Package com.opengamma.analytics.math.matrix

Examples of com.opengamma.analytics.math.matrix.DoubleMatrix1D

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    //TODO set these in sub classes
    int tries = 0;
    int count = 0;
    while (chiSqr > 100.0 * n && count < 5) { //10bps average error
      final DoubleMatrix1D gStart = getGlobalStart(forward, strikes, expiry, impliedVols);
      try {
        final LeastSquareResultsWithTransform glsRes = globalFitter.solve(gStart, gFixed);
        if (glsRes.getChiSq() < chiSqr) {
          gBest = glsRes;
          chiSqr = gBest.getChiSq();
        }
        count++;
      } catch (final Exception e) {
      }
      tries++;
      if (tries > 20) {
        throw new MathException("Cannot fit data");
      }
    }
    if (gBest == null) {
      throw new IllegalStateException("Global estimate was null; should never happen");
    }
    if (n == 3) {
      if (gBest.getChiSq() / n > 1.0) {
        s_logger.debug("chi^2 on fit to ", +n + " points is " + gBest.getChiSq());
      }
      modelParameters.add(toSmileModelData(gBest.getModelParameters()));
    } else {
      final BitSet lFixed = getLocalFixedValues();
      DoubleMatrix1D lStart = gBest.getModelParameters();
      for (int i = 0; i < n - 2; i++) {
        final double[][] temp = getStrikesVolsAndErrors(i, strikes, impliedVols, errors);
        final double[] tStrikes = temp[0];
        final double[] tVols = temp[1];
        final double[] tErrors = temp[2];
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      }
    };

