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org.apache.ignite.ml.math.functions.IgniteBiFunction to org.apache.ignite.ml.preprocessing.Preprocessor
No. of Instances - 87
No. of Commits - 1
No. of Projects - {'ignite'}
Hierarchy/Composition: T_SUPER_R
Primitive Info: -
NameSpace: Internal -> Internal
Mapping:
Add or Remove Method invocation
FeatureLabelExtractorWrapper.wrap(preprocessor)
to
preprocessor
FeatureLabelExtractorWrapper.wrap(preprocessor)
to
preprocessor
FeatureLabelExtractorWrapper.wrap(imputingPreprocessor,lbExtractor)
to
imputingPreprocessor
FeatureLabelExtractorWrapper.wrap(imputingPreprocessor,lbExtractor)
to
imputingPreprocessor
datasetBuilder.build(envBuilder,(env,upstream,upstreamSize) -> new EmptyContext(),(env,upstream,upstreamSize,ctx) -> { double[] sum=null; double[] squaredSum=null; long cnt=0; while (upstream.hasNext()) { UpstreamEntry<K,V> entity=upstream.next(); Vector row=basePreprocessor.apply(entity.getKey(),entity.getValue()); if (sum == null) { sum=new double[row.size()]; squaredSum=new double[row.size()]; } else { assert sum.length == row.size() : "Base preprocessor must return exactly " + sum.length + " features"; } ++cnt; for (int i=0; i < row.size(); i++) { double x=row.get(i); sum[i]+=x; squaredSum[i]+=x * x; } } return new StandardScalerData(sum,squaredSum,cnt); } )
to
datasetBuilder.build(envBuilder,(env,upstream,upstreamSize) -> new EmptyContext(),(env,upstream,upstreamSize,ctx) -> { double[] sum=null; double[] squaredSum=null; long cnt=0; while (upstream.hasNext()) { UpstreamEntry<K,V> entity=upstream.next(); Vector row=basePreprocessor.apply(entity.getKey(),entity.getValue()).features(); if (sum == null) { sum=new double[row.size()]; squaredSum=new double[row.size()]; } else { assert sum.length == row.size() : "Base preprocessor must return exactly " + sum.length + " features"; } ++cnt; for (int i=0; i < row.size(); i++) { double x=row.get(i); sum[i]+=x; squaredSum[i]+=x * x; } } return new StandardScalerData(sum,squaredSum,cnt); } )
FeatureLabelExtractorWrapper.wrap(imputingPreprocessor,lbExtractor)
to
imputingPreprocessor
FeatureLabelExtractorWrapper.wrap(preprocessor)
to
preprocessor
FeatureLabelExtractorWrapper.wrap(normalizationPreprocessor,lbExtractor)
to
normalizationPreprocessor
trainer.fit(ignite,dataCache,FeatureLabelExtractorWrapper.wrap(normalizationPreprocessor,lbExtractor)).withK(1)
to
trainer.fit(ignite,dataCache,normalizationPreprocessor).withK(1)
FeatureLabelExtractorWrapper.wrap(preprocessor,lbExtractor)
to
preprocessor
FeatureLabelExtractorWrapper.wrap(normalizationPreprocessor,lbExtractor)
to
normalizationPreprocessor
FeatureLabelExtractorWrapper.wrap(normalizationPreprocessor,lbExtractor)
to
normalizationPreprocessor
FeatureLabelExtractorWrapper.wrap(preprocessor)
to
preprocessor
FeatureLabelExtractorWrapper.wrap(normalizationPreprocessor,lbExtractor)
to
normalizationPreprocessor
FeatureLabelExtractorWrapper.wrap(normalizationPreprocessor,lbExtractor)
to
normalizationPreprocessor
new PipelineMdl<K,V>().withFeatureExtractor(finalFeatureExtractor).withLabelExtractor(lbExtractor)
to
new PipelineMdl<K,V>().withPreprocessor(finalPreprocessor)
FeatureLabelExtractorWrapper.wrap(normalizationPreprocessor,lbExtractor)
to
normalizationPreprocessor
FeatureLabelExtractorWrapper.wrap(preprocessor)
to
preprocessor
FeatureLabelExtractorWrapper.wrap(preprocessor)
to
preprocessor
Rename Variable
featureExtractor
to
preprocessor
lbExtractor
to
preprocessor
featureExtractor
to
preprocessor
lbExtractor
to
preprocessor
lbExtractor
to
preprocessor
finalFeatureExtractor
to
finalPreprocessor
finalFeatureExtractor
to
finalPreprocessor
finalFeatureExtractor
to
finalPreprocessor
finalFeatureExtractor
to
finalPreprocessor
featureExtractor
to
preprocessor
featureExtractor
to
preprocessor
featureExtractor
to
preprocessor
featureExtractor
to
preprocessor
Cascading Type Change (Similar)
Vector
to
LabeledVector
Vector
to
LabeledVector
Cascading Type Change (Different)
Vector
to
LabeledVector
Object[]
to
LabeledVector<Double>