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Problem solution · C++

Analyze User Website Visit Pattern

Analyze User Website Visit Pattern: a C++ solution using sorting and greedy selection. Learn the idea, check the complexity, and read the full code, with credit to Kamyu LeetCode Solutions.

Technique
Sorting and greedy selection
Source
Kamyu LeetCode Solutions
Length
54 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Sorting and greedy selection

For Analyze User Website Visit Pattern, the implementation first exposes a useful order, then scans that order while making locally justified choices.

  1. Choose the key that reveals the greedy or grouping structure.
  2. Sort the relevant records by that key.
  3. Scan in order, maintaining the invariant that makes each local choice safe.

Code notes

  • 54 lines of C++ from the credited upstream file analyze-user-website-visit-pattern.cpp.
  • The implementation visibly relies on sequence storage, hash lookup.
  • 7 loop blocks detected.

Complexity

Sorting is typically the dominant term unless the subsequent scan uses a more expensive nested operation.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from Kamyu LeetCode Solutions by kamyu104 and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeAnalyze User Website Visit Pattern · C++C++
Use this to learn the idea, then write your own version.
// Time:  O(n^3)// Space: O(n^3) class Solution {public:    vector<string> mostVisitedPattern(vector<string>& username, vector<int>& timestamp, vector<string>& website) {        vector<tuple<int, string, string>> A;        for (int i = 0; i < username.size(); ++i) {            A.emplace_back(timestamp[i], username[i], website[i]);        }        sort(A.begin(), A.end());        unordered_map<string, vector<string>> users;        for (const auto& [t, u, w] : A) {            users[u].emplace_back(w);        }         vector<string> result;        unordered_map<vector<string>, int, VectorHash<string>> count;        for (const auto& [u, webs] : users) {            unordered_set<vector<string>, VectorHash<string>> lookup;            for (int i = 0; i + 2 < webs.size(); ++i) {                for (int j = i + 1; j + 1 < webs.size(); ++j) {                    for (int k = j + 1; k < webs.size(); ++k) {                        vector<string> pattern = {webs[i], webs[j], webs[k]};                        if (lookup.count(pattern)) {                            continue;                        }                        lookup.emplace(pattern);                        ++count[pattern];                        if (result.empty() ||                            count[pattern] > count[result] ||                            (count[pattern] == count[result] && pattern < result)) {                            result = pattern;                        }                    }                }            }        }        return result;    } private:    template<typename T>    struct VectorHash {        size_t operator()(const std::vector<T>& v) const {            size_t seed = 0;            for (const auto& i : v) {                seed ^= std::hash<T>{}(i)  + 0x9e3779b9 + (seed<<6) + (seed>>2);            }            return seed;        }    };};

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