Added a spec for the assignment problem validation functions and added

missing asserts.

dlib/svm/cross_validate_assignment_trainer.h

@@ -23,6 +23,28 @@ namespace dlib
         const std::vector<typename assignment_function::label_type>& labels
     )
     {
+        // make sure requires clause is not broken
+#ifdef ENABLE_ASSERTS
+        if (assigner.forces_assignment())
+        {
+            DLIB_ASSERT(is_forced_assignment_problem(samples, labels),
+                "\t double test_assignment_function()"
+                << "\n\t invalid inputs were given to this function"
+                << "\n\t is_forced_assignment_problem(samples,labels): " << is_forced_assignment_problem(samples,labels)
+                << "\n\t is_assignment_problem(samples,labels):        " << is_assignment_problem(samples,labels)
+                << "\n\t is_learning_problem(samples,labels):          " << is_learning_problem(samples,labels)
+                );
+        }
+        else
+        {
+            DLIB_ASSERT(is_assignment_problem(samples, labels),
+                "\t double test_assignment_function()"
+                << "\n\t invalid inputs were given to this function"
+                << "\n\t is_assignment_problem(samples,labels): " << is_assignment_problem(samples,labels)
+                << "\n\t is_learning_problem(samples,labels):   " << is_learning_problem(samples,labels)
+                );
+        }
+#endif
         double total_right = 0;
         double total = 0;
         for (unsigned long i = 0; i < samples.size(); ++i)
@@ -55,6 +77,37 @@ namespace dlib
         const long folds
     )
     {
+        // make sure requires clause is not broken
+#ifdef ENABLE_ASSERTS
+        if (trainer.forces_assignment())
+        {
+            DLIB_ASSERT(is_forced_assignment_problem(samples, labels) &&
+                        1 < folds && folds <= static_cast<long>(samples.size()),
+                "\t double cross_validate_assignment_trainer()"
+                << "\n\t invalid inputs were given to this function"
+                << "\n\t samples.size(): " << samples.size() 
+                << "\n\t folds:  " << folds 
+                << "\n\t is_forced_assignment_problem(samples,labels): " << is_forced_assignment_problem(samples,labels)
+                << "\n\t is_assignment_problem(samples,labels):        " << is_assignment_problem(samples,labels)
+                << "\n\t is_learning_problem(samples,labels):          " << is_learning_problem(samples,labels)
+                );
+        }
+        else
+        {
+            DLIB_ASSERT(is_assignment_problem(samples, labels) &&
+                        1 < folds && folds <= static_cast<long>(samples.size()),
+                "\t double cross_validate_assignment_trainer()"
+                << "\n\t invalid inputs were given to this function"
+                << "\n\t samples.size(): " << samples.size() 
+                << "\n\t folds:  " << folds 
+                << "\n\t is_assignment_problem(samples,labels): " << is_assignment_problem(samples,labels)
+                << "\n\t is_learning_problem(samples,labels):   " << is_learning_problem(samples,labels)
+                );
+        }
+#endif
+
+
+
         typedef typename trainer_type::sample_type sample_type;
         typedef typename trainer_type::label_type label_type;
 

dlib/svm/cross_validate_assignment_trainer_abstract.h

+// Copyright (C) 2011  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#undef DLIB_CROSS_VALIDATE_ASSiGNEMNT_TRAINER_ABSTRACT_H__
+#ifdef DLIB_CROSS_VALIDATE_ASSiGNEMNT_TRAINER_ABSTRACT_H__
+
+#include <vector>
+#include "../matrix.h"
+#include "svm.h"
+
+
+namespace dlib
+{
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename assignment_function
+        >
+    double test_assignment_function (
+        const assignment_function& assigner,
+        const std::vector<typename assignment_function::sample_type>& samples,
+        const std::vector<typename assignment_function::label_type>& labels
+    );
+    /*!
+        requires
+            - is_assignment_problem(samples, labels)
+            - if (assigner.forces_assignment()) then
+                - is_forced_assignment_problem(samples, labels) 
+            - assignment_function == an instantiation of the dlib::assignment_function
+              template or an object with a compatible interface.
+        ensures
+            - Tests assigner against the given samples and labels and returns the fraction 
+              of assignments predicted correctly.  
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename trainer_type
+        >
+    double cross_validate_assignment_trainer (
+        const trainer_type& trainer,
+        const std::vector<typename trainer_type::sample_type>& samples,
+        const std::vector<typename trainer_type::label_type>& labels,
+        const long folds
+    );
+    /*!
+        requires
+            - is_assignment_problem(samples, labels)
+            - if (trainer.forces_assignment()) then
+                - is_forced_assignment_problem(samples, labels) 
+            - 1 < folds <= samples.size()
+            - trainer_type == dlib::structural_assignment_trainer or an object
+              with a compatible interface.
+        ensures
+            - performs k-fold cross validation by using the given trainer to solve the
+              given assignment learning problem for the given number of folds.  Each fold 
+              is tested using the output of the trainer and the fraction of assignments
+              predicted correctly is returned.
+            - The number of folds used is given by the folds argument.
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+}
+
+#endif // DLIB_CROSS_VALIDATE_ASSiGNEMNT_TRAINER_ABSTRACT_H__
 
 

dlib/svm/structural_assignment_trainer.h

@@ -29,6 +29,8 @@ namespace dlib
 
         typedef assignment_function<feature_extractor> trained_function_type;
 
+        bool forces_assignment(
+        ) const { return false; } // TODO
 
         const assignment_function<feature_extractor> train (  
             const std::vector<sample_type>& x,