RRF-Savant Meta-State Logistic Regression

Model Summary

This repository contains a lightweight logistic regression classifier implemented with scikit-learn.
The model operates on 15-dimensional RRF-Savant meta-state features, derived from the RRF / SavantEngine pipeline, and outputs a binary prediction with associated probabilities.

It is designed as a fast, interpretable decision layer on top of the richer RRF-Savant embedding and resonance machinery.

Model Details

Model type: Logistic Regression (binary classifier)
Framework: scikit-learn
Input dimensionality: 15
Source notebook: RRFSavant_AGI_Core_Colab.ipynb
File format (recommended): joblib (.joblib)

Input Features

Each input is a 15-dimensional feature vector:

RRF-Savant meta-state features, including:
- φ / Φ phase indicators (RRF-Savant “phi” level)
- Ω / omega dynamics or cycle index
- Global coherence / resonance scores
- Spectral features:
  - S_RRF: spectral smoothness
  - C_RRF: spectral concentration
- Energy-like measures (e.g. E_H)
- Dominant frequency or harmonic index
- One-hot encoded Φ nodes / states

Exact semantics and preprocessing are defined in the source notebook
RRFSavant_AGI_Core_Colab.ipynb.

Outputs

y_pred: binary class label (e.g. 0 vs 1)
proba: probability estimates for each class via predict_proba

The precise interpretation of class 0 and class 1 (e.g. baseline vs. “RRF-aligned” state, safe vs. risky, etc.) should be documented alongside your use-case.

Intended Use

Primary use:
- As a meta-controller for RRF-Savant systems, mapping high-level meta-state features to a simple decision (binary label).
- As a fast screening / routing head deciding whether to:
  - escalate to a heavier RRF/Savant pipeline,
  - trigger a specific operating mode,
  - log / flag certain states.
Not intended for:
- Standalone critical decision-making (medical, legal, safety-critical applications) without human oversight.
- Direct real-world risk scoring without proper calibration and validation.

Training

Training framework: scikit-learn LogisticRegression
Data source:
- Internal RRF-Savant meta-state dataset, generated and curated in
  RRFSavant_AGI_Core_Colab.ipynb.
Preprocessing (typical):
- Numeric features scaled (e.g. StandardScaler)
- Categorical / discrete Φ nodes one-hot encoded
- Train/validation split performed inside the notebook

For exact data splits, preprocessing, and hyperparameters, refer to the Colab notebook.

Evaluation

Typical metrics for this model family include:

Accuracy
ROC-AUC
Precision / Recall / F1
Calibration of probabilities

You should log and report:

Metrics on a held-out test set
Any class imbalance handling performed (e.g. class_weight="balanced")

How to Use Assuming model is loaded

import joblib import numpy as np

Load model

clf = joblib.load("rrf_savant_meta_logit.joblib")

Example: single feature vector (15 dims)

x = np.array([ 0.85087634, 0.67296168, 0.74652746, 0.03735409, 0.72399869, 0.66076596, 0.30312352, 0.69585885, 0.98531076, 0.28866375, 0.99602791, 0.69072907, 0.05884264, 0.74298728, 0.75928443 ]).reshape(1, -1)

Prediction

y_pred = clf.predict(x)[0] proba = clf.predict_proba(x)[0] # [P(class 0), P(class 1)]

print("Predicted label:", y_pred) print("Probabilities:", proba)

Install Dependencies

pip install scikit-learn numpy joblib

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RRFSavantMetaLogit