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