Another sorting method, the counting sort, does not require comparison. Instead, you create an integer array whose index range covers the entire range of values in your array to sort. Each time a value occurs in
Insertion Sort and the simple version of QuickSort were stable, but the faster in-place version of Quicksort was not (since it scrambled around elements while sorting). In cases where you care about the original order, it is important to use a stable sorting algorithm. In this challenge, y...
def countInversions(arr): def merge_sort_and_count(arr): if len(arr) <= 1: return arr, 0 mid = len(arr) // 2 left, left_inversions = merge_sort_and_count(arr[:mid]) right, right_inversions = merge_sort_and_count(arr[mid:]) merged, split_inversions = merge_and_count(left, ...
sort(val + 1, val + n + 1); sort(val_2 + 1, val_2 + n + 1); cnt1 = 0; cnt2 = 0; cnt3 = 0; for (i = 1; i <= n; i++) { for (j = i + 1; j <= n; j++) { v = val_2[i] + val_2[j]; pos = lower_bound(val_2 + j + 1, val_2 + n + 1...
Complete the function countInversions in the editor below. It must return an integer representing the number of inversions required to sort the array. countInversions has the following parameter(s): arr: an array of integers to sort .
voidcountSort(vector<vector<string>> arr){ } intmain() { string n_temp; getline(cin, n_temp); intn = stoi(ltrim(rtrim(n_temp))); vector<vector<string>> arr(n); for(inti =0; i < n; i++){ arr[i].resize(2); string arr_row_temp_temp; ...