数组的反转计数表示数组距离排序的距离(或距离)。如果数组已排序,则反转计数为0。如果数组按相反顺序排序,则反转计数为最大值。
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Two elements a[i] and a[j] form an inversion if a[i] > a[j] and i < j. For simplicity, we may assume that all elements are unique. Example: Input: arr[] = {8, 4, 2, 1} Output: 6 Given array has six inversions (8,4), (4,2), (8,2), (8,1), (4,1), (2,1).
我们已经讨论了以下方法。 1) 基于朴素和合并排序的方法。 2) 基于AVL树的方法。
在本文中,使用 在C++ STL中设置 本文对此进行了讨论。
1) Create an empty Set in C++ STL (Note that a Set in C++ STL is implemented using Self-Balancing Binary Search Tree). And insert first element of array into the set. 2) Initialize inversion count as 0. 3) Iterate from 1 to n-1 and do following for every element in arr[i] a) Insert arr[i] into the set. b) Find the first element greater than arr[i] in set using upper_bound() defined Set STL. c) Find distance of above found element from last element in set and add this distance to inversion count. 4) Return inversion count.
// A STL Set based approach for inversion count #include<bits/stdc++.h> using namespace std; // Returns inversion count in arr[0..n-1] int getInvCount( int arr[], int n) { // Create an empty set and insert first element in it multiset< int > set1; set1.insert(arr[0]); int invcount = 0; // Initialize result multiset< int >::iterator itset1; // Iterator for the set // Traverse all elements starting from second for ( int i=1; i<n; i++) { // Insert arr[i] in set (Note that set maintains // sorted order) set1.insert(arr[i]); // Set the iterator to first greater element than arr[i] // in set (Note that set stores arr[0],.., arr[i-1] itset1 = set1.upper_bound(arr[i]); // Get distance of first greater element from end // and this distance is count of greater elements // on left side of arr[i] invcount += distance(itset1, set1.end()); } return invcount; } // Driver program to test above int main() { int arr[] = {8, 4, 2, 1}; int n = sizeof (arr)/ sizeof ( int ); cout << "Number of inversions count are : " << getInvCount(arr,n); return 0; } |
输出:
Number of inversions count are : 6
请注意,上述实现的最坏情况时间复杂度为O(n) 2. )由于STL中的距离函数在最坏的情况下需要O(n)时间,但这种实现比其他实现简单得多,并且平均比Naive方法花费的时间要少得多。
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