Java 类weka.core.DistanceFunction 实例源码

项目：repo.kmeanspp.silhouette_score 文件：NearestNeighbourSearch.java

/**
 * Parses a given list of options. Valid options are:
 * 
 * <!-- options-start --> <!-- options-end -->
 * 
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
@Override
public void setOptions(String[] options) throws Exception {
  String nnSearchClass = Utils.getOption('A', options);
  if (nnSearchClass.length() != 0) {
    String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
    if (nnSearchClassSpec.length == 0) {
      throw new Exception("Invalid DistanceFunction specification string.");
    }
    String className = nnSearchClassSpec[0];
    nnSearchClassSpec[0] = "";

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

  setMeasurePerformance(Utils.getFlag('P', options));
}

项目：autoweka 文件：NearestNeighbourSearch.java

/**
 * Parses a given list of options. Valid options are:
 *
 <!-- options-start -->
 <!-- options-end -->
 *
 * @param options   the list of options as an array of strings
 * @throws Exception    if an option is not supported
 */
public void setOptions(String[] options) throws Exception {
  String nnSearchClass = Utils.getOption('A', options);
  if(nnSearchClass.length() != 0) {
    String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
    if(nnSearchClassSpec.length == 0) { 
      throw new Exception("Invalid DistanceFunction specification string."); 
    }
    String className = nnSearchClassSpec[0];
    nnSearchClassSpec[0] = "";

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

  setMeasurePerformance(Utils.getFlag('P',options));
}

项目：umple 文件：NearestNeighbourSearch.java

/**
 * Parses a given list of options. Valid options are:
 * 
 * <!-- options-start --> <!-- options-end -->
 * 
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
@Override
public void setOptions(String[] options) throws Exception {
  String nnSearchClass = Utils.getOption('A', options);
  if (nnSearchClass.length() != 0) {
    String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
    if (nnSearchClassSpec.length == 0) {
      throw new Exception("Invalid DistanceFunction specification string.");
    }
    String className = nnSearchClassSpec[0];
    nnSearchClassSpec[0] = "";

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

  setMeasurePerformance(Utils.getFlag('P', options));
}

项目：jbossBA 文件：NearestNeighbourSearch.java

/**
 * Parses a given list of options. Valid options are:
 *
 <!-- options-start -->
 <!-- options-end -->
 *
 * @param options   the list of options as an array of strings
 * @throws Exception    if an option is not supported
 */
public void setOptions(String[] options) throws Exception {
  String nnSearchClass = Utils.getOption('A', options);
  if(nnSearchClass.length() != 0) {
    String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
    if(nnSearchClassSpec.length == 0) { 
      throw new Exception("Invalid DistanceFunction specification string."); 
    }
    String className = nnSearchClassSpec[0];
    nnSearchClassSpec[0] = "";

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

  setMeasurePerformance(Utils.getFlag('P',options));
}

项目：seagull 文件：Classification.java

private IBk useCosine() {
    IBk ibk = new IBk();
    Instances data = ClassificationModel.getInstance().getInstances();
    Normalize normalizer = new Normalize(); 

    try {
        normalizer.setInputFormat(data);

        // Euclidean Distance working over normalized instances = Cosine Similarity according to Foundations of Statistical Natural Processing Language p.301
        // As long as attribute normalization is disabled.
        Instances normalizedInstances; 
        normalizedInstances = Filter.useFilter(data, normalizer); 
        ClassificationModel.getInstance().setInstances(normalizedInstances);
        DistanceFunction df = new EuclideanDistance(); 
        ((EuclideanDistance) df).setDontNormalize(true);                                
        ibk.getNearestNeighbourSearchAlgorithm().setDistanceFunction(df); 
    } catch (Exception e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }

    return ibk;
}

项目：repo.kmeanspp.silhouette_score 文件：SimpleKMeans.java

/**
 * sets the distance function to use for instance comparison.
 * 
 * @param df the new distance function to use
 * @throws Exception if instances cannot be processed
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance)
    && !(df instanceof ManhattanDistance)) {
    throw new Exception(
      "SimpleKMeans currently only supports the Euclidean and Manhattan distances.");
  }
  m_DistanceFunction = df;
}

