机器学习知识点(十九)矩阵特征值分解基础知识及Java实现

1、特征值分解基础知识

矩阵乘法Y=AB的数学意义在于变换，以其中一个向量A为中心，则B的作用主要是使A发生伸缩或旋转变换。一个矩阵其实就是一个线性变换，因为一个矩阵乘以一个向量后得到的向量，其实就相当于将这个向量进行了线性变换。

如果说一个向量v是方阵A的特征向量，将一定可以表示成下面的形式：





    这时候λ就被称为特征向量v对应的特征值，一个矩阵的一组特征向量是一组正交向量。特征值分解是将一个矩阵分解成下面的形式：





其中Q是这个矩阵A的特征向量组成的矩阵，Σ是一个对角阵，每一个对角线上的元素就是一个特征值。一个变换方阵的所有特征向量组成了这个变换矩阵的一组基。所谓基，可以理解为坐标系的轴。平常用到直角坐标系，在线性代数中可以把这个坐标系扭曲、拉伸、旋转，称为基变换。可以按需求去设定基，但是基的轴之间必须是线性无关的，也就是保证坐标系的不同轴不要指向同一个方向或可以被别的轴组合而成。从线性空间的角度看，在一个定义了内积的线性空间里，对一个N阶对称方阵进行特征分解，就是产生了该空间的N个标准正交基，然后把矩阵投影到这N个基上。N个特征向量就是N个标准正交基，而特征值的模则代表矩阵在每个基上的投影长度。特征值越大，说明矩阵在对应的特征向量上的方差越大，功率越大，信息量越多。不过，特征值分解也有很多的局限，比如说变换的矩阵必须是方阵。

在机器学习特征提取中，意思就是最大特征值对应的特征向量方向上包含最多的信息量，如果某几个特征值很小，说明这几个方向信息量很小，可以用来降维，也就是删除小特征值对应方向的数据，只保留大特征值方向对应的数据，这样做以后数据量减小，但有用信息量变化不大，PCA降维就是基于这种思路。特征值分解可以得到特征值与特征向量，特征值表示的是这个特征到底有多重要，而特征向量表示这个特征是什么，可以将每一个特征向量理解为一个线性的子空间。

2、Java实现

http://math.nist.gov/javanumerics/jama/Java矩阵计算包，下载Jama-1.0.3.jar引入工程。

下载Jama-1.0.3.zip研究源码。

1）  特征值分解测试类

 

package sk.ml;

import Jama.EigenvalueDecomposition;
import Jama.Matrix;

public class QRTest {
//矩阵特征分解
public static void main(String argv[]){
double[] columnwise = {1.,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.};
Matrix A = new Matrix(columnwise,4);//构造矩阵
A.print(A.getColumnDimension(), A.getRowDimension());
EigenvalueDecomposition Eig = A.eig();
Matrix D = Eig.getD();
Matrix V = Eig.getV();
D.print(D.getColumnDimension(), D.getRowDimension());//打印特征值
V.print(V.getColumnDimension(), V.getRowDimension());//打印特征向量
}
}

 

2）  源码参考Matrix

 

package Jama;

import java.text.NumberFormat;
import java.text.DecimalFormat;
import java.text.DecimalFormatSymbols;
import java.util.Locale;
import java.text.FieldPosition;
import java.io.PrintWriter;
import java.io.BufferedReader;
import java.io.StreamTokenizer;
import Jama.util.*;

/**
Jama = Java Matrix class.
<P>
The Java Matrix Class provides the fundamental operations of numerical
linear algebra.  Various constructors create Matrices from two dimensional
arrays of double precision floating point numbers.  Various "gets" and
"sets" provide access to submatrices and matrix elements.  Several methods
implement basic matrix arithmetic, including matrix addition and
multiplication, matrix norms, and element-by-element array operations.
Methods for reading and printing matrices are also included.  All the
operations in this version of the Matrix Class involve real matrices.
Complex matrices may be handled in a future version.
<P>
Five fundamental matrix decompositions, which consist of pairs or triples
of matrices, permutation vectors, and the like, produce results in five
decomposition classes.  These decompositions are accessed by the Matrix
class to compute solutions of simultaneous linear equations, determinants,
inverses and other matrix functions.  The five decompositions are:
<P><UL>
<LI>Cholesky Decomposition of symmetric, positive definite matrices.
<LI>LU Decomposition of rectangular matrices.
<LI>QR Decomposition of rectangular matrices.
<LI>Singular Value Decomposition of rectangular matrices.
<LI>Eigenvalue Decomposition of both symmetric and nonsymmetric square matrices.
</UL>
<DL>
<DT><B>Example of use:</B></DT>
<P>
<DD>Solve a linear system A x = b and compute the residual norm, ||b - A x||.
<P><PRE>
double[][] vals = {{1.,2.,3},{4.,5.,6.},{7.,8.,10.}};
Matrix A = new Matrix(vals);
Matrix b = Matrix.random(3,1);
Matrix x = A.solve(b);
Matrix r = A.times(x).minus(b);
double rnorm = r.normInf();
</PRE></DD>
</DL>

@author The MathWorks, Inc. and the National Institute of Standards and Technology.
@version 5 August 1998
*/

public class Matrix implements Cloneable, java.io.Serializable {

/* ------------------------
Class variables
* ------------------------ */

/** Array for internal storage of elements.
@serial internal array storage.
*/
private double[][] A;

/** Row and column dimensions.
@serial row dimension.
@serial column dimension.
*/
private int m, n;

/* ------------------------
Constructors
* ------------------------ */

/** Construct an m-by-n matrix of zeros.
@param m    Number of rows.
@param n    Number of colums.
*/

public Matrix (int m, int n) {
this.m = m;
this.n = n;
A = new double[m]
;
}

/** Construct an m-by-n constant matrix.
@param m    Number of rows.
@param n    Number of colums.
@param s    Fill the matrix with this scalar value.
*/

public Matrix (int m, int n, double s) {
this.m = m;
this.n = n;
A = new double[m]
;
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = s;
}
}
}

/** Construct a matrix from a 2-D array.
@param A    Two-dimensional array of doubles.
@exception  IllegalArgumentException All rows must have the same length
@see        #constructWithCopy
*/

public Matrix (double[][] A) {
m = A.length;
n = A[0].length;
for (int i = 0; i < m; i++) {
if (A[i].length != n) {
throw new IllegalArgumentException("All rows must have the same length.");
}
}
this.A = A;
}

