Examples of weka.core.EuclideanDistance

weka.core.EuclideanDistance

kipedia.org/wiki/Euclidean_distance.

BibTeX:

 @misc{missing_id, author = {Wikipedia}, title = {Euclidean distance}, URL = {http://en.wikipedia.org/wiki/Euclidean_distance} }

Valid options are:

 -D Turns off the normalization of attribute  values in distance calculation.

 -R <col1,col2-col4,...> Specifies list of columns to used in the calculation of the  distance. 'first' and 'last' are valid indices. (default: first-last)

 -V Invert matching sense of column indices.

@author Gabi Schmidberger (gabi@cs.waikato.ac.nz) @author Ashraf M. Kibriya (amk14@cs.waikato.ac.nz) @author FracPete (fracpete at waikato dot ac dot nz) @version $Revision: 1.13 $

    // num of clusters to start with
    m_NumClusters =  m_MinNumClusters;


    // set distance function to default
    if (m_DistanceF == null) {
      m_DistanceF = new EuclideanDistance();
    }


    m_DistanceF.setInstances(m_Instances);
    checkInstances();

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      funcSpec[0] = "";
      setDistanceF((DistanceFunction) Utils.forName(DistanceFunction.class,
                funcName, funcSpec));
    }
    else {
      setDistanceF(new EuclideanDistance());
    }


    optionString  = Utils.getOption('N', options);
    if (optionString.length() != 0) {
      setInputCenterFile(new File(optionString));

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      setDistanceFunction( (DistanceFunction)
                           Utils.forName( DistanceFunction.class, 
                                          className, distFunctionClassSpec) );
    }
    else {
      setDistanceFunction(new EuclideanDistance());
    }
    
    m_PreserveOrder = Utils.getFlag("O", options);


    super.setOptions(options);

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      setDistanceFunction( (DistanceFunction)
          Utils.forName( DistanceFunction.class, 
              className, nnSearchClassSpec) );
    }
    else {
      setDistanceFunction(new EuclideanDistance());
    }


    Utils.checkForRemainingOptions(options);
  }

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   */
  protected void buildKDTree(Instances instances) throws Exception {


    checkMissing(instances);
    if (m_EuclideanDistance == null)
      m_DistanceFunction = m_EuclideanDistance = new EuclideanDistance(
          instances);
    else
      m_EuclideanDistance.setInstances(instances);


    m_Instances = instances;

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    if (insts.numInstances() == 0)
      throw new Exception(
    "CoverTree: Empty set of instances. Cannot build tree.");
    checkMissing(insts);
    if (m_EuclideanDistance == null)
      m_DistanceFunction = m_EuclideanDistance = new EuclideanDistance(insts);
    else
      m_EuclideanDistance.setInstances(insts);
    
    Stack<DistanceNode> point_set = new Stack<DistanceNode>();
    Stack<DistanceNode> consumed_set = new Stack<DistanceNode>();

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      setDistanceFunction( (DistanceFunction)
                            Utils.forName( DistanceFunction.class, 
                                           className, nnSearchClassSpec) );
    }
    else {
      setDistanceFunction(new EuclideanDistance());
    }
    
    setMeasurePerformance(Utils.getFlag('P',options));
  }

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    // num of clusters to start with
    m_NumClusters =  m_MinNumClusters;


    // set distance function to default
    if (m_DistanceF == null) {
      m_DistanceF = new EuclideanDistance();
    }


    m_DistanceF.setInstances(m_Instances);
    checkInstances();

View Full Code Here

      funcSpec[0] = "";
      setDistanceF((DistanceFunction) Utils.forName(DistanceFunction.class,
                funcName, funcSpec));
    }
    else {
      setDistanceF(new EuclideanDistance());
    }


    optionString  = Utils.getOption('N', options);
    if (optionString.length() != 0) {
      setInputCenterFile(new File(optionString));

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   */
  protected void buildKDTree(Instances instances) throws Exception {


    checkMissing(instances);
    if (m_EuclideanDistance == null)
      m_DistanceFunction = m_EuclideanDistance = new EuclideanDistance(
          instances);
    else
      m_EuclideanDistance.setInstances(instances);


    m_Instances = instances;

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Related Classes of weka.core.EuclideanDistance

weka.clusterers.HierarchicalClusterer

weka.clusterers.SimpleKMeans

weka.clusterers.XMeans

weka.core.neighboursearch.CoverTree

weka.core.neighboursearch.KDTree

weka.core.neighboursearch.NearestNeighbourSearch

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