using System;
using System.Collections;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace HTEX.Lib.ETL
{
    public class KMeansService
    {

        public static Dictionary<double, List<double>> KMeansOptimized(IEnumerable<double> datas, int optimalK = 0, int maxClusters = 10)
        {
           
            if (optimalK<=0)
            { 
                // 使用肘部法则找到最佳聚类数目
                optimalK = FindOptimalK(datas, maxClusters);
            }
            // 使用 K-means++ 方法初始化质心
            List<double> centroids = InitializeCentroids(datas, optimalK);

            Dictionary<double, List<double>> clustersResults = new Dictionary<double, List<double>>();

            // 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<double, List<double>> clusters = new Dictionary<double, List<double>>();
                foreach (int point in datas)
                {
                    double nearestCentroid = centroids.OrderBy(c => Math.Abs(point - c)).First();
                    if (!clusters.ContainsKey(nearestCentroid))
                    {
                        clusters[nearestCentroid] = new List<double>();
                    }
                    clusters[nearestCentroid].Add(point);
                }

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

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

        private static int FindOptimalK(IEnumerable<double> datas, int maxClusters)
        {
            double[] silhouetteCoefficients = new double[maxClusters];

            for (int k = 2; k <= maxClusters; k++) // 至少需要两个聚类
            {
                var centroids = InitializeCentroids(datas, k);
                var clusters = AssignPointsToClusters(datas, centroids);
                silhouetteCoefficients[k - 1] = CalculateSilhouetteCoefficient(clusters);
            }

            // 寻找最高的轮廓系数对应的聚类数
            int optimalK = 0;
            double maxCoefficient = double.MinValue;
            for (int i = 0; i < silhouetteCoefficients.Length; i++)
            {
                if (silhouetteCoefficients[i] > maxCoefficient)
                {
                    maxCoefficient = silhouetteCoefficients[i];
                    optimalK = i + 2; // 因为我们从2开始计算
                }
            }

            return optimalK;
        }

        private static double CalculateSilhouetteCoefficient(Dictionary<double, List<double>> clusters)
        {
            double silhouetteCoefficient = 0;
            int totalElements = clusters.Sum(cluster => cluster.Value.Count);

            foreach (var cluster in clusters)
            {
                foreach (var element in cluster.Value)
                {
                    double a = AverageDistanceWithinCluster(element, cluster.Value);
                    double b = Double.PositiveInfinity;
                    foreach (var otherCluster in clusters.Where(c => !c.Key.Equals(cluster.Key)))
                    {
                        double dist = AverageDistanceBetweenClusters(element, otherCluster.Value);
                        if (dist < b)
                        {
                            b = dist;
                        }
                    }
                    silhouetteCoefficient += (b - a) / Math.Max(a, b);
                }
            }

            return silhouetteCoefficient / totalElements;
        }
        private static double AverageDistanceWithinCluster(double element, List<double> cluster)
        {
            double sum = 0;
            foreach (var otherElement in cluster.Where(e => !e.Equals(element)))
            {
                sum += Math.Abs(element - otherElement);
            }
            return sum / (cluster.Count - 1);
        }
        private static double AverageDistanceBetweenClusters(double element, List<double> otherCluster)
        {
            double sum = 0;
            foreach (var otherElement in otherCluster)
            {
                sum += Math.Abs(element - otherElement);
            }
            return sum / otherCluster.Count;
        }
        /// <summary>
        /// 随机方法初始化质心
        /// </summary>
        /// <param name="datas"></param>
        /// <param name="k"></param>
        /// <returns></returns>
        //private static List<int> InitializeCentroids(IEnumerable<int> datas, int k)
        //{
        //    // K-means++ 初始化
        //    var centroids = new List<int>();
        //    var random = new Random();
        //    //  centroids.Add(datas.ElementAt(random.Next(datas.Count())));
        //    centroids.Add(1);
        //    while (centroids.Count < k)
        //    {
        //        int[] distancesSquared = datas.Select(dataPoint =>
        //            centroids.Min(centroid => (dataPoint - centroid) * (dataPoint - centroid))).ToArray();
        //        double totalDistance = distancesSquared.Sum();
        //        //double r = random.NextDouble() * totalDistance;
        //        double r = totalDistance;
        //        double accumulator = 0;
        //        for (int i = 0; i < distancesSquared.Length; i++)
        //        {
        //            accumulator += distancesSquared[i];
        //            if (accumulator >= r)
        //            {
        //                centroids.Add(datas.ElementAt(i));
        //                break;
        //            }
        //        }
        //    }
        //    return centroids;
        //}