项目：repo.kmeanspp.silhouette_score 文件：KMeansPlusPlusSC.java

/**
 * sets the distance function to use for instance comparison.
 * 
 * @param df the new distance function to use
 * @throws Exception if instances cannot be processed
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance)
    && !(df instanceof ManhattanDistance)) {
    throw new Exception(
      "KMeansPlusPlus only supports the Euclidean and Manhattan distances.");
  }
  m_DistanceFunction = df;
}

项目：repo.kmeanspp.silhouette_score 文件：KMeansPlusPlus.java

/**
 * sets the distance function to use for instance comparison.
 * 
 * @param df the new distance function to use
 * @throws Exception if instances cannot be processed
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance)
    && !(df instanceof ManhattanDistance)) {
    throw new Exception(
      "KMeansPlusPlus only supports the Euclidean and Manhattan distances.");
  }
  m_DistanceFunction = df;
}

项目：repo.kmeanspp.silhouette_score 文件：FilteredDistance.java

/**
  * Gets the distance specification string, which contains the class name of
  * the distance and any options to the distance
  *
  * @return the distance string.
  */
 protected String getDistanceSpec() {

   DistanceFunction c = getDistance();
   if (c instanceof OptionHandler) {
     return c.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)c).getOptions());
   }
   return c.getClass().getName();
 }

项目：repo.kmeanspp.silhouette_score 文件：CoverTree.java

/**
 * Sets the distance function to use for nearest neighbour search. Currently
 * only EuclideanDistance is supported.
 * 
 * @param df the distance function to use
 * @throws Exception if not EuclideanDistance
 */
@Override
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance)) {
    throw new Exception("CoverTree currently only works with "
      + "EuclideanDistanceFunction.");
  }
  m_DistanceFunction = m_EuclideanDistance = (EuclideanDistance) df;
}

项目：repo.kmeanspp.silhouette_score 文件：KDTree.java

/**
 * sets the distance function to use for nearest neighbour search.
 * 
 * @param df        the distance function to use
 * @throws Exception    if not EuclideanDistance
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance))
    throw new Exception("KDTree currently only works with "
        + "EuclideanDistanceFunction.");
  m_DistanceFunction = m_EuclideanDistance = (EuclideanDistance) df;
}

项目：LibD3C-1.1 文件：SimpleKMeans.java

/**
  * sets the distance function to use for instance comparison.
  * 
  * @param df the new distance function to use
  * @throws Exception if instances cannot be processed
  */
 public void setDistanceFunction(DistanceFunction df) throws Exception {
   if (!(df instanceof EuclideanDistance) && 
!(df instanceof ManhattanDistance))      {
     throw new Exception("SimpleKMeans currently only supports the Euclidean and Manhattan distances.");
   }
   m_DistanceFunction = df;
 }

项目：autoweka 文件：SimpleKMeans.java

/**
  * sets the distance function to use for instance comparison.
  * 
  * @param df the new distance function to use
  * @throws Exception if instances cannot be processed
  */
 public void setDistanceFunction(DistanceFunction df) throws Exception {
   if (!(df instanceof EuclideanDistance) && 
!(df instanceof ManhattanDistance))      {
     throw new Exception("SimpleKMeans currently only supports the Euclidean and Manhattan distances.");
   }
   m_DistanceFunction = df;
 }

项目：autoweka 文件：KDTree.java

/**
 * sets the distance function to use for nearest neighbour search.
 * 
 * @param df        the distance function to use
 * @throws Exception    if not EuclideanDistance
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance))
    throw new Exception("KDTree currently only works with "
        + "EuclideanDistanceFunction.");
  m_DistanceFunction = m_EuclideanDistance = (EuclideanDistance) df;
}

项目：umple 文件：SimpleKMeans.java

/**
 * sets the distance function to use for instance comparison.
 * 
 * @param df the new distance function to use
 * @throws Exception if instances cannot be processed
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance)
    && !(df instanceof ManhattanDistance)) {
    throw new Exception(
      "SimpleKMeans currently only supports the Euclidean and Manhattan distances.");
  }
  m_DistanceFunction = df;
}

