content: /*
   *    This program is free software; you can redistribute it and/or modify
   *    it under the terms of the GNU General Public License as published by
   *    the Free Software Foundation; either version 2 of the License, or
   *    (at your option) any later version.
   *
   *    This program is distributed in the hope that it will be useful,
   *    but WITHOUT ANY WARRANTY; without even the implied warranty of
   *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   *    GNU General Public License for more details.
   *
   *    You should have received a copy of the GNU General Public License
   *    along with this program; if not, write to the Free Software
   *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
   */
  
  /*
   *    RuleSetModel.java
   *    Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.pmml.consumer;
  
  import java.io.Serializable;
  import java.util.ArrayList;
  
  import org.w3c.dom.Element;
  import org.w3c.dom.Node;
  import org.w3c.dom.NodeList;
  
  import weka.classifiers.pmml.consumer.TreeModel.MiningFunction;
  import weka.core.Attribute;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  import weka.core.pmml.MiningSchema;
  
  /**
   * Class implementing import of PMML RuleSetModel. Can be used as a Weka
   * classifier for prediction only (buildClassifier() raises an Exception).
   * 
   * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
   * @version $Revision: 5987 $
   */
  public class RuleSetModel extends PMMLClassifier {
    
    /** For serialization */
    private static final long serialVersionUID = 1993161168811020547L;
  
    /**
     * Abstract inner base class for Rules
     */
    static abstract class Rule implements Serializable {
      
      /** For serialization */
      private static final long serialVersionUID = 6236231263477446102L;
      
      /** The predicate for this rule */
      protected TreeModel.Predicate m_predicate;
      
      public Rule(Element ruleE, MiningSchema miningSchema) throws Exception {
        // Set up the predicate
        m_predicate = TreeModel.Predicate.getPredicate(ruleE, miningSchema);
      }
      
      /**
       * Collect the rule(s) that fire for the supplied incoming instance
       * 
       * @param input a vector of independent and derived independent variables
       * @param ruleCollection the array list to add any firing rules into
       */
      public abstract void fires(double[] input, ArrayList<SimpleRule> ruleCollection);
      
      /**
       * Get a textual description of this Rule
       * 
       * @param prefix prefix string (typically some number of spaces) to prepend
       * @param indent the number of additional spaces to add to the prefix
       * @return a description of this Rule as a String
       */
      public abstract String toString(String prefix, int indent);
      
    }
    
    /**
     * Inner class for representing simple rules
     */
    static class SimpleRule extends Rule {
      
      /** For serialization */
      private static final long serialVersionUID = -2612893679476049682L;
  
      /** The ID for the rule (optional) */
      protected String m_ID;
      
      /** The predicted value when the rule fires (required) */
      protected String m_scoreString;
      
      /** 
       * The predicted value as a number (regression) or index (classification)
       * when the rule fires (required)
       */
      protected double m_score = Utils.missingValue();
      
      /** The number of training/test instances on which the rule fired (optional) */
      protected double m_recordCount = Utils.missingValue();
      
      /** 
       * The number of training/test instances on which the rule fired and the
       * prediction was correct (optional)
       */
      protected double m_nbCorrect = Utils.missingValue();
      
      /** The confidence of the rule (optional) */
      protected double m_confidence = Utils.missingValue();
      
      /** The score distributions for this rule (if any) */
      protected ArrayList<TreeModel.ScoreDistribution> m_scoreDistributions = 
        new ArrayList<TreeModel.ScoreDistribution>();
      
      /**
       *  The relative importance of the rule. May or may not be equal to the
       * confidence (optional).
       */
      protected double m_weight = Utils.missingValue();
      
      public String toString(String prefix, int indent) {
        StringBuffer temp = new StringBuffer();
        
        for (int i = 0; i < indent; i++) {
          prefix += " ";
        }
        
        temp.append(prefix + "Simple rule: " + m_predicate + "\n");
        temp.append(prefix + " => " + m_scoreString + "\n");
        if (!Utils.isMissingValue(m_recordCount)) {
          temp.append(prefix + " recordCount: " + m_recordCount + "\n");
        }
        if (!Utils.isMissingValue(m_nbCorrect)) {
          temp.append(prefix + "   nbCorrect: " + m_nbCorrect + "\n");
        }
        if (!Utils.isMissingValue(m_confidence)) {
          temp.append(prefix + "  confidence: " + m_confidence + "\n");
        }
        if (!Utils.isMissingValue(m_weight)) {
          temp.append(prefix + "      weight: " + m_weight + "\n");
        }
        
        return temp.toString();
      }
      
      public String toString() {
        return toString("", 0);
      }
          
      /**
       * Constructor for a simple rule
       * 
       * @param ruleE the XML element holding the simple rule
       * @param miningSchema the mining schema to use
       * @throws Exception if something goes wrong
       */
      public SimpleRule(Element ruleE, MiningSchema miningSchema) throws Exception {
        super(ruleE, miningSchema);
        
