Change Return Type KNNClassificationModel to NNClassificationModel in method public withK(k int) : NNClassificationModel in class org.apache.ignite.ml.knn.NNClassificationModel |
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Change Variable Type dataset : LabeledDataset to dataset : LabeledVectorSet in method public testAccessMethods() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type training : LabeledDataset to training : LabeledVectorSet in method public testLoadingFileWithMissedData() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type dataset : Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> to dataset : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method public buildDataset(datasetBuilder DatasetBuilder<K,V>, featureExtractor IgniteBiFunction<K,V,Vector>, lbExtractor IgniteBiFunction<K,V,Double>) : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in class org.apache.ignite.ml.knn.KNNUtils |
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Change Variable Type knnMdl : KNNClassificationModel to knnMdl : NNClassificationModel in method public testBinaryClassificationFarPointsWithWeightedStrategy() : void in class org.apache.ignite.ml.knn.KNNClassificationTest |
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Change Variable Type knnMdl : KNNClassificationModel to knnMdl : NNClassificationModel in method public testBinaryClassificationFarPointsWithSimpleStrategy() : void in class org.apache.ignite.ml.knn.KNNClassificationTest |
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Change Variable Type res : LabeledDataset to res : LabeledVectorSet in method public copy() : LabeledVectorSet in class org.apache.ignite.ml.structures.LabeledVectorSet |
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Change Variable Type dataset : Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> to dataset : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method public fit(datasetBuilder DatasetBuilder<K,V>, featureExtractor IgniteBiFunction<K,V,Vector>, lbExtractor IgniteBiFunction<K,V,Double>) : SVMLinearBinaryClassificationModel in class org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer |
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Change Variable Type knnMdl : KNNClassificationModel to knnMdl : NNClassificationModel in method public testBinaryClassification() : void in class org.apache.ignite.ml.knn.KNNClassificationTest |
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Change Variable Type partDataBuilder : PartitionDataBuilder<K,V,EmptyContext,LabeledDataset<Double,LabeledVector>> to partDataBuilder : PartitionDataBuilder<K,V,EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method public fit(datasetBuilder DatasetBuilder<K,V>, featureExtractor IgniteBiFunction<K,V,Vector>, lbExtractor IgniteBiFunction<K,V,Double>) : KMeansModel in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer |
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Change Variable Type importedMdl : KNNClassificationModel to importedMdl : NNClassificationModel in method public importExportKNNModelTest() : void in class org.apache.ignite.ml.LocalModelsTest |
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Change Variable Type training : LabeledDataset to training : LabeledVectorSet in method public testLoadingCorrectTxtFile() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type mdl : KNNClassificationModel to mdl : NNClassificationModel in method public main(args String[]) : void in class org.apache.ignite.examples.ml.tutorial.Step_6_KNN |
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Change Variable Type split4 : LabeledDatasetTestTrainPair to split4 : LabeledVectorSetTestTrainPair in method public testSplitting() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type knnMdl : KNNClassificationModel to knnMdl : NNClassificationModel in method public main(args String[]) : void in class org.apache.ignite.examples.ml.knn.KNNClassificationExample |
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Change Variable Type dataset : LabeledDataset to dataset : LabeledVectorSet in method public testExternalization() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type training : LabeledDataset to training : LabeledVectorSet in method public testLoadingFileWithIncorrectData() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type knnMdl : KNNClassificationModel to knnMdl : NNClassificationModel in method public testBinaryClassificationWithSmallestK() : void in class org.apache.ignite.ml.knn.KNNClassificationTest |
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Change Variable Type split3 : LabeledDatasetTestTrainPair to split3 : LabeledVectorSetTestTrainPair in method public testSplitting() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type training : LabeledDataset to training : LabeledVectorSet in method public testSplitting() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type split2 : LabeledDatasetTestTrainPair to split2 : LabeledVectorSetTestTrainPair in method public testSplitting() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type mdl : KNNClassificationModel to mdl : NNClassificationModel in method public importExportKNNModelTest() : void in class org.apache.ignite.ml.LocalModelsTest |
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Change Variable Type partDataBuilder : PartitionDataBuilder<K,V,EmptyContext,LabeledDataset<Double,LabeledVector>> to partDataBuilder : PartitionDataBuilder<K,V,EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method public buildDataset(datasetBuilder DatasetBuilder<K,V>, featureExtractor IgniteBiFunction<K,V,Vector>, lbExtractor IgniteBiFunction<K,V,Double>) : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in class org.apache.ignite.ml.knn.KNNUtils |
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Change Variable Type split1 : LabeledDatasetTestTrainPair to split1 : LabeledVectorSetTestTrainPair in method public testSplitting() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type partDataBuilder : PartitionDataBuilder<K,V,EmptyContext,LabeledDataset<Double,LabeledVector>> to partDataBuilder : PartitionDataBuilder<K,V,EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method public fit(datasetBuilder DatasetBuilder<K,V>, featureExtractor IgniteBiFunction<K,V,Vector>, lbExtractor IgniteBiFunction<K,V,Double>) : SVMLinearBinaryClassificationModel in class org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer |
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Change Variable Type dataset : LabeledDataset to dataset : LabeledVectorSet in method public testLabels() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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Change Variable Type neighborsToFilter : LabeledDataset<Double,LabeledVector> to neighborsToFilter : LabeledVectorSet<Double,LabeledVector> in method protected findKNearestNeighbors(v Vector) : List<LabeledVector> in class org.apache.ignite.ml.knn.classification.KNNClassificationModel |
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Change Variable Type dataset : Dataset<EmptyContext,LabeledDataset<Double,LabeledVector>> to dataset : Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>> in method public fit(datasetBuilder DatasetBuilder<K,V>, featureExtractor IgniteBiFunction<K,V,Vector>, lbExtractor IgniteBiFunction<K,V,Double>) : KMeansModel in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer |
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Change Variable Type dataset : LabeledDataset to dataset : LabeledVectorSet in method public testFeatureNames() : void in class org.apache.ignite.ml.knn.LabeledVectorSetTest |
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