KNN（k-nearest-neighbor）演算法的思想是找到在輸入新資料時，找到與該資料最接近的k個鄰居，在這k個鄰居中，找到出現次數最多的類別，對其進行歸類。
Iris資料集是常用的分類實驗資料集，由Fisher, 1936收集整理。Iris也稱鳶尾花卉資料集，是一類多重變數分析的資料集。資料集包含150個數據集，分為3類，每類50個數據，每個資料包含4個屬性。可通過花萼長度，花萼寬度，花瓣長度，花瓣寬度4個屬性預測鳶尾花卉屬於（Setosa，Versicolour，Virginica）三個種類中的哪一類。

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

namespace ConsoleApplication2
{
    public class Iris
    {
        // 
        private double sepalLength;
        public double SepalLength
        {
            get { return sepalLength; }
            set { sepalLength = value; }
        }

        // 
        private double sepalWidth;
        public double SepalWidth
        {
            get { return sepalWidth; }
            set { sepalWidth = value; }
        }

        // 
        private double petalLength;
        public double PetalLength
        {
            get { return petalLength; }
            set { petalLength = value; }
        }

        //
        private double petalWidth;
        public double PetalWidth
        {
            get { return petalLength; }
            set { petalLength = value; }
        }

        //
        private string species;
        public string Species
        {
            get { return species; }
            set { species = value; }
        }
    }
}
 

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

namespace ConsoleApplication2
{
    public class KNN
    {
        /// <summary>
        /// 樣本資料
        /// </summary>
        private List<Iris> sampleList;

        /// <summary>
        /// 未分類資料
        /// </summary>
        private List<Iris> unclassifyList;

        /// <summary>
        /// K值
        /// </summary>
        private int k;

        /// <summary>
        /// 建構函式
        /// </summary>
        /// <param name="sampleList">樣本資料</param>
        /// <param name="unclassifyList">未分類資料</param>
        /// <param name="k">k值</param>
        public KNN(List<Iris> sampleList, List<Iris> unclassifyList, int k)
        {
            this.sampleList = sampleList;
            this.unclassifyList = unclassifyList;
            this.k = k;
        }

        /// <summary>
        /// 分類
        /// </summary>
        public void Classify()
        {
            int sampleCount = sampleList.Count;
            int unclassifyCount = unclassifyList.Count;

            // 
            for (int i = 0; i < unclassifyCount; i++)
            {
                Tuple<string, double>[] tupleArray = new Tuple<string, double>[sampleCount];
                for (int j = 0; j < sampleCount; j++)
                {
                    double distance = CalculateDistance(sampleList[j], unclassifyList[i]);
                    string species = sampleList[j].Species;
                    tupleArray[j] = Tuple.Create(species, distance);
                }

                //
                IEnumerable<Tuple<string, double>> selector = tupleArray.OrderBy(t => t.Item2).Take(k);
                Dictionary<string, int> dictionary = new Dictionary<string, int>();
                foreach (Tuple<string, double> tuple in selector)
                {
                    if (dictionary.ContainsKey(tuple.Item1))
                    {
                        dictionary[tuple.Item1]++;
                    }
                    else
                    {
                        dictionary.Add(tuple.Item1, 1);
                    }
                }

                // 
                IEnumerable<KeyValuePair<string, int>> keyValuePair = dictionary.OrderByDescending(t => t.Value).Take(1);
                foreach (KeyValuePair<string, int> kvp in keyValuePair)
                {
                    unclassifyList[i].Species = kvp.Key;
                }

                // 
                sampleList.Add(unclassifyList[i]);
                sampleCount++;
            }
            
        }

        /// <summary>
        /// 計算距離
        /// </summary>
        /// <param name="sample">樣本資料</param>
        /// <param name="unclassify">未分類資料</param>
        /// <returns>兩者歐氏距離</returns>
        public double CalculateDistance(Iris sample, Iris unclassify)
        {
            double delta_SepalLength = unclassify.SepalLength - sample.SepalLength;
            double delta_SepalWidth = unclassify.SepalWidth - sample.SepalWidth;
            double delta_PetalLength = unclassify.PetalLength - sample.PetalLength;
            double delta_PetalWidth = unclassify.PetalWidth - sample.PetalWidth;
            return Math.Sqrt(delta_SepalLength * delta_SepalLength + delta_SepalWidth * delta_SepalWidth + delta_PetalLength * delta_PetalLength + delta_PetalWidth * delta_PetalWidth);
        }

        /// <summary>
        /// 列印
        /// </summary>
        public void Print(string filePath)
        {
            StringBuilder stringBuilder = new StringBuilder();
            for (int i = 0; i < sampleList.Count; i++)
            {
                Iris iris = sampleList[i];
                stringBuilder.AppendLine(i.ToString() + "\t" + iris.SepalLength.ToString() + "\t" + iris.SepalWidth.ToString() + "\t" + iris.PetalLength.ToString() + "\t" + iris.PetalWidth.ToString() + "\t" + iris.Species);
            }

            System.IO.FileStream fs = new System.IO.FileStream(filePath, System.IO.FileMode.Create);
            System.IO.StreamWriter sw = new System.IO.StreamWriter(fs);
            sw.Write(stringBuilder.ToString());
            sw.Flush();
            sw.Close();
            fs.Close();
            fs.Dispose();
        }
    }
}
 

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

namespace ConsoleApplication2
{
    class Program
    {
        static void Main(string[] args)
        {
            List<Iris> sampleList = GetIrisDataset(AppDomain.CurrentDomain.BaseDirectory + "樣本.txt");
            List<Iris> unclassifyList = GetIrisDataset(AppDomain.CurrentDomain.BaseDirectory + "未分類.txt");

            KNN tool = new KNN(sampleList, unclassifyList, 5);
            tool.Classify();
            tool.Print(@"C:\Users\DSF\Desktop\t.txt");

            Console.WriteLine("OK");
        }

        static List<Iris> GetIrisDataset(string filePath)
        {
            System.IO.FileStream fs = new System.IO.FileStream(filePath, System.IO.FileMode.Open);
            System.IO.StreamReader sr = new System.IO.StreamReader(fs);

            //
            List<Iris> list = new List<Iris>();
            string readLine = sr.ReadLine();
            while (!string.IsNullOrEmpty(readLine))
            {
                string[] splitArray = readLine.Split(' ');
                Iris iris = new Iris();
                iris.SepalLength = Convert.ToDouble(splitArray[1]);
                iris.SepalWidth = Convert.ToDouble(splitArray[2]);
                iris.PetalLength = Convert.ToDouble(splitArray[3]);
                iris.PetalWidth = Convert.ToDouble(splitArray[4]);
                iris.Species = splitArray[5];
                list.Add(iris);
                readLine = sr.ReadLine();
            }

            //
            sr.Close();
            fs.Close();
            fs.Dispose();
            return list;
        }
    }

}