示例#1
0
  private String classificationOutput(
      myDataset dataset,
      int ex,
      int data[][],
      int classData[],
      int infoAttr[],
      Vector<Rule> contenedor,
      int nClases) {

    int j, k, l;
    boolean match;
    double tmp1, tmp2;
    int pos = 0, classPredicted;
    double Waip;
    int ejemplo[] = new int[data[0].length];

    for (j = 0; j < ejemplo.length; j++) {
      if (dataset.isMissing(ex, j)) ejemplo[j] = -1;
      else ejemplo[j] = dataset.valueExample(ex, j);
    }

    classPredicted = -1;
    Waip = 0;

    /*Search a match of the example (following by the container)*/
    for (j = contenedor.size() - 1; j >= 0; j--) {
      match = true;
      for (k = 0; k < contenedor.elementAt(j).getRule().length && match; k++) {
        if (ejemplo[contenedor.elementAt(j).getiCondition(k).getAttribute()]
            != contenedor.elementAt(j).getiCondition(k).getValue()) {
          match = false;
        }
      }
      if (match) {
        tmp1 = Double.NEGATIVE_INFINITY;
        for (l = 0; l < nClases; l++) {
          tmp2 = 0;
          for (k = 0; k < contenedor.elementAt(j).getRule().length; k++) {
            tmp2 +=
                RuleSet.computeWeightEvidence(
                    data, classData, contenedor.elementAt(j).getiCondition(k), l, infoAttr);
          }
          if (tmp2 > tmp1) {
            tmp1 = tmp2;
            pos = l;
          }
        }
        if (tmp1 > Waip) {
          classPredicted = pos;
          Waip = tmp1;
        }
      }
    }
    if (classPredicted == -1) return "Unclassified";

    return dataset.getOutputValue(classPredicted);
  }
示例#2
0
  /** It launches the algorithm */
  public void execute() {

    int i, j, k, l;
    int t;
    int ele;
    double prob[];
    double aux;
    double NUmax = 1.5; // used for lineal ranking
    double NUmin = 0.5; // used for lineal ranking
    double pos1, pos2;
    int sel1, sel2;
    int data[][];
    int infoAttr[];
    int classData[];
    Vector<Rule> contenedor = new Vector<Rule>();
    Vector<Rule> conjR = new Vector<Rule>();
    Rule tmpRule;
    Condition tmpCondition[] = new Condition[1];
    RuleSet population[];
    RuleSet hijo1, hijo2;

    if (somethingWrong) { // We do not execute the program
      System.err.println("An error was found, the data-set has numerical values.");
      System.err.println("Aborting the program");
      // We should not use the statement: System.exit(-1);
    } else {
      Randomize.setSeed(seed);

      nClasses = train.getnClasses();

      /*Build the nominal data information*/
      infoAttr = new int[train.getnInputs()];
      for (i = 0; i < infoAttr.length; i++) {
        infoAttr[i] = train.numberValues(i);
      }

      data = new int[train.getnData()][train.getnInputs()];
      for (i = 0; i < data.length; i++) {
        for (j = 0; j < data[i].length; j++) {
          if (train.isMissing(i, j)) data[i][j] = -1;
          else data[i][j] = train.valueExample(i, j);
        }
      }

      classData = new int[train.getnData()];
      for (i = 0; i < classData.length; i++) {
        classData[i] = train.getOutputAsInteger(i);
      }

      /*Find first-order rules which result interesting*/

      for (i = 0; i < nClasses; i++) {
        for (j = 0; j < infoAttr.length; j++) {
          for (k = 0; k < infoAttr[j]; k++) {
            tmpCondition[0] = new Condition(j, k);
            tmpRule = new Rule(tmpCondition);
            if (Math.abs(computeAdjustedResidual(data, classData, tmpRule, i)) > 1.96) {
              if (!contenedor.contains(tmpRule)) {
                contenedor.add(tmpRule);
                conjR.add(tmpRule);
              }
            }
          }
        }
      }

      // Construct the Baker selection roulette
      prob = new double[popSize];
      for (j = 0; j < popSize; j++) {
        aux = (double) (NUmax - NUmin) * ((double) j / (popSize - 1));
        prob[j] = (double) (1.0 / (popSize)) * (NUmax - aux);
      }
      for (j = 1; j < popSize; j++) prob[j] = prob[j] + prob[j - 1];

      /*Steady-State Genetic Algorithm*/
      ele = 2;
      population = new RuleSet[popSize];
      while (conjR.size() >= 2) {
        t = 0;

        System.out.println("Producing rules of level " + ele);

        for (i = 0; i < population.length; i++) {
          population[i] = new RuleSet(conjR);
          population[i].computeFitness(data, classData, infoAttr, contenedor, nClasses);
        }

        Arrays.sort(population);

        while (t < numGenerations && !population[0].equals(population[popSize - 1])) {
          System.out.println("Generation " + t);
          t++;

          /*Baker's selection*/
          pos1 = Randomize.Rand();
          pos2 = Randomize.Rand();
          for (l = 0; l < popSize && prob[l] < pos1; l++) ;
          sel1 = l;
          for (l = 0; l < popSize && prob[l] < pos2; l++) ;
          sel2 = l;

          hijo1 = new RuleSet(population[sel1]);
          hijo2 = new RuleSet(population[sel2]);

          if (Randomize.Rand() < pCross) {
            RuleSet.crossover1(hijo1, hijo2);
          } else {
            RuleSet.crossover2(hijo1, hijo2);
          }

          RuleSet.mutation(hijo1, conjR, pMut, data, classData, infoAttr, contenedor, nClasses);
          RuleSet.mutation(hijo2, conjR, pMut, data, classData, infoAttr, contenedor, nClasses);

          hijo1.computeFitness(data, classData, infoAttr, contenedor, nClasses);
          hijo2.computeFitness(data, classData, infoAttr, contenedor, nClasses);

          population[popSize - 2] = new RuleSet(hijo1);
          population[popSize - 1] = new RuleSet(hijo2);

          Arrays.sort(population);
        }

        /*Decode function*/
        ele++;
        conjR.removeAllElements();
        System.out.println(
            "Fitness of the best chromosome in rule level " + ele + ": " + population[0].fitness);
        for (i = 0; i < population[0].getRuleSet().length; i++) {
          if (Math.abs(computeAdjustedResidual(data, classData, population[0].getRule(i), i))
              > 1.96) {
            if (validarRegla(population[0].getRule(i))
                && !contenedor.contains(population[0].getRule(i))) {
              contenedor.add(population[0].getRule(i));
              conjR.add(population[0].getRule(i));
            }
          }
        }
      }

      // Finally we should fill the training and test output files
      doOutput(this.val, this.outputTr, data, classData, infoAttr, contenedor, nClasses);
      doOutput(this.test, this.outputTst, data, classData, infoAttr, contenedor, nClasses);

      /*Print the rule obtained*/
      for (i = contenedor.size() - 1; i >= 0; i--) {
        if (reglaPositiva(
            this.train, data, classData, infoAttr, nClasses, contenedor.elementAt(i))) {
          Fichero.AnadirtoFichero(outputRule, contenedor.elementAt(i).toString(train));
          Fichero.AnadirtoFichero(
              outputRule,
              " -> "
                  + consecuente(
                      this.train, data, classData, infoAttr, nClasses, contenedor.elementAt(i))
                  + "\n");
        }
      }
      System.out.println("Algorithm Finished");
    }
  }