Janady13/AI_Brain / daemon/seed_statistics.cs

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Field	Recorded value
Source type	GitHub source
Original record	Janady13/AI_Brain / daemon/seed_statistics.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_statistics.cs
Original SHA-256	72821130972ad91b52396a9d0193e56418f21429873f9a88ce23e2004bf85283
Git blob SHA	1d51371540461c136623ef7887080d8a70aa21b6
Reading-copy SHA-256	72821130972ad91b52396a9d0193e56418f21429873f9a88ce23e2004bf85283

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// Generate statistical priors for marketing campaigns

using System;
using System.Collections.Generic;
using System.IO;
using System.Text.Json;

namespace AIBrain.Daemon
{
    public class StatisticalPrior
    {
        public string Feature { get; set; }
        public string FeatureType { get; set; }
        public double MeanRoi { get; set; }
        public double StdRoi { get; set; }
        public double MeanCtr { get; set; }
        public double StdCtr { get; set; }
        public int SampleSize { get; set; }
        public double Weight { get; set; }
        public double BaselineBudget { get; set; }
        public string Notes { get; set; }
    }

    public static class SeedStatistics
    {
        private static readonly string STATISTICAL_PRIORS_PATH = 
            Path.Combine(Path.GetDirectoryName(typeof(SeedStatistics).Assembly.Location) ?? ".", 
                "datasets", "statistical_reasoning.json");

        private static List<StatisticalPrior> LoadPriors()
        {
            try
            {
                if (File.Exists(STATISTICAL_PRIORS_PATH))
                {
                    var json = File.ReadAllText(STATISTICAL_PRIORS_PATH);
                    return JsonSerializer.Deserialize<List<StatisticalPrior>>(json) ?? new List<StatisticalPrior>();
                }
            }
            catch
            {
                // Fall through to default
            }

            return new List<StatisticalPrior>
            {
                new StatisticalPrior
                {
                    Feature = "omni-channel",
                    FeatureType = "channel",
                    MeanRoi = 2.2,
                    StdRoi = 0.5,
                    MeanCtr = 0.038,
                    StdCtr = 0.012,
                    SampleSize = 150,
                    Weight = 0.8,
                    BaselineBudget = 20000,
                    Notes = "Fallback statistical prior when curated dataset missing."
                }
            };
        }

        public static IEnumerable<string> GenStatistics(int limit)
        {
            var priors = LoadPriors();

            var scenarios = new[] 
            { 
                "baseline", "aggressive", "defensive", "innovation", "efficiency", 
                "expansion", "stabilization", "premium", "retention", "accelerated" 
            };

            var confidenceLevels = new[] { 0.68, 0.8, 0.9, 0.95 };
            var budgetMultipliers = new[] { 0.75, 0.9, 1.0, 1.1, 1.25 };

            var zScores = new Dictionary<double, double>
            {
                [0.68] = 0.994,
                [0.8] = 1.282,
                [0.9] = 1.645,
                [0.95] = 1.96
            };

            int produced = 0;

            foreach (var prior in priors)
            {
                var feature = prior.Feature ?? "unspecified";
                var featureType = prior.FeatureType ?? "feature";
                var meanRoi = prior.MeanRoi;
                var stdRoi = prior.StdRoi;
                var meanCtr = prior.MeanCtr;
                var stdCtr = prior.StdCtr;
                var sampleSize = prior.SampleSize;
                var weight = prior.Weight;
                var baselineBudget = prior.BaselineBudget;
                var notes = prior.Notes ?? "Statistical benchmark from curated marketing runs.";

                foreach (var scenario in scenarios)
                {
                    foreach (var confidence in confidenceLevels)
                    {
                        var zScore = zScores.ContainsKey(confidence) ? zScores[confidence] : 1.96;
                        var marginRoi = Math.Round(zScore * stdRoi / Math.Sqrt(Math.Max(sampleSize, 1)), 3);
                        var marginCtr = Math.Round(zScore * stdCtr / Math.Sqrt(Math.Max(sampleSize, 1)), 4);

                        foreach (var multiplier in budgetMultipliers)
                        {
                            var scenarioBudget = Math.Round(baselineBudget * multiplier, 2);
                            var projectedRevenue = Math.Round(scenarioBudget * meanRoi, 2);

                            var statement = 
                                $"Statistical prior: {Capitalize(scenario)} strategy for {featureType} " +
                                $"'{feature}' targets ROI {meanRoi:F3}x ±{marginRoi:F3} ({confidence:P0} CI) " +
                                $"and CTR {meanCtr:F4} ±{marginCtr:F4} from {sampleSize} runs; " +
                                $"allocate ${scenarioBudget:N2} to unlock ${projectedRevenue:N2} with weights {weight:F2}. Notes: {notes}";

                            yield return statement;
                            produced++;

                            if (produced >= limit)
                                yield break;
                        }
                    }
                }
            }
        }

        private static string Capitalize(string s)
        {
            if (string.IsNullOrEmpty(s))
                return s;
            return char.ToUpper(s[0]) + s.Substring(1);
        }

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