add an auto-diff module in unsupported. it is similar to adolc's forward

mode but the advantage of using Eigen's expression template to compute
the derivatives (unless you nest an AutoDiffScalar into an Eigen's
matrix).

1 files changed, 156 insertions, 0 deletions

diff --git a/unsupported/test/autodiff.cpp b/unsupported/test/autodiff.cpp
new file mode 100644
index 000000000..7d619897c
--- /dev/null
+++ b/unsupported/test/autodiff.cpp
@@ -0,0 +1,156 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#include "main.h"
+#include <unsupported/Eigen/AutoDiff>
+
+template<typename Scalar>
+EIGEN_DONT_INLINE Scalar foo(const Scalar& x, const Scalar& y)
+{
+//   return x+std::sin(y);
+  asm("#mybegin");
+  return x*2 - std::pow(x,2) + 2*std::sqrt(y*y) - 4 * std::sin(x) + 2 * std::cos(y) - std::exp(-0.5*x*x);
+//   return y/x;// - y*2;
+  asm("#myend");
+}
+
+template<typename _Scalar, int NX=Dynamic, int NY=Dynamic>
+struct TestFunc1
+{
+  typedef _Scalar Scalar;
+  enum {
+    InputsAtCompileTime = NX,
+    ValuesAtCompileTime = NY
+  };
+  typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
+  typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
+  typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
+  
+  int m_inputs, m_values;
+  
+  TestFunc1() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
+  TestFunc1(int inputs, int values) : m_inputs(inputs), m_values(values) {}
+  
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+
+  template<typename T>
+  void operator() (const Matrix<T,InputsAtCompileTime,1>& x, Matrix<T,ValuesAtCompileTime,1>* _v) const
+  {
+    Matrix<T,ValuesAtCompileTime,1>& v = *_v;
+
+    v[0] = 2 * x[0] * x[0] + x[0] * x[1];
+    v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1];
+    if(inputs()>2)
+    {
+      v[0] += 0.5 * x[2];
+      v[1] += x[2];
+    }
+    if(values()>2)
+    {
+      v[2] = 3 * x[1] * x[0] * x[0];
+    }
+    if (inputs()>2 && values()>2)
+      v[2] *= x[2];
+  }
+
+  void operator() (const InputType& x, ValueType* v, JacobianType* _j) const
+  {
+    (*this)(x, v);
+
+    if(_j)
+    {
+      JacobianType& j = *_j;
+
+      j(0,0) = 4 * x[0] + x[1];
+      j(1,0) = 3 * x[1];
+
+      j(0,1) = x[0];
+      j(1,1) = 3 * x[0] + 2 * 0.5 * x[1];
+
+      if (inputs()>2)
+      {
+        j(0,2) = 0.5;
+        j(1,2) = 1;
+      }
+      if(values()>2)
+      {
+        j(2,0) = 3 * x[1] * 2 * x[0];
+        j(2,1) = 3 * x[0] * x[0];
+      }
+      if (inputs()>2 && values()>2)
+      {
+        j(2,0) *= x[2];
+        j(2,1) *= x[2];
+
+        j(2,2) = 3 * x[1] * x[0] * x[0];
+        j(2,2) = 3 * x[1] * x[0] * x[0];
+      }
+    }
+  }
+};
+
+template<typename Func> void adolc_forward_jacobian(const Func& f)
+{
+    typename Func::InputType x = Func::InputType::Random(f.inputs());
+    typename Func::ValueType y(f.values()), yref(f.values());
+    typename Func::JacobianType j(f.values(),f.inputs()), jref(f.values(),f.inputs());
+
+    jref.setZero();
+    yref.setZero();
+    f(x,&yref,&jref);
+//     std::cerr << y.transpose() << "\n\n";;
+//     std::cerr << j << "\n\n";;
+
+    j.setZero();
+    y.setZero();
+    AutoDiffJacobian<Func> autoj(f);
+    autoj(x, &y, &j);
+//     std::cerr << y.transpose() << "\n\n";;
+//     std::cerr << j << "\n\n";;
+
+    VERIFY_IS_APPROX(y, yref);
+    VERIFY_IS_APPROX(j, jref);
+}
+
+void test_autodiff()
+{
+  std::sqrt(3);
+  std::sin(3);
+  std::cerr << foo<float>(1,2) << "\n";
+  AutoDiffScalar<Vector2f> ax(1,Vector2f::UnitX());
+  AutoDiffScalar<Vector2f> ay(2,Vector2f::UnitY());
+  std::cerr << foo<AutoDiffScalar<Vector2f> >(ax,ay).value() << " <> "
+            << foo<AutoDiffScalar<Vector2f> >(ax,ay).derivatives().transpose() << "\n\n";
+  
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,2,2>()) ));
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,2,3>()) ));
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,2>()) ));
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,3>()) ));
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double>(3,3)) ));
+  }
+  
+//   exit(1);
+}