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上一篇文章跟大家分享了用NumSharp实现简单的线性回归#xff0c;但是没有进行可视化#xff0c;可能对拟合的过程没有直观的感受#xff0c;因此今天跟大家介绍一下使用C#基于Scottplot进行可视化#xff0c;当然Python的代码#xff0c;我也会同步进行可视化。
P…前言
上一篇文章跟大家分享了用NumSharp实现简单的线性回归但是没有进行可视化可能对拟合的过程没有直观的感受因此今天跟大家介绍一下使用C#基于Scottplot进行可视化当然Python的代码我也会同步进行可视化。
Python代码进行可视化
Python代码用matplotlib做了可视化我就不具体介绍了。
修改之后的python代码如下
#The optimal values of m and b can be actually calculated with way less effort than doing a linear regression.
#this is just to demonstrate gradient descentimport numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation# y mx b
# m is slope, b is y-intercept
def compute_error_for_line_given_points(b, m, points):totalError 0for i in range(0, len(points)):x points[i, 0]y points[i, 1]totalError (y - (m * x b)) ** 2return totalError / float(len(points))def step_gradient(b_current, m_current, points, learningRate):b_gradient 0m_gradient 0N float(len(points))for i in range(0, len(points)):x points[i, 0]y points[i, 1]b_gradient -(2/N) * (y - ((m_current * x) b_current))m_gradient -(2/N) * x * (y - ((m_current * x) b_current))new_b b_current - (learningRate * b_gradient)new_m m_current - (learningRate * m_gradient)return [new_b, new_m]def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):b starting_bm starting_margs_data []for i in range(num_iterations):b, m step_gradient(b, m, np.array(points), learning_rate)args_data.append((b,m))return args_dataif __name__ __main__:points np.genfromtxt(data.csv, delimiter,)learning_rate 0.0001initial_b 0 # initial y-intercept guessinitial_m 0 # initial slope guessnum_iterations 10print (Starting gradient descent at b {0}, m {1}, error {2}.format(initial_b, initial_m, compute_error_for_line_given_points(initial_b, initial_m, points)))print (Running...)args_data gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)b args_data[-1][0]m args_data[-1][1]print (After {0} iterations b {1}, m {2}, error {3}.format(num_iterations, b, m, compute_error_for_line_given_points(b, m, points)))data np.array(points).reshape(100,2)x1 data[:,0]y1 data[:,1]x2 np.linspace(20, 80, 100)y2 initial_m * x2 initial_bdata2 np.array(args_data)b_every data2[:,0]m_every data2[:,1]# 创建图形和轴fig, ax plt.subplots()line1, ax.plot(x1, y1, ro)line2, ax.plot(x2,y2)# 添加标签和标题plt.xlabel(x)plt.ylabel(y)plt.title(Graph of y mx b)# 添加网格plt.grid(True)# 定义更新函数def update(frame):line2.set_ydata(m_every[frame] * x2 b_every[frame])ax.set_title(f{frame} Graph of y {m_every[frame]:.2f}x {b_every[frame]:.2f})# 创建动画
animation FuncAnimation(fig, update, frameslen(data2), interval500)# 显示动画
plt.show()
实现的效果如下所示 C#代码进行可视化
这是本文重点介绍的内容本文的C#代码通过Scottplot进行可视化。
Scottplot简介
ScottPlot 是一个免费的开源绘图库用于 .NET可以轻松以交互方式显示大型数据集。
控制台程序可视化
首先我先介绍一下在控制台程序中进行可视化。
首先添加Scottplot包 将上篇文章中的C#代码修改如下
using NumSharp;namespace LinearRegressionDemo
{internal class Program{ static void Main(string[] args){ //创建double类型的列表Listdouble Array new Listdouble();Listdouble ArgsList new Listdouble();// 指定CSV文件的路径string filePath 你的data.csv路径;// 调用ReadCsv方法读取CSV文件数据Array ReadCsv(filePath);var array np.array(Array).reshape(100,2);double learning_rate 0.0001;double initial_b 0;double initial_m 0;double num_iterations 10;Console.WriteLine($Starting gradient descent at b {initial_b}, m {initial_m}, error {compute_error_for_line_given_points(initial_b, initial_m, array)});Console.WriteLine(Running...);ArgsList