dlib C++ Library - oca.cpp

// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include <dlib/optimization.h>
#include <dlib/svm.h>
#include <sstream>
#include <string>
#include <cstdlib>
#include <ctime>
#include <vector>
#include "tester.h"
namespace 
{
 using namespace test;
 using namespace dlib;
 using namespace std;
 logger dlog("test.oca");
// ----------------------------------------------------------------------------------------
 class test_oca : public tester
 {
 public:
 test_oca (
 ) :
 tester ("test_oca",
 "Runs tests on the oca component.")
 {
 }
 void perform_test(
 )
 {
 print_spinner();
 typedef matrix<double,0,1> w_type;
 w_type w;
 decision_function<linear_kernel<w_type> > df;
 svm_c_linear_trainer<linear_kernel<w_type> > trainer;
 trainer.set_c_class1(2);
 trainer.set_c_class1(3);
 trainer.set_learns_nonnegative_weights(true);
 trainer.set_epsilon(1e-12);
 std::vector<w_type> x;
 w_type temp(2);
 temp = -1, 1;
 x.push_back(temp);
 temp = 1, -1;
 x.push_back(temp);
 std::vector<double> y;
 y.push_back(+1);
 y.push_back(-1);
 w_type true_w(3);
 oca solver;
 // test the version without a non-negativity constraint on w.
 solver(make_oca_problem_c_svm<w_type>(2.0, 3.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, 0);
 dlog << LINFO << trans(w);
 true_w = -0.5, 0.5, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 solver.solve_with_elastic_net(make_oca_problem_c_svm<w_type>(2.0, 3.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, 0.5);
 dlog << LINFO << trans(w);
 true_w = -0.5, 0.5, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 print_spinner();
 w_type prior = true_w;
 solver(make_oca_problem_c_svm<w_type>(20.0, 30.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, prior);
 dlog << LINFO << trans(w);
 true_w = -0.5, 0.5, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 prior = 0,0,0;
 solver(make_oca_problem_c_svm<w_type>(20.0, 30.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, prior);
 dlog << LINFO << trans(w);
 true_w = -0.5, 0.5, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 prior = -1,1,0;
 solver(make_oca_problem_c_svm<w_type>(20.0, 30.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, prior);
 dlog << LINFO << trans(w);
 true_w = -1.0, 1.0, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 prior = -0.2,0.2,0;
 solver(make_oca_problem_c_svm<w_type>(20.0, 30.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, prior);
 dlog << LINFO << trans(w);
 true_w = -0.5, 0.5, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 prior = -10.2,-1,0;
 solver(make_oca_problem_c_svm<w_type>(20.0, 30.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, prior);
 dlog << LINFO << trans(w);
 true_w = -10.2, -1.0, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 print_spinner();
 // test the version with a non-negativity constraint on w.
 solver(make_oca_problem_c_svm<w_type>(2.0, 3.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, 9999);
 dlog << LINFO << trans(w);
 true_w = 0, 1, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 df = trainer.train(x,y);
 w = join_cols(df.basis_vectors(0), uniform_matrix<double>(1,1,-df.b));
 true_w = 0, 1, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST_MSG(max(abs(w-true_w)) < 1e-9, max(abs(w-true_w)));
 print_spinner();
 // test the version with a non-negativity constraint on w.
 solver(make_oca_problem_c_svm<w_type>(2.0, 3.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, 2);
 dlog << LINFO << trans(w);
 true_w = 0, 1, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 print_spinner();
 // test the version with a non-negativity constraint on w.
 solver(make_oca_problem_c_svm<w_type>(2.0, 3.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, 1);
 dlog << LINFO << trans(w);
 true_w = 0, 1, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 print_spinner();
 // switching the labels should change which w weight goes negative.
 y.clear();
 y.push_back(-1);
 y.push_back(+1);
 solver(make_oca_problem_c_svm<w_type>(2.0, 3.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, 0);
 dlog << LINFO << trans(w);
 true_w = 0.5, -0.5, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 print_spinner();
 solver(make_oca_problem_c_svm<w_type>(2.0, 3.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, 1);
 dlog << LINFO << trans(w);
 true_w = 0.5, -0.5, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 print_spinner();
 solver(make_oca_problem_c_svm<w_type>(2.0, 3.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, 2);
 dlog << LINFO << trans(w);
 true_w = 1, 0, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 print_spinner();
 solver(make_oca_problem_c_svm<w_type>(2.0, 3.0, mat(x), mat(y), false, 1e-12, 0.0, 40, max_index_plus_one(x)), w, 5);
 dlog << LINFO << trans(w);
 true_w = 1, 0, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 df = trainer.train(x,y);
 w = join_cols(df.basis_vectors(0), uniform_matrix<double>(1,1,-df.b));
 true_w = 1, 0, 0;
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST_MSG(max(abs(w-true_w)) < 1e-9, max(abs(w-true_w)));
 x.clear();
 y.clear();
 temp = -2, 2;
 x.push_back(temp);
 temp = 0, -0;
 x.push_back(temp);
 y.push_back(+1);
 y.push_back(-1);
 trainer.set_c(10);
 df = trainer.train(x,y);
 w = join_cols(df.basis_vectors(0), uniform_matrix<double>(1,1,-df.b));
 true_w = 0, 1, -1;
 dlog << LINFO << "w: " << trans(w);
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 x.clear();
 y.clear();
 temp = -2, 2;
 x.push_back(temp);
 temp = 0, -0;
 x.push_back(temp);
 y.push_back(-1);
 y.push_back(+1);
 trainer.set_c(10);
 df = trainer.train(x,y);
 w = join_cols(df.basis_vectors(0), uniform_matrix<double>(1,1,-df.b));
 true_w = 1, 0, 1;
 dlog << LINFO << "w: " << trans(w);
 dlog << LINFO << "error: "<< max(abs(w-true_w));
 DLIB_TEST(max(abs(w-true_w)) < 1e-10);
 }
 } a;
}