// Copyright (C) 2010 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include <sstream>
#include <string>
#include <cstdlib>
#include <ctime>
#include <dlib/statistics.h>
#include <dlib/statistics/running_gradient.h>
#include <dlib/rand.h>
#include <dlib/svm.h>
#include <algorithm>
#include <dlib/matrix.h>
#include <cmath>
#include <random>
#include "tester.h"
namespace
{
using namespace test;
using namespace dlib;
using namespace std;
logger dlog("test.statistics");
class statistics_tester : public tester
{
public:
statistics_tester (
) :
tester ("test_statistics",
"Runs tests on the statistics component.")
{}
void test_random_subset_selector ()
{
random_subset_selector<double> rand_set;
for (int j = 0; j < 30; ++j)
{
print_spinner();
running_stats<double> rs, rs2;
rand_set.set_max_size(1000);
for (double i = 0; i < 100000; ++i)
{
rs.add(i);
rand_set.add(i);
}
for (unsigned long i = 0; i < rand_set.size(); ++i)
rs2.add(rand_set[i]);
dlog << LDEBUG << "true mean: " << rs.mean();
dlog << LDEBUG << "true sampled: " << rs2.mean();
double ratio = rs.mean()/rs2.mean();
DLIB_TEST_MSG(0.96 < ratio && ratio < 1.04, " ratio: " << ratio);
}
{
random_subset_selector<int> r1, r2;
r1.set_max_size(300);
for (int i = 0; i < 4000; ++i)
r1.add(i);
ostringstream sout;
serialize(r1, sout);
istringstream sin(sout.str());
deserialize(r2, sin);
DLIB_TEST(r1.size() == r2.size());
DLIB_TEST(r1.max_size() == r2.max_size());
DLIB_TEST(r1.next_add_accepts() == r2.next_add_accepts());
DLIB_TEST(std::equal(r1.begin(), r1.end(), r2.begin()));
for (int i = 0; i < 4000; ++i)
{
r1.add(i);
r2.add(i);
}
DLIB_TEST(r1.size() == r2.size());
DLIB_TEST(r1.max_size() == r2.max_size());
DLIB_TEST(r1.next_add_accepts() == r2.next_add_accepts());
DLIB_TEST(std::equal(r1.begin(), r1.end(), r2.begin()));
}
}
void test_random_subset_selector2 ()
{
random_subset_selector<double> rand_set;
DLIB_TEST(rand_set.next_add_accepts() == false);
DLIB_TEST(rand_set.size() == 0);
DLIB_TEST(rand_set.max_size() == 0);
for (int j = 0; j < 30; ++j)
{
print_spinner();
running_stats<double> rs, rs2;
rand_set.set_max_size(1000);
DLIB_TEST(rand_set.next_add_accepts() == true);
for (double i = 0; i < 100000; ++i)
{
rs.add(i);
if (rand_set.next_add_accepts())
rand_set.add(i);
else
rand_set.add();
}
DLIB_TEST(rand_set.size() == 1000);
DLIB_TEST(rand_set.max_size() == 1000);
for (unsigned long i = 0; i < rand_set.size(); ++i)
rs2.add(rand_set[i]);
dlog << LDEBUG << "true mean: " << rs.mean();
dlog << LDEBUG << "true sampled: " << rs2.mean();
double ratio = rs.mean()/rs2.mean();
DLIB_TEST_MSG(0.96 < ratio && ratio < 1.04, " ratio: " << ratio);
}
}
void test_running_cross_covariance ()
{
running_cross_covariance<matrix<double> > rcc1, rcc2;
matrix<double,0,1> xm, ym;
const int num = 40;
dlib::rand rnd;
for (int i = 0; i < num; ++i)
{
matrix<double,0,1> x = randm(4,1,rnd);
matrix<double,0,1> y = randm(4,1,rnd);
xm += x/num;
ym += y/num;
if (i < 15)
rcc1.add(x,y);
else
rcc2.add(x,y);