项目：umple 文件：FilteredDistance.java

/**
  * Gets the distance specification string, which contains the class name of
  * the distance and any options to the distance
  *
  * @return the distance string.
  */
 protected String getDistanceSpec() {

   DistanceFunction c = getDistance();
   if (c instanceof OptionHandler) {
     return c.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)c).getOptions());
   }
   return c.getClass().getName();
 }

项目：umple 文件：CoverTree.java

/**
 * Sets the distance function to use for nearest neighbour search. Currently
 * only EuclideanDistance is supported.
 * 
 * @param df the distance function to use
 * @throws Exception if not EuclideanDistance
 */
@Override
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance)) {
    throw new Exception("CoverTree currently only works with "
      + "EuclideanDistanceFunction.");
  }
  m_DistanceFunction = m_EuclideanDistance = (EuclideanDistance) df;
}

项目：umple 文件：KDTree.java

/**
 * sets the distance function to use for nearest neighbour search.
 * 
 * @param df        the distance function to use
 * @throws Exception    if not EuclideanDistance
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance))
    throw new Exception("KDTree currently only works with "
        + "EuclideanDistanceFunction.");
  m_DistanceFunction = m_EuclideanDistance = (EuclideanDistance) df;
}

项目：jbossBA 文件：SimpleKMeans.java

/**
 * sets the distance function to use for instance comparison.
 * 
 * @param df the new distance function to use
 * @throws Exception if instances cannot be processed
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance)
    && !(df instanceof ManhattanDistance)) {
    throw new Exception(
      "SimpleKMeans currently only supports the Euclidean and Manhattan distances.");
  }
  m_DistanceFunction = df;
}

项目：jbossBA 文件：XMeans.java

/**
  * Gets the distance function specification string, which contains the 
  * class name of the distance function class and any options to it.
  *
  * @return the distance function specification string
  */
 protected String getDistanceFSpec() {

   DistanceFunction d = getDistanceF();
   if (d instanceof OptionHandler) {
     return d.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler) d).getOptions());
   }
   return d.getClass().getName();
 }

项目：jbossBA 文件：KDTree.java

/**
 * sets the distance function to use for nearest neighbour search.
 * 
 * @param df        the distance function to use
 * @throws Exception    if not EuclideanDistance
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance))
    throw new Exception("KDTree currently only works with "
        + "EuclideanDistanceFunction.");
  m_DistanceFunction = m_EuclideanDistance = (EuclideanDistance) df;
}

项目：repo.kmeanspp.silhouette_score 文件：HierarchicalClusterer.java

public DistanceFunction getDistanceFunction() {
  return m_DistanceFunction;
}

项目：repo.kmeanspp.silhouette_score 文件：HierarchicalClusterer.java

public void setDistanceFunction(DistanceFunction distanceFunction) {
  m_DistanceFunction = distanceFunction;
}

项目：repo.kmeanspp.silhouette_score 文件：HierarchicalClusterer.java

/**
 * Parses a given list of options.
 * <p/>
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <!-- options-end -->
 * 
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
@Override
public void setOptions(String[] options) throws Exception {
  m_bPrintNewick = Utils.getFlag('P', options);

  String optionString = Utils.getOption('N', options);
  if (optionString.length() != 0) {
    Integer temp = new Integer(optionString);
    setNumClusters(temp);
  } else {
    setNumClusters(2);
  }

  setDistanceIsBranchLength(Utils.getFlag('B', options));

  String sLinkType = Utils.getOption('L', options);

  if (sLinkType.compareTo("SINGLE") == 0) {
    setLinkType(new SelectedTag(SINGLE, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("COMPLETE") == 0) {
    setLinkType(new SelectedTag(COMPLETE, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("AVERAGE") == 0) {
    setLinkType(new SelectedTag(AVERAGE, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("MEAN") == 0) {
    setLinkType(new SelectedTag(MEAN, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("CENTROID") == 0) {
    setLinkType(new SelectedTag(CENTROID, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("WARD") == 0) {
    setLinkType(new SelectedTag(WARD, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("ADJCOMPLETE") == 0) {
    setLinkType(new SelectedTag(ADJCOMPLETE, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("NEIGHBOR_JOINING") == 0) {
    setLinkType(new SelectedTag(NEIGHBOR_JOINING, TAGS_LINK_TYPE));
  }

