Examples of KDtree

com.nr.cg.KDtree
KD Trees and Nearest-Neighbor Finding Copyright (C) Numerical Recipes Software 1986-2007 Java translation Copyright (C) Huang Wen Hui 2012 @author hwh
com.vividsolutions.jts.index.kdtree.KdTree
An implementation of a 2-D KD-Tree. KD-trees provide fast range searching on point data.
This implementation supports detecting and snapping points which are closer than a given tolerance value. If the same point (up to tolerance) is inserted more than once a new node is not created but the count of the existing node is incremented. @author David Skea @author Martin Davis

weka.core.neighboursearch.KDTree

utonlab.org/autonweb/14665.html} }

Valid options are:

 -S <classname and options> Node splitting method to use. (default: weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide)

 -W <value> Set minimal width of a box (default: 1.0E-2).

 -L Maximal number of instances in a leaf (default: 40).

 -N Normalizing will be done (Select dimension for split, with normalising to universe).

@author Gabi Schmidberger (gabi[at-the-rate]cs[dot]waikato[dot]ac[dot]nz) @author Malcolm Ware (mfw4[at-the-rate]cs[dot]waikato[dot]ac[dot]nz) @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz) @version $Revision: 1.3 $

Examples of com.nr.cg.KDtree

      pvec[i].x[0]=myran.doub();
      pvec[i].x[1]=myran.doub();
    }


    Nearpoints qo = new Nearpoints(2,pvec);
    KDtree kd = new KDtree(2,pvec);


    int[] kdlist=new int[M];
    Point[] qolist=new Point[M];


    testpt.x[0]=0.6;
    testpt.x[1]=0.7;
    r=0.1;
    nkd=kd.locatenear(testpt,r,kdlist,M);
    nqo=qo.locatenear(testpt,r,qolist,M);


    // See if KDtree and Nearpoints found the same number of
    // neighbors withing radius r
    localflag = (nkd != nqo);
    globalflag = globalflag || localflag;
    if (localflag) {
      fail("*** Nearpoints,2D: KDtree found a different number of neighbors inside radius r");
      
    }


    // See if each point from kdlist can be found in qolist
    sbeps=1.e-16;
    if (nkd == nqo) {
      for (i=0;i<nkd;i++) {
        min=1.e99;
        for (j=0;j<nqo;j++) {
          d=dist(kd.ptss[kdlist[i]],qolist[j]);
          if (d < min) min=d;
        }
//        System.out.printf(min);
        localflag = min > sbeps;
        globalflag = globalflag || localflag;
        if (localflag) {
          fail("*** Nearpoints,2D: A point identified by KDtree was not found by Nearpoints");
          
        }
      }
    }


    // Create KDtree and Qotree of same Points<3> in 3D
    Point test3pt=new Point(3);
    Point[] p3vec=new Point[N];  
    for (i=0;i<N;i++) {
      p3vec[i] = new Point(3);
      p3vec[i].x[0]=myran.doub();
      p3vec[i].x[1]=myran.doub();
      p3vec[i].x[2]=myran.doub();
    }


    Nearpoints qo3=new Nearpoints(3,p3vec);
    KDtree kd3=new KDtree(3,p3vec);


    int[] kd3list=new int[M];
    Point[] qo3list=new Point[M];


    test3pt.x[0]=0.6;
    test3pt.x[1]=0.7;
    test3pt.x[2]=0.6;
    r=0.1;
    nkd=kd3.locatenear(test3pt,r,kd3list,M);
    nqo=qo3.locatenear(test3pt,r,qo3list,M);


    // See if KDtree and Nearpoints found the same number of
    // neighbors withing radius r
    localflag = (nkd != nqo);

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Examples of com.nr.cg.KDtree

    for (i=0;i<N;i++) {
      kd2[i] = new Point(2);
      kd2[i].x[0]=myran.doub();
      kd2[i].x[1]=myran.doub();
    }
    KDtree tree2=new KDtree(2,kd2);
    
    // Test locate(Point) method
    Point y=new Point(2);
    int nb;       // number of box containing y
    localflag=false;
    for (i=0;i<M;i++) {
      y.x[0]=myran.doub();
      y.x[1]=myran.doub();
      nb=tree2.locate(y);
//      System.out.printf(nb);
//      System.out.printf(tree2.boxes[nb].lo.x[0] << " %f\n", tree2.boxes[nb].lo.x[1]);
//      System.out.printf(tree2.boxes[nb].hi.x[0] << " %f\n", tree2.boxes[nb].hi.x[1]);
      localflag = localflag || (y.x[0] < tree2.boxes[nb].lo.x[0]);
      localflag = localflag || (y.x[0] > tree2.boxes[nb].hi.x[0]);
      localflag = localflag || (y.x[1] < tree2.boxes[nb].lo.x[1]);
      localflag = localflag || (y.x[1] > tree2.boxes[nb].hi.x[1]);
      globalflag = globalflag || localflag;
      if (localflag) {
        fail("*** KDtree<2>,locate(Point): The located box does not contain selected point");
        
