This change was just to avoid a warning from gcc. The code still

computes exactly the same thing as before.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403519

dlib/svm/svm_c_linear_trainer.h

@@ -16,6 +16,45 @@
 namespace dlib
 {
 
+// ----------------------------------------------------------------------------------------
+
+    template <typename T>
+    typename enable_if<is_matrix<typename T::type>,unsigned long>::type num_dimensions_in_samples (
+        const T& samples
+    ) 
+    {
+        if (samples.size() > 0)
+            return samples(0).size();
+        else
+            return 0;
+    }
+
+    template <typename T>
+    typename disable_if<is_matrix<typename T::type>,unsigned long>::type num_dimensions_in_samples (
+        const T& samples
+    ) 
+    /*!
+        T must be a sparse vector with an integral key type
+    !*/
+    {
+        typedef typename T::type sample_type;
+        // You are getting this error because you are attempting to use sparse sample vectors with
+        // the svm_c_linear_trainer object but you aren't using an unsigned integer as your key type
+        // in the sparse vectors.
+        COMPILE_TIME_ASSERT(sparse_vector::has_unsigned_keys<sample_type>::value);
+
+
+        // these should be sparse samples so look over all them to find the max dimension.
+        unsigned long max_dim = 0;
+        for (long i = 0; i < samples.size(); ++i)
+        {
+            if (samples(i).size() > 0)
+                max_dim = std::max<unsigned long>(max_dim, (--samples(i).end())->first + 1);
+        }
+
+        return max_dim;
+    }
+    
 // ----------------------------------------------------------------------------------------
 
     template <
@@ -203,7 +242,7 @@ namespace dlib
             const matrix_exp<EXP>& sample
         ) const
         {
-            return dot(colm(w,0,w.size()-1), sample);
+            return dot(colm(w,0,sample.size()), sample);
         }
 
         template <typename T>
@@ -219,43 +258,6 @@ namespace dlib
             return temp;
         }
 
-        template <typename T>
-        typename enable_if<is_matrix<typename T::type>,unsigned long>::type num_dimensions_in_samples (
-            const T& samples
-        ) const
-        {
-            if (samples.size() > 0)
-                return samples(0).size();
-            else
-                return 0;
-        }
-
-        template <typename T>
-        typename disable_if<is_matrix<typename T::type>,unsigned long>::type num_dimensions_in_samples (
-            const T& samples
-        ) const
-        /*!
-            T must be a sparse vector with an integral key type
-        !*/
-        {
-            typedef typename T::type sample_type;
-            // You are getting this error because you are attempting to use sparse sample vectors with
-            // the svm_c_linear_trainer object but you aren't using an unsigned integer as your key type
-            // in the sparse vectors.
-            COMPILE_TIME_ASSERT(sparse_vector::has_unsigned_keys<sample_type>::value);
-
-
-            // these should be sparse samples so look over all them to find the max dimension.
-            unsigned long max_dim = 0;
-            for (long i = 0; i < samples.size(); ++i)
-            {
-                if (samples(i).size() > 0)
-                    max_dim = std::max<unsigned long>(max_dim, (--samples(i).end())->first + 1);
-            }
-
-            return max_dim;
-        }
-        
     // -----------------------------------------------------
     // -----------------------------------------------------
 
@@ -619,7 +621,10 @@ namespace dlib
             df.basis_vectors.set_size(1);
             // Copy the plane normal into the output basis vector.  The output vector might be a
             // sparse vector container so we need to use this special kind of copy to handle that case.
-            sparse_vector::assign_dense_to_sparse(df.basis_vectors(0), matrix_cast<scalar_type>(colm(w, 0, w.size()-1)));
+            // As an aside, the reason for using num_dimensions_in_samples() and not just w.size()-1 is because
+            // doing it this way avoids an inane warning from gcc that can occur in some cases.
+            const long out_size = num_dimensions_in_samples(x);
+            sparse_vector::assign_dense_to_sparse(df.basis_vectors(0), matrix_cast<scalar_type>(colm(w, 0, out_size)));
             df.alpha.set_size(1);
             df.alpha(0) = 1;