Package weka.core

Examples of weka.core.Instances.attribute()

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      for (int i = 0; i < data.numAttributes(); i++) {
        if ( data.classIndex()==i )
          continue;
        if ( data.attribute(i).isNominal() )
          numNominal++;
        if ( data.attribute(i).isNumeric() )
          numNumeric++;
      }
      System.err.printf( "%s\t%d\t%d\t%d\t%d\t", curArff.getName(), data.numInstances(), numNumeric, numNominal, data.numClasses() );
    
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        query.append(" ");
        query.append(m_createInt);
        query.append(" PRIMARY KEY,");
      }
      for(int i = 0;i < structure.numAttributes(); i++){
          Attribute att = structure.attribute(i);
          String attName = att.name();
          attName = attName.replaceAll("[^\\w]","_");
          attName = m_DataBaseConnection.maskKeyword(attName);
          if(m_DataBaseConnection.getUpperCase())
              query.append(attName.toUpperCase());
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        newInstances.add(normalizeInstance(instances.instance(iInstance)));
      }
    }
    //sanity check
    for (int iNode = 0; iNode < getNrOfNodes(); iNode++) {
      if (newInstances.attribute(iNode).numValues() != getCardinality(iNode)) {
        throw new Exception("Number of values of node [[[" + getNodeName(iNode) + "]]] differs in (discretized) dataset." );
      }
    }
    // if we got this far, all is ok with the data set and
    // we can replace data set of Bayes net
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        }
        parentsets[nBase + iNode].addParent(nParent, instances);
      }
      // resolve conditional probability table
      int nCardinality = parentsets[nBase + iNode].getCardinalityOfParents();
      int nValues = instances.attribute(nBase + iNode).numValues();
      distributions[nBase + iNode] = new Estimator[nCardinality];
      for (int i = 0; i < nCardinality; i++) {
        distributions[nBase + iNode][i] = new DiscreteEstimatorBayes(nValues, 0.0f);
      }
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    Reader arffReader = null;
    try
    {
      arffReader = new FileReader(arffWithTrainingData);
      Instances trainingData = new Instances(arffReader);
      if (!"class".equals(trainingData.attribute(trainingData.numAttributes()-1).name()))
        throw new IllegalArgumentException("last element is not a class");
      trainingData.setClassIndex(trainingData.numAttributes()-1);
     
      classifier.buildClassifier(trainingData);
    }
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    Reader arffReader = null;
    try
    {
      arffReader = new FileReader(arffWithTrainingData);
      Instances trainingData = new Instances(arffReader);
      if (!"class".equals(trainingData.attribute(trainingData.numAttributes()-1).name()))
        throw new IllegalArgumentException("last element is not a class");
      trainingData.setClassIndex(trainingData.numAttributes()-1);
     
      classifier.buildClassifier(trainingData);
    }
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    Reader arffReader = null;
    try
    {
      arffReader = new FileReader(arffWithTrainingData);
      Instances trainingData = new Instances(arffReader);
      if (!"class".equals(trainingData.attribute(trainingData.numAttributes()-1).name()))
        throw new IllegalArgumentException("last element is not a class");
      trainingData.setClassIndex(trainingData.numAttributes()-1);
     
      classifier.buildClassifier(trainingData);
    }
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       System.out.println("\nElapsed time: "+(duration)+"sec");

      //test on instance
       Instance instance = new DenseInstance(4);
       instance.setValue(data.attribute("Abilities"), "pediatrician");
       instance.setValue(data.attribute("Dispositions"), "cautious");
       instance.setValue(data.attribute("Cross"), "female");
       instance.setDataset(data);
      
       classifier.testInstance(instance);
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       System.out.println("\nElapsed time: "+(duration)+"sec");

      //test on instance
       Instance instance = new DenseInstance(4);
       instance.setValue(data.attribute("Abilities"), "pediatrician");
       instance.setValue(data.attribute("Dispositions"), "cautious");
       instance.setValue(data.attribute("Cross"), "female");
       instance.setDataset(data);
      
       classifier.testInstance(instance);
      
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      //test on instance
       Instance instance = new DenseInstance(4);
       instance.setValue(data.attribute("Abilities"), "pediatrician");
       instance.setValue(data.attribute("Dispositions"), "cautious");
       instance.setValue(data.attribute("Cross"), "female");
       instance.setDataset(data);
      
       classifier.testInstance(instance);
      
       System.out.println(instance);
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