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// SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
// SPDX-FileCopyrightText: Bradley M. Bell <bradbell@seanet.com>
// SPDX-FileContributor: 2003-22 Bradley M. Bell
// ----------------------------------------------------------------------------
/*
{xrst_begin fun_assign.cpp}
ADFun Assignment: Example and Test
##################################
{xrst_literal
// BEGIN C++
// END C++
}
{xrst_end fun_assign.cpp}
*/
// BEGIN C++
# include <cppad/cppad.hpp>
# include <limits>
bool fun_assign(void)
{ bool ok = true;
using CppAD::AD;
using CppAD::NearEqual;
size_t i, j;
// ten times machine percision
double eps = 10. * CppAD::numeric_limits<double>::epsilon();
// an empty ADFun<double> object
CppAD::ADFun<double> g;
// domain space vector
size_t n = 3;
CPPAD_TESTVECTOR(AD<double>) x(n);
for(j = 0; j < n; j++)
x[j] = AD<double>(j + 2);
// declare independent variables and start tape recording
CppAD::Independent(x);
// range space vector
size_t m = 2;
CPPAD_TESTVECTOR(AD<double>) y(m);
y[0] = x[0] + x[0] * x[1];
y[1] = x[1] * x[2] + x[2];
// Store operation sequence, and order zero forward results, in f.
// This assignment will use move semantics
CppAD::ADFun<double> f;
f = CppAD::ADFun<double>(x, y);
// sparsity pattern for the identity matrix
CPPAD_TESTVECTOR(std::set<size_t>) r(n);
for(j = 0; j < n; j++)
r[j].insert(j);
// Store forward mode sparsity pattern in f
f.ForSparseJac(n, r);
// make a copy of f in g
g = f;
// check values that should be equal
ok &= ( g.size_order() == f.size_order() );
ok &= ( (g.size_forward_bool() > 0) == (f.size_forward_bool() > 0) );
ok &= ( (g.size_forward_set() > 0) == (f.size_forward_set() > 0) );
// Use zero order Taylor coefficient from f for first order
// calculation using g.
CPPAD_TESTVECTOR(double) dx(n), dy(m);
for(i = 0; i < n; i++)
dx[i] = 0.;
dx[1] = 1;
dy = g.Forward(1, dx);
ok &= NearEqual(dy[0], x[0], eps, eps); // partial y[0] w.r.t x[1]
ok &= NearEqual(dy[1], x[2], eps, eps); // partial y[1] w.r.t x[1]
// Use forward Jacobian sparsity pattern from f to calculate
// Hessian sparsity pattern using g.
CPPAD_TESTVECTOR(std::set<size_t>) s(1), h(n);
s[0].insert(0); // Compute sparsity pattern for Hessian of y[0]
h = f.RevSparseHes(n, s);
// check sparsity pattern for Hessian of y[0] = x[0] + x[0] * x[1]
ok &= ( h[0].find(0) == h[0].end() ); // zero w.r.t x[0], x[0]
ok &= ( h[0].find(1) != h[0].end() ); // non-zero w.r.t x[0], x[1]
ok &= ( h[0].find(2) == h[0].end() ); // zero w.r.t x[0], x[2]
ok &= ( h[1].find(0) != h[1].end() ); // non-zero w.r.t x[1], x[0]
ok &= ( h[1].find(1) == h[1].end() ); // zero w.r.t x[1], x[1]
ok &= ( h[1].find(2) == h[1].end() ); // zero w.r.t x[1], x[2]
ok &= ( h[2].find(0) == h[2].end() ); // zero w.r.t x[2], x[0]
ok &= ( h[2].find(1) == h[2].end() ); // zero w.r.t x[2], x[1]
ok &= ( h[2].find(2) == h[2].end() ); // zero w.r.t x[2], x[2]
return ok;
}
// END C++
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