/* $Id: eigen_array.cpp 2870 2013-07-28 17:00:59Z bradbell $ */
/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-13 Bradley M. Bell
CppAD is distributed under multiple licenses. This distribution is under
the terms of the
GNU General Public License Version 3.
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 eigen_array.cpp$$
$spell
Eigen
$$
$section Using Eigen Arrays: Example and Test$$
$index array, eigen example$$
$index eigen, array example$$
$index example, eigen array$$
$index test, eigen array$$
$code
$verbatim%example/eigen_array.cpp%0%// BEGIN C++%// END C++%1%$$
$$
$end
*/
// BEGIN C++
# include <cppad/example/cppad_eigen.hpp>
# include <cppad/speed/det_by_minor.hpp>
# include <Eigen/Dense>
bool eigen_array(void)
{ bool ok = true;
using CppAD::AD;
using CppAD::NearEqual;
using Eigen::Matrix;
using Eigen::Dynamic;
//
typedef Matrix< AD<double> , Dynamic, 1 > a_vector;
//
// some temporary indices
size_t i, j;
// domain and range space vectors
size_t n = 10, m = n;
a_vector a_x(n), a_y(m);
// set and declare independent variables and start tape recording
for(j = 0; j < n; j++)
a_x[j] = double(1 + j);
CppAD::Independent(a_x);
// evaluate a component wise function
a_y = a_x.array() + sin(a_x.array());
// create f: x -> y and stop tape recording
CppAD::ADFun<double> f(a_x, a_y);
// compute the derivative of y w.r.t x using CppAD
CPPAD_TESTVECTOR(double) x(n);
for(j = 0; j < n; j++)
x[j] = double(j) + 1.0 / double(j+1);
CPPAD_TESTVECTOR(double) jac = f.Jacobian(x);
// check Jacobian
double eps = 100. * CppAD::numeric_limits<double>::epsilon();
for(i = 0; i < m; i++)
{ for(j = 0; j < n; j++)
{ double check = 1.0 + cos(x[i]);
if( i != j )
check = 0.0;
ok &= NearEqual(jac[i * n + j], check, eps, eps);
}
}
return ok;
}
// END C++