Examples with SparseInstance weka.core.SparseInstance used on opensource projects

Example 6 with SparseInstance

use of weka.core.SparseInstance in project dkpro-tc by dkpro.

the class MekaDataWriter method writeClassifierFormat.

@Override
public void writeClassifierFormat(Collection<Instance> instances) throws AnalysisEngineProcessException {
    try {
        Instances masterInstance = initalConfiguration(instances);
        for (Instance instance : instances) {
            double[] featureValues = getFeatureValues(attributeStore, instance);
            // set class label values
            List<String> instanceOutcome = instance.getOutcomes();
            for (Attribute label : outcomeAttributes) {
                String labelname = label.name();
                featureValues[attributeStore.getAttributeOffset(labelname)] = instanceOutcome.contains(labelname.split(CLASS_ATTRIBUTE_PREFIX)[1]) ? 1.0d : 0.0d;
            }
            weka.core.Instance wekaInstance;
            if (useSparse) {
                wekaInstance = new SparseInstance(1.0, featureValues);
            } else {
                wekaInstance = new DenseInstance(1.0, featureValues);
            }
            wekaInstance.setDataset(masterInstance);
            Double instanceWeight = instance.getWeight();
            if (applyWeighting) {
                wekaInstance.setWeight(instanceWeight);
            }
            saver.writeIncremental(wekaInstance);
        }
    } catch (Exception e) {
        throw new AnalysisEngineProcessException(e);
    }
}

Example 7 with SparseInstance

use of weka.core.SparseInstance in project dkpro-tc by dkpro.

the class WekaDataWriter method writeClassifierFormat.

@Override
public void writeClassifierFormat(Collection<Instance> instances) throws AnalysisEngineProcessException {
    try {
        Instances masterInstance = initalConfiguration(instances);
        for (Instance inst : instances) {
            double[] featureValues = getFeatureValues(attributeStore, inst);
            weka.core.Instance wekaInstance;
            if (useSparse) {
                wekaInstance = new SparseInstance(1.0, featureValues);
            } else {
                wekaInstance = new DenseInstance(1.0, featureValues);
            }
            wekaInstance.setDataset(masterInstance);
            String outcome = inst.getOutcome();
            if (isRegression) {
                wekaInstance.setClassValue(Double.parseDouble(outcome));
            } else {
                wekaInstance.setClassValue(outcome);
            }
            Double instanceWeight = inst.getWeight();
            if (applyWeighting) {
                wekaInstance.setWeight(instanceWeight);
            }
            // preprocessingFilter.input(wekaInstance);
            // saver.writeIncremental(preprocessingFilter.output());
            saver.writeIncremental(wekaInstance);
        }
    } catch (Exception e) {
        throw new AnalysisEngineProcessException(e);
    }
}