worked on the rvm regression

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402457

dlib/svm/rvm.h

@@ -116,13 +116,14 @@ namespace dlib
         {
 
             // make sure requires clause is not broken
-            DLIB_ASSERT(x.nr() > 1 && x.nr() == y.nr() && x.nc() == 1 && y.nc() == 1,
+            DLIB_ASSERT(is_binary_classification_problem(x,y) == true,
                 "\tdecision_function rvm_trainer::train(x,y)"
                 << "\n\t invalid inputs were given to this function"
                 << "\n\t x.nr(): " << x.nr() 
                 << "\n\t y.nr(): " << y.nr() 
                 << "\n\t x.nc(): " << x.nc() 
                 << "\n\t y.nc(): " << y.nc() 
+                << "\n\t is_binary_classification_problem(x,y): " << ((is_binary_classification_problem(x,y))? "true":"false")
                 );
 
             // make a target vector where +1 examples have value 1 and -1 examples
@@ -638,10 +639,10 @@ namespace dlib
             >
         const decision_function<kernel_type> train (
             const in_sample_vector_type& x,
-            const in_scalar_vector_type& y
+            const in_scalar_vector_type& t
         ) const
         {
-            return do_train(vector_to_matrix(x), vector_to_matrix(y));
+            return do_train(vector_to_matrix(x), vector_to_matrix(t));
         }
 
         void swap (
@@ -665,30 +666,20 @@ namespace dlib
             >
         const decision_function<kernel_type> do_train (
             const in_sample_vector_type& x,
-            const in_scalar_vector_type& y
+            const in_scalar_vector_type& t
         ) const
         {
 
             // make sure requires clause is not broken
-            DLIB_ASSERT(x.nr() > 1 && x.nr() == y.nr() && x.nc() == 1 && y.nc() == 1,
-                "\tdecision_function rvm_regression_trainer::train(x,y)"
+            DLIB_ASSERT(x.nr() > 1 && x.nr() == t.nr() && x.nc() == 1 && t.nc() == 1,
+                "\tdecision_function rvm_regression_trainer::train(x,t)"
                 << "\n\t invalid inputs were given to this function"
                 << "\n\t x.nr(): " << x.nr() 
-                << "\n\t y.nr(): " << y.nr() 
+                << "\n\t t.nr(): " << t.nr() 
                 << "\n\t x.nc(): " << x.nc() 
-                << "\n\t y.nc(): " << y.nc() 
+                << "\n\t t.nc(): " << t.nc() 
                 );
 
-            // make a target vector where +1 examples have value 1 and -1 examples
-            // have a value of 0.
-            scalar_vector_type t(y.size());
-            for (long i = 0; i < y.size(); ++i)
-            {
-                if (y(i) == 1)
-                    t(i) = 1;
-                else
-                    t(i) = 0;
-            }
 
             /*! This is the convention for the active_bases variable in the function:
                 - if (active_bases(i) >= 0) then
@@ -725,9 +716,6 @@ namespace dlib
             matrix<scalar_type,1,0,mem_manager_type> tempv2, tempv3;
             scalar_matrix_type tempm;
 
-            scalar_vector_type t_estimate;
-            scalar_vector_type beta;
-
 
             Q.set_size(x.nr());
             S.set_size(x.nr());
@@ -740,8 +728,6 @@ namespace dlib
             while (true)
             {
                 // Compute optimal weights and sigma for current alpha using equation 6. 
-
-                // compute the updated sigma matrix
                 sigma = trans(phi)*phi/var;
                 for (long r = 0; r < alpha.nr(); ++r)
                     sigma(r,r) += alpha(r);
@@ -805,6 +791,8 @@ namespace dlib
                     }
                 }
 
+                // recompute the variance
+                var = length_squared(t - phi*weights)/(x.nr() - weights.size() + trans(alpha)*diag(sigma));
 
                 // next we update the selected alpha.
 
@@ -873,12 +861,10 @@ namespace dlib
                 }
 
 
-                // recompute the variance
-                var = length_squared(t - phi*weights)/(x.nr() - weights.size() + trans(alpha)*diag(sigma));
 
             } // end while(true).  So we have converged on the final answer.
 
-
+       
             // now put everything into a decision_function object and return it
             std_vector_c<sample_type> dictionary;
             std_vector_c<scalar_type> final_weights;