Vectoria 🗜️

Vectoria is a sophisticated, adaptive lossless data compressor written in Python. It intelligently analyzes input data and selects the optimal compression strategy from a suite of advanced techniques, ensuring high compression ratios for a variety of data patterns.

It was created by John Drossos ([email protected]).

## Support This Project

If you find Vectoria useful, please consider supporting its development. Every contribution is greatly appreciated!

❤️ Donate via Stripe (pay as much as you want)

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## Key Features

• 🧠 Adaptive Model Selection: Vectoria doesn't use a single, fixed algorithm. It evaluates multiple configurations and two distinct entropy models to find the one that yields the smallest possible output size for your specific data.
• ⚙️ Sophisticated Compression Pipeline: It combines several proven techniques, including Vector Quantization (VQ), Move-to-Front (MTF), Run-Length Encoding (RLE), Rice coding, and Huffman coding.
• 📊 Precise Cost Analysis: The decision engine is powered by a byte-perfect cost model that accounts for all overhead, including data payloads, headers, codebooks, and even padding bits.
• 🐍 Pure Python: Fully implemented in Python with no external dependencies, making it easy to integrate and understand.

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## Installation

You can install Vectoria directly from PyPI:

pip install vectoria-compressor

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## Quick Start

Using Vectoria is straightforward. The VectorCompressor object handles everything.

from vectoria import VectorCompressor

# 1. Your data (as a string of '0's and '1's)
# This example has a lot of repeating patterns that Vectoria can exploit.
bitstream = ("010111" * 50) + ("000001" * 50) + ("010111000001" * 20)

# 2. Initialize the compressor
# You can configure vector_size and max_codebook_size.
compressor = VectorCompressor(vector_size=2, max_codebook_size=16)

# 3. Encode the data
compressed_form = compressor.encode(bitstream)

# 4. Check the results
original_size = len(bitstream)
compressed_size = compressed_form['model']['bit_accounting']['total_bits']
ratio = original_size / compressed_size

print(f"Original size: {original_size} bits")
print(f"Compressed size: {compressed_size} bits")
print(f"🏆 Best model chosen: {compressed_form['model']['model_type']}")
print(f"Compression Ratio: {ratio:.2f} : 1")

# 5. Decode to get the original data back
reconstructed_data = compressor.decode(compressed_form)

assert bitstream == reconstructed_data
print("\nVerification successful: Data was perfectly reconstructed! ✅")

### Simple API

For even simpler usage, you can use the top-level compress and decompress functions, which handle the creation of the VectorCompressor object for you.

import vectoria

# Your data
bitstream = ("010111" * 50) + ("000001" * 50)

# Compress in one line
# You can still pass configuration options as keyword arguments
compressed_data = vectoria.compress(bitstream, vector_size=2, max_codebook_size=8)

# Decompress in one line
reconstructed_data = vectoria.decompress(compressed_data)

assert bitstream == reconstructed_data
print("Simple API roundtrip successful! ✅")

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## How Vectoria Works

Vectoria's power comes from its ability to choose the best strategy. Think of it as a competition where different compression models compete to see which can represent your data in the fewest bits.

### Step 1: Vectorization

First, the input bitstream is chopped into small, 3-bit chunks (e.g., '010'). These chunks are then grouped into vectors. For a vector_size of 2, the stream '010111001...' becomes ('010', '111'), ('001', ...) and so on. This step turns the raw bitstream into a sequence of higher-level symbols.

### Step 2: The Competition

For a range of possible dictionary sizes (the "codebook"), Vectoria simulates two powerful compression models to see which performs better.

Model A: The MTF-RLE Pipeline

This model is designed to excel with data that has locality of reference (i.e., where the same vectors appear frequently in bursts).

Tokens -> Move-to-Front (MTF) -> Run-Length Encode (RLE) of Zeros -> Huffman/Rice Coding

1. Move-to-Front (MTF): The sequence of vectors is transformed. When a vector is seen, it's replaced by its current position in a dynamic list and then moved to the front. Frequent vectors consistently get encoded as small numbers (like 0, 1, 2).
2. Run-Length Encoding (RLE): Because MTF generates long runs of zeros for the most frequent vector, a specialized RLE process efficiently compresses these runs.
3. Entropy Coding: The resulting symbols and run-lengths are then compressed using Huffman and Rice coding.

Model B: The Contextual Model

This model is designed to excel with data that has strong local patterns, where the next vector is highly predictable from the current vector.

Tokens -> Context-based Huffman Coding

1. Context Modeling: It builds a statistical model where the probability of the next vector depends on the vector that came just before it. For example, it learns that the vector ('010', '111') is very likely to be followed by ('000', '001').
2. Huffman Coding: It uses a different Huffman table for each context, allowing for extremely tight compression when these local patterns are strong.

### Step 3: The Decision

Vectoria runs this competition for multiple codebook sizes (K=1, 2, ... max_codebook_size). Using its precise bit-accounting model, it calculates the exact final compressed size for every single configuration. Finally, it picks the undisputed winner—the combination of codebook size and model type that results in the absolute minimum number of bits—and uses that to perform the final encoding.

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## License

This project is licensed under the MIT License. See the LICENSE file for details.