private static void sgmga_aniso_normalize_test(int dim_num, double[] level_weight)
//****************************************************************************80
//
// Purpose:
//
// SGMGA_ANISO_NORMALIZE_TEST calls SGMGA_ANISO_NORMALIZE.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 27 November 2009
//
// Author:
//
// John Burkardt
//
{
int dim;
int option;
Console.WriteLine("");
Console.WriteLine("SGMGA_ANISO_NORMALIZE_TEST");
Console.WriteLine(" Input weight sum: "
+ typeMethods.r8vec_sum(dim_num, level_weight) + "");
string cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_weight[dim].ToString(CultureInfo.InvariantCulture).PadLeft(12);
}
Console.WriteLine(cout);
for (option = 0; option <= 2; option++)
{
SGMGAniso.sgmga_aniso_normalize(option, dim_num, ref level_weight);
Console.WriteLine(" For OPTION = " + option
+ " Normalized weight sum: "
+ typeMethods.r8vec_sum(dim_num, level_weight) + "");
cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_weight[dim].ToString(CultureInfo.InvariantCulture).PadLeft(12);
}
Console.WriteLine(cout);
}
}
private static void sgmga_aniso_balance_test(double alpha_max, int dim_num,
double[] level_weight)
//****************************************************************************80
//
// Purpose:
//
// SGMGA_ANISO_BALANCE_TEST calls SGMGA_ANISO_BALANCE.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 03 February 2010
//
// Author:
//
// John Burkardt
//
{
int dim;
Console.WriteLine("");
Console.WriteLine("SGMGA_ANISO_BALANCE_TEST");
Console.WriteLine(" ALPHA_MAX = " + alpha_max + "");
Console.WriteLine(" Input weight sum: "
+ typeMethods.r8vec_sum(dim_num, level_weight) + "");
string cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_weight[dim].ToString(CultureInfo.InvariantCulture).PadLeft(12);
}
Console.WriteLine(cout);
double[] level_weight2 = SGMGAniso.sgmga_aniso_balance(alpha_max, dim_num, level_weight);
Console.WriteLine(" Output weight sum: "
+ typeMethods.r8vec_sum(dim_num, level_weight2) + "");
cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_weight2[dim].ToString(CultureInfo.InvariantCulture).PadLeft(12);
}
Console.WriteLine(cout);
}
private static void sgmga_importance_to_aniso_test(int dim_num, double[] importance,
double[] level_weight)
//****************************************************************************80
//
// Purpose:
//
// SGMGA_IMPORTANCE_TO_ANISO_TEST calls SGMGA_IMPORTANCE_TO_ANISO.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 27 November 2009
//
// Author:
//
// John Burkardt
//
{
int dim;
Console.WriteLine("");
Console.WriteLine("SGMGA_IMPORTANCE_TO_ANISO_TEST");
Console.WriteLine(" Importances:");
string cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + importance[dim].ToString(CultureInfo.InvariantCulture).PadLeft(12);
}
Console.WriteLine(cout);
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
Console.WriteLine(" Anisotropic coefficients:");
cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_weight[dim].ToString(CultureInfo.InvariantCulture).PadLeft(12);
}
Console.WriteLine(cout);
}
private static void sgmga_unique_index_tests()
//****************************************************************************80
//
// Purpose:
//
// SGMGA_UNIQUE_INDEX_TESTS calls SGMGA_UNIQUE_INDEX_TEST.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 20 June 2010
//
// Author:
//
// John Burkardt
//
// Local Parameters:
//
// Local, double TOL, a tolerance for point equality.
// A value of sqrt ( eps ) is reasonable, and will allow the code to
// consolidate points which are equal, or very nearly so. A value of
// -1.0, on the other hand, will force the code to use every point,
// regardless of duplication.
//
{
int dim;
Console.WriteLine("");
Console.WriteLine("SGMGA_UNIQUE_INDEX_TESTS");
Console.WriteLine(" Call SGMGA_UNIQUE_INDEX_TEST with various arguments");
//
// Set the point equality tolerance.
//
double tol = Math.Sqrt(typeMethods.r8_epsilon());
Console.WriteLine("");
Console.WriteLine(" All tests will use a point equality tolerance of " + tol + "");
int dim_num = 2;
double[] importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
double[] level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
int level_max_min = 0;
int level_max_max = 2;
int[] np = new int[dim_num];
np[0] = 0;
np[1] = 0;
int np_sum = typeMethods.i4vec_sum(dim_num, np);
double[] p = new double[np_sum];
int[] rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
int[] growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
Func <int, int, double[], double[], double[]>[] gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 3;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np[2] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
rule[2] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
growth[2] = 6;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 3;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np[2] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
rule[2] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
growth[2] = 6;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 3;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = PattersonQuadrature.patterson_lookup_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 4;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 7;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = Burkardt.Laguerre.QuadratureRule.laguerre_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 1;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
p[0] = 1.5;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 8;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = Burkardt.Laguerre.QuadratureRule.gen_laguerre_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 2;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
p[0] = 0.5;
p[1] = 1.5;
rule = new int[dim_num];
rule[0] = 2;
rule[1] = 9;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = Fejer2.fejer2_compute_points_np;
gw_compute_points[1] = JacobiQuadrature.jacobi_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 1;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
p[0] = 2.0;
rule = new int[dim_num];
rule[0] = 6;
rule[1] = 10;
growth = new int[dim_num];
growth[0] = 3;
growth[1] = 4;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = HermiteQuadrature.gen_hermite_compute_points_np;
gw_compute_points[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
dim_num = 3;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np[2] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 4;
rule[2] = 5;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
growth[2] = 3;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_points_np;
gw_compute_points[2] = HermiteQuadrature.hermite_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
//
// Repeat, treating rules #2 and #3 as Golub Welsch rules.
//
dim_num = 3;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 2;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np[2] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 11;
rule[2] = 11;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
growth[2] = 3;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_points_np;
gw_compute_points[2] = HermiteQuadrature.hermite_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
//
// Try a case which includes a dimension of "0 importance".
//
dim_num = 3;
importance = new double[dim_num];
importance[0] = 1.0;
importance[1] = 0.0;
importance[2] = 1.0;
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 3;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np[2] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
rule[2] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
growth[2] = 6;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[2] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
sgmga_unique_index_test(dim_num, importance, level_weight, level_max_min,
level_max_max, rule, growth, np, p, gw_compute_points, tol);
}
private static void sgmga_unique_index_test(int dim_num, double[] importance,
double[] level_weight, int level_max_min, int level_max_max, int[] rule,
int[] growth, int[] np, double[] p,
Func <int, int, double[], double[], double[]>[] gw_compute_points,
double tol)
//***************************************************************************80
//
// Purpose:
//
// SGMGA_UNIQUE_INDEX_TEST tests SGMGA_UNIQUE_INDEX.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 09 June 2010
//
// Author:
//
// John Burkardt
//
// Parameters:
//
// Input, int DIM_NUM, the spatial dimension.
//
// Input, double IMPORTANCE[DIM_NUM], the importance for each dimension.
//
// Input, double LEVEL_WEIGHT[DIM_NUM], the weights for each dimension.
//
// Input, int LEVEL_MAX_MIN, LEVEL_MAX_MAX, the minimum and
// maximum values of LEVEL_MAX.
