matrix.java

MacroWeka扩展了著名数据挖掘工具weka

   * @param b the multiplication matrix
   * @return the product matrix
   */
  public final Matrix multiply(Matrix b) {
    try {
      return new Matrix(getMatrix().times(b.getMatrix()).getArrayCopy());
    }
    catch (Exception e) {
      e.printStackTrace();
      return null;
    }
  }

  /**
   * Performs a (ridged) linear regression.
   *
   * @param y the dependent variable vector
   * @param ridge the ridge parameter
   * @return the coefficients 
   * @throws IllegalArgumentException if not successful
   */
  public final double[] regression(Matrix y, double ridge) {
    return getMatrix().regression(y.getMatrix(), ridge).getCoefficients();
  }

  /**
   * Performs a weighted (ridged) linear regression. 
   *
   * @param y the dependent variable vector
   * @param w the array of data point weights
   * @param ridge the ridge parameter
   * @return the coefficients 
   * @throws IllegalArgumentException if the wrong number of weights were
   * provided.
   */
  public final double[] regression(Matrix y, double [] w, double ridge) {
    return getMatrix().regression(y.getMatrix(), w, ridge).getCoefficients();
  }

  /**
   * Returns the L part of the matrix.
   * This does only make sense after LU decomposition.
   *
   * @return matrix with the L part of the matrix; 
   * @see #LUDecomposition()
   */
  public Matrix getL() throws Exception {
    int nr = numRows();    // num of rows
    int nc = numColumns(); // num of columns
    double[][] ld = new double[nr][nc];

    for (int i = 0; i < nr; i++) {
      for (int j = 0; (j < i) && (j < nc); j++) {
        ld[i][j] = getElement(i, j);
      }
      if (i < nc) ld[i][i] = 1;
    }
    Matrix l = new Matrix(ld);
    return l;
  }

  /**
   * Returns the U part of the matrix.
   * This does only make sense after LU decomposition.
   *
   * @return matrix with the U part of a matrix; 
   * @see #LUDecomposition()
   */
  public Matrix getU() throws Exception {
    int nr = numRows();    // num of rows
    int nc = numColumns(); // num of columns
    double[][] ud = new double[nr][nc];
    
    for (int i = 0; i < nr; i++) {
      for (int j = i; j < nc ; j++) {
        ud[i][j] = getElement(i, j);
      }
    }
    Matrix u = new Matrix(ud);
    return u;
  }
  
  /**
   * Performs a LUDecomposition on the matrix.
   * It changes the matrix into its LU decomposition.
   *
   * @return the indices of the row permutation
   */
  public int[] LUDecomposition() throws Exception {
    // decompose
    weka.core.matrix.LUDecomposition lu = m_Matrix.lu();

    // singular? old class throws Exception!
    if (!lu.isNonsingular())
    	throw new Exception("Matrix is singular");

    weka.core.matrix.Matrix u = lu.getU();
    weka.core.matrix.Matrix l = lu.getL();

    // modify internal matrix
    int nr = numRows();
    int nc = numColumns();
    for (int i = 0; i < nr; i++) {
      for (int j = 0; j < nc; j++) {
        if (j < i)
          setElement(i, j, l.get(i, j));
        else
          setElement(i, j, u.get(i, j));
      }
    }

    u = null;
    l = null;

    return lu.getPivot();
  }
  
  /**
   * Solve A*X = B using backward substitution.
   * A is current object (this). Note that this matrix will be changed! 
   * B parameter bb.
   * X returned in parameter bb.
   *
   * @param bb first vector B in above equation then X in same equation.
   */
  public void solve(double[] bb) throws Exception {
    // solve
    weka.core.matrix.Matrix x = m_Matrix.solve(
                                    new weka.core.matrix.Matrix(bb, bb.length));
    
    // move X into bb
    int nr = x.getRowDimension();
    for (int i = 0; i < nr; i++)
      bb[i] = x.get(i, 0);
  }

  /**
   * Performs Eigenvalue Decomposition using Householder QR Factorization
   *
   * Matrix must be symmetrical.
   * Eigenvectors are return in parameter V, as columns of the 2D array.
   * (Real parts of) Eigenvalues are returned in parameter d.
   *
   * @param V double array in which the eigenvectors are returned 
   * @param d array in which the eigenvalues are returned
   * @throws Exception if matrix is not symmetric
   */
  public void eigenvalueDecomposition(double[][] V, double[] d) 
    throws Exception {

