Add PPM Algorithm Implementation

[LukOlen]

Describe your change:

This pull request introduces a new implementation of the Prediction by Partial Matching (PPM) algorithm to the repository. The PPM algorithm is a statistical data compression technique that utilizes context modeling to achieve efficient compression rates.

Key Features:

Context-Based Compression: The algorithm maintains a context of previous symbols to predict the next symbol, enhancing compression efficiency. The Wikipedia page describes the algorithm way better than I ever could. https://en.wikipedia.org/wiki/Prediction_by_partial_matching

Compression and Decompression Functions:

The implementation includes both compression and decompression methods in one file, allowing for reversible data transformation.

File Handling:

A utility function is provided to read data from a file, making it easy to test the algorithm with real data.

Usage:

To use the PPM algorithm, simply call the ppm function with the path to the file you wish to compress. The algorithm will output the compressed data as probabilities and also provide the decompressed data for verification.

Testing:

I have tested the implementation with various datasets to ensure its functionality and performance. The results demonstrate that the algorithm effectively compresses and decompresses data while maintaining accuracy.

• [x ] Add an algorithm?
• [ ] Fix a bug or typo in an existing algorithm?
• [ ] Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• [ ] Documentation change?

Checklist:

• [ x] I have read CONTRIBUTING.md.
• [x ] This pull request is all my own work -- I have not plagiarized.
• [x ] I know that pull requests will not be merged if they fail the automated tests.
• [x ] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• [x ] All new Python files are placed inside an existing directory.
• [x ] All filenames are in all lowercase characters with no spaces or dashes.
• [x ] All functions and variable names follow Python naming conventions.
• [x ] All function parameters and return values are annotated with Python type hints.
• [x ] All functions have doctests that pass the automated testing.
• [x ] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• [x ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".