//
// Input, int RULE[DIM_NUM], the rule in each dimension.
// 1, "CC", Clenshaw Curtis, Closed Fully Nested.
// 2, "F2", Fejer Type 2, Open Fully Nested.
// 3, "GP", Gauss Patterson, Open Fully Nested.
// 4, "GL", Gauss Legendre, Open Weakly Nested.
// 5, "GH", Gauss Hermite, Open Weakly Nested.
// 6, "GGH", Generalized Gauss Hermite, Open Weakly Nested.
// 7, "LG", Gauss Laguerre, Open Non Nested.
// 8, "GLG", Generalized Gauss Laguerre, Open Non Nested.
// 9, "GJ", Gauss Jacobi, Open Non Nested.
// 10, "HGK", Hermite Genz-Keister, Open Fully Nested.
// 11, "UO", User supplied Open, presumably Non Nested.
// 12, "UC", User supplied Closed, presumably Non Nested.
//
// Input, int GROWTH[DIM_NUM], the desired growth in each dimension.
// 0, "DF", default growth associated with this quadrature rule;
// 1, "SL", slow linear, L+1;
// 2 "SO", slow linear odd, O=1+2((L+1)/2)
// 3, "ML", moderate linear, 2L+1;
// 4, "SE", slow exponential;
// 5, "ME", moderate exponential;
// 6, "FE", full exponential.
//
// Input, int NP[RULE_NUM], the number of parameters used by each rule.
//
// Input, double P[sum(NP[*])], the parameters needed by each rule.
//
// Input, void ( *GW_COMPUTE_POINTS[] ) ( int order, int np, double p[], double x[] ),
// an array of pointers to functions which return the 1D quadrature points
// associated with each spatial dimension for which a Golub Welsch rule
// is used.
//
// Input, double TOL, a tolerance for point equality.
//
{
int dim;
int level_max;
Console.WriteLine("");
Console.WriteLine("SGMGA_UNIQUE_INDEX_TEST");
Console.WriteLine(" SGMGA_UNIQUE_INDEX returns a mapping between");
Console.WriteLine(" the nonunique and unique points in a sparse grid.");
Console.WriteLine("");
string cout = " IMPORTANCE: ";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + importance[dim].ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
cout = " LEVEL_WEIGHT:";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_weight[dim].ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
Console.WriteLine("");
Console.WriteLine(" Dimension Rule Growth rate Parameters");
Console.WriteLine("");
int p_index = 0;
for (dim = 0; dim < dim_num; dim++)
{
double alpha;
int i;
switch (rule[dim])
{
case 1:
case 2:
case 3:
case 4:
case 5:
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8) + "");
break;
case 6:
alpha = p[p_index];
p_index += 1;
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8)
+ " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
break;
case 7:
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8) + "");
break;
case 8:
alpha = p[p_index];
p_index += 1;
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8)
+ " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
break;
case 9:
alpha = p[p_index];
p_index += 1;
double beta = p[p_index];
p_index += 1;
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8)
+ " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " " + beta.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
break;
case 10:
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8) + "");
break;
case 11:
{
cout = " " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8);
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
cout += " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
break;
}
case 12:
{
cout = " " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8);
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
cout += " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
break;
}
default:
Console.WriteLine("");
Console.WriteLine("SGMGA_UNIQUE_INDEX_TEST - Fatal error!");
Console.WriteLine(" Unexpected value of RULE = " + rule[dim] + "");
return;
}
}
for (level_max = level_max_min; level_max <= level_max_max; level_max++)
{
int point_total_num = SGMGAniso.sgmga_size_total(dim_num, level_weight,
level_max, rule, growth);
int point_num = SGMGAniso.sgmga_size(dim_num, level_weight, level_max,
rule, np, p, gw_compute_points, tol, growth);
Console.WriteLine("");
Console.WriteLine(" LEVEL_MAX POINT_NUM POINT_NUM");
Console.WriteLine(" Unique Total");
Console.WriteLine("");
Console.WriteLine(" " + level_max.ToString().PadLeft(8)
+ " " + point_num.ToString().PadLeft(8)
+ " " + point_total_num.ToString().PadLeft(8) + "");
int[] sparse_unique_index = new int[point_total_num];
SGMGAniso.sgmga_unique_index(dim_num, level_weight, level_max, rule,
np, p, gw_compute_points, tol, point_num, point_total_num,
growth, ref sparse_unique_index);
Console.WriteLine("");
Console.WriteLine(" POINT UNIQUE");
Console.WriteLine("");
int point;
for (point = 0; point < point_total_num; point++)
{
Console.WriteLine(" " + point.ToString().PadLeft(8)
+ " " + sparse_unique_index[point].ToString().PadLeft(8) + "");
}
}
}
private static void sgmga_vcn_coef_tests()
//****************************************************************************80
//
// Purpose:
//
// SGMGA_VCN_COEF_TESTS calls SGMGA_VCN_COEF_TEST.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 27 November 2009
//
// Author:
//
// John Burkardt
//
{
int dim;
Console.WriteLine("");
Console.WriteLine("SGMGA_VCN_COEF_TESTS");
Console.WriteLine(" calls SGMGA_VCN_COEF_TEST.");
int dim_num = 2;
double[] importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
double[] level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
int level_max_min = 0;
int level_max_max = 4;
sgmga_vcn_coef_test(dim_num, importance, level_weight, level_max_min,
level_max_max);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 4;
sgmga_vcn_coef_test(dim_num, importance, level_weight, level_max_min,
level_max_max);
dim_num = 3;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 4;
sgmga_vcn_coef_test(dim_num, importance, level_weight, level_max_min,
level_max_max);
dim_num = 3;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 4;
sgmga_vcn_coef_test(dim_num, importance, level_weight, level_max_min,
level_max_max);
dim_num = 4;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 3;
sgmga_vcn_coef_test(dim_num, importance, level_weight, level_max_min,
level_max_max);
//
// Try a case with a dimension of "0 importance".
//
dim_num = 3;
importance = new double[dim_num];
importance[0] = 1.0;
importance[1] = 0.0;
importance[2] = 1.0;
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max_min = 0;
level_max_max = 3;
sgmga_vcn_coef_test(dim_num, importance, level_weight, level_max_min,
level_max_max);
}
private static void sgmga_vcn_coef_test(int dim_num, double[] importance,
double[] level_weight, int level_max_min, int level_max_max)
//****************************************************************************80
//
// Purpose:
//
// SGMGA_VCN_COEF_TEST tests SGMGA_VCN_COEF.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 18 May 2010
//
// Author:
//
// John Burkardt
//
{
int dim;
int level_max;
SGMGAniso.SGMGAData data = new();
string cout = "";
int[] level_1d = new int[dim_num];
int[] level_1d_max = new int[dim_num];
int[] level_1d_min = new int[dim_num];
Console.WriteLine("");
Console.WriteLine("SGMGA_VCN_COEF_TEST");
Console.WriteLine(" For anisotropic problems, a \"combinatorial coefficent\"");
Console.WriteLine(" must be computed for each component product grid.");
Console.WriteLine(" SGMGA_VCN_COEF_NAIVE does this in a simple, inefficient way.");
Console.WriteLine(" SGMGA_VCN_COEF tries to be more efficient.");
Console.WriteLine(" Here, we simply compare COEF1 and COEF2, the same");
Console.WriteLine(" coefficient computed by the naive and efficient ways.");
Console.WriteLine("");
Console.WriteLine(" IMPORTANCE:");
cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + importance[dim].ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
Console.WriteLine(" LEVEL_WEIGHT:");
cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_weight[dim].ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
for (level_max = level_max_min; level_max <= level_max_max; level_max++)
{
int i = 0;
double coef1_sum = 0.0;
double coef2_sum = 0.0;
//
// Initialization.