}
rnd.clear();
matrix<double> cov;
for (int i = 0; i < num; ++i)
{
matrix<double,0,1> x = randm(4,1,rnd);
matrix<double,0,1> y = randm(4,1,rnd);
cov += (x-xm)*trans(y-ym);
}
cov /= num-1;
running_cross_covariance<matrix<double> > rcc = rcc1 + rcc2;
DLIB_TEST(max(abs(rcc.covariance_xy()-cov)) < 1e-14);
DLIB_TEST(max(abs(rcc.mean_x()-xm)) < 1e-14);
DLIB_TEST(max(abs(rcc.mean_y()-ym)) < 1e-14);
}
std::map<unsigned long,double> dense_to_sparse (
const matrix<double,0,1>& x
)
{
std::map<unsigned long,double> temp;
for (long i = 0; i < x.size(); ++i)
temp[i] = x(i);
return temp;
}
void test_running_cross_covariance_sparse()
{
running_cross_covariance<matrix<double> > rcc1, rcc2;
running_covariance<matrix<double> > rc1, rc2;
matrix<double,0,1> xm, ym;
const int num = 40;
rc1.set_dimension(4);
rc2.set_dimension(4);
rcc1.set_dimensions(4,5);
rcc2.set_dimensions(4,5);
dlib::rand rnd;
for (int i = 0; i < num; ++i)
{
matrix<double,0,1> x = randm(4,1,rnd);
matrix<double,0,1> y = randm(5,1,rnd);
xm += x/num;
ym += y/num;
if (i < 15)
{
rcc1.add(x,dense_to_sparse(y));
rc1.add(x);
}
else if (i < 30)
{
rcc2.add(dense_to_sparse(x),y);
rc2.add(dense_to_sparse(x));
}
else
{
rcc2.add(dense_to_sparse(x),dense_to_sparse(y));
rc2.add(x);
}
}
rnd.clear();
matrix<double> cov, cov2;
for (int i = 0; i < num; ++i)
{
matrix<double,0,1> x = randm(4,1,rnd);
matrix<double,0,1> y = randm(5,1,rnd);
cov += (x-xm)*trans(y-ym);
cov2 += (x-xm)*trans(x-xm);
}
cov /= num-1;
cov2 /= num-1;
running_cross_covariance<matrix<double> > rcc = rcc1 + rcc2;
DLIB_TEST_MSG(max(abs(rcc.covariance_xy()-cov)) < 1e-14, max(abs(rcc.covariance_xy()-cov)));
DLIB_TEST(max(abs(rcc.mean_x()-xm)) < 1e-14);
DLIB_TEST(max(abs(rcc.mean_y()-ym)) < 1e-14);
running_covariance<matrix<double> > rc = rc1 + rc2;
DLIB_TEST(max(abs(rc.covariance()-cov2)) < 1e-14);
DLIB_TEST(max(abs(rc.mean()-xm)) < 1e-14);
}
void test_running_covariance (
)
{
dlib::rand rnd;
std::vector<matrix<double,0,1> > vects;
running_covariance<matrix<double,0,1> > cov, cov2;
DLIB_TEST(cov.in_vector_size() == 0);
for (unsigned long dims = 1; dims < 5; ++dims)
{
for (unsigned long samps = 2; samps < 10; ++samps)
{
vects.clear();
cov.clear();
DLIB_TEST(cov.in_vector_size() == 0);
for (unsigned long i = 0; i < samps; ++i)
{
vects.push_back(randm(dims,1,rnd));
cov.add(vects.back());
}
DLIB_TEST(cov.in_vector_size() == (long)dims);
DLIB_TEST(equal(mean(mat(vects)), cov.mean()));
DLIB_TEST_MSG(equal(covariance(mat(vects)), cov.covariance()),
max(abs(covariance(mat(vects)) - cov.covariance()))
<< " dims = " << dims << " samps = " << samps
);
}
}
for (unsigned long dims = 1; dims < 5; ++dims)
{
for (unsigned long samps = 2; samps < 10; ++samps)
{
vects.clear();
cov.clear();
cov2.clear();
DLIB_TEST(cov.in_vector_size() == 0);
for (unsigned long i = 0; i < samps; ++i)
{
vects.push_back(randm(dims,1,rnd));
if ((i%2) == 0)
cov.add(vects.back());
else
cov2.add(vects.back());
}
DLIB_TEST((cov+cov2).in_vector_size() == (long)dims);