gradient_descent_runner(array, initial_b, initial_m, learning_rate, num_iterations);double b ArgsList[ArgsList.Count - 2];double m ArgsList[ArgsList.Count - 1];Console.WriteLine($After {num_iterations} iterations b {b}, m {m}, error {compute_error_for_line_given_points(b, m, array)});Console.ReadLine();var x1 array[$:, 0];var y1 array[$:, 1];var y2 m * x1 b;ScottPlot.Plot myPlot new(400, 300);myPlot.AddScatterPoints(x1.ToArraydouble(), y1.ToArraydouble(), markerSize: 5);myPlot.AddScatter(x1.ToArraydouble(), y2.ToArraydouble(), markerSize: 0);myPlot.Title($y {m:0.00}x {b:0.00});myPlot.SaveFig(图片.png);}static Listdouble ReadCsv(string filePath){Listdouble array new Listdouble();try{// 使用File.ReadAllLines读取CSV文件的所有行string[] lines File.ReadAllLines(filePath); // 遍历每一行数据foreach (string line in lines){// 使用逗号分隔符拆分每一行的数据string[] values line.Split(,);// 打印每一行的数据foreach (string value in values){array.Add(Convert.ToDouble(value));} }}catch (Exception ex){Console.WriteLine(发生错误: ex.Message);}return array;}public static double compute_error_for_line_given_points(double b,double m,NDArray array){double totalError 0;for(int i 0;i array.shape[0];i){double x array[i, 0];double y array[i, 1];totalError Math.Pow((y - (m*xb)),2);}return totalError / array.shape[0];}public static double[] step_gradient(double b_current,double m_current,NDArray array,double learningRate){double[] args new double[2];double b_gradient 0;double m_gradient 0;double N array.shape[0];for (int i 0; i array.shape[0]; i){double x array[i, 0];double y array[i, 1];b_gradient -(2 / N) * (y - ((m_current * x) b_current));m_gradient -(2 / N) * x * (y - ((m_current * x) b_current));}double new_b b_current - (learningRate * b_gradient);double new_m m_current - (learningRate * m_gradient);args[0] new_b;args[1] new_m;return args;}public static Listdouble gradient_descent_runner(NDArray array, double starting_b, double starting_m, double learningRate,double num_iterations){double[] args new double[2];Listdouble argsList new Listdouble();args[0] starting_b;args[1] starting_m;for(int i 0 ; i num_iterations; i) {args step_gradient(args[0], args[1], array, learningRate);argsList.AddRange(args);}return argsList;}}
}
然后得到的图片如下所示 在以上代码中需要注意的地方 var x1 array[$:, 0];var y1 array[$:, 1];是在使用NumSharp中的切片x1表示所有行的第一列y1表示所有行的第二列。
当然我们不满足于只是保存图片在控制台应用程序中再添加一个 ScottPlot.WinForms包 右键控制台项目选择属性将目标OS改为Windows 将上述代码中的 myPlot.SaveFig(图片.png);修改为 var viewer new ScottPlot.FormsPlotViewer(myPlot);viewer.ShowDialog();再次运行结果如下 winform进行可视化
我也想像Python代码中那样画动图因此做了个winform程序进行演示。
首先创建一个winform添加ScottPlot.WinForms包然后从工具箱中添加FormsPlot这个控件 有两种方法实现第一种方法用了定时器
using NumSharp;
namespace WinFormDemo
{public partial class Form1 : Form{System.Windows.Forms.Timer updateTimer new System.Windows.Forms.Timer();int num_iterations;int count 0;NDArray? x1, y1, b_each, m_each;public Form1(){InitializeComponent();}private void button1_Click(object sender, EventArgs e){StartLinearRegression();}public void StartLinearRegression(){//创建double类型的列表Listdouble Array new Listdouble();Listdouble ArgsList new Listdouble();// 指定CSV文件的路径string filePath 你的data.csv路径;// 调用ReadCsv方法读取CSV文件数据Array ReadCsv(filePath);var array np.array(Array).reshape(100, 2);double learning_rate 0.0001;double initial_b 0;double initial_m 0;num_iterations 10;ArgsList gradient_descent_runner(array, initial_b, initial_m, learning_rate, num_iterations);x1 array[$:, 0];y1 array[$:, 1];var argsArr np.array(ArgsList).reshape(num_iterations, 