//
double level_weight_min_pos = typeMethods.r8vec_min_pos(dim_num, level_weight);
double q_min = level_max * level_weight_min_pos
- typeMethods.r8vec_sum(dim_num, level_weight);
double q_max = level_max * level_weight_min_pos;
for (dim = 0; dim < dim_num; dim++)
{
level_1d_min[dim] = 0;
}
for (dim = 0; dim < dim_num; dim++)
{
switch (level_weight[dim])
{
case > 0.0:
{
level_1d_max[dim] = (int)Math.Floor(q_max / level_weight[dim]) + 1;
if (q_max <= (level_1d_max[dim] - 1) * level_weight[dim])
{
level_1d_max[dim] -= 1;
}
break;
}
default:
level_1d_max[dim] = 0;
break;
}
}
bool more_grids = false;
Console.WriteLine("");
Console.WriteLine(" I Q Coef1 Coef2 X");
cout = " MIN" + " " + q_min.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " ";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_1d_min[dim].ToString().PadLeft(2);
}
Console.WriteLine(cout);
//
// Seek all vectors LEVEL_1D which satisfy the constraint:
//
// LEVEL_MAX * LEVEL_WEIGHT_MIN_POS - sum ( LEVEL_WEIGHT )
// < sum ( 0 <= I < DIM_NUM ) LEVEL_WEIGHT[I] * LEVEL_1D[I]
// <= LEVEL_MAX * LEVEL_WEIGHT_MIN_POS.
//
for (;;)
{
SGMGAniso.sgmga_vcn_ordered_naive(ref data, dim_num, level_weight, level_1d_max,
level_1d, q_min, q_max, ref more_grids);
if (!more_grids)
{
break;
}
//
// Compute the combinatorial coefficient.
//
double coef1 = SGMGAniso.sgmga_vcn_coef_naive(dim_num, level_weight, level_1d_max,
level_1d, q_min, q_max);
double coef2 = SGMGAniso.sgmga_vcn_coef(dim_num, level_weight, level_1d, q_max);
i += 1;
double q = 0.0;
for (dim = 0; dim < dim_num; dim++)
{
q += level_weight[dim] * level_1d[dim];
}
coef1_sum += coef1;
coef2_sum += coef2;
cout = " " + i.ToString().PadLeft(4)
+ " " + q.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " " + coef1.ToString(CultureInfo.InvariantCulture).PadLeft(10)
+ " " + coef2.ToString(CultureInfo.InvariantCulture).PadLeft(10);
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_1d[dim].ToString().PadLeft(2);
}
Console.WriteLine(cout);
}
cout = " MAX" + " " + q_max.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " ";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_1d_max[dim].ToString().PadLeft(2);
}
Console.WriteLine(cout);
Console.WriteLine(" SUM "
+ " " + coef1_sum.ToString(CultureInfo.InvariantCulture).PadLeft(10)
+ " " + coef2_sum.ToString(CultureInfo.InvariantCulture).PadLeft(10) + "");
}
}
private static void sgmga_weight_test(int dim_num, double[] importance,
double[] level_weight, int level_max_min, int level_max_max, int[] rule,
int[] growth, int[] np, double[] p,
Func <int, int, double[], double[], double[]>[] gw_compute_points,
Func <int, int, double[], double[], double[]>[] gw_compute_weights,
double tol)
//***************************************************************************80
//
// Purpose:
//
// SGMGA_WEIGHT_TEST checks the sum of the quadrature weights.
//
// Discussion:
//
// If any component rule is of Golub-Welsch type, we cannot compute
// the exact weight sum, which we set, instead, to zero.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 09 June 2010
//
// Author:
//
// John Burkardt
//
// Parameters:
//
// Input, int DIM_NUM, the spatial dimension.
//
// Input, double IMPORTANCE[DIM_NUM], the anisotropic importance.
//
// Input, double LEVEL_WEIGHT[DIM_NUM], the anisotropic weights.
//
// Input, int LEVEL_MAX_MIN, LEVEL_MAX_MAX, the minimum and
// maximum values of LEVEL_MAX.
//
// Input, int RULE[DIM_NUM], the rule in each dimension.
// 1, "CC", Clenshaw Curtis, Closed Fully Nested.
// 2, "F2", Fejer Type 2, Open Fully Nested.
// 3, "GP", Gauss Patterson, Open Fully Nested.
// 4, "GL", Gauss Legendre, Open Weakly Nested.
// 5, "GH", Gauss Hermite, Open Weakly Nested.
// 6, "GGH", Generalized Gauss Hermite, Open Weakly Nested.
// 7, "LG", Gauss Laguerre, Open Non Nested.
// 8, "GLG", Generalized Gauss Laguerre, Open Non Nested.
// 9, "GJ", Gauss Jacobi, Open Non Nested.
// 10, "HGK", Hermite Genz-Keister, Open Fully Nested.
// 11, "UO", User supplied Open, presumably Non Nested.
// 12, "UC", User supplied Closed, presumably Non Nested.
//
// Input, int GROWTH[DIM_NUM], the desired growth in each dimension.
// 0, "DF", default growth associated with this quadrature rule;
// 1, "SL", slow linear, L+1;
// 2 "SO", slow linear odd, O=1+2((L+1)/2)
// 3, "ML", moderate linear, 2L+1;
// 4, "SE", slow exponential;
// 5, "ME", moderate exponential;
// 6, "FE", full exponential.
//
// Input, int NP[RULE_NUM], the number of parameters used by each rule.
//
// Input, double P[sum(NP[*])], the parameters needed by each rule.
//
// Input, void ( *GW_COMPUTE_POINTS[] ) ( int order, int np, double p[], double x[] ),
// an array of pointers to functions which return the 1D quadrature points
// associated with each spatial dimension for which a Golub Welsch rule
// is used.
//
// Input, void ( *GW_COMPUTE_WEIGHTS[] ) ( int order, int np, double p[], double w[] ),
// an array of pointers to functions which return the 1D quadrature weights
// associated with each spatial dimension for which a Golub Welsch rule
// is used.
//
// Input, double TOL, a tolerance for point equality.