DLIB_TEST(equal(mean(mat(vects)), (cov+cov2).mean()));
DLIB_TEST_MSG(equal(covariance(mat(vects)), (cov+cov2).covariance()),
max(abs(covariance(mat(vects)) - (cov+cov2).covariance()))
<< " dims = " << dims << " samps = " << samps
);
}
}
}
void test_running_stats()
{
print_spinner();
running_stats<double> rs, rs2;
running_scalar_covariance<double> rsc1, rsc2;
running_scalar_covariance_decayed<double> rscd1(1000000), rscd2(1000000);
for (double i = 0; i < 100; ++i)
{
rs.add(i);
rsc1.add(i,i);
rsc2.add(i,i);
rsc2.add(i,-i);
rscd1.add(i,i);
rscd2.add(i,i);
rscd2.add(i,-i);
}
// make sure the running_stats and running_scalar_covariance agree
DLIB_TEST_MSG(std::abs(rs.mean() - rsc1.mean_x()) < 1e-10, std::abs(rs.mean() - rsc1.mean_x()));
DLIB_TEST(std::abs(rs.mean() - rsc1.mean_y()) < 1e-10);
DLIB_TEST(std::abs(rs.stddev() - rsc1.stddev_x()) < 1e-10);
DLIB_TEST(std::abs(rs.stddev() - rsc1.stddev_y()) < 1e-10);
DLIB_TEST(std::abs(rs.variance() - rsc1.variance_x()) < 1e-10);
DLIB_TEST(std::abs(rs.variance() - rsc1.variance_y()) < 1e-10);
DLIB_TEST(rs.current_n() == rsc1.current_n());
DLIB_TEST(std::abs(rsc1.correlation() - 1) < 1e-10);
DLIB_TEST(std::abs(rsc2.correlation() - 0) < 1e-10);
DLIB_TEST_MSG(std::abs(rs.mean() - rscd1.mean_x()) < 1e-2, std::abs(rs.mean() - rscd1.mean_x()) << " " << rscd1.mean_x());
DLIB_TEST(std::abs(rs.mean() - rscd1.mean_y()) < 1e-2);
DLIB_TEST_MSG(std::abs(rs.stddev() - rscd1.stddev_x()) < 1e-2, std::abs(rs.stddev() - rscd1.stddev_x()));
DLIB_TEST(std::abs(rs.stddev() - rscd1.stddev_y()) < 1e-2);
DLIB_TEST_MSG(std::abs(rs.variance() - rscd1.variance_x()) < 1e-2, std::abs(rs.variance() - rscd1.variance_x()));
DLIB_TEST(std::abs(rs.variance() - rscd1.variance_y()) < 1e-2);
DLIB_TEST(std::abs(rscd1.correlation() - 1) < 1e-2);
DLIB_TEST(std::abs(rscd2.correlation() - 0) < 1e-2);
// test serialization of running_stats
ostringstream sout;
serialize(rs, sout);
istringstream sin(sout.str());
deserialize(rs2, sin);
// make sure the running_stats and running_scalar_covariance agree
DLIB_TEST_MSG(std::abs(rs2.mean() - rsc1.mean_x()) < 1e-10, std::abs(rs2.mean() - rsc1.mean_x()));
DLIB_TEST(std::abs(rs2.mean() - rsc1.mean_y()) < 1e-10);
DLIB_TEST(std::abs(rs2.stddev() - rsc1.stddev_x()) < 1e-10);
DLIB_TEST(std::abs(rs2.stddev() - rsc1.stddev_y()) < 1e-10);
DLIB_TEST(std::abs(rs2.variance() - rsc1.variance_x()) < 1e-10);
DLIB_TEST(std::abs(rs2.variance() - rsc1.variance_y()) < 1e-10);
DLIB_TEST(rs2.current_n() == rsc1.current_n());
rsc1.clear();
rsc1.add(1, -1);
rsc1.add(0, 0);
rsc1.add(1, -1);
rsc1.add(0, 0);
rsc1.add(1, -1);
rsc1.add(0, 0);
DLIB_TEST(std::abs(rsc1.covariance() - -0.3) < 1e-10);
}
void test_skewness_and_kurtosis_1()
{
dlib::rand rnum;
running_stats<double> rs1;
double tp = 0;
rnum.set_seed("DlibRocks");
for(int i = 0; i< 1000000; i++)
{
tp = rnum.get_random_gaussian();
rs1.add(tp);
}
// check the unbiased skewness and excess kurtosis of one million Gaussian
// draws are both near_vects zero.
