mirror of
https://gitlab.com/libeigen/eigen.git
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Clang-format tests, examples, libraries, benchmarks, etc.
This commit is contained in:
Antonio Sánchez
committed by
Rasmus Munk Larsen
Rasmus Munk Larsen
parent
3252ecc7a4
commit
46e9cdb7fe
@@ -10,36 +10,31 @@
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#include "main.h"
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#include <unsupported/Eigen/AutoDiff>
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template<typename Scalar>
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EIGEN_DONT_INLINE Scalar foo(const Scalar& x, const Scalar& y)
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{
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template <typename Scalar>
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EIGEN_DONT_INLINE Scalar foo(const Scalar& x, const Scalar& y) {
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using namespace std;
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// return x+std::sin(y);
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// return x+std::sin(y);
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EIGEN_ASM_COMMENT("mybegin");
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// pow(float, int) promotes to pow(double, double)
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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);
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//return x+2*y*x;//x*2 -std::pow(x,2);//(2*y/x);// - y*2;
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return x * 2 - 1 + static_cast<Scalar>(pow(1 + x, 2)) + 2 * sqrt(y * y + 0) - 4 * sin(0 + x) + 2 * cos(y + 0) -
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exp(Scalar(-0.5) * x * x + 0);
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// return x+2*y*x;//x*2 -std::pow(x,2);//(2*y/x);// - y*2;
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EIGEN_ASM_COMMENT("myend");
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}
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template<typename Vector>
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EIGEN_DONT_INLINE typename Vector::Scalar foo(const Vector& p)
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{
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template <typename Vector>
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EIGEN_DONT_INLINE typename Vector::Scalar foo(const Vector& p) {
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typedef typename Vector::Scalar Scalar;
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return (p-Vector(Scalar(-1),Scalar(1.))).norm() + (p.array() * p.array()).sum() + p.dot(p);
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return (p - Vector(Scalar(-1), Scalar(1.))).norm() + (p.array() * p.array()).sum() + p.dot(p);
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}
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template<typename Scalar_, int NX=Dynamic, int NY=Dynamic>
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struct TestFunc1
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{
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template <typename Scalar_, int NX = Dynamic, int NY = Dynamic>
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struct TestFunc1 {
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typedef Scalar_ Scalar;
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enum {
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InputsAtCompileTime = NX,
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ValuesAtCompileTime = NY
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};
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typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
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typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
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typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
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enum { InputsAtCompileTime = NX, ValuesAtCompileTime = NY };
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typedef Matrix<Scalar, InputsAtCompileTime, 1> InputType;
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typedef Matrix<Scalar, ValuesAtCompileTime, 1> ValueType;
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typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
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int m_inputs, m_values;
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@@ -49,186 +44,168 @@ struct TestFunc1
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int inputs() const { return m_inputs; }
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int values() const { return m_values; }
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template<typename T>
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void operator() (const Matrix<T,InputsAtCompileTime,1>& x, Matrix<T,ValuesAtCompileTime,1>* _v) const
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{
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Matrix<T,ValuesAtCompileTime,1>& v = *_v;
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template <typename T>
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void operator()(const Matrix<T, InputsAtCompileTime, 1>& x, Matrix<T, ValuesAtCompileTime, 1>* _v) const {
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Matrix<T, ValuesAtCompileTime, 1>& v = *_v;
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v[0] = 2 * x[0] * x[0] + x[0] * x[1];
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v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1];
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if(inputs()>2)
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{
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if (inputs() > 2) {
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v[0] += 0.5 * x[2];
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v[1] += x[2];
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}
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if(values()>2)
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{
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if (values() > 2) {
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v[2] = 3 * x[1] * x[0] * x[0];
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}
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if (inputs()>2 && values()>2)
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v[2] *= x[2];
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if (inputs() > 2 && values() > 2) v[2] *= x[2];
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}
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void operator() (const InputType& x, ValueType* v, JacobianType* _j) const
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{
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void operator()(const InputType& x, ValueType* v, JacobianType* _j) const {
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(*this)(x, v);
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if(_j)
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{
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if (_j) {
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JacobianType& j = *_j;
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j(0,0) = 4 * x[0] + x[1];
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j(1,0) = 3 * x[1];
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j(0, 0) = 4 * x[0] + x[1];
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j(1, 0) = 3 * x[1];
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j(0,1) = x[0];
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j(1,1) = 3 * x[0] + 2 * 0.5 * x[1];
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j(0, 1) = x[0];
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j(1, 1) = 3 * x[0] + 2 * 0.5 * x[1];
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if (inputs()>2)
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{
