Examples of com.opengamma.analytics.math.interpolation.PiecewisePolynomialResultsWithSensitivity

Package com.opengamma.analytics.math.interpolation

Examples of com.opengamma.analytics.math.interpolation.PiecewisePolynomialResultsWithSensitivity

com.opengamma.analytics.math.interpolation.PiecewisePolynomialResultsWithSensitivity
Result of interpolation by piecewise polynomial containing knots: Positions of knots coefMatrix: Coefficient matrix whose i-th row vector is { a_n, a_{n-1}, ...} for the i-th interval, where a_n, a_{n-1},... are coefficients of f(x) = a_n (x-x_i)^n + a_{n-1} (x-x_i)^{n-1} + .... In multidimensional cases, coefficients for the i-th interval of the j-th spline is in (j*(i-1) + i) -th row vector. nIntervals: Number of intervals, which should be (Number of knots) - 1 order: Number of coefficients in polynomial, which is equal to (polynomial degree) + 1 dim: Number of splines which are in the super class, and _coeffSense Node sensitivity of the coefficients _coeffSense[i].getData()[j][k] is \frac{\partial a^i_{n-j}}{\partial y_k}

        }
      }
      diffSense[i] = new DoubleMatrix2D(tmp);
    }


    PiecewisePolynomialResultsWithSensitivity ppDiff = new PiecewisePolynomialResultsWithSensitivity(pp.getKnots(), pp.getCoefMatrix(), nCoefs - 1, pp.getDimensions(), diffSense);
    return nodeSensitivity(ppDiff, xKeys);
  }

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        }
      }
      diffSense[i] = new DoubleMatrix2D(tmp);
    }


    PiecewisePolynomialResultsWithSensitivity ppDiff = new PiecewisePolynomialResultsWithSensitivity(pp.getKnots(), pp.getCoefMatrix(), nCoefs - 2, pp.getDimensions(), diffSense);
    return nodeSensitivity(ppDiff, xKeys);
  }

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      final double xMax = xValues[nData - 1];
      for (int i = 0; i < 10 * nData; ++i) {
        xKeys[i] = xMin + (xMax - xMin) / (10 * nData - 1) * i;
      }


      PiecewisePolynomialResultsWithSensitivity result = interps[k].interpolateWithSensitivity(xValues, yValues);
      for (int j = 0; j < nData; ++j) {
        yValuesUp[j] = yValues[j] * (1. + EPS);
        yValuesDw[j] = yValues[j] * (1. - EPS);
        final PiecewisePolynomialResultsWithSensitivity resultUp = interps[k].interpolateWithSensitivity(xValues, yValuesUp);
        final PiecewisePolynomialResultsWithSensitivity resultDw = interps[k].interpolateWithSensitivity(xValues, yValuesDw);


        final double[] valuesUp = FUNCTION.evaluate(resultUp, xKeys).getData()[0];
        final double[] valuesDw = FUNCTION.evaluate(resultDw, xKeys).getData()[0];
        final double[] diffUp = FUNCTION.differentiate(resultUp, xKeys).getData()[0];
        final double[] diffDw = FUNCTION.differentiate(resultDw, xKeys).getData()[0];

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      final double xMax = xValues[nData - 1];
      for (int i = 0; i < 10 * nData; ++i) {
        xKeys[i] = xMin + (xMax - xMin) / (10 * nData - 1) * i;
      }


      PiecewisePolynomialResultsWithSensitivity result = interps[k].interpolateWithSensitivity(xValues, yValues);
      for (int j = 0; j < nData; ++j) {
        yValuesUp[j] = yValues[j] * (1. + EPS);
        yValuesDw[j] = yValues[j] * (1. - EPS);
        final PiecewisePolynomialResultsWithSensitivity resultUp = interps[k].interpolateWithSensitivity(xValues, yValuesUp);
        final PiecewisePolynomialResultsWithSensitivity resultDw = interps[k].interpolateWithSensitivity(xValues, yValuesDw);


        final double[] diffUp = FUNCTION.differentiateTwice(resultUp, xKeys).getData()[0];
        final double[] diffDw = FUNCTION.differentiateTwice(resultDw, xKeys).getData()[0];
        for (int i = 0; i < 10 * nData; ++i) {
          final double xKeyUp = xKeys[i] * (1. + EPS);

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          final double xMax = xValues[nData - 1];
          for (int i = 0; i < 10 * nData; ++i) {
            xKeys[i] = xMin + (xMax - xMin) / (10 * nData - 1) * i;
          }


          PiecewisePolynomialResultsWithSensitivity result = interps[k].interpolateWithSensitivity(xValues, yValues);
          for (int j = 0; j < nData; ++j) {
            yValuesUp[j + 1] = yValues[j + 1] * (1. + EPS);
            yValuesDw[j + 1] = yValues[j + 1] * (1. - EPS);
            final PiecewisePolynomialResultsWithSensitivity resultUp = interps[k].interpolateWithSensitivity(xValues, yValuesUp);
            final PiecewisePolynomialResultsWithSensitivity resultDw = interps[k].interpolateWithSensitivity(xValues, yValuesDw);


            final double[] valuesUp = FUNCTION.evaluate(resultUp, xKeys).getData()[0];
            final double[] valuesDw = FUNCTION.evaluate(resultDw, xKeys).getData()[0];
            final double[] diffUp = FUNCTION.differentiate(resultUp, xKeys).getData()[0];
            final double[] diffDw = FUNCTION.differentiate(resultDw, xKeys).getData()[0];

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          final double xMax = xValues[nData - 1];
          for (int i = 0; i < 10 * nData; ++i) {
            xKeys[i] = xMin + (xMax - xMin) / (10 * nData - 1) * i;
          }


          PiecewisePolynomialResultsWithSensitivity result = interps[k].interpolateWithSensitivity(xValues, yValues);
          for (int j = 0; j < nData; ++j) {
            yValuesUp[j + 1] = yValues[j + 1] * (1. + EPS);
            yValuesDw[j + 1] = yValues[j + 1] * (1. - EPS);
            final PiecewisePolynomialResultsWithSensitivity resultUp = interps[k].interpolateWithSensitivity(xValues, yValuesUp);
            final PiecewisePolynomialResultsWithSensitivity resultDw = interps[k].interpolateWithSensitivity(xValues, yValuesDw);


            final double[] diffUp = FUNCTION.differentiateTwice(resultUp, xKeys).getData()[0];
            final double[] diffDw = FUNCTION.differentiateTwice(resultDw, xKeys).getData()[0];
            for (int i = 0; i < 10 * nData; ++i) {
              final double xKeyUp = xKeys[i] * (1. + EPS);

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Related Classes of com.opengamma.analytics.math.interpolation.PiecewisePolynomialResultsWithSensitivity

com.opengamma.analytics.math.function.PiecewisePolynomialWithSensitivityFunction1D

com.opengamma.analytics.math.function.PiecewisePolynomialWithSensitivityFunction1DTest

org.apache.commons.lang.NotImplementedException

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