Made test a little more numerically robust.

dlib/test/one_vs_one_trainer.cpp

@@ -116,18 +116,17 @@ namespace
 
             ovo_trainer trainer;
 
-            typedef polynomial_kernel<sample_type> poly_kernel;
+            typedef histogram_intersection_kernel<sample_type> hist_kernel;
             typedef radial_basis_kernel<sample_type> rbf_kernel;
 
             // make the binary trainers and set some parameters
             krr_trainer<rbf_kernel> rbf_trainer;
-            svm_nu_trainer<poly_kernel> poly_trainer;
-            poly_trainer.set_kernel(poly_kernel(0.1, 1, 2));
+            svm_nu_trainer<hist_kernel> hist_trainer;
             rbf_trainer.set_kernel(rbf_kernel(0.1));
 
 
             trainer.set_trainer(rbf_trainer);
-            trainer.set_trainer(poly_trainer, 1, 2);
+            trainer.set_trainer(hist_trainer, 1, 2);
 
             randomize_samples(samples, labels);
             matrix<double> res = cross_validate_multiclass_trainer(trainer, samples, labels, 2);
@@ -143,8 +142,7 @@ namespace
 
             // test using a normalized_function with a one_vs_one_decision_function 
             {
-                poly_trainer.set_kernel(poly_kernel(1.1, 1, 2));
-                trainer.set_trainer(poly_trainer, 1, 2);
+                trainer.set_trainer(hist_trainer, 1, 2);
                 vector_normalizer<sample_type> normalizer;
                 normalizer.train(samples);
                 for (unsigned long i = 0; i < samples.size(); ++i)
@@ -156,8 +154,7 @@ namespace
                 DLIB_TEST(ndf(samples[40])  == labels[40]);
                 DLIB_TEST(ndf(samples[90])  == labels[90]);
                 DLIB_TEST(ndf(samples[120])  == labels[120]);
-                poly_trainer.set_kernel(poly_kernel(0.1, 1, 2));
-                trainer.set_trainer(poly_trainer, 1, 2);
+                trainer.set_trainer(hist_trainer, 1, 2);
                 print_spinner();
             }
 
@@ -173,7 +170,7 @@ namespace
 
 
             one_vs_one_decision_function<ovo_trainer, 
-                decision_function<poly_kernel>,  // This is the output of the poly_trainer
+                decision_function<hist_kernel>,  // This is the output of the hist_trainer
                 decision_function<rbf_kernel>    // This is the output of the rbf_trainer
             > df2, df3;