org.apache.ignite.ml.dataset.feature.extractor.Vectorizer to org.apache.ignite.ml.preprocessing.Preprocessor
No. of Instances - 106
No. of Commits - 1
No. of Projects - {'ignite'}
Hierarchy/Composition: SIBLING
Primitive Info: -
NameSpace: Internal -> Internal
Mapping:
- Rename Variable
- vectorizer to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- featureExtractor to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- vectorizer to preprocessor
- featureExtractor to preprocessor
- featureExtractor to preprocessor
- vectorizer to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- extractor to preprocessor
- vectorizer to preprocessor
- Cascading Type Change (Different)
- Add or Remove Method invocation
- datasetBuilder.build(envBuilder,(env,upstream,upstreamSize) -> new EmptyContext(),(env,upstream,upstreamSize,ctx) -> { DiscreteNaiveBayesSumsHolder res=new DiscreteNaiveBayesSumsHolder(); while (upstream.hasNext()) { UpstreamEntry<K,V> entity=upstream.next(); LabeledVector featureExtractor=CompositionUtils.asFeatureExtractor(extractor); Double lbExtractor=CompositionUtils.asLabelExtractor(extractor); Vector features=featureExtractor.apply(entity.getKey(),entity.getValue()); Double lb=lbExtractor.apply(entity.getKey(),entity.getValue()); long[][] valuesInBucket; int size=features.size(); if (!res.valuesInBucketPerLbl.containsKey(lb)) { valuesInBucket=new long[size][]; for (int i=0; i < size; i++) { valuesInBucket[i]=new long[bucketThresholds[i].length + 1]; Arrays.fill(valuesInBucket[i],0L); } res.valuesInBucketPerLbl.put(lb,valuesInBucket); } if (!res.featureCountersPerLbl.containsKey(lb)) res.featureCountersPerLbl.put(lb,0); res.featureCountersPerLbl.put(lb,res.featureCountersPerLbl.get(lb) + 1); valuesInBucket=res.valuesInBucketPerLbl.get(lb); for (int j=0; j < size; j++) { double x=features.get(j); int bucketNum=toBucketNumber(x,bucketThresholds[j]); valuesInBucket[j][bucketNum]+=1; } } return res; } ) to datasetBuilder.build(envBuilder,(env,upstream,upstreamSize) -> new EmptyContext(),(env,upstream,upstreamSize,ctx) -> { DiscreteNaiveBayesSumsHolder res=new DiscreteNaiveBayesSumsHolder(); while (upstream.hasNext()) { UpstreamEntry<K,V> entity=upstream.next(); LabeledVector lv=extractor.apply(entity.getKey(),entity.getValue()); Vector features=lv.features(); Double lb=(Double)lv.label(); long[][] valuesInBucket; int size=features.size(); if (!res.valuesInBucketPerLbl.containsKey(lb)) { valuesInBucket=new long[size][]; for (int i=0; i < size; i++) { valuesInBucket[i]=new long[bucketThresholds[i].length + 1]; Arrays.fill(valuesInBucket[i],0L); } res.valuesInBucketPerLbl.put(lb,valuesInBucket); } if (!res.featureCountersPerLbl.containsKey(lb)) res.featureCountersPerLbl.put(lb,0); res.featureCountersPerLbl.put(lb,res.featureCountersPerLbl.get(lb) + 1); valuesInBucket=res.valuesInBucketPerLbl.get(lb); for (int j=0; j < size; j++) { double x=features.get(j); int bucketNum=toBucketNumber(x,bucketThresholds[j]); valuesInBucket[j][bucketNum]+=1; } } return res; } )