        String id = ruleE.getAttribute("id");
        if (id != null && id.length() > 0) {
          m_ID = id;
        }
        
        m_scoreString = ruleE.getAttribute("score");
        Attribute classAtt = miningSchema.getFieldsAsInstances().classAttribute(); 
        if (classAtt.isNumeric()) {
          m_score = Double.parseDouble(m_scoreString);
        } else {
          if (classAtt.indexOfValue(m_scoreString) < 0) {
            throw new Exception("[SimpleRule] class value " + m_scoreString + 
                "does not exist in class attribute " + classAtt.name());
          }
          m_score = classAtt.indexOfValue(m_scoreString);
        }
        
        String recordCount = ruleE.getAttribute("recordCount");
        if (recordCount != null && recordCount.length() > 0) {
          m_recordCount = Double.parseDouble(recordCount);
        }
        
        String nbCorrect = ruleE.getAttribute("nbCorrect");
        if (nbCorrect != null && nbCorrect.length() > 0) {
          m_nbCorrect = Double.parseDouble(nbCorrect);
        }
        
        String confidence = ruleE.getAttribute("confidence");
        if (confidence != null && confidence.length() > 0) {
          m_confidence = Double.parseDouble(confidence);
        }
        
        String weight = ruleE.getAttribute("weight");
        if (weight != null && weight.length() > 0) {
          m_weight = Double.parseDouble(weight);
        }
        
        // get the ScoreDistributions (if any)
        if (miningSchema.getFieldsAsInstances().classAttribute().isNominal()) {
          // see if we have any ScoreDistribution entries
          NodeList scoreChildren = ruleE.getChildNodes();
                  
          for (int i = 0; i < scoreChildren.getLength(); i++) {
            Node child = scoreChildren.item(i);
            if (child.getNodeType() == Node.ELEMENT_NODE) {
              String tagName = ((Element)child).getTagName();
              if (tagName.equals("ScoreDistribution")) {
                TreeModel.ScoreDistribution newDist = 
                  new TreeModel.ScoreDistribution((Element)child, 
                    miningSchema, m_recordCount);
                m_scoreDistributions.add(newDist);
              }
            }
          }
          
          // check that we have as many score distribution elements as there
          // are class labels in the data
          if (m_scoreDistributions.size() > 0 && 
              m_scoreDistributions.size() != 
                miningSchema.getFieldsAsInstances().classAttribute().numValues()) {
            throw new Exception("[SimpleRule] Number of score distribution elements is "
                + " different than the number of class labels!");
          }
          
          //backfit the confidence values (if necessary)
          if (Utils.isMissingValue(m_recordCount)) {
            double baseCount = 0;
            for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
              baseCount += s.getRecordCount();
            }
            
            for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
              s.deriveConfidenceValue(baseCount);
            }
          }
        }
      }
      
      /**
       * Collect the rule(s) that fire for the supplied incoming instance
       * 
       * @param input a vector of independent and derived independent variables
       * @param ruleCollection the array list to add any firing rules into
       */
      public void fires(double[] input, ArrayList<SimpleRule> ruleCollection) {
        if (m_predicate.evaluate(input) == TreeModel.Predicate.Eval.TRUE) {
          ruleCollection.add(this);
        }      
      }
      