DLIB_TEST(abs(rs1.skewness()) < 0.1);
DLIB_TEST(abs(rs1.ex_kurtosis()) < 0.1);
}
void test_skewness_and_kurtosis_2()
{
string str = "DlibRocks";
std::minstd_rand shuffle_random;
for(int j = 0; j<5 ; j++)
{
matrix<double,1,100000> dat;
dlib::rand rnum;
running_stats<double> rs1;
double tp = 0;
double n = 100000;
double xb = 0;
double sknum = 0;
double skdenom = 0;
double unbi_skew = 0;
double exkurnum = 0;
double exkurdenom = 0;
double unbi_exkur = 0;
std::shuffle(str.begin(), str.end(), shuffle_random);
rnum.set_seed(str);
for(int i = 0; i<n; i++)
{
tp = rnum.get_random_gaussian();
rs1.add(tp);
dat(i)=tp;
xb += dat(i);
}
xb = xb/n;
for(int i = 0; i < n; i++ )
{
sknum += pow(dat(i) - xb,3);
skdenom += pow(dat(i) - xb,2);
exkurnum += pow(dat(i) - xb,4);
exkurdenom += pow(dat(i)-xb,2);
}
sknum = sknum/n;
skdenom = pow(skdenom/n,1.5);
exkurnum = exkurnum/n;
exkurdenom = pow(exkurdenom/n,2);
unbi_skew = sqrt(n*(n-1))/(n-2)*sknum/skdenom;
unbi_exkur = (n-1)*((n+1)*(exkurnum/exkurdenom-3)+6)/((n-2)*(n-3));
dlog << LINFO << "Skew Diff: " << unbi_skew - rs1.skewness();
dlog << LINFO << "Kur Diff: " << unbi_exkur - rs1.ex_kurtosis();
// Test an alternative implementation of the unbiased skewness and excess
// kurtosis against the one in running_stats.
DLIB_TEST(abs(unbi_skew - rs1.skewness()) < 1e-10);
DLIB_TEST(abs(unbi_exkur - rs1.ex_kurtosis()) < 1e-10);
}
}
void test_randomize_samples()
{
std::vector<unsigned int> t(15),u(15),v(15);
for (unsigned long i = 0; i < t.size(); ++i)
{
t[i] = i;
u[i] = i+1;
v[i] = i+2;
}
randomize_samples(t,u,v);
DLIB_TEST(t.size() == 15);
DLIB_TEST(u.size() == 15);
DLIB_TEST(v.size() == 15);
for (unsigned long i = 0; i < t.size(); ++i)
{
const unsigned long val = t[i];
DLIB_TEST(u[i] == val+1);
DLIB_TEST(v[i] == val+2);
}
}
void test_randomize_samples2()
{
dlib::matrix<int,15,1> t(15),u(15),v(15);
for (long i = 0; i < t.size(); ++i)
{
t(i) = i;
u(i) = i+1;
v(i) = i+2;
}
randomize_samples(t,u,v);
DLIB_TEST(t.size() == 15);
DLIB_TEST(u.size() == 15);
DLIB_TEST(v.size() == 15);
for (long i = 0; i < t.size(); ++i)
{
const long val = t(i);
DLIB_TEST(u(i) == val+1);
DLIB_TEST(v(i) == val+2);
}
}
void another_test()
{
std::vector<double> a;
running_stats<double> rs1, rs2;
for (int i = 0; i < 10; ++i)
{
rs1.add(i);
a.push_back(i);
}
DLIB_TEST(std::abs(variance(mat(a)) - rs1.variance()) < 1e-13);
DLIB_TEST(std::abs(stddev(mat(a)) - rs1.stddev()) < 1e-13);
DLIB_TEST(std::abs(mean(mat(a)) - rs1.mean()) < 1e-13);
for (int i = 10; i < 20; ++i)
{
rs2.add(i);
a.push_back(i);
}
DLIB_TEST(std::abs(variance(mat(a)) - (rs1+rs2).variance()) < 1e-13);
DLIB_TEST(std::abs(mean(mat(a)) - (rs1+rs2).mean()) < 1e-13);
DLIB_TEST((rs1+rs2).current_n() == 20);
running_scalar_covariance<double> rc1, rc2, rc3;
dlib::rand rnd;
for (double i = 0; i < 10; ++i)
{
const double a = i + rnd.get_random_gaussian();