/** Construct a matrix quickly without checking arguments.
@param A    Two-dimensional array of doubles.
@param m    Number of rows.
@param n    Number of colums.
*/

public Matrix (double[][] A, int m, int n) {
this.A = A;
this.m = m;
this.n = n;
}

/** Construct a matrix from a one-dimensional packed array
@param vals One-dimensional array of doubles, packed by columns (ala Fortran).
@param m    Number of rows.
@exception  IllegalArgumentException Array length must be a multiple of m.
*/

public Matrix (double vals[], int m) {
this.m = m;
n = (m != 0 ? vals.length/m : 0);
if (m*n != vals.length) {
throw new IllegalArgumentException("Array length must be a multiple of m.");
}
A = new double[m]
;
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = vals[i+j*m];
}
}
}

/* ------------------------
Public Methods
* ------------------------ */

/** Construct a matrix from a copy of a 2-D array.
@param A    Two-dimensional array of doubles.
@exception  IllegalArgumentException All rows must have the same length
*/

public static Matrix constructWithCopy(double[][] A) {
int m = A.length;
int n = A[0].length;
Matrix X = new Matrix(m,n);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
if (A[i].length != n) {
throw new IllegalArgumentException
("All rows must have the same length.");
}
for (int j = 0; j < n; j++) {
C[i][j] = A[i][j];
}
}
return X;
}

/** Make a deep copy of a matrix
*/

public Matrix copy () {
Matrix X = new Matrix(m,n);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[i][j] = A[i][j];
}
}
return X;
}

/** Clone the Matrix object.
*/

public Object clone () {
return this.copy();
}

/** Access the internal two-dimensional array.
@return     Pointer to the two-dimensional array of matrix elements.
*/

public double[][] getArray () {
return A;
}

/** Copy the internal two-dimensional array.
@return     Two-dimensional array copy of matrix elements.
*/

public double[][] getArrayCopy () {
double[][] C = new double[m]
;
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[i][j] = A[i][j];
}
}
return C;
}

/** Make a one-dimensional column packed copy of the internal array.
@return     Matrix elements packed in a one-dimensional array by columns.
*/

public double[] getColumnPackedCopy () {
double[] vals = new double[m*n];
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
vals[i+j*m] = A[i][j];
}
}
return vals;
}

/** Make a one-dimensional row packed copy of the internal array.
@return     Matrix elements packed in a one-dimensional array by rows.
*/

public double[] getRowPackedCopy () {
double[] vals = new double[m*n];
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
vals[i*n+j] = A[i][j];
}
}
return vals;
}

/** Get row dimension.
@return     m, the number of rows.
*/

public int getRowDimension () {
return m;
}

/** Get column dimension.
@return     n, the number of columns.
*/

public int getColumnDimension () {
return n;
}

/** Get a single element.
@param i    Row index.
@param j    Column index.
@return     A(i,j)
@exception  ArrayIndexOutOfBoundsException
*/

public double get (int i, int j) {
return A[i][j];
}

/** Get a submatrix.
@param i0   Initial row index
@param i1   Final row index
@param j0   Initial column index
@param j1   Final column index
@return     A(i0:i1,j0:j1)
@exception  ArrayIndexOutOfBoundsException Submatrix indices
*/

public Matrix getMatrix (int i0, int i1, int j0, int j1) {
Matrix X = new Matrix(i1-i0+1,j1-j0+1);
double[][] B = X.getArray();
try {
for (int i = i0; i <= i1; i++) {
for (int j = j0; j <= j1; j++) {
B[i-i0][j-j0] = A[i][j];
}
}
} catch(ArrayIndexOutOfBoundsException e) {
throw new ArrayIndexOutOfBoundsException("Submatrix indices");
}
return X;
}

/** Get a submatrix.
@param r    Array of row indices.
@param c    Array of column indices.
@return     A(r(:),c(:))
@exception  ArrayIndexOutOfBoundsException Submatrix indices
*/

public Matrix getMatrix (int[] r, int[] c) {
Matrix X = new Matrix(r.length,c.length);
double[][] B = X.getArray();
try {
for (int i = 0; i < r.length; i++) {
for (int j = 0; j < c.length; j++) {
B[i][j] = A[r[i]][c[j]];
}
}
} catch(ArrayIndexOutOfBoundsException e) {
throw new ArrayIndexOutOfBoundsException("Submatrix indices");
}
return X;
}

/** Get a submatrix.
@param i0   Initial row index
@param i1   Final row index
@param c    Array of column indices.
@return     A(i0:i1,c(:))
@exception  ArrayIndexOutOfBoundsException Submatrix indices
*/

public Matrix getMatrix (int i0, int i1, int[] c) {
Matrix X = new Matrix(i1-i0+1,c.length);
double[][] B = X.getArray();
try {
for (int i = i0; i <= i1; i++) {
for (int j = 0; j < c.length; j++) {
B[i-i0][j] = A[i][c[j]];
}
}
} catch(ArrayIndexOutOfBoundsException e) {
throw new ArrayIndexOutOfBoundsException("Submatrix indices");
}
return X;
}

/** Get a submatrix.
@param r    Array of row indices.
@param j0   Initial column index
@param j1   Final column index
@return     A(r(:),j0:j1)
@exception  ArrayIndexOutOfBoundsException Submatrix indices
*/

public Matrix getMatrix (int[] r, int j0, int j1) {
Matrix X = new Matrix(r.length,j1-j0+1);
double[][] B = X.getArray();
try {
for (int i = 0; i < r.length; i++) {
for (int j = j0; j <= j1; j++) {
B[i][j-j0] = A[r[i]][j];
}
}
} catch(ArrayIndexOutOfBoundsException e) {
throw new ArrayIndexOutOfBoundsException("Submatrix indices");
}
return X;
}

/** Set a single element.
@param i    Row index.
@param j    Column index.
@param s    A(i,j).
@exception  ArrayIndexOutOfBoundsException
*/

public void set (int i, int j, double s) {
A[i][j] = s;
}

/** Set a submatrix.
@param i0   Initial row index
@param i1   Final row index
@param j0   Initial column index
@param j1   Final column index
@param X    A(i0:i1,j0:j1)
@exception  ArrayIndexOutOfBoundsException Submatrix indices
*/

public void setMatrix (int i0, int i1, int j0, int j1, Matrix X) {
try {
for (int i = i0; i <= i1; i++) {
for (int j = j0; j <= j1; j++) {
A[i][j] = X.get(i-i0,j-j0);
}
}
} catch(ArrayIndexOutOfBoundsException e) {
throw new ArrayIndexOutOfBoundsException("Submatrix indices");
}
}

