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// 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_all_trainer");
class test_one_vs_all_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_all_trainer (
) :
tester (
"test_one_vs_all_trainer", // the command line argument name for this test
"Run tests on the one_vs_all_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 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, norm_samples;
std::vector<label_type> labels;
// First, get our labeled set of training data
generate_data(samples, labels);
typedef one_vs_all_trainer<any_trainer<sample_type,scalar_type>,label_type > ova_trainer;
ova_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);
randomize_samples(samples, labels);
matrix<double> 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);
// test using a normalized_function with a one_vs_all_decision_function
{
poly_trainer.set_kernel(poly_kernel(1.1, 1, 2));
trainer.set_trainer(poly_trainer, 1);
vector_normalizer<sample_type> normalizer;
normalizer.train(samples);
for (unsigned long i = 0; i < samples.size(); ++i)
norm_samples.push_back(normalizer(samples[i]));
normalized_function<one_vs_all_decision_function<ova_trainer> > ndf;
ndf.function = trainer.train(norm_samples, labels);
ndf.normalizer = normalizer;
DLIB_TEST(ndf(samples[0]) == labels[0]);
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);
print_spinner();
}
one_vs_all_decision_function<ova_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_all_decision_function<ova_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);
}
template <typename label_type, typename scalar_type>
void run_probabilistic_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_all_trainer<any_trainer<sample_type,scalar_type>,label_type > ova_trainer;
ova_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(probabilistic(rbf_trainer, 3));
trainer.set_trainer(probabilistic(poly_trainer, 3), 1);
randomize_samples(samples, labels);
matrix<double> 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_all_decision_function<ova_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_all_decision_function<ova_trainer,
probabilistic_function<decision_function<poly_kernel> >, // This is the output of the poly_trainer
probabilistic_function<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>();
dlog << LINFO << "run_probabilistic_test<double,double>()";
run_probabilistic_test<double,double>();
dlog << LINFO << "run_probabilistic_test<int,double>()";
run_probabilistic_test<int,double>();
dlog << LINFO << "run_probabilistic_test<double,float>()";
run_probabilistic_test<double,float>();
dlog << LINFO << "run_probabilistic_test<int,float>()";
run_probabilistic_test<int,float>();
}
};
test_one_vs_all_trainer a;
}
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