//
{
double alpha;
double beta;
int dim;
int i;
int level_max;
string cout = "";
Console.WriteLine("");
Console.WriteLine("SGMGA_WEIGHT_TEST");
Console.WriteLine(" Compute the weights of a sparse grid.");
Console.WriteLine("");
Console.WriteLine(" Each sparse grid is of spatial dimension DIM_NUM,");
Console.WriteLine(" and is made up of product grids of levels up to LEVEL_MAX.");
Console.WriteLine("");
cout = " IMPORTANCE: ";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + importance[dim].ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
cout = " LEVEL_WEIGHT:";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_weight[dim].ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
Console.WriteLine("");
Console.WriteLine(" Dimension Rule Growth rate Parameters");
Console.WriteLine("");
int p_index = 0;
for (dim = 0; dim < dim_num; dim++)
{
switch (rule[dim])
{
case 1:
case 2:
case 3:
case 4:
case 5:
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8) + "");
break;
case 6:
alpha = p[p_index];
p_index += 1;
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8)
+ " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
break;
case 7:
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8) + "");
break;
case 8:
alpha = p[p_index];
p_index += 1;
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8)
+ " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
break;
case 9:
alpha = p[p_index];
p_index += 1;
beta = p[p_index];
p_index += 1;
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8)
+ " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " " + beta.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
break;
case 10:
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8) + "");
break;
case 11:
{
cout = " " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8);
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
cout += " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
break;
}
case 12:
{
cout = " " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + growth[dim].ToString().PadLeft(8);
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
cout += " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
break;
}
default:
Console.WriteLine("");
Console.WriteLine("SGMGA_WEIGHT_TEST - Fatal error!");
Console.WriteLine(" Unexpected value of RULE = " + rule[dim] + "");
return;
}
}
double weight_sum_exact = 1.0;
p_index = 0;
for (dim = 0; dim < dim_num; dim++)
{
switch (rule[dim])
{
case 1:
case 2:
case 3:
case 4:
weight_sum_exact *= 2.0;
break;
case 5:
weight_sum_exact *= Math.Sqrt(Math.PI);
break;
case 6:
alpha = p[p_index];
p_index += 1;
weight_sum_exact *= typeMethods.r8_gamma(0.5 * (alpha + 1.0));
break;
case 7:
weight_sum_exact *= 1.0;
break;
case 8:
alpha = p[p_index];
p_index += 1;
weight_sum_exact *= typeMethods.r8_gamma(alpha + 1.0);
break;
case 9:
alpha = p[p_index];
p_index += 1;
beta = p[p_index];
p_index += 1;
double arg1 = -alpha;
double arg2 = 1.0;
double arg3 = beta + 2.0;
double arg4 = -1.0;
double value1 = typeMethods.r8_hyper_2f1(arg1, arg2, arg3, arg4);
arg1 = -beta;
arg2 = 1.0;
arg3 = alpha + 2.0;
arg4 = -1.0;
double value2 = typeMethods.r8_hyper_2f1(arg1, arg2, arg3, arg4);
weight_sum_exact *= value1 / (beta + 1.0) + value2 / (alpha + 1.0);
break;
case 10:
weight_sum_exact *= Math.Sqrt(Math.PI);
break;
case 11:
{
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
}
weight_sum_exact = 0.0;
break;
}
case 12:
{
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
}
weight_sum_exact = 0.0;
break;
}
default:
Console.WriteLine("");
Console.WriteLine("SGMGA_WEIGHT_TEST - Fatal error!");
Console.WriteLine(" Unexpected value of RULE[" + dim + "] = "
+ rule[dim] + ".");
return;
}
}
switch (weight_sum_exact)
{
case 0.0:
Console.WriteLine("");
Console.WriteLine(" Because this rule includes Golub-Welsch components,");
Console.WriteLine(" we do not try to compute the exact weight sum.");
break;
default:
Console.WriteLine("");
Console.WriteLine(" As a simple test, sum these weights.");
Console.WriteLine(" They should sum to exactly " + weight_sum_exact + "");
break;
}
Console.WriteLine("");
Console.WriteLine(" Level Weight sum Expected sum Difference");
Console.WriteLine("");
for (level_max = level_max_min; level_max <= level_max_max; level_max++)
{
int point_total_num = SGMGAniso.sgmga_size_total(dim_num, level_weight,
level_max, rule, growth);
int point_num = SGMGAniso.sgmga_size(dim_num, level_weight, level_max,
rule, np, p, gw_compute_points, tol, growth);
int[] sparse_unique_index = new int[point_total_num];
SGMGAniso.sgmga_unique_index(dim_num, level_weight, level_max, rule,
np, p, gw_compute_points, tol, point_num, point_total_num,
growth, ref sparse_unique_index);
double[] sparse_weight = new double[point_num];
SGMGAniso.sgmga_weight(dim_num, level_weight, level_max, rule, np,
p, gw_compute_weights, point_num, point_total_num, sparse_unique_index,
growth, sparse_weight);
double weight_sum = typeMethods.r8vec_sum(point_num, sparse_weight);
double weight_sum_error = typeMethods.r8_abs(weight_sum - weight_sum_exact);
Console.WriteLine(" " + level_max.ToString().PadLeft(8)
+ " " + weight_sum.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " " + weight_sum_exact.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " " + weight_sum_error.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
}
}
private static void sgmga_write_tests()
//****************************************************************************80
//
// Purpose:
//
// SGMGA_WRITE_TESTS calls SGMGA_WRITE_TEST.
//
// Discussion:
//
// We can't test Golub-Welsch rules in this routine, because the program
// that writes out the files needs to know the integration region for each
// component, and we have not specified how that would be done with
// Golub Welsch rules.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 20 June 2010
//
// Author:
//
// John Burkardt
//
// Local Parameters:
//
// Local, double TOL, a tolerance for point equality.
// A value of sqrt ( eps ) is reasonable, and will allow the code to
// consolidate points which are equal, or very nearly so. A value of
// -1.0, on the other hand, will force the code to use every point,
// regardless of duplication.
//
{
int dim;
Console.WriteLine("");
Console.WriteLine("SGMGA_WRITE_TESTS");
Console.WriteLine(" Call SGMGA_WRITE_TEST with various arguments.");
//
// Set the point equality tolerance.
