LU Decomposition
//package aima.core.util.math;
import java.io.BufferedReader;
import java.io.PrintWriter;
import java.io.StreamTokenizer;
import java.text.DecimalFormat;
import java.text.DecimalFormatSymbols;
import java.text.NumberFormat;
import java.util.List;
import java.util.Locale;
/**
* LU Decomposition.
* <P>
* For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n unit
* lower triangular matrix L, an n-by-n upper triangular matrix U, and a
* permutation vector piv of length m so that A(piv,:) = L*U. If m < n, then L
* is m-by-m and U is m-by-n.
* <P>
* The LU decompostion with pivoting always exists, even if the matrix is
* singular, so the constructor will never fail. The primary use of the LU
* decomposition is in the solution of square systems of simultaneous linear
* equations. This will fail if isNonsingular() returns false.
*/
public class LUDecomposition implements java.io.Serializable {
private static final long serialVersionUID = 1L;
/*
* ------------------------ Class variables ------------------------
*/
/**
* Array for internal storage of decomposition.
*
* @serial internal array storage.
*/
private final double[][] LU;
/**
* Row and column dimensions, and pivot sign.
*
* @serial column dimension.
* @serial row dimension.
* @serial pivot sign.
*/
private final int m, n;
private int pivsign;
/**
* Internal storage of pivot vector.
*
* @serial pivot vector.
*/
private final int[] piv;
/*
* ------------------------ Constructor ------------------------
*/
/**
* LU Decomposition, a structure to access L, U and piv.
*
* @param A
* Rectangular matrix
*/
public LUDecomposition(Matrix A) {
// Use a "left-looking", dot-product, Crout/Doolittle algorithm.
LU = A.getArrayCopy();
m = A.getRowDimension();
n = A.getColumnDimension();
piv = new int[m];
for (int i = 0; i < m; i++) {
piv[i] = i;
}
pivsign = 1;
double[] LUrowi;
double[] LUcolj = new double[m];
// Outer loop.
for (int j = 0; j < n; j++) {
// Make a copy of the j-th column to localize references.
for (int i = 0; i < m; i++) {
LUcolj[i] = LU[i][j];
}
// Apply previous transformations.
for (int i = 0; i < m; i++) {
LUrowi = LU[i];
// Most of the time is spent in the following dot product.
int kmax = Math.min(i, j);
double s = 0.0;
for (int k = 0; k < kmax; k++) {
s += LUrowi[k] * LUcolj[k];
}
LUrowi[j] = LUcolj[i] -= s;
}
// Find pivot and exchange if necessary.
int p = j;
for (int i = j + 1; i < m; i++) {
if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {
p = i;
}
}
if (p != j) {
for (int k = 0; k < n; k++) {
double t = LU[p][k];
LU[p][k] = LU[j][k];
LU[j][k] = t;
}
int k = piv[p];
piv[p] = piv[j];
piv[j] = k;
pivsign = -pivsign;
}
// Compute multipliers.
if (j < m & LU[j][j] != 0.0) {
for (int i = j + 1; i < m; i++) {
LU[i][j] /= LU[j][j];
}
}
}
}
/*
* ------------------------ Temporary, experimental code.
* ------------------------\
*
* \ LU Decomposition, computed by Gaussian elimination. <P> This
* constructor computes L and U with the "daxpy"-based elimination algorithm
* used in LINPACK and MATLAB. In Java, we suspect the dot-product, Crout
* algorithm will be faster. We have temporarily included this constructor
* until timing experiments confirm this suspicion. <P> @param A Rectangular
* matrix @param linpackflag Use Gaussian elimination. Actual value ignored.
*
* @return Structure to access L, U and piv. \
*
* public LUDecomposition (Matrix A, int linpackflag) { // Initialize. LU =
* A.getArrayCopy(); m = A.getRowDimension(); n = A.getColumnDimension();
* piv = new int[m]; for (int i = 0; i < m; i++) { piv[i] = i; } pivsign =
* 1; // Main loop. for (int k = 0; k < n; k++) { // Find pivot. int p = k;
* for (int i = k+1; i < m; i++) { if (Math.abs(LU[i][k]) >
* Math.abs(LU[p][k])) { p = i; } } // Exchange if necessary. if (p != k) {
* for (int j = 0; j < n; j++) { double t = LU[p][j]; LU[p][j] = LU[k][j];
* LU[k][j] = t; } int t = piv[p]; piv[p] = piv[k]; piv[k] = t; pivsign =
* -pivsign; } // Compute multipliers and eliminate k-th column. if
* (LU[k][k] != 0.0) { for (int i = k+1; i < m; i++) { LU[i][k] /= LU[k][k];
* for (int j = k+1; j < n; j++) { LU[i][j] -= LU[i][k]LU[k][j]; } } } } } \
* ------------------------ End of temporary code. ------------------------
*/
/*
* ------------------------ Public Methods ------------------------
*/
/**
* Is the matrix nonsingular?
