Examples of weka.core.Instance

weka.core.Instance
te empty instance with three attribute values
Instance inst = new Instance(3);

// Set instance's values for the attributes "length", "weight", and "position"
inst.setValue(length, 5.3);
inst.setValue(weight, 300);
inst.setValue(position, "first");

// Set instance's dataset to be the dataset "race"
inst.setDataset(race);

// Print the instance
System.out.println("The instance: " + inst);
...

All methods that change an instance are safe, ie. a change of an instance does not affect any other instances. All methods that change an instance's attribute values clone the attribute value vector before it is changed. If your application heavily modifies instance values, it may be faster to create a new instance from scratch. @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5970 $

   */
  @Override
  public Set<ReasonerAdapter> select() {
    Set<ReasonerAdapter> selectedSet = new HashSet<ReasonerAdapter>();
    
    Instance instance = translator.getInstance(this.ontoProperties, this.ontology, this.query);
    String selectedReasonerName = predict(instance);
    
    System.out.println("Selected Reasoner: " + selectedReasonerName);
    
    for(ReasonerAdapter reasoner: reasoners){

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    return ontologyAnalyser.getFeatures(ontology);
  }
  
  public Instance getInstance(Properties ontoProperties, OWLOntology ontology, Query query){
    List<String> featureList = getFeatures(ontoProperties, ontology, query);
    Instance instance = new Instance(featureList.size() + 1);
    instance.setDataset(dataset);
    for(int i = 0; i < featureList.size(); i ++){
      Attribute attr = instance.attribute(i);
      if(attr.isNumeric()){
        instance.setValue(i, Double.valueOf(featureList.get(i)));
      }else{
        instance.setValue(i, featureList.get(i));
      }
    }
    return instance;
  }

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        {
          try
          {
            int []comparisonResults = new int[dataCollector.getInstanceLength()];
            dataCollector.fillInPairDetails(comparisonResults,p, nonNegPairs);
            Instance instance = dataCollector.constructInstance(comparisonResults, true);
            double distribution[]=classifier.distributionForInstance(instance);
            long quality = obtainMeasureOfQualityFromDistribution(distribution,classTrue);
            if ( quality >= 0 )// && p.getScore() > 0)
            {
              possibleResults.add(new PairScore(p.getQ(), p.getR(), p.getScore(), quality));

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        // potentially meaningful pair, check with the classifier
        try
        {
          int []comparisonResults = new int[dataCollector.getInstanceLength()];
          dataCollector.fillInPairDetails(comparisonResults,p, pairs);
          Instance instance = dataCollector.constructInstance(comparisonResults, true);
          double distribution[]=classifier.distributionForInstance(instance);
          long quality = obtainMeasureOfQualityFromDistribution(distribution,classFalse);
          if ( quality > 0 )
          {
            if (pairBestToReturnAsRed == null || quality >pairBestToReturnAsRed.getAnotherScore())

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        {// meaningful pairs, check with the classifier
          try
          {
            int []comparisonResults = new int[dataCollector.getInstanceLength()];
            dataCollector.fillInPairDetails(comparisonResults,p, pairs);
            Instance instance = dataCollector.constructInstance(comparisonResults, true);
            double distribution[]=classifier.distributionForInstance(instance);
            long quality = obtainMeasureOfQualityFromDistribution(distribution,classTrue);
            if ( quality >= 0 )
            {
              sum+=quality;

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        {// meaningful pairs, check with the classifier
          try
          {
            int []comparisonResults = new int[dataCollector.getInstanceLength()];
            dataCollector.fillInPairDetails(comparisonResults,p, pairs);
            Instance instance = dataCollector.constructInstance(comparisonResults, true);
            double distribution[]=classifier.distributionForInstance(instance);
            long quality = obtainMeasureOfQualityFromDistribution(distribution,classTrue);
            if ( quality >= 0 )
            {
              sum+=quality;

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        {
          try
          {
            int []comparisonResults = new int[dataCollector.getInstanceLength()];
            dataCollector.fillInPairDetails(comparisonResults,p, nonNegPairs);
            Instance instance = dataCollector.constructInstance(comparisonResults, true);
            double distribution[]=classifier.distributionForInstance(instance);
            long quality = obtainMeasureOfQualityFromDistribution(distribution,classTrue);
            if ( quality >= 0 )// && p.getScore() > 0)
            {
              possibleResults.add(new PairScore(p.getQ(), p.getR(), p.getScore(), quality));

View Full Code Here

        // potentially meaningful pair, check with the classifier
        try
        {
          int []comparisonResults = new int[dataCollector.getInstanceLength()];
          dataCollector.fillInPairDetails(comparisonResults,p, pairs);
          Instance instance = dataCollector.constructInstance(comparisonResults, true);
          double distribution[]=classifier.distributionForInstance(instance);
          long quality = obtainMeasureOfQualityFromDistribution(distribution,classFalse);
          if ( quality > 0 )
          {
            if (pairBestToReturnAsRed == null || quality >pairBestToReturnAsRed.getAnotherScore())

View Full Code Here

        {// meaningful pairs, check with the classifier
          try
          {
            int []comparisonResults = new int[dataCollector.getInstanceLength()];
            dataCollector.fillInPairDetails(comparisonResults,p, pairs);
            Instance instance = dataCollector.constructInstance(comparisonResults, true);
            double distribution[]=classifier.distributionForInstance(instance);
            long quality = obtainMeasureOfQualityFromDistribution(distribution,classTrue);
            if ( quality >= 0 )
            {
              sum+=quality;

View Full Code Here

        {
          try
          {
            int []comparisonResults = new int[dataCollector.getInstanceLength()];
            dataCollector.fillInPairDetails(comparisonResults,p, nonNegPairs);
            Instance instance = dataCollector.constructInstance(comparisonResults, true);
            double distribution[]=classifier.distributionForInstance(instance);
            long quality = obtainMeasureOfQualityFromDistribution(distribution,classTrue);
            if ( quality >= 0 )// && p.getScore() > 0)
            {
              possibleResults.add(new PairScore(p.getQ(), p.getR(), p.getScore(), quality));

View Full Code Here

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