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| 1 | +/** |
| 2 | + * @file sparse_table.cpp |
| 3 | + * @brief Implementation of [Sparse Table](https://en.wikipedia.org/wiki/Range_minimum_query) data structure |
| 4 | + * |
| 5 | + * @details |
| 6 | + * Sparse Table is a data structure, that allows answering range queries. |
| 7 | + * It can answer most range queries in O(logn), but its true power is answering range minimum queries |
| 8 | + * or equivalent range maximum queries). For those queries it can compute the answer in O(1) time. |
| 9 | + * |
| 10 | + * * Running Time Complexity \n |
| 11 | + * * Build : O(NlogN) \n |
| 12 | + * * Range Query : O(1) \n |
| 13 | +*/ |
| 14 | + |
| 15 | +#include <vector> |
| 16 | +#include <cassert> |
| 17 | +#include <iostream> |
| 18 | +#include <algorithm> |
| 19 | + |
| 20 | +/** |
| 21 | + * @namespace range_queries |
| 22 | + * @brief Range Queries algorithms |
| 23 | + */ |
| 24 | +namespace range_queries { |
| 25 | +/** |
| 26 | + * @namespace sparse_table |
| 27 | + * @brief Range queries using sparse-tables |
| 28 | + */ |
| 29 | + namespace sparse_table { |
| 30 | +/** |
| 31 | + * This function precomputes intial log table for further use. |
| 32 | + * @param n value of the size of the input array |
| 33 | + * @return corresponding vector of the log table |
| 34 | + */ |
| 35 | +template<typename T> |
| 36 | +std::vector<T> computeLogs(const std::vector<T>& A) { |
| 37 | + int n = A.size(); |
| 38 | + std::vector<T> logs(n); |
| 39 | + logs[1] = 0; |
| 40 | + for (int i = 2 ; i < n ; i++) { |
| 41 | + logs[i] = logs[i/2] + 1; |
| 42 | + } |
| 43 | + return logs; |
| 44 | +} |
| 45 | + |
| 46 | +/** |
| 47 | + * This functions builds the primary data structure sparse table |
| 48 | + * @param n value of the size of the input array |
| 49 | + * @param A array of the input integers |
| 50 | + * @param logs array of the log table |
| 51 | + * @return created sparse table data structure |
| 52 | + */ |
| 53 | +template<typename T> |
| 54 | +std::vector<std::vector<T> > buildTable(const std::vector<T>& A, const std::vector<T>& logs) { |
| 55 | + int n = A.size(); |
| 56 | + std::vector<std::vector<T> > table(20, std::vector<T>(n+5, 0)); |
| 57 | + int curLen = 0; |
| 58 | + for (int i = 0 ; i <= logs[n] ; i++) { |
| 59 | + curLen = 1 << i; |
| 60 | + for (int j = 0 ; j + curLen < n ; j++) { |
| 61 | + if (curLen == 1) { |
| 62 | + table[i][j] = A[j]; |
| 63 | + } |
| 64 | + else { |
| 65 | + table[i][j] = std::min(table[i-1][j], table[i-1][j + curLen/2]); |
| 66 | + } |
| 67 | + } |
| 68 | + } |
| 69 | + return table; |
| 70 | +} |
| 71 | + |
| 72 | +/** |
| 73 | + * This function is the query function to get the range minimum value |
| 74 | + * @param beg beginning index of the query range |
| 75 | + * @param end ending index of the query range |
| 76 | + * @param logs array of the log table |
| 77 | + * @param table sparse table data structure for the input array |
| 78 | + * @return minimum value for the [beg, end] range for the input array |
| 79 | + */ |
| 80 | +template<typename T> |
| 81 | +int getMinimum(int beg, int end, const std::vector<T>& logs, const std::vector<std::vector<T> >& table) { |
| 82 | + int p = logs[end - beg + 1]; |
| 83 | + int pLen = 1 << p; |
| 84 | + return std::min(table[p][beg], table[p][end - pLen + 1]); |
| 85 | +} |
| 86 | +} |
| 87 | +} // namespace range_queries |
| 88 | + |
| 89 | +/** |
| 90 | + * Main function |
| 91 | + */ |
| 92 | +int main() { |
| 93 | + std::vector<int> A{1, 2, 0, 3, 9}; |
| 94 | + std::vector<int> logs = range_queries::sparse_table::computeLogs(A); |
| 95 | + std::vector<std::vector<int> > table = range_queries::sparse_table::buildTable(A, logs); |
| 96 | + assert(range_queries::sparse_table::getMinimum(0, 0, logs, table) == 1); |
| 97 | + assert(range_queries::sparse_table::getMinimum(0, 4, logs, table) == 0); |
| 98 | + assert(range_queries::sparse_table::getMinimum(2, 4, logs, table) == 0); |
| 99 | + return 0; |
| 100 | +} |
| 101 | + |
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