Ejemplo n.º 1
0
  @Test
  public void testBigHierarchicalSearch() {
    PruningConeTree tree = new PruningConeTree();
    int totalNodes = 100000;
    List<double[]> vectors = new ArrayList<double[]>(totalNodes);
    for (int i = 0; i < 10; i++) {
      vectors.addAll(
          populateRandom(
              tree, (int) totalNodes / 10, DIMENSIONS, 100, getRandomVector(DIMENSIONS, 500)));
    }
    DoubleVector queryVector = new DoubleVector(getRandomVector(DIMENSIONS, 700));

    double maxDot = Double.MIN_VALUE;
    double[] maxVec = null;
    for (double[] vector : vectors) {
      double dotproduct = queryVector.similarity(new DoubleVector(vector));
      if (dotproduct > maxDot) {
        maxDot = dotproduct;
        maxVec = vector;
      }
    }

    Query query = new Query(queryVector);
    DoubleVector resp = (DoubleVector) tree.query(query);

    Assert.assertArrayEquals(resp.getCoords(), maxVec, 0.0001);
  }
Ejemplo n.º 2
0
  @Test
  public void testSearchInLowDimensions() {
    int nodeSize = 10;
    PruningConeTree tree = new PruningConeTree(Math.PI / 4, 0.5, 10);
    // List<double[]> vectors = populateRandom(tree, 30, 3, 100);
    // printVectors(vectors);
    List<double[]> vectors = new ArrayList<double[]>();
    vectors.add(new double[] {77.70176498116555, 182.6366419604867, 184.452422671186});
    vectors.add(new double[] {90.13444552156517, 156.90522877473836, 153.23459682303144});
    vectors.add(new double[] {70.43588291695296, 144.8240112865408, 115.7531937610102});
    vectors.add(new double[] {23.349186085639374, 113.19096637157797, 187.54977875948117});
    vectors.add(new double[] {31.04071146108659, 110.78655892809655, 112.92078923295179});
    vectors.add(new double[] {72.22910855217609, 172.27772491706648, 110.32037633402018});
    vectors.add(new double[] {94.34170872652678, 102.45934588283333, 149.4878045057245});
    vectors.add(new double[] {105.68702732458162, 166.25938221913952, 162.19513156527415});
    vectors.add(new double[] {46.58118264957257, 151.06725028011198, 180.6410164182393});
    vectors.add(new double[] {71.69344310865992, 107.26092561097482, 171.57921708069});
    vectors.add(new double[] {25.038714451813615, 138.10223915595077, 164.27147252323573});
    vectors.add(new double[] {38.97679317048615, 150.82083709699555, 152.51262540208901});
    vectors.add(new double[] {61.81117130728214, 143.44251179438444, 111.95605794091571});
    vectors.add(new double[] {77.90073124668673, 112.97005024793842, 125.74635215730162});
    vectors.add(new double[] {46.544915711859844, 173.01871199807874, 179.77932460899223});
    vectors.add(new double[] {37.585176293531845, 173.7705404031444, 126.61341696436185});
    vectors.add(new double[] {44.34670084088936, 159.48843542649, 123.10315924282001});
    vectors.add(new double[] {15.832494787392415, 153.46597548386046, 145.93231494634745});
    vectors.add(new double[] {41.91796947788818, 150.18595095888628, 186.68982929279534});
    vectors.add(new double[] {67.72330669889762, 140.03793569584553, 138.6252847051797});
    vectors.add(new double[] {100.61718468456104, 180.15787518063735, 145.7509501754609});
    vectors.add(new double[] {103.65857401807077, 137.93601237109522, 182.5964986012226});
    vectors.add(new double[] {49.05077320626245, 160.80311686168446, 179.23778086485714});
    vectors.add(new double[] {32.384171323896254, 92.07817287500691, 104.74635493288034});
    vectors.add(new double[] {40.257302271967774, 131.37008678729097, 154.65153976674605});
    vectors.add(new double[] {76.81703020188893, 174.85617081237018, 137.2859753338546});
    vectors.add(new double[] {55.85123992860376, 129.62679067417406, 155.41835621128808});
    vectors.add(new double[] {46.41113193518079, 148.72396506481599, 174.68655198958726});
    vectors.add(new double[] {48.83904528997557, 146.14809434485014, 144.96119986350794});
    vectors.add(new double[] {78.90816781548723, 85.8272470132913, 167.01441870496808});
    for (double[] vector : vectors) {
      tree.add(vector);
    }
    //        double[] queryVector = getRandomVector(3, 100);
    //        System.out.println("Query: " + (new Vector(queryVector)));
    double[] queryVector = {38.76117074251793, 78.43972538522617, 55.35981227709609};
    Vector q = new DoubleVector(queryVector);
    double maxDot = Double.MIN_VALUE;
    double[] maxVec = null;
    for (double[] vector : vectors) {
      double dotproduct = q.similarity(new DoubleVector(vector));
      if (dotproduct > maxDot) {
        maxDot = dotproduct;
        maxVec = vector;
      }
    }

    DoubleVector resp = (DoubleVector) tree.query(queryVector);
    Assert.assertArrayEquals(resp.getCoords(), maxVec, 0.0001);
  }
Ejemplo n.º 3
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 private List<double[]> populateRandom(
     PruningConeTree tree, int howMany, int dimensions, int max, double[] center) {
   List<double[]> vectors = new ArrayList<double[]>();
   for (int i = 0; i < howMany; i++) {
     double[] vector = getRandomVector(dimensions, max, center);
     vectors.add(vector);
     tree.add(vector);
   }
   return vectors;
 }
Ejemplo n.º 4
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  @Test
  public void testSimpleHierarchicalSearch() {
    int nodeSize = 10;
    PruningConeTree tree = new PruningConeTree();
    List<double[]> vectors = populateRandom(tree, 20, DIMENSIONS, 100);
    double[] queryVector = getRandomVector(DIMENSIONS, 100);
    Vector q = new DoubleVector(queryVector);
    double maxDot = Double.MIN_VALUE;
    double[] maxVec = null;
    for (double[] vector : vectors) {
      double dotproduct = q.similarity(new DoubleVector(vector));
      if (dotproduct > maxDot) {
        maxDot = dotproduct;
        maxVec = vector;
      }
    }

    DoubleVector resp = (DoubleVector) tree.query(queryVector);
    Assert.assertArrayEquals(resp.getCoords(), maxVec, 0.0001);
  }
Ejemplo n.º 5
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 @Test
 public void testAdd() {
   PruningConeTree tree = new PruningConeTree();
   tree.add(getRandomVector(DIMENSIONS, 100));
 }