X-Git-Url: http://gitweb.fperrin.net/?a=blobdiff_plain;f=tim%2Fprune%2Ffunction%2Festimate%2Fjama%2FQRDecomposition.java;fp=tim%2Fprune%2Ffunction%2Festimate%2Fjama%2FQRDecomposition.java;h=0000000000000000000000000000000000000000;hb=ce6f2161b8596f7018d6a76bff79bc9e571f35fd;hp=199c67e5ef1df774b62834aa13db481ac1aa7a57;hpb=2d8cb72e84d5cc1089ce77baf1e34ea3ea2f8465;p=GpsPrune.git

diff --git a/tim/prune/function/estimate/jama/QRDecomposition.java b/tim/prune/function/estimate/jama/QRDecomposition.java
deleted file mode 100644
index 199c67e..0000000
--- a/tim/prune/function/estimate/jama/QRDecomposition.java
+++ /dev/null
@@ -1,146 +0,0 @@
-package tim.prune.function.estimate.jama;
-
-/**
- * QR Decomposition.
- *
- * For an m-by-n matrix A with m >= n, the QR decomposition is an m-by-n
- * orthogonal matrix Q and an n-by-n upper triangular matrix R so that A = Q*R.
- *
- * The QR decomposition always exists, even if the matrix does not have full
- * rank, so the constructor will never fail. The primary use of the QR
- * decomposition is in the least squares solution of nonsquare systems of
- * simultaneous linear equations. This will fail if isFullRank() returns false.
- *
- * Original authors The MathWorks, Inc. and the National Institute of Standards and Technology
- * The original public domain code has now been modified and reduced,
- * and is placed under GPL2 with the rest of the GpsPrune code.
- */
-public class QRDecomposition
-{
-
-	/** Array for internal storage of decomposition */
-	private double[][] _QR;
-
-	/** Row and column dimensions */
-	private int _m, _n;
-
-	/** Array for internal storage of diagonal of R */
-	private double[] _Rdiag;
-
-
-	/**
-	 * QR Decomposition, computed by Householder reflections.
-	 *
-	 * @param inA   Rectangular matrix
-	 * @return Structure to access R and the Householder vectors and compute Q.
-	 */
-	public QRDecomposition(Matrix inA)
-	{
-		// Initialize.
-		_QR = inA.getArrayCopy();
-		_m = inA.getNumRows();
-		_n = inA.getNumColumns();
-		_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.pythag(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;
-		}
-	}
-
-	/*
-	 * ------------------------ Public Methods ------------------------
-	 */
-
-	/**
-	 * Is the matrix full rank?
-	 * @return true if R, and hence A, has full rank.
-	 */
-	public boolean isFullRank()
-	{
-		for (int j = 0; j < _n; j++) {
-			if (_Rdiag[j] == 0)
-				return false;
-		}
-		return true;
-	}
-
-
-	/**
-	 * 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 if matrix dimensions don't agree
-	 * @exception RuntimeException         if Matrix is rank deficient.
-	 */
-	public Matrix solve(Matrix B)
-	{
-		if (B.getNumRows() != _m) {
-			throw new IllegalArgumentException("Matrix row dimensions must agree.");
-		}
-		if (!isFullRank()) {
-			throw new RuntimeException("Matrix is rank deficient.");
-		}
-
-		// Copy right hand side
-		int nx = B.getNumColumns();
-		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));
-	}
-}