    //TODO replace this with an explicit polynomial fitter
    final NonLinearLeastSquare ls = new NonLinearLeastSquare();
    final LeastSquareResults lsRes = ls.solve(new DoubleMatrix1D(x), new DoubleMatrix1D(impliedVols), func, new DoubleMatrix1D(0.1, 0.0, 0.0));
    final DoubleMatrix1D fitP = lsRes.getFitParameters();
    return fitP;
  }
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          sensitivity[loopccypv][loopnode] = pvDiff.getAmount(ccyList.get(loopccypv)) / (2 * _shift);
        }
      }
      final String name = black.getMulticurveProvider().getName(ccy);
      for (int loopccypv = 0; loopccypv < nbCcy; loopccypv++) {
        result = result.plus(new ObjectsPair<>(name, ccyList.get(loopccypv)), new DoubleMatrix1D(sensitivity[loopccypv]));
      }
    }
    // Forward ON
    final Set<IndexON> indexON = black.getMulticurveProvider().getIndexesON();
    for (final IndexON index : indexON) {
      final YieldAndDiscountCurve curve = black.getMulticurveProvider().getCurve(index);
      ArgumentChecker.isTrue(curve instanceof YieldCurve, "Curve should be a YieldCurve");
      final YieldCurve curveYield = (YieldCurve) curve;
      ArgumentChecker.isTrue(curveYield.getCurve() instanceof InterpolatedDoublesCurve, "Yield curve should be based on InterpolatedDoublesCurve");
      final InterpolatedDoublesCurve curveInt = (InterpolatedDoublesCurve) curveYield.getCurve();
      final int nbNodePoint = curveInt.getXDataAsPrimitive().length;
      final double[][] sensitivity = new double[nbCcy][nbNodePoint];
      for (int loopnode = 0; loopnode < nbNodePoint; loopnode++) {
        final double[] yieldBumpedPlus = curveInt.getYDataAsPrimitive().clone();
        yieldBumpedPlus[loopnode] += _shift;
        final YieldAndDiscountCurve dscBumpedPlus = new YieldCurve(curveInt.getName(), new InterpolatedDoublesCurve(curveInt.getXDataAsPrimitive(), yieldBumpedPlus, curveInt.getInterpolator(), true));
        final BlackSTIRFuturesSmileProviderDiscount marketFwdBumpedPlus = new BlackSTIRFuturesSmileProviderDiscount(black.getMulticurveProvider().withForward(index, dscBumpedPlus),
            black.getBlackParameters(), black.getFuturesIndex());
        final MultipleCurrencyAmount pvBumpedPlus = instrument.accept(_valueCalculator, marketFwdBumpedPlus);
        final double[] yieldBumpedMinus = curveInt.getYDataAsPrimitive().clone();
        yieldBumpedMinus[loopnode] -= _shift;
        final YieldAndDiscountCurve dscBumpedMinus = new YieldCurve(curveInt.getName(),
            new InterpolatedDoublesCurve(curveInt.getXDataAsPrimitive(), yieldBumpedMinus, curveInt.getInterpolator(), true));
        final BlackSTIRFuturesSmileProviderDiscount marketFwdBumpedMinus = new BlackSTIRFuturesSmileProviderDiscount(black.getMulticurveProvider().withForward(index, dscBumpedMinus),
            black.getBlackParameters(), black.getFuturesIndex());
        final MultipleCurrencyAmount pvBumpedMinus = instrument.accept(_valueCalculator, marketFwdBumpedMinus);
        final MultipleCurrencyAmount pvDiff = pvBumpedPlus.plus(pvBumpedMinus.multipliedBy(-1.0));
        for (int loopccypv = 0; loopccypv < nbCcy; loopccypv++) {
          sensitivity[loopccypv][loopnode] = pvDiff.getAmount(ccyList.get(loopccypv)) / (2 * _shift);
        }
      }
      final String name = black.getMulticurveProvider().getName(index);
      for (int loopccypv = 0; loopccypv < nbCcy; loopccypv++) {
        result = result.plus(new ObjectsPair<>(name, ccyList.get(loopccypv)), new DoubleMatrix1D(sensitivity[loopccypv]));
      }
    }
    // Forward Ibor
    final Set<IborIndex> indexForward = black.getMulticurveProvider().getIndexesIbor();
    for (final IborIndex index : indexForward) {
      final YieldAndDiscountCurve curve = black.getMulticurveProvider().getCurve(index);
      ArgumentChecker.isTrue(curve instanceof YieldCurve, "Curve should be a YieldCurve");
      final YieldCurve curveYield = (YieldCurve) curve;
      ArgumentChecker.isTrue(curveYield.getCurve() instanceof InterpolatedDoublesCurve, "Yield curve should be based on InterpolatedDoublesCurve");
      final InterpolatedDoublesCurve curveInt = (InterpolatedDoublesCurve) curveYield.getCurve();
      final int nbNodePoint = curveInt.getXDataAsPrimitive().length;
      final double[][] sensitivity = new double[nbCcy][nbNodePoint];
      for (int loopnode = 0; loopnode < nbNodePoint; loopnode++) {
        final double[] yieldBumpedPlus = curveInt.getYDataAsPrimitive().clone();
        yieldBumpedPlus[loopnode] += _shift;
        final YieldAndDiscountCurve dscBumpedPlus = new YieldCurve(curveInt.getName(), new InterpolatedDoublesCurve(curveInt.getXDataAsPrimitive(), yieldBumpedPlus, curveInt.getInterpolator(), true));
        final BlackSTIRFuturesSmileProviderDiscount marketFwdBumpedPlus = new BlackSTIRFuturesSmileProviderDiscount(black.getMulticurveProvider().withForward(index, dscBumpedPlus),
            black.getBlackParameters(), black.getFuturesIndex());
        final MultipleCurrencyAmount pvBumpedPlus = instrument.accept(_valueCalculator, marketFwdBumpedPlus);
        final double[] yieldBumpedMinus = curveInt.getYDataAsPrimitive().clone();
        yieldBumpedMinus[loopnode] -= _shift;
        final YieldAndDiscountCurve dscBumpedMinus = new YieldCurve(curveInt.getName(),
            new InterpolatedDoublesCurve(curveInt.getXDataAsPrimitive(), yieldBumpedMinus, curveInt.getInterpolator(), true));
        final BlackSTIRFuturesSmileProviderDiscount marketFwdBumpedMinus = new BlackSTIRFuturesSmileProviderDiscount(black.getMulticurveProvider().withForward(index, dscBumpedMinus),
            black.getBlackParameters(), black.getFuturesIndex());
        final MultipleCurrencyAmount pvBumpedMinus = instrument.accept(_valueCalculator, marketFwdBumpedMinus);
        final MultipleCurrencyAmount pvDiff = pvBumpedPlus.plus(pvBumpedMinus.multipliedBy(-1.0));
        for (int loopccypv = 0; loopccypv < nbCcy; loopccypv++) {
          sensitivity[loopccypv][loopnode] = pvDiff.getAmount(ccyList.get(loopccypv)) / (2 * _shift);
        }
      }
      final String name = black.getMulticurveProvider().getName(index);
      for (int loopccypv = 0; loopccypv < nbCcy; loopccypv++) {
        result = result.plus(new ObjectsPair<>(name, ccyList.get(loopccypv)), new DoubleMatrix1D(sensitivity[loopccypv]));
      }
    }
    return result;
  }
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  }