const double b = i + rnd.get_random_gaussian();
rc1.add(a,b);
rc3.add(a,b);
}
for (double i = 11; i < 20; ++i)
{
const double a = i + rnd.get_random_gaussian();
const double b = i + rnd.get_random_gaussian();
rc2.add(a,b);
rc3.add(a,b);
}
DLIB_TEST(std::abs((rc1+rc2).mean_x() - rc3.mean_x()) < 1e-13);
DLIB_TEST(std::abs((rc1+rc2).mean_y() - rc3.mean_y()) < 1e-13);
DLIB_TEST_MSG(std::abs((rc1+rc2).variance_x() - rc3.variance_x()) < 1e-13, std::abs((rc1+rc2).variance_x() - rc3.variance_x()));
DLIB_TEST(std::abs((rc1+rc2).variance_y() - rc3.variance_y()) < 1e-13);
DLIB_TEST(std::abs((rc1+rc2).covariance() - rc3.covariance()) < 1e-13);
DLIB_TEST((rc1+rc2).current_n() == rc3.current_n());
}
void test_average_precision()
{
std::vector<bool> items;
DLIB_TEST(average_precision(items) == 1);
DLIB_TEST(average_precision(items,1) == 0);
items.push_back(true);
DLIB_TEST(average_precision(items) == 1);
DLIB_TEST(std::abs(average_precision(items,1) - 0.5) < 1e-14);
items.push_back(true);
DLIB_TEST(average_precision(items) == 1);
DLIB_TEST(std::abs(average_precision(items,1) - 2.0/3.0) < 1e-14);
items.push_back(false);
DLIB_TEST(average_precision(items) == 1);
DLIB_TEST(std::abs(average_precision(items,1) - 2.0/3.0) < 1e-14);
items.push_back(true);
DLIB_TEST(std::abs(average_precision(items) - (2.0+3.0/4.0)/3.0) < 1e-14);
items.push_back(true);
DLIB_TEST(std::abs(average_precision(items) - (2.0 + 4.0/5.0 + 4.0/5.0)/4.0) < 1e-14);
DLIB_TEST(std::abs(average_precision(items,1) - (2.0 + 4.0/5.0 + 4.0/5.0)/5.0) < 1e-14);
}
template <typename sample_type>
void check_distance_metrics (
const std::vector<frobmetric_training_sample<sample_type> >& samples
)
{
running_stats<double> rs;
for (unsigned long i = 0; i < samples.size(); ++i)
{
for (unsigned long j = 0; j < samples[i].near_vects.size(); ++j)
{
const double d1 = length_squared(samples[i].anchor_vect - samples[i].near_vects[j]);
for (unsigned long k = 0; k < samples[i].far_vects.size(); ++k)
{
const double d2 = length_squared(samples[i].anchor_vect - samples[i].far_vects[k]);
rs.add(d2-d1);
}
}
}
dlog << LINFO << "dist gap max: "<< rs.max();
dlog << LINFO << "dist gap min: "<< rs.min();
dlog << LINFO << "dist gap mean: "<< rs.mean();
dlog << LINFO << "dist gap stddev: "<< rs.stddev();
DLIB_TEST(rs.min() >= 0.99);
DLIB_TEST(rs.mean() >= 0.9999);
}
void test_vector_normalizer_frobmetric(dlib::rand& rnd)
{
print_spinner();
typedef matrix<double,0,1> sample_type;
vector_normalizer_frobmetric<sample_type> normalizer;
std::vector<frobmetric_training_sample<sample_type> > samples;
frobmetric_training_sample<sample_type> samp;
const long key = 1;
const long dims = 5;
// Lets make some two class training data. Each sample will have dims dimensions but
// only the one with index equal to key will be meaningful. In particular, if the key
// dimension is > 0 then the sample is class +1 and -1 otherwise.
