// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include <dlib/matrix.h>
#include <sstream>
#include <string>
#include <cstdlib>
#include <ctime>
#include <vector>
#include "../stl_checked.h"
#include "../array.h"
#include "../rand.h"
#include "checkerboard.h"
#include <dlib/statistics.h>
#include "tester.h"
#include <dlib/svm_threaded.h>
namespace
{
using namespace test;
using namespace dlib;
using namespace std;
logger dlog("test.probabilistic");
// ----------------------------------------------------------------------------------------
class test_probabilistic : public tester
{
public:
test_probabilistic (
) :
tester ("test_probabilistic",
"Runs tests on the probabilistic trainer adapter.")
{}
void perform_test (
)
{
print_spinner();
typedef double scalar_type;
typedef matrix<scalar_type,2,1> sample_type;
std::vector<sample_type> x;
std::vector<matrix<double,0,1> > x_linearized;
std::vector<scalar_type> y;
get_checkerboard_problem(x,y, 1000, 2);
random_subset_selector<sample_type> rx;
random_subset_selector<scalar_type> ry;
rx.set_max_size(x.size());
ry.set_max_size(x.size());
dlog << LINFO << "pos labels: "<< sum(mat(y) == +1);
dlog << LINFO << "neg labels: "<< sum(mat(y) == -1);
for (unsigned long i = 0; i < x.size(); ++i)
{
rx.add(x[i]);
ry.add(y[i]);
}
const scalar_type gamma = 2.0;
typedef radial_basis_kernel<sample_type> kernel_type;
krr_trainer<kernel_type> krr_trainer;
krr_trainer.use_classification_loss_for_loo_cv();
krr_trainer.set_kernel(kernel_type(gamma));
krr_trainer.set_basis(randomly_subsample(x, 100));
probabilistic_decision_function<kernel_type> df;
dlog << LINFO << "cross validation: " << cross_validate_trainer(krr_trainer, rx,ry, 4);
print_spinner();
running_stats<scalar_type> rs_pos, rs_neg;
print_spinner();
df = probabilistic(krr_trainer,3).train(x, y);
for (unsigned long i = 0; i < x.size(); ++i)
{
if (y[i] > 0)
rs_pos.add(df(x[i]));
else
rs_neg.add(df(x[i]));
}
dlog << LINFO << "rs_pos.mean(): "<< rs_pos.mean();
dlog << LINFO << "rs_neg.mean(): "<< rs_neg.mean();
DLIB_TEST_MSG(rs_pos.mean() > 0.95, rs_pos.mean());
DLIB_TEST_MSG(rs_neg.mean() < 0.05, rs_neg.mean());
rs_pos.clear();
rs_neg.clear();
print_spinner();
df = probabilistic(krr_trainer,3).train(rx, ry);
for (unsigned long i = 0; i < x.size(); ++i)
{
if (y[i] > 0)
rs_pos.add(df(x[i]));
else
rs_neg.add(df(x[i]));
}
dlog << LINFO << "rs_pos.mean(): "<< rs_pos.mean();
dlog << LINFO << "rs_neg.mean(): "<< rs_neg.mean();
DLIB_TEST_MSG(rs_pos.mean() > 0.95, rs_pos.mean());
DLIB_TEST_MSG(rs_neg.mean() < 0.05, rs_neg.mean());
rs_pos.clear();
rs_neg.clear();
}
} a;
}