  @Override
  protected DoubleMatrix1D getGlobalStart(final double forward, final double[] strikes, final double expiry, final double[] impliedVols) {
    final RandomEngine random = getRandom();
    final DoubleMatrix1D fitP = getPolynomialFit(forward, strikes, impliedVols);

    final double a = fitP.getEntry(0);
    final double b = fitP.getEntry(1);
    final double c = fitP.getEntry(2);

    if (Math.abs(b) < 1e-3 && Math.abs(c) < 1e-3) { //almost flat smile
      final double theta = Math.PI / 2 - 0.01;
      return new DoubleMatrix1D(a, 0.01, theta, theta);
    }
    final double theta = Math.PI / 2 * random.nextDouble();
    return new DoubleMatrix1D(a * (0.8 + 0.4 * random.nextDouble()), a * 0.5 * random.nextDouble(), theta, theta);
  }
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      deltaX[i] = x[i + 1] - x[i];
      oneOverDeltaX[i] = 1.0 / deltaX[i];
      deltaYOverDeltaX[i] = (y[i + 1] - y[i]) * oneOverDeltaX[i];
    }
    final DoubleMatrix2D inverseTriDiag = getInverseTridiagonalMatrix(deltaX);
    final DoubleMatrix1D rhsVector = getRHSVector(deltaYOverDeltaX);
    return ((DoubleMatrix1D) OG_ALGEBRA.multiply(inverseTriDiag, rhsVector)).getData();
  }
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    }

    if (!_rightNatural) {
      res[n - 1] = _rightFirstDev - deltaYOverDeltaX[n - 2];
    }
    return new DoubleMatrix1D(res);
  }
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  public Double interpolate(final Interpolator1DDataBundle data, final Double value) {
    Validate.notNull(value, "value");
    Validate.notNull(data, "data bundle");
    Validate.isTrue(data instanceof Interpolator1DPiecewisePoynomialDataBundle);
    final Interpolator1DPiecewisePoynomialDataBundle polyData = (Interpolator1DPiecewisePoynomialDataBundle) data;
    final DoubleMatrix1D res = FUNC.evaluate(polyData.getPiecewisePolynomialResultsWithSensitivity(), value);
    return res.getEntry(0);
  }
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  public double firstDerivative(final Interpolator1DDataBundle data, final Double value) {
    Validate.notNull(value, "value");
    Validate.notNull(data, "data bundle");
    Validate.isTrue(data instanceof Interpolator1DPiecewisePoynomialDataBundle);
    final Interpolator1DPiecewisePoynomialDataBundle polyData = (Interpolator1DPiecewisePoynomialDataBundle) data;
    final DoubleMatrix1D res = FUNC.differentiate(polyData.getPiecewisePolynomialResultsWithSensitivity(), value);
    return res.getEntry(0);
  }
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  public double[] getNodeSensitivitiesForValue(final Interpolator1DDataBundle data, final Double value) {
    Validate.notNull(value, "value");
    Validate.notNull(data, "data bundle");
    Validate.isTrue(data instanceof Interpolator1DPiecewisePoynomialDataBundle);
    final Interpolator1DPiecewisePoynomialDataBundle polyData = (Interpolator1DPiecewisePoynomialDataBundle) data;
    final DoubleMatrix1D res = FUNC.nodeSensitivity(polyData.getPiecewisePolynomialResultsWithSensitivity(), value);
    return res.getData();
  }
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              oldValue = xData[i];
              xData[i] += _eps;
              res[i] = (function.evaluate(x) - y) / _eps;
              xData[i] = oldValue;
            }
            return new DoubleMatrix1D(res);
          }
        };
      case CENTRAL:
        return new Function1D<DoubleMatrix1D, DoubleMatrix1D>() {

          @SuppressWarnings("synthetic-access")
          @Override
          public DoubleMatrix1D evaluate(final DoubleMatrix1D x) {
            Validate.notNull(x, "x");
            final int n = x.getNumberOfElements();
            final double[] xData = x.getData();
            double oldValue;
            double up, down;
            final double[] res = new double[n];
            for (int i = 0; i < n; i++) {
              oldValue = xData[i];
              xData[i] += _eps;
              up = function.evaluate(x);
              xData[i] -= _twoEps;
              down = function.evaluate(x);
              res[i] = (up - down) / _twoEps;
              xData[i] = oldValue;
            }
            return new DoubleMatrix1D(res);
          }
        };
      case BACKWARD:
        return new Function1D<DoubleMatrix1D, DoubleMatrix1D>() {

          @SuppressWarnings("synthetic-access")
          @Override
          public DoubleMatrix1D evaluate(final DoubleMatrix1D x) {
            Validate.notNull(x, "x");
            final double y = function.evaluate(x);
            final int n = x.getNumberOfElements();
            final double[] xData = x.getData();
            double oldValue;
            final double[] res = new double[n];
            for (int i = 0; i < n; i++) {
              oldValue = xData[i];
              xData[i] -= _eps;
              res[i] = (y - function.evaluate(x)) / _eps;
              xData[i] = oldValue;
            }
            return new DoubleMatrix1D(res);
          }
        };
      default:
        throw new IllegalArgumentException("Can only handle forward, backward and central differencing");
    }
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