long k = 0;
for (int i = 0; i < 50; ++i)
{
samp.clear();
samp.anchor_vect = gaussian_randm(dims,1,k++);
if (samp.anchor_vect(key) > 0)
samp.anchor_vect(key) = rnd.get_random_double() + 5;
else
samp.anchor_vect(key) = -(rnd.get_random_double() + 5);
matrix<double,0,1> temp;
for (int j = 0; j < 5; ++j)
{
// Don't always put an equal number of near_vects and far_vects vectors into the
// training samples.
const int numa = rnd.get_random_32bit_number()%2 + 1;
const int numb = rnd.get_random_32bit_number()%2 + 1;
for (int num = 0; num < numa; ++num)
{
temp = gaussian_randm(dims,1,k++); temp(key) = 0.1;
//temp = gaussian_randm(dims,1,k++); temp(key) = std::abs(temp(key));
if (samp.anchor_vect(key) > 0) samp.near_vects.push_back(temp);
else samp.far_vects.push_back(temp);
}
for (int num = 0; num < numb; ++num)
{
temp = gaussian_randm(dims,1,k++); temp(key) = -0.1;
//temp = gaussian_randm(dims,1,k++); temp(key) = -std::abs(temp(key));
if (samp.anchor_vect(key) < 0) samp.near_vects.push_back(temp);
else samp.far_vects.push_back(temp);
}
}
samples.push_back(samp);
}
normalizer.set_epsilon(0.0001);
normalizer.set_c(100);
normalizer.set_max_iterations(6000);
normalizer.train(samples);
dlog << LINFO << "learned transform: \n" << normalizer.transform();
matrix<double,0,1> total;
for (unsigned long i = 0; i < samples.size(); ++i)
{
samples[i].anchor_vect = normalizer(samples[i].anchor_vect);
total += samples[i].anchor_vect;
for (unsigned long j = 0; j < samples[i].near_vects.size(); ++j)
samples[i].near_vects[j] = normalizer(samples[i].near_vects[j]);
for (unsigned long j = 0; j < samples[i].far_vects.size(); ++j)
samples[i].far_vects[j] = normalizer(samples[i].far_vects[j]);
}
total /= samples.size();
dlog << LINFO << "sample transformed means: "<< trans(total);
DLIB_TEST(length(total) < 1e-9);
check_distance_metrics(samples);
// make sure serialization works
stringstream os;
serialize(normalizer, os);
vector_normalizer_frobmetric<sample_type> normalizer2;
deserialize(normalizer2, os);
DLIB_TEST(equal(normalizer.transform(), normalizer2.transform()));
DLIB_TEST(equal(normalizer.transformed_means(), normalizer2.transformed_means()));
DLIB_TEST(normalizer.in_vector_size() == normalizer2.in_vector_size());
DLIB_TEST(normalizer.out_vector_size() == normalizer2.out_vector_size());
DLIB_TEST(normalizer.get_max_iterations() == normalizer2.get_max_iterations());
DLIB_TEST(std::abs(normalizer.get_c() - normalizer2.get_c()) < 1e-14);
DLIB_TEST(std::abs(normalizer.get_epsilon() - normalizer2.get_epsilon()) < 1e-14);
}
void prior_frobnorm_test()
{
frobmetric_training_sample<matrix<double,0,1> > sample;
std::vector<frobmetric_training_sample<matrix<double,0,1> > > samples;
matrix<double,3,1> x, near_, far_;
x = 0,0,0;
near_ = 1,0,0;
far_ = 0,1,0;
sample.anchor_vect = x;
sample.near_vects.push_back(near_);
sample.far_vects.push_back(far_);
samples.push_back(sample);
vector_normalizer_frobmetric<matrix<double,0,1> > trainer;
trainer.set_c(100);