      /**
       * Score the incoming instance
       * 
       * @param instance a vector containing the incoming independent and
       * derived independent variables
       * @param classAtt the class attribute
       * @param rsm the rule selection method (ignored by simple rules)
       * @return a probability distribution over the class labels or
       * the predicted value (in element zero of the array if the class is numeric)
       * @throws Exception if something goes wrong
       */
      public double[] score(double[] instance, Attribute classAtt) 
        throws Exception {
        
        double[] preds;
        if (classAtt.isNumeric()) {
          preds = new double[1];
          preds[0] = m_score;
        } else {
          preds = new double[classAtt.numValues()];
          if (m_scoreDistributions.size() > 0) {
            for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
              preds[s.getClassLabelIndex()] = s.getConfidence();
            }
          } else if (!Utils.isMissingValue(m_confidence)) {
            preds[classAtt.indexOfValue(m_scoreString)] = m_confidence;
          } else {
            preds[classAtt.indexOfValue(m_scoreString)] = 1.0;
          }
        }      
        
        return preds;
      }
      
      /**
       * Get the weight of the rule
       * 
       * @return the weight of the rule
       */
      public double getWeight() {
        return m_weight;
      }
      
      /**
       * Get the ID of the rule
       * 
       * @return the ID of the rule
       */
      public String getID() {
        return m_ID;
      }
      
      /**
       * Get the predicted value of this rule (either a number
       * for regression problems or an index of a class label for
       * classification problems)
       * 
       * @return the predicted value of this rule
       */
      public double getScore() {
        return m_score;
      }
    }
    
    /**
     * Inner class representing a compound rule
     */
    static class CompoundRule extends Rule {
      
      /** For serialization */
      private static final long serialVersionUID = -2853658811459970718L;
      
      /** The child rules of this compound rule */
      ArrayList<Rule> m_childRules = new ArrayList<Rule>();
      
      public String toString(String prefix, int indent) {
        StringBuffer temp = new StringBuffer();
  
        for (int i = 0; i < indent; i++) {
          prefix += " ";
        }
        
        temp.append(prefix + "Compound rule: " + m_predicate + "\n");
        
        for (Rule r : m_childRules) {
          temp.append(r.toString(prefix, indent + 1));
        }
  
        return temp.toString();
      }
      
      public String toString() {
        return toString("", 0);
      }
      
      /**
       * Constructor.
       * 
       * @param ruleE XML node holding the rule
       * @param miningSchema the mining schema to use
       * @throws Exception if something goes wrong
       */
      public CompoundRule(Element ruleE, MiningSchema miningSchema) throws Exception {
        
        // get the Predicate
        super(ruleE, miningSchema);
        
        // get the nested rules
        NodeList ruleChildren = ruleE.getChildNodes();
        for (int i = 0; i < ruleChildren.getLength(); i++) {
          Node child = ruleChildren.item(i);
          if (child.getNodeType() == Node.ELEMENT_NODE) {
            String tagName = ((Element)child).getTagName();
            if (tagName.equals("SimpleRule")) {
              Rule childRule = new SimpleRule(((Element)child), miningSchema);
              m_childRules.add(childRule);
            } else if (tagName.equals("CompoundRule")) {
              Rule childRule = new CompoundRule(((Element)child), miningSchema);
              m_childRules.add(childRule);
            }
          }
        }
      }
      
      /**
       * Collect the rule(s) that fire for the supplied incoming instance
       * 
       * @param input a vector of independent and derived independent variables
       * @param ruleCollection the array list to add any firing rules into
       */
      public void fires(double[] input, ArrayList<SimpleRule> ruleCollection) {
        
        // evaluate our predicate first
        if (m_predicate.evaluate(input) == TreeModel.Predicate.Eval.TRUE) {
          // now check the child rules
          for (Rule r : m_childRules) {
            r.fires(input, ruleCollection);
          }
        }      
      }
    }
    
    /**
     * Inner class representing a set of rules 
     */
    static class RuleSet implements Serializable {
      
      /** For serialization */
      private static final long serialVersionUID = -8718126887943074376L;
  
      enum RuleSelectionMethod {
        WEIGHTEDSUM("weightedSum"),
        WEIGHTEDMAX("weightedMax"),
        FIRSTHIT("firstHit");
        
        private final String m_stringVal;
        
        RuleSelectionMethod(String name) {
          m_stringVal = name;
        }
        
        public String toString() {
          return m_stringVal;
        }
      }
      
      /** 
       * The number of training/test cases to which the ruleset was
       * applied to generate support and confidence measures for individual
       * rules (optional)
       */
      private double m_recordCount = Utils.missingValue();
      
      /** 
       * The number of training/test cases for which the default
       * score is correct (optional)
       */
      private double m_nbCorrect = Utils.missingValue();
      
