List of usage examples for org.apache.commons.math3.linear QRDecomposition QRDecomposition
public QRDecomposition(RealMatrix matrix)
From source file:com.opengamma.strata.math.impl.linearalgebra.QRDecompositionCommons.java
@Override public QRDecompositionResult apply(DoubleMatrix x) { ArgChecker.notNull(x, "x"); RealMatrix temp = CommonsMathWrapper.wrap(x); QRDecomposition qr = new QRDecomposition(temp); return new QRDecompositionCommonsResult(qr); }
From source file:edu.washington.gs.skyline.model.quantification.LinearModel.java
public LinearModel(RealMatrix designMatrix, RealMatrix contrastValues) { this.designMatrix = designMatrix; this.contrastValues = contrastValues; qrDecomposition = new QRDecomposition(designMatrix); independentColumnIndices = IntStream.range(0, designMatrix.getColumnDimension()).toArray(); RealMatrix matrixCrossproduct = computeMatrixCrossproduct(designMatrix, independentColumnIndices); matrixCrossproductInverse = new LUDecomposition(matrixCrossproduct).getSolver().getInverse(); }
From source file:com.joptimizer.algebra.QRSparseFactorizationTest.java
public void testDecompose() throws Exception { log.debug("testDecompose"); final double[][] A = new double[][] { { 1, 0, 0, 2 }, { 0, 0, 2, 0 }, { 2, 3, 0, 0 }, { 0, 0, 4, 4 } }; double[][] EQ = { { 0.447214, -0.894427, 0., 0. }, { 0., 0., 0.447214, -0.894427 }, { 0.894427, 0.447214, 0., 0. }, { 0., 0., 0.894427, 0.447214 } }; double[][] ER = { { 2.23607, 2.68328, 0., 0.894427 }, { 0., 1.34164, 0., -1.78885 }, { 0., 0., 4.47214, 3.57771 }, { 0., 0., 0., 1.78885 } }; QRDecomposition dFact = new QRDecomposition(new Array2DRowRealMatrix(A)); RealMatrix Q = dFact.getQ();//w w w .j a v a 2s . c om RealMatrix R = dFact.getR(); RealMatrix H = dFact.getH(); log.debug("Q: " + ArrayUtils.toString(Q.getData())); log.debug("R: " + ArrayUtils.toString(R.getData())); //log.debug("H: " + ArrayUtils.toString(H.getData())); SparseDoubleMatrix2D S = new SparseDoubleMatrix2D(A); QRSparseFactorization qr = new QRSparseFactorization(S); qr.factorize(); log.debug("R: " + ArrayUtils.toString(qr.getR().toArray())); for (int i = 0; i < R.getRowDimension(); i++) { for (int j = 0; j < R.getColumnDimension(); j++) { assertEquals(ER[i][j], qr.getR().getQuick(i, j), 1.e-5); } } assertTrue(qr.hasFullRank()); }
From source file:com.joptimizer.util.UtilsTest.java
public void testQRdecomposition() throws Exception { log.debug("testQRdecomposition"); double[][] A = new double[][] { { 1, 0, 0 }, { 2, 2, 0 }, { 3, 3, 3 } }; QRDecomposition qrFact = new QRDecomposition(MatrixUtils.createRealMatrix(A)); qrFact.getQ();//from ww w .ja va 2 s . co m }
From source file:edu.cudenver.bios.matrix.OrthogonalPolynomials.java
/** * Computes orthogonal polynomial contrasts for the specified data values. Currently only * supports fitting (not prediction contrasts). * /*from ww w .ja v a 2 s. c o m*/ * @param x the points at which the polynomials will be evaluated * @param maxDegree contrasts will be computed for degrees 1 to maxDegree * @return matrix containing 0th,1st, 2nd,...maxDegree-th degree contrasts in each column * @throws IllegalArgumentException */ public static RealMatrix orthogonalPolynomialCoefficients(double[] x, int maxDegree) throws IllegalArgumentException { if (x == null) throw new IllegalArgumentException("no data specified"); if (maxDegree < 1) throw new IllegalArgumentException("max polynomial