        /// <summary>
        /// 分位数法 来初始化质心,将数据按照一定的百分位数划分来确定初始质心的位置。确保质心在整个数据范围内的分布更加均匀
        /// </summary>
        /// <param name="datas"></param>
        /// <param name="k"></param>
        /// <returns></returns>
        //private static List<int> InitializeCentroids(IEnumerable<int> datas, int k)
        //{
        //    // 排序数据
        //    var sortedData = datas.OrderBy(d => d).ToArray();

        //    // 计算每个质心的位置
        //    List<int> centroids = new List<int>();
        //    for (int i = 0; i < k; i++)
        //    {
        //        // 计算第i个质心应该位于哪个位置
        //        int index = (sortedData.Length * i + 1) / k - 1;
        //        if (index < 0 || index >= sortedData.Length) continue; // 防止索引越界
        //        centroids.Add(sortedData[index]);
        //    }

        //    return centroids;
        //}
        private static List<double> InitializeCentroids(IEnumerable<double> datas, int k)
        {
            // 计算每个数据点的局部密度
            var densities = CalculateLocalDensities(datas);

            // 选择高密度点作为初始质心
            var centroids = new List<double>();
            foreach (var density in densities.OrderByDescending(d => d.Value).Take(k))
            {
                centroids.Add(density.Key);
            }

            return centroids;
        }

        private static Dictionary<double, int> CalculateLocalDensities(IEnumerable<double> datas, int radius = 10)
        {
            // 计算每个数据点周围的邻居数量作为局部密度
            var densities = new Dictionary<double, int>();
            foreach (var dataPoint in datas)
            {
                int localDensity = datas.Count(p => Math.Abs(p - dataPoint) <= radius);
                densities[dataPoint] = localDensity;
            }

            return densities;
        }

        private static Dictionary<double, List<double>> AssignPointsToClusters(IEnumerable<double> datas, List<double> centroids)
        {
            // 分配数据点到最近的质心
            var clusters = new Dictionary<double, List<double>>();
            foreach (var point in datas)
            {
                double nearestCentroid = centroids.OrderBy(c => Math.Abs(point - c)).First();
                if (!clusters.ContainsKey(nearestCentroid))
                {
                    clusters[nearestCentroid] = new List<double>();
                }
                clusters[nearestCentroid].Add(point);
            }
            return clusters;
        }

        public static Dictionary<double, List<double>> KMeans(IEnumerable<double> datas)
        {

            // 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<double> centroids = new List<double>();

            centroids.Add(datas.Min());
            centroids.Add(datas.Max());
            centroids.Sort(); // 对质心进行排序以避免重复

            Dictionary<double, List<double>> clustersResults = new Dictionary<double, List<double>>();
            // 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<double, List<double>> clusters = new Dictionary<double, List<double>>();
                foreach (int point in datas)
                {
                    double nearestCentroid = centroids.OrderBy(c => Math.Abs(point - c)).First();
                    if (!clusters.ContainsKey(nearestCentroid))
                    {
                        clusters[nearestCentroid] = new List<double>();
                    }
                    clusters[nearestCentroid].Add(point);
                }

                // 更新质心位置
                List<double> newCentroids = new List<double>();
                foreach (KeyValuePair<double, List<double>> cluster in clusters)
                {
                    double sum = cluster.Value.Sum();
                    int count = cluster.Value.Count;
                    double newCentroid = sum *1.0/ 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();

                clustersResults=clusters;

                if (newCentroids.SequenceEqual(centroids))
                {
                    break;
                }
                centroids = newCentroids;
            }
            return clustersResults;
        }

        public static Dictionary<double, List<int>> KMeans(IEnumerable<int> datas)
        {
            // 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<double> centroids = new List<double>();

            centroids.Add(datas.Min());
            centroids.Add(datas.Max());
            centroids.Sort(); // 对质心进行排序以避免重复

            Dictionary<double, List<int>> clustersResults = new Dictionary<double, 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<double, List<int>> clusters = new Dictionary<double, List<int>>();
                foreach (int point in datas)
                {
                    double 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<double> newCentroids = new List<double>();
                foreach (KeyValuePair<double, List<int>> cluster in clusters)
                {
                    int sum = cluster.Value.Sum();
                    int count = cluster.Value.Count;
                    double newCentroid = sum *1.0/ 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();

                clustersResults=clusters;

                if (newCentroids.SequenceEqual(centroids))
                {
                    break;
                }
                centroids = newCentroids;
            }
            return clustersResults;
        }

    }
}