32 #include "gtest/gtest.h" 40 for (
size_t n = 0; n < N; ++n)
46 td.
m_txid = crypto::null_hash;
47 td.
m_txid.data[0] = n & 0xff;
48 td.
m_txid.data[1] = (n >> 8) & 0xff;
49 td.
m_txid.data[2] = (n >> 16) & 0xff;
50 td.
m_txid.data[3] = (n >> 24) & 0xff;
57 auto i = std::find(unused_indices.begin(), unused_indices.end(), idx); \ 58 ASSERT_TRUE(i != unused_indices.end()); \ 59 unused_indices.erase(i); \ 60 selected.push_back(idx); \ 63 #define PICK(expected) \ 65 size_t idx = w.pop_best_value_from(transfers, unused_indices, selected); \ 66 ASSERT_EQ(expected, idx); \ 67 selected.push_back(idx); \ 70 TEST(select_outputs, one_out_of_N)
78 transfers[6].m_block_height = 700;
79 std::vector<size_t> unused_indices({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
80 std::vector<size_t> selected;
85 TEST(select_outputs, order)
91 transfers[0].m_block_height = 700;
92 transfers[1].m_block_height = 700;
93 transfers[2].m_block_height = 704;
94 transfers[3].m_block_height = 716;
95 transfers[4].m_block_height = 701;
96 std::vector<size_t> unused_indices({0, 1, 2, 3, 4});
97 std::vector<size_t> selected;
105 #define MKOFFSETS(N, n) \ 108 for (auto &offset: offsets) \ 110 offset = n_outs += (n); \ 115 std::vector<uint64_t> offsets;
119 std::vector<double> ages(100000);
120 double age_scale = 120. * (offsets.size() / (double)n_outs);
121 for (
size_t i = 0; i < ages.size(); )
126 ages[i] = (n_outs - 1 - o) * age_scale;
128 ASSERT_LE(ages[i], offsets.size() * 120);
139 static const size_t NPICKS = 1000000;
140 std::vector<uint64_t> offsets;
145 std::vector<int> picks(offsets.size(), 0);
146 for (
int i = 0; i < NPICKS; )
151 auto it = std::lower_bound(offsets.begin(), offsets.end(), o);
152 auto idx = std::distance(offsets.begin(), it);
158 for (
int d = 1; d < 0x20; ++d)
162 size_t count_selected = 0, count_chain = 0;
163 for (
size_t i = 0; i < offsets.size(); ++i)
165 size_t n_outputs = offsets[i] - (i == 0 ? 0 : offsets[i - 1]);
168 count_selected += picks[i];
172 float selected_ratio = count_selected / (float)NPICKS;
173 float chain_ratio = count_chain / (float)n_outs;
174 MDEBUG(count_selected <<
"/" << NPICKS <<
" outputs selected in blocks of density " << d <<
", " << 100.0f * selected_ratio <<
"%");
175 MDEBUG(count_chain <<
"/" << offsets.size() <<
" outputs in blocks of density " << d <<
", " << 100.0f * chain_ratio <<
"%");
176 ASSERT_LT(fabsf(selected_ratio - chain_ratio), 0.025f);
180 TEST(select_outputs, same_distribution)
182 static const size_t NPICKS = 1000000;
183 std::vector<uint64_t> offsets;
188 std::vector<int> chain_picks(offsets.size(), 0);
189 std::vector<int> output_picks(n_outs, 0);
190 for (
int i = 0; i < NPICKS; )
195 auto it = std::lower_bound(offsets.begin(), offsets.end(), o);
196 auto idx = std::distance(offsets.begin(), it);
204 std::vector<int> chain_norm(100, 0), output_norm(100, 0);
205 for (
size_t i = 0; i < output_picks.size(); ++i)
206 output_norm[i * 100 / output_picks.size()] += output_picks[i];
207 for (
size_t i = 0; i < chain_picks.size(); ++i)
208 chain_norm[i * 100 / chain_picks.size()] += chain_picks[i];
210 double max_dev = 0.0, avg_dev = 0.0;
211 for (
size_t i = 0; i < 100; ++i)
213 const double diff = (double)output_norm[i] - (
double)chain_norm[i];
214 double dev = fabs(2.0 * diff / (output_norm[i] + chain_norm[i]));
219 MDEBUG(
"avg_dev: " << avg_dev);
TEST(select_outputs, one_out_of_N)
#define ASSERT_GE(val1, val2)
void rand(size_t N, uint8_t *bytes)
unsigned __int64 uint64_t
#define ASSERT_LT(val1, val2)
type_vec_type median(std::vector< type_vec_type > &v)
#define ASSERT_LE(val1, val2)