  String nnSearchClass = Utils.getOption('A', options);
  if (nnSearchClass.length() != 0) {
    String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
    if (nnSearchClassSpec.length == 0) {
      throw new Exception("Invalid DistanceFunction specification string.");
    }
    String className = nnSearchClassSpec[0];
    nnSearchClassSpec[0] = "";

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

  super.setOptions(options);

  Utils.checkForRemainingOptions(options);
}

项目：repo.kmeanspp.silhouette_score 文件：BallNode.java

/**
 * Calculates the radius of a node.
 * 
 * @param start The start index of the portion in indices array 
 * that belongs to the node.
 * @param end The end index of the portion in indices array 
 * that belongs to the node. 
 * @param instList The indices array holding indices of 
 * instances. 
 * @param insts The actual instances. instList points to 
 * instances in this object. 
 * @param pivot The centre/pivot of the node. 
 * @param distanceFunction The distance function to use to 
 * calculate the radius. 
 * @return The radius of the node. 
 * @throws Exception If there is some problem calculating the 
 * radius. 
 */
public static double calcRadius(int start, int end, int[] instList, 
                               Instances insts, Instance pivot, 
                               DistanceFunction distanceFunction) 
                                                           throws Exception {
  double radius = Double.NEGATIVE_INFINITY;

  for(int i=start; i<=end; i++) {
    double dist = distanceFunction.distance(pivot, 
                                            insts.instance(instList[i]), Double.POSITIVE_INFINITY);

    if(dist>radius)
      radius = dist;
  }
  return Math.sqrt(radius);
}

项目：autoweka 文件：HierarchicalClusterer.java

/**
 * Parses a given list of options. <p/>
 *
  <!-- options-start -->
 * Valid options are: <p/>
 * 
  <!-- options-end -->
 *
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
public void setOptions(String[] options) throws Exception {
  m_bPrintNewick = Utils.getFlag('P', options);

  String optionString = Utils.getOption('N', options); 
  if (optionString.length() != 0) {
    Integer temp = new Integer(optionString);
    setNumClusters(temp);
  }
  else {
    setNumClusters(2);
  }

  setDebug(Utils.getFlag('D', options));
  setDistanceIsBranchLength(Utils.getFlag('B', options));

  String sLinkType = Utils.getOption('L', options);


  if (sLinkType.compareTo("SINGLE") == 0) {setLinkType(new SelectedTag(SINGLE, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("COMPLETE") == 0) {setLinkType(new SelectedTag(COMPLETE, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("AVERAGE") == 0) {setLinkType(new SelectedTag(AVERAGE, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("MEAN") == 0) {setLinkType(new SelectedTag(MEAN, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("CENTROID") == 0) {setLinkType(new SelectedTag(CENTROID, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("WARD") == 0) {setLinkType(new SelectedTag(WARD, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("ADJCOMLPETE") == 0) {setLinkType(new SelectedTag(ADJCOMLPETE, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("NEIGHBOR_JOINING") == 0) {setLinkType(new SelectedTag(NEIGHBOR_JOINING, TAGS_LINK_TYPE));}

  String nnSearchClass = Utils.getOption('A', options);
  if(nnSearchClass.length() != 0) {
    String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
    if(nnSearchClassSpec.length == 0) { 
      throw new Exception("Invalid DistanceFunction specification string."); 
    }
    String className = nnSearchClassSpec[0];
    nnSearchClassSpec[0] = "";