//
double tol = Math.Sqrt(typeMethods.r8_epsilon());
Console.WriteLine("");
Console.WriteLine(" All tests will use a point equality tolerance of " + tol + "");
int dim_num = 2;
double[] importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
double[] level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
int level_max = 2;
int[] rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
int[] growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
int[] np = new int[dim_num];
np[0] = 0;
np[1] = 0;
int np_sum = typeMethods.i4vec_sum(dim_num, np);
double[] p = new double[np_sum];
Func <int, int, double[], double[], double[]>[] gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
Func <int, int, double[], double[], double[]>[] gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
string file_name = "sgmga_d2_l2_ccxcc_iso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
file_name = "sgmga_d2_l2_ccxcc_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
dim_num = 3;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
rule[2] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
growth[2] = 6;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np[2] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[2] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[2] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
file_name = "sgmga_d3_l2_ccxccxcc_iso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
dim_num = 3;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
rule[2] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
growth[2] = 6;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np[2] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[2] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[2] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
file_name = "sgmga_d3_l2_ccxccxcc_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 3;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 3;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = PattersonQuadrature.patterson_lookup_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = PattersonQuadrature.patterson_lookup_weights_np;
file_name = "sgmga_d2_l3_ccxgp_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 4;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_weights_np;
file_name = "sgmga_d2_l2_ccxgl_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 7;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = Burkardt.Laguerre.QuadratureRule.laguerre_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = Burkardt.Laguerre.QuadratureRule.laguerre_compute_weights_np;
file_name = "sgmga_d2_l2_ccxlg_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 8;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
np = new int[dim_num];
np[0] = 1;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
p[0] = 1.5;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = Burkardt.Laguerre.QuadratureRule.gen_laguerre_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = Burkardt.Laguerre.QuadratureRule.gen_laguerre_compute_weights_np;
file_name = "sgmga_d2_l2_ccxglg_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
rule = new int[dim_num];
rule[0] = 2;
rule[1] = 9;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
np = new int[dim_num];
np[0] = 0;
np[1] = 2;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
p[0] = 0.5;
p[1] = 1.5;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = Fejer2.fejer2_compute_points_np;
gw_compute_points[1] = JacobiQuadrature.jacobi_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = Fejer2.fejer2_compute_weights_np;
gw_compute_weights[1] = JacobiQuadrature.jacobi_compute_weights_np;
file_name = "sgmga_d2_l2_f2xgj_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
rule = new int[dim_num];
rule[0] = 6;
rule[1] = 10;
growth = new int[dim_num];
growth[0] = 3;
growth[1] = 4;
np = new int[dim_num];
np[0] = 1;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
p[0] = 2.0;
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = HermiteQuadrature.gen_hermite_compute_points_np;
gw_compute_points[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = HermiteQuadrature.gen_hermite_compute_weights_np;
gw_compute_weights[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_weights_np;
file_name = "sgmga_d2_l2_gghxhgk_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
//
// LEVEL_MAX = 1
//
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 1;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
file_name = "sgmga_d2_l1_ccxcc_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
//
// LEVEL_MAX = 2 (already done)
//
// LEVEL_MAX = 3
//
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 3;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
file_name = "sgmga_d2_l3_ccxcc_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
//
// LEVEL_MAX = 4
//
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 4;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
file_name = "sgmga_d2_l4_ccxcc_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
//
// LEVEL_MAX = 5
//
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 5;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 1;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
file_name = "sgmga_d2_l5_ccxcc_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
//
// Dimension 3
//
dim_num = 3;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
rule = new int[dim_num];
rule[0] = 1;
rule[1] = 4;
rule[2] = 5;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 3;
growth[2] = 3;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np[2] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = ClenshawCurtis.clenshaw_curtis_compute_points_np;
gw_compute_points[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_points_np;
gw_compute_points[2] = HermiteQuadrature.hermite_compute_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = ClenshawCurtis.clenshaw_curtis_compute_weights_np;
gw_compute_weights[1] = Burkardt.Legendre.QuadratureRule.legendre_compute_weights_np;
gw_compute_weights[2] = HermiteQuadrature.hermite_compute_weights_np;
file_name = "sgmga_d3_l2_ccxglxgh_aniso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
//
// Rule 3, LEVEL_MAX = 4
//
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 4;
rule = new int[dim_num];
rule[0] = 3;
rule[1] = 3;
growth = new int[dim_num];
growth[0] = 6;
growth[1] = 6;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = PattersonQuadrature.patterson_lookup_points_np;
gw_compute_points[1] = PattersonQuadrature.patterson_lookup_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = PattersonQuadrature.patterson_lookup_weights_np;
gw_compute_weights[1] = PattersonQuadrature.patterson_lookup_weights_np;
file_name = "sgmga_d2_l4_gpxgp_iso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
//
// Rule 3, Slow Exponential Growth, LEVEL_MAX = 4
//
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 4;
rule = new int[dim_num];
rule[0] = 3;
rule[1] = 3;
growth = new int[dim_num];
growth[0] = 4;
growth[1] = 4;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = PattersonQuadrature.patterson_lookup_points_np;
gw_compute_points[1] = PattersonQuadrature.patterson_lookup_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = PattersonQuadrature.patterson_lookup_weights_np;
gw_compute_weights[1] = PattersonQuadrature.patterson_lookup_weights_np;
file_name = "sgmga_d2_l4_gpsexgpse_iso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
//
// Rule 3, Moderate Exponential Growth, LEVEL_MAX = 4
//
dim_num = 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 4;
rule = new int[dim_num];
rule[0] = 3;
rule[1] = 3;
growth = new int[dim_num];
growth[0] = 5;
growth[1] = 5;
np = new int[dim_num];
np[0] = 0;
np[1] = 0;
np_sum = typeMethods.i4vec_sum(dim_num, np);
p = new double[np_sum];
gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_points[0] = PattersonQuadrature.patterson_lookup_points_np;
gw_compute_points[1] = PattersonQuadrature.patterson_lookup_points_np;
gw_compute_weights = new Func <int, int, double[], double[], double[]> [dim_num];
gw_compute_weights[0] = PattersonQuadrature.patterson_lookup_weights_np;
gw_compute_weights[1] = PattersonQuadrature.patterson_lookup_weights_np;
file_name = "sgmga_d2_l4_gpmexgpme_iso";
sgmga_write_test(dim_num, level_weight, level_max, rule, growth, np,
p, gw_compute_points, gw_compute_weights, tol, file_name);
}
private static void sgmga_write_test(int dim_num, double[] level_weight, int level_max,
int[] rule, int[] growth, int[] np, double[] p,
Func <int, int, double[], double[], double[]>[] gw_compute_points,
Func <int, int, double[], double[], double[]>[] gw_compute_weights,
double tol, string file_name)
//***************************************************************************80
//
// Purpose:
//
// SGMGA_WRITE_TEST tests SGMGA_WRITE.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 07 June 2010
//
// Author:
//
// John Burkardt
//
// Parameters:
//
// Input, integer DIM_NUM, the spatial dimension.
//
// Input, double LEVEL_WEIGHT[DIM_NUM], the weights for each dimension.
//
// Input, integer LEVEL_MAX, the level that defines the grid.
//
// Input, int RULE[DIM_NUM], the rule in each dimension.
// 1, "CC", Clenshaw Curtis, Closed Fully Nested.
// 2, "F2", Fejer Type 2, Open Fully Nested.
// 3, "GP", Gauss Patterson, Open Fully Nested.
// 4, "GL", Gauss Legendre, Open Weakly Nested.
// 5, "GH", Gauss Hermite, Open Weakly Nested.
// 6, "GGH", Generalized Gauss Hermite, Open Weakly Nested.
// 7, "LG", Gauss Laguerre, Open Non Nested.
// 8, "GLG", Generalized Gauss Laguerre, Open Non Nested.
// 9, "GJ", Gauss Jacobi, Open Non Nested.
// 10, "HGK", Hermite Genz-Keister, Open Fully Nested.
// 11, "UO", User supplied Open, presumably Non Nested.
// 12, "UC", User supplied Closed, presumably Non Nested.
//
// Input, int GROWTH[DIM_NUM], the desired growth in each dimension.
// 0, "DF", default growth associated with this quadrature rule;
// 1, "SL", slow linear, L+1;
// 2 "SO", slow linear odd, O=1+2((L+1)/2)
// 3, "ML", moderate linear, 2L+1;
// 4, "SE", slow exponential;
// 5, "ME", moderate exponential;
// 6, "FE", full exponential.
//
// Input, int NP[RULE_NUM], the number of parameters used by each rule.
//
// Input, double P[sum(NP[*])], the parameters needed by each rule.