      }
    }


    // Test locate(int) method
    int n0;
    localflag=false;
    for (i=0;i<M;i++) {
      n0=myran.int32p() % N;
      nb=tree2.locate(n0);
//      System.out.printf(nb);
//      System.out.printf(tree2.boxes[nb].lo.x[0] << " %f\n", tree2.boxes[nb].lo.x[1]);
//      System.out.printf(tree2.boxes[nb].hi.x[0] << " %f\n", tree2.boxes[nb].hi.x[1]);
      localflag = localflag || (kd2[n0].x[0] < tree2.boxes[nb].lo.x[0]);
      localflag = localflag || (kd2[n0].x[0] > tree2.boxes[nb].hi.x[0]);
      localflag = localflag || (kd2[n0].x[1] < tree2.boxes[nb].lo.x[1]);
      localflag = localflag || (kd2[n0].x[1] > tree2.boxes[nb].hi.x[1]);
      globalflag = globalflag || localflag;
      if (localflag) {
        fail("*** KDtree<2>,locate(int): The located box does not contain selected point");
        
      }
    }


    // Test disti() method
    int n1,n2;
    sbeps=1.e-15;
    localflag=false;
    for (i=0;i<M;i++) {
      n1=myran.int32p() % N;
      n2=myran.int32p() % N;
      double dis=tree2.disti(n1,n2);
//      System.out.printf(dis << " %f\n", sqrt(SQR(kd2[n1].x[0]-kd2[n2].x[0]) 
//        + SQR(kd2[n1].x[1]-kd2[n2].x[1])));
      if (n1 == n2) 
        localflag = localflag || dis < 1.e99;
      else
        localflag = localflag || (dis-sqrt(SQR(kd2[n1].x[0]-kd2[n2].x[0]) 
          + SQR(kd2[n1].x[1]-kd2[n2].x[1]))) > sbeps;
      globalflag = globalflag || localflag;
      if (localflag) {
        fail("*** KDtree<2>,disti(): The disti() method gave an incorrect result");
        
      }
    }


    // Test nearest() method
    int j,n3;
    double dis2sq;
    localflag=false;
    for (i=0;i<M;i++) {
      y.x[0]=myran.doub();
      y.x[1]=myran.doub();
      n3=tree2.nearest(y);
      dis2sq=SQR(kd2[n3].x[0]-y.x[0])+SQR(kd2[n3].x[1]-y.x[1]);
      for (j=0;j<N;j++) {
        if (j != n3) localflag = localflag || 
          (SQR(kd2[j].x[0]-y.x[0])+ SQR(kd2[j].x[1]-y.x[1])) < dis2sq;
      }
      globalflag = globalflag || localflag;
      if (localflag) {
        fail("*** KDtree<2>,nearest(): Exhaustive search found closer point");
        
      }
    }


    // Test nnearest() method
    int n4,n,nfar,K=5;
    boolean test;
    double far;
    int[]nn=new int[K];
    double[] dn=new double[K];
    localflag=false;
    for (i=0;i<M;i++) {
      n4=myran.int32p()%N;
      tree2.nnearest(n4,nn,dn,K);   // Find K nearest Points
      // See if there is anything closer than the furthest
      // of these points, other than the other points in the list nn[]
      far=0.0;
      nfar=0;
      for (j=0;j<K;j++)
        if (dn[j] > far) {
          far=dn[j];
          nfar=nn[j];
        }
      test=false;
      for (j=0;j<N;j++) {
        for (n=0;n<K;n++)
          test = test || (nn[n] == j);
        if (!test)
          localflag = localflag || (tree2.disti(j,n4) < far);
        globalflag = globalflag || localflag;
        if (localflag) {
          fail("*** KDtree<2>,nnearest(): Found a nearer point than the supposed n nearest");
          
        }
      }
    }


    // Test locatenear() method
    int nmax=N/10;
    int[] list=new int[nmax];
    double r=0.1;
    y.x[0]=0.5;
    y.x[1]=0.5;
    int nret=tree2.locatenear(y,r,list,nmax);
    // Check the result by brute force
    int[] list2=new int[nmax];
    int nret2=0;
    for (i=0;i<N;i++) {
      if ((dist(y,kd2[i]) < r) && (nret2 < nmax)) {
        list2[nret2]=i;
        nret2++;
      }
    }


//    System.out.printf(nret << " %f\n", nret2);
    localflag = (nret != nret2);
    globalflag = globalflag || localflag;
    if (localflag) {
      fail("*** KDtree<2>,locatenear(): Found different number of points closer than radius r");
      
