Examples of opennlp.maxent.GISModel.eval()

opennlp.maxent.GISModel.eval()
Use this model to evaluate a context and return an array of the likelihood of each outcome given that context. @param context The names of the predicates which have been observed at the present decision point. @return The normalized probabilities for the outcomes given the context.The indexes of the double[] are the outcome ids, and the actual string representation of the outcomes can be obtained from the method getOutcome(int i).

      GISModel gm = GIS.trainModel(100, di);
      
      //ClassificationEvaluator ce = new ClassificationEvaluator();
      
      for(Event event : testEvents) {
        double [] results = gm.eval(event.getContext());
        String result = results[gm.getIndex("TRUE")] > 0.5 ? "TRUE" : "FALSE";
        //String result = gm.getBestOutcome(results);
        //System.out.println(event.getOutcome() + "\t" + result + "\t" + results[gm.getIndex(event.getOutcome())] + "\t" + StringTools.arrayToList(event.getContext()));
        ce.logEvent(event.getOutcome(), result);
      }

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    int mp = 0;
    int mn = 0;
    for(int i=numDocs/2;i<numDocs;i++) { 
      Event e = docToEvent(ir, i, c);
      //System.out.println(gm.getBestOutcome(gm.eval(e.getContext())));
      double prob = gm.eval(e.getContext())[gm.getIndex("TRUE")];
      //System.out.println(ir.document(i).getField("filename").stringValue().replaceAll("markedup", "source"));
      String name = ir.document(i).getField("filename").stringValue().replaceAll("markedup", "source");
      byProb.put(name, prob);
      res.put(name, e.getOutcome());
      if(e.getOutcome().equals("TRUE")) {

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