2);b_each argsArr[$:, 0];m_each argsArr[$:, 1];double b b_each[-1];double m m_each[-1];var y2 m * x1 b;formsPlot1.Plot.AddScatterPoints(x1.ToArraydouble(), y1.ToArraydouble(), markerSize: 5);//formsPlot1.Plot.AddScatter(x1.ToArraydouble(), y2.ToArraydouble(), markerSize: 0);formsPlot1.Render();}static Listdouble ReadCsv(string filePath){Listdouble array new Listdouble();try{// 使用File.ReadAllLines读取CSV文件的所有行string[] lines File.ReadAllLines(filePath);// 遍历每一行数据foreach (string line in lines){// 使用逗号分隔符拆分每一行的数据string[] values line.Split(,);// 打印每一行的数据foreach (string value in values){array.Add(Convert.ToDouble(value));}}}catch (Exception ex){Console.WriteLine(发生错误: ex.Message);}return array;}public static double compute_error_for_line_given_points(double b, double m, NDArray array){double totalError 0;for (int i 0; i array.shape[0]; i){double x array[i, 0];double y array[i, 1];totalError Math.Pow((y - (m * x b)), 2);}return totalError / array.shape[0];}public static double[] step_gradient(double b_current, double m_current, NDArray array, double learningRate){double[] args new double[2];double b_gradient 0;double m_gradient 0;double N array.shape[0];for (int i 0; i array.shape[0]; i){double x array[i, 0];double y array[i, 1];b_gradient -(2 / N) * (y - ((m_current * x) b_current));m_gradient -(2 / N) * x * (y - ((m_current * x) b_current));}double new_b b_current - (learningRate * b_gradient);double new_m m_current - (learningRate * m_gradient);args[0] new_b;args[1] new_m;return args;}public static Listdouble gradient_descent_runner(NDArray array, double starting_b, double starting_m, double learningRate, double num_iterations){double[] args new double[2];Listdouble argsList new Listdouble();args[0] starting_b;args[1] starting_m;for (int i 0; i num_iterations; i){args step_gradient(args[0], args[1], array, learningRate);argsList.AddRange(args);}return argsList;}private void button2_Click(object sender, EventArgs e){// 初始化定时器updateTimer.Interval 1000; // 设置定时器触发间隔毫秒updateTimer.Tick UpdateTimer_Tick;updateTimer.Start();}private void UpdateTimer_Tick(object? sender, EventArgs e){if (count num_iterations){updateTimer.Stop();}else{UpdatePlot(count);}count;}public void UpdatePlot(int count){double b b_each?[count];double m m_each?[count];var y2 m * x1 b;formsPlot1.Plot.Clear();formsPlot1.Plot.AddScatterPoints(x1?.ToArraydouble(), y1?.ToArraydouble(), markerSize: 5);formsPlot1.Plot.AddScatter(x1?.ToArraydouble(), y2.ToArraydouble(), markerSize: 0);formsPlot1.Plot.Title($第{count 1}次迭代y {m:0.00}x {b:0.00});formsPlot1.Render();}private void button3_Click(object sender, EventArgs e){updateTimer.Stop();}private void Form1_Load(object sender, EventArgs e){}}
}
简单介绍一下思路首先创建Listdouble argsList用来保存每次迭代生成的参数b、m然后用 var argsArr np.array(ArgsList).reshape(num_iterations, 2); 将argsList通过np.array()方法转化为NDArray然后再调用reshape方法转化成行数等于迭代次数列数为2即每一行对应一组参数值b、m。 b_each argsArr[$:, 0];m_each argsArr[$:, 1];argsArr[$:, 0]表示每一行中第一列的值也就是每一个bargsArr[$:, 1]表示每一行中第二列的值。 double b b_each[-1];double m m_each[-1];b_each[-1]用了NumSharp的功能表示b_each最后一个元素。
实现效果如下所示 另一种方法可以通过异步实现
using NumSharp;namespace WinFormDemo