//
// Input, void ( *GW_COMPUTE_POINTS[] ) ( int order, int np, double p[], double x[] ),
// an array of pointers to functions which return the 1D quadrature points
// associated with each spatial dimension for which a Golub Welsch rule
// is used.
//
// Input, void ( *GW_COMPUTE_WEIGHTS[] ) ( int order, int np, double p[], double w[] ),
// an array of pointers to functions which return the 1D quadrature weights
// associated with each spatial dimension for which a Golub Welsch rule
// is used.
//
// Input, double TOL, a tolerance for point equality.
//
// Input, string FILE_NAME, the main name of the output files.
//
{
Console.WriteLine("");
Console.WriteLine("SGMGA_WRITE_TEST");
Console.WriteLine(" SGMGA_WRITE writes a sparse grid rule to files.");
//
// Compute necessary data.
//
int point_total_num = SGMGAniso.sgmga_size_total(dim_num, level_weight,
level_max, rule, growth);
int point_num = SGMGAniso.sgmga_size(dim_num, level_weight, level_max,
rule, np, p, gw_compute_points, tol, growth);
int[] sparse_unique_index = new int[point_total_num];
SGMGAniso.sgmga_unique_index(dim_num, level_weight, level_max, rule,
np, p, gw_compute_points, tol, point_num, point_total_num,
growth, ref sparse_unique_index);
int[] sparse_order = new int[dim_num * point_num];
int[] sparse_index = new int[dim_num * point_num];
SGMGAniso.sgmga_index(dim_num, level_weight, level_max, rule, point_num,
point_total_num, sparse_unique_index, growth, ref sparse_order, ref sparse_index);
//
// Compute points and weights.
//
double[] sparse_point = new double [dim_num * point_num];
SGMGAniso.sgmga_point(dim_num, level_weight, level_max, rule, np,
p, gw_compute_points, point_num, sparse_order, sparse_index,
growth, ref sparse_point);
double[] sparse_weight = new double[point_num];
SGMGAniso.sgmga_weight(dim_num, level_weight, level_max, rule, np,
p, gw_compute_weights, point_num, point_total_num, sparse_unique_index,
growth, sparse_weight);
//
// Write points and weights to files.
//
SGMGAniso.sgmga_write(dim_num, level_weight, rule, np, p,
point_num, sparse_weight, sparse_point, file_name);
}
private static void sgmga_size_tabulate(int rule_1d, int growth_1d, int np_1d, double[] p_1d,
int dim_min, int dim_max, int level_max_min, int level_max_max,
Func <int, int, double[], double[], double[]> gw_compute_points_1d)
//****************************************************************************80
//
// Purpose:
//
// SGMGA_SIZE_TABULATE tests SGMGA_SIZE.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 07 June 2010
//
// Author:
//
// John Burkardt
//
// Parameters:
//
// Input, int RULE_1D, the 1D rule.
// 1, "CC", Clenshaw Curtis, Closed Fully Nested.
// 2, "F2", Fejer Type 2, Open Fully Nested.
// 3, "GP", Gauss Patterson, Open Fully Nested.
// 4, "GL", Gauss Legendre, Open Weakly Nested.
// 5, "GH", Gauss Hermite, Open Weakly Nested.
// 6, "GGH", Generalized Gauss Hermite, Open Weakly Nested.
// 7, "LG", Gauss Laguerre, Open Non Nested.
// 8, "GLG", Generalized Gauss Laguerre, Open Non Nested.
// 9, "GJ", Gauss Jacobi, Open Non Nested.
// 10, "HGK", Hermite Genz-Keister, Open Fully Nested.
// 11, "UO", User supplied Open, presumably Non Nested.
// 12, "UC", User supplied Closed, presumably Non Nested.
//
// Input, int GROWTH_1D, the desired growth in each dimension.
// 0, "DF", default growth associated with this quadrature rule;
// 1, "SL", slow linear, L+1;
// 2 "SO", slow linear odd, O=1+2((L+1)/2)
// 3, "ML", moderate linear, 2L+1;
// 4, "SE", slow exponential;
// 5, "ME", moderate exponential;
// 6, "FE", full exponential.
//
// Input, int NP_1D, the number of parameters in the 1D rule.
//
// Input, double P_1D[NP_1D], the parameters.
//
// Input, int DIM_MIN, the minimum spatial dimension to consider.
//
// Input, int DIM_MAX, the maximum spatial dimension to consider.
//
// Input, int LEVEL_MAX_MIN, the minimum value of LEVEL_MAX to consider.
//
// Input, int LEVEL_MAX_MAX, the maximum value of LEVEL_MAX to consider.
//
// Input, GW_COMPUTE_POINTS_1D ( int order, int np, double p[], double x[] ),
// a function which return the 1D quadrature points.
//
{
int dim_num;
int level_max;
Console.WriteLine("");
Console.WriteLine("SGMGA_SIZE_TABULATE");
Console.WriteLine(" SGMGA_SIZE returns the number of distinct");
Console.WriteLine(" points in a sparse grid.");
Console.WriteLine("");
Console.WriteLine(" We use the same rule in all dimensions, and count the points,");
Console.WriteLine(" for a range of dimensions and levels.");
Console.WriteLine("");
Console.WriteLine(" 1D rule index = " + rule_1d + "");
Console.WriteLine(" 1D growth rule = " + growth_1d + "");
Console.WriteLine("");
double tol = Math.Sqrt(typeMethods.r8_epsilon());
string cout = " DIM: ";
for (dim_num = dim_min; dim_num <= dim_max; dim_num++)
{
cout += " " + dim_num.ToString().PadLeft(8);
}
Console.WriteLine(cout);
Console.WriteLine("");
Console.WriteLine(" LEVEL_MAX");
Console.WriteLine("");
for (level_max = level_max_min; level_max <= level_max_max; level_max++)
{
cout = " " + level_max.ToString().PadLeft(4);
for (dim_num = dim_min; dim_num <= dim_max; dim_num++)
{
double[] level_weight = new double[dim_num];
int[] rule = new int[dim_num];
int[] growth = new int[dim_num];
int[] np = new int[dim_num];
int np_sum = dim_num * np_1d;
double[] p = new double[np_sum];
Func <int, int, double[], double[], double[]>[] gw_compute_points = new Func <int, int, double[], double[], double[]> [dim_num];
int dim;
for (dim = 0; dim < dim_num; dim++)
{
level_weight[dim] = 1.0;
rule[dim] = rule_1d;
growth[dim] = growth_1d;
np[dim] = np_1d;
int i;
for (i = 0; i < np_1d; i++)
{
p[i + dim * np_1d] = p_1d[i];
}
gw_compute_points[dim] = gw_compute_points_1d;
}
int point_num = SGMGAniso.sgmga_size(dim_num, level_weight, level_max, rule,
np, p, gw_compute_points, tol, growth);
cout += " " + point_num.ToString().PadLeft(8);
}
Console.WriteLine(cout);
}
}
private static void sgmga_vcn_ordered_tests()
//****************************************************************************80
//
// Purpose:
//
// SGMGA_VCN_ORDERED_TESTS calls SGMGA_VCN_ORDERED_TEST.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 21 May 2010
//
// Author:
//
// John Burkardt
//
{
int dim;
int dim_num;
int[] dim_num_array =
{
2, 2, 2, 2, 2,
3, 3, 3, 3, 3,
4, 4
};
double[] importance;
int level_max;
int[] level_max_array =
{
0, 1, 2, 3, 4,
0, 1, 2, 3, 4,
2, 3
};
double[] level_weight;
double q_max;
double q_min;
int test;
const int test_num = 12;
Console.WriteLine("");
Console.WriteLine("SGMGA_VCN_ORDERED_TESTS");
Console.WriteLine(" calls SGMGA_VCN_ORDERED_TEST.");
//
// Isotropic examples.
