protected MixtureOfGaussians extract(IRecord<URL> item) { return this.gmmExtract.extractFeature(item); }
protected MixtureOfGaussians extract(IRecord<URL> item) { return this.gmmExtract.extractFeature(item); }
protected void prepareFeats() { if (feats != null) return; final int numInstances = data.size(); feats = new double[numInstances][]; int index = 0; for (final T d : this.data) { feats[index++] = this.extractor.extractFeature(d).values; } }
protected void prepareFeats() { if (feats != null) return; final int numInstances = data.size(); feats = new double[numInstances][]; int index = 0; for (final T d : this.data) { feats[index++] = this.extractor.extractFeature(d).values; } }
public List<MixtureOfGaussians> extractGroupGaussians( UKBenchListDataset<IRecord<URL>> ukbenchObject) { List<MixtureOfGaussians> gaussians = new ArrayList<MixtureOfGaussians>(); int i = 0; for (IRecord<URL> imageURL : ukbenchObject) { MixtureOfGaussians gmm = gmmExtract.extractFeature(imageURL); gaussians.add(gmm); } return gaussians; }
public List<MixtureOfGaussians> extractGroupGaussians( UKBenchListDataset<IRecord<URL>> ukbenchObject) { List<MixtureOfGaussians> gaussians = new ArrayList<MixtureOfGaussians>(); int i = 0; for (IRecord<URL> imageURL : ukbenchObject) { MixtureOfGaussians gmm = gmmExtract.extractFeature(imageURL); gaussians.add(gmm); } return gaussians; }
Feature[] computeFeature(OBJECT object) { final FeatureVector feature = extractor.extractFeature(object); return LiblinearHelper.convert(feature, bias); }
@Override public FV extractFeature(OBJECT object) { return extractor.extractFeature(object).getFeatureVector(); }
@Override public void put(int id, DATA data) { index.put(id, extractor.extractFeature(data)); }
@Override public FEATURE get(int index) { return extractor.extractFeature(input.get(index)); }
double[] computeFeatureDense(OBJECT object) { final FeatureVector feature = extractor.extractFeature(object); return LiblinearHelper.convertDense(feature, bias); }
@Override public FV extractFeature(OBJECT object) { return extractor.extractFeature(object).getFeatureVector(); }
@Override public FEATURE get(int index) { return extractor.extractFeature(input.get(index)); }
@Override public void train(final Annotated<OBJECT, ANNOTATION> annotated) { this.nn = null; this.features.add(this.extractor.extractFeature(annotated.getObject())); final Collection<ANNOTATION> anns = annotated.getAnnotations(); this.annotations.add(anns); this.annotationsSet.addAll(anns); }
@Override public List<ScoredAnnotation<ANNOTATION>> annotate(OBJECT object) { final FEATURE f = extractor.extractFeature(object); final List<ScoredAnnotation<ANNOTATION>> result = new ArrayList<ScoredAnnotation<ANNOTATION>>(); result.add(new ScoredAnnotation<ANNOTATION>(model.predict(f), 1)); return result; } }
@Override public FEATURE extractFeature(OBJECT object) { final FEATURE cachedFeature = this.cache.get(object.getID()); FEATURE feature = null; if (!force && cachedFeature != null) { feature = cachedFeature; if (feature != null) return feature; } feature = extractor.extractFeature(object); this.cache.put(object.getID(), feature); return feature; }
@Override public FEATURE extractFeature(OBJECT object) { final FEATURE cachedFeature = this.cache.get(object.getID()); FEATURE feature = null; if (!force && cachedFeature != null) { feature = cachedFeature; if (feature != null) return feature; } feature = extractor.extractFeature(object); this.cache.put(object.getID(), feature); return feature; }
@Override public FEATURE get(int index) { return extractor.extractFeature( data.get(indices.get(index)).getObject() ); }
@Override public FEATURE get(int index) { return extractor.extractFeature( data.get(selectedIndices.get(index)).getObject() ); }
@Override public DoubleFV extractFeature(T object) { return map.evaluate(inner.extractFeature(object).asDoubleFV()); } }