    // old class only worked with symmetric matrices!
    if (!this.isSymmetric())
      throw new Exception("EigenvalueDecomposition: Matrix must be symmetric.");
    
    // perform eigenvalue decomposition
    weka.core.matrix.EigenvalueDecomposition eig = m_Matrix.eig();
    weka.core.matrix.Matrix v = eig.getV();
    double[] d2 = eig.getRealEigenvalues();
    
    // transfer data
    int nr = numRows();
    int nc = numColumns();
    for (int i = 0; i < nr; i++)
      for (int j = 0; j < nc; j++)
        V[i][j] = v.get(i, j);

    for (int i = 0; i < d2.length; i++)
      d[i] = d2[i];
  }   

  /**
   * Returns sqrt(a^2 + b^2) without under/overflow.
   *   
   * @param a length of one side of rectangular triangle
   * @param b length of other side of rectangular triangle
   * @return lenght of third side
   */
  protected static double hypot(double a, double b) {
    return weka.core.matrix.Maths.hypot(a, b);
  }

  /**
   * converts the Matrix into a single line Matlab string: matrix is enclosed 
   * by parentheses, rows are separated by semicolon and single cells by
   * blanks, e.g., [1 2; 3 4].
   * @return      the matrix in Matlab single line format
   */
  public String toMatlab() {
    return getMatrix().toMatlab();
  }

  /**
   * creates a matrix from the given Matlab string.
   * @param matlab  the matrix in matlab format
   * @return        the matrix represented by the given string
   * @see           #toMatlab()
   */
  public static Matrix parseMatlab(String matlab) throws Exception {
    return new Matrix(weka.core.matrix.Matrix.parseMatlab(matlab).getArray());
  }
  
  /**
   * Main method for testing this class.
   */
  public static void main(String[] ops) {

    double[] first = {2.3, 1.2, 5};
    double[] second = {5.2, 1.4, 9};
    double[] response = {4, 7, 8};
    double[] weights = {1, 2, 3};

    try {
      // test eigenvaluedecomposition
      double[][] m = {{1, 2, 3}, {2, 5, 6},{3, 6, 9}};
      Matrix M = new Matrix(m);
      int n = M.numRows();
      double[][] V = new double[n][n];
      double[] d = new double[n];
      double[] e = new double[n];
      M.eigenvalueDecomposition(V, d);
      Matrix v = new Matrix(V);
      // M.testEigen(v, d, );
      // end of test-eigenvaluedecomposition
      
      Matrix a = new Matrix(2, 3);
      Matrix b = new Matrix(3, 2);
      System.out.println("Number of columns for a: " + a.numColumns());
      System.out.println("Number of rows for a: " + a.numRows());
      a.setRow(0, first);
      a.setRow(1, second);
      b.setColumn(0, first);
      b.setColumn(1, second);
      System.out.println("a:\n " + a);
      System.out.println("b:\n " + b);
      System.out.println("a (0, 0): " + a.getElement(0, 0));
      System.out.println("a transposed:\n " + a.transpose());
      System.out.println("a * b:\n " + a.multiply(b));
      Matrix r = new Matrix(3, 1);
      r.setColumn(0, response);
      System.out.println("r:\n " + r);
      System.out.println("Coefficients of regression of b on r: ");
      double[] coefficients = b.regression(r, 1.0e-8);
      for (int i = 0; i < coefficients.length; i++) {
	System.out.print(coefficients[i] + " ");
      }
      System.out.println();
      System.out.println("Weights: ");
      for (int i = 0; i < weights.length; i++) {
	System.out.print(weights[i] + " ");
      }
      System.out.println();
      System.out.println("Coefficients of weighted regression of b on r: ");
      coefficients = b.regression(r, weights, 1.0e-8);
      for (int i = 0; i < coefficients.length; i++) {
	System.out.print(coefficients[i] + " ");
      }
      System.out.println();
      a.setElement(0, 0, 6);
      System.out.println("a with (0, 0) set to 6:\n " + a);
      a.write(new java.io.FileWriter("main.matrix"));
      System.out.println("wrote matrix to \"main.matrix\"\n" + a);
      a = new Matrix(new java.io.FileReader("main.matrix"));
      System.out.println("read matrix from \"main.matrix\"\n" + a);
    } catch (Exception e) {
      e.printStackTrace();
    }
  }
}