Implement Trie Data Structure

[staging-devin-ai-integration[bot]]

Trie Data Structure Implementation

This pull request (#88) implements a Trie data structure in Python for the marcosfede/algorithms repository. The Trie is a tree-like data structure used for efficient storage and retrieval of strings, particularly useful for tasks such as autocomplete and spell-checking.

Features

• Implemented in trie/trie.py:

  • TrieNode class for representing nodes in the Trie
  • Trie class with the following methods:
    • insert(word): Insert a word into the Trie
    • search(word, is_prefix=False): Search for a word or prefix in the Trie
    • delete(word): Delete a word from the Trie

• Time complexity:

  • Insert: O(m), where m is the length of the word
  • Search: O(m), where m is the length of the word
  • Delete: O(m), where m is the length of the word

• Space complexity: O(n * m), where n is the number of words and m is the average length of words

Implementation Details

• The delete operation uses an iterative approach to handle long words efficiently
• Empty string cases are handled appropriately
• The search method includes an is_prefix parameter to support prefix searching

Testing

Comprehensive unit tests implemented in trie/test_trie.py cover:

• Basic insertion, search, and deletion operations
• Edge cases (empty string, long words, single-character words)
• Common prefixes and their handling
• Prefix searching functionality
• Case sensitivity and special characters
• Unicode character support
• Large datasets (10,000 words)
• Complex operations and mixed scenarios

New test cases added for more comprehensive coverage:

• test_unicode_characters: Ensures correct handling of non-ASCII characters
• test_large_dataset: Verifies performance with a large number of words
• test_prefix_matching: Checks prefix search functionality
• test_edge_cases: Tests boundary conditions and edge scenarios

These additional tests ensure the robustness and reliability of the Trie implementation across various use cases.

Improvements

• Implemented prefix search functionality
• Enhanced test coverage for edge cases and large datasets
• Improved code organization and documentation
• Cleaned up commit history through rebasing

Next Steps

• Consider adding more advanced Trie operations (e.g., autocomplete, wildcard search)
• Optimize memory usage for large datasets
• Implement serialization/deserialization for persistent storage

This implementation provides a solid foundation for Trie-based algorithms and can be easily extended for various applications.

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PR: #88 URL: https://github.com/marcosfede/algorithms/pull/88 Requested by: Federico Link to Devin run: https://staging.itsdev.in/devin/21a67400cd63476a80b6a041168e9615

[marcosfede]

hey devin, can you clean up this PR?

[marcosfede]

test

[staging-devin-ai-integration[bot]]

Closing due to inactivity.