using Azure;
using HTEX.Test.Service;
using MathNet.Numerics;
using System.Configuration;
using System.Diagnostics;
using System.Linq;
using System.Text.RegularExpressions;
using TEAMModelOS.SDK;
using TEAMModelOS.SDK.DI;
using TEAMModelOS.SDK.Extension;
using TEAMModelOS.SDK.Models;
using static HTEX.Test.Controllers.LessonRecordController;
namespace HTEX.Test
{
    public class Program
    {
        public static void Main(string[] args)
        {
            Test();


            var builder = WebApplication.CreateBuilder(args);

            // Add services to the container.

            builder.Services.AddControllers();
            builder.Services.AddAzureStorage(builder.Configuration.GetValue<string>("Azure:Storage:ConnectionString"));
            builder.Services.AddAzureRedis(builder.Configuration.GetValue<string>("Azure:Redis:ConnectionString"));
            builder.Services.AddAzureCosmos(builder.Configuration.GetValue<string>("Azure:Cosmos:ConnectionString"));
            builder.Services.AddAzureServiceBus(builder.Configuration.GetValue<string>("Azure:ServiceBus:ConnectionString"));
            builder.Services.AddAzureSignalR(builder.Configuration.GetValue<string>("Azure:SignalR:ConnectionString"));
            builder.Services.AddSnowflakeId(Convert.ToInt64(builder.Configuration.GetValue<string>("Option:LocationNum")), 1);
            builder.Services.AddHttpClient();
            var app = builder.Build();

            // Configure the HTTP request pipeline.

            app.UseHttpsRedirection();

            app.UseAuthorization();


            app.MapControllers();

            app.Run();
        }

        public static string Test2() 
        {
            // 创建一个包含三万个元素的数组
            int[] data = Enumerable.Range(1, 30000).Select(i => new Random().Next(1, 31)).ToArray();

            // 设置聚类的数量
            int k = 2;
            // 初始化质心，可以选择数据中的k个随机点作为初始质心
            List<int> centroids = new List<int>();
            Random rand = new Random();
            for (int i = 0; i < k; i++)
            {
                centroids.Add(rand.Next(1, 31));
            }
            centroids.Sort(); // 对质心进行排序以避免重复

            Dictionary<int, List<int>> clustersD= new Dictionary<int, List<int>>();
            // 迭代次数
            int maxIterations = 100;
            for (int iteration = 0; iteration < maxIterations; iteration++)
            {
                // 将数据点分配给最近的质心
                Dictionary<int, List<int>> clusters = new Dictionary<int, List<int>>();
                foreach (int point in data)
                {
                    int nearestCentroid = centroids.OrderBy(c => Math.Abs(point - c)).First();
                    if (!clusters.ContainsKey(nearestCentroid))
                    {
                        clusters[nearestCentroid] = new List<int>();
                    }
                    clusters[nearestCentroid].Add(point);
                }

                // 更新质心位置
                List<int> newCentroids = new List<int>();
                foreach (KeyValuePair<int, List<int>> cluster in clusters)
                {
                    int sum = cluster.Value.Sum();
                    int count = cluster.Value.Count;
                    int newCentroid = sum / count; // 计算均值
                    newCentroids.Add(newCentroid);

                    // 输出当前聚类的信息
                    Console.WriteLine($"Cluster with centroid {cluster.Key}:");
                    Console.WriteLine($"Min: {cluster.Value.Min()}, Max: {cluster.Value.Max()}, Average: {sum / count}");
                }

                // 检查质心是否改变
                newCentroids.Sort();
                clustersD=clusters;
                if (newCentroids.SequenceEqual(centroids))
                {
                   
                    break;
                }
                centroids = newCentroids;
            }
            return "";
        }

        public static string Test()
        {
            //  Test2();
            //var n = 2.0;
            //var w = 2.0;
            //var r = 1.0;
            //var p = InteractWeight.TT;
            //var j = InteractWeight.T1;
            //var data = 190/(1+Math.Exp(-(n/4*(w*w/n+p/j * w/n * r/w *r))/n))-95;
            var n = 3.0;
            var m = 5.6;
            var w = 3.0;
            var r = 3.0;
            var p = InteractWeight.TT;
            var j = InteractWeight.T1;
            //var data = 190.0/(1+Math.Exp(-((n/m.avg)*(w*(w/n)+(p/j)* (w/n)* (r/w) *r))/n))-95.0;
            var data = 190.0/(1+Math.Exp(-((n/m)*(w*(w/n)+(p/j)* (r/n) *r))/n))-95.0;

            //int[] a = new int[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 };
            //int [] b = new int[] { 2192, 2379, 2070, 1813, 1662, 1421, 1134, 1043, 861, 763, 608, 563, 451, 358, 291, 252, 213, 170, 152, 133, 132, 95, 82, 71, 73, 63, 43 };
            //var lib= b.Sum();
            //List<int > ds= new List<int>();
            //for(int i = 0; i<a.Length; i++) 
            //{
            //    int d=   b[i]*  a[i];
            //    ds.Add(d);
            //}
            //var q = ds.Sum() *1.0/lib;
            //Console.WriteLine(q);
            //   var per= GetPersent(array,3);
            //   Console.WriteLine(per);

