Janady13/AI_Brain / daemon/seed_math_models.cs

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Field	Recorded value
Source type	GitHub source
Original record	Janady13/AI_Brain / daemon/seed_math_models.cs
Date	Not specified in source
Repository	Janady13/AI_Brain
Revision	6d0767aad08781eb5c4e1eb14a42ec92c437be78
Branch	copilot/convert-python-files-to-csharp
Path or archive member	daemon/seed_math_models.cs
Original SHA-256	1a2c3bf43daf52bec5d0f79ad35f81fa527bd66b2d6af22569f47c09e97d88a7
Git blob SHA	d721add980efaaf4498b06b1b0d4e0531353645e
Reading-copy SHA-256	1a2c3bf43daf52bec5d0f79ad35f81fa527bd66b2d6af22569f47c09e97d88a7

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// Generate 500 distinct model descriptions via combinatorics

using System;
using System.Collections.Generic;

namespace AIBrain.Daemon
{
    public static class SeedMathModels
    {
        public static IEnumerable<string> GenMathModels(int n)
        {
            var families = new[]
            {
                "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"
            };

            var tasks = new[]
            {
                "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"
            };

            var qualifiers = new[]
            {
                "hierarchical", "conjugate prior", "robust", "sparse", "regularized", "nonparametric",
                "semi-supervised", "self-supervised", "transfer-learning", "online", "streaming",
                "batched", "stochastic", "deterministic", "Bayes-optimal", "approximate", "ensemble"
            };

            var domains = new[]
            {
                "vision", "NLP", "speech", "finance", "healthcare", "operations", "geospatial",
                "energy", "retail", "manufacturing", "security", "recommendation systems"
            };

            var seen = new HashSet<string>();
            int i = 0;

            foreach (var f in families)
            {
                foreach (var q in qualifiers)
                {
                    foreach (var t in tasks)
                    {
                        var phrase = $"{f} {q} model for {t}";
                        if (seen.Contains(phrase))
                            continue;

                        seen.Add(phrase);
                        yield return $"Model: {phrase}";
                        i++;

                        if (i >= n)
                            yield break;
                    }
                }
            }

            if (i < n)
            {
                foreach (var f in families)
                {
                    foreach (var d in domains)
                    {
                        var phrase = $"{f} approach applied to {d}";
                        if (seen.Contains(phrase))
                            continue;

                        seen.Add(phrase);
                        yield return $"Model: {phrase}";
                        i++;

                        if (i >= n)
                            yield break;
                    }
                }
            }
        }

        public static void Main(string[] args)
        {
            SeedCommon.RunSeed("math_model", GenMathModels);
        }
    }
}