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@(@\newcommand{\W}[1]{ \; #1 \; } \newcommand{\R}[1]{ {\rm #1} } \newcommand{\B}[1]{ {\bf #1} } \newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} } \newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} } \newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} } \newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }@)@
Creating Your Own Interface to an ADFun Object

# include <cppad/cppad.hpp>

namespace {

     // This class is an example of a different interface to an AD function object
     template <class Base>
     class my_ad_fun {

     private:
          CppAD::ADFun<Base> f;

     public:
          // default constructor
          my_ad_fun(void)
          { }

          // destructor
          ~ my_ad_fun(void)
          { }

          // Construct an my_ad_fun object with an operation sequence.
          // This is the same as for ADFun<Base> except that no zero
          // order forward sweep is done. Note Hessian and Jacobian do
          // their own zero order forward mode sweep.
          template <class ADvector>
          my_ad_fun(const ADvector& x, const ADvector& y)
          {     f.Dependent(x, y); }

          // same as ADFun<Base>::Jacobian
          template <class VectorBase>
          VectorBase jacobian(const VectorBase& x)
          {     return f.Jacobian(x); }

          // same as ADFun<Base>::Hessian
             template <typename VectorBase>
          VectorBase hessian(const VectorBase &x, const VectorBase &w)
          {     return f.Hessian(x, w); }
     };

} // End empty namespace

bool ad_fun(void)
{     // This example is similar to example/jacobian.cpp, except that it
     // uses my_ad_fun instead of ADFun.

     bool ok = true;
     using CppAD::AD;
     using CppAD::NearEqual;
     double eps99 = 99.0 * std::numeric_limits<double>::epsilon();
     using CppAD::exp;
     using CppAD::sin;
     using CppAD::cos;

     // domain space vector
     size_t n = 2;
     CPPAD_TESTVECTOR(AD<double>)  X(n);
     X[0] = 1.;
     X[1] = 2.;

     // declare independent variables and starting recording
     CppAD::Independent(X);

     // a calculation between the domain and range values
     AD<double> Square = X[0] * X[0];

     // range space vector
     size_t m = 3;
     CPPAD_TESTVECTOR(AD<double>)  Y(m);
     Y[0] = Square * exp( X[1] );
     Y[1] = Square * sin( X[1] );
     Y[2] = Square * cos( X[1] );

     // create f: X -> Y and stop tape recording
     my_ad_fun<double> f(X, Y);

     // new value for the independent variable vector
     CPPAD_TESTVECTOR(double) x(n);
     x[0] = 2.;
     x[1] = 1.;

     // compute the derivative at this x
     CPPAD_TESTVECTOR(double) jac( m * n );
     jac = f.jacobian(x);

     /*
     F'(x) = [ 2 * x[0] * exp(x[1]) ,  x[0] * x[0] * exp(x[1]) ]
             [ 2 * x[0] * sin(x[1]) ,  x[0] * x[0] * cos(x[1]) ]
             [ 2 * x[0] * cos(x[1]) , -x[0] * x[0] * sin(x[i]) ]
     */
     ok &=  NearEqual( 2.*x[0]*exp(x[1]), jac[0*n+0], eps99, eps99);
     ok &=  NearEqual( 2.*x[0]*sin(x[1]), jac[1*n+0], eps99, eps99);
     ok &=  NearEqual( 2.*x[0]*cos(x[1]), jac[2*n+0], eps99, eps99);

     ok &=  NearEqual( x[0] * x[0] *exp(x[1]), jac[0*n+1], eps99, eps99);
     ok &=  NearEqual( x[0] * x[0] *cos(x[1]), jac[1*n+1], eps99, eps99);
     ok &=  NearEqual(-x[0] * x[0] *sin(x[1]), jac[2*n+1], eps99, eps99);

     return ok;
}


Input File: example/general/ad_fun.cpp