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  1. #include <iostream>
  2. #include <complex>
  3. #include <functional>
  4.  
  5.  
  6. class Kernel {
  7. public: 
  8.     /*
  9.     Covariance function : return the covariance between two R.V. for the entire kernel's domain definition. 
  10.     */
  11.     virtual double covarianceFunction(
  12.         double   X,
  13.         double   Y
  14.     )const = 0 ;
  15.     ~Kernel() = default;
  16. };
  17.  
  18.  
  19. template <typename Func>
  20. class FooKernel : public Kernel {
  21. public:
  22.  
  23.     FooKernel(Func&& fun) : fun_(std::forward<Func>(fun)) {}
  24.     double covarianceFunction(
  25.         double   X,
  26.         double   Y
  27.     ) const {
  28.         return fun_(X, Y);
  29.     }
  30.     template<class T>
  31.     auto operator+(const T b) const {
  32.         return make_foo_kernel([b, this](double X, double Y) -> double {
  33.             return this->covarianceFunction(X, Y) + b.covarianceFunction(X, Y);
  34.         });
  35.     }
  36.     FooKernel operator=(const FooKernel other) const {
  37.         return other;
  38.     }
  39. private:
  40.    Func fun_;
  41. };
  42.  
  43. template <typename Func>
  44. auto make_foo_kernel(Func&& fun)
  45. {
  46.     return FooKernel<Func>(std::forward<Func>(fun));
  47. }
  48.  
  49.  
  50. class GaussianKernel : public Kernel {
  51. public:
  52.     GaussianKernel(double sigma, double scale) : m_sigma(sigma), m_scale(scale) {}
  53.     GaussianKernel(double sigma) : m_sigma(sigma), m_scale(1) {}
  54.     /*
  55.     A well known covariance function that enforces smooth deformations
  56.     Ref : Shape modeling using Gaussian process Morphable Models, Luethi et al.
  57.     */
  58.     double covarianceFunction(
  59.         double   X,
  60.         double   Y
  61.     ) const 
  62.     {
  63.         //use diagonal matrix
  64.     double result;
  65.     result = m_scale  *  exp(-std::norm(X - Y) / (m_sigma*m_sigma));
  66.     return result;      
  67.     }
  68.     template<class T>
  69.     auto operator+(const T b) const {
  70.         return make_foo_kernel([b, this](double X, double Y) -> double {
  71.             auto debugBval = b.covarianceFunction(X, Y);
  72.             auto debugAval = this->covarianceFunction(X, Y);
  73.             auto test = debugBval + debugAval;
  74.             return test;
  75.         });
  76.     }
  77. private:
  78.     double m_sigma;
  79.     double m_scale;
  80. };
  81.  
  82. int main()
  83. {
  84.     auto C = GaussianKernel(50,60) + GaussianKernel(100,200);
  85.     auto result = C.covarianceFunction(30.0,40.0);
  86.  
  87.     return 0;
  88. }
Success #stdin #stdout 0s 4584KB
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https://ideone.com/2aRGJ1
language:
C++14 (gcc 8.3)
created:
6 years ago
visibility:
public

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