Java EuclideanDistance类代码示例

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * Set the dummy priming data (two-instance dataset that contains global
 * min/max for numeric attributes) for the distance function to use when
 * normalizing numeric attributes. This method should be called when filters
 * that transform the data are being used, and *after* the first iteration of
 * k-means has completed. At this point, the reduce task can compute global
 * min/max for transformed attributes using the partial summary metadata for
 * the clusters computed in the first iteration
 *
 * @param priming the dummy priming data to use in the distance function
 */
public void setDummyDistancePrimingData(Instances priming)
  throws DistributedWekaException {
  if (m_kMeans == null) {
    throw new DistributedWekaException("Must call init() first");
  }

  m_distanceFunction = new EuclideanDistance();
  m_distanceFunction.setInstances(priming);
  try {
    m_kMeans.setDistanceFunction(m_distanceFunction);
    m_updateDistanceFunction = false;
  } catch (Exception ex) {
    throw new DistributedWekaException(ex);
  }
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * Builds the BallTree on the supplied set of 
 * instances/points (supplied with setInstances(Instances) 
 * method and referenced by the m_Instances field). This
 * method should not be called by outside classes. They
 * should only use setInstances(Instances) method.
 * 
 * @throws Exception If no instances are supplied 
 * (m_Instances is null), or if some other error in the 
 * supplied BallTreeConstructor occurs while building 
 * the tree.  
 */
protected void buildTree() throws Exception {
  if(m_Instances==null)
    throw new Exception("No instances supplied yet. Have to call " +
                        "setInstances(instances) with a set of Instances " +
                        "first.");
  m_InstList = new int[m_Instances.numInstances()];
  
  for(int i=0; i<m_InstList.length; i++) {
    m_InstList[i] = i;
  } //end for
  
  m_DistanceFunction.setInstances(m_Instances);
  m_TreeConstructor.setInstances(m_Instances);
  m_TreeConstructor.setInstanceList(m_InstList);
  m_TreeConstructor.setEuclideanDistanceFunction(
                    (EuclideanDistance)m_DistanceFunction);
  
  m_Root = m_TreeConstructor.buildTree();
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * 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));
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * Builds the ball tree top down.
 * 
 * @return The root node of the tree.
 * @throws Exception If there is problem building the tree.
 */
@Override
public BallNode buildTree() throws Exception {
  BallNode root;

  m_NumNodes = m_MaxDepth = 0;
  m_NumLeaves = 1;

  m_Splitter.setInstances(m_Instances);
  m_Splitter.setInstanceList(m_InstList);
  m_Splitter
    .setEuclideanDistanceFunction((EuclideanDistance) m_DistanceFunction);

  root = new BallNode(0, m_InstList.length - 1, 0);
  root.setPivot(BallNode.calcCentroidPivot(m_InstList, m_Instances));
  root.setRadius(BallNode.calcRadius(m_InstList, m_Instances,
    root.getPivot(), m_DistanceFunction));

  splitNodes(root, m_MaxDepth + 1, root.m_Radius);

  return root;
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
  * clusters an instance that has been through the filters.
  *
  * @param instance the instance to assign a cluster to
  * @param updateErrors if true, update the within clusters sum of errors
  * @param useFastDistCalc whether to use the fast distance calculation or not
  * @return a cluster number
  */
 private int clusterProcessedInstance(Instance instance, boolean updateErrors, boolean useFastDistCalc) {
   double minDist = Integer.MAX_VALUE;
   int bestCluster = 0;
   for (int i = 0; i < m_NumClusters; i++) {
     double dist;
     if (useFastDistCalc)
dist = m_DistanceFunction.distance(instance, m_ClusterCentroids.instance(i), minDist);
     else
dist = m_DistanceFunction.distance(instance, m_ClusterCentroids.instance(i));
     if (dist < minDist) {
minDist = dist;
bestCluster = i;
     }
   }
   if (updateErrors) {
     if (m_DistanceFunction instanceof EuclideanDistance) {
       //Euclidean distance to Squared Euclidean distance
       minDist *= minDist;
     }
     m_squaredErrors[bestCluster] += minDist;
   }
   return bestCluster;
 }
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * performs initialization of members
 */
@Override
protected void initializeMembers() {
  super.initializeMembers();
  
  m_TrainsetNew          = null;
  m_TestsetNew           = null;
  m_Alpha                = 0.99;
  m_Sigma                = 1.0;
  m_Repeats              = 0;
  m_SequenceLimit        = SEQ_LIMIT_GRAPHKERNEL;
  m_filterType           = SMO.FILTER_NORMALIZE;
  m_IncludeNumAttributes = true;
  m_MatrixY              = null;
  m_MatrixW              = null;
  m_MatrixD              = null;
  m_MatrixS              = null;
  m_MatrixFStar          = null;
  m_Data                 = null;
  m_DistanceFunction     = new EuclideanDistance();
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * 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));
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * Builds the ball tree top down. 
 * @return The root node of the tree. 
 * @throws Exception If there is problem building
 * the tree.
 */
public BallNode buildTree() throws Exception {
  BallNode root;
  
