/// <summary>
/// Assume a tensor has been flattened to a vector as {A_{0,0}, A_{0,1},...._A_{0,m}, A_{1,0}, A_{1,1},...._A_{1,m},...,A_{n,0}, A_{n,1},...._A_{n,m}}
/// (see <seealso cref="#flattenMatrix"/>) that is, the <b>last</b> index changes most rapidly. This produces the sum of penalty matrices (or order given by k) with each scaled
/// by lambda. </summary>
/// <param name="numElements"> The range of each index. In the example above, this would be {n,m} </param>
/// <param name="k"> The difference order for each dimension </param>
/// <param name="lambda"> The scaling for each dimension </param>
/// <returns> A penalty matrix </returns>
public static DoubleMatrix getPenaltyMatrix(int[] numElements, int[] k, double[] lambda)
{
ArgChecker.notEmpty(numElements, "size");
ArgChecker.notEmpty(k, "k");
ArgChecker.notEmpty(lambda, "lambda");
int dim = numElements.Length;
ArgChecker.isTrue(dim == k.Length, "k different length to size");
ArgChecker.isTrue(dim == lambda.Length, "lambda different lenght to size");
DoubleMatrix p = (DoubleMatrix)MA.scale(getPenaltyMatrix(numElements, k[0], 0), lambda[0]);
for (int i = 1; i < dim; i++)
{
DoubleMatrix temp = (DoubleMatrix)MA.scale(getPenaltyMatrix(numElements, k[i], i), lambda[i]);
p = (DoubleMatrix)MA.add(p, temp);
}
return(p);
}
// jacobian indirect, merging groups
private static DoubleMatrix jacobianIndirect(DoubleMatrix res, DoubleMatrix pDmCurrentMatrix, int nbTrades, int totalParamsGroup, int totalParamsPrevious, ImmutableList <CurveParameterSize> orderPrevious, ImmutableMap <CurveName, JacobianCalibrationMatrix> jacobiansPrevious)
{
if (totalParamsPrevious == 0)
{
return(DoubleMatrix.EMPTY);
}
//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[][] nonDirect = new double[totalParamsGroup][totalParamsPrevious];
double[][] nonDirect = RectangularArrays.ReturnRectangularDoubleArray(totalParamsGroup, totalParamsPrevious);
for (int i = 0; i < nbTrades; i++)
{
Array.Copy(res.rowArray(i), 0, nonDirect[i], 0, totalParamsPrevious);
}
DoubleMatrix pDpPreviousMatrix = (DoubleMatrix)MATRIX_ALGEBRA.scale(MATRIX_ALGEBRA.multiply(pDmCurrentMatrix, DoubleMatrix.copyOf(nonDirect)), -1d);
// all curves: order and size
int[] startIndexBefore = new int[orderPrevious.size()];
for (int i = 1; i < orderPrevious.size(); i++)
{
startIndexBefore[i] = startIndexBefore[i - 1] + orderPrevious.get(i - 1).ParameterCount;
}
// transition Matrix: all curves from previous groups
//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[][] transition = new double[totalParamsPrevious][totalParamsPrevious];
double[][] transition = RectangularArrays.ReturnRectangularDoubleArray(totalParamsPrevious, totalParamsPrevious);
for (int i = 0; i < orderPrevious.size(); i++)
{
int paramCountOuter = orderPrevious.get(i).ParameterCount;
JacobianCalibrationMatrix thisInfo = jacobiansPrevious.get(orderPrevious.get(i).Name);
DoubleMatrix thisMatrix = thisInfo.JacobianMatrix;
int startIndexInner = 0;
for (int j = 0; j < orderPrevious.size(); j++)
{
int paramCountInner = orderPrevious.get(j).ParameterCount;
if (thisInfo.containsCurve(orderPrevious.get(j).Name))
{ // If not, the matrix stay with 0
for (int k = 0; k < paramCountOuter; k++)
{
Array.Copy(thisMatrix.rowArray(k), startIndexInner, transition[startIndexBefore[i] + k], startIndexBefore[j], paramCountInner);
}
}
startIndexInner += paramCountInner;
}
}
DoubleMatrix transitionMatrix = DoubleMatrix.copyOf(transition);