//
for (test = 0; test < test_num; test++)
{
dim_num = dim_num_array[test];
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = level_max_array[test];
q_min = level_max - typeMethods.r8vec_sum(dim_num, level_weight);
q_max = level_max;
sgmga_vcn_ordered_test(dim_num, importance, level_weight, q_min, q_max);
}
//
// Anisotropic examples.
//
for (test = 0; test < test_num; test++)
{
dim_num = dim_num_array[test];
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = level_max_array[test];
q_min = level_max - typeMethods.r8vec_sum(dim_num, level_weight);
q_max = level_max;
sgmga_vcn_ordered_test(dim_num, importance, level_weight, q_min, q_max);
}
}
private static void sgmga_vcn_timing_test(int dim_num, double[] importance,
double[] level_weight, double q_min, double q_max)
//****************************************************************************80
//
// Purpose:
//
// SGMGA_VCN_TIMING_TEST times SGMGA_VCN and SGMGA_VCN_NAIVE.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 26 April 2011
//
// Author:
//
// John Burkardt
//
{
int dim;
SGMGAniso.SGMGAData data = new();
string cout;
int[] level_1d = new int[dim_num];
int[] level_1d_max = new int[dim_num];
int[] level_1d_min = new int[dim_num];
Console.WriteLine("");
Console.WriteLine("SGMGA_VCN_TIMING_TEST");
Console.WriteLine(" Consider vectors 0 <= LEVEL_1D(1:N) <= LEVEL_1D_MAX(1:N),");
Console.WriteLine(" Set Q = sum ( LEVEL_WEIGHT(1:N) * LEVEL_1D(1:N) )");
Console.WriteLine(" Accept vectors for which Q_MIN < Q <= Q_MAX");
Console.WriteLine(" No particular order is imposed on the LEVEL_1D values.");
Console.WriteLine(" SGMGA_VCN_NAIVE uses a naive approach;");
Console.WriteLine(" SGMGA_VCN tries to be more efficient.");
Console.WriteLine(" Here, we compare the timings.");
for (dim = 0; dim < dim_num; dim++)
{
switch (level_weight[dim])
{
case > 0.0:
{
level_1d_max[dim] = (int)Math.Floor(q_max / level_weight[dim]) + 1;
if (q_max <= (level_1d_max[dim] - 1) * level_weight[dim])
{
level_1d_max[dim] -= 1;
}
break;
}
default:
level_1d_max[dim] = 0;
break;
}
}
for (dim = 0; dim < dim_num; dim++)
{
level_1d_min[dim] = 0;
}
bool more_grids = false;
Console.WriteLine("");
Console.WriteLine(" IMPORTANCE:");
cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + importance[dim].ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
Console.WriteLine(" LEVEL_WEIGHT:");
cout = "";
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_weight[dim].ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
Console.WriteLine("");
Console.WriteLine(" SGMGA_VCN_NAIVE");
Console.WriteLine(" I Q X");
cout = " MIN" + " " + q_min.ToString(CultureInfo.InvariantCulture).PadLeft(14);
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_1d_min[dim].ToString().PadLeft(2);
}
Console.WriteLine(cout);
DateTime t1 = DateTime.Now;
for (;;)
{
SGMGAniso.sgmga_vcn_naive(dim_num, level_weight, level_1d_max, level_1d,
q_min, q_max, ref more_grids);
if (!more_grids)
{
break;
}
}
DateTime t2 = DateTime.Now;
cout = " MAX" + " " + q_max.ToString(CultureInfo.InvariantCulture).PadLeft(14);
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_1d_max[dim].ToString().PadLeft(2);
}
Console.WriteLine(cout);
Console.WriteLine(" TIME" + " "
+ (t2 - t1).TotalSeconds.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
Console.WriteLine("");
Console.WriteLine(" SGMGA_VCN");
Console.WriteLine(" I Q X");
cout = " MIN" + " " + q_min.ToString(CultureInfo.InvariantCulture).PadLeft(14);
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_1d_min[dim].ToString().PadLeft(2);
}
Console.WriteLine(cout);
t1 = DateTime.Now;
for (;;)
{
SGMGAniso.sgmga_vcn(ref data, dim_num, level_weight, ref level_1d, q_min, q_max,
ref more_grids);
if (!more_grids)
{
break;
}
}
t2 = DateTime.Now;
cout = " MAX" + " " + q_max.ToString(CultureInfo.InvariantCulture).PadLeft(14);
for (dim = 0; dim < dim_num; dim++)
{
cout += " " + level_1d_max[dim].ToString().PadLeft(2);
}
Console.WriteLine(cout);
Console.WriteLine(" TIME" + " "
+ (t2 - t1).TotalSeconds.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
}
private static void sgmga_vcn_timing_tests()
//****************************************************************************80
//
// Purpose:
//
// SGMGA_VCN_TIMING_TESTS calls SGMGA_VCN_TIMING_TEST.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 21 May 2010
//
// Author:
//
// John Burkardt
//
{
int dim;
double[] importance;
int level_max;
double[] level_weight;
double q_max;
double q_min;
int test;
Console.WriteLine("");
Console.WriteLine("SGMGA_VCN_TIMING_TESTS");
Console.WriteLine(" calls SGMGA_VCN_TIMING_TEST.");
//
// Isotropic examples.
//
int dim_num = 2;
for (test = 0; test < 2; test++)
{
dim_num *= 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = 1.0;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
q_min = level_max - typeMethods.r8vec_sum(dim_num, level_weight);
q_max = level_max;
sgmga_vcn_timing_test(dim_num, importance, level_weight, q_min, q_max);
}
//
// Anisotropic examples.
//
dim_num = 2;
for (test = 0; test < 2; test++)
{
dim_num *= 2;
importance = new double[dim_num];
for (dim = 0; dim < dim_num; dim++)
{
importance[dim] = dim + 1;
}
level_weight = new double[dim_num];
SGMGAniso.sgmga_importance_to_aniso(dim_num, importance, ref level_weight);
level_max = 2;
q_min = level_max - typeMethods.r8vec_sum(dim_num, level_weight);
q_max = level_max;
sgmga_vcn_timing_test(dim_num, importance, level_weight, q_min, q_max);
}
}
private static void sgmga_product_weight_test(int dim_num, int[] order_1d,
int order_nd, int[] rule, int[] np, double[] p,
Func <int, int, double[], double[], double[]>[] gw_compute_weights)
//***************************************************************************80
//
// Purpose:
//
// SGMGA_PRODUCT_WEIGHT_TEST: weights of a mixed factor product rule.
//
// Discussion:
//
// This routine computes a sparse grid and compares the sum of the weights
// to the expected exact value.