    }


    // Make sure list[] and list2[] are the same
    int[] llist=buildVector(list),llist2=buildVector(list2);
    Arrays.sort(llist);
    Arrays.sort(llist2);
    localflag=false;
    for (i=0;i<nret;i++) {
//      System.out.printf(llist[i] << " %f\n", llist2[i]);
      localflag=localflag || (llist[i] != llist2[i]);
    }
    globalflag = globalflag || localflag;
    if (localflag) {
      fail("*** KDtree<2>,locatenear(): The list of nearby points is not correct");
      
    }


    // Repeat all the tests in 3D
    // Test KDtree in 3D
    for (i=0;i<N;i++) {
      kd3[i] = new Point(3);
      kd3[i].x[0]=myran.doub();
      kd3[i].x[1]=myran.doub();
      kd3[i].x[2]=myran.doub();
    }
    KDtree tree3 = new KDtree(3,kd3);


    // Test locate(Point) method in 3D
    Point yy=new Point(3);
    localflag=false;
    for (i=0;i<M;i++) {
      yy.x[0]=myran.doub();
      yy.x[1]=myran.doub();
      yy.x[2]=myran.doub();
      nb=tree3.locate(yy);
//      System.out.printf(nb);
//      System.out.printf(tree3.boxes[nb].lo.x[0] << " %f\n", tree3.boxes[nb].lo.x[1] 
//        << tree3.boxes[nb].lo.x[2]);
//      System.out.printf(tree3.boxes[nb].hi.x[0] << " %f\n", tree3.boxes[nb].hi.x[1] 
//        << tree3.boxes[nb].hi.x[2]);
      localflag = localflag || (yy.x[0] < tree3.boxes[nb].lo.x[0]);
      localflag = localflag || (yy.x[0] > tree3.boxes[nb].hi.x[0]);
      localflag = localflag || (yy.x[1] < tree3.boxes[nb].lo.x[1]);
      localflag = localflag || (yy.x[1] > tree3.boxes[nb].hi.x[1]);
      localflag = localflag || (yy.x[2] < tree3.boxes[nb].lo.x[2]);
      localflag = localflag || (yy.x[2] > tree3.boxes[nb].hi.x[2]);
      globalflag = globalflag || localflag;
      if (localflag) {
        fail("*** KDtree<3>,locate(Point): The located box does not contain selected point");
        
      }
    }


    // Test locate(int) method in 3D
    localflag=false;
    for (i=0;i<M;i++) {
      n0=myran.int32p() % N;
      nb=tree3.locate(n0);
//      System.out.printf(nb);
//      System.out.printf(tree3.boxes[nb].lo.x[0] << " %f\n", tree3.boxes[nb].lo.x[1] 
//        tree3.boxes[nb].lo.x[2]);
//      System.out.printf(tree3.boxes[nb].hi.x[0] << " %f\n", tree3.boxes[nb].hi.x[1] 
//        tree3.boxes[nb].hi.x[2]);
      localflag = localflag || (kd3[n0].x[0] < tree3.boxes[nb].lo.x[0]);
      localflag = localflag || (kd3[n0].x[0] > tree3.boxes[nb].hi.x[0]);
      localflag = localflag || (kd3[n0].x[1] < tree3.boxes[nb].lo.x[1]);
      localflag = localflag || (kd3[n0].x[1] > tree3.boxes[nb].hi.x[1]);
      localflag = localflag || (kd3[n0].x[2] < tree3.boxes[nb].lo.x[2]);
      localflag = localflag || (kd3[n0].x[2] > tree3.boxes[nb].hi.x[2]);
      globalflag = globalflag || localflag;
      if (localflag) {
        fail("*** KDtree<3>,locate(int): The located box does not contain selected point");
        
      }
    }


    // Test disti() method in 3D
    sbeps=1.e-15;
    localflag=false;
    for (i=0;i<M;i++) {
      n1=myran.int32p() % N;
      n2=myran.int32p() % N;
      double dis=tree3.disti(n1,n2);
//      System.out.printf(dis << " %f\n", sqrt(SQR(kd3[n1].x[0]-kd3[n2].x[0]) 
//        + SQR(kd3[n1].x[1]-kd3[n2].x[1])
//        + SQR(kd3[n1].x[2]-kd3[n2].x[2])));
      if (n1 == n2) 
        localflag = localflag || dis < 1.e99;
      else
        localflag = localflag || (dis-sqrt(SQR(kd3[n1].x[0]-kd3[n2].x[0]) 
          + SQR(kd3[n1].x[1]-kd3[n2].x[1])
          + SQR(kd3[n1].x[2]-kd3[n2].x[2]))) > sbeps;
      globalflag = globalflag || localflag;
      if (localflag) {
        fail("*** KDtree<3>,disti(): The disti() method gave an incorrect result");
        
