Added an initial cut of the rvm stuff

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402456

dlib/svm.h

@@ -11,6 +11,7 @@
 #include "svm/rbf_network.h"
 #include "svm/linearly_independent_subset_finder.h"
 #include "svm/reduced.h"
+#include "svm/rvm.h"
 
 #endif // DLIB_SVm_HEADER
 

dlib/svm/rvm_abstract.h

+// Copyright (C) 2008  Davis E. King (davisking@users.sourceforge.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#undef DLIB_RVm_ABSTRACT_
+#ifdef DLIB_RVm_ABSTRACT_
+
+#include "rvm_abstract.h"
+#include <cmath>
+#include <limits>
+#include "../matrix.h"
+#include "../algs.h"
+#include "function.h"
+#include "kernel.h"
+
+namespace dlib
+{
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename kern_type 
+        >
+    class rvm_trainer 
+    {
+        /*!
+            REQUIREMENTS ON kern_type
+                is a kernel function object as defined in dlib/svm/kernel_abstract.h 
+
+            WHAT THIS OBJECT REPRESENTS
+                This object implements a trainer for a relevance vector machine for 
+                solving binary classification problems.
+
+                The implementation of the RVM training algorithm used by this object is based
+                on the following excellent paper:
+                    Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation 
+                    for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings 
+                    of the Ninth International Workshop on Artificial Intelligence and Statistics, 
+                    Key West, FL, Jan 3-6.
+        !*/
+
+    public:
+        typedef kern_type 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;
+
+        rvm_trainer (
+        );
+        /*!
+            ensures
+                - This object is properly initialized and ready to be used
+                  to train a relevance vector machine.
+                - #get_epsilon() == 0.001
+        !*/
+
+        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_epsilon (
+            scalar_type eps_
+        );
+        /*!
+            requires
+                - eps > 0
+            ensures
+                - #get_epsilon() == eps 
+        !*/
+
+        const scalar_type get_epsilon (
+        ) const;
+        /*!
+            ensures
+                - returns the error epsilon that determines when training should stop.
+                  Generally a good value for this is 0.001.  Smaller values may result
+                  in a more accurate solution but take longer to execute.
+        !*/
+
+        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
+                - is_binary_classification_problem(x,y) == true
+                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                  Also, x should contain sample_type objects.
+                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                  Also, y should contain scalar_type objects.
+            ensures
+                - trains a relevance vector classifier given the training samples in x and 
+                  labels in y.  Training is done when the error is less than get_epsilon().
+                - returns a decision function F with the following properties:
+                    - if (new_x is a sample predicted have +1 label) then
+                        - F(new_x) >= 0
+                    - else
+                        - F(new_x) < 0
+            throws
+                - std::bad_alloc
+        !*/
+
+        void swap (
+            rvm_trainer& item
+        );
+        /*!
+            ensures
+                - swaps *this and item
+        !*/
+
+    };  
+
+// ----------------------------------------------------------------------------------------
+
+    template <typename K>
+    void swap (
+        rvm_trainer<K>& a,
+        rvm_trainer<K>& b
+    ) { a.swap(b); }
+    /*!
+        provides a global swap
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+}
+
+#endif // DLIB_RVm_ABSTRACT_
+

dlib/svm/svm_abstract.h

@@ -119,7 +119,6 @@ namespace dlib
             ensures
                 - This object is properly initialized and ready to be used
                   to train a support vector machine.
-                - #get_kernel() == kernel_type() 
                 - #get_nu() == 0.1 
                 - #get_cache_size() == 200
                 - #get_epsilon() == 0.001
@@ -229,9 +228,9 @@ namespace dlib
         /*!
             requires
                 - is_binary_classification_problem(x,y) == true
-                - x == a matrix or something convertable to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via vector_to_matrix().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertable to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via vector_to_matrix().
                   Also, y should contain scalar_type objects.
             ensures
                 - trains a nu support vector classifier given the training samples in x and