/** Set a submatrix.
@param r    Array of row indices.
@param c    Array of column indices.
@param X    A(r(:),c(:))
@exception  ArrayIndexOutOfBoundsException Submatrix indices
*/

public void setMatrix (int[] r, int[] c, Matrix X) {
try {
for (int i = 0; i < r.length; i++) {
for (int j = 0; j < c.length; j++) {
A[r[i]][c[j]] = X.get(i,j);
}
}
} catch(ArrayIndexOutOfBoundsException e) {
throw new ArrayIndexOutOfBoundsException("Submatrix indices");
}
}

/** Set a submatrix.
@param r    Array of row indices.
@param j0   Initial column index
@param j1   Final column index
@param X    A(r(:),j0:j1)
@exception  ArrayIndexOutOfBoundsException Submatrix indices
*/

public void setMatrix (int[] r, int j0, int j1, Matrix X) {
try {
for (int i = 0; i < r.length; i++) {
for (int j = j0; j <= j1; j++) {
A[r[i]][j] = X.get(i,j-j0);
}
}
} catch(ArrayIndexOutOfBoundsException e) {
throw new ArrayIndexOutOfBoundsException("Submatrix indices");
}
}

/** Set a submatrix.
@param i0   Initial row index
@param i1   Final row index
@param c    Array of column indices.
@param X    A(i0:i1,c(:))
@exception  ArrayIndexOutOfBoundsException Submatrix indices
*/

public void setMatrix (int i0, int i1, int[] c, Matrix X) {
try {
for (int i = i0; i <= i1; i++) {
for (int j = 0; j < c.length; j++) {
A[i][c[j]] = X.get(i-i0,j);
}
}
} catch(ArrayIndexOutOfBoundsException e) {
throw new ArrayIndexOutOfBoundsException("Submatrix indices");
}
}

/** Matrix transpose.
@return    A'
*/

public Matrix transpose () {
Matrix X = new Matrix(n,m);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[j][i] = A[i][j];
}
}
return X;
}

/** One norm
@return    maximum column sum.
*/

public double norm1 () {
double f = 0;
for (int j = 0; j < n; j++) {
double s = 0;
for (int i = 0; i < m; i++) {
s += Math.abs(A[i][j]);
}
f = Math.max(f,s);
}
return f;
}

/** Two norm
@return    maximum singular value.
*/

public double norm2 () {
return (new SingularValueDecomposition(this).norm2());
}

/** Infinity norm
@return    maximum row sum.
*/

public double normInf () {
double f = 0;
for (int i = 0; i < m; i++) {
double s = 0;
for (int j = 0; j < n; j++) {
s += Math.abs(A[i][j]);
}
f = Math.max(f,s);
}
return f;
}

/** Frobenius norm
@return    sqrt of sum of squares of all elements.
*/

public double normF () {
double f = 0;
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
f = Maths.hypot(f,A[i][j]);
}
}
return f;
}

/**  Unary minus
@return    -A
*/

public Matrix uminus () {
Matrix X = new Matrix(m,n);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[i][j] = -A[i][j];
}
}
return X;
}

/** C = A + B
@param B    another matrix
@return     A + B
*/

public Matrix plus (Matrix B) {
checkMatrixDimensions(B);
Matrix X = new Matrix(m,n);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[i][j] = A[i][j] + B.A[i][j];
}
}
return X;
}

/** A = A + B
@param B    another matrix
@return     A + B
*/

public Matrix plusEquals (Matrix B) {
checkMatrixDimensions(B);
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = A[i][j] + B.A[i][j];
}
}
return this;
}

/** C = A - B
@param B    another matrix
@return     A - B
*/

public Matrix minus (Matrix B) {
checkMatrixDimensions(B);
Matrix X = new Matrix(m,n);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[i][j] = A[i][j] - B.A[i][j];
}
}
return X;
}

/** A = A - B
@param B    another matrix
@return     A - B
*/

public Matrix minusEquals (Matrix B) {
checkMatrixDimensions(B);
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = A[i][j] - B.A[i][j];
}
}
return this;
}

/** Element-by-element multiplication, C = A.*B
@param B    another matrix
@return     A.*B
*/

public Matrix arrayTimes (Matrix B) {
checkMatrixDimensions(B);
Matrix X = new Matrix(m,n);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[i][j] = A[i][j] * B.A[i][j];
}
}
return X;
}

/** Element-by-element multiplication in place, A = A.*B
@param B    another matrix
@return     A.*B
*/

public Matrix arrayTimesEquals (Matrix B) {
checkMatrixDimensions(B);
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = A[i][j] * B.A[i][j];
}
}
return this;
}

/** Element-by-element right division, C = A./B
@param B    another matrix
@return     A./B
*/

public Matrix arrayRightDivide (Matrix B) {
checkMatrixDimensions(B);
Matrix X = new Matrix(m,n);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[i][j] = A[i][j] / B.A[i][j];
}
}
return X;
}

/** Element-by-element right division in place, A = A./B
@param B    another matrix
@return     A./B
*/

public Matrix arrayRightDivideEquals (Matrix B) {
checkMatrixDimensions(B);
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = A[i][j] / B.A[i][j];
}
}
return this;
}

/** Element-by-element left division, C = A.\B
@param B    another matrix
@return     A.\B
*/

public Matrix arrayLeftDivide (Matrix B) {
checkMatrixDimensions(B);
Matrix X = new Matrix(m,n);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[i][j] = B.A[i][j] / A[i][j];
}
}
return X;
}

/** Element-by-element left division in place, A = A.\B
@param B    another matrix
@return     A.\B
*/

public Matrix arrayLeftDivideEquals (Matrix B) {
checkMatrixDimensions(B);
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = B.A[i][j] / A[i][j];
}
}
return this;
}

/** Multiply a matrix by a scalar, C = s*A
@param s    scalar
@return     s*A
*/

public Matrix times (double s) {
Matrix X = new Matrix(m,n);
double[][] C = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
C[i][j] = s*A[i][j];
}
}
return X;
}