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

  Utils.checkForRemainingOptions(options);
}

项目：autoweka 文件：BallNode.java

/**
 * Calculates the radius of a node.
 * 
 * @param start The start index of the portion in indices array 
 * that belongs to the node.
 * @param end The end index of the portion in indices array 
 * that belongs to the node. 
 * @param instList The indices array holding indices of 
 * instances. 
 * @param insts The actual instances. instList points to 
 * instances in this object. 
 * @param pivot The centre/pivot of the node. 
 * @param distanceFunction The distance function to use to 
 * calculate the radius. 
 * @return The radius of the node. 
 * @throws Exception If there is some problem calculating the 
 * radius. 
 */
public static double calcRadius(int start, int end, int[] instList, 
                               Instances insts, Instance pivot, 
                               DistanceFunction distanceFunction) 
                                                           throws Exception {
  double radius = Double.NEGATIVE_INFINITY;

  for(int i=start; i<=end; i++) {
    double dist = distanceFunction.distance(pivot, 
                                            insts.instance(instList[i]), Double.POSITIVE_INFINITY);

    if(dist>radius)
      radius = dist;
  }
  return Math.sqrt(radius);
}

项目：umple 文件：HierarchicalClusterer.java

public DistanceFunction getDistanceFunction() {
  return m_DistanceFunction;
}

项目：umple 文件：HierarchicalClusterer.java

public void setDistanceFunction(DistanceFunction distanceFunction) {
  m_DistanceFunction = distanceFunction;
}

项目：umple 文件：HierarchicalClusterer.java

/**
 * Parses a given list of options.
 * <p/>
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <!-- options-end -->
 * 
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
@Override
public void setOptions(String[] options) throws Exception {
  m_bPrintNewick = Utils.getFlag('P', options);

  String optionString = Utils.getOption('N', options);
  if (optionString.length() != 0) {
    Integer temp = new Integer(optionString);
    setNumClusters(temp);
  } else {
    setNumClusters(2);
  }

  setDistanceIsBranchLength(Utils.getFlag('B', options));

  String sLinkType = Utils.getOption('L', options);

  if (sLinkType.compareTo("SINGLE") == 0) {
    setLinkType(new SelectedTag(SINGLE, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("COMPLETE") == 0) {
    setLinkType(new SelectedTag(COMPLETE, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("AVERAGE") == 0) {
    setLinkType(new SelectedTag(AVERAGE, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("MEAN") == 0) {
    setLinkType(new SelectedTag(MEAN, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("CENTROID") == 0) {
    setLinkType(new SelectedTag(CENTROID, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("WARD") == 0) {
    setLinkType(new SelectedTag(WARD, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("ADJCOMPLETE") == 0) {
    setLinkType(new SelectedTag(ADJCOMPLETE, TAGS_LINK_TYPE));
  }
  if (sLinkType.compareTo("NEIGHBOR_JOINING") == 0) {
    setLinkType(new SelectedTag(NEIGHBOR_JOINING, TAGS_LINK_TYPE));
  }

  String nnSearchClass = Utils.getOption('A', options);
  if (nnSearchClass.length() != 0) {
    String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
    if (nnSearchClassSpec.length == 0) {
      throw new Exception("Invalid DistanceFunction specification string.");
    }
    String className = nnSearchClassSpec[0];
    nnSearchClassSpec[0] = "";

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

  super.setOptions(options);

  Utils.checkForRemainingOptions(options);
}

项目：umple 文件：BallNode.java

/**
 * Calculates the radius of a node.
 * 
 * @param start The start index of the portion in indices array 
 * that belongs to the node.
 * @param end The end index of the portion in indices array 
 * that belongs to the node. 
 * @param instList The indices array holding indices of 
 * instances. 
 * @param insts The actual instances. instList points to 
 * instances in this object. 
 * @param pivot The centre/pivot of the node. 
 * @param distanceFunction The distance function to use to 
 * calculate the radius. 
 * @return The radius of the node. 
 * @throws Exception If there is some problem calculating the 
 * radius. 
 */
public static double calcRadius(int start, int end, int[] instList, 
                               Instances insts, Instance pivot, 
                               DistanceFunction distanceFunction) 
                                                           throws Exception {
  double radius = Double.NEGATIVE_INFINITY;

  for(int i=start; i<=end; i++) {
    double dist = distanceFunction.distance(pivot, 
                                            insts.instance(instList[i]), Double.POSITIVE_INFINITY);

    if(dist>radius)
      radius = dist;
  }
  return Math.sqrt(radius);
}

项目：jbossBA 文件：HierarchicalClusterer.java

/**
 * Parses a given list of options. <p/>
 *
  <!-- options-start -->
 * Valid options are: <p/>
 * 
  <!-- options-end -->
 *
 * @param options the list of options as an array of strings
 * @throws Exception if an option is not supported
 */
public void setOptions(String[] options) throws Exception {
  m_bPrintNewick = Utils.getFlag('P', options);

