Added an epsilon-insensitive support vector regression class.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404025

dlib/svm.h

@@ -26,6 +26,7 @@
 #include "svm/sort_basis_vectors.h"
 #include "svm/svm_c_trainer.h"
 #include "svm/svm_one_class_trainer.h"
+#include "svm/svr_trainer.h"
 
 #endif // DLIB_SVm_HEADER
 

dlib/svm/svr_trainer_abstract.h

+// Copyright (C) 2010  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#undef DLIB_SVm_EPSILON_REGRESSION_TRAINER_ABSTRACT_
+#ifdef DLIB_SVm_EPSILON_REGRESSION_TRAINER_ABSTRACT_
+
+#include <cmath>
+#include <limits>
+#include "../matrix/matrix_abstract.h"
+#include "../algs.h"
+#include "function_abstract.h"
+#include "kernel_abstract.h"
+#include "../optimization/optimization_solve_qp3_using_smo_abstract.h"
+
+namespace dlib
+{
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename K 
+        >
+    class svr_trainer
+    {
+        /*!
+            REQUIREMENTS ON K 
+                is a kernel function object as defined in dlib/svm/kernel_abstract.h 
+
+            WHAT THIS OBJECT REPRESENTS
+                This object implements a trainer for performing epsilon-insensitive support 
+                vector regression.  It is implemented using the SMO algorithm.
+
+                The implementation of the eps-SVR training algorithm used by this object is based
+                on the following paper:
+                    - Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector 
+                      machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
+        !*/
+
+    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;
+
+        svr_trainer (
+        );
+        /*!
+            ensures
+                - This object is properly initialized and ready to be used
+                  to train a support vector machine.
+                - #get_c() == 1
+                - #get_epsilon_insensitivity() == 0.1
+                - #get_cache_size() == 200
+                - #get_epsilon() == 0.001
+        !*/
+
+        void set_cache_size (
+            long cache_size
+        );
+        /*!
+            requires
+                - cache_size > 0
+            ensures
+                - #get_cache_size() == cache_size 
+        !*/
+
+        const long get_cache_size (
+        ) const;
+        /*!
+            ensures
+                - returns the number of megabytes of cache this object will use
+                  when it performs training via the this->train() function.
+                  (bigger values of this may make training go faster but won't affect 
+                  the result.  However, too big a value will cause you to run out of 
+                  memory, obviously.)
+        !*/
+
+        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.
+        !*/
+
+        void set_epsilon_insensitivity (
+            scalar_type eps
+        );
+        /*!
+            requires
+                - eps > 0
+            ensures
+                - #get_epsilon_insensitivity() == eps
+        !*/
+
+        const scalar_type get_epsilon_insensitivity (
+        ) const;
+        /*!
+            ensures
+                - This object tries to find a function which minimizes the
+                  regression error on a training set.  This error is measured
+                  in the following way:
+                    - if (abs(predicted_value - true_labeled_value) < eps) then
+                        - The error is 0.  That is, any function which gets within
+                          eps of the correct output is good enough.
+                    - else
+                        - The error grows linearly once it gets bigger than eps
+                 
+                  So epsilon-insensitive regression means we do regression but 
+                  stop trying to fit a data point once it is "close enough".  
+                  This function returns that eps value which controls what we 
+                  mean by "close enough".
+        !*/
+
+        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_c (
+            scalar_type C 
+        );
+        /*!
+            requires
+                - C > 0
+            ensures
+                - #get_c() == C 
+        !*/
+
+        const scalar_type get_c (
+        ) const;
+        /*!
+            ensures
+                - returns the SVR regularization parameter.  It is the parameter that 
+                  determines the trade-off between trying to reduce the training error 
+                  or allowing more errors but hopefully improving the generalization 
+                  of the resulting decision_function.  Larger values encourage exact 
+                  fitting while smaller values of C may encourage better generalization. 
+        !*/
+
+        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_learning_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
+                - performs support vector regression given the training samples in x and 
+                  target values in y.  
+                - returns a decision_function F with the following properties:
+                    - F(new_x) == predicted y value
+        !*/
+
+        void swap (
+            svr_trainer& item
+        );
+        /*!
+            ensures
+                - swaps *this and item
+        !*/
+    }; 
+
+    template <typename K>
+    void swap (
+        svr_trainer<K>& a,
+        svr_trainer<K>& b
+    ) { a.swap(b); }
+    /*!
+        provides a global swap
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+}
+
+#endif // DLIB_SVm_EPSILON_REGRESSION_TRAINER_ABSTRACT_
+
+
+