      }
    }


    // Test nearest() method in 3D
    double dis3sq;
    localflag=false;
    for (i=0;i<M;i++) {
      yy.x[0]=myran.doub();
      yy.x[1]=myran.doub();
      yy.x[2]=myran.doub();
      n3=tree3.nearest(yy);
      dis3sq=SQR(kd3[n3].x[0]-yy.x[0])+SQR(kd3[n3].x[1]-yy.x[1])
        +SQR(kd3[n3].x[2]-yy.x[2]);
      for (j=0;j<N;j++) {
        if (j != n3) localflag = localflag || 
          (SQR(kd3[j].x[0]-yy.x[0]) + SQR(kd3[j].x[1]-yy.x[1])
          + SQR(kd3[j].x[2]-yy.x[2])) < dis3sq;
      }
      globalflag = globalflag || localflag;
      if (localflag) {
        fail("*** KDtree<3>,nearest(): Exhaustive search found closer point");
        
      }
    }


    // Test nnearest() method in 3D
    localflag=false;
    for (i=0;i<M;i++) {
      n4=myran.int32p() % N;
      tree3.nnearest(n4,nn,dn,K);   // Find K nearest Points
      // See if there is anything closer than the furthest
      // of these points, other than the other points in the list nn[]
      far=0.0;
      nfar=0;
      for (j=0;j<K;j++)
        if (dn[j] > far) {
          far=dn[j];
          nfar=nn[j];
        }
      test=false;
      for (j=0;j<N;j++) {
        for (n=0;n<K;n++)
          test = test || (nn[n] == j);
        if (!test)
          localflag = localflag || (tree3.disti(j,n4) < far);
        globalflag = globalflag || localflag;
        if (localflag) {
          fail("*** KDtree<3>,nnearest(): Found a nearer point than the supposed n nearest");
          
        }
      }
    }


    // Test locatenear() method in 3D
    r=0.2;
    yy.x[0]=0.5;
    yy.x[1]=0.5;
    yy.x[2]=0.5;
    nret=tree3.locatenear(yy,r,list,nmax);
    // Check the result by brute force
    nret2=0;
    for (i=0;i<N;i++) {
      if ((dist(yy,kd3[i]) < r) && (nret2 < nmax)) {
        list2[nret2]=i;

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Examples of com.vividsolutions.jts.index.kdtree.KdTree

   */
  public ConformingDelaunayTriangulator(Collection initialVertices,
      double tolerance) {
    this.initialVertices = new ArrayList(initialVertices);
    this.tolerance = tolerance;
    kdt = new KdTree(tolerance);
  }

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Examples of com.vividsolutions.jts.index.kdtree.KdTree

   */
  public ConformingDelaunayTriangulator(Collection initialVertices,
      double tolerance) {
    this.initialVertices = new ArrayList(initialVertices);
    this.tolerance = tolerance;
    kdt = new KdTree(tolerance);
  }

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Examples of weka.core.neighboursearch.KDTree

   *
   * @return the KDTree string.
   */
  protected String getKDTreeSpec() {
    
    KDTree c = getKDTree();
    if (c instanceof OptionHandler) {
      return c.getClass().getName() + " "
  + Utils.joinOptions(((OptionHandler)c).getOptions());
    }
    return c.getClass().getName();
  }

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Examples of weka.core.neighboursearch.KDTree

  String funcName = funcSpec[0];
  funcSpec[0] = "";
  setKDTree((KDTree) Utils.forName(KDTree.class, funcName, funcSpec));
      }
      else {
  setKDTree(new KDTree());
      }
    }
    else {
      setKDTree(new KDTree());
    }


    optionString = Utils.getOption('C', options);
    if (optionString.length() != 0)
      setCutOffFactor(Double.parseDouble(optionString));

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Examples of weka.core.neighboursearch.KDTree

   *
   * @return the KDTree string.
   */
  protected String getKDTreeSpec() {
    
    KDTree c = getKDTree();
    if (c instanceof OptionHandler) {
      return c.getClass().getName() + " "
  + Utils.joinOptions(((OptionHandler)c).getOptions());
    }
    return c.getClass().getName();
  }

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Examples of weka.core.neighboursearch.KDTree

  String funcName = funcSpec[0];
  funcSpec[0] = "";
  setKDTree((KDTree) Utils.forName(KDTree.class, funcName, funcSpec));
      }
      else {
  setKDTree(new KDTree());
      }
    }
    else {
      setKDTree(new KDTree());
    }


    optionString = Utils.getOption('C', options);
    if (optionString.length() != 0)
      setCutOffFactor(Double.parseDouble(optionString));

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