/** Multiply a matrix by a scalar in place, A = s*A
@param s    scalar
@return     replace A by s*A
*/

public Matrix timesEquals (double s) {
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = s*A[i][j];
}
}
return this;
}

/** Linear algebraic matrix multiplication, A * B
@param B    another matrix
@return     Matrix product, A * B
@exception  IllegalArgumentException Matrix inner dimensions must agree.
*/

public Matrix times (Matrix B) {
if (B.m != n) {
throw new IllegalArgumentException("Matrix inner dimensions must agree.");
}
Matrix X = new Matrix(m,B.n);
double[][] C = X.getArray();
double[] Bcolj = new double
;
for (int j = 0; j < B.n; j++) {
for (int k = 0; k < n; k++) {
Bcolj[k] = B.A[k][j];
}
for (int i = 0; i < m; i++) {
double[] Arowi = A[i];
double s = 0;
for (int k = 0; k < n; k++) {
s += Arowi[k]*Bcolj[k];
}
C[i][j] = s;
}
}
return X;
}

/** LU Decomposition
@return     LUDecomposition
@see LUDecomposition
*/

public LUDecomposition lu () {
return new LUDecomposition(this);
}

/** QR Decomposition
@return     QRDecomposition
@see QRDecomposition
*/

public QRDecomposition qr () {
return new QRDecomposition(this);
}

/** Cholesky Decomposition
@return     CholeskyDecomposition
@see CholeskyDecomposition
*/

public CholeskyDecomposition chol () {
return new CholeskyDecomposition(this);
}

/** Singular Value Decomposition
@return     SingularValueDecomposition
@see SingularValueDecomposition
*/

public SingularValueDecomposition svd () {
return new SingularValueDecomposition(this);
}

/** Eigenvalue Decomposition
@return     EigenvalueDecomposition
@see EigenvalueDecomposition
*/

public EigenvalueDecomposition eig () {
return new EigenvalueDecomposition(this);
}

/** Solve A*X = B
@param B    right hand side
@return     solution if A is square, least squares solution otherwise
*/

public Matrix solve (Matrix B) {
return (m == n ? (new LUDecomposition(this)).solve(B) :
(new QRDecomposition(this)).solve(B));
}

/** Solve X*A = B, which is also A'*X' = B'
@param B    right hand side
@return     solution if A is square, least squares solution otherwise.
*/

public Matrix solveTranspose (Matrix B) {
return transpose().solve(B.transpose());
}

/** Matrix inverse or pseudoinverse
@return     inverse(A) if A is square, pseudoinverse otherwise.
*/

public Matrix inverse () {
return solve(identity(m,m));
}

/** Matrix determinant
@return     determinant
*/

public double det () {
return new LUDecomposition(this).det();
}

/** Matrix rank
@return     effective numerical rank, obtained from SVD.
*/

public int rank () {
return new SingularValueDecomposition(this).rank();
}

/** Matrix condition (2 norm)
@return     ratio of largest to smallest singular value.
*/

public double cond () {
return new SingularValueDecomposition(this).cond();
}

/** Matrix trace.
@return     sum of the diagonal elements.
*/

public double trace () {
double t = 0;
for (int i = 0; i < Math.min(m,n); i++) {
t += A[i][i];
}
return t;
}

/** Generate matrix with random elements
@param m    Number of rows.
@param n    Number of colums.
@return     An m-by-n matrix with uniformly distributed random elements.
*/

public static Matrix random (int m, int n) {
Matrix A = new Matrix(m,n);
double[][] X = A.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
X[i][j] = Math.random();
}
}
return A;
}

/** Generate identity matrix
@param m    Number of rows.
@param n    Number of colums.
@return     An m-by-n matrix with ones on the diagonal and zeros elsewhere.
*/

public static Matrix identity (int m, int n) {
Matrix A = new Matrix(m,n);
double[][] X = A.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
X[i][j] = (i == j ? 1.0 : 0.0);
}
}
return A;
}

/** Print the matrix to stdout.   Line the elements up in columns
* with a Fortran-like 'Fw.d' style format.
@param w    Column width.
@param d    Number of digits after the decimal.
*/

public void print (int w, int d) {
print(new PrintWriter(System.out,true),w,d); }

/** Print the matrix to the output stream.   Line the elements up in
* columns with a Fortran-like 'Fw.d' style format.
@param output Output stream.
@param w      Column width.
@param d      Number of digits after the decimal.
*/

public void print (PrintWriter output, int w, int d) {
DecimalFormat format = new DecimalFormat();
format.setDecimalFormatSymbols(new DecimalFormatSymbols(Locale.US));
format.setMinimumIntegerDigits(1);
format.setMaximumFractionDigits(d);
format.setMinimumFractionDigits(d);
format.setGroupingUsed(false);
print(output,format,w+2);
}

/** Print the matrix to stdout.  Line the elements up in columns.
* Use the format object, and right justify within columns of width
* characters.
* Note that is the matrix is to be read back in, you probably will want
* to use a NumberFormat that is set to US Locale.
@param format A  Formatting object for individual elements.
@param width     Field width for each column.
@see java.text.DecimalFormat#setDecimalFormatSymbols
*/

public void print (NumberFormat format, int width) {
print(new PrintWriter(System.out,true),format,width); }

// DecimalFormat is a little disappointing coming from Fortran or C's printf.
// Since it doesn't pad on the left, the elements will come out different
// widths.  Consequently, we'll pass the desired column width in as an
// argument and do the extra padding ourselves.

