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

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.times()

de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix.times()
Multiply a matrix by a scalar, C = s*A @param s scalar @return s*A

      for(int i = 0; i < k; i++) {
        V difference = x.minus(means.get(i));
        Matrix differenceRow = difference.getRowVector();
        Vector differenceCol = difference.getColumnVector();
        Matrix rowTimesCov = differenceRow.times(invCovMatr.get(i));
        Vector rowTimesCovTimesCol = rowTimesCov.times(differenceCol);
        double power = rowTimesCovTimesCol.get(0, 0) / 2.0;
        double prob = normDistrFactor.get(i) * Math.exp(-power);
        if(logger.isDebuggingFinest()) {
          logger.debugFinest(" difference vector= ( " + difference.toString() + " )\n" + " differenceRow:\n" + FormatUtil.format(differenceRow, "    ") + "\n" + " differenceCol:\n" + FormatUtil.format(differenceCol, "    ") + "\n" + " rowTimesCov:\n" + FormatUtil.format(rowTimesCov, "    ") + "\n" + " rowTimesCovTimesCol:\n" + FormatUtil.format(rowTimesCovTimesCol, "    ") + "\n" + " power= " + power + "\n" + " prob=" + prob + "\n" + " inv cov matrix: \n" + FormatUtil.format(invCovMatr.get(i), "     "));
        }

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      }
    }


    // TODO: unnecessary copy.
    Matrix V = getEigenvectors();
    adapatedStrongEigenvectors = V.times(e_czech).times(Matrix.identity(dim, localdim));
    m_hat = V.times(e_hat).timesTranspose(V);
    m_czech = V.times(e_czech).timesTranspose(V);
  }


  /**

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    }


    // TODO: unnecessary copy.
    Matrix V = getEigenvectors();
    adapatedStrongEigenvectors = V.times(e_czech).times(Matrix.identity(dim, localdim));
    m_hat = V.times(e_hat).timesTranspose(V);
    m_czech = V.times(e_czech).timesTranspose(V);
  }


  /**
   * Returns a copy of the matrix of strong eigenvectors after passing the eigen

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    // TODO: unnecessary copy.
    Matrix V = getEigenvectors();
    adapatedStrongEigenvectors = V.times(e_czech).times(Matrix.identity(dim, localdim));
    m_hat = V.times(e_hat).timesTranspose(V);
    m_czech = V.times(e_czech).timesTranspose(V);
  }


  /**
   * Returns a copy of the matrix of strong eigenvectors after passing the eigen
   * pair filter.

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        log.append(FormatUtil.format(transposedWeakEigenvectors, NF)).append('\n');
        log.append("Eigenvalues:\n");
        log.append(FormatUtil.format(pcares.getEigenvalues(), " , ", 2));
        logger.debugFine(log.toString());
      }
      Vector B = transposedWeakEigenvectors.times(centroid);
      if(logger.isDebugging()) {
        StringBuilder log = new StringBuilder();
        log.append("Centroid:\n").append(centroid).append('\n');
        log.append("tEV * Centroid\n");
        log.append(B);

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   * @param matrix the matrix to be centered
   * @return centered matrix (for convenience)
   */
  public static Matrix centerMatrix(final Matrix matrix) {
    final Matrix normalizingMatrix = new Matrix(matrix.getRowDimensionality(), matrix.getColumnDimensionality(), 1.0 / matrix.getColumnDimensionality());
    return matrix.minusEquals(normalizingMatrix.times(matrix)).minusEquals(matrix.times(normalizingMatrix)).plusEquals(normalizingMatrix.times(matrix).times(normalizingMatrix));
  }


  @Override
  public String toString() {
    return super.toString();

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   * @param matrix the matrix to be centered
   * @return centered matrix (for convenience)
   */
  public static Matrix centerMatrix(final Matrix matrix) {
    final Matrix normalizingMatrix = new Matrix(matrix.getRowDimensionality(), matrix.getColumnDimensionality(), 1.0 / matrix.getColumnDimensionality());
    return matrix.minusEquals(normalizingMatrix.times(matrix)).minusEquals(matrix.times(normalizingMatrix)).plusEquals(normalizingMatrix.times(matrix).times(normalizingMatrix));
  }


  @Override
  public String toString() {
    return super.toString();

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      }
    }


    // TODO: unnecessary copy.
    Matrix V = getEigenvectors();
    adapatedStrongEigenvectors = V.times(e_czech).times(Matrix.identity(dim, localdim));
    m_hat = V.times(e_hat).timesTranspose(V);
    m_czech = V.times(e_czech).timesTranspose(V);
  }


  /**

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    }


    // TODO: unnecessary copy.
    Matrix V = getEigenvectors();
    adapatedStrongEigenvectors = V.times(e_czech).times(Matrix.identity(dim, localdim));
    m_hat = V.times(e_hat).timesTranspose(V);
    m_czech = V.times(e_czech).timesTranspose(V);
  }


  /**
   * Returns a copy of the matrix of strong eigenvectors after passing the eigen

View Full Code Here


    // TODO: unnecessary copy.
    Matrix V = getEigenvectors();
    adapatedStrongEigenvectors = V.times(e_czech).times(Matrix.identity(dim, localdim));
    m_hat = V.times(e_hat).timesTranspose(V);
    m_czech = V.times(e_czech).timesTranspose(V);
  }


  /**
   * Returns a copy of the matrix of strong eigenvectors after passing the eigen
   * pair filter.

View Full Code Here

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