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Added unit tests for the vector_normalizer_frobmetric object.

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Davis King 2013-09-24 22:04:34 -04:00
parent df250a8fd7
commit ab0f41de5f
1 changed files with 124 additions and 0 deletions
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124
dlib/test/statistics.cpp
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@ -573,9 +573,133 @@ namespace
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.size(); ++j)
{
const double d1 = length_squared(samples[i].anchor - samples[i].near[j]);
for (unsigned long k = 0; k < samples[i].far.size(); ++k)
{
const double d2 = length_squared(samples[i].anchor - samples[i].far[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 = gaussian_randm(dims,1,k++);
if (samp.anchor(key) > 0)
samp.anchor(key) = rnd.get_random_double() + 5;
else
samp.anchor(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 and far 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(key) > 0) samp.near.push_back(temp);
else samp.far.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(key) < 0) samp.near.push_back(temp);
else samp.far.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 = normalizer(samples[i].anchor);
total += samples[i].anchor;
for (unsigned long j = 0; j < samples[i].near.size(); ++j)
samples[i].near[j] = normalizer(samples[i].near[j]);
for (unsigned long j = 0; j < samples[i].far.size(); ++j)
samples[i].far[j] = normalizer(samples[i].far[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 perform_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();