Examples of com.opengamma.analytics.math.linearalgebra.DecompositionResult

Package com.opengamma.analytics.math.linearalgebra

Examples of com.opengamma.analytics.math.linearalgebra.DecompositionResult

com.opengamma.analytics.math.linearalgebra.DecompositionResult
Contains the results of matrix decomposition. The decomposed matrices (e.g. the L and U matrices for LU decomposition) are stored in this class. There are methods that allow calculations to be performed using these matrices.

    final int nObs = observedValues.getNumberOfElements();
    Validate.isTrue(nObs == sigma.getNumberOfElements(), "observedValues and sigma must be same length");
    ArgumentChecker.isTrue(allowedValue.evaluate(startPos), "The start position {} is not valid for this model. Please choose a valid start position", startPos);


    DoubleMatrix2D alpha;
    DecompositionResult decmp;
    DoubleMatrix1D theta = startPos;


    double lambda = 0.0; // TODO debug if the model is linear, it will be solved in 1 step
    double newChiSqr, oldChiSqr;
    DoubleMatrix1D error = getError(func, observedValues, sigma, theta);


    DoubleMatrix1D newError;
    DoubleMatrix2D jacobian = getJacobian(jac, sigma, theta);


    oldChiSqr = getChiSqr(error);
    double p = getANorm(penalty, theta);
    oldChiSqr += p;


    // If we start at the solution we are done
    if (oldChiSqr == 0.0) {
      return finish(oldChiSqr, jacobian, theta, sigma);
    }


    DoubleMatrix1D beta = getChiSqrGrad(error, jacobian);
    DoubleMatrix1D temp = (DoubleMatrix1D) _algebra.multiply(penalty, theta);
    beta = (DoubleMatrix1D) _algebra.subtract(beta, temp);


    for (int count = 0; count < MAX_ATTEMPTS; count++) {
      alpha = getModifiedCurvatureMatrix(jacobian, lambda, penalty);


      DoubleMatrix1D deltaTheta;
      try {
        decmp = _decomposition.evaluate(alpha);
        deltaTheta = decmp.solve(beta);
      } catch (final Exception e) {
        throw new MathException(e);
      }


      DoubleMatrix1D trialTheta = (DoubleMatrix1D) _algebra.add(theta, deltaTheta);

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   */
  public DoubleMatrix2D calInverseJacobian(final DoubleMatrix1D sigma, final Function1D<DoubleMatrix1D, DoubleMatrix2D> jac, final DoubleMatrix1D originalSolution) {
    final DoubleMatrix2D jacobian = getJacobian(jac, sigma, originalSolution);
    final DoubleMatrix2D a = getModifiedCurvatureMatrix(jacobian, 0.0);
    final DoubleMatrix2D bT = getBTranspose(jacobian, sigma);
    final DecompositionResult decRes = _decomposition.evaluate(a);
    return decRes.solve(bT);
  }

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    return decRes.solve(bT);
  }


  private LeastSquareResults finish(final double newChiSqr, final DoubleMatrix2D jacobian, final DoubleMatrix1D newTheta, final DoubleMatrix1D sigma) {
    final DoubleMatrix2D alpha = getModifiedCurvatureMatrix(jacobian, 0.0);
    final DecompositionResult decmp = _decomposition.evaluate(alpha);
    return finish(alpha, decmp, newChiSqr, jacobian, newTheta, sigma);
  }

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    final int nParms = startPos.getNumberOfElements();
    Validate.isTrue(nObs == sigma.getNumberOfElements(), "observedValues and sigma must be same length");
    ArgumentChecker.isTrue(nObs >= nParms, "must have data points greater or equal to number of parameters. #date points = {}, #parameters = {}", nObs, nParms);
    ArgumentChecker.isTrue(constraints.evaluate(startPos), "The inital value of the parameters (startPos) is {} - this is not an allowed value", startPos);
    DoubleMatrix2D alpha;
    DecompositionResult decmp;
    DoubleMatrix1D theta = startPos;


    double lambda = 0.0; //TODO debug if the model is linear, it will be solved in 1 step
    double newChiSqr, oldChiSqr;
    DoubleMatrix1D error = getError(func, observedValues, sigma, theta);


