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Add unit test for Hessian via AutoDiffScalar

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This commit is contained in:
Gael Guennebaud 2015-11-05 14:54:05 +01:00
parent 9ceaa8e445
commit 589b839ad0
1 changed files with 50 additions and 3 deletions
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53
unsupported/test/autodiff.cpp
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@ -129,6 +129,7 @@ template<typename Func> void forward_jacobian(const Func& f)
// TODO also check actual derivatives!
template <int>
void test_autodiff_scalar()
{
Vector2f p = Vector2f::Random();
@ -140,6 +141,7 @@ void test_autodiff_scalar()
}
// TODO also check actual derivatives!
template <int>
void test_autodiff_vector()
{
Vector2f p = Vector2f::Random();
@ -153,6 +155,7 @@ void test_autodiff_vector()
VERIFY_IS_APPROX(res.value(), foo(p));
}
template <int>
void test_autodiff_jacobian()
{
CALL_SUBTEST(( forward_jacobian(TestFunc1<double,2,2>()) ));
@ -162,12 +165,56 @@ void test_autodiff_jacobian()
CALL_SUBTEST(( forward_jacobian(TestFunc1<double>(3,3)) ));
}
template <int>
void test_autodiff_hessian()
{
typedef AutoDiffScalar<VectorXd> AD;
typedef Matrix<AD,Eigen::Dynamic,1> VectorAD;
typedef AutoDiffScalar<VectorAD> ADD;
typedef Matrix<ADD,Eigen::Dynamic,1> VectorADD;
VectorADD x(2);
double s1 = internal::random<double>(), s2 = internal::random<double>(), s3 = internal::random<double>(), s4 = internal::random<double>();
x(0).value()=s1;
x(1).value()=s2;
//set unit vectors for the derivative directions (partial derivatives of the input vector)
x(0).derivatives().resize(2);
x(0).derivatives().setZero();
x(0).derivatives()(0)= 1;
x(1).derivatives().resize(2);
x(1).derivatives().setZero();
x(1).derivatives()(1)=1;
//repeat partial derivatives for the inner AutoDiffScalar
x(0).value().derivatives() = VectorXd::Unit(2,0);
x(1).value().derivatives() = VectorXd::Unit(2,1);
//set the hessian matrix to zero
for(int idx=0; idx<2; idx++) {
x(0).derivatives()(idx).derivatives() = VectorXd::Zero(2);
x(1).derivatives()(idx).derivatives() = VectorXd::Zero(2);
}
ADD y = sin(AD(s3)*x(0) + AD(s4)*x(1));
VERIFY_IS_APPROX(y.value().derivatives()(0), y.derivatives()(0).value());
VERIFY_IS_APPROX(y.value().derivatives()(1), y.derivatives()(1).value());
VERIFY_IS_APPROX(y.value().derivatives()(0), s3*std::cos(s1*s3+s2*s4));
VERIFY_IS_APPROX(y.value().derivatives()(1), s4*std::cos(s1*s3+s2*s4));
VERIFY_IS_APPROX(y.derivatives()(0).derivatives(), -std::sin(s1*s3+s2*s4)*Vector2d(s3*s3,s4*s3));
VERIFY_IS_APPROX(y.derivatives()(1).derivatives(), -std::sin(s1*s3+s2*s4)*Vector2d(s3*s4,s4*s4));
}
void test_autodiff()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( test_autodiff_scalar() );
CALL_SUBTEST_2( test_autodiff_vector() );
CALL_SUBTEST_3( test_autodiff_jacobian() );
CALL_SUBTEST_1( test_autodiff_scalar<1>() );
CALL_SUBTEST_2( test_autodiff_vector<1>() );
CALL_SUBTEST_3( test_autodiff_jacobian<1>() );
CALL_SUBTEST_4( test_autodiff_hessian<1>() );
}
}