Package com.opengamma.analytics.math.matrix

Examples of com.opengamma.analytics.math.matrix.DoubleMatrix2D

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    fD[1][1] = (fp - fD[0][1]) / _cutOffStrike;
    fD[1][2] = (fp - 2 * fD[0][1]) / (_cutOffStrike * _cutOffStrike);
    fD[2][0] = fpp;
    fD[2][1] = (fpp + fD[0][2] * (2 * (_mu + 1) + 2 * _parameter[1] / _cutOffStrike + 4 * _parameter[2] / (_cutOffStrike * _cutOffStrike))) / _cutOffStrike;
    fD[2][2] = (fpp + fD[0][2] * (2 * (2 * _mu + 3) + 4 * _parameter[1] / _cutOffStrike + 8 * _parameter[2] / (_cutOffStrike * _cutOffStrike))) / (_cutOffStrike * _cutOffStrike);
    final DoubleMatrix2D fDmatrix = new DoubleMatrix2D(fD);
    // Derivative of abc with respect to forward
    final ColtMatrixAlgebra algebra = new ColtMatrixAlgebra();
    final DoubleMatrix2D fDInverse = algebra.getInverse(fDmatrix);
    final OGMatrixAlgebra algebraOG = new OGMatrixAlgebra();
    final DoubleMatrix1D derivativeF = (DoubleMatrix1D) algebraOG.multiply(fDInverse, pDFvector);
    return derivativeF.getData();
  }
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    fD[1][1] = (fp - fD[0][1]) / _cutOffStrike;
    fD[1][2] = (fp - 2 * fD[0][1]) / (_cutOffStrike * _cutOffStrike);
    fD[2][0] = fpp;
    fD[2][1] = (fpp + fD[0][2] * (2 * (_mu + 1) + 2 * _parameter[1] / _cutOffStrike + 4 * _parameter[2] / (_cutOffStrike * _cutOffStrike))) / _cutOffStrike;
    fD[2][2] = (fpp + fD[0][2] * (2 * (2 * _mu + 3) + 4 * _parameter[1] / _cutOffStrike + 8 * _parameter[2] / (_cutOffStrike * _cutOffStrike))) / (_cutOffStrike * _cutOffStrike);
    final DoubleMatrix2D fDmatrix = new DoubleMatrix2D(fD);
    // Derivative of abc with respect to forward
    final ColtMatrixAlgebra algebra = new ColtMatrixAlgebra();
    final DoubleMatrix2D fDInverse = algebra.getInverse(fDmatrix);
    final OGMatrixAlgebra algebraOG = new OGMatrixAlgebra();
    for (int loopparam = 0; loopparam < 4; loopparam++) {
      final DoubleMatrix1D pDSABRvector = new DoubleMatrix1D(pDSABR[loopparam]);
      final DoubleMatrix1D derivativeSABR = (DoubleMatrix1D) algebraOG.multiply(fDInverse, pDSABRvector);
      result[loopparam] = derivativeSABR.getData();
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  @Override
  protected DoubleMatrix1D[] combinedMatrixEqnSolver(final double[][] doubMat1, final double[] doubVec, final double[][] doubMat2) {
    final int size = doubVec.length;
    final DoubleMatrix1D[] res = new DoubleMatrix1D[size + 1];
    final DoubleMatrix2D doubMat2Matrix = new DoubleMatrix2D(doubMat2);

    final double[] u = new double[size - 1];
    final double[] d = new double[size];
    final double[] l = new double[size - 1];

    for (int i = 0; i < size - 1; ++i) {
      u[i] = doubMat1[i][i + 1];
      d[i] = doubMat1[i][i];
      l[i] = doubMat1[i + 1][i];
    }
    d[size - 1] = doubMat1[size - 1][size - 1];
    final TridiagonalMatrix m = new TridiagonalMatrix(d, u, l);
    res[0] = new DoubleMatrix1D(solvTriDag(m, doubVec));
    for (int i = 0; i < size; ++i) {
      final double[] doubMat2Colum = doubMat2Matrix.getColumnVector(i).getData();
      res[i + 1] = new DoubleMatrix1D(solvTriDag(m, doubMat2Colum));
    }

    return res;
  }
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      }
      final double bound = Math.max(Math.abs(ref) + Math.abs(yValuesSrt[i + 1]), 1.e-1);
      ArgumentChecker.isTrue(Math.abs(ref - yValuesSrt[i + 1]) < ERROR * bound, "Input is too large/small or data points are too close");
    }

