From 3b0f4ff135a2ab24b86b568d9424ad9048495a02 Mon Sep 17 00:00:00 2001
From: Davis King <davis@dlib.net>
Date: Thu, 3 Jan 2013 22:17:10 -0500
Subject: [PATCH] Added more unit tests for the forces_last_weight_to_1 stuff.

---
 dlib/test/ranking.cpp          | 36 +++++++++++++++++++++++++---------
 dlib/test/svm_c_linear_dcd.cpp | 16 ++++++++++++++-
 2 files changed, 42 insertions(+), 10 deletions(-)

diff --git a/dlib/test/ranking.cpp b/dlib/test/ranking.cpp
index 66314885d..07a5b4d59 100644
--- a/dlib/test/ranking.cpp
+++ b/dlib/test/ranking.cpp
@@ -223,7 +223,7 @@ namespace
 
 // ----------------------------------------------------------------------------------------
 
-    template <typename K>
+    template <typename K, bool use_dcd_trainer>
     class simple_rank_trainer
     {
     public:
@@ -250,18 +250,35 @@ namespace
                 }
             }
 
-            svm_c_linear_dcd_trainer<K> trainer;
-            trainer.set_c(1.0/samples.size());
-            trainer.set_epsilon(1e-10);
-            trainer.force_last_weight_to_1(true);
-            //trainer.be_verbose();
-            return trainer.train(samples, labels);
+            if (use_dcd_trainer)
+            {
+                svm_c_linear_dcd_trainer<K> trainer;
+                trainer.set_c(1.0/samples.size());
+                trainer.set_epsilon(1e-10);
+                trainer.force_last_weight_to_1(true);
+                //trainer.be_verbose();
+                return trainer.train(samples, labels);
+            }
+            else
+            {
+                svm_c_linear_trainer<K> trainer;
+                trainer.set_c(1.0);
+                trainer.set_epsilon(1e-13);
+                trainer.force_last_weight_to_1(true);
+                //trainer.be_verbose();
+                decision_function<K> df = trainer.train(samples, labels);
+                DLIB_TEST_MSG(df.b == 0, df.b);
+                return df;
+            }
         }
     };
 
+    template <bool use_dcd_trainer>
     void test_svmrank_weight_force_dense()
     {
         print_spinner();
+        dlog << LINFO << "use_dcd_trainer: "<< use_dcd_trainer;
+
         typedef matrix<double,10,1> sample_type;
         typedef linear_kernel<sample_type> kernel_type;
 
@@ -291,7 +308,7 @@ namespace
         dlog << LINFO << "ranking accuracy: " << acc1;
         DLIB_TEST(std::abs(acc1 - 1) == 0);
 
-        simple_rank_trainer<kernel_type> strainer;
+        simple_rank_trainer<kernel_type,use_dcd_trainer> strainer;
         decision_function<kernel_type> df2;
         df2 = strainer.train(pair);
         dlog << LINFO << "weights: "<< trans(df2.basis_vectors(0));
@@ -325,7 +342,8 @@ namespace
             test_count_ranking_inversions();
             dotest1();
             dotest_sparse_vectors();
-            test_svmrank_weight_force_dense();
+            test_svmrank_weight_force_dense<true>();
+            test_svmrank_weight_force_dense<false>();
 
         }
     } a;
diff --git a/dlib/test/svm_c_linear_dcd.cpp b/dlib/test/svm_c_linear_dcd.cpp
index f69e9145a..b9bc9974c 100644
--- a/dlib/test/svm_c_linear_dcd.cpp
+++ b/dlib/test/svm_c_linear_dcd.cpp
@@ -250,6 +250,7 @@ namespace
         typedef linear_kernel<sample_type> kernel_type;
 
 
+        svm_c_linear_trainer<kernel_type> linear_trainer_cpa;
 
         svm_c_linear_dcd_trainer<kernel_type> linear_trainer;
 
@@ -257,7 +258,9 @@ namespace
 
         const double C = 1;
         linear_trainer.set_epsilon(1e-10);
+        linear_trainer_cpa.set_epsilon(1e-11);
 
+        linear_trainer_cpa.force_last_weight_to_1(force_weight);
 
         linear_trainer.force_last_weight_to_1(force_weight);
         linear_trainer.include_bias(have_bias);
@@ -268,7 +271,7 @@ namespace
         // make an instance of a sample vector so we can use it below
         sample_type sample;
 
-        decision_function<kernel_type> df;
+        decision_function<kernel_type> df, df2;
 
         running_stats<double> rs;
 
@@ -299,11 +302,22 @@ namespace
             labels.push_back(label);
 
             linear_trainer.set_c(C);
+            linear_trainer_cpa.set_c(C*samples.size());
 
             df = linear_trainer.train(samples, labels, state);
 
             if (force_weight)
+            {
                 DLIB_TEST(std::abs(df.basis_vectors(0)(9) - 1) < 1e-8);
+                DLIB_TEST(std::abs(df.b) < 1e-8);
+
+                if (samples.size() > 1)
+                {
+                    df2 = linear_trainer_cpa.train(samples, labels);
+                    DLIB_TEST_MSG( max(abs(df.basis_vectors(0) - df2.basis_vectors(0))) < 1e-7, max(abs(df.basis_vectors(0) - df2.basis_vectors(0))));
+                    DLIB_TEST( std::abs(df.b - df2.b) < 1e-7);
+                }
+            }
 
             if (!have_bias)
                 DLIB_TEST(std::abs(df.b) < 1e-8);