PolyerGalio

---

Overview

PolyerGalio (Greek for "multiple tools") is a collection of implemented machine learning methods ranging from
data encoding and processing pipelines to supervised learning and clustering.

The focus of this repository is:

• Classic and alternative ML algorithms implemented with a unified interface
• Numerical stability and performance
• Novel extensions and original research contributions

All algorithms are implemented in NumPy and SciPy, with minimal external dependencies.

---

Installation

pip install polyergalio

For local development:

pip install -e ".[test]"

---

Implemented Methods

🔹 Encoding and Embedding Creation

src/polyergalio/encoders/*

• Categorical variable pipeline
• Chronological variable (cyclical and absolute) pipeline
• Numeric (normalized and raw) pipeline
• Trainable Fourier Embedding pipeline
• Trainable Text embedding pipeline
• tokenization with sentencepiece

🔹 Toy Dataset Generation

src/polyergalio/generators/*

🔹 Supervised Learning

src/polyergalio/models/supervised/*

Scaled Conjugate Gradient (SCG)

• SCG for gradient descent applied to regression and logistic regression (Møller); (Anderson)
• SCG regression with Elastic Net regularization (novel)
• SCG classification:
  • Binary
  • Multinomial
  • Multilabel

Relative Weights (RW)

• Johnson’s Relative Weights regression (Johnson)
• Relative Weights applied to logistic regression
  (Solís & Pasquier); (Tonidandel & LeBreton)

Tree Algorithms (EBM / EBTM)

• Tree algorithms - Explainable Boosted-Tree Model (EBM)

🔹 Unsupervised Learning & Clustering

src/polyergalio/models/clustering/*

Self-Organizing Maps

• Self Organizing Maps, Parameterless Self-Organizing Maps - PLSOM
  (Kohonen); (Berglund & Sitte)
  • Clustering and dimensionality reduction without hyperparameter adjustment
• Growing Self Organizing Maps, Parameterless (grid)
• Grid-free Growing Parameterless Self Organizing Maps
  • FreeSOM - grows and shrinks under conditional updates
  • (novel) fusion of Neural Gas and PLSOM

Centroid Neural Networks (CENTNN)

• Novel Centroid Neural Network for fast clustering and optimization
  (Park, Dong-Chul)
• CENTNN with N-dimensional density modeling
• (novel)

---

Status

Active research / experimental
APIs may change as methods are refined and extended.

---

Authors and Contributors

• "David Graey", "graeyband@gmail.com"
• "Dr Charles Anderson", "Chuck.Anderson@colostate.edu"

---

References

Primary academic references are cited inline.
Full bibliographic references may be added in /docs in the future.

https://packaging.python.org/en/latest/tutorials/packaging-projects/

https://packaging.python.org/en/latest/tutorials/creating-documentation/

Visuals & Diagrams

https://mermaid.js.org/config/Tutorials.html

flowchart LR;
    A --> B;
    A --> C;