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

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Vector

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    final double bestY = minmaxY.getMin();
    double bestX = Double.POSITIVE_INFINITY;
    int bestI = -1;
    Iterator<Vector> iter = this.points.iterator();
    for(int i = 0; iter.hasNext(); i++) {
      Vector vec = iter.next();
      if(vec.get(1) == bestY && vec.get(0) < bestX) {
        bestX = vec.get(0);
        bestI = i;
      }
    }
    assert (bestI >= 0);
    // Bring the reference object to the head.
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    Stack<Vector> stack = new Stack<Vector>();
    // Start with the first two points on the stack
    stack.add(iter.next());
    stack.add(iter.next());
    while(iter.hasNext()) {
      Vector next = iter.next();
      Vector curr = stack.pop();
      Vector prev = stack.peek();
      while((stack.size() > 1) && !isConvex(prev, curr, next)) {
        curr = stack.pop();
        prev = stack.peek();
      }
      stack.add(curr);
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    // if only one dimension was given, expand to all dimensions.
    if(min.getRowDimensionality() == 1 && max.getRowDimensionality() == 1) {
      if(min.get(0) >= max.get(0)) {
        throw new UnableToComplyException("Clipping range empty.");
      }
      clipmin = new Vector(dim);
      clipmax = new Vector(dim);
      for(int i = 0; i < dim; i++) {
        clipmin.set(i, 0, min.get(0));
        clipmax.set(i, 0, max.get(0));
      }
      return;
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      int i = 0;
      for(Distribution axis : axes) {
        d[i] = axis.generate();
        i++;
      }
      Vector p = new Vector(d);
      if(trans != null) {
        p = trans.apply(p);
      }
      if(testClipping(p)) {
        retries--;
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   *
   * @see de.lmu.ifi.dbs.elki.data.synthetic.bymodel.GeneratorInterface#getDensity(de.lmu.ifi.dbs.elki.math.linearalgebra.Vector)
   */
  @Override
  public double getDensity(Vector p) {
    Vector o = p;
    if(trans != null) {
      o = trans.applyInverse(p);
    }

    double density = 1.0;
    int i = 0;
    for(Distribution axis : axes) {
      density = density * axis.explain(o.get(i));
      i++;
    }
    return density * densitycorrection;
  }
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    final double fakt = (1.0 / (Math.sqrt(Math.pow(MathUtil.TWOPI, DatabaseUtil.dimensionality(relation)) * covarianceMatrix.det())));

    // for each object compute Mahalanobis distance
    for(DBID id : relation.iterDBIDs()) {
      V x = relation.get(id);
      Vector x_minus_mean = x.minus(mean).getColumnVector();
      // Gaussian PDF
      final double mDist = x_minus_mean.transposeTimes(covarianceTransposed).times(x_minus_mean).get(0, 0);
      final double prob = fakt * Math.exp(-mDist / 2.0);

      mm.put(prob);
      oscores.put(id, prob);
    }
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    try {
      SVGSimpleLinearAxis.drawAxis(svgp, layer, yscale, 0, ysize, 0, 0, true, false, context.getStyleLibrary());

      // draw axes that are non-trivial
      final int dimensionality = DatabaseUtil.dimensionality(relation);
      double orig = proj.fastProjectScaledToRender(new Vector(dimensionality));
      for(int d = 0; d < dimensionality; d++) {
        Vector v = new Vector(dimensionality);
        v.set(d, 1);
        // projected endpoint of axis
        double ax = proj.fastProjectScaledToRender(v);
        if(ax != orig) {
          final double left = (orig / Projection.SCALE + 0.5) * xsize;
          final double right = (ax / Projection.SCALE + 0.5) * xsize;
 
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    }
  }

  @Override
  public Vector getColumnVector() {
    return new Vector(Util.convertToDoubles(values));
  }
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    FiniteProgress rocloop = logger.isVerbose() ? new FiniteProgress("Computing ROC AUC values", relation.size(), logger) : null;

    // sort neighbors
    for(Cluster<?> clus : split) {
      ArrayList<FCPair<Double, DBID>> cmem = new ArrayList<FCPair<Double, DBID>>(clus.size());
      Vector av = averages.get(clus).getColumnVector();
      Matrix covm = covmats.get(clus);

      for(DBID i1 : clus.getIDs()) {
        Double d = MathUtil.mahalanobisDistance(covm, av.minus(relation.get(i1).getColumnVector()));
        cmem.add(new FCPair<Double, DBID>(d, i1));
      }
      Collections.sort(cmem);

      for(int ind = 0; ind < cmem.size(); ind++) {
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  public Vector getColumnVector() {
    double[] data = new double[values.length];
    for(int i = 0; i < values.length; i++) {
      data[i] = values[i];
    }
    return new Vector(data);
  }
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Related Classes of de.lmu.ifi.dbs.elki.math.linearalgebra.Vector

  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.LMCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ORCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.EM
  • de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans
  • de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyGeneratedInitialMeans
  • de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator
  • de.lmu.ifi.dbs.elki.algorithm.outlier.ABOD
  • de.lmu.ifi.dbs.elki.algorithm.outlier.GaussianModel
  • de.lmu.ifi.dbs.elki.algorithm.outlier.GaussianUniformMixture
  • de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMeanMultipleAttributes
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