      /** 
       * The default value to predict when no rule in the
       * ruleset fires (as a String; optional) 
       * */
      private String m_defaultScore;
      
      /** 
       * The default value to predict (either a real value or an
       * index) 
       * */
      private double m_defaultPrediction = Utils.missingValue();
      
      /** 
       * The default distribution to predict when no rule in the
       * ruleset fires (nominal class only, optional)
       */
      private ArrayList<TreeModel.ScoreDistribution> m_scoreDistributions =
        new ArrayList<TreeModel.ScoreDistribution>();
      
      /**
       * The default confidence value to return along with a score
       * when no rules in the set fire (optional)
       */
      private double m_defaultConfidence = Utils.missingValue();
      
      /** The active rule selection method */
      private RuleSelectionMethod m_currentMethod;
      
      /** The selection of rule selection methods allowed */
      private ArrayList<RuleSelectionMethod> m_availableRuleSelectionMethods = 
        new ArrayList<RuleSelectionMethod>();
      
      /** The rules contained in the rule set */
      private ArrayList<Rule> m_rules = new ArrayList<Rule>();
      
      /* (non-Javadoc)
       * @see java.lang.Object#toString()
       */
      public String toString() {
        StringBuffer temp = new StringBuffer();
        
        temp.append("Rule selection method: " + m_currentMethod + "\n");
        if (m_defaultScore != null) {
          temp.append("Default prediction: " + m_defaultScore + "\n");
          
          if (!Utils.isMissingValue(m_recordCount)) {
            temp.append("       recordCount: " + m_recordCount + "\n");
          }
          if (!Utils.isMissingValue(m_nbCorrect)) {
            temp.append("         nbCorrect: " + m_nbCorrect + "\n");
          }
          if (!Utils.isMissingValue(m_defaultConfidence)) {
            temp.append(" defaultConfidence: " + m_defaultConfidence + "\n");
          }
          
          temp.append("\n");
        }
        
        for (Rule r : m_rules) {
          temp.append(r + "\n");
        }
        
        return temp.toString();
      }
      
      /**
       * Constructor for a RuleSet.
       * 
       * @param ruleSetNode the XML node holding the RuleSet
       * @param miningSchema the mining schema to use
       * @throws Exception if something goes wrong
       */
      public RuleSet(Element ruleSetNode, MiningSchema miningSchema) 
        throws Exception {
        
        String recordCount = ruleSetNode.getAttribute("recordCount");
        if (recordCount != null && recordCount.length() > 0) {
          m_recordCount = Double.parseDouble(recordCount);
        }
        
        String nbCorrect = ruleSetNode.getAttribute("nbCorrect");
        if (nbCorrect != null & nbCorrect.length() > 0) {
          m_nbCorrect = Double.parseDouble(nbCorrect);
        }
        
        String defaultScore = ruleSetNode.getAttribute("defaultScore");
        if (defaultScore != null && defaultScore.length() > 0) {
          m_defaultScore = defaultScore;
          
          Attribute classAtt = miningSchema.getFieldsAsInstances().classAttribute();
          if (classAtt == null) {
            throw new Exception("[RuleSet] class attribute not set!");
          }
          
          if (classAtt.isNumeric()) {
            m_defaultPrediction = Double.parseDouble(defaultScore);
          } else {
            if (classAtt.indexOfValue(defaultScore) < 0) {
              throw new Exception("[RuleSet] class value " + defaultScore + 
                  " not found!");
            }
            m_defaultPrediction = classAtt.indexOfValue(defaultScore);
          }
        }
        
        String defaultConfidence = ruleSetNode.getAttribute("defaultConfidence");
        if (defaultConfidence != null && defaultConfidence.length() > 0) {
          m_defaultConfidence = Double.parseDouble(defaultConfidence);
        }
        