  m_NumNodes = m_MaxDepth = 0;
  m_NumLeaves = 1;
  
  m_Splitter.setInstances(m_Instances);
  m_Splitter.setInstanceList(m_InstList);
  m_Splitter.
  setEuclideanDistanceFunction((EuclideanDistance)m_DistanceFunction);
  
  root = new BallNode(0, m_InstList.length-1, 0);
  root.setPivot(BallNode.calcCentroidPivot(m_InstList, m_Instances));
  root.setRadius(BallNode.calcRadius(m_InstList, m_Instances, root.getPivot(), m_DistanceFunction));
  
  splitNodes(root, m_MaxDepth+1, root.m_Radius);
  
  return root; 
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * clusters an instance that has been through the filters
 * 
 * @param instance the instance to assign a cluster to
 * @param updateErrors if true, update the within clusters sum of errors
 * @return a cluster number
 */
private int clusterProcessedInstance(Instance instance, boolean updateErrors) {
  double minDist = Integer.MAX_VALUE;
  int bestCluster = 0;
  for (int i = 0; i < m_NumClusters; i++) {
    double dist = m_DistanceFunction.distance(instance,
      m_ClusterCentroids.instance(i));
    if (dist < minDist) {
      minDist = dist;
      bestCluster = i;
    }
  }
  if (updateErrors) {
    if (m_DistanceFunction instanceof EuclideanDistance) {
      // Euclidean distance to Squared Euclidean distance
      minDist *= minDist;
    }
    m_squaredErrors[bestCluster] += minDist;
  }
  return bestCluster;
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
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;
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * Make the final PreconstructedKMeans clusterer to wrap the centroids and
 * stats found during map-reduce.
 * 
 * @param best the best result from the runs of k-means that were performed in
 *          parallel
 * @param preprocess any pre-processing filters applied
 * @param initialStartingPoints the initial starting centroids
 * @param finalNumIterations the final number of iterations performed
 * @return a final clusterer object
 * @throws DistributedWekaException if a problem occurs
 */
protected Clusterer makeFinalClusterer(KMeansReduceTask best,
  Filter preprocess, Instances initialStartingPoints, int finalNumIterations)
  throws DistributedWekaException {

  Clusterer finalClusterer = null;
  PreconstructedKMeans finalKMeans = new PreconstructedKMeans();
  // global priming data for the distance function (this will be in
  // the transformed space if we're using preprocessing filters)
  Instances globalPrimingData = best.getGlobalDistanceFunctionPrimingData();
  NormalizableDistance dist = new EuclideanDistance();
  dist.setInstances(globalPrimingData);
  finalKMeans.setClusterCentroids(best.getCentroidsForRun());
  finalKMeans.setFinalNumberOfIterations(finalNumIterations + 1);
  if (initialStartingPoints != null) {
    finalKMeans.setInitialStartingPoints(initialStartingPoints);
  }
  try {
    finalKMeans.setDistanceFunction(dist);
    finalKMeans.setClusterStats(best.getAggregatedCentroidSummaries());
  } catch (Exception e) {
    throw new DistributedWekaException(e);
  }

  if (!getInitWithRandomCentroids()) {
    finalKMeans.setInitializationMethod(new SelectedTag(
      SimpleKMeans.KMEANS_PLUS_PLUS, SimpleKMeans.TAGS_SELECTION));
  }

  finalKMeans.setDisplayStdDevs(getDisplayCentroidStdDevs());

  finalClusterer = finalKMeans;

  if (preprocess != null) {
    PreconstructedFilteredClusterer fc =
      new PreconstructedFilteredClusterer();
    fc.setFilter(preprocess);
    fc.setClusterer(finalKMeans);
    finalClusterer = fc;
  }

  return finalClusterer;
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * 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;
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * 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;
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * Builds the tree on the given set of instances. P.S.: For internal use only.
 * Outside classes should call setInstances().
 * 
 * @param insts The instances on which to build the cover tree.
 * @throws Exception If the supplied set of Instances is empty, or if there
 *           are missing values.
 */
protected void buildCoverTree(Instances insts) throws Exception {
  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>();

  Instance point_p = insts.instance(0);
  int p_idx = 0;
  double max_dist = -1, dist = 0.0;

  for (int i = 1; i < insts.numInstances(); i++) {
    DistanceNode temp = new DistanceNode();
    temp.dist = new Stack<Double>();
    dist = Math.sqrt(m_DistanceFunction.distance(point_p, insts.instance(i),
      Double.POSITIVE_INFINITY));
    if (dist > max_dist) {
      max_dist = dist;
      insts.instance(i);
    }
    temp.dist.push(dist);
    temp.idx = i;
    point_set.push(temp);
  }

  max_dist = max_set(point_set);
  m_Root = batch_insert(p_idx, get_scale(max_dist), get_scale(max_dist),
    point_set, consumed_set);
}
 

import weka.core.EuclideanDistance; //导入依赖的package包/类
/**
 * 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;
}