Janady13/AI_Brain / daemon/seed_math_models.py

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Field	Recorded value
Source type	GitHub source
Original record	Janady13/AI_Brain / daemon/seed_math_models.py
Date	Not specified in source
Repository	Janady13/AI_Brain
Revision	11aa50ef5d7692e8abd1b8d74ae31c5c29201b14
Branch	codex/build-online-github-ani-irqa7y
Path or archive member	daemon/seed_math_models.py
Original SHA-256	5409790d2a187ff03b6276abfd848b8fe9fb0ca2b6be5447fbac0028a0780e47
Git blob SHA	44d9d00b442e2ad9a6017bbccbf634884989ff61
Reading-copy SHA-256	5409790d2a187ff03b6276abfd848b8fe9fb0ca2b6be5447fbac0028a0780e47

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Original record	Branch or archive	Revision / identifier	Original location
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-irqa7y	11aa50ef5d7692e8abd1b8d74ae31c5c29201b14	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-hr4y0z	366ab848896ae49159cfb3f6c201b8c5a731e9c8	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-i3cigl	3a6e002311c48c27ffd41ef4a2a31c63947128cd	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani	49db80a641b10ab62d16cc7643bd0b367f93951c	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-hrtsoc	50285aff347685f043e7a72e4d72891eebaa6cd6	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-7v4kug	5cd0bbf744b0aee9445913337e852889d9a8098e	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-nbgsix	840945d70b88674d8f40d330001cc63d871dc8e2	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	copilot/vscode1760283895982	9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-jf5olo	c6653627c453ab428d665226167fa5f4aa05e9a5	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	feature/seeder-engine-tests	cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-7nmb21	d0f50071c85cddaaebbf9c4dd1313bd7676986da	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-0etkns	e250b19d446bdffd542c9eea31cead12e30800d6	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-hr4y0z	366ab848896ae49159cfb3f6c201b8c5a731e9c8	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-irqa7y	11aa50ef5d7692e8abd1b8d74ae31c5c29201b14	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-i3cigl	3a6e002311c48c27ffd41ef4a2a31c63947128cd	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-7v4kug	5cd0bbf744b0aee9445913337e852889d9a8098e	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-nbgsix	840945d70b88674d8f40d330001cc63d871dc8e2	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	copilot/vscode1760283895982	9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-7nmb21	d0f50071c85cddaaebbf9c4dd1313bd7676986da	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	feature/seeder-engine-tests	cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani	49db80a641b10ab62d16cc7643bd0b367f93951c	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-jf5olo	c6653627c453ab428d665226167fa5f4aa05e9a5	Open original
Janady13/AI_Brain / daemon/seed_math_models.py	codex/build-online-github-ani-hrtsoc	50285aff347685f043e7a72e4d72891eebaa6cd6	Open original

Recovered source transcript

Download complete source text ↓62 lines · 2.6 KB

#!/usr/bin/env python3
from seed_common import run_seed

# Generate 500 distinct model descriptions via combinatorics

def gen_math_models(n: int):
    families = [
        "Bayesian", "Frequentist", "Gaussian Process", "Kalman Filter", "Hidden Markov",
        "ARIMA", "GARCH", "Markov Decision Process", "Monte Carlo", "Variational Inference",
        "MCMC", "Principal Component", "Independent Component", "t-SNE", "UMAP",
        "Support Vector Machine", "Random Forest", "Gradient Boosting", "XGBoost", "LightGBM",
        "CatBoost", "Elastic Net", "Lasso", "Ridge", "K-Means",
        "DBSCAN", "Spectral Clustering", "Hierarchical Clustering", "Nonnegative Matrix Factorization",
        "Autoencoder", "Variational Autoencoder", "GAN", "Diffusion Model", "Transformer",
        "Graph Neural Network", "Temporal Convolutional Network", "Long Short-Term Memory",
        "Convolutional Neural Network", "Bayesian Optimization", "Multi-Armed Bandit",
    ]
    tasks = [
        "binary classification", "multiclass classification", "regression", "time-series forecasting",
        "anomaly detection", "causal inference", "counterfactual estimation", "dimensionality reduction",
        "topic modeling", "recommendation", "segmentation", "clustering", "survival analysis",
        "reinforcement learning", "policy optimization", "control", "simulation",
    ]
    qualifiers = [
        "hierarchical", "conjugate prior", "robust", "sparse", "regularized", "nonparametric",
        "semi-supervised", "self-supervised", "transfer-learning", "online", "streaming",
        "batched", "stochastic", "deterministic", "Bayes-optimal", "approximate", "ensemble",
    ]
    domains = [
        "vision", "NLP", "speech", "finance", "healthcare", "operations", "geospatial",
        "energy", "retail", "manufacturing", "security", "recommendation systems",
    ]

    seen = set()
    i = 0
    for f in families:
        for q in qualifiers:
            for t in tasks:
                phrase = f"{f} {q} model for {t}"
                if phrase in seen:
                    continue
                seen.add(phrase)
                yield f"Model: {phrase}"
                i += 1
                if i >= n:
                    return
    # If not enough combinations, add domain variants
    if i < n:
        for f in families:
            for d in domains:
                phrase = f"{f} approach applied to {d}"
                if phrase in seen:
                    continue
                seen.add(phrase)
                yield f"Model: {phrase}"
                i += 1
                if i >= n:
                    return

if __name__ == "__main__":
    run_seed("math_model", gen_math_models)