/* $Id: interface2c.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 interface2c.cpp$$
$spell
	
$$

$section Interfacing to C: Example and Test$$
$index C, interface to$$
$index interface, to C$$
$index difference, central$$
$index central difference$$

$code
$verbatim%example/interface2c.cpp%0%// BEGIN C++%// END C++%1%$$
$$

$end
*/
// BEGIN C++
# include <cppad/cppad.hpp>  // CppAD utilities
# include <cassert>        // assert macro

namespace { // Begin empty namespace
/*
Compute the value of a sum of Gaussians defined by a and evaluated at x
	y = sum_{i=1}^n a[3*i] exp( (x - a[3*i+1])^2 / a[3*i+2])^2 )
where the floating point type is a template parameter
*/
template <class Float>
Float sumGauss(const Float &x, const CppAD::vector<Float> &a)   
{ 
	// number of components in a
	size_t na = a.size();

	// number of Gaussians
	size_t n = na / 3;

	// check the restricitons on na 
	assert( na == n * 3 );

	// declare temporaries used inside of loop
	Float ex, arg;

	// initialize sum
  	Float y = 0.; 

	// loop with respect to Gaussians
	size_t i;
	for(i = 0; i < n; i++)
	{
		arg =   (x - a[3*i+1]) / a[3*i+2]; 
		ex  =   exp(-arg * arg); 
		y  +=   a[3*i] * ex; 
	} 
	return y;
}
/*
Create a C function interface that computes both
	y = sum_{i=1}^n a[3*i] exp( (x - a[3*i+1])^2 / a[3*i+2])^2 )
and its derivative with respect to the parameter vector a.
*/
extern "C"
void sumGauss(float x, float a[], float *y, float dyda[], size_t na)   
{	// Note that any simple vector could replace CppAD::vector; 
	// for example, std::vector, std::valarray

	// check the restrictions on na
	assert( na % 3 == 0 );  // mod(na, 3) = 0

	// use the shorthand ADfloat for the type CppAD::AD<float>
	typedef CppAD::AD<float> ADfloat;

	// vector for indpendent variables
	CppAD::vector<ADfloat> A(na);      // used with template function above
	CppAD::vector<float>   acopy(na);  // used for derivative calculations

	// vector for the dependent variables (there is only one)
	CppAD::vector<ADfloat> Y(1); 

	// copy the independent variables from C vector to CppAD vectors
	size_t i;
	for(i = 0; i < na; i++)
 		A[i] = acopy[i] = a[i];

	// declare that A is the independent variable vector
	CppAD::Independent(A);

	// value of x as an ADfloat object
	ADfloat X = x;

	// Evaluate template version of sumGauss with ADfloat as the template 
	// parameter. Set the independent variable to the resulting value
	Y[0] = sumGauss(X, A); 

	// create the AD function object F : A -> Y
	CppAD::ADFun<float> F(A, Y);

	// use Value to convert Y[0] to float and return y = F(a)  
	*y = CppAD::Value(Y[0]);

	// evaluate the derivative F'(a)
	CppAD::vector<float> J(na);
	J = F.Jacobian(acopy);

	// return the value of dyda = F'(a) as a C vector
	for(i = 0; i < na; i++)
		dyda[i] = J[i];

	return;
}
/*
Link CppAD::NearEqual so do not have to use namespace notation in Interface2C
*/
bool NearEqual(float x, float y, float r, float a)
{	return CppAD::NearEqual(x, y, r, a);
}

} // End empty namespace

bool Interface2C(void)
{	// This routine is intentionally coded as if it were a C routine
	// except for the fact that it uses the predefined type bool.
	bool ok = true; 

	// declare variables
	float x, a[6], y, dyda[6], tmp[6];
	size_t na, i;

	// number of parameters (3 for each Gaussian)
	na = 6;

	// number of Gaussians: n  = na / 3;

	// value of x
	x = 1.;

	// value of the parameter vector a
	for(i = 0; i < na; i++)
		a[i] = (float) (i+1);

	// evaulate function and derivative
	sumGauss(x, a, &y, dyda, na);

	// compare dyda to central difference approximation for deriative
	for(i = 0; i < na; i++)
	{	// local variables
		float small, ai, yp, ym, dy_da;

		// We assume that the type float has at least 7 digits of 
		// precision, so we choose small to be about pow(10., -7./2.).
		small  = (float) 3e-4;

		// value of this component of a
		ai    = a[i];

		// evaluate F( a + small * ei )
		a[i]  = ai + small;
		sumGauss(x, a, &yp, tmp, na);

		// evaluate F( a - small * ei )
		a[i]  = ai - small;
		sumGauss(x, a, &ym, tmp, na);

		// evaluate central difference approximates for partial
		dy_da = (yp - ym) / (2 * small);

		// restore this component of a
		a[i]  = ai;

		ok   &= NearEqual(dyda[i], dy_da, small, small); 
	}
	return ok;
}
// END C++
