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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
// ----------------------------------------------------------------------------
/*
Test of CondExp with AD< AD< Base > > types
*/
# include <cppad/cppad.hpp>
typedef CppAD::AD< double > ADdouble;
typedef CppAD::AD< ADdouble > ADADdouble;
namespace { // BEGIN empty namespace
bool CondExpADOne(void)
{ bool ok = true;
using namespace CppAD;
size_t n = 3;
size_t m = 8;
// ADdouble independent variable vector
CPPAD_TESTVECTOR( ADdouble ) Xa(n);
Xa[0] = -1.;
Xa[1] = 0.;
Xa[2] = 1.;
Independent(Xa);
// ADdouble independent variable vector
CPPAD_TESTVECTOR( ADADdouble ) Xaa(n);
Xaa[0] = Xa[0];
Xaa[1] = Xa[1];
Xaa[2] = Xa[2];
Independent(Xaa);
// ADADdouble parameter
ADADdouble p = ADADdouble(Xa[0]);
ADADdouble q = ADADdouble(Xa[1]);
ADADdouble r = ADADdouble(Xa[2]);
// ADADdouble dependent variable vector
CPPAD_TESTVECTOR( ADADdouble ) Yaa(m);
// CondExp(parameter, parameter, parameter)
Yaa[0] = CondExp(p, q, r);
// CondExp(parameter, parameter, variable)
Yaa[1] = CondExp(p, q, Xaa[2]);
// CondExp(parameter, variable, parameter)
Yaa[2] = CondExp(p, Xaa[1], r);
// CondExp(parameter, variable, variable)
Yaa[3] = CondExp(p, Xaa[1], Xaa[2]);
// CondExp(variable, variable, variable)
Yaa[5] = CondExp(Xaa[0], Xaa[1], Xaa[2]);
// CondExp(variable, variable, parameter)
Yaa[4] = CondExp(Xaa[0], Xaa[1], r);
// CondExp(variable, parameter, variable)
Yaa[6] = CondExp(Xaa[0], q, Xaa[2]);
// CondExp(variable, parameter, parameter)
Yaa[7] = CondExp(Xaa[0], q, r);
// create fa: Xaa -> Yaa function object
ADFun< ADdouble > fa(Xaa, Yaa);
// function values
CPPAD_TESTVECTOR( ADdouble ) Ya(m);
Ya = fa.Forward(0, Xa);
// create f: Xa -> Ya function object
ADFun<double> f(Xa, Ya);
// check result of function evaluation
CPPAD_TESTVECTOR(double) x(n);
CPPAD_TESTVECTOR(double) y(m);
x[0] = 1.;
x[1] = 0.;
x[2] = -1.;
y = f.Forward(0, x);
size_t i;
for(i = 0; i < m; i++)
{ // y[i] = CondExp(x[0], x[1], x[2])
if( x[0] > 0 )
ok &= (y[i] == x[1]);
else
ok &= (y[i] == x[2]);
}
// check forward mode derivatives
CPPAD_TESTVECTOR(double) dx(n);
CPPAD_TESTVECTOR(double) dy(m);
dx[0] = 1.;
dx[1] = 2.;
dx[2] = 3.;
dy = f.Forward(1, dx);
for(i = 0; i < m; i++)
{ if( x[0] > 0. )
ok &= (dy[i] == dx[1]);
else
ok &= (dy[i] == dx[2]);
}
// calculate Jacobian
CPPAD_TESTVECTOR(double) J(m * n);
size_t j;
for(i = 0; i < m; i++)
{ for(j = 0; j < n; j++)
J[i * n + j] = 0.;
if( x[0] > 0. )
J[i * n + 1] = 1.;
else
J[i * n + 2] = 1.;
}
// check reverse mode derivatives
for(i = 0; i < m; i++)
dy[i] = double(i);
dx = f.Reverse(1, dy);
double sum;
for(j = 0; j < n; j++)
{ sum = 0;
for(i = 0; i < m; i++)
sum += dy[i] * J[i * n + j];
ok &= (sum == dx[j]);
}
// forward mode computation of sparsity pattern
CPPAD_TESTVECTOR(bool) Px(n * n);
for(i = 0; i < n; i++)
{ for(j = 0; j < n; j++)
Px[i * n + j] = false;
Px[i * n + i] = true;
}
CPPAD_TESTVECTOR(bool) Py(m * n);
Py = f.ForSparseJac(n, Px);
for(i = 0; i < m; i++)
{ ok &= Py[ i * n + 0 ] == false;
ok &= Py[ i * n + 1 ] == true;
ok &= Py[ i * n + 2 ] == true;
}
// reverse mode computation of sparsity pattern
Py.resize(m * m);
for(i = 0; i < m; i++)
{ for(j = 0; j < m; j++)
Py[i * m + j] = false;
Py[i * m + i] = true;
}
Px.resize(m * n);
Px = f.RevSparseJac(m, Py);
for(i = 0; i < m; i++)
{ for(j = 0; j < n; j++)
ok &= ( Px[i * n + j] == ( j > 0 ) );
}
