Sum of Prefix Scores of Strings | Trie | Full Easy Dry Run | Leetcode 2416 | codestorywithMIK
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This is the 7th Video of our Playlist "TRIE : Popular Interview Problems" by codestorywithMIK
In this video we will try to solve a very good Trie problem : Sum of Prefix Scores of Strings | Trie | Full Easy Dry Run | Leetcode 2416 | codestorywithMIK
I will explain the intuition so easily that you will never forget and start seeing this as cakewalk EASYYY.
We will do live coding after explanation and see if we are able to pass all the test cases.
Also, please note that my Github solution link below contains both C++ as well as JAVA code.
Problem Name : Sum of Prefix Scores of Strings | Trie | Full Easy Dry Run | Leetcode 2416 | codestorywithMIK
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Leetcode Link : https://leetcode.com/problems/sum-of-...
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Summary :
The approach uses a Trie (prefix tree) to efficiently calculate the sum of prefix scores for each word in a list. Here's a breakdown:
Trie Construction:
A Trie is built where each node represents a letter of the alphabet.
As words are inserted into the Trie, a countP (prefix count) at each node tracks how many words share that prefix.
Insertion of Words:
Each word is inserted letter by letter into the Trie.
For every new letter, the corresponding Trie node's countP is incremented, indicating that this prefix has been encountered one more time.
Calculating Scores:
For each word, the score is calculated by summing the countP values for each node along the path of its prefixes.
This gives the sum of prefix scores for that word, as the score reflects how many words share the same prefixes.
Result:
The algorithm computes the prefix score for each word, stores it in a result array, and returns it.
This approach ensures efficient prefix matching and score calculation with a time complexity of approximately O(N * L), where N is the number of words and L is the average word length.
✨ Timelines✨
00:00 - Introduction
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