Added some unit tests for the one_vs_one multiclass training code.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404054

dlib/test/CMakeLists.txt

@@ -62,6 +62,7 @@ set (tests
    member_function_pointer.cpp
    metaprogramming.cpp
    multithreaded_object.cpp
+   one_vs_one_trainer.cpp
    optimization.cpp
    optimization_test_functions.cpp
    opt_qp_solver.cpp

dlib/test/one_vs_one_trainer.cpp

@@ -0,0 +1,197 @@
+// Copyright (C) 2010  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+
+#include "tester.h"
+#include <dlib/svm.h>
+#include <vector>
+#include <sstream>
+
+namespace  
+{
+    using namespace test;
+    using namespace dlib;
+    using namespace std;
+    dlib::logger dlog("test.one_vs_one_trainer");
+
+
+    class test_one_vs_one_trainer : public tester
+    {
+        /*!
+            WHAT THIS OBJECT REPRESENTS
+                This object represents a unit test.  When it is constructed
+                it adds itself into the testing framework.
+        !*/
+    public:
+        test_one_vs_one_trainer (
+        ) :
+            tester (
+                "test_one_vs_one_trainer",       // the command line argument name for this test
+                "Run tests on the one_vs_one_trainer stuff.", // the command line argument description
+                0                     // the number of command line arguments for this test
+            )
+        {
+        }
+
+
+
+        template <typename sample_type, typename label_type>
+        void generate_data (
+            std::vector<sample_type>& samples,
+            std::vector<label_type>& labels
+        )
+        {
+            const long num = 50;
+
+            sample_type m;
+
+            dlib::rand::float_1a rnd;
+
+
+            // make some samples near the origin
+            double radius = 0.5;
+            for (long i = 0; i < num+10; ++i)
+            {
+                double sign = 1;
+                if (rnd.get_random_double() < 0.5)
+                    sign = -1;
+                m(0) = 2*radius*rnd.get_random_double()-radius;
+                m(1) = sign*sqrt(radius*radius - m(0)*m(0));
+
+                // add this sample to our set of samples we will run k-means 
+                samples.push_back(m);
+                labels.push_back(1);
+            }
+
+            // make some samples in a circle around the origin but far away
+            radius = 10.0;
+            for (long i = 0; i < num+20; ++i)
+            {
+                double sign = 1;
+                if (rnd.get_random_double() < 0.5)
+                    sign = -1;
+                m(0) = 2*radius*rnd.get_random_double()-radius;
+                m(1) = sign*sqrt(radius*radius - m(0)*m(0));
+
+                // add this sample to our set of samples we will run k-means 
+                samples.push_back(m);
+                labels.push_back(2);
+            }
+
+            // make some samples in a circle around the point (25,25) 
+            radius = 4.0;
+            for (long i = 0; i < num+30; ++i)
+            {
+                double sign = 1;
+                if (rnd.get_random_double() < 0.5)
+                    sign = -1;
+                m(0) = 2*radius*rnd.get_random_double()-radius;
+                m(1) = sign*sqrt(radius*radius - m(0)*m(0));
+
+                // translate this point away from the origin
+                m(0) += 25;
+                m(1) += 25;
+
+                // add this sample to our set of samples we will run k-means 
+                samples.push_back(m);
+                labels.push_back(3);
+            }
+        }
+
+        template <typename label_type, typename scalar_type>
+        void run_test (
+        )
+        {
+            print_spinner();
+            typedef matrix<scalar_type,2,1> sample_type;
+
+            std::vector<sample_type> samples;
+            std::vector<label_type> labels;
+
+            // First, get our labeled set of training data
+            generate_data(samples, labels);
+
+            typedef one_vs_one_trainer<any_trainer<sample_type,scalar_type>,label_type > ovo_trainer;
+
+
+            ovo_trainer trainer;
+
+            typedef polynomial_kernel<sample_type> poly_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));
+            rbf_trainer.set_kernel(rbf_kernel(0.1));
+
+
+            trainer.set_trainer(rbf_trainer);
+            trainer.set_trainer(poly_trainer, 1, 2);
+
+            randomize_samples(samples, labels);
+            matrix<scalar_type> res = cross_validate_multiclass_trainer(trainer, samples, labels, 2);
+
+            print_spinner();
+
+            matrix<scalar_type> ans(3,3);
+            ans = 60,  0,  0, 
+                  0, 70,  0, 
+                  0,  0, 80;
+
+            DLIB_TEST_MSG(ans == res, "res: \n" << res);
+
+            one_vs_one_decision_function<ovo_trainer> df = trainer.train(samples, labels);
+
+            DLIB_TEST(df.number_of_classes() == 3);
+
+            DLIB_TEST(df(samples[0])  == labels[0])
+            DLIB_TEST(df(samples[90])  == labels[90])
+
+
+            one_vs_one_decision_function<ovo_trainer, 
+                decision_function<poly_kernel>,  // This is the output of the poly_trainer
+                decision_function<rbf_kernel>    // This is the output of the rbf_trainer
+            > df2, df3;
+
+
+            df2 = df;
+            ofstream fout("df.dat", ios::binary);
+            serialize(df2, fout);
+            fout.close();
+
+            // load the function back in from disk and store it in df3.  
+            ifstream fin("df.dat", ios::binary);
+            deserialize(df3, fin);
+
+
+            DLIB_TEST(df3(samples[0])  == labels[0])
+            DLIB_TEST(df3(samples[90])  == labels[90])
+            res = test_multiclass_decision_function(df3, samples, labels);
+
+            DLIB_TEST(res == ans);
+
+
+        }
+
+        void perform_test (
+        )
+        {
+            dlog << LINFO << "run_test<double,double>()";
+            run_test<double,double>();
+
+            dlog << LINFO << "run_test<int,double>()";
+            run_test<int,double>();
+
+            dlog << LINFO << "run_test<double,float>()";
+            run_test<double,float>();
+
+            dlog << LINFO << "run_test<int,float>()";
+            run_test<int,float>();
+        }
+    };
+
+    test_one_vs_one_trainer a;
+
+}
+
+