{public partial class Form2 : Form{ int num_iterations;NDArray? x1, y1, b_each, m_each;public Form2(){InitializeComponent();}private void button1_Click(object sender, EventArgs e){StartLinearRegression();}public void StartLinearRegression(){//创建double类型的列表Listdouble Array new Listdouble();Listdouble ArgsList new Listdouble();// 指定CSV文件的路径string filePath 你的data.csv路径;// 调用ReadCsv方法读取CSV文件数据Array ReadCsv(filePath);var array np.array(Array).reshape(100, 2);double learning_rate 0.0001;double initial_b 0;double initial_m 0;num_iterations 10;ArgsList gradient_descent_runner(array, initial_b, initial_m, learning_rate, num_iterations);x1 array[$:, 0];y1 array[$:, 1];var argsArr np.array(ArgsList).reshape(num_iterations, 2);b_each argsArr[$:, 0];m_each argsArr[$:, 1];double b b_each[-1];double m m_each[-1];var y2 m * x1 b;formsPlot1.Plot.AddScatterPoints(x1.ToArraydouble(), y1.ToArraydouble(), markerSize: 5); formsPlot1.Render();}static Listdouble ReadCsv(string filePath){Listdouble array new Listdouble();try{// 使用File.ReadAllLines读取CSV文件的所有行string[] lines File.ReadAllLines(filePath);// 遍历每一行数据foreach (string line in lines){// 使用逗号分隔符拆分每一行的数据string[] values line.Split(,);// 打印每一行的数据foreach (string value in values){array.Add(Convert.ToDouble(value));}}}catch (Exception ex){Console.WriteLine(发生错误: ex.Message);}return array;}public static double compute_error_for_line_given_points(double b, double m, NDArray array){double totalError 0;for (int i 0; i array.shape[0]; i){double x array[i, 0];double y array[i, 1];totalError Math.Pow((y - (m * x b)), 2);}return totalError / array.shape[0];}public static double[] step_gradient(double b_current, double m_current, NDArray array, double learningRate){double[] args new double[2];double b_gradient 0;double m_gradient 0;double N array.shape[0];for (int i 0; i array.shape[0]; i){double x array[i, 0];double y array[i, 1];b_gradient -(2 / N) * (y - ((m_current * x) b_current));m_gradient -(2 / N) * x * (y - ((m_current * x) b_current));}double new_b b_current - (learningRate * b_gradient);double new_m m_current - (learningRate * m_gradient);args[0] new_b;args[1] new_m;return args;}public static Listdouble gradient_descent_runner(NDArray array, double starting_b, double starting_m, double learningRate, double num_iterations){double[] args new double[2];Listdouble argsList new Listdouble();args[0] starting_b;args[1] starting_m;for (int i 0; i num_iterations; i){args step_gradient(args[0], args[1], array, learningRate);argsList.AddRange(args);}return argsList;}private void Form2_Load(object sender, EventArgs e){}public async Task UpdateGraph(){for (int i 0; i num_iterations; i){double b b_each?[i];double m m_each?[i];var y2 m * x1 b;formsPlot1.Plot.Clear();formsPlot1.Plot.AddScatterPoints(x1?.ToArraydouble(), y1?.ToArraydouble(), markerSize: 5);formsPlot1.Plot.AddScatter(x1?.ToArraydouble(), y2.ToArraydouble(), markerSize: 0);formsPlot1.Plot.Title($第{i 1}次迭代y {m:0.00}x {b:0.00});formsPlot1.Render();await Task.Delay(1000);}}private async void button2_Click(object sender, EventArgs e){await UpdateGraph();}}
}
点击更新按钮开始执行异步任务 private async void button2_Click(object sender, EventArgs e){await UpdateGraph();}public async Task UpdateGraph(){for (int i 0; i num_iterations; i){double b b_each?[i];double m m_each?[i];var y2 m * x1 b;formsPlot1.Plot.Clear();formsPlot1.Plot.AddScatterPoints(x1?.ToArraydouble(), y1?.ToArraydouble(), markerSize: 5);formsPlot1.Plot.AddScatter(x1?.ToArraydouble(), y2.ToArraydouble(), markerSize: 0);formsPlot1.Plot.Title($第{i 1}次迭代y {m:0.00}x {b:0.00});formsPlot1.Render();await Task.Delay(1000);}实现效果如下 总结
本文以一个控制台应用与一个winform程序为例向大家介绍了C#如何基于ScottPlot进行数据可视化并介绍了实现动态绘图的两种方式一种是使用定时器另一种是使用异步操作希望对你有所帮助。
