We are given an integer array (Arr) and an array of queries.
A query will be one of the two types below.
Type 1, format: [1, idx, val], where you are to update Arr[idx]=val, and we do not need to return anything
Type 2, format [2, left_idx, right_idx], where you are to count the number of elements with odd frequency within the left_idx and right_idx inclusive, and we return the count. The index is 1-based indexing
Examples:
Arr = [1,3,5,5]
queries = [[2,1,4], [1,4,2], [2,1,4]]
The output should be [2, 4]
For queries[0], which is [2,1,4], the frequency of the elements is {1:1, 3:1, 5:2} so there are 2 elements (1,3) with odd frequency and the output for this query is 2.
queries[1] changes Arr from [1,3,5,5] to [1,3,5,4], and there will be no output.
queries[2], which is [2,1,4], the frequency of the elements is {1:1, 3:1, 5:1, 4:1}, and there are 4 elements (1,3,5,4) with odd frequency and the output will be 4.
Hence the overall output is [2,4].
I tried solving this question by doing Type 1 query in O(1) (just updating the array) time and Type 2 query in O(n) time (iterating from left to right to get the answer), but this failed the time limit given.
I was wondering if there is any solution with a better time complexity.
Some simple but not so efficient way to solve this is to use segment tree or sqrt decomposition with bitset. In each stage you memorize bitset where bt[i] is the number of elements i modulo 2. To merge segments you can simpli xor bitsets
Mb something with segtree