return((DoubleMatrix)MATRIX_ALGEBRA.multiply(pDpPreviousMatrix, transitionMatrix));
}
public virtual void linearTest()
{
bool print = false;
if (print)
{
Console.WriteLine("NonLinearLeastSquareWithPenaltyTest.linearTest");
}
int nWeights = 20;
int diffOrder = 2;
double lambda = 100.0;
DoubleMatrix penalty = (DoubleMatrix)MA.scale(getPenaltyMatrix(nWeights, diffOrder), lambda);
int[] onIndex = new int[] { 1, 4, 11, 12, 15, 17 };
double[] obs = new double[] { 0, 1.0, 1.0, 1.0, 0.0, 0.0 };
int n = onIndex.Length;
System.Func <DoubleArray, DoubleArray> func = (DoubleArray x) =>
{
return(DoubleArray.of(n, i => x.get(onIndex[i])));
};
System.Func <DoubleArray, DoubleMatrix> jac = (DoubleArray x) =>
{
return(DoubleMatrix.of(n, nWeights, (i, j) => j == onIndex[i] ? 1d : 0d));
};
Well44497b random = new Well44497b(0L);
DoubleArray start = DoubleArray.of(nWeights, i => random.NextDouble());
LeastSquareWithPenaltyResults lsRes = NLLSWP.solve(DoubleArray.copyOf(obs), DoubleArray.filled(n, 0.01), func, jac, start, penalty);
if (print)
{
Console.WriteLine("chi2: " + lsRes.ChiSq);
Console.WriteLine(lsRes.FitParameters);
}
for (int i = 0; i < n; i++)
{
assertEquals(obs[i], lsRes.FitParameters.get(onIndex[i]), 0.01);
}
double expPen = 20.87912357454752;
assertEquals(expPen, lsRes.Penalty, 1e-9);
}
private DoubleArray[] fdSenseCal(double[] xValues, double[] yValues, double[] xx)
{
int nData = yValues.Length;
double eps = 1e-6;
double scale = 0.5 / eps;
DoubleArray[] res = new DoubleArray[nData];
double[] temp = new double[nData];
PiecewisePolynomialResult pp;
for (int i = 0; i < nData; i++)
{
Array.Copy(yValues, 0, temp, 0, nData);
temp[i] += eps;
pp = PCHIP.interpolate(xValues, temp);
DoubleArray yUp = PPVAL.evaluate(pp, xx).row(0);
temp[i] -= 2 * eps;
pp = PCHIP.interpolate(xValues, temp);
DoubleArray yDown = PPVAL.evaluate(pp, xx).row(0);
res[i] = (DoubleArray)MA.scale(MA.subtract(yUp, yDown), scale);
}
return(res);
}
private GeneralizedLeastSquareResults <T> solveImp <T>(IList <T> x, IList <double> y, IList <double> sigma, IList <System.Func <T, double> > basisFunctions, double lambda, int differenceOrder)
{
int n = x.Count;
int m = basisFunctions.Count;
double[] b = new double[m];
double[] invSigmaSqr = new double[n];
//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[][] f = new double[m][n];
double[][] f = RectangularArrays.ReturnRectangularDoubleArray(m, n);
int i, j, k;
for (i = 0; i < n; i++)
{
double temp = sigma[i];
ArgChecker.isTrue(temp > 0, "sigma must be greater than zero");
invSigmaSqr[i] = 1.0 / temp / temp;
}
for (i = 0; i < m; i++)
{
for (j = 0; j < n; j++)
{
f[i][j] = basisFunctions[i](x[j]);
}
}
double sum;
for (i = 0; i < m; i++)
{
sum = 0;
for (k = 0; k < n; k++)
{
sum += y[k] * f[i][k] * invSigmaSqr[k];
}
b[i] = sum;
}
DoubleArray mb = DoubleArray.copyOf(b);
DoubleMatrix ma = getAMatrix(f, invSigmaSqr);
if (lambda > 0.0)
{
DoubleMatrix d = getDiffMatrix(m, differenceOrder);
ma = (DoubleMatrix)_algebra.add(ma, _algebra.scale(d, lambda));
}
DecompositionResult decmp = _decomposition.apply(ma);
DoubleArray w = decmp.solve(mb);
DoubleMatrix covar = decmp.solve(DoubleMatrix.identity(m));
double chiSq = 0;
for (i = 0; i < n; i++)
{
double temp = 0;
for (k = 0; k < m; k++)
{
temp += w.get(k) * f[k][i];
}
chiSq += FunctionUtils.square(y[i] - temp) * invSigmaSqr[i];
}
return(new GeneralizedLeastSquareResults <T>(basisFunctions, chiSq, w, covar));
}
