c#一元线性回归总结

迈不过友情╰ 2024-04-18 10:20 51阅读 0赞

方法是网上搜的，说下自己的理解，就是根据参数来计算出k和b，不能直接就使用,对多个参数进行拟合一下

using System;  
using System.Collections.Generic;  
   
namespace LinearRegression  
\{  
    class Program  
    \{  
        static void Main(string\[\] args)  
        \{  
            List<Point> \_PList = new List<Point>();  
            \_PList.Add(new Point(1, 3.09));  
            \_PList.Add(new Point(2, 5.06));  
            \_PList.Add(new Point(3, 7.03));  
            \_PList.Add(new Point(4, 9.12));  
            \_PList.Add(new Point(5, 10.96));  
            \_PList.Add(new Point(6, 12.91));  
            \_PList.Add(new Point(7, 15.01));  
   
            Line line = LinearRegressionSolve(\_PList);  
            Console.WriteLine("Y = " + line.K + " X " + "+ " + line.B);  
   
            line = LinearRegressionSolve2(\_PList);  
            Console.WriteLine("Y = " + line.K + " X " + "+ " + line.B);  
   
            line = LinearRegressionSolve3(\_PList);  
            Console.WriteLine("Y = " + line.K + " X " + "+ " + line.B);  
            Console.ReadKey();  
        \}  
   
        static Line LinearRegressionSolve(List<Point> \_plist)  
        \{  
            double k = 0, b = 0;  
            double sumX = 0, sumY = 0;  
            double avgX = 0, avgY = 0;  
            foreach (var v in \_plist)  
            \{  
                sumX += v.X;  
                sumY += v.Y;  
            \}  
            avgX = sumX / (\_plist.Count + 0.0);  
            avgY = sumY / (\_plist.Count + 0.0);  
   
            //sumA=(x-avgX)(y-avgY)的和 sumB=(x-avgX)平方  
            double sumA = 0, sumB = 0;  
            foreach (var v in \_plist)  
            \{  
                sumA += (v.X - avgX) \* (v.Y - avgY);  
                sumB += (v.X - avgX) \* (v.X - avgX);  
            \}  
            k = sumA / (sumB + 0.0);  
            b = avgY - k \* avgX;  
   
            Line line = new Line(k, b);  
            return line;  
        \}  
   
        static Line LinearRegressionSolve2(List<Point> \_plist)  
        \{  
            double k = 0, b = 0;  
            double sumX = 0, sumY = 0;  
            double sumXY = 0, sumXX = 0;  
            foreach (var v in \_plist)  
            \{  
                sumX += v.X;  
                sumY += v.Y;  
                sumXY += v.X \* v.Y;  
                sumXX += v.X \* v.X;  
            \}  
   
            k = (sumX \* sumY / (\_plist.Count + 0.0) - sumXY) / (sumX \* sumX / (\_plist.Count + 0.0) - sumXX);  
            b = (sumY - k \* sumX) / (\_plist.Count + 0.00);  
   
            Line line = new Line(k, b);  
            return line;  
        \}  
   
        static Line LinearRegressionSolve3(List<Point> \_plist)  
        \{  
            double k = 0, b = 0;  
            double sumX = 0, sumY = 0;  
            double avgX = 0, avgY = 0;  
            double sumXY = 0, sumXX = 0;  
            foreach (var v in \_plist)  
            \{  
                sumX += v.X;  
                sumY += v.Y;  
                sumXY += v.X \* v.Y;  
                sumXX += v.X \* v.X;  
            \}  
            avgX = sumX / (\_plist.Count + 0.0);  
            avgY = sumY / (\_plist.Count + 0.0);  
   
            k = (sumXY-avgY\*sumX) / (sumXX - avgX \* sumX);  
            b = avgY - k \* avgX;  
   
            Line line = new Line(k, b);  
            return line;  
        \}  
   
    \}  
   
    public class Line  
    \{  
        public double K \{ get; set; \}  
        public double B \{ get; set; \}  
   
        public Line(double k, double b)  
        \{  
            K = k;  
            B = b;  
        \}  
    \}  
   
    public class Point  
    \{  
        public double X \{ get; set; \}  
        public double Y \{ get; set; \}  
   
        public Point(double x, double y)  
        \{  
            X = x;  
            Y = y;  
        \}  
    \}  
   
\}