Added the option to set a prior to svm_rank_trainer.

dlib/svm/svm_rank_trainer.h

@@ -297,6 +297,8 @@ namespace dlib
         )
         {
             last_weight_1 = should_last_weight_be_1;
+            if (last_weight_1)
+                prior.set_size(0);
         }
 
         void set_oca (
@@ -326,6 +328,33 @@ namespace dlib
         )
         {
             learn_nonnegative_weights = value;
+            if (learn_nonnegative_weights)
+                prior.set_size(0); 
+        }
+
+        void set_prior (
+            const trained_function_type& prior_
+        )
+        {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(prior_.basis_vectors.size() == 1 &&
+                        prior_.alpha(0) == 1,
+                "\t void svm_rank_trainer::set_prior()"
+                << "\n\t The supplied prior could not have been created by this object's train() method."
+                << "\n\t prior_.basis_vectors.size(): " << prior_.basis_vectors.size() 
+                << "\n\t prior_.alpha(0):             " << prior_.alpha(0) 
+                << "\n\t this: " << this
+                );
+
+            prior = prior_.basis_vectors(0);
+            learn_nonnegative_weights = false;
+            last_weight_1 = false;
+        }
+
+        bool has_prior (
+        ) const
+        {
+            return prior.size() != 0;
         }
 
         void set_c (
@@ -379,10 +408,30 @@ namespace dlib
                 force_weight_1_idx = num_dims-1;
             }
 
+            if (has_prior())
+            {
+                if (is_matrix<sample_type>::value)
+                {
+                    // make sure requires clause is not broken
+                    DLIB_CASSERT(num_dims == (unsigned long)prior.size(),
+                        "\t decision_function svm_rank_trainer::train(samples)"
+                        << "\n\t The dimension of the training vectors must match the dimension of\n"
+                        << "\n\t those used to create the prior."
+                        << "\n\t num_dims:     " << num_dims 
+                        << "\n\t prior.size(): " << prior.size() 
+                    );
+                }
+                solver( make_oca_problem_ranking_svm<w_type>(C, samples, verbose, eps, max_iterations), 
+                    w, 
+                    prior);
+            }
+            else
+            {
                 solver( make_oca_problem_ranking_svm<w_type>(C, samples, verbose, eps, max_iterations), 
                     w, 
                     num_nonnegative,
                     force_weight_1_idx);
+            }
 
 
             // put the solution into a decision function and then return it
@@ -415,6 +464,7 @@ namespace dlib
         unsigned long max_iterations;
         bool learn_nonnegative_weights;
         bool last_weight_1;
+        matrix<scalar_type,0,1> prior;
     }; 
 
 // ----------------------------------------------------------------------------------------

dlib/svm/svm_rank_trainer_abstract.h

@@ -58,6 +58,7 @@ namespace dlib
                 - #get_max_iterations() == 10000
                 - #learns_nonnegative_weights() == false
                 - #forces_last_weight_to_1() == false
+                - #has_prior() == false
         !*/
 
         explicit svm_rank_trainer (
@@ -76,6 +77,7 @@ namespace dlib
                 - #get_max_iterations() == 10000
                 - #learns_nonnegative_weights() == false
                 - #forces_last_weight_to_1() == false
+                - #has_prior() == false
         !*/
 
         void set_epsilon (
@@ -146,6 +148,8 @@ namespace dlib
         /*!
             ensures
                 - #forces_last_weight_to_1() == should_last_weight_be_1
+                - if (should_last_weight_be_1 == true) then
+                    - #has_prior() == false
         !*/
 
         void set_oca (
@@ -190,6 +194,39 @@ namespace dlib
         /*!
             ensures
                 - #learns_nonnegative_weights() == value
+                - if (value == true) then
+                    - #has_prior() == false
+        !*/
+
+        void set_prior (
+            const trained_function_type& prior
+        );
+        /*!
+            requires
+                - prior == a function produced by a call to this class's train() function.  
+                  Therefore, it must be the case that:
+                    - prior.basis_vectors.size() == 1
+                    - prior.alpha(0) == 1
+            ensures
+                - Subsequent calls to train() will try to learn a function similar to the
+                  given prior.
+                - #has_prior() == true
+                - #learns_nonnegative_weights() == false
+                - #forces_last_weight_to_1() == false
+        !*/
+
+        bool has_prior (
+        ) const
+        /*!
+            ensures
+                - returns true if a prior has been set and false otherwise.  Having a prior
+                  set means that you have called set_prior() and supplied a previously
+                  trained function as a reference.  In this case, any call to train() will
+                  try to learn a function that matches the behavior of the prior as close
+                  as possible but also fits the supplied training data.  In more technical
+                  detail, having a prior means we replace the ||w||^2 regularizer with one
+                  of the form ||w-prior||^2 where w is the set of parameters for a learned
+                  function.
         !*/
 
         void set_c (
@@ -219,6 +256,9 @@ namespace dlib
         /*!
             requires
                 - is_ranking_problem(samples) == true
+                - if (has_prior()) then
+                    - The vectors in samples must have the same dimensionality as the
+                      vectors used to train the prior given to set_prior().  
             ensures
                 - trains a ranking support vector classifier given the training samples.  
                 - returns a decision function F with the following properties:
@@ -237,6 +277,9 @@ namespace dlib
         /*!
             requires
                 - is_ranking_problem(std::vector<ranking_pair<sample_type> >(1, sample)) == true
+                - if (has_prior()) then
+                    - The vectors in samples must have the same dimensionality as the
+                      vectors used to train the prior given to set_prior().  
             ensures
                 - This is just a convenience routine for calling the above train()
                   function.  That is, it just copies sample into a std::vector object and

dlib/test/ranking.cpp

@@ -73,6 +73,40 @@ namespace
         }
     }
 
+// ----------------------------------------------------------------------------------------
+
+    void run_prior_test()
+    {
+        print_spinner();
+        typedef matrix<double,3,1> sample_type;
+        typedef linear_kernel<sample_type> kernel_type;
+
+        svm_rank_trainer<kernel_type> trainer;
+
+        ranking_pair<sample_type> data;
+
+        sample_type samp;
+        samp = 0, 0, 1; data.relevant.push_back(samp); 
+        samp = 0, 1, 0; data.nonrelevant.push_back(samp); 
+
+        trainer.set_c(10);
+        decision_function<kernel_type> df = trainer.train(data);
+
+        trainer.set_prior(df);
+
+        data.relevant.clear();
+        data.nonrelevant.clear();
+        samp = 1, 0, 0; data.relevant.push_back(samp); 
+        samp = 0, 1, 0; data.nonrelevant.push_back(samp); 
+
+        df = trainer.train(data);
+
+        dlog << LINFO << trans(df.basis_vectors(0));
+        DLIB_TEST(df.basis_vectors(0)(0) > 0);
+        DLIB_TEST(df.basis_vectors(0)(1) < 0);
+        DLIB_TEST(df.basis_vectors(0)(2) > 0);
+    }
+
 // ----------------------------------------------------------------------------------------
 
     void dotest1()
@@ -355,6 +389,7 @@ namespace
             dotest_sparse_vectors();
             test_svmrank_weight_force_dense<true>();
             test_svmrank_weight_force_dense<false>();
+            run_prior_test();
 
         }
     } a;