Machine Gnostics¶
Welcome to Machine Gnostics, an innovative Python library designed to implement the principles of Mathematical Gnostics for robust data analysis, modeling, and inference. Unlike traditional statistical approaches that depend heavily on probabilistic assumptions, Machine Gnostics harnesses deterministic algebraic and geometric structures. This unique foundation enables the library to deliver exceptional resilience against outliers, noise, and corrupted data, making it a powerful tool for challenging real-world scenarios.
Machine Gnostics
Laws of Nature, Encoded—For Everyone!
graph LR
Entropy["Entropy"]
Curvature["Space Curvature"]
Bounds["[0, ∞] Bounds"]
Laws["Laws of Nature"]
MG["Machine Gnostics"]
AI["**AI** built with 'Laws of Nature'"]
Universe["Universe"]
Universe --> Entropy
Universe --> Curvature
Universe --> Bounds
Entropy --> Laws
Curvature --> Laws
Bounds --> Laws
Laws --> MG
Laws -.-> AI
MG --> AI
%% Help center Laws visually
Entropy -.-> MG
Curvature -.-> MG
Bounds -.-> MG
style Universe stroke-width:2px
style Laws stroke-width:3px
style MG stroke-width:2px
style AI stroke-width:2pxMachine Gnostics is an open-source initiative that seeks to redefine the mathematical underpinnings of machine learning. While most conventional ML libraries are grounded in probabilistic and statistical frameworks, Machine Gnostics explores alternative paradigms—drawing from deterministic algebra, information theory, and geometric methods. This approach opens new avenues for building robust, interpretable, and reliable analysis tools that can withstand the limitations of traditional models.
Machine Gnostics
As a pioneering project, Machine Gnostics invites users to adopt a fresh perspective and develop a new understanding of machine learning. The library is currently in its infancy, and as such, some features may require refinement and fixes. We are actively working to expand its capabilities, with new models and methods planned for the near future. Community support and collaboration are essential to realizing Machine Gnostics’ full potential. Together, let’s build a new AI grounded in a rational and resilient paradigm.
Machine Gnostics challenges the limitations of traditional, probabilistic models. Instead of relying on assumptions and large data samples, it encodes the very laws of nature—geometry, physics, entropy—into algorithms that extract truth from data, even when samples are small, noisy, or corrupted.
Data Science Rooted in Nature¶
Machine Gnostics challenges the limitations of traditional, probabilistic models. Instead of relying on assumptions and large data samples, it encodes the very laws of nature—geometry, physics, entropy—into algorithms that extract truth from data, even when samples are small, noisy, or corrupted.
“Let data speak for themselves.” Machine Gnostics empowers you to uncover the real structure of your data, free from statistical dogma.
Why Machine Gnostics?¶
- Beyond Statistics: Move past fragile, assumption-heavy models. MG is built for the real world—messy, complex, and unpredictable.
- Nature-Inspired Algorithms: Deterministic, axiomatic, and robust—rooted in geometry, physics, and information theory.
- Resilient to Outliers & Noise: Analyze small, corrupted, or outlier-ridden datasets with confidence.
- Universal & Open: Free, open-source, and adaptable for science, engineering, and industry.
Core Features¶
- Advanced Gnostic Data Analysis: Unlock sophisticated exploratory data analysis (EDA) with algorithms that reveal hidden structures, relationships, and patterns in your data. Designed for data scientists, analysts, and researchers, Machine Gnostics provides tools that go far beyond traditional statistics—enabling deeper, more meaningful insights for both small and complex datasets.
- Industry-Ready Machine Learning:
Enjoy seamless integration with standard machine learning workflows. Machine Gnostics models support familiar
fitandpredictmethods, making them easy to adopt in any pipeline. With built-in MLflow integration, you can track, version, and deploy models effortlessly—bridging the gap between research and real-world industry applications. - Next-Generation Deep Learning (MAGNET): Prepare for the future with MAGNET (Machine Gnostics Networks), our upcoming deep learning framework. Rooted in the gnostic theorem and the laws of nature, MAGNET will offer a new paradigm for building robust, interpretable neural networks. Stay tuned as we develop this groundbreaking extension to the Machine Gnostics ecosystem.
How It Works¶
Machine Gnostics encodes the “gnostic cycle” of observation and feedback, letting you model uncertainty as a consequence of real, measurable conditions—not just randomness. See the Concepts page for a deep dive into the science and philosophy behind MG.
Real-World Impact¶
- Testimonials:Hear from scientists and engineers who have solved unsolvable problems with MG.See Testimonials & History.
- Case Studies: Explore real applications in thermodynamics, environmental science, and more. See Examples.
Get Involved¶
Machine Gnostics is open source and community-driven.