print_spinner();
trainer.train(samples);
matrix<double,3,3> correct;
correct = 0, 0, 0,
0, 1, 0,
0, 0, 0;
dlog << LDEBUG << trainer.transform();
DLIB_TEST(max(abs(trainer.transform()-correct)) < 1e-8);
trainer.set_uses_identity_matrix_prior(true);
print_spinner();
trainer.train(samples);
correct = 1, 0, 0,
0, 2, 0,
0, 0, 1;
dlog << LDEBUG << trainer.transform();
DLIB_TEST(max(abs(trainer.transform()-correct)) < 1e-8);
}
void test_lda ()
{
// This test makes sure we pick the right direction in a simple 2D -> 1D LDA
typedef matrix<double,2,1> sample_type;
std::vector<unsigned long> labels;
std::vector<sample_type> samples;
for (int i=0; i<4; i++)
{
sample_type s;
s(0) = i;
s(1) = i+1;
samples.push_back(s);
labels.push_back(1);
sample_type s1;
s1(0) = i+1;
s1(1) = i;
samples.push_back(s1);
labels.push_back(2);
}
matrix<double> X;
X.set_size(8,2);
for (int i=0; i<8; i++){
X(i,0) = samples[i](0);
X(i,1) = samples[i](1);
}
matrix<double,0,1> mean;
dlib::compute_lda_transform(X,mean,labels,1);
std::vector<double> vals1, vals2;
for (unsigned long i = 0; i < samples.size(); ++i)
{
double val = X*samples[i]-mean;
if (i%2 == 0)
vals1.push_back(val);
else
vals2.push_back(val);
dlog << LINFO << "1D LDA output: " << val;
}
if (vals1[0] > vals2[0])
swap(vals1, vals2);
const double err = equal_error_rate(vals1, vals2).first;
dlog << LINFO << "LDA ERR: " << err;
DLIB_TEST(err == 0);
DLIB_TEST(equal_error_rate(vals2, vals1).first == 1);
}
void test_equal_error_rate()
{
auto result = equal_error_rate({}, {});
DLIB_TEST(result.first == 0);
DLIB_TEST(result.second == 0);
// no error case
result = equal_error_rate({1,1,1}, {2,2,2});
DLIB_TEST_MSG(result.first == 0, result.first);
DLIB_TEST_MSG(result.second == 2, result.second);
// max error case
result = equal_error_rate({2,2,2}, {1,1,1});
DLIB_TEST_MSG(result.first == 1, result.first);
DLIB_TEST_MSG(result.second == 2, result.second);
// Another way to have max error
result = equal_error_rate({1,1,1}, {1,1,1});
DLIB_TEST_MSG(result.second == 1, result.second);
DLIB_TEST_MSG(result.first == 1, result.first);
// wildly unbalanced
result = equal_error_rate({}, {1,1,1});
DLIB_TEST_MSG(result.first == 0, result.first);
// wildly unbalanced
result = equal_error_rate({1,1,1}, {});
DLIB_TEST_MSG(result.first == 0, result.first);
// 25% error case
result = equal_error_rate({1,1,1,3}, {2, 2, 0, 2});
DLIB_TEST_MSG(result.first == 0.25, result.first);
DLIB_TEST_MSG(result.second == 2, result.second);
}
void test_running_stats_decayed()
{
print_spinner();
std::vector<double> tmp(300);
std::vector<double> tmp_var(tmp.size());
dlib::rand rnd;
const int num_rounds = 100000;
for (int rounds = 0; rounds < num_rounds; ++rounds)
{
running_stats_decayed<double> rs(100);
for (size_t i = 0; i < tmp.size(); ++i)
{
rs.add(rnd.get_random_gaussian() + 1);
tmp[i] += rs.mean();
if (i > 0)
tmp_var[i] += rs.variance();
}
}
// should print all 1s basically since the mean and variance should always be 1.
