Examples of etc.aloe.cscw2013.SMOFeatureWeighting

Package etc.aloe.cscw2013

Examples of etc.aloe.cscw2013.SMOFeatureWeighting

etc.aloe.cscw2013.TrainingImpl
Extracts top features and feature weights from a linear support vector machine (SMO) classifier. Also works with a CostSensitiveClassifier wrapping an SMO. @author Michael Brooks

    @Override
    public Balancing constructBalancing() {
        if (options instanceof TrainOptionsImpl) {
            TrainOptionsImpl trainOpts = (TrainOptionsImpl) options;
            if (trainOpts.useDownsampling) {
                return new DownsampleBalancing(trainOpts.falsePositiveCost, trainOpts.falseNegativeCost);
            } else if (trainOpts.useUpsampling) {
                return new UpsampleBalancing(trainOpts.falsePositiveCost, trainOpts.falseNegativeCost);
            } else {
                return null;
            }

View Full Code Here

        return new WekaModel();
    }


    @Override
    public FeatureExtraction constructFeatureExtraction() {
        return new FeatureExtractionImpl();
    }

View Full Code Here

    public FeatureGeneration constructFeatureGeneration() {
        if (options instanceof TrainOptionsImpl) {
            TrainOptionsImpl trainOpts = (TrainOptionsImpl) options;
            //Read the emoticons
            List<String> termList = loadTermList(trainOpts.emoticonFile);
            FeatureGenerationImpl featureGen = new FeatureGenerationImpl(termList);
            featureGen.setParticipantFeatureCount(trainOpts.participantFeatures);
            return featureGen;
        } else {
            throw new IllegalArgumentException("Options not for Training");
        }
    }

View Full Code Here

        }
    }


    @Override
    public LabelMapping constructLabelMapping() {
        return new LabelMappingImpl();
    }

View Full Code Here

        } else {
            throw new IllegalArgumentException("Options should be for Training or Labeling");
        }


        if (disableSegmentation) {
            return new NullSegmentation();
        } else {
            Segmentation segmentation = new ThresholdSegmentation(segmentationThresholdSeconds,
                    !ignoreParticipants);
            segmentation.setSegmentResolution(new ResolutionImpl());
            return segmentation;

View Full Code Here

        return new LabelMappingImpl();
    }


    @Override
    public SegmentResolution constructSegmentResolution() {
        return new ResolutionImpl();
    }

View Full Code Here

        if (disableSegmentation) {
            return new NullSegmentation();
        } else {
            Segmentation segmentation = new ThresholdSegmentation(segmentationThresholdSeconds,
                    !ignoreParticipants);
            segmentation.setSegmentResolution(new ResolutionImpl());
            return segmentation;
        }
    }

View Full Code Here

        return new ResolutionImpl();
    }


    @Override
    public FeatureWeighting constructFeatureWeighting() {
        return new SMOFeatureWeighting();
    }

View Full Code Here

        }


        if (disableSegmentation) {
            return new NullSegmentation();
        } else {
            Segmentation segmentation = new ThresholdSegmentation(segmentationThresholdSeconds,
                    !ignoreParticipants);
            segmentation.setSegmentResolution(new ResolutionImpl());
            return segmentation;
        }
    }

View Full Code Here

    @Override
    public Training constructTraining() {
        if (options instanceof TrainOptionsImpl) {
            TrainOptionsImpl trainOpts = (TrainOptionsImpl) options;


            TrainingImpl trainingImpl = new TrainingImpl();
            
            trainingImpl.setBuildLogisticModel(true);
            
            if (trainOpts.useMinCost || trainOpts.useReweighting) {
                trainingImpl.setUseCostTraining(true);
                trainingImpl.setFalsePositiveCost(trainOpts.falsePositiveCost);
                trainingImpl.setFalseNegativeCost(trainOpts.falseNegativeCost);
                trainingImpl.setUseReweighting(trainOpts.useReweighting);
            }


            return trainingImpl;
        } else {
            throw new IllegalArgumentException("Options must be for Training");

View Full Code Here

0 1

TOP

Related Classes of etc.aloe.cscw2013.SMOFeatureWeighting

etc.aloe.cscw2013.WekaModel

etc.aloe.cscw2013.WekaModelTest

etc.aloe.data.ExampleSet

etc.aloe.data.FeatureSpecification

etc.aloe.data.Message

etc.aloe.data.MessageSet

etc.aloe.data.Predictions

etc.aloe.data.Segment

etc.aloe.data.SegmentSet

etc.aloe.factories.CSCW2013

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.