dlib C++ Library - rbf_network_abstract.h

// Copyright (C) 2008 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_RBf_NETWORK_ABSTRACT_
#ifdef DLIB_RBf_NETWORK_ABSTRACT_
#include "../algs.h"
#include "function_abstract.h"
#include "kernel_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
 template <
 typename K 
 >
 class rbf_network_trainer 
 {
 /*!
 REQUIREMENTS ON K 
 is a kernel function object as defined in dlib/svm/kernel_abstract.h 
 (since this is supposed to be a RBF network it is probably reasonable
 to use some sort of radial basis kernel)
 INITIAL VALUE
 - get_num_centers() == 10 
 WHAT THIS OBJECT REPRESENTS
 This object implements a trainer for a radial basis function network.
 The implementation of this algorithm follows the normal RBF training 
 process. For more details see the code or the Wikipedia article
 about RBF networks. 
 !*/
 public:
 typedef K kernel_type;
 typedef typename kernel_type::scalar_type scalar_type;
 typedef typename kernel_type::sample_type sample_type;
 typedef typename kernel_type::mem_manager_type mem_manager_type;
 typedef decision_function<kernel_type> trained_function_type;
 rbf_network_trainer (
 ); 
 /*!
 ensures
 - this object is properly initialized
 !*/
 void set_kernel (
 const kernel_type& k
 );
 /*!
 ensures
 - #get_kernel() == k 
 !*/
 const kernel_type& get_kernel (
 ) const;
 /*!
 ensures
 - returns a copy of the kernel function in use by this object
 !*/
 void set_num_centers (
 const unsigned long num_centers
 );
 /*!
 ensures
 - #get_num_centers() == num_centers
 !*/
 const unsigned long get_num_centers (
 ) const;
 /*!
 ensures
 - returns the maximum number of centers (a.k.a. basis_vectors in the 
 trained decision_function) you will get when you train this object on data.
 !*/
 template <
 typename in_sample_vector_type,
 typename in_scalar_vector_type
 >
 const decision_function<kernel_type> train (
 const in_sample_vector_type& x,
 const in_scalar_vector_type& y
 ) const
 /*!
 requires
 - x == a matrix or something convertible to a matrix via mat().
 Also, x should contain sample_type objects.
 - y == a matrix or something convertible to a matrix via mat().
 Also, y should contain scalar_type objects.
 - is_learning_problem(x,y) == true
 ensures
 - trains a RBF network given the training samples in x and 
 labels in y and returns the resulting decision_function
 throws
 - std::bad_alloc
 !*/
 void swap (
 rbf_network_trainer& item
 );
 /*!
 ensures
 - swaps *this and item
 !*/
 }; 
// ----------------------------------------------------------------------------------------
 template <typename K>
 void swap (
 rbf_network_trainer<K>& a,
 rbf_network_trainer<K>& b
 ) { a.swap(b); }
 /*!
 provides a global swap
 !*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_RBf_NETWORK_ABSTRACT_