| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | #include "mityaeva_d_min_v_rows_matrix/seq/include/ops_seq.hpp" | ||
| 2 | |||
| 3 | #include <algorithm> | ||
| 4 | #include <cstddef> | ||
| 5 | #include <vector> | ||
| 6 | |||
| 7 | #include "mityaeva_d_min_v_rows_matrix/common/include/common.hpp" | ||
| 8 | |||
| 9 | namespace mityaeva_d_min_v_rows_matrix { | ||
| 10 | |||
| 11 |
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48 | MinValuesInRowsSEQ::MinValuesInRowsSEQ(const InType &in) { |
| 12 | SetTypeOfTask(GetStaticTypeOfTask()); | ||
| 13 |
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48 | GetInput() = in; |
| 14 |
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48 | GetOutput() = std::vector<int>{0}; |
| 15 | 48 | } | |
| 16 | |||
| 17 |
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48 | bool MinValuesInRowsSEQ::ValidationImpl() { |
| 18 | const auto &input = GetInput(); | ||
| 19 | |||
| 20 |
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48 | if (input.empty() || input.size() < 2) { |
| 21 | return false; | ||
| 22 | } | ||
| 23 | |||
| 24 | 48 | int rows = input[0]; | |
| 25 | 48 | int cols = input[1]; | |
| 26 | |||
| 27 |
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48 | if (rows <= 0 || cols <= 0) { |
| 28 | return false; | ||
| 29 | } | ||
| 30 | |||
| 31 | 48 | size_t expected_size = 2 + (static_cast<size_t>(rows) * static_cast<size_t>(cols)); | |
| 32 | 48 | return input.size() == expected_size; | |
| 33 | } | ||
| 34 | |||
| 35 | 48 | bool MinValuesInRowsSEQ::PreProcessingImpl() { | |
| 36 | 48 | return true; | |
| 37 | } | ||
| 38 | |||
| 39 |
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48 | bool MinValuesInRowsSEQ::RunImpl() { |
| 40 | const auto &input = GetInput(); | ||
| 41 | |||
| 42 | try { | ||
| 43 | 48 | int rows = input[0]; | |
| 44 | 48 | int cols = input[1]; | |
| 45 | |||
| 46 | 48 | std::vector<int> result; | |
| 47 |
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48 | result.reserve(rows); |
| 48 | |||
| 49 | size_t data_index = 2; | ||
| 50 | |||
| 51 |
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160 | for (int i = 0; i < rows; ++i) { |
| 52 |
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112 | if (cols == 0) { |
| 53 | ✗ | result.push_back(0); | |
| 54 | ✗ | continue; | |
| 55 | } | ||
| 56 | |||
| 57 | 112 | int min_val = input[data_index]; | |
| 58 | |||
| 59 |
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312 | for (int j = 1; j < cols; ++j) { |
| 60 | 200 | int current_val = input[data_index + j]; | |
| 61 | 200 | min_val = std::min(current_val, min_val); | |
| 62 | } | ||
| 63 | |||
| 64 | result.push_back(min_val); | ||
| 65 | 112 | data_index += cols; | |
| 66 | } | ||
| 67 | |||
| 68 | auto &output = GetOutput(); | ||
| 69 | output.clear(); | ||
| 70 |
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48 | output.reserve(rows + 1); |
| 71 |
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48 | output.push_back(static_cast<int>(result.size())); |
| 72 | |||
| 73 |
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160 | for (int val : result) { |
| 74 | output.push_back(val); | ||
| 75 | } | ||
| 76 | |||
| 77 | return true; | ||
| 78 | |||
| 79 | ✗ | } catch (...) { | |
| 80 | return false; | ||
| 81 | ✗ | } | |
| 82 | } | ||
| 83 | |||
| 84 |
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48 | bool MinValuesInRowsSEQ::PostProcessingImpl() { |
| 85 | const auto &output = GetOutput(); | ||
| 86 |
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48 | return !output.empty() && output[0] > 0; |
| 87 | } | ||
| 88 | |||
| 89 | } // namespace mityaeva_d_min_v_rows_matrix | ||
| 90 |