degree must be greater than 1"); // count number of unique values HashSet<Double> s = new HashSet<Double>(); for (double i : x) s.add(i); int uniqueCount = s.size(); if (maxDegree >= uniqueCount) throw new IllegalArgumentException( "max polynomial degree must be less than the number of unique points"); // center the data double xBar = StatUtils.mean(x); double[] xCentered = new double[x.length]; for (int i = 0; i < x.length; i++) xCentered[i] = x[i] - xBar; // compute an "outer product" of the centered x vector and a vector // containing the sequence 0 to maxDegree-1, but raise the x values // to the power in the sequence array double[][] xOuter = new double[x.length][maxDegree + 1]; int row = 0; for (double xValue : xCentered) { for (int col = 0; col <= maxDegree; col++) { xOuter[row][col] = Math.pow(xValue, col); } row++; } // do some mysterious QR decomposition stuff. See Emerson (1968) RealMatrix outerVector = new Array2DRowRealMatrix(xOuter); QRDecomposition qrDecomp = new QRDecomposition(outerVector); RealMatrix z = MatrixUtils.getDiagonalMatrix(qrDecomp.getR()); RealMatrix raw = qrDecomp.getQ().multiply(z); // column sum of squared elements in raw double[] normalizingConstants = new double[raw.getColumnDimension()]; for (int col = 0; col < raw.getColumnDimension(); col++) { normalizingConstants[col] = 0; for (row = 0; row < raw.getRowDimension(); row++) { double value = raw.getEntry(row, col); normalizingConstants[col] += value * value; } } // now normalize the raw values for (int col = 0; col < raw.getColumnDimension(); col++) { double normalConstantSqrt = Math.sqrt(normalizingConstants[col]); for (row = 0; row < raw.getRowDimension(); row++) { raw.setEntry(row, col, raw.getEntry(row, col) / normalConstantSqrt); } } return raw; }
From source file:com.ibm.bi.dml.runtime.matrix.data.LibCommonsMath.java
/** * Function to solve a given system of equations. * // www.j a va 2s.c o m * @param in1 * @param in2 * @return * @throws DMLRuntimeException */ private static MatrixBlock computeSolve(MatrixObject in1, MatrixObject in2) throws DMLRuntimeException { Array2DRowRealMatrix matrixInput = DataConverter.convertToArray2DRowRealMatrix(in1); Array2DRowRealMatrix vectorInput = DataConverter.convertToArray2DRowRealMatrix(in2); /*LUDecompositionImpl ludecompose = new LUDecompositionImpl(matrixInput); DecompositionSolver lusolver = ludecompose.getSolver(); RealMatrix solutionMatrix = lusolver.solve(vectorInput);*/ // Setup a solver based on QR Decomposition QRDecomposition qrdecompose = new QRDecomposition(matrixInput); DecompositionSolver solver = qrdecompose.getSolver(); // Invoke solve RealMatrix solutionMatrix = solver.solve(vectorInput); MatrixBlock solution = DataConverter.convertToMatrixBlock(solutionMatrix.getData()); return solution; }
From source file:com.ibm.bi.dml.runtime.matrix.data.LibCommonsMath.java
/** * Function to perform QR decomposition on a given matrix. * /*from w w w .j a v a2s .com*/ * @param in * @return * @throws DMLRuntimeException */ private static MatrixBlock[] computeQR(MatrixObject in) throws DMLRuntimeException { Array2DRowRealMatrix matrixInput = DataConverter.convertToArray2DRowRealMatrix(in); // Perform QR decomposition QRDecomposition qrdecompose = new QRDecomposition(matrixInput); RealMatrix H = qrdecompose.getH(); RealMatrix R = qrdecompose.getR(); // Read the results into native format MatrixBlock mbH = DataConverter.convertToMatrixBlock(H.getData()); MatrixBlock mbR = DataConverter.convertToMatrixBlock(R.getData()); return new MatrixBlock[] { mbH, mbR }; }