            //var data =  MLService.GetNormalCluster(array.ToArray(),3,0.3);
            //  var group =  array.GroupBy(x=>x).Select(x=>new { key = x.Key, list = x.ToList()}).OrderBy(x=>x.key);
            // Console.WriteLine(group.Select(x => x.key).ToJsonString());
            //Console.WriteLine(group.Select(x => x.list.Count()).ToJsonString());

            //int count = 10;
            //int score = 10;
            //for (int i = 0; i < 10; i++) 
            //{
            //    int work = Random.Shared.Next(1, count);
            //    int right = Random.Shared.Next(1, work);
            //    double xIRS =  (work+right);
            //    var exsIRS = 150/(1+Math.Exp(-(xIRS/2)))-75;
            //    Console.WriteLine($"{i}=》答题 {work}-----{right}: {exsIRS}");

            //    double xPIC = (work+right*3);
            //    var exsPIC = 40/(1+Math.Exp(-(xPIC/5)))-20;
            //    Console.WriteLine($"{i}=》挑人 {work}-----{right}: {exsPIC}");


            //    double xBUS = (work+right*3+(work-right));
            //    var exsBUZ = 80/(1+Math.Exp(-(xBUS/3)))-40;
            //    Console.WriteLine($"{i}=》抢权 {work}-----{right}: {exsBUZ}");

            //    int pscr = Random.Shared.Next(1, 10)*10;
            //    int gscr = Random.Shared.Next(1, 10)*10;
            //    double xSCR = (pscr + gscr );
            //    var exsSCR= 60/(1+Math.Exp(-(xSCR/8)))-30;
            //    Console.WriteLine($"{i}=》计分 {pscr}-----{gscr}: {exsSCR}");


            //    double xWRK = (work);
            //    var exsWRK = 150/(1+Math.Exp(-(xWRK)))-75;
            //    Console.WriteLine($"{i}=》作品 {work}-----{right}: {exsBUZ}");


            //    int vt = Random.Shared.Next(1, 3);
            //    int sc = Random.Shared.Next(1, 3);
            //    int cm = Random.Shared.Next(1, 3);
            //    int pt = Random.Shared.Next(1, 3);
            //    double xCMT = (vt + sc+cm*2+pt);
            //    var exsCMT = 100/(1+Math.Exp(-(xSCR/8)))-50;
            //    Console.WriteLine($"{i}=》评分 {vt}-----{sc}---{cm}---{pt}: {exsCMT}");

            //    int b = Random.Shared.Next(1, 100)/20;
            //    int m = Random.Shared.Next(1, 100)/20;
            //    int g = Random.Shared.Next(1, 10)/2;
            //    int t = Random.Shared.Next(1, 10)/2;
            //    double xXZ = (b + m+g+t);
            //    var exsXZ= 150/(1+Math.Exp(-(xSCR/8)))-75;
            //    Console.WriteLine($"{i}=》协作 {b}-----{m}---{g}---{t}: {exsXZ}");

            //    var xALL = xXZ+xCMT+xWRK+xSCR+xBUS+exsPIC+xIRS;
            //    var exsALL = 190/(1+Math.Exp(-(xALL/25)))-95;
            //    Console.WriteLine($"{i}=》所有: {exsALL}");
            //}


            //List<int> nums = new List<int>();
            //for (int i = 0; i < 100; i++)
            //{
            //    nums.Add(Random.Shared.Next(1,5));
            //}
            //for (int i = 0; i < 100; i++)
            //{
            //    nums.Add(Random.Shared.Next(4, 8));
            //}
            //for (int i = 0; i < 100; i++)
            //{
            //    nums.Add(Random.Shared.Next(5, 20));
            //}
            //for (int i = 0; i < 50; i++)
            //{
            //    nums.Add(Random.Shared.Next(15, 36));
            //}
            //var per = GetPersent(nums, 11);
            //bool[] anomalies = MarkAnomalies(nums);

            //for (int i = 0; i < nums.Count; i++)
            //{
            //    if (anomalies[i]) { Console.WriteLine($"Number{ i}: {nums[i]} is an anomaly."); }

            //}
            return "Hello World!";
        }

        static bool[] MarkAnomalies(List<int> array)
        {
            if (array.Count == 0) return new bool[0];

            double average = array.Average();
            double variance = array.Average(x => Math.Pow(x - average, 2));
            double standardDeviation = Math.Sqrt(variance);

            // 定义异常值的阈值，这里使用2倍标准差
            double threshold =3* standardDeviation;

            bool[] anomalies = new bool[array.Count];
            for (int i = 0; i < array.Count; i++)
            {
                double deviation = Math.Abs(array[i] - average);
                anomalies[i] = deviation > threshold;
            }

            return anomalies;
        }
        /// <summary>
        /// 计算当前元素在集合中超过了多少百分比的值
        /// </summary>
        /// <param name="nums"></param>
        /// <param name="curr"></param>
        /// <returns></returns>
        public static double GetPersent(IEnumerable<double> nums, int curr)
        {
            int count = 0;
            foreach (var op in nums.OrderBy(x => x))
            {
                if (op < curr)
                {
                    count++;
                }
                else if (op == curr)
                {
                    count++;
                }
                else
                {
                    break;
                }
            }
            return count *1.0/ nums.Count() * 100;
        }


        public class KeyCount
        {
            public int count { get; set;}
            public int key { get; set;}
        }
    }
}