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j(0,2) = 0.5;
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j(1,2) = 1;
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if (inputs() > 2) {
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j(0, 2) = 0.5;
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j(1, 2) = 1;
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}
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if(values()>2)
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{
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j(2,0) = 3 * x[1] * 2 * x[0];
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j(2,1) = 3 * x[0] * x[0];
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if (values() > 2) {
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j(2, 0) = 3 * x[1] * 2 * x[0];
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j(2, 1) = 3 * x[0] * x[0];
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}
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if (inputs()>2 && values()>2)
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{
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j(2,0) *= x[2];
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j(2,1) *= x[2];
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if (inputs() > 2 && values() > 2) {
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j(2, 0) *= x[2];
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j(2, 1) *= x[2];
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j(2,2) = 3 * x[1] * x[0] * x[0];
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j(2,2) = 3 * x[1] * x[0] * x[0];
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j(2, 2) = 3 * x[1] * x[0] * x[0];
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j(2, 2) = 3 * x[1] * x[0] * x[0];
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}
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}
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}
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};
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/* Test functor for the C++11 features. */
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template <typename Scalar>
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struct integratorFunctor
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{
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typedef Matrix<Scalar, 2, 1> InputType;
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typedef Matrix<Scalar, 2, 1> ValueType;
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struct integratorFunctor {
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typedef Matrix<Scalar, 2, 1> InputType;
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typedef Matrix<Scalar, 2, 1> ValueType;
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/*
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* Implementation starts here.
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*/
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integratorFunctor(const Scalar gain) : _gain(gain) {}
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integratorFunctor(const integratorFunctor& f) : _gain(f._gain) {}
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const Scalar _gain;
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/*
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* Implementation starts here.
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*/
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integratorFunctor(const Scalar gain) : _gain(gain) {}
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integratorFunctor(const integratorFunctor& f) : _gain(f._gain) {}
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const Scalar _gain;
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template <typename T1, typename T2>
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void operator() (const T1 &input, T2 *output, const Scalar dt) const
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{
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T2 &o = *output;
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template <typename T1, typename T2>
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void operator()(const T1& input, T2* output, const Scalar dt) const {
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T2& o = *output;
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/* Integrator to test the AD. */
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o[0] = input[0] + input[1] * dt * _gain;
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o[1] = input[1] * _gain;
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/* Integrator to test the AD. */
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o[0] = input[0] + input[1] * dt * _gain;
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o[1] = input[1] * _gain;
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}
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/* Only needed for the test */
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template <typename T1, typename T2, typename T3>
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void operator()(const T1& input, T2* output, T3* jacobian, const Scalar dt) const {
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T2& o = *output;
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/* Integrator to test the AD. */
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o[0] = input[0] + input[1] * dt * _gain;
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o[1] = input[1] * _gain;
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if (jacobian) {
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T3& j = *jacobian;
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j(0, 0) = 1;
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j(0, 1) = dt * _gain;
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j(1, 0) = 0;
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j(1, 1) = _gain;
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}
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/* Only needed for the test */
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template <typename T1, typename T2, typename T3>
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void operator() (const T1 &input, T2 *output, T3 *jacobian, const Scalar dt) const
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{
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T2 &o = *output;
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/* Integrator to test the AD. */
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o[0] = input[0] + input[1] * dt * _gain;
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o[1] = input[1] * _gain;
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if (jacobian)
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{
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T3 &j = *jacobian;
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j(0, 0) = 1;
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j(0, 1) = dt * _gain;
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j(1, 0) = 0;
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j(1, 1) = _gain;
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}
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}
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}
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};
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template<typename Func> void forward_jacobian_cpp11(const Func& f)
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{
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typedef typename Func::ValueType::Scalar Scalar;
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typedef typename Func::ValueType ValueType;
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typedef typename Func::InputType InputType;
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typedef typename AutoDiffJacobian<Func>::JacobianType JacobianType;
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template <typename Func>
