UnitTestSetup() {
      rtol = 1e-4;
      atol = 1e-5;
      crtol = 1e-12;
      catol = 1e-12;
      a = 3.1;
      const OrdinalType d = 2;
      const OrdinalType p = 7;
      
      // Create product basis
      Teuchos::Array< Teuchos::RCP<const Stokhos::OneDOrthogPolyBasis<OrdinalType,ValueType> > > bases(d);
      for (OrdinalType i=0; i<d; i++)
	bases[i] = 
	  Teuchos::rcp(new Stokhos::LegendreBasis<OrdinalType,ValueType>(p));
      basis =
	Teuchos::rcp(new Stokhos::CompletePolynomialBasis<OrdinalType,ValueType>(bases));
      
      // Constant expansion
      exp = 
	Teuchos::rcp(new Stokhos::ConstantOrthogPolyExpansion<OrdinalType,ValueType>());
      
      // Create approximation
      cx.reset(basis, 1);
      cx.term(0, 0) = a;
      cu.reset(basis, 1);
      cu2.reset(basis, 1);
    }
Esempio n. 2
0
    UnitTestSetup() {
      rtol = 1e-4;
      atol = 1e-5;
      crtol = 1e-12;
      catol = 1e-12;
      a = 3.1;
      const OrdinalType d = 2;
      const OrdinalType p = 7;
      
      // Create product basis
      Teuchos::Array< Teuchos::RCP<const Stokhos::OneDOrthogPolyBasis<OrdinalType,ValueType> > > bases(d);
      for (OrdinalType i=0; i<d; i++)
	bases[i] = 
	  Teuchos::rcp(new Stokhos::LegendreBasis<OrdinalType,ValueType>(p));

      basis =
	Teuchos::rcp(new product_basis_type(bases));
      
      // Tensor product quadrature
      quad = 
	Teuchos::rcp(new Stokhos::TensorProductQuadrature<OrdinalType,ValueType>(basis));

      // Tensor product pseudospectral operator
      Teuchos::Array< Stokhos::EvenGrowthRule<OrdinalType> > point_growth(d);
      ps_op = 
	Teuchos::rcp(new Stokhos::QuadraturePseudoSpectralOperator<OrdinalType,ValueType>(*basis, *quad));

      // Triple product tensor
      Cijk = basis->computeTripleProductTensor();
      Cijk_linear = basis->computeLinearTripleProductTensor();
      
      // Quadrature expansion
      exp = 
	Teuchos::rcp(new Stokhos::PseudoSpectralOrthogPolyExpansion<OrdinalType,ValueType>(basis, Cijk, ps_op));
      exp_linear = 
	Teuchos::rcp(new Stokhos::PseudoSpectralOrthogPolyExpansion<OrdinalType,ValueType>(basis, Cijk_linear, ps_op));
      
      // Create approximation
      sz = basis->size();
      x.reset(basis);
      y.reset(basis);
      u.reset(basis); 
      u2.reset(basis);
      cx.reset(basis, 1);
      x.term(0, 0) = 1.0;
      cx.term(0, 0) = a;
      cu.reset(basis);
      cu2.reset(basis, 1);
      sx.reset(basis, d+1);
      su.reset(basis, d+1);
      su2.reset(basis, d+1);
      for (OrdinalType i=0; i<d; i++) {
	x.term(i, 1) = 0.1;
	sx.term(i, 1) = 0.0;
      }
      y.term(0, 0) = 2.0;
      for (OrdinalType i=0; i<d; i++)
	y.term(i, 1) = 0.25;
    }
ordinal_type
Stokhos::ProductLanczosPCEBasis<ordinal_type, value_type>::
isInvariant(const Stokhos::OrthogPolyApprox<ordinal_type, value_type>& pce) const
{
  Teuchos::RCP<const Stokhos::OrthogPolyBasis<ordinal_type,value_type> > basis =
    pce.basis();
  ordinal_type dim = basis->dimension();
  value_type tol = 1.0e-15;

