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

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

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

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

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

      Matrix m1_czech = pca1.dissimilarityMatrix();
      for(int i = 0; i < v2_strong.getColumnDimensionality(); i++) {
        Matrix v2_i = v2_strong.getColumn(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).get(0, 0) - v2_i.transposeTimes(m1_czech).times(v2_i).get(0, 0));


        // 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 m1_czech = pca1.dissimilarityMatrix();
      for(int i = 0; i < v2_strong.getColumnDimensionality(); i++) {
        Matrix v2_i = v2_strong.getColumn(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).get(0, 0) - v2_i.transposeTimes(m1_czech).times(v2_i).get(0, 0));


        // 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++) {
        Matrix v1_i = v1_strong.getColumn(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).get(0, 0) - v1_i.transposeTimes(m2_czech).times(v1_i).get(0, 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++) {
        Matrix v1_i = v1_strong.getColumn(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).get(0, 0) - v1_i.transposeTimes(m2_czech).times(v1_i).get(0, 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++) {
      Matrix v2_i = v2_strong.getColumn(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).get(0, 0) - v2_i.transposeTimes(m1_czech).times(v2_i).get(0, 0));


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

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    Matrix v2_strong = pca2.adapatedStrongEigenvectors();
    for(int i = 0; i < v2_strong.getColumnDimensionality(); i++) {
      Matrix v2_i = v2_strong.getColumn(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).get(0, 0) - v2_i.transposeTimes(m1_czech).times(v2_i).get(0, 0));


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

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