/** Print the matrix to the output stream.  Line the elements up in columns.
* Use the format object, and right justify within columns of width
* characters.
* Note that is the matrix is to be read back in, you probably will want
* to use a NumberFormat that is set to US Locale.
@param output the output stream.
@param format A formatting object to format the matrix elements
@param width  Column width.
@see java.text.DecimalFormat#setDecimalFormatSymbols
*/

public void print (PrintWriter output, NumberFormat format, int width) {
output.println();  // start on new line.
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
String s = format.format(A[i][j]); // format the number
int padding = Math.max(1,width-s.length()); // At _least_ 1 space
for (int k = 0; k < padding; k++)
output.print(' ');
output.print(s);
}
output.println();
}
output.println();   // end with blank line.
}

/** Read a matrix from a stream.  The format is the same the print method,
* so printed matrices can be read back in (provided they were printed using
* US Locale).  Elements are separated by
* whitespace, all the elements for each row appear on a single line,
* the last row is followed by a blank line.
@param input the input stream.
*/

public static Matrix read (BufferedReader input) throws java.io.IOException {
StreamTokenizer tokenizer= new StreamTokenizer(input);

// Although StreamTokenizer will parse numbers, it doesn't recognize
// scientific notation (E or D); however, Double.valueOf does.
// The strategy here is to disable StreamTokenizer's number parsing.
// We'll only get whitespace delimited words, EOL's and EOF's.
// These words should all be numbers, for Double.valueOf to parse.

tokenizer.resetSyntax();
tokenizer.wordChars(0,255);
tokenizer.whitespaceChars(0, ' ');
tokenizer.eolIsSignificant(true);
java.util.Vector<Double> vD = new java.util.Vector<Double>();

// Ignore initial empty lines
while (tokenizer.nextToken() == StreamTokenizer.TT_EOL);
if (tokenizer.ttype == StreamTokenizer.TT_EOF)
throw new java.io.IOException("Unexpected EOF on matrix read.");
do {
vD.addElement(Double.valueOf(tokenizer.sval)); // Read & store 1st row.
} while (tokenizer.nextToken() == StreamTokenizer.TT_WORD);

int n = vD.size();  // Now we've got the number of columns!
double row[] = new double
;
for (int j=0; j<n; j++)  // extract the elements of the 1st row.
row[j]=vD.elementAt(j).doubleValue();
java.util.Vector<double[]> v = new java.util.Vector<double[]>();
v.addElement(row);  // Start storing rows instead of columns.
while (tokenizer.nextToken() == StreamTokenizer.TT_WORD) {
// While non-empty lines
v.addElement(row = new double
);
int j = 0;
do {
if (j >= n) throw new java.io.IOException
("Row " + v.size() + " is too long.");
row[j++] = Double.valueOf(tokenizer.sval).doubleValue();
} while (tokenizer.nextToken() == StreamTokenizer.TT_WORD);
if (j < n) throw new java.io.IOException
("Row " + v.size() + " is too short.");
}
int m = v.size();  // Now we've got the number of rows.
double[][] A = new double[m][];
v.copyInto(A);  // copy the rows out of the vector
return new Matrix(A);
}

/* ------------------------
Private Methods
* ------------------------ */

/** Check if size(A) == size(B) **/

private void checkMatrixDimensions (Matrix B) {
if (B.m != m || B.n != n) {
throw new IllegalArgumentException("Matrix dimensions must agree.");
}
}

private static final long serialVersionUID = 1;
}

 

3）源码参考EigenvalueDecomposition

可重点研读如何实现特征值分解。

package Jama;
import Jama.util.*;

/** Eigenvalues and eigenvectors of a real matrix.
<P>
If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is
diagonal and the eigenvector matrix V is orthogonal.
I.e. A = V.times(D.times(V.transpose())) and
V.times(V.transpose()) equals the identity matrix.
<P>
If A is not symmetric, then the eigenvalue matrix D is block diagonal
with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda].  The
columns of V represent the eigenvectors in the sense that A*V = V*D,
i.e. A.times(V) equals V.times(D).  The matrix V may be badly
conditioned, or even singular, so the validity of the equation
A = V*D*inverse(V) depends upon V.cond().
**/

public class EigenvalueDecomposition implements java.io.Serializable {

/* ------------------------
Class variables
* ------------------------ */

/** Row and column dimension (square matrix).
@serial matrix dimension.
*/
private int n;

/** Symmetry flag.
@serial internal symmetry flag.
*/
private boolean issymmetric;

/** Arrays for internal storage of eigenvalues.
@serial internal storage of eigenvalues.
*/
private double[] d, e;

/** Array for internal storage of eigenvectors.
@serial internal storage of eigenvectors.
*/
private double[][] V;

/** Array for internal storage of nonsymmetric Hessenberg form.
@serial internal storage of nonsymmetric Hessenberg form.
*/
private double[][] H;

/** Working storage for nonsymmetric algorithm.
@serial working storage for nonsymmetric algorithm.
*/
private double[] ort;

/* ------------------------
Private Methods
* ------------------------ */

// Symmetric Householder reduction to tridiagonal form.

private void tred2 () {

//  This is derived from the Algol procedures tred2 by
//  Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
//  Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
//  Fortran subroutine in EISPACK.

for (int j = 0; j < n; j++) {
d[j] = V[n-1][j];
}

// Householder reduction to tridiagonal form.

for (int i = n-1; i > 0; i--) {

// Scale to avoid under/overflow.

double scale = 0.0;
double h = 0.0;
for (int k = 0; k < i; k++) {
scale = scale + Math.abs(d[k]);
}
if (scale == 0.0) {
e[i] = d[i-1];
for (int j = 0; j < i; j++) {
d[j] = V[i-1][j];
V[i][j] = 0.0;
V[j][i] = 0.0;
}
} else {

// Generate Householder vector.

for (int k = 0; k < i; k++) {
d[k] /= scale;
h += d[k] * d[k];
}
double f = d[i-1];
double g = Math.sqrt(h);
if (f > 0) {
g = -g;
}
e[i] = scale * g;
h = h - f * g;
d[i-1] = f - g;
for (int j = 0; j < i; j++) {
e[j] = 0.0;
}

// Apply similarity transformation to remaining columns.

for (int j = 0; j < i; j++) {
f = d[j];
V[j][i] = f;
g = e[j] + V[j][j] * f;
for (int k = j+1; k <= i-1; k++) {
g += V[k][j] * d[k];
e[k] += V[k][j] * f;
}
e[j] = g;
}
f = 0.0;
for (int j = 0; j < i; j++) {
e[j] /= h;
f += e[j] * d[j];
}
double hh = f / (h + h);
for (int j = 0; j < i; j++) {
e[j] -= hh * d[j];
}
for (int j = 0; j < i; j++) {
f = d[j];
g = e[j];
for (int k = j; k <= i-1; k++) {
V[k][j] -= (f * e[k] + g * d[k]);
}
d[j] = V[i-1][j];
V[i][j] = 0.0;
}
}
d[i] = h;
}

// Accumulate transformations.

