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