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 "SMOreg : No model built yet.";    }    try {      text.append("SMOreg\n\n");	          text.append("Kernel used : \n");      if(m_useRBF) {	text.append("  RBF kernel : K(x,y) = e^-(" + m_gamma + "* <x-y,x-y>^2)");      } else if (m_exponent == 1){	text.append("  Linear Kernel : K(x,y) = <x,y>");      } else {	if (m_featureSpaceNormalization) {	  if (m_lowerOrder){	    text.append("  Normalized Poly Kernel with lower order : K(x,y) = (<x,y>+1)^" + m_exponent + "/" + 			"((<x,x>+1)^" + m_exponent + "*" + "(<y,y>+1)^" + m_exponent + ")^(1/2)");		    	  } else {	    text.append("  Normalized Poly Kernel : K(x,y) = <x,y>^" + m_exponent + "/" + "(<x,x>^" + 			m_exponent + "*" + "<y,y>^" + m_exponent + ")^(1/2)");	  }	} else {	  if (m_lowerOrder){	    text.append("  Poly Kernel with lower order : K(x,y) = (<x,y> + 1)^" + m_exponent);	  } else {	    text.append("  Poly Kernel : K(x,y) = <x,y>^" + m_exponent);			  }	}      }      text.append("\n\n");      // display the linear transformation      String trans = "";      if (m_filterType == FILTER_STANDARDIZE) {	//text.append("LINEAR TRANSFORMATION APPLIED : \n");	trans = "(standardized) ";	//text.append(trans + m_data.classAttribute().name() + "  = " + 	//	    m_Alin + " * " + m_data.classAttribute().name() + " + " + m_Blin + "\n\n");      } else if (m_filterType == FILTER_NORMALIZE) {	//text.append("LINEAR TRANSFORMATION APPLIED : \n");	trans = "(normalized) ";	//text.append(trans + m_data.classAttribute().name() + "  = " + 	//	    m_Alin + " * " + m_data.classAttribute().name() + " + " + m_Blin + "\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");			// We can assume that the weight vector is stored in sparse	// format because the classifier has been built	text.append(trans + m_data.classAttribute().name() + " =\n");	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 {	text.append("Support Vector Expansion :\n");	text.append(trans + m_data.classAttribute().name() + " =\n");	printed = 0;	for (int i = 0; i < m_alpha.length; i++) {	  double val = m_alpha[i] - m_alpha_[i];	  if (java.lang.Math.abs(val) < 1e-4)	    continue;	  if (printed > 0) {	    text.append(" + ");	  } else {	    text.append("   ");		    	  }	  text.append(Utils.doubleToString(val, 12, 4) 		      + " * K[X(" + i + "), 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: " + printed);      }      text.append("\n\nNumber of kernel evaluations: " + m_kernel.numEvals()+ "\n");    } catch (Exception e) {      return "Can't print the classifier.";    }    return text.toString();  }