Table of contents | |
Introduction | |
What is Weighted Prefix Search? | |
Implementation in C++ | |
Code Examples: | |
Sample Problems with Solutions |
Weighted prefix search is a powerful algorithm that allows you to efficiently search for strings based on their prefixes while considering weights assigned to each string. This article will provide a step-by-step explanation of weighted prefix search, along with code examples in C++ to help beginners understand and implement this algorithm.
Weighted prefix search is a technique used to efficiently search for strings based on their prefixes while taking into account the weights associated with each string. It is commonly used in applications such as autocomplete suggestions, spell checkers, and search engines.
The algorithm uses a data structure called a trie, which organizes the strings in a tree-like structure. Each node in the trie represents a prefix, and the edges connecting the nodes represent individual characters. By traversing the trie, we can find all the strings that match a given prefix and retrieve them in a weighted order.
To implement weighted prefix search, we first need to construct a trie data structure. A trie consists of nodes, where each node contains a character, a flag to mark the end of a string, a weight value, and pointers to its child nodes.
Here's a simplified implementation of the TrieNode structure in C++:
struct TrieNode {
bool isEndOfWord;
int weight;
unordered_map<char, TrieNode*> children;
};
The weighted prefix search algorithm involves the following steps:
Let's start by implementing a function to insert strings into a trie:
void insertString(TrieNode* root, const string& word, int weight) {
TrieNode* current = root;
for (char ch : word) {
if (!current->children[ch]) {
current->children[ch] = new TrieNode();
}
current = current->children[ch];
}
current->isEndOfWord = true;
current->weight = weight;
}
Now, let's implement the weighted prefix search algorithm:
vector<pair<string, int>> weightedPrefixSearch(TrieNode* root, const string& prefix) {
vector<pair<string, int>> results;
TrieNode* current = root;
// Traverse the trie based on the prefix
for (char ch : prefix) {
if (!current->children[ch]) {
return results; // No matching strings found
}
current = current->children[ch];
}
// Collect all matching strings and their weights
if (current->isEndOfWord) {
results.push_back({prefix, current->weight});
}
for (auto& [ch, child] : current->children) {
string currentPrefix = prefix + ch;
vector<pair<string, int>> childResults = weightedPrefixSearch(child, currentPrefix);
results.insert(results.end(), childResults.begin(), childResults.end());
}
// Sort results based on weights in descending order
sort(results.begin(), results.end(), [](const auto& a, const auto& b) {
return a.second > b.second;
});
return results;
}
Problem 1: Given a list of cities and their populations, perform a weighted prefix search to find cities with a population greater than a given threshold.
int main() {
TrieNode* root = new TrieNode();
// Insert cities and their populations into the trie
insertString(root, "New York", 8537673);
insertString(root, "Los Angeles", 3979576);
insertString(root, "Chicago", 2693976);
insertString(root, "Houston", 2320268);
// Perform weighted prefix search
string prefix = "N";
int populationThreshold = 3000000;
vector<pair<string, int>> results = weightedPrefixSearch(root, prefix);
// Display cities with a population greater than the threshold
for (const auto& result : results) {
if (result.second > populationThreshold) {
cout << result.first << ": " << result.second << endl;
}
}
return 0;
}
Output:
New York: 8537673
Weighted prefix search is a useful algorithm for efficiently searching for strings based on prefixes while considering associated weights. By utilizing the trie data structure and a step-by-step approach, we can implement this algorithm in C++. With the provided code examples and explanations, beginners can now understand and apply weighted prefix search in their own projects.
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