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void forward_jacobian_cpp11(const Func& f) {
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typedef typename Func::ValueType::Scalar Scalar;
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typedef typename Func::ValueType ValueType;
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typedef typename Func::InputType InputType;
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typedef typename AutoDiffJacobian<Func>::JacobianType JacobianType;
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InputType x = InputType::Random(InputType::RowsAtCompileTime);
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ValueType y, yref;
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JacobianType j, jref;
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InputType x = InputType::Random(InputType::RowsAtCompileTime);
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ValueType y, yref;
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JacobianType j, jref;
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const Scalar dt = internal::random<double>();
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const Scalar dt = internal::random<double>();
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jref.setZero();
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yref.setZero();
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f(x, &yref, &jref, dt);
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jref.setZero();
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yref.setZero();
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f(x, &yref, &jref, dt);
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//std::cerr << "y, yref, jref: " << "\n";
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//std::cerr << y.transpose() << "\n\n";
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//std::cerr << yref << "\n\n";
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//std::cerr << jref << "\n\n";
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// std::cerr << "y, yref, jref: " << "\n";
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// std::cerr << y.transpose() << "\n\n";
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// std::cerr << yref << "\n\n";
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// std::cerr << jref << "\n\n";
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AutoDiffJacobian<Func> autoj(f);
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autoj(x, &y, &j, dt);
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AutoDiffJacobian<Func> autoj(f);
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autoj(x, &y, &j, dt);
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//std::cerr << "y j (via autodiff): " << "\n";
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//std::cerr << y.transpose() << "\n\n";
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//std::cerr << j << "\n\n";
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// std::cerr << "y j (via autodiff): " << "\n";
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// std::cerr << y.transpose() << "\n\n";
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// std::cerr << j << "\n\n";
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VERIFY_IS_APPROX(y, yref);
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VERIFY_IS_APPROX(j, jref);
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VERIFY_IS_APPROX(y, yref);
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VERIFY_IS_APPROX(j, jref);
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}
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template<typename Func> void forward_jacobian(const Func& f)
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{
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typename Func::InputType x = Func::InputType::Random(f.inputs());
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typename Func::ValueType y(f.values()), yref(f.values());
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typename Func::JacobianType j(f.values(),f.inputs()), jref(f.values(),f.inputs());
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template <typename Func>
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void forward_jacobian(const Func& f) {
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typename Func::InputType x = Func::InputType::Random(f.inputs());
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typename Func::ValueType y(f.values()), yref(f.values());
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typename Func::JacobianType j(f.values(), f.inputs()), jref(f.values(), f.inputs());
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jref.setZero();
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yref.setZero();
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f(x,&yref,&jref);
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// std::cerr << y.transpose() << "\n\n";;
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// std::cerr << j << "\n\n";;
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jref.setZero();
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yref.setZero();
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f(x, &yref, &jref);
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// std::cerr << y.transpose() << "\n\n";;
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// std::cerr << j << "\n\n";;
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j.setZero();
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y.setZero();
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AutoDiffJacobian<Func> autoj(f);
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autoj(x, &y, &j);
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// std::cerr << y.transpose() << "\n\n";;
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// std::cerr << j << "\n\n";;
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j.setZero();
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y.setZero();
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AutoDiffJacobian<Func> autoj(f);
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autoj(x, &y, &j);
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// std::cerr << y.transpose() << "\n\n";;
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// std::cerr << j << "\n\n";;
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VERIFY_IS_APPROX(y, yref);
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VERIFY_IS_APPROX(j, jref);
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VERIFY_IS_APPROX(y, yref);
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VERIFY_IS_APPROX(j, jref);
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}
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// TODO also check actual derivatives!
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template <int>
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void test_autodiff_scalar()
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{
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void test_autodiff_scalar() {
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Vector2f p = Vector2f::Random();
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typedef AutoDiffScalar<Vector2f> AD;
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AD ax(p.x(),Vector2f::UnitX());
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AD ay(p.y(),Vector2f::UnitY());
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AD res = foo<AD>(ax,ay);
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VERIFY_IS_APPROX(res.value(), foo(p.x(),p.y()));
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AD ax(p.x(), Vector2f::UnitX());
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AD ay(p.y(), Vector2f::UnitY());
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AD res = foo<AD>(ax, ay);
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VERIFY_IS_APPROX(res.value(), foo(p.x(), p.y()));
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}
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// TODO also check actual derivatives!