        // get the rule selection methods
        NodeList selectionNL = ruleSetNode.getElementsByTagName("RuleSelectionMethod");
        for (int i = 0; i < selectionNL.getLength(); i++) {
          Node selectN = selectionNL.item(i);
          if (selectN.getNodeType() == Node.ELEMENT_NODE) {
            Element sN = (Element)selectN;
            String criterion = sN.getAttribute("criterion");
            for (RuleSelectionMethod m : RuleSelectionMethod.values()) {
              if (m.toString().equals(criterion)) {
                m_availableRuleSelectionMethods.add(m);
                if (i == 0) {
                  // set the default (first specified one)
                  m_currentMethod = m;
                }
              }
            }
          }
        }
        
        if (miningSchema.getFieldsAsInstances().classAttribute().isNominal()) {
          // see if we have any ScoreDistribution entries
          NodeList scoreChildren = ruleSetNode.getChildNodes();
          for (int i = 0; i < scoreChildren.getLength(); i++) {
            Node child = scoreChildren.item(i);
            if (child.getNodeType() == Node.ELEMENT_NODE) {
              String tagName = ((Element)child).getTagName();
              if (tagName.equals("ScoreDistribution")) {
                TreeModel.ScoreDistribution newDist = 
                  new TreeModel.ScoreDistribution((Element)child, 
                    miningSchema, m_recordCount);
                m_scoreDistributions.add(newDist);
              }
            }
          }
          
          //backfit the confidence values (if necessary)
          if (Utils.isMissingValue(m_recordCount)) {
            double baseCount = 0;
            for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
              baseCount += s.getRecordCount();
            }
            
            for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
              s.deriveConfidenceValue(baseCount);
            }
          }
        }
        
        // Get the rules in this rule set
        NodeList ruleChildren = ruleSetNode.getChildNodes();
        for (int i = 0; i < ruleChildren.getLength(); i++) {
          Node child = ruleChildren.item(i);
          if (child.getNodeType() == Node.ELEMENT_NODE) {
            String tagName = ((Element)child).getTagName();
            if (tagName.equals("SimpleRule")) {
              Rule tempRule = new SimpleRule(((Element)child), miningSchema);
              m_rules.add(tempRule);
            } else if (tagName.equals("CompoundRule")) {
              Rule tempRule = new CompoundRule(((Element)child), miningSchema);
              m_rules.add(tempRule);
            }
          }
        }
      }
      
      /**
       * Score an incoming instance by collecting all rules that fire.
       * 
       * @param instance a vector of incoming attribte and derived field values
       * @param classAtt the class attribute
       * @return a predicted probability distribution
       * @throws Exception is something goes wrong
       */
      protected double[] score(double[] instance, Attribute classAtt)
        throws Exception {
        
        double[] preds = null;
        if (classAtt.isNumeric()) {
          preds = new double[1];
        } else {
          preds = new double[classAtt.numValues()];
        }
        
        // holds the rules that fire for this test case
        ArrayList<SimpleRule> firingRules = new ArrayList<SimpleRule>();
        
        for (Rule r : m_rules) {
          r.fires(instance, firingRules);
        }
        
        if (firingRules.size() > 0) {
          if (m_currentMethod == RuleSelectionMethod.FIRSTHIT) {
            preds = firingRules.get(0).score(instance, classAtt);
          } else if (m_currentMethod == RuleSelectionMethod.WEIGHTEDMAX) {
            double wMax = Double.NEGATIVE_INFINITY;
            SimpleRule best = null;
            for (SimpleRule s : firingRules) {
              if (Utils.isMissingValue(s.getWeight())) {
                throw new Exception("[RuleSet] Scoring criterion is WEIGHTEDMAX, but " +
                		"rule " + s.getID() + " does not have a weight defined!");
              }
              if (s.getWeight() > wMax) {
                wMax = s.getWeight();
                best = s;
              }
            }
            if (best == null) {
              throw new Exception("[RuleSet] Unable to determine the best rule under " +
              		"the WEIGHTEDMAX criterion!");
            }
            preds = best.score(instance, classAtt);          
          } else if (m_currentMethod == RuleSelectionMethod.WEIGHTEDSUM) {
            double sumOfWeights = 0;
            for (SimpleRule s : firingRules) {
              if (Utils.isMissingValue(s.getWeight())) {
                throw new Exception("[RuleSet] Scoring criterion is WEIGHTEDSUM, but " +
                          "rule " + s.getID() + " does not have a weight defined!");
              }            
              if (classAtt.isNumeric()) {
                sumOfWeights += s.getWeight();
                preds[0] += (s.getScore() * s.getWeight());
              } else {
                preds[(int)s.getScore()] += s.getWeight();
              }
            }
            if (classAtt.isNumeric()) {
              if (sumOfWeights == 0) {
                throw new Exception("[RuleSet] Sum of weights is zero!");
              }
              preds[0] /= sumOfWeights;
            } else {
              // array gets normalized in the distributionForInstance() method
            }
          }
        } else {
          // default prediction
          if (classAtt.isNumeric()) {
            preds[0] = m_defaultPrediction;
          } else {
            if (m_scoreDistributions.size() > 0) {
              for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
                preds[s.getClassLabelIndex()] = s.getConfidence();
              }
            } else if (!Utils.isMissingValue(m_defaultConfidence)) {
              preds[(int)m_defaultPrediction] = m_defaultConfidence;
            } else {
              preds[(int)m_defaultPrediction] = 1.0;
            }
          }
        }
        
