Design Add And Search Words
Detailed guide and Python implementation for the 'Design Add And Search Words' problem.
1. Concept Overview
The 'Design Add And Search Words' problem is a key challenge in the Trie section.
This implementation focuses on easy-level logic in Python.
We prioritize technical accuracy and code readability in our provided solutions.
2. Real-World Applications
3. Visual Intuition
Visualizing the logic flow for Design Add And Search Words.
4. Prerequisites
5. Step-by-Step Thinking
1. Understand the problem
Read the problem statement for Design Add And Search Words carefully.
2. Formulate brute force
Draft a simple iterative solution.
3. Identify inefficiency
Look for redundant calculations.
4. Optimize search path
Use hashing or sorting to speed up the process.
5. Final Implementation
Clean up the code for production standards.
Problem Statement
Design a data structure that supports adding new words and finding if a string matches any previously added string.
Implement the WordDictionary class:
- WordDictionary() Initializes the object.
- addWord(word: str) Adds word to the data structure, it can be matched later.
- search(word: str) -> bool Returns True if there is any string in the data structure that matches word or False otherwise. word may contain dots '.' where dots can be matched with any letter.
Input is a list of operations and arguments. Implement a function wordDictionary(operations: list, arguments: list) -> list that returns a list of results (None for constructor/addWord, bool for search).
- •1 <= len(word) <= 25
- •word in addWord consists of lowercase English letters
- •word in search consists of '.' or lowercase English letters
- •At most 10^4 calls will be made to addWord and search
Examples
operations = ["WordDictionary", "addWord", "addWord", "addWord", "search", "search", "search", "search"], arguments = [[], ["bad"], ["dad"], ["mad"], ["pad"], ["bad"], [".ad"], ["b.."]]
[None, None, None, None, False, True, True, True]
Initialize. Add "bad", "dad", "mad". search("pad") -> False. search("bad") -> True. search(".ad") -> True. search("b..") -> True.
Need a Hint?
Edge Cases to Watch
- Empty input structures
- Single element inputs
- Large numerical bounds
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Interview Insights & Variations
Complexity Analysis Breakdown
Why Time: Directly evaluates all possibilities.
Why Space: Uses standard local memory.
Why Time: Optimized paths reduce total operations.
Why Space: May trade memory for speed.
Optimized Solution Python Code
Optimized Solution Python Code
class TrieNode:
def __init__(self):
self.children = {}
self.end = False
class WordDictionaryOpt:
def __init__(self):
self.root = TrieNode()
def addWord(self, word):
curr = self.root
for c in word:
if c not in curr.children: curr.children[c] = TrieNode()
curr = curr.children[c]
curr.end = True
def search(self, word):
def dfs(j, root):
curr = root
for i in range(j, len(word)):
c = word[i]
if c == ".":
for child in curr.children.values():
if dfs(i + 1, child): return True
return False
else:
if c not in curr.children: return False
curr = curr.children[c]
return curr.end
return dfs(0, self.root)Brute Force Code (Spoiler Guarded)
Brute Force Code (Spoiler Guarded)
class WordDictionaryBrute:
def __init__(self):
self.words = set()
def addWord(self, word):
self.words.add(word)
def search(self, word):
if '.' not in word: return word in self.words
for w in self.words:
if len(w) == len(word):
match = True
for i in range(len(word)):
if word[i] != '.' and word[i] != w[i]:
match = False; break
if match: return True
return FalseAlgorithm Pattern Checklist
When dealing with Trie data patterns.
Core Prerequisites
Revision Key Notes
Common Mistakes & Pitfalls
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