for (int i = 0; i < n-1; i++) {
V[n-1][i] = V[i][i];
V[i][i] = 1.0;
double h = d[i+1];
if (h != 0.0) {
for (int k = 0; k <= i; k++) {
d[k] = V[k][i+1] / h;
}
for (int j = 0; j <= i; j++) {
double g = 0.0;
for (int k = 0; k <= i; k++) {
g += V[k][i+1] * V[k][j];
}
for (int k = 0; k <= i; k++) {
V[k][j] -= g * d[k];
}
}
}
for (int k = 0; k <= i; k++) {
V[k][i+1] = 0.0;
}
}
for (int j = 0; j < n; j++) {
d[j] = V[n-1][j];
V[n-1][j] = 0.0;
}
V[n-1][n-1] = 1.0;
e[0] = 0.0;
}

// Symmetric tridiagonal QL algorithm.

private void tql2 () {

//  This is derived from the Algol procedures tql2, by
//  Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
//  Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
//  Fortran subroutine in EISPACK.

for (int i = 1; i < n; i++) {
e[i-1] = e[i];
}
e[n-1] = 0.0;

double f = 0.0;
double tst1 = 0.0;
double eps = Math.pow(2.0,-52.0);
for (int l = 0; l < n; l++) {

// Find small subdiagonal element

tst1 = Math.max(tst1,Math.abs(d[l]) + Math.abs(e[l]));
int m = l;
while (m < n) {
if (Math.abs(e[m]) <= eps*tst1) {
break;
}
m++;
}

// If m == l, d[l] is an eigenvalue,
// otherwise, iterate.

if (m > l) {
int iter = 0;
do {
iter = iter + 1;  // (Could check iteration count here.)

// Compute implicit shift

double g = d[l];
double p = (d[l+1] - g) / (2.0 * e[l]);
double r = Maths.hypot(p,1.0);
if (p < 0) {
r = -r;
}
d[l] = e[l] / (p + r);
d[l+1] = e[l] * (p + r);
double dl1 = d[l+1];
double h = g - d[l];
for (int i = l+2; i < n; i++) {
d[i] -= h;
}
f = f + h;

// Implicit QL transformation.

p = d[m];
double c = 1.0;
double c2 = c;
double c3 = c;
double el1 = e[l+1];
double s = 0.0;
double s2 = 0.0;
for (int i = m-1; i >= l; i--) {
c3 = c2;
c2 = c;
s2 = s;
g = c * e[i];
h = c * p;
r = Maths.hypot(p,e[i]);
e[i+1] = s * r;
s = e[i] / r;
c = p / r;
p = c * d[i] - s * g;
d[i+1] = h + s * (c * g + s * d[i]);

// Accumulate transformation.

for (int k = 0; k < n; k++) {
h = V[k][i+1];
V[k][i+1] = s * V[k][i] + c * h;
V[k][i] = c * V[k][i] - s * h;
}
}
p = -s * s2 * c3 * el1 * e[l] / dl1;
e[l] = s * p;
d[l] = c * p;

// Check for convergence.

} while (Math.abs(e[l]) > eps*tst1);
}
d[l] = d[l] + f;
e[l] = 0.0;
}

// Sort eigenvalues and corresponding vectors.

for (int i = 0; i < n-1; i++) {
int k = i;
double p = d[i];
for (int j = i+1; j < n; j++) {
if (d[j] < p) {
k = j;
p = d[j];
}
}
if (k != i) {
d[k] = d[i];
d[i] = p;
for (int j = 0; j < n; j++) {
p = V[j][i];
V[j][i] = V[j][k];
V[j][k] = p;
}
}
}
}

// Nonsymmetric reduction to Hessenberg form.

private void orthes () {

//  This is derived from the Algol procedures orthes and ortran,
//  by Martin and Wilkinson, Handbook for Auto. Comp.,
//  Vol.ii-Linear Algebra, and the corresponding
//  Fortran subroutines in EISPACK.

int low = 0;
int high = n-1;

for (int m = low+1; m <= high-1; m++) {

// Scale column.

double scale = 0.0;
for (int i = m; i <= high; i++) {
scale = scale + Math.abs(H[i][m-1]);
}
if (scale != 0.0) {

// Compute Householder transformation.

double h = 0.0;
for (int i = high; i >= m; i--) {
ort[i] = H[i][m-1]/scale;
h += ort[i] * ort[i];
}
double g = Math.sqrt(h);
if (ort[m] > 0) {
g = -g;
}
h = h - ort[m] * g;
ort[m] = ort[m] - g;

// Apply Householder similarity transformation
// H = (I-u*u'/h)*H*(I-u*u')/h)

for (int j = m; j < n; j++) {
double f = 0.0;
for (int i = high; i >= m; i--) {
f += ort[i]*H[i][j];
}
f = f/h;
for (int i = m; i <= high; i++) {
H[i][j] -= f*ort[i];
}
}

for (int i = 0; i <= high; i++) {
double f = 0.0;
for (int j = high; j >= m; j--) {
f += ort[j]*H[i][j];
}
f = f/h;
for (int j = m; j <= high; j++) {
H[i][j] -= f*ort[j];
}
}
ort[m] = scale*ort[m];
H[m][m-1] = scale*g;
}
}

// Accumulate transformations (Algol's ortran).

for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
V[i][j] = (i == j ? 1.0 : 0.0);
}
}

for (int m = high-1; m >= low+1; m--) {
if (H[m][m-1] != 0.0) {
for (int i = m+1; i <= high; i++) {
ort[i] = H[i][m-1];
}
for (int j = m; j <= high; j++) {
double g = 0.0;
for (int i = m; i <= high; i++) {
g += ort[i] * V[i][j];
}
// Double division avoids possible underflow
g = (g / ort[m]) / H[m][m-1];
for (int i = m; i <= high; i++) {
V[i][j] += g * ort[i];
}
}
}
}
}

// Complex scalar division.

private transient double cdivr, cdivi;
private void cdiv(double xr, double xi, double yr, double yi) {
double r,d;
if (Math.abs(yr) > Math.abs(yi)) {
r = yi/yr;
d = yr + r*yi;
cdivr = (xr + r*xi)/d;
cdivi = (xi - r*xr)/d;
} else {
r = yr/yi;
d = yi + r*yr;
cdivr = (r*xr + xi)/d;
cdivi = (r*xi - xr)/d;
}
}

// Nonsymmetric reduction from Hessenberg to real Schur form.

private void hqr2 () {

//  This is derived from the Algol procedure hqr2,
//  by Martin and Wilkinson, Handbook for Auto. Comp.,
//  Vol.ii-Linear Algebra, and the corresponding
//  Fortran subroutine in EISPACK.