    return new PiecewisePolynomialResult(new DoubleMatrix1D(xValuesSrt), new DoubleMatrix2D(coefs), 4, 1);
  }
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      final double[] intervals = _solver.intervalsCalculator(xValuesSrt);
      final double[] slopes = _solver.slopesCalculator(yValuesSrt, intervals);
      final double[] first = firstDerivativeCalculator(slopes);

      coefMatrix[i] = new DoubleMatrix2D(_solver.solve(yValuesSrt, intervals, slopes, first));

      for (int k = 0; k < intervals.length; ++k) {
        double ref = 0.;
        for (int j = 0; j < 4; ++j) {
          ref += coefMatrix[i].getData()[k][j] * Math.pow(intervals[k], 3 - j);
          ArgumentChecker.isFalse(Double.isNaN(coefMatrix[i].getData()[k][j]), "Too large input");
          ArgumentChecker.isFalse(Double.isInfinite(coefMatrix[i].getData()[k][j]), "Too large input");
        }
        final double bound = Math.max(Math.abs(ref) + Math.abs(yValuesSrt[k + 1]), 1.e-1);
        ArgumentChecker.isTrue(Math.abs(ref - yValuesSrt[k + 1]) < ERROR * bound, "Input is too large/small or data points are too close");
      }
    }

    final int nIntervals = coefMatrix[0].getNumberOfRows();
    final int nCoefs = coefMatrix[0].getNumberOfColumns();
    double[][] resMatrix = new double[dim * nIntervals][nCoefs];

    for (int i = 0; i < nIntervals; ++i) {
      for (int j = 0; j < dim; ++j) {
        resMatrix[dim * i + j] = coefMatrix[j].getRowVector(i).getData();
      }
    }

    return new PiecewisePolynomialResult(new DoubleMatrix1D(xValuesSrt), new DoubleMatrix2D(resMatrix), nCoefs, dim);
  }
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    final double[][] slopeSensitivity = _solver.slopeSensitivityCalculator(intervals);
    final DoubleMatrix1D[] firstWithSensitivity = firstDerivativeWithSensitivityCalculator(yValues, intervals, slopes, slopeSensitivity);
    final DoubleMatrix2D[] resMatrix = _solver.solveWithSensitivity(yValues, intervals, slopes, slopeSensitivity, firstWithSensitivity);

    for (int k = 0; k < nDataPts; k++) {
      DoubleMatrix2D m = resMatrix[k];
      final int rows = m.getNumberOfRows();
      final int cols = m.getNumberOfColumns();
      for (int i = 0; i < rows; ++i) {
        for (int j = 0; j < cols; ++j) {
          ArgumentChecker.isTrue(Doubles.isFinite(m.getEntry(i, j)), "Matrix contains a NaN or infinite");
        }
      }
    }

    final DoubleMatrix2D coefMatrix = resMatrix[0];
    for (int i = 0; i < nDataPts - 1; ++i) {
      double ref = 0.;
      for (int j = 0; j < 4; ++j) {
        ref += coefMatrix.getData()[i][j] * Math.pow(intervals[i], 3 - j);
      }
      final double bound = Math.max(Math.abs(ref) + Math.abs(yValues[i + 1]), 1.e-1);
      ArgumentChecker.isTrue(Math.abs(ref - yValues[i + 1]) < ERROR * bound, "Input is too large/small or data points are too close");
    }
    final DoubleMatrix2D[] coefSenseMatrix = new DoubleMatrix2D[nDataPts - 1];
    System.arraycopy(resMatrix, 1, coefSenseMatrix, 0, nDataPts - 1);
    final int nCoefs = coefMatrix.getNumberOfColumns();