  String optionString = Utils.getOption('N', options); 
  if (optionString.length() != 0) {
    Integer temp = new Integer(optionString);
    setNumClusters(temp);
  }
  else {
    setNumClusters(2);
  }

  setDebug(Utils.getFlag('D', options));
  setDistanceIsBranchLength(Utils.getFlag('B', options));

  String sLinkType = Utils.getOption('L', options);


  if (sLinkType.compareTo("SINGLE") == 0) {setLinkType(new SelectedTag(SINGLE, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("COMPLETE") == 0) {setLinkType(new SelectedTag(COMPLETE, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("AVERAGE") == 0) {setLinkType(new SelectedTag(AVERAGE, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("MEAN") == 0) {setLinkType(new SelectedTag(MEAN, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("CENTROID") == 0) {setLinkType(new SelectedTag(CENTROID, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("WARD") == 0) {setLinkType(new SelectedTag(WARD, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("ADJCOMLPETE") == 0) {setLinkType(new SelectedTag(ADJCOMLPETE, TAGS_LINK_TYPE));}
  if (sLinkType.compareTo("NEIGHBOR_JOINING") == 0) {setLinkType(new SelectedTag(NEIGHBOR_JOINING, TAGS_LINK_TYPE));}

  String nnSearchClass = Utils.getOption('A', options);
  if(nnSearchClass.length() != 0) {
    String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
    if(nnSearchClassSpec.length == 0) { 
      throw new Exception("Invalid DistanceFunction specification string."); 
    }
    String className = nnSearchClassSpec[0];
    nnSearchClassSpec[0] = "";

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

  Utils.checkForRemainingOptions(options);
}

项目：jbossBA 文件：BallNode.java

/**
 * Calculates the radius of a node.
 * 
 * @param start The start index of the portion in indices array 
 * that belongs to the node.
 * @param end The end index of the portion in indices array 
 * that belongs to the node. 
 * @param instList The indices array holding indices of 
 * instances. 
 * @param insts The actual instances. instList points to 
 * instances in this object. 
 * @param pivot The centre/pivot of the node. 
 * @param distanceFunction The distance function to use to 
 * calculate the radius. 
 * @return The radius of the node. 
 * @throws Exception If there is some problem calculating the 
 * radius. 
 */
public static double calcRadius(int start, int end, int[] instList, 
                               Instances insts, Instance pivot, 
                               DistanceFunction distanceFunction) 
                                                           throws Exception {
  double radius = Double.NEGATIVE_INFINITY;

  for(int i=start; i<=end; i++) {
    double dist = distanceFunction.distance(pivot, 
                                            insts.instance(instList[i]), Double.POSITIVE_INFINITY);

    if(dist>radius)
      radius = dist;
  }
  return Math.sqrt(radius);
}

项目：repo.kmeanspp.silhouette_score 文件：BallNode.java

/**
 * Calculates the radius of node.
 *  
 * @param instList The indices array containing the indices of the 
 * instances inside the node. 
 * @param insts The actual instances object. instList points to 
 * instances in this object.
 * @param pivot The centre/pivot of the node.
 * @param distanceFunction The distance fuction to use to calculate 
 * the radius. 
 * @return The radius of the node. 
 * @throws Exception If there is some problem in calculating the 
 * radius. 
 */
public static double calcRadius(int[] instList, Instances insts,Instance pivot, 
                               DistanceFunction distanceFunction) 
                                                throws Exception {
  return calcRadius(0, instList.length-1, instList, insts, 
                    pivot, distanceFunction);
}

