dlib C++ Library - probabilistic.cpp

// 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;
}