// Initialize

int nn = this.n;
int n = nn-1;
int low = 0;
int high = nn-1;
double eps = Math.pow(2.0,-52.0);
double exshift = 0.0;
double p=0,q=0,r=0,s=0,z=0,t,w,x,y;

// Store roots isolated by balanc and compute matrix norm

double norm = 0.0;
for (int i = 0; i < nn; i++) {
if (i < low | i > high) {
d[i] = H[i][i];
e[i] = 0.0;
}
for (int j = Math.max(i-1,0); j < nn; j++) {
norm = norm + Math.abs(H[i][j]);
}
}

// Outer loop over eigenvalue index

int iter = 0;
while (n >= low) {

// Look for single small sub-diagonal element

int l = n;
while (l > low) {
s = Math.abs(H[l-1][l-1]) + Math.abs(H[l][l]);
if (s == 0.0) {
s = norm;
}
if (Math.abs(H[l][l-1]) < eps * s) {
break;
}
l--;
}

// Check for convergence
// One root found

if (l == n) {
H

= H

+ exshift;
d
= H

;
e
= 0.0;
n--;
iter = 0;

// Two roots found

} else if (l == n-1) {
w = H
[n-1] * H[n-1]
;
p = (H[n-1][n-1] - H

) / 2.0;
q = p * p + w;
z = Math.sqrt(Math.abs(q));
H

= H

+ exshift;
H[n-1][n-1] = H[n-1][n-1] + exshift;
x = H

;

// Real pair

if (q >= 0) {
if (p >= 0) {
z = p + z;
} else {
z = p - z;
}
d[n-1] = x + z;
d
= d[n-1];
if (z != 0.0) {
d
= x - w / z;
}
e[n-1] = 0.0;
e
= 0.0;
x = H
[n-1];
s = Math.abs(x) + Math.abs(z);
p = x / s;
q = z / s;
r = Math.sqrt(p * p+q * q);
p = p / r;
q = q / r;

// Row modification

for (int j = n-1; j < nn; j++) {
z = H[n-1][j];
H[n-1][j] = q * z + p * H
[j];
H
[j] = q * H
[j] - p * z;
}

// Column modification

for (int i = 0; i <= n; i++) {
z = H[i][n-1];
H[i][n-1] = q * z + p * H[i]
;
H[i]
= q * H[i]
- p * z;
}

// Accumulate transformations

for (int i = low; i <= high; i++) {
z = V[i][n-1];
V[i][n-1] = q * z + p * V[i]
;
V[i]
= q * V[i]
- p * z;
}

// Complex pair

} else {
d[n-1] = x + p;
d
= x + p;
e[n-1] = z;
e
= -z;
}
n = n - 2;
iter = 0;

// No convergence yet

} else {

// Form shift

x = H

;
y = 0.0;
w = 0.0;
if (l < n) {
y = H[n-1][n-1];
w = H
[n-1] * H[n-1]
;
}

// Wilkinson's original ad hoc shift

if (iter == 10) {
exshift += x;
for (int i = low; i <= n; i++) {
H[i][i] -= x;
}
s = Math.abs(H
[n-1]) + Math.abs(H[n-1][n-2]);
x = y = 0.75 * s;
w = -0.4375 * s * s;
}

// MATLAB's new ad hoc shift

if (iter == 30) {
s = (y - x) / 2.0;
s = s * s + w;
if (s > 0) {
s = Math.sqrt(s);
if (y < x) {
s = -s;
}
s = x - w / ((y - x) / 2.0 + s);
for (int i = low; i <= n; i++) {
H[i][i] -= s;
}
exshift += s;
x = y = w = 0.964;
}
}

iter = iter + 1;   // (Could check iteration count here.)

// Look for two consecutive small sub-diagonal elements

int m = n-2;
while (m >= l) {
z = H[m][m];
r = x - z;
s = y - z;
p = (r * s - w) / H[m+1][m] + H[m][m+1];
q = H[m+1][m+1] - z - r - s;
r = H[m+2][m+1];
s = Math.abs(p) + Math.abs(q) + Math.abs(r);
p = p / s;
q = q / s;
r = r / s;
if (m == l) {
break;
}
if (Math.abs(H[m][m-1]) * (Math.abs(q) + Math.abs(r)) <
eps * (Math.abs(p) * (Math.abs(H[m-1][m-1]) + Math.abs(z) +
Math.abs(H[m+1][m+1])))) {
break;
}
m--;
}

for (int i = m+2; i <= n; i++) {
H[i][i-2] = 0.0;
if (i > m+2) {
H[i][i-3] = 0.0;
}
}

// Double QR step involving rows l:n and columns m:n

for (int k = m; k <= n-1; k++) {
boolean notlast = (k != n-1);
if (k != m) {
p = H[k][k-1];
q = H[k+1][k-1];
r = (notlast ? H[k+2][k-1] : 0.0);
x = Math.abs(p) + Math.abs(q) + Math.abs(r);
if (x == 0.0) {
continue;
}
p = p / x;
q = q / x;
r = r / x;
}

s = Math.sqrt(p * p + q * q + r * r);
if (p < 0) {
s = -s;
}
if (s != 0) {
if (k != m) {
H[k][k-1] = -s * x;
} else if (l != m) {
H[k][k-1] = -H[k][k-1];
}
p = p + s;
x = p / s;
y = q / s;
z = r / s;
q = q / p;
r = r / p;

// Row modification

for (int j = k; j < nn; j++) {
p = H[k][j] + q * H[k+1][j];
if (notlast) {
p = p + r * H[k+2][j];
H[k+2][j] = H[k+2][j] - p * z;
}
H[k][j] = H[k][j] - p * x;
H[k+1][j] = H[k+1][j] - p * y;
}

// Column modification

for (int i = 0; i <= Math.min(n,k+3); i++) {
p = x * H[i][k] + y * H[i][k+1];
if (notlast) {
p = p + z * H[i][k+2];
H[i][k+2] = H[i][k+2] - p * r;
}
H[i][k] = H[i][k] - p;
H[i][k+1] = H[i][k+1] - p * q;
}

