GCC Code Coverage Report

Directory: ./
File: tasks/levonychev_i_multistep_2d_optimization/seq/src/ops_seq.cpp
Date: 2026-01-10 02:40:41
Exec Total Coverage
Lines: 56 56 100.0%
Functions: 5 5 100.0%
Branches: 37 84 44.0%
Line Branch Exec Source
1 #include "levonychev_i_multistep_2d_optimization/seq/include/ops_seq.hpp"
2
3 #include <algorithm>
4 #include <cstddef>
5 #include <limits>
6 #include <vector>
7
8 #include "levonychev_i_multistep_2d_optimization/common/include/common.hpp"
9 #include "levonychev_i_multistep_2d_optimization/common/include/optimization_common.hpp"
10
11 namespace levonychev_i_multistep_2d_optimization {
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13
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 24 LevonychevIMultistep2dOptimizationSEQ::LevonychevIMultistep2dOptimizationSEQ(const InType &in) {
14 SetTypeOfTask(GetStaticTypeOfTask());
15
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 24 GetInput() = in;
16 24 GetOutput() = OptimizationResult();
17 24 }
18
19 24 bool LevonychevIMultistep2dOptimizationSEQ::ValidationImpl() {
20 const auto &params = GetInput();
21
22
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 24 bool is_correct_ranges = (params.x_min < params.x_max) && (params.y_min < params.y_max);
23 bool isnt_correct_func = !params.func;
24
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 24 bool is_correct_params = (params.num_steps > 0) && (params.grid_size_step1 > 0) && (params.candidates_per_step > 0);
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 24 return is_correct_ranges && !isnt_correct_func && is_correct_params;
27 }
28
29 24 bool LevonychevIMultistep2dOptimizationSEQ::PreProcessingImpl() {
30 24 GetOutput() = OptimizationResult();
31 24 return true;
32 }
33
34 24 bool LevonychevIMultistep2dOptimizationSEQ::RunImpl() {
35 const auto &params = GetInput();
36 auto &result = GetOutput();
37
38 24 SearchRegion current_region(params.x_min, params.x_max, params.y_min, params.y_max);
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40
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 96 for (int step = 0; step < params.num_steps; ++step) {
41 72 int grid_size = params.grid_size_step1 * (1 << step);
42
43 72 std::vector<Point> local_points;
44 72 size_t total_points = static_cast<size_t>(grid_size) * static_cast<size_t>(grid_size);
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 72 local_points.reserve(total_points);
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 72 SearchInRegion(local_points, params.func, current_region, grid_size, 1);
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 72 std::vector<Point> local_candidates;
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 72 int num_local_candidates = std::min(params.candidates_per_step, static_cast<int>(local_points.size()));
50 72 local_candidates.assign(local_points.begin(), local_points.begin() + num_local_candidates);
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 72 std::vector<Point> all_candidates = local_candidates;
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54 72 std::vector<Point> valid_candidates;
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 360 for (const auto &cand : all_candidates) {
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 288 if (cand.value < std::numeric_limits<double>::max()) {
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 288 valid_candidates.push_back(cand);
58 }
59 }
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 432 std::ranges::sort(valid_candidates, [](const Point &a, const Point &b) { return a.value < b.value; });
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 72 int num_global = std::min(params.candidates_per_step, static_cast<int>(valid_candidates.size()));
63 72 all_candidates.assign(valid_candidates.begin(), valid_candidates.begin() + num_global);
64
65 72 std::vector<SearchRegion> new_regions;
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 360 for (const auto &cand : all_candidates) {
67 288 double margin_x = (params.x_max - params.x_min) * 0.05 / (1 << step);
68 288 double margin_y = (params.y_max - params.y_min) * 0.05 / (1 << step);
69
70 288 SearchRegion new_region(std::max(params.x_min, cand.x - margin_x), std::min(params.x_max, cand.x + margin_x),
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 288 std::max(params.y_min, cand.y - margin_y), std::min(params.y_max, cand.y + margin_y));
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 288 new_regions.push_back(new_region);
73 }
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 72 if (!new_regions.empty()) {
75 72 current_region = new_regions[0];
76 }
77 }
78
79 int power_of_2 = 1;
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 72 for (int i = 0; i < params.num_steps - 1; ++i) {
81 48 power_of_2 *= 2;
82 }
83 24 int final_grid_size = params.grid_size_step1 * power_of_2;
84 24 std::vector<Point> local_points;
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 24 local_points.reserve(static_cast<size_t>(final_grid_size) * static_cast<size_t>(final_grid_size));
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 24 SearchInRegion(local_points, params.func, current_region, final_grid_size, 1);
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88 Point best_point;
89 24 best_point = local_points[0];
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 24 if (params.use_local_optimization) {
91 24 best_point = LocalOptimization(params.func, best_point.x, best_point.y, params.x_min, params.x_max, params.y_min,
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 24 params.y_max);
93 }
94
95 24 result.x_min = best_point.x;
96 24 result.y_min = best_point.y;
97 24 result.value = best_point.value;
98
99 24 return true;
100 }
101
102 24 bool LevonychevIMultistep2dOptimizationSEQ::PostProcessingImpl() {
103 const auto &params = GetInput();
104 auto &result = GetOutput();
105
106
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 24 return result.x_min >= params.x_min && result.x_min <= params.x_max && result.y_min >= params.y_min &&
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 24 result.y_min <= params.y_max;
108 }
109
110 } // namespace levonychev_i_multistep_2d_optimization
111