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wallet2: "output lineup" fake out selection

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Based on python code by sarang:
https://github.com/SarangNoether/skunkworks/blob/outputs/outputs/simulate.py
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moneromooo-monero 2019-04-02 14:16:45 +00:00
parent 7973fb6a69
commit 35e0a968bd
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3 changed files with 189 additions and 60 deletions
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117
tests/unit_tests/output_selection.cpp
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@ -101,3 +101,120 @@ TEST(select_outputs, order)
PICK(1); // then the one that's on the same height
}
#define MKOFFSETS(N, n) \
offsets.resize(N); \
size_t n_outs = 0; \
for (auto &offset: offsets) \
{ \
offset = n_outs += (n); \
}
TEST(select_outputs, gamma)
{
std::vector<uint64_t> offsets;
MKOFFSETS(300000, 1);
tools::gamma_picker picker(offsets);
std::vector<double> ages(100000);
double age_scale = 120. * (offsets.size() / (double)n_outs);
for (size_t i = 0; i < ages.size(); )
{
uint64_t o = picker.pick();
if (o >= n_outs)
continue;
ages[i] = (n_outs - 1 - o) * age_scale;
ASSERT_GE(ages[i], 0);
ASSERT_LE(ages[i], offsets.size() * 120);
++i;
}
double median = epee::misc_utils::median(ages);
MDEBUG("median age: " << median / 86400. << " days");
ASSERT_GE(median, 1.3 * 86400);
ASSERT_LE(median, 1.4 * 86400);
}
TEST(select_outputs, density)
{
static const size_t NPICKS = 1000000;
std::vector<uint64_t> offsets;
MKOFFSETS(300000, 1 + (rand() & 0x1f));
tools::gamma_picker picker(offsets);
std::vector<int> picks(/*n_outs*/offsets.size(), 0);
for (int i = 0; i < NPICKS; )
{
uint64_t o = picker.pick();
if (o >= n_outs)
continue;
auto it = std::lower_bound(offsets.begin(), offsets.end(), o);
auto idx = std::distance(offsets.begin(), it);
ASSERT_LT(idx, picks.size());
++picks[idx];
++i;
}
for (int d = 1; d < 0x20; ++d)
{
// count the number of times an output in a block of d outputs was selected
// count how many outputs are in a block of d outputs
size_t count_selected = 0, count_chain = 0;
for (size_t i = 0; i < offsets.size(); ++i)
{
size_t n_outputs = offsets[i] - (i == 0 ? 0 : offsets[i - 1]);
if (n_outputs == d)
{
count_selected += picks[i];
count_chain += d;
}
}
float selected_ratio = count_selected / (float)NPICKS;
float chain_ratio = count_chain / (float)n_outs;
MDEBUG(count_selected << "/" << NPICKS << " outputs selected in blocks of density " << d << ", " << 100.0f * selected_ratio << "%");
MDEBUG(count_chain << "/" << offsets.size() << " outputs in blocks of density " << d << ", " << 100.0f * chain_ratio << "%");
ASSERT_LT(fabsf(selected_ratio - chain_ratio), 0.02f);
}
}
TEST(select_outputs, same_distribution)
{
static const size_t NPICKS = 1000000;
std::vector<uint64_t> offsets;
MKOFFSETS(300000, 1 + (rand() & 0x1f));
tools::gamma_picker picker(offsets);
std::vector<int> chain_picks(offsets.size(), 0);
std::vector<int> output_picks(n_outs, 0);
for (int i = 0; i < NPICKS; )
{
uint64_t o = picker.pick();
if (o >= n_outs)
continue;
auto it = std::lower_bound(offsets.begin(), offsets.end(), o);
auto idx = std::distance(offsets.begin(), it);
ASSERT_LT(idx, chain_picks.size());
++chain_picks[idx];
++output_picks[o];
++i;
}
// scale them both to 0-100
std::vector<int> chain_norm(100, 0), output_norm(100, 0);
for (size_t i = 0; i < output_picks.size(); ++i)
output_norm[i * 100 / output_picks.size()] += output_picks[i];
for (size_t i = 0; i < chain_picks.size(); ++i)
chain_norm[i * 100 / chain_picks.size()] += chain_picks[i];
double max_dev = 0.0, avg_dev = 0.0;
for (size_t i = 0; i < 100; ++i)
{
const double diff = (double)output_norm[i] - (double)chain_norm[i];
double dev = fabs(2.0 * diff / (output_norm[i] + chain_norm[i]));
ASSERT_LT(dev, 0.1);
avg_dev += dev;
}
avg_dev /= 100;
MDEBUG("avg_dev: " << avg_dev);
ASSERT_LT(avg_dev, 0.015);
}