58 lines
1.9 KiB
C++
58 lines
1.9 KiB
C++
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#ifndef DEF_AUTOMATIC_DIFFERENTIATION_STATIC
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#define DEF_AUTOMATIC_DIFFERENTIATION_STATIC
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#undef __Dual_DualBase
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#undef __Dual_VectorT
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#undef __Dual_bdynamic
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#define __Dual_DualBase DualS
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#define __Dual_VectorT Eigen::Array<Scalar, N, 1>
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#define __Dual_bdynamic 0
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#include "AutomaticDifferentiation_base.hpp"
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/// Function object to evaluate the derivative of a univariate function anywhere without explicitely using the dual numbers.
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///
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/// Here is an example of use :
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///
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/// template<typename T> fct(T x) { return exp(-x*x); }
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///
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/// Scalar x = 3.14;
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/// auto gradFct = GradFunc1(fct, x); // x is only passed to the function so that the full type of GradFunc1 does not need to be written manually.
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/// Scalar dfdx = gradFct(x); // Evaluation of the gradient of fct at x.
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///
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/// // Alternatively :
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/// Scalar fx, dfdx;
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/// gradFct.get_f_grad(x, fx, dfdx); // Evaluation of the gradient of fct at x.
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template<typename Func, typename Scalar>
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struct GradFunc1
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{
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Func f;
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/// Constructor of the gradient function object. The second parameter IS NOT USED FOR THE COMPUTATION OF THE GRADIENT, but only as a convenience for the
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/// automatic detection of the Scalar variable type.
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GradFunc1(Func f_, Scalar) : f(f_) {}
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// Function that returns the 1st derivative of the function f at x.
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Scalar operator()(Scalar const& x)
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{
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// differentiate using the dual number and return the .b component.
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DualS<Scalar, 1> X(x);
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X.diff(0);
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DualS<Scalar, 1> Y = f(X);
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return Y.d(0);
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}
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/// Function that returns both the function value and the gradient of f at x. Use this preferably over separate calls to f and to gradf.
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void get_f_grad(Scalar const& x, Scalar & fx, Scalar & gradfx)
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{
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// differentiate using the dual number and return the .b component.
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DualS<Scalar, 1> X(x);
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X.diff(0);
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DualS<Scalar, 1> Y = f(X);
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fx = Y.x();
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gradfx = Y.d(0);
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}
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};
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#endif
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