Examples of CosineDistance

com.jgaap.distances.CosineDistance
Cosine Distance or normalized dot product. This is YA distance for Nearest Neighbor algorithms, based on John's research at JHU. NOTE: The cosine distance was modified slightly as we need to make it nonnegative and we want smaller distances to imply similarity. @author Noecker @version 1.0
de.jungblut.distance.CosineDistance

Examples of com.jgaap.distances.CosineDistance

   * @throws AnalyzeException 
   */
  @Test
  public void testAnalyze() throws AnalyzeException {
    NearestNeighborDriver nearest = new NearestNeighborDriver();
    CosineDistance cosine = new CosineDistance();
    nearest.setDistance(cosine);


    EventSet known1 = new EventSet();
    EventSet known2 = new EventSet();
    EventSet unknown = new EventSet();

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Examples of de.jungblut.distance.CosineDistance


  @Test
  public void testVectorInvertedIndex() {


    InvertedIndex<DoubleVector, Integer> invIndex = InvertedIndex
        .createVectorIndex(new CosineDistance());
    DoubleVector v1 = new SparseDoubleVector(4);
    v1.set(1, 0.6931471805599453);
    v1.set(0, 1.3862943611198906);
    DoubleVector v2 = new SparseDoubleVector(4);
    v2.set(2, 0.6931471805599453);

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Examples of de.jungblut.distance.CosineDistance


  @Test
  public void testSparseKNN() {


    SparseKNearestNeighbours neighbours = new SparseKNearestNeighbours(2, 2,
        new CosineDistance());


    // we seperate stuff in two dimensions each
    DoubleVector left = new SingleEntryDoubleVector(0d);
    DoubleVector right = new SingleEntryDoubleVector(1d);
    DoubleVector v1 = new SparseDoubleVector(4);

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Examples of de.jungblut.distance.CosineDistance

   * entity tokens count and m is the number of the context vertices. Alpha is
   * set to 0.5 and the cosine distance is used.
   */
  public IterativeSimilarityAggregation(String[] seedTokens,
      Tuple<String[], DoubleMatrix> bipartiteGraph) {
    this(seedTokens, bipartiteGraph, 0.5d, new CosineDistance());
  }

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