    DoubleMatrix1D newError;
    DoubleMatrix2D jacobian = getJacobian(jac, sigma, theta);
    oldChiSqr = getChiSqr(error);


    //If we start at the solution we are done
    if (oldChiSqr == 0.0) {
      return finish(oldChiSqr, jacobian, theta, sigma);
    }


    DoubleMatrix1D beta = getChiSqrGrad(error, jacobian);


    for (int count = 0; count < MAX_ATTEMPTS; count++) {
      alpha = getModifiedCurvatureMatrix(jacobian, lambda);


      DoubleMatrix1D deltaTheta;
      try {
        decmp = _decomposition.evaluate(alpha);
        deltaTheta = decmp.solve(beta);
      } catch (final Exception e) {
        throw new MathException(e);
      }


      DoubleMatrix1D trialTheta = (DoubleMatrix1D) _algebra.add(theta, deltaTheta);

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  public DoubleMatrix2D calInverseJacobian(final DoubleMatrix1D sigma, final Function1D<DoubleMatrix1D, DoubleMatrix1D> func,
      final Function1D<DoubleMatrix1D, DoubleMatrix2D> jac, final DoubleMatrix1D originalSolution) {
    final DoubleMatrix2D jacobian = getJacobian(jac, sigma, originalSolution);
    final DoubleMatrix2D a = getModifiedCurvatureMatrix(jacobian, 0.0);
    final DoubleMatrix2D bT = getBTranspose(jacobian, sigma);
    final DecompositionResult decRes = _decomposition.evaluate(a);
    return decRes.solve(bT);
  }

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    return decRes.solve(bT);
  }


  private LeastSquareResults finish(final double newChiSqr, final DoubleMatrix2D jacobian, final DoubleMatrix1D newTheta, final DoubleMatrix1D sigma) {
    final DoubleMatrix2D alpha = getModifiedCurvatureMatrix(jacobian, 0.0);
    final DecompositionResult decmp = _decomposition.evaluate(alpha);
    return finish(alpha, decmp, newChiSqr, jacobian, newTheta, sigma);
  }

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    if (lambda > 0.0) {
      final DoubleMatrix2D d = getDiffMatrix(m, differenceOrder);
      ma = (DoubleMatrix2D) _algebra.add(ma, _algebra.scale(d, lambda));
    }


    final DecompositionResult decmp = _decomposition.evaluate(ma);
    final DoubleMatrix1D w = decmp.solve(mb);
    final DoubleMatrix2D covar = decmp.solve(DoubleMatrixUtils.getIdentityMatrix2D(m));


    double chiSq = 0;
    for (i = 0; i < n; i++) {
      double temp = 0;
      for (k = 0; k < m; k++) {

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        final DoubleMatrix2D d = getDiffMatrix(sizes, differenceOrder[i], i);
        ma = (DoubleMatrix2D) _algebra.add(ma, _algebra.scale(d, lambda[i]));
      }
    }


    final DecompositionResult decmp = _decomposition.evaluate(ma);
    final DoubleMatrix1D w = decmp.solve(mb);
    final DoubleMatrix2D covar = decmp.solve(DoubleMatrixUtils.getIdentityMatrix2D(m));


    double chiSq = 0;
    for (i = 0; i < n; i++) {
      double temp = 0;
      for (k = 0; k < m; k++) {

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    return res;
  }


  private double[] solve(final double[][] v, final double[] y, final Decomposition<?> decomp) {
    final DecompositionResult decompRes = decomp.evaluate(new DoubleMatrix2D(v));
    final DoubleMatrix1D res = decompRes.solve(new DoubleMatrix1D(y));
    return res.getData();
  }

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      return res;
    }


    @SuppressWarnings("synthetic-access")
    private double[] solveLU(final TridiagonalMatrix lM, final double[] y) {
      final DecompositionResult res = DCOMP.evaluate(lM.toDoubleMatrix2D());
      return res.solve(y);
    }

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