/// <summary>
/// Left extrapolation by linear function unless extra node is added on the left
/// </summary>
private PiecewisePolynomialResult extrapolateByLinearFunction(PiecewisePolynomialResult result, double[] xValues)
{
int nIntervalsAll = result.NumberOfIntervals;
double[] nodes = result.Knots.toArray();
if (Math.Abs(xValues[xValues.Length - 1] - nodes[nIntervalsAll]) < EPS)
{
double lastNodeX = nodes[nIntervalsAll];
double lastNodeY = FUNC.evaluate(result, lastNodeX).get(0);
double extraNode = 2.0 * nodes[nIntervalsAll] - nodes[nIntervalsAll - 1];
double extraDerivative = FUNC.differentiate(result, lastNodeX).get(0);
double[] newKnots = new double[nIntervalsAll + 2];
Array.Copy(nodes, 0, newKnots, 0, nIntervalsAll + 1);
newKnots[nIntervalsAll + 1] = extraNode; // dummy node, outside the data range
double[][] newCoefMatrix = new double[nIntervalsAll + 1][];
for (int i = 0; i < nIntervalsAll; ++i)
{
newCoefMatrix[i] = Arrays.copyOf(result.CoefMatrix.row(i).toArray(), result.Order);
}
newCoefMatrix[nIntervalsAll] = new double[result.Order];
newCoefMatrix[nIntervalsAll][result.Order - 1] = lastNodeY;
newCoefMatrix[nIntervalsAll][result.Order - 2] = extraDerivative;
if (result is PiecewisePolynomialResultsWithSensitivity)
{
PiecewisePolynomialResultsWithSensitivity resultCast = (PiecewisePolynomialResultsWithSensitivity)result;
double[] extraSense = FUNC.nodeSensitivity(resultCast, lastNodeX).toArray();
double[] extraSenseDer = FUNC.differentiateNodeSensitivity(resultCast, lastNodeX).toArray();
DoubleMatrix[] newCoefSense = new DoubleMatrix[nIntervalsAll + 1];
for (int i = 0; i < nIntervalsAll; ++i)
{
newCoefSense[i] = resultCast.getCoefficientSensitivity(i);
}
//JAVA TO C# CONVERTER NOTE: The following call to the 'RectangularArrays' helper class reproduces the rectangular array initialization that is automatic in Java:
//ORIGINAL LINE: double[][] extraCoefSense = new double[resultCast.Order][extraSense.Length];
double[][] extraCoefSense = RectangularArrays.ReturnRectangularDoubleArray(resultCast.Order, extraSense.Length);
extraCoefSense[resultCast.Order - 1] = Arrays.copyOf(extraSense, extraSense.Length);
extraCoefSense[resultCast.Order - 2] = Arrays.copyOf(extraSenseDer, extraSenseDer.Length);
newCoefSense[nIntervalsAll] = DoubleMatrix.copyOf(extraCoefSense);
return(new PiecewisePolynomialResultsWithSensitivity(DoubleArray.copyOf(newKnots), DoubleMatrix.copyOf(newCoefMatrix), resultCast.Order, 1, newCoefSense));
}
return(new PiecewisePolynomialResult(DoubleArray.copyOf(newKnots), DoubleMatrix.copyOf(newCoefMatrix), result.Order, 1));
}
return(result);
}
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: @Override public PiecewisePolynomialResultsWithSensitivity interpolateWithSensitivity(final double[] xValues, final double[] yValues)
public override PiecewisePolynomialResultsWithSensitivity interpolateWithSensitivity(double[] xValues, double[] yValues)
{
ArgChecker.notNull(xValues, "xValues");
ArgChecker.notNull(yValues, "yValues");
ArgChecker.isTrue(xValues.Length == yValues.Length | xValues.Length + 2 == yValues.Length, "(xValues length = yValues length) or (xValues length + 2 = yValues length)");
ArgChecker.isTrue(xValues.Length > 2, "Data points should be more than 2");
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int nDataPts = xValues.length;
int nDataPts = xValues.Length;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int yValuesLen = yValues.length;
int yValuesLen = yValues.Length;
for (int i = 0; i < nDataPts; ++i)
{
ArgChecker.isFalse(double.IsNaN(xValues[i]), "xValues containing NaN");
ArgChecker.isFalse(double.IsInfinity(xValues[i]), "xValues containing Infinity");
}
for (int i = 0; i < yValuesLen; ++i)
{
ArgChecker.isFalse(double.IsNaN(yValues[i]), "yValues containing NaN");
