Package com.opengamma.analytics.math.matrix

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

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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(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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    final double[][] res = new double[nbParameters][nbParameters];
    for (int loopinstrument = 0; loopinstrument < _data.getNumberOfInstruments(); loopinstrument++) {
      final InstrumentDerivative deriv = _data.getInstrument(loopinstrument);
      res[loopinstrument] = _parameterSensitivityCalculator.calculateSensitivity(deriv, bundle, curvesSet).getData();
    }
    return new DoubleMatrix2D(res);
  }
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          }
        }
        offset += _data.getCurveNodePointsForCurve(name).length;
      }
    }
    return new DoubleMatrix2D(res);
  }
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      final InstrumentDerivativeVisitor<MulticurveProviderInterface, MulticurveSensitivity> sensitivityCalculator) {
    final GeneratorMulticurveProviderForward generator = new GeneratorMulticurveProviderForward(knownData, discountingMap, forwardIborMap, forwardONMap, generatorsMap);
    final MulticurveProviderForwardBuildingData data = new MulticurveProviderForwardBuildingData(instruments, generator);
    final Function1D<DoubleMatrix1D, DoubleMatrix2D> jacobianCalculator = new MulticurveProviderForwardFinderJacobian(
        new ParameterSensitivityMulticurveMatrixCalculator(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 double[][] res = new double[nbParameters][nbParameters];
    for (int loopinstrument = 0; loopinstrument < _data.getNumberOfInstruments(); loopinstrument++) {
      final InstrumentDerivative deriv = _data.getInstrument(loopinstrument);
      res[loopinstrument] = _parameterSensitivityCalculator.calculateSensitivity(deriv, bundle, curvesSet).getData();
    }
    return new DoubleMatrix2D(res);
  }
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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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        vega[loopexp][loopstrike] = nodeWeight[loopexp][loopstrike] * pointSensitivity.getVega().getMap().get(point);
      }
    }
    return new PresentValueForexBlackVolatilityNodeSensitivityDataBundle(optionForex.getUnderlyingOption().getUnderlyingForex().getCurrency1(),
        optionForex.getUnderlyingOption().getUnderlyingForex().getCurrency2(), new DoubleMatrix1D(volatilityModel.getTimeToExpiration()),
        new DoubleMatrix1D(volatilityModel.getDeltaFull()), new DoubleMatrix2D(vega));
  }
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    final int nData = xValues.length;

    if (nData == 2) {
      final double[][] res = new double[][] {{
          yValues[1] / intervals[0] - yValues[0] / intervals[0] - intervals[0] * solnVector[0] / 2. - intervals[0] * solnVector[1] / 6. + intervals[0] * solnVector[0] / 6., yValues[0] } };
      return new DoubleMatrix2D(res);
    }
    if (nData == 3) {
      final double[][] res = new double[][] {{solnVector[0] / 2., yValues[1] / intervals[0] - yValues[0] / intervals[0] - intervals[0] * solnVector[0] / 2., yValues[0] } };
      return new DoubleMatrix2D(res);
    }
    return getCommonSplineCoeffs(xValues, yValues, intervals, solnVector);
  }
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    final int nData = xValues.length;

    if (nData == 2) {
      final DoubleMatrix2D[] res = new DoubleMatrix2D[nData];
      final double[][] coef = new double[][] {{yValues[1] / intervals[0] - yValues[0] / intervals[0], yValues[0] } };
      res[0] = new DoubleMatrix2D(coef);
      final double[][] coefSense = new double[2][];
      coefSense[0] = new double[] {-1. / intervals[0], 1. / intervals[0] };
      coefSense[1] = new double[] {1., 0. };
      res[1] = new DoubleMatrix2D(coefSense);
      return res;
    }
    if (nData == 3) {
      final DoubleMatrix2D[] res = new DoubleMatrix2D[2];
      final DoubleMatrix1D[] soln = combinedMatrixEqnSolver(toBeInv, vector, vecSensitivity);
      final double[][] coef = new double[][] {{soln[0].getData()[0] / 2.,
          yValues[1] / intervals[0] - yValues[0] / intervals[0] - intervals[0] * soln[0].getData()[0] / 2., yValues[0] } };
      res[0] = new DoubleMatrix2D(coef);
      final double[][] coefSense = new double[3][0];
      coefSense[0] = new double[] {soln[1].getData()[0] / 2., soln[2].getData()[0] / 2., soln[3].getData()[0] / 2. };
      coefSense[1] = new double[]
      {-1. / intervals[0] - intervals[0] * soln[1].getData()[0] / 2., 1. / intervals[0] - intervals[0] * soln[2].getData()[0] / 2., -intervals[0] * soln[3].getData()[0] / 2. };
      coefSense[2] = new double[] {1., 0., 0. };
      res[1] = new DoubleMatrix2D(coefSense);
      return res;
    }
    final DoubleMatrix2D[] res = new DoubleMatrix2D[nData];
    final DoubleMatrix1D[] soln = combinedMatrixEqnSolver(toBeInv, vector, vecSensitivity);
    res[0] = getCommonSplineCoeffs(xValues, yValues, intervals, soln[0].getData());
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