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Added an initial cut of the rvm stuff
--HG-- extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402456
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Davis King
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@ -11,6 +11,7 @@
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#include "svm/rbf_network.h"
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#include "svm/linearly_independent_subset_finder.h"
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#include "svm/reduced.h"
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#include "svm/rvm.h"
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#endif // DLIB_SVm_HEADER
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1081
dlib/svm/rvm.h
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1081
dlib/svm/rvm.h
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File diff suppressed because it is too large
Load Diff
143
dlib/svm/rvm_abstract.h
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143
dlib/svm/rvm_abstract.h
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@ -0,0 +1,143 @@
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// Copyright (C) 2008 Davis E. King (davisking@users.sourceforge.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#undef DLIB_RVm_ABSTRACT_
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#ifdef DLIB_RVm_ABSTRACT_
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#include "rvm_abstract.h"
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#include <cmath>
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#include <limits>
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#include "../matrix.h"
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#include "../algs.h"
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#include "function.h"
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#include "kernel.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 kern_type
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>
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class rvm_trainer
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{
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/*!
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REQUIREMENTS ON kern_type
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is a kernel function object as defined in dlib/svm/kernel_abstract.h
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WHAT THIS OBJECT REPRESENTS
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This object implements a trainer for a relevance vector machine for
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solving binary classification problems.
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The implementation of the RVM training algorithm used by this object is based
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on the following excellent paper:
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Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation
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for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings
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of the Ninth International Workshop on Artificial Intelligence and Statistics,
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Key West, FL, Jan 3-6.
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!*/
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public:
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typedef kern_type 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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rvm_trainer (
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);
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/*!
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ensures
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- This object is properly initialized and ready to be used
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to train a relevance vector machine.
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- #get_epsilon() == 0.001
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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_epsilon (
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scalar_type eps_
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);
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/*!
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requires
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- eps > 0
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ensures
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- #get_epsilon() == eps
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!*/
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const scalar_type get_epsilon (
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) const;
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/*!
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ensures
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- returns the error epsilon that determines when training should stop.
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Generally a good value for this is 0.001. Smaller values may result
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in a more accurate solution but take longer to execute.
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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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- is_binary_classification_problem(x,y) == true
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- x == a matrix or something convertible to a matrix via vector_to_matrix().
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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 vector_to_matrix().
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Also, y should contain scalar_type objects.
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ensures
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- trains a relevance vector classifier given the training samples in x and
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labels in y. Training is done when the error is less than get_epsilon().
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- returns a decision function F with the following properties:
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- if (new_x is a sample predicted have +1 label) then
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- F(new_x) >= 0
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- else
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- F(new_x) < 0
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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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rvm_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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rvm_trainer<K>& a,
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rvm_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_RVm_ABSTRACT_
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@ -119,7 +119,6 @@ namespace dlib
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ensures
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- This object is properly initialized and ready to be used
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to train a support vector machine.
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- #get_kernel() == kernel_type()
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- #get_nu() == 0.1
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- #get_cache_size() == 200
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- #get_epsilon() == 0.001
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@ -229,9 +228,9 @@ namespace dlib
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/*!
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requires
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- is_binary_classification_problem(x,y) == true
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- x == a matrix or something convertable to a matrix via vector_to_matrix().
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- x == a matrix or something convertible to a matrix via vector_to_matrix().
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Also, x should contain sample_type objects.
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- y == a matrix or something convertable to a matrix via vector_to_matrix().
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- y == a matrix or something convertible to a matrix via vector_to_matrix().
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Also, y should contain scalar_type objects.
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ensures
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- trains a nu support vector classifier given the training samples in x and
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