TEAMModelOS/TEAMModelOS.Extension/HTEX.Lib/ETL/KMeansService.cs

using System;
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>> 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;
        }

    }
}