/**
* 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;
}
/**
* 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;
}
/**
* 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;
}
/**
* 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;
}
/**
* 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;
}
/**
* 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;
}
/**
* Return the Householder vectors
*
* @return Lower trapezoidal matrix whose columns define the reflections
*/
public Matrix getH() {
Matrix X = new Matrix(m, n);
double[][] H = X.getArray();
for (int i = 0; i < m; i++) {
for (int j = 0; j < n; j++) {
if (i >= j) {
H[i][j] = QR[i][j];
} else {
H[i][j] = 0.0;
}
}
}
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;
}
/**
* 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 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;
}
/**
* 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;
}
/**
* Return the upper triangular factor
*
* @return R
*/
public Matrix getR() {
Matrix X = new Matrix(n, n);
double[][] R = X.getArray();
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
if (i < j) {
R[i][j] = QR[i][j];
} else if (i == j) {
R[i][j] = Rdiag[i];
} else {
R[i][j] = 0.0;
}
}
}
return X;
}
/**
* 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 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");
}
}
/**
* QR Decomposition, computed by Householder reflections.
*
* @param A Rectangular matrix
* @return Structure to access R and the Householder vectors and compute Q.
*/
public QRDecomposition(Matrix A) {
// Initialize.
QR = A.getArrayCopy();
m = A.getRowDimension();
n = A.getColumnDimension();
Rdiag = new double[n];
// Main loop.
for (int k = 0; k < n; k++) {
// Compute 2-norm of k-th column without under/overflow.
double nrm = 0;
for (int i = k; i < m; i++) {
nrm = Maths.hypot(nrm, QR[i][k]);
}
if (nrm != 0.0) {
// Form k-th Householder vector.
if (QR[k][k] < 0) {
nrm = -nrm;
}
for (int i = k; i < m; i++) {
QR[i][k] /= nrm;
}
QR[k][k] += 1.0;
// Apply transformation to remaining columns.
for (int j = k + 1; j < n; j++) {
double s = 0.0;
for (int i = k; i < m; i++) {
s += QR[i][k] * QR[i][j];
}
s = -s / QR[k][k];
for (int i = k; i < m; i++) {
QR[i][j] += s * QR[i][k];
}
}
}
Rdiag[k] = -nrm;
}
}
/**
* 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;
}
/**
* Least squares solution of A*X = B
*
* @param B A Matrix with as many rows as A and any number of columns.
* @return X that minimizes the two norm of Q*R*X-B.
* @exception IllegalArgumentException Matrix row dimensions must agree.
* @exception RuntimeException Matrix is rank deficient.
*/
public Matrix solve(Matrix B) {
if (B.getRowDimension() != m) {
throw new IllegalArgumentException("Matrix row dimensions must agree.");
}
if (!this.isFullRank()) {
throw new RuntimeException("Matrix is rank deficient.");
}
// Copy right hand side
int nx = B.getColumnDimension();
double[][] X = B.getArrayCopy();
// Compute Y = transpose(Q)*B
for (int k = 0; k < n; k++) {
for (int j = 0; j < nx; j++) {
double s = 0.0;
for (int i = k; i < m; i++) {
s += QR[i][k] * X[i][j];
}
s = -s / QR[k][k];
for (int i = k; i < m; i++) {
X[i][j] += s * QR[i][k];
}
}
}
// Solve R*X = Y;
for (int k = n - 1; k >= 0; k--) {
for (int j = 0; j < nx; j++) {
X[k][j] /= Rdiag[k];
}
for (int i = 0; i < k; i++) {
for (int j = 0; j < nx; j++) {
X[i][j] -= X[k][j] * QR[i][k];
}
}
}
return (new Matrix(X, n, nx).getMatrix(0, n - 1, 0, nx - 1));
}
/**
* Generate and return the (economy-sized) orthogonal factor
*
* @return Q
*/
public Matrix getQ() {
Matrix X = new Matrix(m, n);
double[][] Q = X.getArray();
for (int k = n - 1; k >= 0; k--) {
for (int i = 0; i < m; i++) {
Q[i][k] = 0.0;
}
Q[k][k] = 1.0;
for (int j = k; j < n; j++) {
if (QR[k][k] != 0) {
double s = 0.0;
for (int i = k; i < m; i++) {
s += QR[i][k] * Q[i][j];
}
s = -s / QR[k][k];
for (int i = k; i < m; i++) {
Q[i][j] += s * QR[i][k];
}
}
}
}
return X;
}
/**
* 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());
}