ArgChecker.isFalse(double.IsInfinity(yValues[i]), "yValues containing Infinity");
}
for (int i = 0; i < nDataPts - 1; ++i)
{
for (int j = i + 1; j < nDataPts; ++j)
{
ArgChecker.isFalse(xValues[i] == xValues[j], "xValues should be distinct");
}
}
double[] yValuesSrt = new double[nDataPts];
if (nDataPts == yValuesLen)
{
yValuesSrt = Arrays.copyOf(yValues, nDataPts);
}
else
{
yValuesSrt = Arrays.copyOfRange(yValues, 1, nDataPts + 1);
}
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] intervals = _solver.intervalsCalculator(xValues);
double[] intervals = _solver.intervalsCalculator(xValues);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] slopes = _solver.slopesCalculator(yValuesSrt, intervals);
double[] slopes = _solver.slopesCalculator(yValuesSrt, intervals);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final PiecewisePolynomialResultsWithSensitivity resultWithSensitivity = _method.interpolateWithSensitivity(xValues, yValues);
PiecewisePolynomialResultsWithSensitivity resultWithSensitivity = _method.interpolateWithSensitivity(xValues, yValues);
ArgChecker.isTrue(resultWithSensitivity.Order == 4, "Primary interpolant is not cubic");
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[] initialFirst = _function.differentiate(resultWithSensitivity, xValues).rowArray(0);
double[] initialFirst = _function.differentiate(resultWithSensitivity, xValues).rowArray(0);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final double[][] slopeSensitivity = _solver.slopeSensitivityCalculator(intervals);
double[][] slopeSensitivity = _solver.slopeSensitivityCalculator(intervals);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final com.opengamma.strata.collect.array.DoubleArray[] initialFirstSense = _function.differentiateNodeSensitivity(resultWithSensitivity, xValues);
DoubleArray[] initialFirstSense = _function.differentiateNodeSensitivity(resultWithSensitivity, xValues);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final com.opengamma.strata.collect.array.DoubleArray[] firstWithSensitivity = firstDerivativeWithSensitivityCalculator(yValuesSrt, intervals, initialFirst, initialFirstSense);
DoubleArray[] firstWithSensitivity = firstDerivativeWithSensitivityCalculator(yValuesSrt, intervals, initialFirst, initialFirstSense);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final com.opengamma.strata.collect.array.DoubleMatrix[] resMatrix = _solver.solveWithSensitivity(yValuesSrt, intervals, slopes, slopeSensitivity, firstWithSensitivity);
DoubleMatrix[] resMatrix = _solver.solveWithSensitivity(yValuesSrt, intervals, slopes, slopeSensitivity, firstWithSensitivity);
for (int k = 0; k < nDataPts; k++)
{
DoubleMatrix m = resMatrix[k];
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int rows = m.rowCount();
int rows = m.rowCount();
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int cols = m.columnCount();
int cols = m.columnCount();
for (int i = 0; i < rows; ++i)
{
for (int j = 0; j < cols; ++j)
{
ArgChecker.isTrue(Doubles.isFinite(m.get(i, j)), "Matrix contains a NaN or infinite");
}
}
}
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final com.opengamma.strata.collect.array.DoubleMatrix coefMatrix = resMatrix[0];
DoubleMatrix coefMatrix = resMatrix[0];
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final com.opengamma.strata.collect.array.DoubleMatrix[] coefSenseMatrix = new com.opengamma.strata.collect.array.DoubleMatrix[nDataPts - 1];
DoubleMatrix[] coefSenseMatrix = new DoubleMatrix[nDataPts - 1];
Array.Copy(resMatrix, 1, coefSenseMatrix, 0, nDataPts - 1);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final int nCoefs = coefMatrix.columnCount();
int nCoefs = coefMatrix.columnCount();
return(new PiecewisePolynomialResultsWithSensitivity(DoubleArray.copyOf(xValues), coefMatrix, nCoefs, 1, coefSenseMatrix));
}