using Azure;
using Bogus;
using HTEX.Test.Service;
using MathNet.Numerics;
using System;
using System.Configuration;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Reflection;
using System.Text.RegularExpressions;
using TEAMModelOS.SDK;
using TEAMModelOS.SDK.DI;
using TEAMModelOS.SDK.Extension;
using TEAMModelOS.SDK.Models;
using TEAMModelOS.SDK.Models.Cosmos.Student;
using static HTEX.Test.Controllers.LessonRecordController;
using static Microsoft.Azure.Amqp.Serialization.SerializableType;
using static System.Runtime.InteropServices.JavaScript.JSType;
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()
        {
             
            string jsons =   System.IO.File.ReadAllText( $"F:\\lesson-local\\analysis.json");
            LessonDataAnalysis lessonDataAnlysis = jsons.ToObject<LessonDataAnalysis>();
            var n = 5;
            //聚类分数量大和数量小的类群。判断当前出题数在两个类群的最大范围内，再取其质心。
            var m = n<=lessonDataAnlysis.clustersInteract.First().Value.Max() ? lessonDataAnlysis.clustersInteract.First().Value: lessonDataAnlysis.clustersInteract.Last().Value;
            var persent = GetPersent(lessonDataAnlysis.interactNormal, n);
            //Console.WriteLine(persent);
            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;}
        }
        class WeightedItem
        {
            public int Value { get; set; }
            public double Weight { get; set; }
        }
    }
}