List of transposeTimesTimes() Examples

Examples of transposeTimesTimes()

de.lmu.ifi.dbs.elki.math.linearalgebra.Vector.transposeTimesTimes()
Linear algebraic matrix multiplication, a^T * B * c @param B matrix @param c vector on the right @return Matrix product, a^T * B

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

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


      double dist1 = v1Mv2.transposeTimesTimes(m1, v1Mv2);
      double dist2 = v2Mv1.transposeTimesTimes(m2, v2Mv1);


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

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

        }
      }


      Matrix m = null; // index.getLocalProjection(v.getID()).similarityMatrix();
      Vector rv1Mrv2 = v.getColumnVector().minusEquals(new Vector(r));
      double dist = rv1Mrv2.transposeTimesTimes(m, rv1Mrv2);


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


    // TODO: Remove?

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

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

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


    DoubleMinMax minmax = new DoubleMinMax();
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC);
    for(DBID id : attributes.iterDBIDs()) {
      Vector temp = deltas.get(id).minus(mean);
      final double score = temp.transposeTimesTimes(cmati, temp);
      minmax.put(score);
      scores.putDouble(id, score);
    }


    Relation<Double> scoreResult = new MaterializedRelation<Double>("mean multiple attributes spatial outlier", "mean-multipleattributes-outlier", TypeUtil.DOUBLE, scores, attributes.getDBIDs());

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


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


      mm.put(prob);
      oscores.putDouble(id, prob);
    }

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


    DoubleMinMax minmax = new DoubleMinMax();
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC);
    for(DBID id : attributes.iterDBIDs()) {
      Vector temp = deltas.get(id).minus(mean);
      final double score = temp.transposeTimesTimes(cmati, temp);
      minmax.put(score);
      scores.putDouble(id, score);
    }


    Relation<Double> scoreResult = new MaterializedRelation<Double>("Median multiple attributes outlier", "median-outlier", TypeUtil.DOUBLE, scores, attributes.getDBIDs());

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

    double covarianceDet = covarianceMatrix.det();
    double fakt = 1.0 / Math.sqrt(Math.pow(MathUtil.TWOPI, DatabaseUtil.dimensionality(database)) * covarianceDet);
    // for each object compute probability and sum
    for(DBID id : objids) {
      Vector x = database.get(id).getColumnVector().minusEquals(mean);
      double mDist = x.transposeTimesTimes(covInv, x);
      prob += Math.log(fakt * Math.exp(-mDist / 2.0));
    }
    return prob;
  }

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

    for(DBID id : database.iterDBIDs()) {
      Vector x = database.get(id).getColumnVector();
      List<Double> probabilities = new ArrayList<Double>(k);
      for(int i = 0; i < k; i++) {
        Vector difference = x.minus(means.get(i));
        double rowTimesCovTimesCol = difference.transposeTimesTimes(invCovMatr.get(i), difference);
        double power = rowTimesCovTimesCol / 2.0;
        double prob = normDistrFactor.get(i) * Math.exp(-power);
        if(logger.isDebuggingFinest()) {
          logger.debugFinest(" difference vector= ( " + difference.toString() + " )\n" + " difference:\n" + FormatUtil.format(difference, "    ") + "\n" + " rowTimesCovTimesCol:\n" + rowTimesCovTimesCol + "\n" + " power= " + power + "\n" + " prob=" + prob + "\n" + " inv cov matrix: \n" + FormatUtil.format(invCovMatr.get(i), "     "));
        }

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