for (size_t i = 0; i < tmp.size(); ++i)
{
DLIB_TEST(std::abs(1-tmp[i]/num_rounds) < 0.001);
if (i > 1)
DLIB_TEST(std::abs(1-tmp_var[i]/num_rounds) < 0.01);
}
}
void test_running_scalar_covariance_decayed()
{
print_spinner();
std::vector<double> tmp(300);
std::vector<double> tmp_var(tmp.size());
std::vector<double> tmp_covar(tmp.size());
dlib::rand rnd;
const int num_rounds = 500000;
for (int rounds = 0; rounds < num_rounds; ++rounds)
{
running_scalar_covariance_decayed<double> rs(100);
for (size_t i = 0; i < tmp.size(); ++i)
{
rs.add(rnd.get_random_gaussian() + 1, rnd.get_random_gaussian() + 1);
tmp[i] += (rs.mean_y()+rs.mean_x())/2;
if (i > 0)
{
tmp_var[i] += (rs.variance_y()+rs.variance_x())/2;
tmp_covar[i] += rs.covariance();
}
}
}
// should print all 1s basically since the mean and variance should always be 1.
for (size_t i = 0; i < tmp.size(); ++i)
{
DLIB_TEST(std::abs(1-tmp[i]/num_rounds) < 0.001);
if (i > 1)
{
DLIB_TEST(std::abs(1-tmp_var[i]/num_rounds) < 0.01);
DLIB_TEST(std::abs(tmp_covar[i]/num_rounds) < 0.001);
}
}
}
void test_probability_values_are_increasing() {
DLIB_TEST(probability_values_are_increasing(std::vector<double>{1,2,3,4,5,6,7,8}) > 0.99);
DLIB_TEST(probability_values_are_increasing(std::vector<double>{8,7,6,5,4,4,3,2}) < 0.01);
DLIB_TEST(probability_values_are_increasing_robust(std::vector<double>{1,2,3,4,5,6,7,8}) > 0.99);
DLIB_TEST(probability_values_are_increasing_robust(std::vector<double>{8,7,6,5,4,4,3,2}) < 0.01);
DLIB_TEST(probability_values_are_increasing(std::vector<double>{1,2,1e10,3,4,5,6,7,8}) < 0.3);
DLIB_TEST(probability_values_are_increasing_robust(std::vector<double>{1,2,1e100,3,4,5,6,7,8}) > 0.99);
}
void test_event_corr()
{
print_spinner();
DLIB_TEST(event_correlation(1000,1000,500,2000) == 0);
DLIB_TEST(std::abs(event_correlation(1000,1000,300,2000) + 164.565757010104) < 1e-11);
DLIB_TEST(std::abs(event_correlation(1000,1000,700,2000) - 164.565757010104) < 1e-11);
DLIB_TEST(event_correlation(10,1000,5,2000) == 0);
DLIB_TEST(event_correlation(1000,10,5,2000) == 0);
DLIB_TEST(std::abs(event_correlation(10,1000,1,2000) - event_correlation(1000,10,1,2000)) < 1e-11);
DLIB_TEST(std::abs(event_correlation(10,1000,9,2000) - event_correlation(1000,10,9,2000)) < 1e-11);
DLIB_TEST(std::abs(event_correlation(10,1000,1,2000) + 3.69672251700842) < 1e-11);
DLIB_TEST(std::abs(event_correlation(10,1000,9,2000) - 3.69672251700842) < 1e-11);
}
void perform_test (
)
{
prior_frobnorm_test();
dlib::rand rnd;
for (int i = 0; i < 5; ++i)
test_vector_normalizer_frobmetric(rnd);
test_random_subset_selector();
test_random_subset_selector2();
test_running_covariance();
test_running_cross_covariance();
test_running_cross_covariance_sparse();
test_running_stats();
test_skewness_and_kurtosis_1();
test_skewness_and_kurtosis_2();
test_randomize_samples();
test_randomize_samples2();
another_test();
test_average_precision();
test_lda();
test_event_corr();
test_running_stats_decayed();
test_running_scalar_covariance_decayed();
test_equal_error_rate();
test_probability_values_are_increasing();
}
} a;
}