similator 0.1.1

A Python library for validating and comparing text data using bytearrays.

Similator

Similator is a powerful Python library designed for efficient text validation and comparison at the byte level. With features like customizable similarity thresholds, case-sensitive or case-insensitive comparisons, and an optional caching mechanism, Similator is ideal for tasks requiring precise text matching and validation.

🚀 Features

• Byte-Level Text Validation and Comparison: Leverage the power of bytearrays for fast and accurate text operations.
• Customizable Similarity Search: Set thresholds to find the most relevant matches in your dataset.
• Automatic Caching: Enable caching to store and reuse search results, boosting performance in repetitive tasks.
• Advanced Scoring Mechanism: A sophisticated scoring system that rewards larger and more significant matches, making your similarity searches more meaningful.
• Case Sensitivity Options: Choose between case-sensitive and case-insensitive operations based on your needs.

📦 Installation

Install Similator quickly and easily using pip:

pip install similator

🌟 Quickstart Guide

Here's a quick example to get you up and running with Similator:

1. Import and Initialize

from similator import TextSimilator, ValidData

# Example data
valid_strings = ["Hello", "World", "Text", "Example", "Python"]

# Initialize ValidData
valid_data_instance = ValidData(valid_strings, encoding='utf-8', case_sensitive=False)

# Initialize TextSimilator with ValidData
text_similator = TextSimilator(valid_data_instance, encoding='utf-8', case_sensitive=False)

2. Perform a Search

Search for a string within the valid data with a similarity threshold:

search_value = "hello"
results = text_similator.search(search_value, threshold=0.85)
print(results)
# Output: [Score(value='hello', points=2.0)]

3. Compare Two Strings

Directly compare two strings to obtain a similarity score:

value1 = "hello"
value2 = "hell"
similarity_score = text_similator.compare(value1, value2)
print(similarity_score)
# Output: 1.94

Advanced Usage

Enabling Caching for Repeated Searches

If your application involves repeated searches with similar queries, you can enable caching to improve performance:

# Enable caching with a maximum size of 50 cached results
text_similator_with_cache = TextSimilator(valid_data_instance, auto_cached=True, max_cache_size=50)

# Perform a search and it will be cached
results_cached = text_similator_with_cache.search("python", threshold=0.9)

Exporting and Loading Cached Data

You can export the cache to a file and reload it later for persistent storage:

# Export the current cache to a JSON file
text_similator_with_cache.memory.export_memory("cache.json")

# Load the cache from a JSON file
text_similator_with_cache.memory.load_memory("cache.json")

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💬 Contact

If you have any questions, suggestions, or just want to say hello, feel free to contact me:

• Email: [e-mail]
• GitHub: DSAV-code
• Twitter/X: @dsav_v2

🛠️ Contributing

Contributions are welcome! If you have any ideas, suggestions, or issues, feel free to open an issue or submit a pull request.

📝 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

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