From source file:edu.stanford.cfuller.imageanalysistools.fitting.BisquareLinearFit.java
/** * Calculates the leverages of data points for least squares fitting (assuming equal variances). * * @param indVarValues The values of the independent variable used for the fitting. * @return a RealVector containing a leverage value for each independent variable value. *//* w w w. ja v a 2s . c o m*/ protected RealVector calculateLeverages(RealVector indVarValues) { RealMatrix indVarMatrix = null; if (this.noIntercept) { indVarMatrix = new Array2DRowRealMatrix(indVarValues.getDimension(), 1); } else { indVarMatrix = new Array2DRowRealMatrix(indVarValues.getDimension(), 2); } indVarMatrix.setColumnVector(0, indVarValues); if (!this.noIntercept) { indVarMatrix.setColumnVector(1, indVarValues.mapMultiply(0).mapAdd(1)); } RealVector leverages = new ArrayRealVector(indVarValues.getDimension()); QRDecomposition xQR = new QRDecomposition(indVarMatrix); RealMatrix xR = xQR.getR(); int smallerDim = xR.getRowDimension() < xR.getColumnDimension() ? xR.getRowDimension() : xR.getColumnDimension(); RealMatrix xRSq = xR.getSubMatrix(0, smallerDim - 1, 0, smallerDim - 1); QRDecomposition xRQR = new QRDecomposition(xRSq); RealMatrix xRInv = xRQR.getSolver().getInverse(); RealMatrix xxRInv = indVarMatrix.multiply(xRInv); for (int i = 0; i < indVarValues.getDimension(); i++) { double sum = 0; for (int j = 0; j < xxRInv.getColumnDimension(); j++) { sum += Math.pow(xxRInv.getEntry(i, j), 2); } leverages.setEntry(i, sum); } return leverages; }
From source file:gamlss.utilities.WLSMultipleLinearRegression.java
/** * {@inheritDoc}/*from w w w.ja va 2 s . c om*/ * <p>This implementation computes and caches the QR decomposition of the X matrix * once it is successfully loaded.</p> */ protected void newXSampleData(double[][] x, ArrayRealVector w) { if (x == null) { throw new NullArgumentException(); } if (x.length == 0) { throw new NoDataException(); } if (this.isNoIntercept()) { this.xMatrix = new Array2DRowRealMatrix(x, true); } else { // Augment design matrix with initial unitary column final int nVars = x[0].length; final double[][] xAug = new double[x.length][nVars + 1]; for (int i = 0; i < x.length; i++) { if (x[i].length != nVars) { throw new DimensionMismatchException(x[i].length, nVars); } xAug[i][0] = w.getEntry(i); System.arraycopy(x[i], 0, xAug[i], 1, nVars); } this.xMatrix = new Array2DRowRealMatrix(xAug, false); } this.qr = new QRDecomposition(getX()); }
From source file:com.ibm.bi.dml.runtime.matrix.data.LibCommonsMath.java
/** * Function to compute matrix inverse via matrix decomposition. * //from ww w. j a v a 2 s .c o m * @param in * @return * @throws DMLRuntimeException */ private static MatrixBlock computeMatrixInverse(Array2DRowRealMatrix in) throws DMLRuntimeException { if (!in.isSquare()) throw new DMLRuntimeException("Input to inv() must be square matrix -- given: a " + in.getRowDimension() + "x" + in.getColumnDimension() + " matrix."); QRDecomposition qrdecompose = new QRDecomposition(in); DecompositionSolver solver = qrdecompose.getSolver(); RealMatrix inverseMatrix = solver.getInverse(); MatrixBlock inverse = DataConverter.convertToMatrixBlock(inverseMatrix.getData()); return inverse; }