Added unit tests for the svr_trainer and svm_one_class_trainer.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404027

dlib/test/svm.cpp

@@ -175,8 +175,15 @@ namespace
         krls<kernel_type> test(kernel_type(0.1),0.001);
         rvm_regression_trainer<kernel_type> rvm_test;
         rvm_test.set_kernel(test.get_kernel());
+
         krr_trainer<kernel_type> krr_test;
         krr_test.set_kernel(test.get_kernel());
+
+        svr_trainer<kernel_type> svr_test;
+        svr_test.set_kernel(test.get_kernel());
+        svr_test.set_epsilon_insensitivity(0.0001);
+        svr_test.set_c(10);
+
         rbf_network_trainer<kernel_type> rbf_test;
         rbf_test.set_kernel(test.get_kernel());
         rbf_test.set_num_centers(13);
@@ -202,6 +209,8 @@ namespace
         print_spinner();
         decision_function<kernel_type> test4 = krr_test.train(samples, labels);
         print_spinner();
+        decision_function<kernel_type> test5 = svr_test.train(samples, labels);
+        print_spinner();
 
         // now we output the value of the sinc function for a few test points as well as the 
         // value predicted by krls object.
@@ -224,6 +233,11 @@ namespace
         m(0) = 0.1; dlog << LDEBUG << "krr: " << sinc(m(0)) << "   " << test4(m); DLIB_TEST(abs(sinc(m(0)) - test4(m)) < 0.01);
         m(0) = -4;  dlog << LDEBUG << "krr: " << sinc(m(0)) << "   " << test4(m); DLIB_TEST(abs(sinc(m(0)) - test4(m)) < 0.01);
         m(0) = 5.0; dlog << LDEBUG << "krr: " << sinc(m(0)) << "   " << test4(m); DLIB_TEST(abs(sinc(m(0)) - test4(m)) < 0.01);
+
+        m(0) = 2.5; dlog << LDEBUG << "svr: " << sinc(m(0)) << "   " << test5(m); DLIB_TEST(abs(sinc(m(0)) - test5(m)) < 0.01);
+        m(0) = 0.1; dlog << LDEBUG << "svr: " << sinc(m(0)) << "   " << test5(m); DLIB_TEST(abs(sinc(m(0)) - test5(m)) < 0.01);
+        m(0) = -4;  dlog << LDEBUG << "svr: " << sinc(m(0)) << "   " << test5(m); DLIB_TEST(abs(sinc(m(0)) - test5(m)) < 0.01);
+        m(0) = 5.0; dlog << LDEBUG << "svr: " << sinc(m(0)) << "   " << test5(m); DLIB_TEST(abs(sinc(m(0)) - test5(m)) < 0.01);
         dlog << LINFO << "   end test_regression()";
     }
 
@@ -249,6 +263,13 @@ namespace
         // Here we have set it to 0.01.
         kcentroid<kernel_type> test(kernel_type(0.1),0.01);
 
+
+        svm_one_class_trainer<kernel_type> one_class_trainer;
+        one_class_trainer.set_nu(0.4);
+        one_class_trainer.set_kernel(kernel_type(0.2));
+
+        std::vector<sample_type> samples;
+
         // now we train our object on a few samples of the sinc function.
         sample_type m;
         for (double x = -15; x <= 8; x += 1)
@@ -256,8 +277,11 @@ namespace
             m(0) = x;
             m(1) = sinc(x);
             test.train(m);
+            samples.push_back(m);
         }
 
+        decision_function<kernel_type> df = one_class_trainer.train(samples);
+
         running_stats<double> rs;
 
         // Now lets output the distance from the centroid to some points that are from the sinc function.
@@ -281,6 +305,15 @@ namespace
         m(0) = -1.5; m(1) = sinc(m(0)); DLIB_TEST_MSG(rs.scale(test(m)) < 2, rs.scale(test(m)));
         m(0) = -0.5; m(1) = sinc(m(0)); DLIB_TEST_MSG(rs.scale(test(m)) < 2, rs.scale(test(m)));
 
+        const double thresh = 0.01;
+        m(0) = -1.5; m(1) = sinc(m(0)); DLIB_TEST_MSG(df(m)+thresh > 0, df(m));
+        m(0) = -1.5; m(1) = sinc(m(0)); DLIB_TEST_MSG(df(m)+thresh > 0, df(m));
+        m(0) = -0;   m(1) = sinc(m(0)); DLIB_TEST_MSG(df(m)+thresh > 0, df(m));
+        m(0) = -0.5; m(1) = sinc(m(0)); DLIB_TEST_MSG(df(m)+thresh > 0, df(m));
+        m(0) = -4.1; m(1) = sinc(m(0)); DLIB_TEST_MSG(df(m)+thresh > 0, df(m));
+        m(0) = -1.5; m(1) = sinc(m(0)); DLIB_TEST_MSG(df(m)+thresh > 0, df(m));
+        m(0) = -0.5; m(1) = sinc(m(0)); DLIB_TEST_MSG(df(m)+thresh > 0, df(m));
+
         dlog << LDEBUG;
         // Lets output the distance from the centroid to some points that are NOT from the sinc function.
         // These numbers should all be significantly bigger than previous set of numbers.  We will also
@@ -291,30 +324,37 @@ namespace
         m(0) = -1.5; m(1) = sinc(m(0))+4;   
         dlog << LDEBUG << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc.";
         DLIB_TEST_MSG(rs.scale(test(m)) > 6, rs.scale(test(m)));
+        DLIB_TEST_MSG(df(m) + thresh < 0, df(m));
 
         m(0) = -1.5; m(1) = sinc(m(0))+3;   
         dlog << LDEBUG << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc.";
         DLIB_TEST_MSG(rs.scale(test(m)) > 6, rs.scale(test(m)));
+        DLIB_TEST_MSG(df(m) + thresh < 0, df(m));
 
         m(0) = -0;   m(1) = -sinc(m(0));    
         dlog << LDEBUG << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc.";
         DLIB_TEST_MSG(rs.scale(test(m)) > 6, rs.scale(test(m)));
+        DLIB_TEST_MSG(df(m) + thresh < 0, df(m));
 
         m(0) = -0.5; m(1) = -sinc(m(0));    
         dlog << LDEBUG << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc.";
         DLIB_TEST_MSG(rs.scale(test(m)) > 6, rs.scale(test(m)));
+        DLIB_TEST_MSG(df(m) + thresh < 0, df(m));
 
         m(0) = -4.1; m(1) = sinc(m(0))+2;   
         dlog << LDEBUG << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc.";
         DLIB_TEST_MSG(rs.scale(test(m)) > 6, rs.scale(test(m)));
+        DLIB_TEST_MSG(df(m) + thresh < 0, df(m));
 
         m(0) = -1.5; m(1) = sinc(m(0))+0.9; 
         dlog << LDEBUG << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc.";
         DLIB_TEST_MSG(rs.scale(test(m)) > 6, rs.scale(test(m)));
+        DLIB_TEST_MSG(df(m) + thresh < 0, df(m));
 
         m(0) = -0.5; m(1) = sinc(m(0))+1;   
         dlog << LDEBUG << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc.";
         DLIB_TEST_MSG(rs.scale(test(m)) > 6, rs.scale(test(m)));
+        DLIB_TEST_MSG(df(m) + thresh < 0, df(m));
 
         dlog << LINFO << "   end test_anomaly_detection()";
     }