    return new PiecewisePolynomialResultsWithSensitivity(new DoubleMatrix1D(xValues), coefMatrix, nCoefs, 1, coefSenseMatrix);
  }
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      for (int loopstrike = 0; loopstrike < volatilityModel.getNumberStrike(); loopstrike++) {
        vega[loopexp][loopstrike] = nodeWeight[loopexp][loopstrike] * pointSensitivity.getVega().getMap().get(point);
      }
    }
    return new PresentValueForexBlackVolatilityNodeSensitivityDataBundle(optionForex.getUnderlyingForex().getCurrency1(), optionForex.getUnderlyingForex().getCurrency2(),
        new DoubleMatrix1D(volatilityModel.getTimeToExpiration()), new DoubleMatrix1D(volatilityModel.getDeltaFull()), new DoubleMatrix2D(vega));
  }
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  private DoubleMatrix1D[] firstDerivativeWithSensitivityCalculator(final double[] yValues, final double[] intervals, final double[] slopes, final double[][] slopeSensitivity) {
    final int nData = yValues.length;
    final double[] slopesExt = getExtraPoints(slopes);
    final double[][] slopeSensitivityExtTransp = new double[nData][nData + 3];
    final DoubleMatrix1D[] res = new DoubleMatrix1D[nData + 1];
    DoubleMatrix2D senseMat = new DoubleMatrix2D(slopeSensitivity);

    for (int i = 0; i < nData; ++i) {
      slopeSensitivityExtTransp[i] = getExtraPoints(senseMat.getColumnVector(i).getData());
    }

    final DoubleMatrix1D[] modSlopesWithSensitivity = modSlopesWithSensitivityCalculator(slopesExt, slopeSensitivityExtTransp);

    final double[] first = new double[nData];
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      final LinkedHashMap<String, IndexON[]> forwardONMap, final LinkedHashMap<String, GeneratorYDCurve> generatorsMap,
      final InstrumentDerivativeVisitor<HullWhiteOneFactorProviderInterface, MulticurveSensitivity> sensitivityCalculator) {
    final GeneratorHullWhiteProviderDiscount generator = new GeneratorHullWhiteProviderDiscount(knownData, discountingMap, forwardIborMap, forwardONMap, generatorsMap);
    final HullWhiteProviderDiscountBuildingData data = new HullWhiteProviderDiscountBuildingData(instruments, generator);
    final Function1D<DoubleMatrix1D, DoubleMatrix2D> jacobianCalculator = new HullWhiteProviderDiscountFinderJacobian(new ParameterSensitivityHullWhiteMatrixCalculator(sensitivityCalculator), data);
    final DoubleMatrix2D jacobian = jacobianCalculator.evaluate(new DoubleMatrix1D(parameters));
    final DoubleMatrix2D inverseJacobian = MATRIX_ALGEBRA.getInverse(jacobian);
    final double[][] matrixTotal = inverseJacobian.getData();
    final DoubleMatrix2D[] result = new DoubleMatrix2D[nbParameters.length];
    int startCurve = 0;
    for (int loopmat = 0; loopmat < nbParameters.length; loopmat++) {
      final double[][] matrixCurve = new double[nbParameters[loopmat]][matrixTotal.length];
      for (int loopparam = 0; loopparam < nbParameters[loopmat]; loopparam++) {
        matrixCurve[loopparam] = matrixTotal[startBlock + startCurve + loopparam].clone();
      }
      result[loopmat] = new DoubleMatrix2D(matrixCurve);
      startCurve += nbParameters[loopmat];
    }
    return result;
  }
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      final InstrumentDerivativeVisitor<InflationProviderInterface, InflationSensitivity> sensitivityCalculator) {
    final GeneratorInflationProviderDiscount generator = new GeneratorInflationProviderDiscount(knownData, inflationMap, generatorsMap);
    final InflationDiscountBuildingData data = new InflationDiscountBuildingData(instruments, generator);
    final Function1D<DoubleMatrix1D, DoubleMatrix2D> jacobianCalculator = new InflationDiscountFinderJacobian(new ParameterSensitivityInflationMatrixCalculator(sensitivityCalculator),
        data);
    final DoubleMatrix2D jacobian = jacobianCalculator.evaluate(new DoubleMatrix1D(parameters));
    final DoubleMatrix2D inverseJacobian = MATRIX_ALGEBRA.getInverse(jacobian);
    final double[][] matrixTotal = inverseJacobian.getData();
    final DoubleMatrix2D[] result = new DoubleMatrix2D[nbParameters.length];
    int startCurve = 0;
    for (int loopmat = 0; loopmat < nbParameters.length; loopmat++) {
      final double[][] matrixCurve = new double[nbParameters[loopmat]][matrixTotal.length];
      for (int loopparam = 0; loopparam < nbParameters[loopmat]; loopparam++) {
        matrixCurve[loopparam] = matrixTotal[startBlock + startCurve + loopparam].clone();
      }
      result[loopmat] = new DoubleMatrix2D(matrixCurve);
      startCurve += nbParameters[loopmat];
    }
    return result;
  }
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