*
* @return true if U, and hence A, is nonsingular.
*/
public boolean isNonsingular() {
for (int j = 0; j < n; j++) {
if (LU[j][j] == 0)
return false;
}
return true;
}
/**
* Return lower triangular factor
*
* @return L
*/
public Matrix getL() {
Matrix X = new Matrix(m, n);
double[][] L = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
if (i > j) {
L[i][j] = LU[i][j];
} else if (i == j) {
L[i][j] = 1.0;
} else {
L[i][j] = 0.0;
}
}
}
return X;
}
/**
* Return upper triangular factor
*
* @return U
*/
public Matrix getU() {
Matrix X = new Matrix(n, n);
double[][] U = X.getArray();
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
if (i <= j) {
U[i][j] = LU[i][j];
} else {
U[i][j] = 0.0;
}
}
}
return X;
}
/**
* Return pivot permutation vector
*
* @return piv
*/
public int[] getPivot() {
int[] p = new int[m];
for (int i = 0; i < m; i++) {
p[i] = piv[i];
}
return p;
}
/**
* Return pivot permutation vector as a one-dimensional double array
*
* @return (double) piv
*/
public double[] getDoublePivot() {
double[] vals = new double[m];
for (int i = 0; i < m; i++) {
vals[i] = piv[i];
}
return vals;
}
/**
* Determinant
*
* @return det(A)
* @exception IllegalArgumentException
* Matrix must be square
*/
public double det() {
if (m != n) {
throw new IllegalArgumentException("Matrix must be square.");
}
double d = pivsign;
for (int j = 0; j < n; j++) {
d *= LU[j][j];
}
return d;
}
/**
* Solve A*X = B
*
* @param B
* A Matrix with as many rows as A and any number of columns.
* @return X so that L*U*X = B(piv,:)
* @exception IllegalArgumentException
* Matrix row dimensions must agree.
* @exception RuntimeException
* Matrix is singular.
*/
public Matrix solve(Matrix B) {
if (B.getRowDimension() != m) {
throw new IllegalArgumentException(
"Matrix row dimensions must agree.");
}
if (!this.isNonsingular()) {
throw new RuntimeException("Matrix is singular.");
}
// Copy right hand side with pivoting
int nx = B.getColumnDimension();
Matrix Xmat = B.getMatrix(piv, 0, nx - 1);
double[][] X = Xmat.getArray();
// Solve L*Y = B(piv,:)
for (int k = 0; k < n; k++) {
for (int i = k + 1; i < n; i++) {
for (int j = 0; j < nx; j++) {
X[i][j] -= X[k][j] * LU[i][k];
}
}
}
// Solve U*X = Y;
for (int k = n - 1; k >= 0; k--) {
for (int j = 0; j < nx; j++) {
X[k][j] /= LU[k][k];
}
for (int i = 0; i < k; i++) {
for (int j = 0; j < nx; j++) {
X[i][j] -= X[k][j] * LU[i][k];
}
}
}
return Xmat;
}
}
/**
* 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
*/
class Matrix implements Cloneable, java.io.Serializable {
private static final long serialVersionUID = 1L;
/*
* ------------------------ Class variables ------------------------
*/
/**
* Array for internal storage of elements.
*
* @serial internal array storage.
*/
private final double[][] A;
/**
* Row and column dimensions.
*
* @serial row dimension.
* @serial column dimension.
*/
private final int m, n;
/*
* ------------------------ Constructors ------------------------
*/
/** Construct a diagonal Matrix from the given List of doubles */
public static Matrix createDiagonalMatrix(List<Double> values) {
Matrix m = new Matrix(values.size(), values.size(), 0);
for (int i = 0; i < values.size(); i++) {
m.set(i, i, values.get(i));
}
return m;
}
/**
* 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][n];
}
/**
* 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][n];
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][n];
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.
*/
@Override
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][n];
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[n];
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) {
// assumed m == n
return new LUDecomposition(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 v = new java.util.Vector();
// 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 {
v.addElement(Double.valueOf(tokenizer.sval)); // Read & store 1st
// row.
} while (tokenizer.nextToken() == StreamTokenizer.TT_WORD);
int n = v.size(); // Now we've got the number of columns!
double row[] = new double[n];
for (int j = 0; j < n; j++)
// extract the elements of the 1st row.
row[j] = ((Double) v.elementAt(j)).doubleValue();
v.removeAllElements();
v.addElement(row); // Start storing rows instead of columns.
while (tokenizer.nextToken() == StreamTokenizer.TT_WORD) {
// While non-empty lines
v.addElement(row = new double[n]);
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);
}
@Override
public String toString() {
StringBuffer buf = new StringBuffer();
for (int i = 0; i < getRowDimension(); i++) {
for (int j = 0; j < getColumnDimension(); j++) {
buf.append(get(i, j));
buf.append(" ");
}
buf.append("\n");
}
return buf.toString();
}
/*
* ------------------------ 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.");
}
}
}
Related examples in the same category
