Examples of weka.core.Attribute.indexOfValue()

weka.core.Attribute.indexOfValue()
Returns the index of a given attribute value. (The index of the first occurence of this value.) @param value the value for which the index is to be returned @return the index of the given attribute value if attributeis nominal or a string, -1 if it is not or the value can't be found

      String targetCat = machineElement.getAttribute("targetCategory");
      if (targetCat != null && targetCat.length() > 0) {
        m_targetCategory = targetCat;
        Attribute classAtt = m_miningSchema.getFieldsAsInstances().classAttribute(); 
        if (classAtt.isNominal()) {
          int index = classAtt.indexOfValue(m_targetCategory);
          
          if (index < 0) {
            throw new Exception("[SupportVectorMachine] : can't find target category: "
                + m_targetCategory + " in the class attribute!");
          }

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          m_targetCategoryIndex = index;
          
          // now check for the PMML 4.0 alternateTargetCategory
          String altTargetCat = machineElement.getAttribute("alternateTargetCategory");
          if (altTargetCat != null && altTargetCat.length() > 0) {
            index = classAtt.indexOfValue(altTargetCat);
            if (index < 0) {
              throw new Exception("[SupportVectorMachine] : can't find alternate target category: "
                  + altTargetCat + " in the class attribute!");
            }            
            m_localAlternateTargetCategoryIndex = index;

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                  }
                } else {
                  // it is a nominal attribute in the mining schema so find
                  // the index that correponds to this value
                  Attribute att = miningSchemaI.attribute(p.m_miningSchemaIndex); 
                  expOrIndex = att.indexOfValue(value);
                  if (expOrIndex == -1) {
                    throw new Exception("[GeneralRegression] unable to find PPCell value "
                                        + value + " in mining schema attribute "
                                        + att.name());
                  }

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                              + paramName + " in parameter list");
        }


        if (targetCatName != null && targetCatName.length() > 0) {
          if (classAtt.isNominal() || classAtt.isString()) {
            targetCategoryIndex = classAtt.indexOfValue(targetCatName);
          } else {
            throw new Exception("[GeneralRegression] found a PCell with a named "
                                + "target category: " + targetCatName
                                + " but class attribute is numeric in "
                                + "mining schema");

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          // means that there were no Value elements defined in the 
          // data dictionary (and hence the mining schema).
          // We add our value here.
          att.addStringValue(m_valueName);
        }
        m_valueIndex = att.indexOfValue(m_valueName);
        /*        for (int i = 0; i < att.numValues(); i++) {
          if (att.value(i).equals(m_valueName)) {
            m_valueIndex = i;
          }
          }*/

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        }
        
        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);
        }

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        } 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) {

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            throw new Exception("[TreeNode] class is numeric but unable to parse score " 
                + m_scoreString + " as a number!");
          }
        } else {
          // store the index of this class value
          m_scoreIndex = classAtt.indexOfValue(m_scoreString);
          
          if (m_scoreIndex < 0) {
            throw new Exception("[TreeNode] can't find match for predicted value " 
                + m_scoreString + " in class attribute!");
          }

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      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);
      }

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      } 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);

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