        return preds;
      }
    }
    
    /** The mining function */
    protected MiningFunction m_functionType = MiningFunction.CLASSIFICATION;
    
    /** The model name (if defined) */
    protected String m_modelName;
    
    /** The algorithm name (if defined) */
    protected String m_algorithmName;
    
    /** The set of rules */
    protected RuleSet m_ruleSet;
  
    /**
     * Constructor for a RuleSetModel
     * 
     * @param model the XML element encapsulating the RuleSetModel
     * @param dataDictionary the data dictionary to use
     * @param miningSchema the mining schema to use
     * @throws Exception if something goes wrong
     */
    public RuleSetModel(Element model, Instances dataDictionary,
        MiningSchema miningSchema) throws Exception {
      
      super(dataDictionary, miningSchema);
      
      if (!getPMMLVersion().equals("3.2")) {
        // TODO: might have to throw an exception and only support 3.2
      }
      
      String fn = model.getAttribute("functionName");
      if (fn.equals("regression")) {
        m_functionType = MiningFunction.REGRESSION;
      }
      
      String modelName = model.getAttribute("modelName");
      if (modelName != null && modelName.length() > 0) {
        m_modelName = modelName;
      }
      
      String algoName = model.getAttribute("algorithmName");
      if (algoName != null && algoName.length() > 0) {
        m_algorithmName = algoName;
      }    
      
      NodeList ruleset = model.getElementsByTagName("RuleSet");
      if (ruleset.getLength() == 1) {
        Node ruleSetNode = ruleset.item(0);
        if (ruleSetNode.getNodeType() == Node.ELEMENT_NODE) {
          m_ruleSet = new RuleSet((Element)ruleSetNode, miningSchema);
        }
      } else {
        throw new Exception ("[RuleSetModel] Should only have a single RuleSet!");
      }
    }
    
    /**                                                                                                             
     * Classifies the given test instance. The instance has to belong to a                                          
     * dataset when it's being classified.                                                          
     *                                                                                                              
     * @param inst the instance to be classified                                                                
     * @return the predicted most likely class for the instance or                                                  
     * Utils.missingValue() if no prediction is made                                                             
     * @exception Exception if an error occurred during the prediction                                              
     */
    public double[] distributionForInstance(Instance inst) throws Exception {
      if (!m_initialized) {
        mapToMiningSchema(inst.dataset());
      }
      double[] preds = null;
      
      if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
        preds = new double[1];
      } else {
        preds = new double[m_miningSchema.getFieldsAsInstances().classAttribute().numValues()];
      }
      
      double[] incoming = m_fieldsMap.instanceToSchema(inst, m_miningSchema);
      
      preds = m_ruleSet.score(incoming, 
          m_miningSchema.getFieldsAsInstances().classAttribute());
      
      if (m_miningSchema.getFieldsAsInstances().classAttribute().isNominal()) {
        Utils.normalize(preds);
      }
      
      return preds;
    }
    
    /**
     * Return a textual description of this model.
     * 
     * @return a textual description of this model
     */
    public String toString() {
      StringBuffer temp = new StringBuffer();
      
      temp.append("PMML version " + getPMMLVersion());
      if (!getCreatorApplication().equals("?")) {
        temp.append("\nApplication: " + getCreatorApplication());
      }
      temp.append("\nPMML Model: RuleSetModel");
      temp.append("\n\n");
      temp.append(m_miningSchema);
      
      if (m_algorithmName != null) {
        temp.append("\nAlgorithm: " + m_algorithmName + "\n");
      }
      
      temp.append(m_ruleSet);
    
      return temp.toString();
    }
  
    /**
     * Get the revision string for this class
     * 
     * @return the revision string
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5987 $");
    }
    
  }
  