MDL Optimal Histogram Density Estimation

This package provides a Cython-accelerated implementation of the Minimum Description Length (MDL) optimal histogram density estimation algorithm from Kontkanen & Myllymaki (2007). It uses information-theoretic principles to automatically determine optimal variable-width bins for density estimation.

Freedman-Diaconis vs. MDL-Optimization

Features

MDL Principle: Uses stochastic complexity for model selection
Dynamic Programming: Efficient O(E²·K_max) optimization (cache parametric complexity computation, speed up)
Score of each Kth bin: The score of each bin is returned to understand the performance of different properties of the same dataset.
Variable-Width Bins: Adapts to data density variations
Automatic Bin Count: No manual parameter tuning required (except maximum bin count to consider $K_{max}$ and data resolution $\epsilon$)
Cython Acceleration: Critical operations compiled to C

Installation

You can install the package using pip:

pip install MDL-Density-Histogram

Alternatively, you can install it from source by cloning the repository and running:

# From project root directory
pip install .

Requires:

Python 3.11+
NumPy
Cython
C compiler (GCC/Clang/MSVC)

Usage Example

import numpy as np
from mdl_density_hist import mdl_optimal_histogram

# Generate sample data
data = np.random.normal(0, 1, 1000)

# Compute optimal histogram
cut_points, K_scores = mdl_optimal_histogram(data, epsilon=0.1)

# Print score of each bin
print(f"K_scores: {K_scores}")

# Visualize result
import matplotlib.pyplot as plt
plt.hist(data, bins=cut_points, density=True)
plt.title('MDL Optimal Histogram')
plt.show()

Parameters

data: Input array (1D numpy array)
epsilon: Quantization precision (default: 0.1)
K_max: Maximum number of bins (default: 10)

Algorithm Highlights

Uses Ramanujan's factorial approximation for efficient parametric complexity
Cache parameteric complexity to speed up computation

Paper Citation

Kontkanen, P., & Myllymäki, P. (2007).
MDL Histogram Density Estimation
Journal of Machine Learning Research 8 (2007) PDF

License

Apache 2.0 License - See LICENSE file

Project Structure

src/
├── mdl_density_hist/
│   ├── __init__.py
│   └── mdl_hist.pyx  # Core Cython implementation
└── pyproject.toml

Performance Notes

Precomputed parametric complexity using dynamic programming
Memory-optimized array operations via NumPy
Candidate cut point pruning for reduced search space

For implementation details, see the paper and inline code comments.

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MDL-Density-Histogram 1.2.2

MDL Optimal Histogram Density Estimation

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BLAKE2b-256	`d428a6b887ea71846c97c115edd3e4d243638ebcd72afd8f8fe1a05ef2b23aec`

Algorithm	Hash digest
SHA256	`8c7e973498efd273572cf48be872a9d67268dfbf608b72704605e4972f03028f`
MD5	`2d42c983d87741b89a209e46b72cbb0f`
BLAKE2b-256	`ca8314635f13d0845fa50480b917bd3f04f27b49e8953f823b442dcebd5f1355`

Algorithm	Hash digest
SHA256	`1b3297b5e6984bd12b95711a466c13f72f36f3463820b9d3a723ab2a081cf7cb`
MD5	`cb56536028684a4c5506d4dcf408665d`
BLAKE2b-256	`8e2a4c452bedb55081f9a85a2683135e450f2074ae76f0fe8dbe9de5c278d163`