Examples of weka.core.Instances.classAttribute()

weka.core.Instances.classAttribute()
Returns the class attribute. @return the class attribute @throws UnassignedClassException if the class is not set

      }
      FastVector newVec = new FastVector(data.numAttributes());
      for (int i = 0; i < data.numAttributes(); i++) {
  if (i == data.classIndex()) {
    newVec.addElement(new Attribute(data.classAttribute().name(), values, 
            data.classAttribute().getMetadata()));
  } else {
    newVec.addElement(data.attribute(i));
  }
      }
      Instances newInsts = new Instances(data.relationName(), newVec, 0);

View Full Code Here

    if (outputFormatPeek() == null) {
      Instances toFilter = getInputFormat();
      Instances[] toFilterIgnoringAttributes;


      // Make subsets if class is nominal
      if ((toFilter.classIndex() >= 0) && toFilter.classAttribute().isNominal()) {
  toFilterIgnoringAttributes = new Instances[toFilter.numClasses()];
  for (int i = 0; i < toFilter.numClasses(); i++) {
    toFilterIgnoringAttributes[i] = new Instances(toFilter, toFilter.numInstances());
  }
  for (int i = 0; i < toFilter.numInstances(); i++) {

View Full Code Here

      // filter out attributes if necessary
      for (int i = 0; i < toFilterIgnoringAttributes.length; i++)
  toFilterIgnoringAttributes[i] = removeIgnored(toFilterIgnoringAttributes[i]);


      // build the clusterers
      if ((toFilter.classIndex() <= 0) || !toFilter.classAttribute().isNominal()) {
  m_clusterers = AbstractDensityBasedClusterer.makeCopies(m_clusterer, 1);
  m_clusterers[0].buildClusterer(toFilterIgnoringAttributes[0]);
      } else {
  m_clusterers = AbstractDensityBasedClusterer.makeCopies(m_clusterer, toFilter.numClasses());
  for (int i = 0; i < m_clusterers.length; i++) {

View Full Code Here

      attInfo.addElement(new Attribute("pCluster_" + j + "_" + i));
    }
  }
      }
      if (toFilter.classIndex() >= 0) {
  attInfo.addElement(toFilter.classAttribute().copy());
      }
      attInfo.trimToSize();
      Instances filtered = new Instances(toFilter.relationName()+"_clusterMembership",
           attInfo, 0);
      if (toFilter.classIndex() >= 0) {

View Full Code Here

        // train using stock_training_data.arff:
        fc.buildClassifier(training_data);
        // test using stock_testing_data.arff:
        for (int i = 0; i < testing_data.numInstances(); i++) {
          double pred = fc.classifyInstance(testing_data.instance(i));
          System.out.print("given value: " + testing_data.classAttribute().value((int)testing_data.instance(i).classValue()));
          System.out.println(". predicted value: " + testing_data.classAttribute().value((int) pred));
        }


  }

View Full Code Here

        fc.buildClassifier(training_data);
        // test using stock_testing_data.arff:
        for (int i = 0; i < testing_data.numInstances(); i++) {
          double pred = fc.classifyInstance(testing_data.instance(i));
          System.out.print("given value: " + testing_data.classAttribute().value((int)testing_data.instance(i).classValue()));
          System.out.println(". predicted value: " + testing_data.classAttribute().value((int) pred));
        }


  }


}

View Full Code Here

    m_BaseFormat = new Instances(data, 0);
    newData.deleteWithMissingClass();
    
    Random random = new Random(m_Seed);
    newData.randomize(random);
    if (newData.classAttribute().isNominal()) {
      newData.stratify(m_NumFolds);
    }


    // Create meta level
    generateMetaLevel(newData, random);

View Full Code Here

    
    if (newData.numInstances() == 0) {
      m_Classifier.buildClassifier(newData);
      return;
    }
    if (newData.classAttribute().isNominal()) {
      m_numClasses = newData.classAttribute().numValues();
    } else {
      m_numClasses = 1;
    }

View Full Code Here

    if (newData.numInstances() == 0) {
      m_Classifier.buildClassifier(newData);
      return;
    }
    if (newData.classAttribute().isNominal()) {
      m_numClasses = newData.classAttribute().numValues();
    } else {
      m_numClasses = 1;
    }


    Instances resampledData = null;

View Full Code Here

    props = new Vector();
    props.add("Filename: " + panel.getFilename());
    props.add("Relation name: " + inst.relationName());
    props.add("# of instances: " + inst.numInstances());
    props.add("# of attributes: " + inst.numAttributes());
    props.add("Class attribute: " + inst.classAttribute().name());
    props.add("# of class labels: " + inst.numClasses());
    
    dialog = new ListSelectorDialog(getParentFrame(), new JList(props));
    dialog.showDialog();
  }

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.