output_selection.cpp File Reference

#include "gtest/gtest.h"
#include "wallet/wallet2.h"
#include <string>

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Macros
#define	SELECT(idx)
#define	PICK(expected)
#define	MKOFFSETS(N, n)

Functions
	TEST (select_outputs, one_out_of_N)
	TEST (select_outputs, order)
	TEST (select_outputs, gamma)
	TEST (select_outputs, density)
	TEST (select_outputs, same_distribution)

Macro Definition Documentation

MKOFFSETS

#define MKOFFSETS	(		N,
			n )

Value:

offsets.resize(N); \
size_t n_outs = 0; \
for (auto &offset: offsets) \
{ \
  offset = n_outs += (n); \
}

Definition at line 105 of file output_selection.cpp.

#define MKOFFSETS(N, n) \
  offsets.resize(N); \
  size_t n_outs = 0; \
  for (auto &offset: offsets) \
  { \
    offset = n_outs += (n); \
  }

PICK

#define PICK

(

expected

)

Value:

do { \
  size_t idx = w.pop_best_value_from(transfers, unused_indices, selected); \
  ASSERT_EQ(expected, idx); \
  selected.push_back(idx); \
} while(0)

Definition at line 63 of file output_selection.cpp.

#define PICK(expected) \
  do { \
    size_t idx = w.pop_best_value_from(transfers, unused_indices, selected); \
    ASSERT_EQ(expected, idx); \
    selected.push_back(idx); \
  } while(0)

SELECT

#define SELECT

(

idx

)

Value:

do { \
  auto i = std::find(unused_indices.begin(), unused_indices.end(), idx); \
  ASSERT_TRUE(i != unused_indices.end()); \
  unused_indices.erase(i); \
  selected.push_back(idx); \
} while(0)

Definition at line 55 of file output_selection.cpp.

#define SELECT(idx) \
  do { \
    auto i = std::find(unused_indices.begin(), unused_indices.end(), idx); \
    ASSERT_TRUE(i != unused_indices.end()); \
    unused_indices.erase(i); \
    selected.push_back(idx); \
  } while(0)

Function Documentation

TEST() [1/5]

TEST	(	select_outputs	,
		density	)

Definition at line 137 of file output_selection.cpp.

{
  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), 0.025f);
  }
}

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TEST() [2/5]

TEST	(	select_outputs	,
		gamma	)

Definition at line 113 of file output_selection.cpp.

{
  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);
}

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TEST() [3/5]

TEST	(	select_outputs	,
		one_out_of_N	)

Definition at line 70 of file output_selection.cpp.

{
  tools::wallet2 w;
 
  // check that if there are N-1 outputs of the same height, one of them
  // already selected, the next one selected is the one that's from a
  // different height
  tools::wallet2::transfer_container transfers = make_transfers_container(10);
  transfers[6].m_block_height = 700;
  std::vector<size_t> unused_indices({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
  std::vector<size_t> selected;
  SELECT(2);
  PICK(6);
}

TEST() [4/5]

TEST	(	select_outputs	,
		order	)

Definition at line 85 of file output_selection.cpp.

{
  tools::wallet2 w;
 
  // check that most unrelated heights are picked in order
  tools::wallet2::transfer_container transfers = make_transfers_container(5);
  transfers[0].m_block_height = 700;
  transfers[1].m_block_height = 700;
  transfers[2].m_block_height = 704;
  transfers[3].m_block_height = 716;
  transfers[4].m_block_height = 701;
  std::vector<size_t> unused_indices({0, 1, 2, 3, 4});
  std::vector<size_t> selected;
  SELECT(0);
  PICK(3); // first the one that's far away
  PICK(2); // then the one that's close
  PICK(4); // then the one that's adjacent
  PICK(1); // then the one that's on the same height
}

TEST() [5/5]

TEST	(	select_outputs	,
		same_distribution	)

Definition at line 180 of file output_selection.cpp.

{
  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);
}

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