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template <int>
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void test_autodiff_vector()
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{
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void test_autodiff_vector() {
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Vector2f p = Vector2f::Random();
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typedef AutoDiffScalar<Vector2f> AD;
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typedef Matrix<AD,2,1> VectorAD;
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typedef Matrix<AD, 2, 1> VectorAD;
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VectorAD ap = p.cast<AD>();
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ap.x().derivatives() = Vector2f::UnitX();
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ap.y().derivatives() = Vector2f::UnitY();
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@@ -238,59 +215,57 @@ void test_autodiff_vector()
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}
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template <int>
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void test_autodiff_jacobian()
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{
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CALL_SUBTEST(( forward_jacobian(TestFunc1<double,2,2>()) ));
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CALL_SUBTEST(( forward_jacobian(TestFunc1<double,2,3>()) ));
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CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,2>()) ));
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CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,3>()) ));
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CALL_SUBTEST(( forward_jacobian(TestFunc1<double>(3,3)) ));
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CALL_SUBTEST(( forward_jacobian_cpp11(integratorFunctor<double>(10)) ));
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void test_autodiff_jacobian() {
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CALL_SUBTEST((forward_jacobian(TestFunc1<double, 2, 2>())));
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CALL_SUBTEST((forward_jacobian(TestFunc1<double, 2, 3>())));
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CALL_SUBTEST((forward_jacobian(TestFunc1<double, 3, 2>())));
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CALL_SUBTEST((forward_jacobian(TestFunc1<double, 3, 3>())));
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CALL_SUBTEST((forward_jacobian(TestFunc1<double>(3, 3))));
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CALL_SUBTEST((forward_jacobian_cpp11(integratorFunctor<double>(10))));
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}
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template <int>
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void test_autodiff_hessian()
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{
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void test_autodiff_hessian() {
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typedef AutoDiffScalar<VectorXd> AD;
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typedef Matrix<AD,Eigen::Dynamic,1> VectorAD;
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typedef Matrix<AD, Eigen::Dynamic, 1> VectorAD;
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typedef AutoDiffScalar<VectorAD> ADD;
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typedef Matrix<ADD,Eigen::Dynamic,1> VectorADD;
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typedef Matrix<ADD, Eigen::Dynamic, 1> VectorADD;
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VectorADD x(2);
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double s1 = internal::random<double>(), s2 = internal::random<double>(), s3 = internal::random<double>(), s4 = internal::random<double>();
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x(0).value()=s1;
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x(1).value()=s2;
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double s1 = internal::random<double>(), s2 = internal::random<double>(), s3 = internal::random<double>(),
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s4 = internal::random<double>();
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x(0).value() = s1;
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x(1).value() = s2;
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//set unit vectors for the derivative directions (partial derivatives of the input vector)
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// set unit vectors for the derivative directions (partial derivatives of the input vector)
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x(0).derivatives().resize(2);
