
Problem Statement
In this problem, we have to work with two arrays, A and B, each containing a permutation of integers from 1 to n. Our goal is to construct a new array C such that each element C[i] captures the count of numbers that appear in both A and B up to the index i. This concept is described as the prefix common array for A and B.
The challenge involves efficiently comparing elements up to index i in both permutations and counting common numbers to form each corresponding index in array C. We are essentially tracking the accumulation of commonalities between the two arrays as we progress from the start to the end of the arrays.
Examples
Example 1
Input:
A = [1,3,2,4], B = [3,1,2,4]
Output:
[0,2,3,4]
Explanation:
At i = 0: no number is common, so C[0] = 0. At i = 1: 1 and 3 are common in A and B, so C[1] = 2. At i = 2: 1, 2, and 3 are common in A and B, so C[2] = 3. At i = 3: 1, 2, 3, and 4 are common in A and B, so C[3] = 4.
Example 2
Input:
A = [2,3,1], B = [3,1,2]
Output:
[0,1,3]
Explanation:
At i = 0: no number is common, so C[0] = 0. At i = 1: only 3 is common in A and B, so C[1] = 1. At i = 2: 1, 2, and 3 are common in A and B, so C[2] = 3.
Constraints
1 <= A.length == B.length == n <= 501 <= A[i], B[i] <= nIt is guaranteed that A and B are both a permutation of n integers.
Approach and Intuition
Given the constraints and the nature of the problem, a direct approach can be practical:
Initialize an empty array
Cwhich will store the count of common numbers for each index. Also, set up a Set data structure,seen, to keep track of numbers encountered inBas we iterate.For each index
ifrom 0 ton-1:- Add the number at the current index of
Bto theseenset. This step continually updates which numbers we've encountered inBup to the current index. - Initialize a variable,
common_count, to zero. This variable will hold the count of common elements for the current indexi. - Iterate over array
Afrom index 0 toi.- For each number in this slice of
A, check if it also exists inseen(indicates the current number has been seen inBup to indexi). - If a number is found in
seen, increment thecommon_count.
- For each number in this slice of
- After finishing this check for all indices up to
i, appendcommon_countto arrayC.
- Add the number at the current index of
This approach takes advantage of the fact that by using a set, checking for existence is average-case O(1), making it efficient to determine if elements of A up to index i have appeared in B. We deliberately accept a probably higher complexity for clarity and directness due to manageable constraint limits (n <= 50). This method ensures that we correctly build the prefix common array by leveraging the properties of sets for quick lookups and aggregating counts as we progress through the arrays.
Solutions
- C++
- Java
- Python
class Solution {
public:
vector<int> calculateCommonPrefix(vector<int>& array1, vector<int>& array2) {
int length = array1.size();
vector<int> result(length), elementCounts(length + 1, 0);
int countShared = 0;
for (int i = 0; i < length; ++i) {
if (++elementCounts[array1[i]] == 2) ++countShared;
if (++elementCounts[array2[i]] == 2) ++countShared;
result[i] = countShared;
}
return result;
}
};
The provided C++ code defines a function calculateCommonPrefix within a class named Solution. This function aims to find the common prefix array of two integer arrays based on the following principles:
- The function takes two vectors
array1andarray2as input. - It initializes a vector
resultto store the cumulative number of common prefix elements between the two arrays up to each index. - Another vector
elementCountsis used to track the occurrences of each element across both arrays. - A variable
countSharedkeeps track of the count of shared elements between the two input vectors at each point.
The key steps in the function are:
- Iterate through the elements of the input arrays using a for-loop, simultaneously processing elements from both arrays.
- Increment the count of each element in
elementCountsas it appears in either array. If after incrementing, an element's count reaches two, this indicates that the element now exists in botharray1andarray2. - For every element found in both arrays, increment
countShared. - Store the current value of
countSharedin theresultvector at the current index, which represents the total number of shared elements up to that point between the two arrays.
Finally, the vector result is returned, which contains the count of common elements up to each index of the input arrays. This function efficiently computes the number of elements common between any two positions of the two arrays using a single loop and provides a direct count by leveraging the indexing property of vectors.
class Solution {
public int[] computeIntersectionCounts(int[] arr1, int[] arr2) {
int len = arr1.length;
int[] resultCounts = new int[len];
int[] elemCount = new int[len + 1];
int intersectCount = 0;
// Processing each element for intersections
for (int idx = 0; idx < len; ++idx) {
// Increment element counts for current elements from both arrays
elemCount[arr1[idx]] += 1;
if (elemCount[arr1[idx]] == 2) ++intersectCount;
elemCount[arr2[idx]] += 1;
if (elemCount[arr2[idx]] == 2) ++intersectCount;
// Accumulate intersect counts in result array
resultCounts[idx] = intersectCount;
}
// Return the array of intersect counts
return resultCounts;
}
}
In the provided Java solution, focus on determining the intersection counts between elements of two arrays at each index. The method computeIntersectionCounts in the Solution class achieves this by:
- Initializing
lento the length of the first array (arr1). - Setting up
resultCounts, an array to store the result, andelemCountto keep track of the occurrences of each element across both arrays. - A loop iterates through each index of the arrays:
- For each index, the method increments the count of the current elements from both arrays in
elemCount. - If the count of an element reaches 2 (indicating that element is found in both arrays up to this index), the
intersectCountis incremented. - The total
intersectCountup to the current index is stored inresultCounts.
- For each index, the method increments the count of the current elements from both arrays in
- The method returns
resultCounts, which provides the cumulative intersection count at each index.
This solution ensures you can track how many times elements of two arrays have matched up to each index, providing a detailed intersection profile across the arrays.
class Solution:
def computeCommonPrefixes(self, numbersA: List[int], numbersB: List[int]) -> List[int]:
size = len(numbersA)
result_arr = [0] * size
occurrences = [0] * (size + 1)
common_element_count = 0
for idx in range(size):
occurrences[numbersA[idx]] += 1
if occurrences[numbersA[idx]] == 2:
common_element_count += 1
occurrences[numbersB[idx]] += 1
if occurrences[numbersB[idx]] == 2:
common_element_count += 1
result_arr[idx] = common_element_count
return result_arr
The given Python code defines a method to compute the prefix common array between two integer arrays, numbersA and numbersB. The function returns an array wherein each index captures the count of common elements encountered up to that point in the two input arrays. Here's how the computation process works:
- Determine the size of the input arrays, assuming both are of equal length, and initialize
result_arrwith zero values of that size. - Create an
occurrencesarray, sized one larger than the input arrays, initialized with zeros to keep track of the occurrence count of numbers from both arrays. - Initialize
common_element_countto zero to count the number of common elements. - Traverse through the input arrays:
- For each number in
numbersA, increase its count inoccurrences. If the count reaches two, incrementcommon_element_count. - Similarly, update counts for
numbersBand adjustcommon_element_countaccordingly. - Update
result_arrat the current index with the value ofcommon_element_count.
- For each number in
- Return
result_arr, which holds the cumulative count of common elements at each index.
This operation helps in understanding how many common elements are found progressively when moving through both arrays simultaneously.