Examples of de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix

Package de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Examples of de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix

de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix
Provides a class for storing the kernel matrix and several extraction methods for convenience. @author Simon Paradies @apiviz.uses de.lmu.ifi.dbs.elki.distance.similarityfunction.PrimitiveSimilarityFunction

  public OutlierResult getRanking(Relation<V> relation, int k) {
    // Fix a static set of IDs
    staticids = DBIDUtil.newArray(relation.getDBIDs());
    Collections.sort(staticids);


    KernelMatrix kernelMatrix = new KernelMatrix(primitiveKernelFunction, relation, staticids);
    PriorityQueue<FCPair<Double, DBID>> pq = new PriorityQueue<FCPair<Double, DBID>>(relation.size(), Collections.reverseOrder());


    // preprocess kNN neighborhoods
    assert (k == this.k);
    KNNQuery<V, DoubleDistance> knnQuery = QueryUtil.getKNNQuery(relation, getDistanceFunction(), k);

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  public OutlierResult getFastRanking(Relation<V> relation, int k, int sampleSize) {
    // Fix a static set of IDs
    staticids = DBIDUtil.newArray(relation.getDBIDs());
    Collections.sort(staticids);


    KernelMatrix kernelMatrix = new KernelMatrix(primitiveKernelFunction, relation, staticids);


    PriorityQueue<FCPair<Double, DBID>> pq = new PriorityQueue<FCPair<Double, DBID>>(relation.size(), Collections.reverseOrder());
    // get Candidate Ranking
    for(DBID aKey : relation.iterDBIDs()) {
      HashMap<DBID, Double> dists = new HashMap<DBID, Double>(relation.size());

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   * 
   * @param data to get explanations for
   */
  // TODO: this should be done by the result classes.
  public void getExplanations(Relation<V> data) {
    KernelMatrix kernelMatrix = new KernelMatrix(primitiveKernelFunction, data, staticids);
    // PQ for Outlier Ranking
    PriorityQueue<FCPair<Double, DBID>> pq = new PriorityQueue<FCPair<Double, DBID>>(data.size(), Collections.reverseOrder());
    HashMap<DBID, LinkedList<DBID>> explaintab = new HashMap<DBID, LinkedList<DBID>>();
    // test all objects
    for(DBID objKey : data.iterDBIDs()) {

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  public OutlierResult getRanking(Relation<V> relation, int k) {
    // Fix a static set of IDs
    staticids = DBIDUtil.newArray(relation.getDBIDs());
    staticids.sort();


    KernelMatrix kernelMatrix = new KernelMatrix(primitiveKernelFunction, relation, staticids);
    Heap<DoubleObjPair<DBID>> pq = new Heap<DoubleObjPair<DBID>>(relation.size(), Collections.reverseOrder());


    // preprocess kNN neighborhoods
    assert (k == this.k);
    KNNQuery<V, DoubleDistance> knnQuery = QueryUtil.getKNNQuery(relation, getDistanceFunction(), k);

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  public OutlierResult getFastRanking(Relation<V> relation, int k, int sampleSize) {
    // Fix a static set of IDs
    staticids = DBIDUtil.newArray(relation.getDBIDs());
    staticids.sort();


    KernelMatrix kernelMatrix = new KernelMatrix(primitiveKernelFunction, relation, staticids);


    Heap<DoubleObjPair<DBID>> pq = new Heap<DoubleObjPair<DBID>>(relation.size(), Collections.reverseOrder());
    // get Candidate Ranking
    for(DBID aKey : relation.iterDBIDs()) {
      HashMap<DBID, Double> dists = new HashMap<DBID, Double>(relation.size());

View Full Code Here

   * 
   * @param data to get explanations for
   */
  // TODO: this should be done by the result classes.
  public void getExplanations(Relation<V> data) {
    KernelMatrix kernelMatrix = new KernelMatrix(primitiveKernelFunction, data, staticids);
    // PQ for Outlier Ranking
    Heap<DoubleObjPair<DBID>> pq = new Heap<DoubleObjPair<DBID>>(data.size(), Collections.reverseOrder());
    HashMap<DBID, DBIDs> explaintab = new HashMap<DBID, DBIDs>();
    // test all objects
    for(DBID objKey : data.iterDBIDs()) {

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Related Classes of de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix

de.lmu.ifi.dbs.elki.algorithm.outlier.ABOD

de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix

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