// Accumulate transformations

for (int i = low; i <= high; i++) {
p = x * V[i][k] + y * V[i][k+1];
if (notlast) {
p = p + z * V[i][k+2];
V[i][k+2] = V[i][k+2] - p * r;
}
V[i][k] = V[i][k] - p;
V[i][k+1] = V[i][k+1] - p * q;
}
}  // (s != 0)
}  // k loop
}  // check convergence
}  // while (n >= low)

// Backsubstitute to find vectors of upper triangular form

if (norm == 0.0) {
return;
}

for (n = nn-1; n >= 0; n--) {
p = d
;
q = e
;

// Real vector

if (q == 0) {
int l = n;
H

= 1.0;
for (int i = n-1; i >= 0; i--) {
w = H[i][i] - p;
r = 0.0;
for (int j = l; j <= n; j++) {
r = r + H[i][j] * H[j]
;
}
if (e[i] < 0.0) {
z = w;
s = r;
} else {
l = i;
if (e[i] == 0.0) {
if (w != 0.0) {
H[i]
= -r / w;
} else {
H[i]
= -r / (eps * norm);
}

// Solve real equations

} else {
x = H[i][i+1];
y = H[i+1][i];
q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
t = (x * s - z * r) / q;
H[i]
= t;
if (Math.abs(x) > Math.abs(z)) {
H[i+1]
= (-r - w * t) / x;
} else {
H[i+1]
= (-s - y * t) / z;
}
}

// Overflow control

t = Math.abs(H[i]
);
if ((eps * t) * t > 1) {
for (int j = i; j <= n; j++) {
H[j]
= H[j]
/ t;
}
}
}
}

// Complex vector

} else if (q < 0) {
int l = n-1;

// Last vector component imaginary so matrix is triangular

if (Math.abs(H
[n-1]) > Math.abs(H[n-1]
)) {
H[n-1][n-1] = q / H
[n-1];
H[n-1]
= -(H

- p) / H
[n-1];
} else {
cdiv(0.0,-H[n-1]
,H[n-1][n-1]-p,q);
H[n-1][n-1] = cdivr;
H[n-1]
= cdivi;
}
H
[n-1] = 0.0;
H

= 1.0;
for (int i = n-2; i >= 0; i--) {
double ra,sa,vr,vi;
ra = 0.0;
sa = 0.0;
for (int j = l; j <= n; j++) {
ra = ra + H[i][j] * H[j][n-1];
sa = sa + H[i][j] * H[j]
;
}
w = H[i][i] - p;

if (e[i] < 0.0) {
z = w;
r = ra;
s = sa;
} else {
l = i;
if (e[i] == 0) {
cdiv(-ra,-sa,w,q);
H[i][n-1] = cdivr;
H[i]
= cdivi;
} else {

// Solve complex equations

x = H[i][i+1];
y = H[i+1][i];
vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;
vi = (d[i] - p) * 2.0 * q;
if (vr == 0.0 & vi == 0.0) {
vr = eps * norm * (Math.abs(w) + Math.abs(q) +
Math.abs(x) + Math.abs(y) + Math.abs(z));
}
cdiv(x*r-z*ra+q*sa,x*s-z*sa-q*ra,vr,vi);
H[i][n-1] = cdivr;
H[i]
= cdivi;
if (Math.abs(x) > (Math.abs(z) + Math.abs(q))) {
H[i+1][n-1] = (-ra - w * H[i][n-1] + q * H[i]
) / x;
H[i+1]
= (-sa - w * H[i]
- q * H[i][n-1]) / x;
} else {
cdiv(-r-y*H[i][n-1],-s-y*H[i]
,z,q);
H[i+1][n-1] = cdivr;
H[i+1]
= cdivi;
}
}

// Overflow control

t = Math.max(Math.abs(H[i][n-1]),Math.abs(H[i]
));
if ((eps * t) * t > 1) {
for (int j = i; j <= n; j++) {
H[j][n-1] = H[j][n-1] / t;
H[j]
= H[j]
/ t;
}
}
}
}
}
}

// Vectors of isolated roots

for (int i = 0; i < nn; i++) {
if (i < low | i > high) {
for (int j = i; j < nn; j++) {
V[i][j] = H[i][j];
}
}
}

// Back transformation to get eigenvectors of original matrix

for (int j = nn-1; j >= low; j--) {
for (int i = low; i <= high; i++) {
z = 0.0;
for (int k = low; k <= Math.min(j,high); k++) {
z = z + V[i][k] * H[k][j];
}
V[i][j] = z;
}
}
}

/* ------------------------
Constructor
* ------------------------ */

/** Check for symmetry, then construct the eigenvalue decomposition
Structure to access D and V.
@param Arg    Square matrix
*/

public EigenvalueDecomposition (Matrix Arg) {
double[][] A = Arg.getArray();
n = Arg.getColumnDimension();
V = new double

;
d = new double
;
e = new double
;

issymmetric = true;
for (int j = 0; (j < n) & issymmetric; j++) {
for (int i = 0; (i < n) & issymmetric; i++) {
issymmetric = (A[i][j] == A[j][i]);
}
}

if (issymmetric) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
V[i][j] = A[i][j];
}
}

// Tridiagonalize.
tred2();

// Diagonalize.
tql2();

} else {
H = new double

;
ort = new double
;

for (int j = 0; j < n; j++) {
for (int i = 0; i < n; i++) {
H[i][j] = A[i][j];
}
}

// Reduce to Hessenberg form.
orthes();

// Reduce Hessenberg to real Schur form.
hqr2();
}
}

/* ------------------------
Public Methods
* ------------------------ */

/** Return the eigenvector matrix
@return     V
*/

public Matrix getV () {
return new Matrix(V,n,n);
}

/** Return the real parts of the eigenvalues
@return     real(diag(D))
*/

public double[] getRealEigenvalues () {
return d;
}

/** Return the imaginary parts of the eigenvalues
@return     imag(diag(D))
*/

public double[] getImagEigenvalues () {
return e;
}

/** Return the block diagonal eigenvalue matrix
@return     D
*/

public Matrix getD () {
Matrix X = new Matrix(n,n);
double[][] D = X.getArray();
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
D[i][j] = 0.0;
}
D[i][i] = d[i];
if (e[i] > 0) {
D[i][i+1] = e[i];
} else if (e[i] < 0) {
D[i][i-1] = e[i];
}
}
return X;
}
private static final long serialVersionUID = 1;
}