Fixed some compiler warnings and also a bug in the cached version of the

batch_trainer.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403146

dlib/svm/pegasos.h

@@ -313,7 +313,7 @@ namespace dlib
 
                 cache.reset(new cache_type);
                 cache->frequency_of_use.resize(samps.size());
-                for (unsigned long i = 0; i < samps.size(); ++i)
+                for (long i = 0; i < samps.size(); ++i)
                     cache->frequency_of_use[i] = std::make_pair(0, i);
 
                 // Set the cache build/rebuild threshold so that we have to have
@@ -328,18 +328,18 @@ namespace dlib
                 const sample_type& b
             )  const
             { 
-                const long a_loc = cache->sample_location[a];
-                const long b_loc = cache->sample_location[b];
-
-                cache->frequency_of_use[a].first += 1;
-                cache->frequency_of_use[b].first += 1;
-
                 // rebuild the cache every so often
                 if (counter > counter_threshold )
                 {
                     build_cache();
                 }
 
+                const long a_loc = cache->sample_location[a];
+                const long b_loc = cache->sample_location[b];
+
+                cache->frequency_of_use[a].first += 1;
+                cache->frequency_of_use[b].first += 1;
+
                 if (a_loc != -1)
                 {
                     return cache->kernel(a_loc, b);
@@ -377,13 +377,13 @@ namespace dlib
                 cache->sample_location.assign(samples->size(), -1);
 
                 // loop over all the samples in the cache
-                for (unsigned long i = 0; i < cache_size; ++i)
+                for (long i = 0; i < cache_size; ++i)
                 {
                     const long cur = cache->frequency_of_use[i].second;
                     cache->sample_location[cur] = i;
 
                     // now populate all possible kernel products with the current sample
-                    for (unsigned long j = 0; j < samples->size(); ++j)
+                    for (long j = 0; j < samples->size(); ++j)
                     {
                         cache->kernel(i, j) = real_kernel((*samples)(cur), (*samples)(j));
                     }
@@ -391,7 +391,7 @@ namespace dlib
                 }
 
                 // reset the frequency of use metrics
-                for (unsigned long i = 0; i < samples->size(); ++i)
+                for (long i = 0; i < samples->size(); ++i)
                     cache->frequency_of_use[i] = std::make_pair(0, i);
             }
 
@@ -547,7 +547,8 @@ namespace dlib
             ckernel_type ck(trainer.get_kernel(), x, cache_size);
 
             // now rebind the trainer to use the caching kernel
-            typename trainer_type::template rebind<ckernel_type>::other my_trainer;
+            typedef typename trainer_type::template rebind<ckernel_type>::other rebound_trainer_type;
+            rebound_trainer_type my_trainer;
             my_trainer.set_kernel(ck);
             replicate_settings(trainer, my_trainer);
 

dlib/test/svm.cpp

@@ -359,16 +359,23 @@ namespace
         print_spinner();
         matrix<scalar_type> peg_cv = cross_validate_trainer_threaded(batch(pegasos_trainer,1.0), x,y, 4, 2);
         print_spinner();
+        matrix<scalar_type> peg_c_cv = cross_validate_trainer_threaded(batch_cached(pegasos_trainer,1.0), x,y, 4, 2);
+        print_spinner();
 
         dlog << LDEBUG << "rvm cv: " << rvm_cv;
         dlog << LDEBUG << "svm cv: " << svm_cv;
         dlog << LDEBUG << "rbf cv: " << rbf_cv;
         dlog << LDEBUG << "peg cv: " << peg_cv;
+        dlog << LDEBUG << "peg cached cv: " << peg_c_cv;
+
+        // make sure the cached version of pegasos computes the same result
+        DLIB_TEST(sum(abs(peg_cv - peg_c_cv)) < std::sqrt(std::numeric_limits<double>::epsilon()));
 
         DLIB_TEST_MSG(mean(rvm_cv) > 0.9, rvm_cv);
         DLIB_TEST_MSG(mean(svm_cv) > 0.9, svm_cv);
         DLIB_TEST_MSG(mean(rbf_cv) > 0.9, rbf_cv);
-        DLIB_TEST_MSG(mean(peg_cv) > 0.9, rbf_cv);
+        DLIB_TEST_MSG(mean(peg_cv) > 0.9, peg_cv);
+        DLIB_TEST_MSG(mean(peg_c_cv) > 0.9, peg_c_cv);
 
         const long num_sv = trainer.train(x,y).support_vectors.size();
         print_spinner();