项目：repo.kmeanspp.silhouette_score 文件：BallNode.java

/**
 * Calculates the radius of a node based on its two 
 * child nodes (if merging two nodes).
 * @param child1 The first child of the node.
 * @param child2 The second child of the node.
 * @param pivot The centre/pivot of the node. 
 * @param distanceFunction The distance function to 
 * use to calculate the radius
 * @return The radius of the node. 
 * @throws Exception If there is some problem 
 * in calculating the radius.
 */
public static double calcRadius(BallNode child1, BallNode child2, 
                                Instance pivot, 
                                DistanceFunction distanceFunction) 
                                                           throws Exception {
  Instance p1 = child1.getPivot(), p2 = child2.getPivot();                                                               

  double radius = child1.getRadius() + distanceFunction.distance(p1, p2) + 
                  child2.getRadius();

  return radius/2;
}

项目：autoweka 文件：BallNode.java

/**
 * Calculates the radius of node.
 *  
 * @param instList The indices array containing the indices of the 
 * instances inside the node. 
 * @param insts The actual instances object. instList points to 
 * instances in this object.
 * @param pivot The centre/pivot of the node.
 * @param distanceFunction The distance fuction to use to calculate 
 * the radius. 
 * @return The radius of the node. 
 * @throws Exception If there is some problem in calculating the 
 * radius. 
 */
public static double calcRadius(int[] instList, Instances insts,Instance pivot, 
                               DistanceFunction distanceFunction) 
                                                throws Exception {
  return calcRadius(0, instList.length-1, instList, insts, 
                    pivot, distanceFunction);
}

项目：autoweka 文件：BallNode.java

/**
 * Calculates the radius of a node based on its two 
 * child nodes (if merging two nodes).
 * @param child1 The first child of the node.
 * @param child2 The second child of the node.
 * @param pivot The centre/pivot of the node. 
 * @param distanceFunction The distance function to 
 * use to calculate the radius
 * @return The radius of the node. 
 * @throws Exception If there is some problem 
 * in calculating the radius.
 */
public static double calcRadius(BallNode child1, BallNode child2, 
                                Instance pivot, 
                                DistanceFunction distanceFunction) 
                                                           throws Exception {
  Instance p1 = child1.getPivot(), p2 = child2.getPivot();                                                               

  double radius = child1.getRadius() + distanceFunction.distance(p1, p2) + 
                  child2.getRadius();

  return radius/2;
}

项目：autoweka 文件：CoverTree.java

/**
 * Sets the distance function to use for nearest neighbour search.
 * Currently only EuclideanDistance is supported.
 * 
 * @param df        the distance function to use 
 * @throws Exception    if not EuclideanDistance
 */
public void setDistanceFunction(DistanceFunction df) throws Exception {
  if (!(df instanceof EuclideanDistance))
    throw new Exception("CoverTree currently only works with "
 + "EuclideanDistanceFunction.");
  m_DistanceFunction = m_EuclideanDistance = (EuclideanDistance) df;
}

项目：umple 文件：BallNode.java

/**
 * Calculates the radius of node.
 *  
 * @param instList The indices array containing the indices of the 
 * instances inside the node. 
 * @param insts The actual instances object. instList points to 
 * instances in this object.
 * @param pivot The centre/pivot of the node.
 * @param distanceFunction The distance fuction to use to calculate 
 * the radius. 
 * @return The radius of the node. 
 * @throws Exception If there is some problem in calculating the 
 * radius. 
 */
public static double calcRadius(int[] instList, Instances insts,Instance pivot, 
                               DistanceFunction distanceFunction) 
                                                throws Exception {
  return calcRadius(0, instList.length-1, instList, insts, 
                    pivot, distanceFunction);
}

项目：umple 文件：BallNode.java

/**
 * Calculates the radius of a node based on its two 
 * child nodes (if merging two nodes).
 * @param child1 The first child of the node.
 * @param child2 The second child of the node.
 * @param pivot The centre/pivot of the node. 
 * @param distanceFunction The distance function to 
 * use to calculate the radius
 * @return The radius of the node. 
 * @throws Exception If there is some problem 
 * in calculating the radius.
 */
public static double calcRadius(BallNode child1, BallNode child2, 
                                Instance pivot, 
                                DistanceFunction distanceFunction) 
                                                           throws Exception {
  Instance p1 = child1.getPivot(), p2 = child2.getPivot();                                                               

  double radius = child1.getRadius() + distanceFunction.distance(p1, p2) + 
                  child2.getRadius();

  return radius/2;
}