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laplacian_kernel.hpp
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1 
12 #ifndef MLPACK_CORE_KERNELS_LAPLACIAN_KERNEL_HPP
13 #define MLPACK_CORE_KERNELS_LAPLACIAN_KERNEL_HPP
14 
15 #include <mlpack/prereqs.hpp>
16 
17 namespace mlpack {
18 namespace kernel {
19 
31 {
32  public:
36  LaplacianKernel() : bandwidth(1.0)
37  { }
38 
44  LaplacianKernel(double bandwidth) :
45  bandwidth(bandwidth)
46  { }
47 
60  template<typename VecTypeA, typename VecTypeB>
61  double Evaluate(const VecTypeA& a, const VecTypeB& b) const
62  {
63  // The precalculation of gamma saves us a little computation time.
64  return exp(-metric::EuclideanDistance::Evaluate(a, b) / bandwidth);
65  }
66 
75  double Evaluate(const double t) const
76  {
77  // The precalculation of gamma saves us a little computation time.
78  return exp(-t / bandwidth);
79  }
80 
90  double Gradient(const double t) const {
91  return exp(-t / bandwidth) / -bandwidth;
92  }
93 
95  double Bandwidth() const { return bandwidth; }
97  double& Bandwidth() { return bandwidth; }
98 
100  template<typename Archive>
101  void Serialize(Archive& ar, const unsigned int /* version */)
102  {
103  ar & data::CreateNVP(bandwidth, "bandwidth");
104  }
105 
106  private:
108  double bandwidth;
109 };
110 
112 template<>
114 {
115  public:
117  static const bool IsNormalized = true;
119  static const bool UsesSquaredDistance = false;
120 };
121 
122 } // namespace kernel
123 } // namespace mlpack
124 
125 #endif
This is a template class that can provide information about various kernels.
double Gradient(const double t) const
Evaluation of the gradient of the Laplacian kernel given the distance between two points...
FirstShim< T > CreateNVP(T &t, const std::string &name, typename boost::enable_if< HasSerialize< T >>::type *=0)
Call this function to produce a name-value pair; this is similar to BOOST_SERIALIZATION_NVP(), but should be used for types that have a Serialize() function (or contain a type that has a Serialize() function) instead of a serialize() function.
The core includes that mlpack expects; standard C++ includes and Armadillo.
double Evaluate(const VecTypeA &a, const VecTypeB &b) const
Evaluation of the Laplacian kernel.
static VecTypeA::elem_type Evaluate(const VecTypeA &a, const VecTypeB &b)
Computes the distance between two points.
double & Bandwidth()
Modify the bandwidth.
LaplacianKernel()
Default constructor; sets bandwidth to 1.0.
double Evaluate(const double t) const
Evaluation of the Laplacian kernel given the distance between two points.
void Serialize(Archive &ar, const unsigned int)
Serialize the kernel.
LaplacianKernel(double bandwidth)
Construct the Laplacian kernel with a custom bandwidth.
The standard Laplacian kernel.
static const bool IsNormalized
If true, then the kernel is normalized: K(x, x) = K(y, y) = 1 for all x.
double Bandwidth() const
Get the bandwidth.
static const bool UsesSquaredDistance
If true, then the kernel include a squared distance, ||x - y||^2 .