211-design-add-and-search-words-data-structure

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Description

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.
• void addWord(word) Adds word to the data structure, it can be matched later.
• bool search(word) 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.

 

Example:

Input
["WordDictionary","addWord","addWord","addWord","search","search","search","search"]
[[],["bad"],["dad"],["mad"],["pad"],["bad"],[".ad"],["b.."]]
Output
[null,null,null,null,false,true,true,true]

Explanation
WordDictionary wordDictionary = new WordDictionary();
wordDictionary.addWord("bad");
wordDictionary.addWord("dad");
wordDictionary.addWord("mad");
wordDictionary.search("pad"); // return False
wordDictionary.search("bad"); // return True
wordDictionary.search(".ad"); // return True
wordDictionary.search("b.."); // return True

 

Constraints:

• 1 <= word.length <= 500
• word in addWord consists lower-case English letters.
• word in search consist of  '.' or lower-case English letters.
• At most 50000 calls will be made to addWord and search.

Solution(Python)

"""
Trie Data structure has 
    key - denotes current char
    children = set of next values (for contant time lookup)
    isEnd = boolean to mark end of the word
"""


class TrieNode:
    def __init__(self):
        self.children = {}
        self.isEnd = False


class WordDictionary:
    """
    intialize empty trie and store in memory
    """

    def __init__(self):
        self.trie = TrieNode()

    """
    algorithm
        initialize curr trie node
        iterate char in word
            check if children has char 
                if true:
                    set curr node as that node
                else:
                    create a trienode with char
                    set curr node as new node
        set curr node isEnd as true
                    
    """

    def addWord(self, word: str) -> None:
        curr = self.trie

        for ch in word:
            if ch not in curr.children:
                curr.children[ch] = TrieNode()
            curr = curr.children[ch]
        curr.isEnd = True

    """
    algorithm
        initialize curr trie node
        fun dfs(index)
            iterate char in word
                if index reaches n:
                return True
                if char is .
                    for all children
                    if dfs(children): return True
                elif char in children:
                    dfs(children of char)
            return  false
                
        return curr.isEnd
            
    """

    def search(self, word: str) -> bool:
        return self.dfs(self.trie, word, 0, len(word))

    def dfs(self, root, word, i, n):
        if i == n:
            return root.isEnd
        if word[i] == ".":
            for child in root.children:
                if self.dfs(root.children[child], word, i + 1, n):
                    return True
        elif word[i] in root.children:
            return self.dfs(root.children[word[i]], word, i + 1, n)
        return False


# Your WordDictionary object will be instantiated and called as such:
# obj = WordDictionary()
# obj.addWord(word)
# param_2 = obj.search(word)