diff options
Diffstat (limited to 'unsupported/test/autodiff.cpp')
| -rw-r--r-- | unsupported/test/autodiff.cpp | 153 |
1 files changed, 150 insertions, 3 deletions
diff --git a/unsupported/test/autodiff.cpp b/unsupported/test/autodiff.cpp index 374f86df9..85743137e 100644 --- a/unsupported/test/autodiff.cpp +++ b/unsupported/test/autodiff.cpp @@ -16,7 +16,8 @@ EIGEN_DONT_INLINE Scalar foo(const Scalar& x, const Scalar& y) using namespace std; // return x+std::sin(y); EIGEN_ASM_COMMENT("mybegin"); - return static_cast<Scalar>(x*2 - 1 + pow(1+x,2) + 2*sqrt(y*y+0) - 4 * sin(0+x) + 2 * cos(y+0) - exp(-0.5*x*x+0)); + // pow(float, int) promotes to pow(double, double) + return x*2 - 1 + static_cast<Scalar>(pow(1+x,2)) + 2*sqrt(y*y+0) - 4 * sin(0+x) + 2 * cos(y+0) - exp(Scalar(-0.5)*x*x+0); //return x+2*y*x;//x*2 -std::pow(x,2);//(2*y/x);// - y*2; EIGEN_ASM_COMMENT("myend"); } @@ -104,6 +105,89 @@ struct TestFunc1 } }; + +#if EIGEN_HAS_VARIADIC_TEMPLATES +/* Test functor for the C++11 features. */ +template <typename Scalar> +struct integratorFunctor +{ + typedef Matrix<Scalar, 2, 1> InputType; + typedef Matrix<Scalar, 2, 1> ValueType; + + /* + * Implementation starts here. + */ + integratorFunctor(const Scalar gain) : _gain(gain) {} + integratorFunctor(const integratorFunctor& f) : _gain(f._gain) {} + const Scalar _gain; + + template <typename T1, typename T2> + void operator() (const T1 &input, T2 *output, const Scalar dt) const + { + T2 &o = *output; + + /* Integrator to test the AD. */ + o[0] = input[0] + input[1] * dt * _gain; + o[1] = input[1] * _gain; + } + + /* Only needed for the test */ + template <typename T1, typename T2, typename T3> + void operator() (const T1 &input, T2 *output, T3 *jacobian, const Scalar dt) const + { + T2 &o = *output; + + /* Integrator to test the AD. */ + o[0] = input[0] + input[1] * dt * _gain; + o[1] = input[1] * _gain; + + if (jacobian) + { + T3 &j = *jacobian; + + j(0, 0) = 1; + j(0, 1) = dt * _gain; + j(1, 0) = 0; + j(1, 1) = _gain; + } + } + +}; + +template<typename Func> void forward_jacobian_cpp11(const Func& f) +{ + typedef typename Func::ValueType::Scalar Scalar; + typedef typename Func::ValueType ValueType; + typedef typename Func::InputType InputType; + typedef typename AutoDiffJacobian<Func>::JacobianType JacobianType; + + InputType x = InputType::Random(InputType::RowsAtCompileTime); + ValueType y, yref; + JacobianType j, jref; + + const Scalar dt = internal::random<double>(); + + jref.setZero(); + yref.setZero(); + f(x, &yref, &jref, dt); + + //std::cerr << "y, yref, jref: " << "\n"; + //std::cerr << y.transpose() << "\n\n"; + //std::cerr << yref << "\n\n"; + //std::cerr << jref << "\n\n"; + + AutoDiffJacobian<Func> autoj(f); + autoj(x, &y, &j, dt); + + //std::cerr << "y j (via autodiff): " << "\n"; + //std::cerr << y.transpose() << "\n\n"; + //std::cerr << j << "\n\n"; + + VERIFY_IS_APPROX(y, yref); + VERIFY_IS_APPROX(j, jref); +} +#endif + template<typename Func> void forward_jacobian(const Func& f) { typename Func::InputType x = Func::InputType::Random(f.inputs()); @@ -127,7 +211,6 @@ template<typename Func> void forward_jacobian(const Func& f) VERIFY_IS_APPROX(j, jref); } - // TODO also check actual derivatives! template <int> void test_autodiff_scalar() @@ -140,6 +223,7 @@ void test_autodiff_scalar() VERIFY_IS_APPROX(res.value(), foo(p.x(),p.y())); } + // TODO also check actual derivatives! template <int> void test_autodiff_vector() @@ -150,7 +234,7 @@ void test_autodiff_vector() VectorAD ap = p.cast<AD>(); ap.x().derivatives() = Vector2f::UnitX(); ap.y().derivatives() = Vector2f::UnitY(); - + AD res = foo<VectorAD>(ap); VERIFY_IS_APPROX(res.value(), foo(p)); } @@ -163,6 +247,9 @@ void test_autodiff_jacobian() CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,2>()) )); CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,3>()) )); CALL_SUBTEST(( forward_jacobian(TestFunc1<double>(3,3)) )); +#if EIGEN_HAS_VARIADIC_TEMPLATES + CALL_SUBTEST(( forward_jacobian_cpp11(integratorFunctor<double>(10)) )); +#endif } @@ -204,9 +291,64 @@ void test_autodiff_hessian() 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)); + + ADD z = x(0)*x(1); + VERIFY_IS_APPROX(z.derivatives()(0).derivatives(), Vector2d(0,1)); + VERIFY_IS_APPROX(z.derivatives()(1).derivatives(), Vector2d(1,0)); +} + +double bug_1222() { + typedef Eigen::AutoDiffScalar<Eigen::Vector3d> AD; + const double _cv1_3 = 1.0; + const AD chi_3 = 1.0; + // this line did not work, because operator+ returns ADS<DerType&>, which then cannot be converted to ADS<DerType> + const AD denom = chi_3 + _cv1_3; + return denom.value(); +} + +double bug_1223() { + using std::min; + typedef Eigen::AutoDiffScalar<Eigen::Vector3d> AD; + + const double _cv1_3 = 1.0; + const AD chi_3 = 1.0; + const AD denom = 1.0; + + // failed because implementation of min attempts to construct ADS<DerType&> via constructor AutoDiffScalar(const Real& value) + // without initializing m_derivatives (which is a reference in this case) + #define EIGEN_TEST_SPACE + const AD t = min EIGEN_TEST_SPACE (denom / chi_3, 1.0); + + const AD t2 = min EIGEN_TEST_SPACE (denom / (chi_3 * _cv1_3), 1.0); + + return t.value() + t2.value(); +} + +// regression test for some compilation issues with specializations of ScalarBinaryOpTraits +void bug_1260() { + Matrix4d A; + Vector4d v; + A*v; } +// check a compilation issue with numext::max +double bug_1261() { + typedef AutoDiffScalar<Matrix2d> AD; + typedef Matrix<AD,2,1> VectorAD; + + VectorAD v; + const AD maxVal = v.maxCoeff(); + const AD minVal = v.minCoeff(); + return maxVal.value() + minVal.value(); +} +double bug_1264() { + typedef AutoDiffScalar<Vector2d> AD; + const AD s; + const Matrix<AD, 3, 1> v1; + const Matrix<AD, 3, 1> v2 = (s + 3.0) * v1; + return v2(0).value(); +} void test_autodiff() { @@ -216,5 +358,10 @@ void test_autodiff() CALL_SUBTEST_3( test_autodiff_jacobian<1>() ); CALL_SUBTEST_4( test_autodiff_hessian<1>() ); } + + bug_1222(); + bug_1223(); + bug_1260(); + bug_1261(); } |