if (i != (int)m_classIndex) {
if (m_sparseIndices[i] != (int)m_classIndex) {
    public String toString() {      StringBuffer text = new StringBuffer();      int printed = 0;      if ((m_alpha == null) && (m_sparseWeights == null)) {	return "BinarySMO: No model built yet.\n";      }      try {	text.append("BinarySMO\n\n");	// If machine linear, print weight vector	if (!m_useRBF && m_exponent == 1.0) {	  text.append("Machine linear: showing attribute weights, ");	  text.append("not support vectors.\n\n");	  // We can assume that the weight vector is stored in sparse	  // format because the classifier has been built	  for (int i = 0; i < m_sparseWeights.length; i++) {	    if (i != (int)m_classIndex) {	      if (printed > 0) {		text.append(" + ");	      } else {		text.append("   ");	      }	      text.append(Utils.doubleToString(m_sparseWeights[i], 12, 4) +			  " * ");	      if (m_filterType == FILTER_STANDARDIZE) {		text.append("(standardized) ");	      } else if (m_filterType == FILTER_NORMALIZE) {		text.append("(normalized) ");	      }	      if (!m_checksTurnedOff) {		text.append(m_data.attribute(m_sparseIndices[i]).name()+"\n");	      } else {		text.append("attribute with index " + 			    m_sparseIndices[i] +"\n");	      }	      printed++;	    }	  }	} else {	  for (int i = 0; i < m_alpha.length; i++) {	    if (m_supportVectors.contains(i)) {	      double val = m_alpha[i];	      if (m_class[i] == 1) {		if (printed > 0) {		  text.append(" + ");		}	      } else {		text.append(" - ");	      }	      text.append(Utils.doubleToString(val, 12, 4) 			  + " * <");	      for (int j = 0; j < m_data.numAttributes(); j++) {		if (j != m_data.classIndex()) {		  text.append(m_data.instance(i).toString(j));		}		if (j != m_data.numAttributes() - 1) {		  text.append(" ");		}	      }	      text.append("> * X]\n");	      printed++;	    }	  }	}	if (m_b > 0) {	  text.append(" - " + Utils.doubleToString(m_b, 12, 4));	} else {	  text.append(" + " + Utils.doubleToString(-m_b, 12, 4));	}	if (m_useRBF || m_exponent != 1.0) {	  text.append("\n\nNumber of support vectors: " + 		      m_supportVectors.numElements());	}	int numEval = 0;	if(m_kernel != null)	  numEval = m_kernel.numEvals();	else	  numEval = 0;		text.append("\n\nNumber of kernel evaluations: " + numEval);      } catch (Exception e) {	e.printStackTrace();	return "Can't print BinarySMO classifier.";      }          return text.toString();    }