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sustaining_gazes/lib/3rdParty/dlib/include/dlib/test/svm_c_linear.cpp

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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 <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.h>
namespace
{
using namespace test;
using namespace dlib;
using namespace std;
logger dlog("test.svm_c_linear");
typedef matrix<double, 0, 1> sample_type;
typedef std::vector<std::pair<unsigned int, double> > sparse_sample_type;
// ----------------------------------------------------------------------------------------
void run_prior_test()
{
typedef matrix<double,3,1> sample_type;
typedef linear_kernel<sample_type> kernel_type;
svm_c_linear_trainer<kernel_type> trainer;
std::vector<sample_type> samples;
std::vector<double> labels;
sample_type samp;
samp = 0, 0, 1; samples.push_back(samp); labels.push_back(+1);
samp = 0, 1, 0; samples.push_back(samp); labels.push_back(-1);
trainer.set_c(10);
decision_function<kernel_type> df = trainer.train(samples, labels);
trainer.set_prior(df);
samples.clear();
labels.clear();
samp = 1, 0, 0; samples.push_back(samp); labels.push_back(+1);
samp = 0, 1, 0; samples.push_back(samp); labels.push_back(-1);
df = trainer.train(samples, labels);
samp = 0, 0, 1; samples.push_back(samp); labels.push_back(+1);
matrix<double,1,2> rs = test_binary_decision_function(df, samples, labels);
dlog << LINFO << rs;
DLIB_TEST(rs(0) == 1);
DLIB_TEST(rs(1) == 1);
dlog << LINFO << trans(df.basis_vectors(0));
DLIB_TEST(df.basis_vectors(0)(0) > 0);
DLIB_TEST(df.basis_vectors(0)(1) < 0);
DLIB_TEST(df.basis_vectors(0)(2) > 0);
}
void run_prior_sparse_test()
{
typedef std::map<unsigned long,double> sample_type;
typedef sparse_linear_kernel<sample_type> kernel_type;
svm_c_linear_trainer<kernel_type> trainer;
std::vector<sample_type> samples;
std::vector<double> labels;
sample_type samp;
samp[0] = 1; samples.push_back(samp); labels.push_back(+1); samp.clear();
samp[1] = 1; samples.push_back(samp); labels.push_back(-1); samp.clear();
trainer.set_c(10);
decision_function<kernel_type> df = trainer.train(samples, labels);
trainer.set_prior(df);
samples.clear();
labels.clear();
samp[2] = 1; samples.push_back(samp); labels.push_back(+1); samp.clear();
samp[1] = 1; samples.push_back(samp); labels.push_back(-1); samp.clear();
df = trainer.train(samples, labels);
matrix<double,1,2> rs = test_binary_decision_function(df, samples, labels);
dlog << LINFO << rs;
DLIB_TEST(rs(0) == 1);
DLIB_TEST(rs(1) == 1);
matrix<double,0,1> w = sparse_to_dense(df.basis_vectors(0));
dlog << LINFO << trans(w);
DLIB_TEST(w(0) > 0.1);
DLIB_TEST(w(1) < -0.1);
DLIB_TEST(w(2) > 0.1);
}
void get_simple_points (
std::vector<sample_type>& samples,
std::vector<double>& labels
)
{
samples.clear();
labels.clear();
sample_type samp(2);
samp = 0,0;
samples.push_back(samp);
labels.push_back(-1);
samp = 0,1;
samples.push_back(samp);
labels.push_back(-1);
samp = 3,0;
samples.push_back(samp);
labels.push_back(+1);
samp = 3,1;
samples.push_back(samp);
labels.push_back(+1);
}
// ----------------------------------------------------------------------------------------
void get_simple_points_sparse (
std::vector<sparse_sample_type>& samples,
std::vector<double>& labels
)
{
samples.clear();
labels.clear();
