The Algorithm to Find Anagram Mappings between Two Arrays

  • 时间：2020-10-11 15:48:46
  • 分类：网络文摘
  • 阅读：78 次

Given two lists Aand B, and B is an anagram of A. B is an anagram of A means B is made by randomizing the order of the elements in A. We want to find an index mapping P, from A to B. A mapping P[i] = j means the ith element in A appears in B at index j. These lists A and B may contain duplicates. If there are multiple answers, output any of them.

For example, given
A = [12, 28, 46, 32, 50]
B = [50, 12, 32, 46, 28]
We should return
[1, 4, 3, 2, 0]

as P[0] = 1 because the 0th element of A appears at B[1], and P[1] = 4 because the 1st element of A appears at B[4], and so on.

Note:
A, B have equal lengths in range [1, 100].
A[i], B[i] are integers in range [0, 10^5].

If the mapping index cannot be re-used (as there might be duplicates in the arrays), we need to maintain a vector of indices. The C++ code below illustrates the mapping algorithm

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class Solution {
public:
    vector<int> anagramMappings(vector<int>& A, vector<int>& B) {
        unordered_map<int, vector<int>> table;
        for (int i = 0; i < B.size(); ++ i) {
            if (table.find(B[i]) == table.end()) {
                table[B[i]] = {i};
            } else {
                table[B[i]].push_back(i); // add duplicate index
            }
        }
        vector<int> r;
        for (int i = 0; i < A.size(); ++ i) {
            r.push_back(table[A[i]].back());  // pick a mapping index
            table[A[i]].pop_back();  // remove it from the queue
        }
        return r;
    }
};
class Solution {
public:
    vector<int> anagramMappings(vector<int>& A, vector<int>& B) {
        unordered_map<int, vector<int>> table;
        for (int i = 0; i < B.size(); ++ i) {
            if (table.find(B[i]) == table.end()) {
                table[B[i]] = {i};
            } else {
                table[B[i]].push_back(i); // add duplicate index
            }
        }
        vector<int> r;
        for (int i = 0; i < A.size(); ++ i) {
            r.push_back(table[A[i]].back());  // pick a mapping index
            table[A[i]].pop_back();  // remove it from the queue
        }
        return r;
    }
};

The complexity is O(N) and the space complexity is O(N) as well. If the same index can be used to map duplicate numbers, the above can be simplified by using a hash map i.e. unordered_map in C++.

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class Solution {
public:
    vector<int> anagramMappings(vector<int>& A, vector<int>& B) {
        unordered_map<int, int> table;
        for (int i = 0; i < B.size(); ++ i) {
            table[B[i]] = {i};            
        }
        vector<int> r;
        for (int i = 0; i < A.size(); ++ i) {
            r.push_back(table[A[i]]);
        }
        return r;
    }
};
class Solution {
public:
    vector<int> anagramMappings(vector<int>& A, vector<int>& B) {
        unordered_map<int, int> table;
        for (int i = 0; i < B.size(); ++ i) {
            table[B[i]] = {i};            
        }
        vector<int> r;
        for (int i = 0; i < A.size(); ++ i) {
            r.push_back(table[A[i]]);
        }
        return r;
    }
};

The same anagram mapping algorithm can be implemented by Java using the HashMap i.e. O(N) both time and space complexity.

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class Solution {
    public int[] anagramMappings(int[] A, int[] B) {
        Map<Integer, Integer> D = new HashMap();
        for (int i = 0; i < B.length; ++i) {
            D.put(B[i], i);
        }
        int[] ans = new int[A.length];
        int t = 0;
        for (int x: A) {
            ans[t++] = D.get(x);
        }
        return ans;
    }
}
class Solution {
    public int[] anagramMappings(int[] A, int[] B) {
        Map<Integer, Integer> D = new HashMap();
        for (int i = 0; i < B.length; ++i) {
            D.put(B[i], i);
        }
        int[] ans = new int[A.length];
        int t = 0;
        for (int x: A) {
            ans[t++] = D.get(x);
        }
        return ans;
    }
}

–EOF (The Ultimate Computing & Technology Blog) —

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