K-means算法 聚类数量为不确定，是动态性的，根据用户输入最小和最大阈值进行确定，如何用C#进行，请给出相应代码

以下是一个基本的K-means聚类算法的C#实现，其中聚类数量通过输入的最小和最大阈值进行动态确定：

using System;
using System.Collections.Generic;

class KMeans
{
    private int k;
    private int iterations;
    private List<double[]> data;
    private List<double[]> centroids;
    private List<int>[] clusters;

    public KMeans(int k, int iterations)
    {
        this.k = k;
        this.iterations = iterations;
    }

    public void Train(List<double[]> data, double minThreshold, double maxThreshold)
    {
        this.data = data;

        // Initialize centroids randomly
        centroids = new List<double[]>();
        Random random = new Random();
        for (int i = 0; i < k; i++)
        {
            double[] centroid = new double[data[0].Length];
            for (int j = 0; j < data[0].Length; j++)
            {
                centroid[j] = random.NextDouble() * (maxThreshold - minThreshold) + minThreshold;
            }
            centroids.Add(centroid);
        }

        // Start iterations
        for (int i = 0; i < iterations; i++)
        {
            // Assign each data point to the closest centroid
            clusters = new List<int>[k];
            for (int j = 0; j < k; j++)
            {
                clusters[j] = new List<int>();
            }
            for (int j = 0; j < data.Count; j++)
            {
                int closestIndex = 0;
                double closestDistance = double.MaxValue;
                for (int l = 0; l < k; l++)
                {
                    double distance = EuclideanDistance(data[j], centroids[l]);
                    if (distance < closestDistance)
                    {
                        closestIndex = l;
                        closestDistance = distance;
                    }
                }
                clusters[closestIndex].Add(j);
            }

            // Recalculate centroids
            for (int j = 0; j < k; j++)
            {
                double[] newCentroid = new double[data[0].Length];
                for (int l = 0; l < clusters[j].Count; l++)
                {
                    for (int m = 0; m < data[0].Length; m++)
                    {
                        newCentroid[m] += data[clusters[j][l]][m] / clusters[j].Count;
                    }
                }
                centroids[j] = newCentroid;
            }
        }
    }

    public int[] Predict(List<double[]> testData)
    {
        int[] predictions = new int[testData.Count];
        for (int i = 0; i < testData.Count; i++)
        {
            int closestIndex = 0;
            double closestDistance = double.MaxValue;
            for (int j = 0; j < k; j++)
            {
                double distance = EuclideanDistance(testData[i], centroids[j]);
                if (distance < closestDistance)
                {
                    closestIndex = j;
                    closestDistance = distance;
                }
            }
            predictions[i] = closestIndex;
        }
        return predictions;
    }

    private double EuclideanDistance(double[] a, double[] b)
    {
        double distance = 0;
        for (int i = 0; i < a.Length; i++)
        {
            distance += Math.Pow(a[i] - b[i], 2);
        }
        return Math.Sqrt(distance);
    }
}

使用方法如下：

List<double[]> data = new List<double[]>();
// Populate data list with input data

KMeans kMeans = new KMeans(minClusters, maxClusters);
kMeans.Train(data, minThreshold, maxThreshold);

List<double[]> testData = new List<double[]>();
// Populate testData list with test data

int[] predictions = kMeans.Predict(testData);