if (sumj[nj - 1] > 0.0 && total_missing != sum) {
if (sumj[nj - 1] > 0.0) {
  public double evaluateAttribute (int attribute)    throws Exception  {    int i, j, ii, jj;    int nnj, nni, ni, nj;    double sum = 0.0;    ni = m_trainInstances.attribute(attribute).numValues() + 1;    nj = m_numClasses + 1;    double[] sumi, sumj;    Instance inst;    double temp = 0.0;    sumi = new double[ni];    sumj = new double[nj];    double[][] counts = new double[ni][nj];    sumi = new double[ni];    sumj = new double[nj];    for (i = 0; i < ni; i++) {      sumi[i] = 0.0;      for (j = 0; j < nj; j++) {        sumj[j] = 0.0;        counts[i][j] = 0.0;      }    }    // Fill the contingency table    for (i = 0; i < m_numInstances; i++) {      inst = m_trainInstances.instance(i);      if (inst.isMissing(attribute)) {        ii = ni - 1;      }      else {        ii = (int)inst.value(attribute);      }      if (inst.isMissing(m_classIndex)) {        jj = nj - 1;      }      else {        jj = (int)inst.value(m_classIndex);      }      counts[ii][jj]++;    }    // get the row totals    for (i = 0; i < ni; i++) {      sumi[i] = 0.0;      for (j = 0; j < nj; j++) {        sumi[i] += counts[i][j];        sum += counts[i][j];      }    }    // get the column totals    for (j = 0; j < nj; j++) {      sumj[j] = 0.0;      for (i = 0; i < ni; i++) {        sumj[j] += counts[i][j];      }    }    // distribute missing counts    if (m_missing_merge) {      double[] i_copy = new double[sumi.length];      double[] j_copy = new double[sumj.length];      double[][] counts_copy = new double[sumi.length][sumj.length];      for (i = 0; i < ni; i++) {        System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length);      }      System.arraycopy(sumi, 0, i_copy, 0, sumi.length);      System.arraycopy(sumj, 0, j_copy, 0, sumj.length);      double total_missing = (sumi[ni - 1] + sumj[nj - 1] - 			      counts[ni - 1][nj - 1]);      // do the missing i's      if (sumi[ni - 1] > 0.0) {        for (j = 0; j < nj - 1; j++) {          if (counts[ni - 1][j] > 0.0) {            for (i = 0; i < ni - 1; i++) {              temp = ((i_copy[i]/(sum - i_copy[ni - 1]))*counts[ni - 1][j]);              counts[i][j] += temp;              sumi[i] += temp;            }            counts[ni - 1][j] = 0.0;          }        }      }      sumi[ni - 1] = 0.0;      // do the missing j's      if (sumj[nj - 1] > 0.0 && total_missing != sum) {        for (i = 0; i < ni - 1; i++) {          if (counts[i][nj - 1] > 0.0) {            for (j = 0; j < nj - 1; j++) {              temp = ((j_copy[j]/(sum - j_copy[nj - 1]))*counts[i][nj - 1]);              counts[i][j] += temp;              sumj[j] += temp;            }            counts[i][nj - 1] = 0.0;          }        }      }      sumj[nj - 1] = 0.0;      // do the both missing      if (counts[ni - 1][nj - 1] > 0.0) {        for (i = 0; i < ni - 1; i++) {          for (j = 0; j < nj - 1; j++) {            temp = (counts_copy[i][j]/(sum - total_missing)) * 	      counts_copy[ni - 1][nj - 1];            counts[i][j] += temp;            sumi[i] += temp;            sumj[j] += temp;          }        }        counts[ni - 1][nj - 1] = 0.0;      }    }    return  ContingencyTables.gainRatio(counts);  }