  // Check if basis is a product basis
  Teuchos::RCP<const Stokhos::ProductBasis<ordinal_type,value_type> > prod_basis = Teuchos::rcp_dynamic_cast<const Stokhos::ProductBasis<ordinal_type,value_type> >(basis);
  if (prod_basis == Teuchos::null)
    return -2;

  // Build list of dimensions pce depends on by looping over each dimension, 
  // computing norm of pce with just that dimension -- note we don't include
  // the constant term
  Teuchos::Array<ordinal_type> dependent_dims;
  tmp_pce.reset(basis);
  for (ordinal_type i=0; i<dim; i++) {
    ordinal_type p = prod_basis->getCoordinateBases()[i]->order();
    tmp_pce.init(0.0);
    for (ordinal_type j=1; j<=p; j++)
      tmp_pce.term(i,j) = pce.term(i,j);
    value_type nrm = tmp_pce.two_norm();
    if (nrm > tol) dependent_dims.push_back(i);
  }

  // If dependent_dims has length 1, pce a function of a single variable,
  // which is an invariant subspace
  if (dependent_dims.size() == 1)
    return dependent_dims[0];

  // If dependent_dims has length 0, pce is constant
  else if (dependent_dims.size() == 0)
    return -1;

  // Otherwise pce depends on more than one variable
  return -2;
}
Esempio n. 4
0
    UnitTestSetup() {
      rtol = 1e-10;//4
      atol = 1e-10;//5
      crtol = 1e-12;
      catol = 1e-12;
      a = 3.1;
      const OrdinalType d = 2;
      const OrdinalType p = 7;
      
      // Create product basis
      Teuchos::Array< Teuchos::RCP<const Stokhos::OneDOrthogPolyBasis<OrdinalType,ValueType> > > bases(d);
      for (OrdinalType i=0; i<d; i++)
	bases[i] = 
	  Teuchos::rcp(new Stokhos::LegendreBasis<OrdinalType,ValueType>(p));
      basis =
	Teuchos::rcp(new Stokhos::CompletePolynomialBasis<OrdinalType,ValueType>(bases));

      // Tensor product quadrature
      quad = 
	Teuchos::rcp(new Stokhos::TensorProductQuadrature<OrdinalType,ValueType>(basis));

      // Triple product tensor
      Cijk = basis->computeTripleProductTensor(basis->size());
      Cijk_linear = basis->computeTripleProductTensor(basis->dimension()+1);
      
      // Algebraic expansion
      exp = 
	Teuchos::rcp(new Stokhos::QuadOrthogPolyExpansion<OrdinalType,ValueType>(basis, Cijk, quad));
      exp_linear = 
	Teuchos::rcp(new Stokhos::QuadOrthogPolyExpansion<OrdinalType,ValueType>(basis, Cijk_linear, quad));

      // Quadrature expansion
      qexp = 
	Teuchos::rcp(new Stokhos::QuadOrthogPolyExpansion<OrdinalType,ValueType>(basis, Cijk, quad));
      //Dense Direct Division Operator
      direct_division_strategy =
         Teuchos::rcp(new Stokhos::DenseDirectDivisionExpansionStrategy<int,double,Stokhos::StandardStorage<int, double> >(basis, Cijk));

      
      // Create approximation
//      sz = basis->size();
      x.reset(basis);
      y.reset(basis);
      u.reset(basis); 
      u2.reset(basis);
      cx.reset(basis, 1);
      x.term(0, 0) = 1.0;
//      y.term(0, 0) = 1.0:
      cx.term(0, 0) = a;
      cu.reset(basis);
//      cu2.reset(basis, 1);
//      sx.reset(basis, d+1);
//      su.reset(basis, d+1);
//      su2.reset(basis, d+1);
      for (OrdinalType i=0; i<d; i++) {
	x.term(i, 1) = 1.0;
//	y.term(i, 1) = 0.1;
      }
//      y.term(0, 0) = 2.0;
//      for (OrdinalType i=0; i<d; i++)
//	y.term(i, 1) = 0.25;
      
      c1.reset(basis);
      c1.term(0,0)=1;
      exp->exp(cu, x);

     }