Examples of com.opengamma.analytics.math.statistics.leastsquare.NonLinearLeastSquare.solve()

com.opengamma.analytics.math.statistics.leastsquare.NonLinearLeastSquare.solve()
Use this when the model is in the ParameterizedFunction form and analytic parameter sensitivity is not available @param x Set of measurement points @param y Set of measurement values @param func The model in ParameterizedFunction form (i.e. takes measurement points and a set of parameters and returns a model value) @param startPos Initial value of the parameters @return A LeastSquareResults object

      objective.setEndIndex(_instrumentIndex.get((loopblock + 1) * _nbInstrumentsBlock) - 1);
      // Implementation note: the index start is from the first instrument of the block and the index end is from the last instrument of the block.
      final DoubleMatrix1D observedValues = new DoubleMatrix1D(_nbInstrumentsBlock, 0.0);
      @SuppressWarnings("unused")
      final
      LeastSquareResults result = ls.solve(observedValues, getCalibrationObjective(), new DoubleMatrix1D(1.0, 0.0));
      // Implementation note: the start value is a multiplicative factor of one and an additive term of 0 (parameters unchanged).
      //   The observed values are 0 as the function returns the difference between the calculated prices and the targets.
    }
  }

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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;
  }


  private void validateStrikes(final double[] strikes) {

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      objective.setEndIndex(_instrumentIndex.get((loopblock + 1) * _nbInstrumentsBlock) - 1);
      // Implementation note: the index start is from the first instrument of the block and the index end is from the last instrument of the block.
      final DoubleMatrix1D observedValues = new DoubleMatrix1D(_nbInstrumentsBlock, 0.0);
      @SuppressWarnings("unused")
      final
      LeastSquareResults result = ls.solve(observedValues, _calibrationObjective, new DoubleMatrix1D(1.0, 0.0));
      // Implementation note: the start value is a multiplicative factor of one and an additive term of 0 (parameters unchanged).
      //   The observed values are 0 as the function returns the difference between the calculated prices and the targets.
    }
  }

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      sigma[i] = 0.01; //1% error
    }


    final NonLinearLeastSquare ls = new NonLinearLeastSquare();
    //solve approx first
    LeastSquareResults solverRes = ls.solve(new DoubleMatrix1D(mrkVols), new DoubleMatrix1D(sigma), funcAppox, TRANSFORMS.transform(initialGuess));
    // now solve pde model
    solverRes = ls.solve(new DoubleMatrix1D(mrkVols), new DoubleMatrix1D(sigma), func, solverRes.getFitParameters());
    return new LeastSquareResultsWithTransform(solverRes, TRANSFORMS);
  }

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    final NonLinearLeastSquare ls = new NonLinearLeastSquare();
    //solve approx first
    LeastSquareResults solverRes = ls.solve(new DoubleMatrix1D(mrkVols), new DoubleMatrix1D(sigma), funcAppox, TRANSFORMS.transform(initialGuess));
    // now solve pde model
    solverRes = ls.solve(new DoubleMatrix1D(mrkVols), new DoubleMatrix1D(sigma), func, solverRes.getFitParameters());
    return new LeastSquareResultsWithTransform(solverRes, TRANSFORMS);
  }


  /**
   * Transforms the price data (in PDEFullResults1D form) to implied volatility in a form used by 2D interpolator

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    final double[] sigma = new double[MARKET_PRICES.length];
    Arrays.fill(sigma, 0.0001); // 1bps


    final NonLinearLeastSquare ls = new NonLinearLeastSquare();
    final LeastSquareResults lsRes = ls.solve(new DoubleMatrix1D(MARKET_VOLS), new DoubleMatrix1D(sigma), func, jac, START);


    final DoubleMatrix1D res = lsRes.getFitParameters();


    assertTrue("chi^2 too large", lsRes.getChiSq() < 0.3);

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    Arrays.fill(start, NODES.length + 1, 2 * NODES.length + 1, TRANSFORMS.get(NAMES[2]).transform(0.0));
    Arrays.fill(start, 2 * NODES.length + 1, 3 * NODES.length + 1, TRANSFORMS.get(NAMES[3]).transform(0.35));
    final DoubleMatrix1D vStart = new DoubleMatrix1D(start);


    final NonLinearLeastSquare ls = new NonLinearLeastSquare();
    final LeastSquareResults lsRes = ls.solve(new DoubleMatrix1D(MARKET_VOLS), new DoubleMatrix1D(SIGMA), func, jac, vStart);


    System.out.println("CapletStrippingMarketDataTest");
    System.out.println("chi2: " + lsRes.getChiSq() + "\n");


    final SABRTermStructureParameters sabrTS = (SABRTermStructureParameters) VOL_MODEL_PROVIDER.evaluate(lsRes.getFitParameters());

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