Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Vector.transposeTimes()

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

Class de.lmu.ifi.dbs.elki.math.linearalgebra.Vector

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Vector.transposeTimes()

de.lmu.ifi.dbs.elki.math.linearalgebra.Vector.transposeTimes()
Linear algebraic matrix multiplication, A^T * B @param B another matrix @return Matrix product, A^T * B @throws IllegalArgumentException Matrix inner dimensions must agree.

    // 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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    // for each object compute probability and sum
    for(DBID id : objids) {
      V x = database.get(id);


      Vector x_minus_mean = x.minus(mean).getColumnVector();
      double mDist = x_minus_mean.transposeTimes(covInv).times(x_minus_mean).get(0, 0);
      prob += Math.log(fakt * Math.exp(-mDist / 2.0));
    }
    return prob;
  }

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      Matrix m1_czech = pca1.dissimilarityMatrix();
      for(int i = 0; i < v2_strong.getColumnDimensionality(); i++) {
        Vector v2_i = v2_strong.getCol(i);
        // check, if distance of v2_i to the space of rv1 > delta
        // (i.e., if v2_i spans up a new dimension)
        double dist = Math.sqrt(v2_i.transposeTimes(v2_i) - v2_i.transposeTimesTimes(m1_czech, v2_i));


        // if so, insert v2_i into v1 and adjust v1
        // and compute m1_czech new, increase lambda1
        if(lambda1 < dimensionality && dist > delta) {
          adjust(v1, e1_czech, v2_i, lambda1++);

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      Matrix m2_czech = pca2.dissimilarityMatrix();
      for(int i = 0; i < v1_strong.getColumnDimensionality(); i++) {
        Vector v1_i = v1_strong.getCol(i);
        // check, if distance of v1_i to the space of rv2 > delta
        // (i.e., if v1_i spans up a new dimension)
        double dist = Math.sqrt(v1_i.transposeTimes(v1_i) - v1_i.transposeTimes(m2_czech).times(v1_i).get(0));


        // if so, insert v1_i into v2 and adjust v2
        // and compute m2_czech new , increase lambda2
        if(lambda2 < dimensionality && dist > delta) {
          adjust(v2, e2_czech, v1_i, lambda2++);

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      Matrix m2_czech = pca2.dissimilarityMatrix();
      for(int i = 0; i < v1_strong.getColumnDimensionality(); i++) {
        Vector v1_i = v1_strong.getCol(i);
        // check, if distance of v1_i to the space of rv2 > delta
        // (i.e., if v1_i spans up a new dimension)
        double dist = Math.sqrt(v1_i.transposeTimes(v1_i) - v1_i.transposeTimes(m2_czech).times(v1_i).get(0));


        // if so, insert v1_i into v2 and adjust v2
        // and compute m2_czech new , increase lambda2
        if(lambda2 < dimensionality && dist > delta) {
          adjust(v2, e2_czech, v1_i, lambda2++);

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    Matrix v2_strong = pca2.adapatedStrongEigenvectors();
    for(int i = 0; i < v2_strong.getColumnDimensionality(); i++) {
      Vector v2_i = v2_strong.getCol(i);
      // check, if distance of v2_i to the space of pca_1 > delta
      // (i.e., if v2_i spans up a new dimension)
      double dist = Math.sqrt(v2_i.transposeTimes(v2_i) - v2_i.transposeTimesTimes(m1_czech, v2_i));


      // if so, return false
      if(dist > delta) {
        return false;
      }

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      V v2 = relation.get(id2);
      Vector v1Mv2 = v1.getColumnVector().minus(v2.getColumnVector());
      Vector v2Mv1 = v2.getColumnVector().minus(v1.getColumnVector());


      double dist1 = v1Mv2.transposeTimes(m1).times(v1Mv2).get(0, 0);
      double dist2 = v2Mv1.transposeTimes(m2).times(v2Mv1).get(0, 0);


      if(dist1 < 0) {
        if(-dist1 < 0.000000000001) {
          dist1 = 0;
        }

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        }
      }


      Matrix m = null; // index.getLocalProjection(v.getID()).similarityMatrix();
      Vector rv1Mrv2 = v.getColumnVector().minus(new Vector(r));
      double dist = rv1Mrv2.transposeTimes(m).times(rv1Mrv2).get(0, 0);


      return new DoubleDistance(Math.sqrt(dist));
    }


    // TODO: Remove?

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      Vector v_i = vectors.get(i);
      Vector u_i = v_i.copy();
      // System.out.println("Vector " + i + ":" + partialSol);
      for(int j = 0; j < i; j++) {
        Vector v_j = ret.getCol(j);
        double f = v_i.transposeTimes(v_j) / v_j.transposeTimes(v_j);
        if(Double.isNaN(f)) {
          if(logger.isDebuggingFine()) {
            logger.debugFine("Zero vector encountered? " + v_j);
          }
          return null;

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