// 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;
}