sparse_sample_type samp;
samp.push_back(make_pair(0, 0.0));
samp.push_back(make_pair(1, 0.0));
samples.push_back(samp);
labels.push_back(-1);
samp.clear();
samp.push_back(make_pair(0, 0.0));
samp.push_back(make_pair(1, 1.0));
samples.push_back(samp);
labels.push_back(-1);
samp.clear();
samp.push_back(make_pair(0, 3.0));
samp.push_back(make_pair(1, 0.0));
samples.push_back(samp);
labels.push_back(+1);
samp.clear();
samp.push_back(make_pair(0, 3.0));
samp.push_back(make_pair(1, 1.0));
samples.push_back(samp);
labels.push_back(+1);
}
// ----------------------------------------------------------------------------------------
void test_sparse (
)
{
print_spinner();
dlog << LINFO << "test with sparse vectors";
std::vector<sparse_sample_type> samples;
std::vector<double> labels;
sample_type samp;
get_simple_points_sparse(samples,labels);
svm_c_linear_trainer<sparse_linear_kernel<sparse_sample_type> > trainer;
trainer.set_c(1e4);
//trainer.be_verbose();
trainer.set_epsilon(1e-11);
double obj;
decision_function<sparse_linear_kernel<sparse_sample_type> > df = trainer.train(samples, labels, obj);
dlog << LDEBUG << "obj: "<< obj;
DLIB_TEST_MSG(abs(obj - 0.72222222222) < 1e-7, obj);
DLIB_TEST(abs(df(samples[0]) - (-1)) < 1e-6);
DLIB_TEST(abs(df(samples[1]) - (-1)) < 1e-6);
DLIB_TEST(abs(df(samples[2]) - (1)) < 1e-6);
DLIB_TEST(abs(df(samples[3]) - (1)) < 1e-6);
// While we are at it, make sure the krr_trainer works with sparse samples
krr_trainer<sparse_linear_kernel<sparse_sample_type> > krr;
df = krr.train(samples, labels);
DLIB_TEST(abs(df(samples[0]) - (-1)) < 1e-6);
DLIB_TEST(abs(df(samples[1]) - (-1)) < 1e-6);
DLIB_TEST(abs(df(samples[2]) - (1)) < 1e-6);
DLIB_TEST(abs(df(samples[3]) - (1)) < 1e-6);
// Now test some of the sparse helper functions
DLIB_TEST(max_index_plus_one(samples) == 2);
DLIB_TEST(max_index_plus_one(samples[0]) == 2);
matrix<double,3,1> m;
m = 1;
add_to(m, samples[3]);
DLIB_TEST(m(0) == 1 + samples[3][0].second);
DLIB_TEST(m(1) == 1 + samples[3][1].second);
DLIB_TEST(m(2) == 1);
m = 1;
subtract_from(m, samples[3]);
DLIB_TEST(m(0) == 1 - samples[3][0].second);
DLIB_TEST(m(1) == 1 - samples[3][1].second);
DLIB_TEST(m(2) == 1);
m = 1;
add_to(m, samples[3], 2);
DLIB_TEST(m(0) == 1 + 2*samples[3][0].second);
DLIB_TEST(m(1) == 1 + 2*samples[3][1].second);
DLIB_TEST(m(2) == 1);
m = 1;
subtract_from(m, samples[3], 2);
DLIB_TEST(m(0) == 1 - 2*samples[3][0].second);
DLIB_TEST(m(1) == 1 - 2*samples[3][1].second);
DLIB_TEST(m(2) == 1);
}
// ----------------------------------------------------------------------------------------
void test_dense (
)
{
print_spinner();
dlog << LINFO << "test with dense vectors";
std::vector<sample_type> samples;
std::vector<double> labels;
sample_type samp;
get_simple_points(samples,labels);
svm_c_linear_trainer<linear_kernel<sample_type> > trainer;
trainer.set_c(1e4);
//trainer.be_verbose();
trainer.set_epsilon(1e-11);
double obj;
decision_function<linear_kernel<sample_type> > df = trainer.train(samples, labels, obj);
dlog << LDEBUG << "obj: "<< obj;
DLIB_TEST_MSG(abs(obj - 0.72222222222) < 1e-7, abs(obj - 0.72222222222));
// There shouldn't be any margin violations since this dataset is so trivial. So that means the objective
// should be exactly the squared norm of the decision plane (times 0.5).
