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Change Parameter Type mdl : Model<double[],Double> to mdl : Model<Vector,Double> in method package ModelOnFeaturesSubspace(featuresMapping Map<Integer,Integer>, mdl Model<Vector,Double>) in class org.apache.ignite.ml.composition.ModelsComposition.ModelOnFeaturesSubspace |
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Change Parameter Type doubles : double[] to doubles : Vector in method public apply(doubles Vector) : Double in class org.apache.ignite.ml.tree.DecisionTreeLeafNode |
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Change Parameter Type basePreprocessor : IgniteBiFunction<K,V,String[]> to basePreprocessor : IgniteBiFunction<K,V,Object[]> in method public StringEncoderPreprocessor(encodingValues Map<String,Integer>[], basePreprocessor IgniteBiFunction<K,V,Object[]>, handledIndices Set<Integer>) in class org.apache.ignite.ml.preprocessing.encoding.stringencoder.StringEncoderPreprocessor |
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Change Parameter Type mdl : Model<double[],L> to mdl : Model<Vector,L> in method public CacheBasedTruthWithPredictionCursor(upstreamCache IgniteCache<K,V>, filter IgniteBiPredicate<K,V>, featureExtractor IgniteBiFunction<K,V,double[]>, lbExtractor IgniteBiFunction<K,V,L>, mdl Model<Vector,L>) in class org.apache.ignite.ml.selection.score.util.CacheBasedTruthWithPredictionCursor |
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Change Parameter Type row : String[] to row : Object[] in method private calculateFrequencies(row Object[], categoryFrequencies Map<String,Integer>[]) : Map<String,Integer>[] in class org.apache.ignite.ml.preprocessing.encoding.stringencoder.StringEncoderTrainer |
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Change Parameter Type mdl : Model<double[],L> to mdl : Model<Vector,L> in method public LocalTruthWithPredictionCursor(upstreamMap Map<K,V>, filter IgniteBiPredicate<K,V>, featureExtractor IgniteBiFunction<K,V,double[]>, lbExtractor IgniteBiFunction<K,V,L>, mdl Model<Vector,L>) in class org.apache.ignite.ml.selection.score.util.LocalTruthWithPredictionCursor |
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Change Parameter Type features : double[] to features : Vector in method public apply(features Vector) : Double in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode |
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Change Parameter Type features : double[] to features : Vector in method public apply(features Vector) : Double in class org.apache.ignite.ml.composition.ModelsComposition |
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Change Parameter Type basePreprocessor : IgniteBiFunction<K,V,String[]> to basePreprocessor : IgniteBiFunction<K,V,Object[]> in method public fit(datasetBuilder DatasetBuilder<K,V>, basePreprocessor IgniteBiFunction<K,V,Object[]>) : StringEncoderPreprocessor<K,V> in class org.apache.ignite.ml.preprocessing.encoding.stringencoder.StringEncoderTrainer |
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Change Attribute Type basePreprocessor : IgniteBiFunction<K,V,String[]> to basePreprocessor : IgniteBiFunction<K,V,Object[]> in class org.apache.ignite.ml.preprocessing.encoding.stringencoder.StringEncoderPreprocessor |
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Change Attribute Type mdl : Model<double[],L> to mdl : Model<Vector,L> in class org.apache.ignite.ml.selection.score.util.CacheBasedTruthWithPredictionCursor |
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Change Attribute Type model : Model<double[],Double> to mdl : Model<Vector,Double> in class org.apache.ignite.ml.composition.ModelsComposition.ModelOnFeaturesSubspace |
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Change Attribute Type mdl : Model<double[],L> to mdl : Model<Vector,L> in class org.apache.ignite.ml.selection.score.util.LocalTruthWithPredictionCursor |
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