1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
5 //
6 // This Source Code Form is subject to the terms of the Mozilla
7 // Public License v. 2.0. If a copy of the MPL was not distributed
8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9
10 #include "main.h"
11 #include <unsupported/Eigen/AutoDiff>
12
13 template<typename Scalar>
foo(const Scalar & x,const Scalar & y)14 EIGEN_DONT_INLINE Scalar foo(const Scalar& x, const Scalar& y)
15 {
16 using namespace std;
17 // return x+std::sin(y);
18 EIGEN_ASM_COMMENT("mybegin");
19 return static_cast<Scalar>(x*2 - pow(x,2) + 2*sqrt(y*y) - 4 * sin(x) + 2 * cos(y) - exp(-0.5*x*x));
20 //return x+2*y*x;//x*2 -std::pow(x,2);//(2*y/x);// - y*2;
21 EIGEN_ASM_COMMENT("myend");
22 }
23
24 template<typename Vector>
foo(const Vector & p)25 EIGEN_DONT_INLINE typename Vector::Scalar foo(const Vector& p)
26 {
27 typedef typename Vector::Scalar Scalar;
28 return (p-Vector(Scalar(-1),Scalar(1.))).norm() + (p.array() * p.array()).sum() + p.dot(p);
29 }
30
31 template<typename _Scalar, int NX=Dynamic, int NY=Dynamic>
32 struct TestFunc1
33 {
34 typedef _Scalar Scalar;
35 enum {
36 InputsAtCompileTime = NX,
37 ValuesAtCompileTime = NY
38 };
39 typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
40 typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
41 typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
42
43 int m_inputs, m_values;
44
TestFunc1TestFunc145 TestFunc1() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
TestFunc1TestFunc146 TestFunc1(int inputs, int values) : m_inputs(inputs), m_values(values) {}
47
inputsTestFunc148 int inputs() const { return m_inputs; }
valuesTestFunc149 int values() const { return m_values; }
50
51 template<typename T>
operator ()TestFunc152 void operator() (const Matrix<T,InputsAtCompileTime,1>& x, Matrix<T,ValuesAtCompileTime,1>* _v) const
53 {
54 Matrix<T,ValuesAtCompileTime,1>& v = *_v;
55
56 v[0] = 2 * x[0] * x[0] + x[0] * x[1];
57 v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1];
58 if(inputs()>2)
59 {
60 v[0] += 0.5 * x[2];
61 v[1] += x[2];
62 }
63 if(values()>2)
64 {
65 v[2] = 3 * x[1] * x[0] * x[0];
66 }
67 if (inputs()>2 && values()>2)
68 v[2] *= x[2];
69 }
70
operator ()TestFunc171 void operator() (const InputType& x, ValueType* v, JacobianType* _j) const
72 {
73 (*this)(x, v);
74
75 if(_j)
76 {
77 JacobianType& j = *_j;
78
79 j(0,0) = 4 * x[0] + x[1];
80 j(1,0) = 3 * x[1];
81
82 j(0,1) = x[0];
83 j(1,1) = 3 * x[0] + 2 * 0.5 * x[1];
84
85 if (inputs()>2)
86 {
87 j(0,2) = 0.5;
88 j(1,2) = 1;
89 }
90 if(values()>2)
91 {
92 j(2,0) = 3 * x[1] * 2 * x[0];
93 j(2,1) = 3 * x[0] * x[0];
94 }
95 if (inputs()>2 && values()>2)
96 {
97 j(2,0) *= x[2];
98 j(2,1) *= x[2];
99
100 j(2,2) = 3 * x[1] * x[0] * x[0];
101 j(2,2) = 3 * x[1] * x[0] * x[0];
102 }
103 }
104 }
105 };
106
forward_jacobian(const Func & f)107 template<typename Func> void forward_jacobian(const Func& f)
108 {
109 typename Func::InputType x = Func::InputType::Random(f.inputs());
110 typename Func::ValueType y(f.values()), yref(f.values());
111 typename Func::JacobianType j(f.values(),f.inputs()), jref(f.values(),f.inputs());
112
113 jref.setZero();
114 yref.setZero();
115 f(x,&yref,&jref);
116 // std::cerr << y.transpose() << "\n\n";;
117 // std::cerr << j << "\n\n";;
118
119 j.setZero();
120 y.setZero();
121 AutoDiffJacobian<Func> autoj(f);
122 autoj(x, &y, &j);
123 // std::cerr << y.transpose() << "\n\n";;
124 // std::cerr << j << "\n\n";;
125
126 VERIFY_IS_APPROX(y, yref);
127 VERIFY_IS_APPROX(j, jref);
128 }
129
130
131 // TODO also check actual derivatives!
test_autodiff_scalar()132 void test_autodiff_scalar()
133 {
134 Vector2f p = Vector2f::Random();
135 typedef AutoDiffScalar<Vector2f> AD;
136 AD ax(p.x(),Vector2f::UnitX());
137 AD ay(p.y(),Vector2f::UnitY());
138 AD res = foo<AD>(ax,ay);
139 VERIFY_IS_APPROX(res.value(), foo(p.x(),p.y()));
140 }
141
142 // TODO also check actual derivatives!
test_autodiff_vector()143 void test_autodiff_vector()
144 {
145 Vector2f p = Vector2f::Random();
146 typedef AutoDiffScalar<Vector2f> AD;
147 typedef Matrix<AD,2,1> VectorAD;
148 VectorAD ap = p.cast<AD>();
149 ap.x().derivatives() = Vector2f::UnitX();
150 ap.y().derivatives() = Vector2f::UnitY();
151
152 AD res = foo<VectorAD>(ap);
153 VERIFY_IS_APPROX(res.value(), foo(p));
154 }
155
test_autodiff_jacobian()156 void test_autodiff_jacobian()
157 {
158 CALL_SUBTEST(( forward_jacobian(TestFunc1<double,2,2>()) ));
159 CALL_SUBTEST(( forward_jacobian(TestFunc1<double,2,3>()) ));
160 CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,2>()) ));
161 CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,3>()) ));
162 CALL_SUBTEST(( forward_jacobian(TestFunc1<double>(3,3)) ));
163 }
164
test_autodiff()165 void test_autodiff()
166 {
167 for(int i = 0; i < g_repeat; i++) {
168 CALL_SUBTEST_1( test_autodiff_scalar() );
169 CALL_SUBTEST_2( test_autodiff_vector() );
170 CALL_SUBTEST_3( test_autodiff_jacobian() );
171 }
172 }
173
174