DLIB_TEST_MSG(abs(length_squared(df.basis_vectors(0))*0.5 + df.b*df.b*0.5 - 0.72222222222) < 1e-7,
length_squared(df.basis_vectors(0))*0.5 + df.b*df.b*0.5);
DLIB_TEST(abs(df(samples[0]) - (-1)) < 1e-6);
DLIB_TEST(abs(df(samples[1]) - (-1)) < 1e-6);
DLIB_TEST(abs(df(samples[2]) - (1)) < 1e-6);
DLIB_TEST(abs(df(samples[3]) - (1)) < 1e-6);
}
// ----------------------------------------------------------------------------------------
class tester_svm_c_linear : public tester
{
public:
tester_svm_c_linear (
) :
tester ("test_svm_c_linear",
"Runs tests on the svm_c_linear_trainer.")
{}
void perform_test (
)
{
test_dense();
test_sparse();
run_prior_test();
run_prior_sparse_test();
// test mixed sparse and dense dot products
{
std::map<unsigned int, double> sv;
matrix<double,0,1> dv(4);
dv = 1,2,3,4;
sv[0] = 1;
sv[3] = 1;
DLIB_TEST(dot(sv,dv) == 5);
DLIB_TEST(dot(dv,sv) == 5);
DLIB_TEST(dot(dv,dv) == 30);
DLIB_TEST(dot(sv,sv) == 2);
sv[10] = 9;
DLIB_TEST(dot(sv,dv) == 5);
}
// test mixed sparse dense assignments
{
std::map<unsigned int, double> sv, sv2;
std::vector<std::pair<unsigned int, double> > sv3;
matrix<double,0,1> dv(4), dv2;
dv = 1,2,3,4;
sv[0] = 1;
sv[3] = 1;
assign(dv2, dv);
DLIB_TEST(dv2.size() == 4);
DLIB_TEST(dv2(0) == 1);
DLIB_TEST(dv2(1) == 2);
DLIB_TEST(dv2(2) == 3);
DLIB_TEST(dv2(3) == 4);
assign(sv2, dv);
DLIB_TEST(sv2.size() == 4);
DLIB_TEST(sv2[0] == 1);
DLIB_TEST(sv2[1] == 2);
DLIB_TEST(sv2[2] == 3);
DLIB_TEST(sv2[3] == 4);
assign(sv2, sv);
DLIB_TEST(sv2.size() == 2);
DLIB_TEST(sv2[0] == 1);
DLIB_TEST(sv2[1] == 0);
DLIB_TEST(sv2[2] == 0);
DLIB_TEST(sv2[3] == 1);
assign(sv3, sv);
DLIB_TEST(sv3.size() == 2);
DLIB_TEST(sv3[0].second == 1);
DLIB_TEST(sv3[1].second == 1);
DLIB_TEST(sv3[0].first == 0);
DLIB_TEST(sv3[1].first == 3);
assign(sv3, dv);
DLIB_TEST(sv3.size() == 4);
DLIB_TEST(sv3[0].second == 1);
DLIB_TEST(sv3[1].second == 2);
DLIB_TEST(sv3[2].second == 3);
DLIB_TEST(sv3[3].second == 4);
DLIB_TEST(sv3[0].first == 0);
DLIB_TEST(sv3[1].first == 1);
DLIB_TEST(sv3[2].first == 2);
DLIB_TEST(sv3[3].first == 3);
assign(sv3, sv);
DLIB_TEST(sv3.size() == 2);
DLIB_TEST(sv3[0].second == 1);
DLIB_TEST(sv3[1].second == 1);
DLIB_TEST(sv3[0].first == 0);
DLIB_TEST(sv3[1].first == 3);
sv.clear();
assign(sv, sv3);
DLIB_TEST(sv.size() == 2);
DLIB_TEST(sv[0] == 1);
DLIB_TEST(sv[1] == 0);
DLIB_TEST(sv[2] == 0);
DLIB_TEST(sv[3] == 1);
}
}
} a;
}
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