return ok;
}
bool CondExpADTwo(void)
{ bool ok = true;
using namespace CppAD;
size_t n = 3;
size_t m = 8;
// ADdouble independent variable vector
CPPAD_TESTVECTOR( ADdouble ) Xa(n);
Xa[0] = -1.;
Xa[1] = 0.;
Xa[2] = 1.;
Independent(Xa);
// use VecAD so that sparsity results are local
VecAD<double> Va(1);
ADdouble zero = 0.;
Va[zero] = Xa[0];
// ADdouble independent variable vector
CPPAD_TESTVECTOR( ADADdouble ) Xaa(n);
Xaa[0] = ADdouble( Va[zero] );
Xaa[1] = Xa[1];
Xaa[2] = Xa[2];
Independent(Xaa);
// ADADdouble parameter
ADADdouble p = ADADdouble(Xa[0]);
ADADdouble q = ADADdouble(Xa[1]);
ADADdouble r = ADADdouble(Xa[2]);
// ADADdouble dependent variable vector
CPPAD_TESTVECTOR( ADADdouble ) Yaa(m);
// CondExp(parameter, parameter, parameter)
Yaa[0] = CondExp(p, q, r);
// CondExp(parameter, parameter, variable)
Yaa[1] = CondExp(p, q, Xaa[2]);
// CondExp(parameter, variable, parameter)
Yaa[2] = CondExp(p, Xaa[1], r);
// CondExp(parameter, variable, variable)
Yaa[3] = CondExp(p, Xaa[1], Xaa[2]);
// CondExp(variable, variable, variable)
Yaa[5] = CondExp(Xaa[0], Xaa[1], Xaa[2]);
// CondExp(variable, variable, parameter)
Yaa[4] = CondExp(Xaa[0], Xaa[1], r);
// CondExp(variable, parameter, variable)
Yaa[6] = CondExp(Xaa[0], q, Xaa[2]);
// CondExp(variable, parameter, parameter)
Yaa[7] = CondExp(Xaa[0], q, r);
// create fa: Xaa -> Yaa function object
ADFun< ADdouble > fa(Xaa, Yaa);
// function values
CPPAD_TESTVECTOR( ADdouble ) Ya(m);
Ya = fa.Forward(0, Xa);
// create f: Xa -> Ya function object
ADFun<double> f(Xa, Ya);
// check use_VecAD
ok &= f.use_VecAD();
// check result of function evaluation
CPPAD_TESTVECTOR(double) x(n);
CPPAD_TESTVECTOR(double) y(m);
x[0] = 1.;
x[1] = 0.;
x[2] = -1.;
y = f.Forward(0, x);
size_t i;
for(i = 0; i < m; i++)
{ // y[i] = CondExp(x[0], x[1], x[2])
if( x[0] > 0 )
ok &= (y[i] == x[1]);
else
ok &= (y[i] == x[2]);
}
// check forward mode derivatives
CPPAD_TESTVECTOR(double) dx(n);
CPPAD_TESTVECTOR(double) dy(m);
dx[0] = 1.;
dx[1] = 2.;
dx[2] = 3.;
dy = f.Forward(1, dx);
for(i = 0; i < m; i++)
{ if( x[0] > 0. )
ok &= (dy[i] == dx[1]);
else
ok &= (dy[i] == dx[2]);
}
// calculate Jacobian
CPPAD_TESTVECTOR(double) J(m * n);
size_t j;
for(i = 0; i < m; i++)
{ for(j = 0; j < n; j++)
J[i * n + j] = 0.;
if( x[0] > 0. )
J[i * n + 1] = 1.;
else
J[i * n + 2] = 1.;
}
// check reverse mode derivatives
for(i = 0; i < m; i++)
dy[i] = double(i);
dx = f.Reverse(1, dy);
double sum;
for(j = 0; j < n; j++)
{ sum = 0;
for(i = 0; i < m; i++)
sum += dy[i] * J[i * n + j];
ok &= (sum == dx[j]);
}
// forward mode computation of sparsity pattern
CPPAD_TESTVECTOR(bool) Px(n * n);
for(i = 0; i < n; i++)
{ for(j = 0; j < n; j++)
Px[i * n + j] = false;
Px[i * n + i] = true;
}
CPPAD_TESTVECTOR(bool) Py(m * n);
Py = f.ForSparseJac(n, Px);
for(i = 0; i < m; i++)
{ for(j = 0; j < n; j++)
// sparsity pattern works for both true and false cases.
ok &= ( Py[i * n + j] == (j > 0) );
}
// reverse mode computation of sparsity pattern
Py.resize(m * m);
for(i = 0; i < m; i++)
{ for(j = 0; j < m; j++)
Py[i * m + j] = false;
Py[i * m + i] = true;
}
Px.resize(m * n);
Px = f.RevSparseJac(m, Py);
for(i = 0; i < m; i++)
{ for(j = 0; j < n; j++)
ok &= ( Px[i * n + j] == (j > 0) );
}
return ok;
}
} // END empty namespace
bool CondExpAD(void)
{ bool ok = true;
ok &= CondExpADOne();
ok &= CondExpADTwo();
return ok;
}
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