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// Copyright (C) 2013 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 <ctime>
#include <vector>
#include <dlib/statistics.h>
#include "tester.h"
#include <dlib/svm.h>
namespace
{
using namespace test;
using namespace dlib;
using namespace std;
logger dlog("test.svr_linear_trainer");
typedef matrix<double, 0, 1> sample_type;
typedef std::vector<std::pair<unsigned int, double> > sparse_sample_type;
// ----------------------------------------------------------------------------------------
double sinc(double x)
{
if (x == 0)
return 1;
return sin(x)/x;
}
template <typename scalar_type>
void test1()
{
typedef matrix<scalar_type,0,1> sample_type;
typedef radial_basis_kernel<sample_type> kernel_type;
print_spinner();
std::vector<sample_type> samples;
std::vector<scalar_type> targets;
// The first thing we do is pick a few training points from the sinc() function.
sample_type m(1);
for (scalar_type x = -10; x <= 4; x += 1)
{
m(0) = x;
samples.push_back(m);
targets.push_back(sinc(x)+1.1);
}
randomize_samples(samples, targets);
empirical_kernel_map<kernel_type> ekm;
ekm.load(kernel_type(0.1), samples);
for (unsigned long i = 0; i < samples.size(); ++i)
samples[i] = ekm.project(samples[i]);
svr_linear_trainer<linear_kernel<sample_type> > linear_trainer;
linear_trainer.set_epsilon(0.0001);
linear_trainer.set_c(30);
linear_trainer.set_epsilon_insensitivity(0.001);
matrix<double> res = cross_validate_regression_trainer(linear_trainer, samples, targets, 5);
dlog << LINFO << "MSE and R-Squared: "<< res;
DLIB_TEST(res(0) < 1e-4);
DLIB_TEST(res(1) > 0.99);
dlib::rand rnd;
samples.clear();
targets.clear();
std::vector<scalar_type> noisefree_targets;
for (scalar_type x = 0; x <= 5; x += 0.1)
{
m(0) = x;
samples.push_back(matrix_cast<scalar_type>(linpiece(m, linspace(0,5,20))));
targets.push_back(x*x + rnd.get_random_gaussian());
noisefree_targets.push_back(x*x);
}
linear_trainer.set_learns_nonnegative_weights(true);
linear_trainer.set_epsilon_insensitivity(1.0);
decision_function<linear_kernel<sample_type> > df2 = linear_trainer.train(samples, targets);
print_spinner();
res = test_regression_function(df2, samples, noisefree_targets);
dlog << LINFO << "MSE and R-Squared: "<< res;
DLIB_TEST(res(0) < 0.15);
DLIB_TEST(res(1) > 0.98);
DLIB_TEST(df2.basis_vectors.size()==1);
DLIB_TEST(max(df2.basis_vectors(0)) >= 0);
linear_trainer.force_last_weight_to_1(true);
df2 = linear_trainer.train(samples, targets);
DLIB_TEST(std::abs(df2.basis_vectors(0)(samples[0].size()-1) - 1.0) < 1e-14);
res = test_regression_function(df2, samples, noisefree_targets);
dlog << LINFO << "MSE and R-Squared: "<< res;
DLIB_TEST(res(0) < 0.20);
DLIB_TEST(res(1) > 0.98);
// convert into sparse vectors and try it out
typedef std::vector<std::pair<unsigned long, scalar_type> > sparse_samp;
std::vector<sparse_samp> ssamples;
for (unsigned long i = 0; i < samples.size(); ++i)
{
sparse_samp s;
for (long j = 0; j < samples[i].size(); ++j)
s.push_back(make_pair(j,samples[i](j)));
ssamples.push_back(s);
}
svr_linear_trainer<sparse_linear_kernel<sparse_samp> > strainer;
strainer.set_learns_nonnegative_weights(true);
strainer.set_epsilon_insensitivity(1.0);
strainer.set_c(30);
decision_function<sparse_linear_kernel<sparse_samp> > df;
df = strainer.train(ssamples, targets);
res = test_regression_function(df, ssamples, noisefree_targets);
dlog << LINFO << "MSE and R-Squared: "<< res;
DLIB_TEST(res(0) < 0.15);
DLIB_TEST(res(1) > 0.98);
DLIB_TEST(df2.basis_vectors.size()==1);
DLIB_TEST(max(sparse_to_dense(df2.basis_vectors(0))) >= 0);
}
// ----------------------------------------------------------------------------------------
class tester_svr_linear_trainer : public tester
{
public:
tester_svr_linear_trainer (
) :
tester ("test_svr_linear_trainer",
"Runs tests on the svr_linear_trainer.")
{}
void perform_test (
)
{
dlog << LINFO << "TEST double";
test1<double>();
dlog << LINFO << "TEST float";
test1<float>();
}
} a;
}
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