Google Applied CS - Day 3
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This document provides code snippets and explanations for implementing a trie data structure for storing and searching words. It includes: 1. Code for adding a word to the trie by recursively traversing the trie and adding child nodes for each character in the word. 2. Methods for checking if a word is contained in the trie, and retrieving any word starting with a given prefix by traversing the trie down the prefix path. 3. Explanations of the trie node structure using a HashMap of children nodes indexed by character, and marking leaf nodes as containing a full word. 4. The trie allows fast dictionary lookups and prefix searches in O(L) time where L is the length of the query string.
Google Applied CS - Day 3
- 2. High Time
- 3. Ghost
- 4. How-to 1. Build dictionary 2. Set button click listeners 3. Decide the turn
- 5. Decide the turn if (user's turn) { listen to onKeyUp -> (k) { if k is a valid letter { append it to TextView } else ignore } } else { if word is of length 4 or more && it is a valid word { Computer wins } else { put next character } }
- 6. Put next character It should form a valid word because this is a computer!
- 7. Ta-da ! while (low < high) { mid = (low + high) / 2; t = words.get(mid); if(t.startsWith(prefix)) { return t; } else if(prefix.compareTo(t) > 0) { // LHS is bigger low = mid + 1; } else { // RHS is bigger high = mid - 1; } }
- 8. Best case: O(1) Average/Worst case: O(log n)
- 12. Basics 1. HashMap<Character, TrieNode> children 2. root.add(word) 3. root.isWord(word) 4. root.getAnyWordStartingWith(word)
- 13. Add a word to Trie void add(String s, int position) { if (position >= s.length()) return; char c = s.charAt(position); TrieNode n = children.get(c); if (n == null) { n = new TrieNode(); children.put(c, n); } if (position == s.length() - 1) { n.isWord = true; } n.add(s, position + 1); }
- 14. And a bit of method overloading
- 15. void add(String s) { add(s, 0) } void add(String s, int position) { ... }
- 16. Same logic, different application 1. root.isWord(word) 2. root.getAnyWordStartingWith(word)
- 17. Fast Dictionary ! An efficient lookup structure
- 18. This is it for today
- 19. Any doubts?