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autodiff:
* fix namespace issue * simplify Jacobian code * fix issue with "Dynamic derivatives"
This commit is contained in:
Gael Guennebaud
parent
0927ba1fd3
commit
1503043981
@ -51,7 +51,8 @@ public:
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typedef typename Functor::ValueType ValueType;
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typedef typename Functor::JacobianType JacobianType;
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typedef AutoDiffScalar<Matrix<double,InputsAtCompileTime,1> > ActiveScalar;
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typedef Matrix<double,InputsAtCompileTime,1> DerivativeType;
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typedef AutoDiffScalar<DerivativeType> ActiveScalar;
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typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput;
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typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue;
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@ -71,24 +72,18 @@ public:
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ActiveValue av(jac.rows());
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if(InputsAtCompileTime==Dynamic)
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{
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for (int j=0; j<jac.cols(); j++)
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ax[j].derivatives().resize(this->inputs());
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for (int j=0; j<jac.rows(); j++)
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av[j].derivatives().resize(this->inputs());
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}
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for (int j=0; j<jac.cols(); j++)
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for (int i=0; i<jac.cols(); i++)
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ax[i].derivatives().coeffRef(j) = i==j ? 1 : 0;
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for (int i=0; i<jac.cols(); i++)
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ax[i].derivatives() = DerivativeType::Unit(this->inputs(),i);
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Functor::operator()(ax, &av);
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for (int i=0; i<jac.rows(); i++)
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{
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(*v)[i] = av[i].value();
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for (int j=0; j<jac.cols(); j++)
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jac.coeffRef(i,j) = av[i].derivatives().coeff(j);
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jac.row(i) = av[i].derivatives();
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}
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}
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protected:
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@ -27,6 +27,18 @@
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namespace Eigen {
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template<typename A, typename B>
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struct ei_make_coherent_impl {
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static void run(A& a, B& b) {}
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};
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// resize a to match b is a.size()==0, and conversely.
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template<typename A, typename B>
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void ei_make_coherent(const A& a, const B&b)
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{
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ei_make_coherent_impl<A,B>::run(a.const_cast_derived(), b.const_cast_derived());
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}
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/** \class AutoDiffScalar
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* \brief A scalar type replacement with automatic differentation capability
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*
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@ -100,6 +112,7 @@ class AutoDiffScalar
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inline const AutoDiffScalar<CwiseBinaryOp<ei_scalar_sum_op<Scalar>,DerType,OtherDerType> >
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operator+(const AutoDiffScalar<OtherDerType>& other) const
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{
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ei_make_coherent(m_derivatives, other.derivatives());
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return AutoDiffScalar<CwiseBinaryOp<ei_scalar_sum_op<Scalar>,DerType,OtherDerType> >(
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m_value + other.value(),
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m_derivatives + other.derivatives());
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@ -117,6 +130,7 @@ class AutoDiffScalar
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inline const AutoDiffScalar<CwiseBinaryOp<ei_scalar_difference_op<Scalar>, DerType,OtherDerType> >
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operator-(const AutoDiffScalar<OtherDerType>& other) const
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{
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ei_make_coherent(m_derivatives, other.derivatives());
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return AutoDiffScalar<CwiseBinaryOp<ei_scalar_difference_op<Scalar>, DerType,OtherDerType> >(
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m_value - other.value(),
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m_derivatives - other.derivatives());
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@ -178,6 +192,7 @@ class AutoDiffScalar
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NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherDerType> > > > > >
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operator/(const AutoDiffScalar<OtherDerType>& other) const
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{
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ei_make_coherent(m_derivatives, other.derivatives());
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return AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>,
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NestByValue<CwiseBinaryOp<ei_scalar_difference_op<Scalar>,
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NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >,
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@ -193,6 +208,7 @@ class AutoDiffScalar
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NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherDerType> > > >
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operator*(const AutoDiffScalar<OtherDerType>& other) const
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{
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ei_make_coherent(m_derivatives, other.derivatives());
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return AutoDiffScalar<CwiseBinaryOp<ei_scalar_sum_op<Scalar>,
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NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >,
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NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherDerType> > > >(
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@ -219,12 +235,57 @@ class AutoDiffScalar
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};
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template<typename A_Scalar, int A_Rows, int A_Cols, int A_Options, int A_MaxRows, int A_MaxCols, typename B>
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struct ei_make_coherent_impl<Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols>, B> {
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typedef Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols> A;
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static void run(A& a, B& b) {
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if((A_Rows==Dynamic || A_Cols==Dynamic) && (a.size()==0))
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{
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a.resize(b.size());
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a.setZero();
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}
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}
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};
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template<typename A, typename B_Scalar, int B_Rows, int B_Cols, int B_Options, int B_MaxRows, int B_MaxCols>
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struct ei_make_coherent_impl<A, Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> > {
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typedef Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> B;
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static void run(A& a, B& b) {
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if((B_Rows==Dynamic || B_Cols==Dynamic) && (b.size()==0))
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{
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b.resize(a.size());
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b.setZero();
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}
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}
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};
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template<typename A_Scalar, int A_Rows, int A_Cols, int A_Options, int A_MaxRows, int A_MaxCols,
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typename B_Scalar, int B_Rows, int B_Cols, int B_Options, int B_MaxRows, int B_MaxCols>
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struct ei_make_coherent_impl<Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols>,
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Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> > {
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typedef Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols> A;
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typedef Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> B;
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static void run(A& a, B& b) {
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if((A_Rows==Dynamic || A_Cols==Dynamic) && (a.size()==0))
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{
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a.resize(b.size());
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a.setZero();
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}
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else if((B_Rows==Dynamic || B_Cols==Dynamic) && (b.size()==0))
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{
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b.resize(a.size());
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b.setZero();
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}
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}
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};
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}
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#define EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(FUNC,CODE) \
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template<typename DerType> \
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inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<typename ei_traits<DerType>::Scalar>, DerType> > \
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FUNC(const AutoDiffScalar<DerType>& x) { \
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inline const Eigen::AutoDiffScalar<Eigen::CwiseUnaryOp<Eigen::ei_scalar_multiple_op<typename Eigen::ei_traits<DerType>::Scalar>, DerType> > \
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FUNC(const Eigen::AutoDiffScalar<DerType>& x) { \
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using namespace Eigen; \
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typedef typename ei_traits<DerType>::Scalar Scalar; \
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typedef AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> > ReturnType; \
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CODE; \
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@ -253,9 +314,10 @@ namespace std
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return ReturnType(std::log(x.value),x.derivatives() * (Scalar(1).x.value()));)
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template<typename DerType>
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inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<typename ei_traits<DerType>::Scalar>, DerType> >
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pow(const AutoDiffScalar<DerType>& x, typename ei_traits<DerType>::Scalar y)
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inline const Eigen::AutoDiffScalar<Eigen::CwiseUnaryOp<Eigen::ei_scalar_multiple_op<typename Eigen::ei_traits<DerType>::Scalar>, DerType> >
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pow(const Eigen::AutoDiffScalar<DerType>& x, typename Eigen::ei_traits<DerType>::Scalar y)
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{
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using namespace Eigen;
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typedef typename ei_traits<DerType>::Scalar Scalar;
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return AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >(
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std::pow(x.value(),y),
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