//
// The routine cannot produce a result for rules that include one or more
// component rules of type 10, that is, Golub-Welsch rules.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 09 June 2010
//
// Author:
//
// John Burkardt
//
// Parameters:
//
// Input, int DIM_NUM, the spatial dimension.
//
// Input, int ORDER_1D[DIM_NUM], the order of the 1D rules.
//
// Input, int ORDER_ND, the order of the product rule.
//
// Input, int RULE[DIM_NUM], the rule in each dimension.
// 1, "CC", Clenshaw Curtis, Closed Fully Nested.
// 2, "F2", Fejer Type 2, Open Fully Nested.
// 3, "GP", Gauss Patterson, Open Fully Nested.
// 4, "GL", Gauss Legendre, Open Weakly Nested.
// 5, "GH", Gauss Hermite, Open Weakly Nested.
// 6, "GGH", Generalized Gauss Hermite, Open Weakly Nested.
// 7, "LG", Gauss Laguerre, Open Non Nested.
// 8, "GLG", Generalized Gauss Laguerre, Open Non Nested.
// 9, "GJ", Gauss Jacobi, Open Non Nested.
// 10, "HGK", Hermite Genz-Keister, Open Fully Nested.
// 11, "UO", User supplied Open, presumably Non Nested.
// 12, "UC", User supplied Closed, presumably Non Nested.
//
// Input, int NP[RULE_NUM], the number of parameters used by each rule.
//
// Input, double P[sum(NP[*])], the parameters needed by each rule.
//
// Input, void ( *GW_COMPUTE_WEIGHTS[] ) ( int order, int np, double p[], double w[] ),
// an array of pointers to functions which return the 1D quadrature weights
// associated with each spatial dimension for which a Golub Welsch rule
// is used.
//
{
double alpha;
double beta;
int dim;
int i;
//
// Determine the integral of 1 over the multidimensional weighted region.
//
int p_index = 0;
double weight_sum_exact = 1.0;
for (dim = 0; dim < dim_num; dim++)
{
switch (rule[dim])
{
case 1:
case 2:
case 3:
case 4:
weight_sum_exact *= 2.0;
break;
case 5:
weight_sum_exact *= Math.Sqrt(Math.PI);
break;
case 6:
alpha = p[p_index];
p_index += 1;
weight_sum_exact *= typeMethods.r8_gamma(0.5 * (alpha + 1.0));
break;
case 7:
weight_sum_exact *= 1.0;
break;
case 8:
alpha = p[p_index];
p_index += 1;
weight_sum_exact *= typeMethods.r8_gamma(alpha + 1.0);
break;
case 9:
alpha = p[p_index];
p_index += 1;
beta = p[p_index];
p_index += 1;
double arg1 = -alpha;
double arg2 = 1.0;
double arg3 = beta + 2.0;
double arg4 = -1.0;
double value1 = typeMethods.r8_hyper_2f1(arg1, arg2, arg3, arg4);
arg1 = -beta;
arg2 = 1.0;
arg3 = alpha + 2.0;
arg4 = -1.0;
double value2 = typeMethods.r8_hyper_2f1(arg1, arg2, arg3, arg4);
weight_sum_exact *= value1 / (beta + 1.0) + value2 / (alpha + 1.0);
break;
case 10:
weight_sum_exact *= Math.Sqrt(Math.PI);
break;
case 11:
{
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
}
weight_sum_exact = 0.0;
break;
}
case 12:
{
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
}
weight_sum_exact = 0.0;
break;
}
default:
Console.WriteLine("");
Console.WriteLine("SGMGA_PRODUCT_WEIGHT_TEST - Fatal error!");
Console.WriteLine(" Unexpected value of RULE[" + dim + "] = "
+ rule[dim] + ".");
return;
}
}
Console.WriteLine("");
Console.WriteLine("SGMGA_PRODUCT_WEIGHT_TEST:");
Console.WriteLine(" Compute the weights of a mixed factor product grid.");
if (weight_sum_exact != 0.0)
{
Console.WriteLine("");
Console.WriteLine(" As a simple test, sum these weights.");
Console.WriteLine(" They should sum to exactly " + weight_sum_exact + "");
}
Console.WriteLine("");
Console.WriteLine(" Spatial dimension DIM_NUM = " + dim_num + "");
Console.WriteLine("");
Console.WriteLine(" Dimension Rule Growth Parameters");
Console.WriteLine("");
p_index = 0;
for (dim = 0; dim < dim_num; dim++)
{
switch (rule[dim])
{
case 1:
case 2:
case 3:
case 4:
case 5:
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + order_1d[dim].ToString().PadLeft(8) + "");
break;
case 6:
alpha = p[p_index];
p_index += 1;
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + order_1d[dim].ToString().PadLeft(8)
+ " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
break;
case 7:
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + order_1d[dim].ToString().PadLeft(8) + "");
break;
case 8:
alpha = p[p_index];
p_index += 1;
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + order_1d[dim].ToString().PadLeft(8)
+ " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
break;
case 9:
alpha = p[p_index];
p_index += 1;
beta = p[p_index];
p_index += 1;
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + order_1d[dim].ToString().PadLeft(8)
+ " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " " + beta.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
break;
case 10:
Console.WriteLine(" " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + order_1d[dim].ToString().PadLeft(8) + "");
break;
case 11:
{
string cout = " " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + order_1d[dim].ToString().PadLeft(8);
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
cout += " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
break;
}
case 12:
{
string cout = " " + dim.ToString().PadLeft(8)
+ " " + rule[dim].ToString().PadLeft(8)
+ " " + order_1d[dim].ToString().PadLeft(8);
for (i = 0; i < np[dim]; i++)
{
alpha = p[p_index];
p_index += 1;
cout += " " + alpha.ToString(CultureInfo.InvariantCulture).PadLeft(14);
}
Console.WriteLine(cout);
break;
}
default:
Console.WriteLine("");
Console.WriteLine("SGMGA_PRODUCT_WEIGHT_TEST - Fatal error!");
Console.WriteLine(" Cannot perform test for rule = " + rule[dim] + "");
return;
}
}
//
// Compute the weights.
//
double[] weight = new double[order_nd];
SGMGAniso.sgmga_product_weight(dim_num, order_1d, order_nd, rule,
np, p, gw_compute_weights, ref weight);
//
// Sum the weights to get the approximation to the integral of 1.
//
double weight_sum = typeMethods.r8vec_sum(order_nd, weight);
//
// Compare the exact and estimated integrals.
//
double weight_sum_error = typeMethods.r8_abs(weight_sum - weight_sum_exact);
if (weight_sum_exact != 0.0)
{
Console.WriteLine("");
Console.WriteLine(" Weight sum Expected sum Difference");
Console.WriteLine("");
Console.WriteLine(" " + weight_sum.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " " + weight_sum_exact.ToString(CultureInfo.InvariantCulture).PadLeft(14)
+ " " + weight_sum_error.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
}
else
{
Console.WriteLine("");
Console.WriteLine(" Weight sum");
Console.WriteLine("");
Console.WriteLine(" " + weight_sum.ToString(CultureInfo.InvariantCulture).PadLeft(14) + "");
}
}
