/* $Id: ad_fun.cpp 2506 2012-10-24 19:36:49Z bradbell $ */
/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-12 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the 
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */

/*
$begin ad_fun.cpp$$
$spell
$$

$section Creating Your Own Interface to an ADFun Object$$

$index ADFun, example$$
$index example, ADFun$$
$index test, ADFun$$

$code
$verbatim%example/ad_fun.cpp%0%// BEGIN C++%// END C++%1%$$
$$

$end
*/
// BEGIN C++

# 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;
	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], 1e-10, 1e-10 );
	ok &=  NearEqual( 2.*x[0]*sin(x[1]), jac[1*n+0], 1e-10, 1e-10 );
	ok &=  NearEqual( 2.*x[0]*cos(x[1]), jac[2*n+0], 1e-10, 1e-10 );

	ok &=  NearEqual( x[0] * x[0] *exp(x[1]), jac[0*n+1], 1e-10, 1e-10 );
	ok &=  NearEqual( x[0] * x[0] *cos(x[1]), jac[1*n+1], 1e-10, 1e-10 );
	ok &=  NearEqual(-x[0] * x[0] *sin(x[1]), jac[2*n+1], 1e-10, 1e-10 );

	return ok;
}


// END C++