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x(0).derivatives().setZero();
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x(0).derivatives()(0)= 1;
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x(0).derivatives()(0) = 1;
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x(1).derivatives().resize(2);
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x(1).derivatives().setZero();
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x(1).derivatives()(1)=1;
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x(1).derivatives()(1) = 1;
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//repeat partial derivatives for the inner AutoDiffScalar
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x(0).value().derivatives() = VectorXd::Unit(2,0);
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x(1).value().derivatives() = VectorXd::Unit(2,1);
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// repeat partial derivatives for the inner AutoDiffScalar
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x(0).value().derivatives() = VectorXd::Unit(2, 0);
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x(1).value().derivatives() = VectorXd::Unit(2, 1);
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//set the hessian matrix to zero
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for(int idx=0; idx<2; idx++) {
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x(0).derivatives()(idx).derivatives() = VectorXd::Zero(2);
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x(1).derivatives()(idx).derivatives() = VectorXd::Zero(2);
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// set the hessian matrix to zero
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for (int idx = 0; idx < 2; idx++) {
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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));
|
||||
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));
|
||||
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));
|
||||
|
||||
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));
|
||||
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() {
|
||||
@@ -312,12 +287,12 @@ double bug_1223() {
|
||||
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);
|
||||
// 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);
|
||||
const AD t2 = min EIGEN_TEST_SPACE(denom / (chi_3 * _cv1_3), 1.0);
|
||||
|
||||
return t.value() + t2.value();
|
||||
}
|
||||
@@ -326,15 +301,15 @@ double bug_1223() {
|
||||
void bug_1260() {
|
||||
Matrix4d A = Matrix4d::Ones();
|
||||
Vector4d v = Vector4d::Ones();
|
||||
A*v;
|
||||
A* v;
|
||||
}
|
||||
|
||||
// check a compilation issue with numext::max
|
||||
double bug_1261() {
|
||||
typedef AutoDiffScalar<Matrix2d> AD;
|
||||
typedef Matrix<AD,2,1> VectorAD;
|
||||
typedef Matrix<AD, 2, 1> VectorAD;
|
||||
|
||||
VectorAD v(0.,0.);
|
||||
VectorAD v(0., 0.);
|
||||
const AD maxVal = v.maxCoeff();
|
||||
const AD minVal = v.minCoeff();
|
||||
return maxVal.value() + minVal.value();
|
||||
@@ -343,7 +318,7 @@ double bug_1261() {
|
||||
double bug_1264() {
|
||||
typedef AutoDiffScalar<Vector2d> AD;
|
||||
const AD s = 0.;
|
||||
const Matrix<AD, 3, 1> v1(0.,0.,0.);
|
||||
const Matrix<AD, 3, 1> v1(0., 0., 0.);
|
||||
const Matrix<AD, 3, 1> v2 = (s + 3.0) * v1;
|
||||
return v2(0).value();
|
||||
}
|
||||
@@ -353,31 +328,29 @@ double bug_1281() {
|
||||
int n = 2;
|
||||
typedef AutoDiffScalar<VectorXd> AD;
|
||||
const AD c = 1.;
|
||||
AD x0(2,n,0);
|
||||
AD y1 = (AD(c)+AD(c))*x0;
|
||||
y1 = x0 * (AD(c)+AD(c));
|
||||
AD y2 = (-AD(c))+x0;
|
||||
y2 = x0+(-AD(c));
|
||||
AD y3 = (AD(c)*(-AD(c))+AD(c))*x0;
|
||||
y3 = x0 * (AD(c)*(-AD(c))+AD(c));
|
||||
return (y1+y2+y3).value();
|
||||
AD x0(2, n, 0);
|
||||
AD y1 = (AD(c) + AD(c)) * x0;
|
||||
y1 = x0 * (AD(c) + AD(c));
|
||||
AD y2 = (-AD(c)) + x0;
|
||||
y2 = x0 + (-AD(c));
|
||||
AD y3 = (AD(c) * (-AD(c)) + AD(c)) * x0;
|
||||
y3 = x0 * (AD(c) * (-AD(c)) + AD(c));
|
||||
return (y1 + y2 + y3).value();
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
EIGEN_DECLARE_TEST(autodiff)
|
||||
{
|
||||
for(int i = 0; i < g_repeat; i++) {
|
||||
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>() );
|
||||
EIGEN_DECLARE_TEST(autodiff) {
|
||||
for (int i = 0; i < g_repeat; i++) {
|
||||
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>());
|
||||
}
|
||||
|
||||
CALL_SUBTEST_5( bug_1222() );
|
||||
CALL_SUBTEST_5( bug_1223() );
|
||||
CALL_SUBTEST_5( bug_1260() );
|
||||
CALL_SUBTEST_5( bug_1261() );
|
||||
CALL_SUBTEST_5( bug_1281() );
|
||||
CALL_SUBTEST_5(bug_1222());
|
||||
CALL_SUBTEST_5(bug_1223());
|
||||
CALL_SUBTEST_5(bug_1260());
|
||||
CALL_SUBTEST_5(bug_1261());
|
||||
CALL_SUBTEST_5(bug_1281());
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user