C#基于ScottPlot进行可视化
<h1 id="c基于scottplot进行可视化">C#基于ScottPlot进行可视化</h1><h2 id="前言">前言</h2>
<p>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。</p>
<h2 id="python代码进行可视化">Python代码进行可视化</h2>
<p>Python代码用matplotlib做了可视化，我就不具体介绍了。</p>
<p>修改之后的python代码如下：</p>
<pre><code class="language-python">#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 descent
import 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 = 0
for i in range(0, len(points)):
x = points
y = points
totalError += (y - (m * x + b)) ** 2
return totalError / float(len(points))
def step_gradient(b_current, m_current, points, learningRate):
b_gradient = 0
m_gradient = 0
N = float(len(points))
for i in range(0, len(points)):
x = points
y = points
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
def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):
b = starting_b
m = starting_m
args_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_data
if __name__ == '__main__':
points = np.genfromtxt("data.csv", delimiter=",")
learning_rate = 0.0001
initial_b = 0 # initial y-intercept guess
initial_m = 0 # initial slope guess
num_iterations = 10
print ("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]
m = args_data[-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_b
data2 = 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 * x2 + b_every)
ax.set_title(f'{frame} Graph of y = {m_every:.2f}x + {b_every:.2f}')
# 创建动画
animation = FuncAnimation(fig, update, frames=len(data2), interval=500)
# 显示动画
plt.show()
</code></pre>
<p>实现的效果如下所示：</p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/python%E7%94%BB%E5%9B%BE%E6%95%88%E6%9E%9C.gif"></p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/image-20240113200232614.png"></p>
<h2 id="c代码进行可视化">C#代码进行可视化</h2>
<p>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。</p>
<h3 id="scottplot简介">Scottplot简介</h3>
<p>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。</p>
<h3 id="控制台程序可视化">控制台程序可视化</h3>
<p>首先我先介绍一下在控制台程序中进行可视化。</p>
<p>首先添加Scottplot包：</p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/image-20240113201207374.png"></p>
<p>将上篇文章中的C#代码修改如下：</p>
<pre><code class="language-csharp">using NumSharp;
namespace LinearRegressionDemo
{
internal class Program
{
static void Main(string[] args)
{
//创建double类型的列表
List<double> Array = new List<double>();
List<double> ArgsList = new List<double>();
// 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($"Starting gradient descent at b = {initial_b}, m = {initial_m}, error = {compute_error_for_line_given_points(initial_b, initial_m, array)}");
Console.WriteLine("Running...");
ArgsList = gradient_descent_runner(array, initial_b, initial_m, learning_rate, num_iterations);
double b = ArgsList;
double m = ArgsList;
Console.WriteLine($"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.ToArray<double>(), y1.ToArray<double>(), markerSize: 5);
myPlot.AddScatter(x1.ToArray<double>(), y2.ToArray<double>(), markerSize: 0);
myPlot.Title($"y = {m:0.00}x + {b:0.00}");
myPlot.SaveFig("图片.png");
}
static List<double> ReadCsv(string filePath)
{
List<double> array = new List<double>();
try
{
// 使用File.ReadAllLines读取CSV文件的所有行
string[] lines = File.ReadAllLines(filePath);
// 遍历每一行数据
foreach (string line in lines)
{
// 使用逗号分隔符拆分每一行的数据
string[] values = line.Split(',');
// 打印每一行的数据
foreach (string value in values)
{
array.Add(Convert.ToDouble(value));
}
}
}
catch (Exception ex)
{
Console.WriteLine("发生错误: " + ex.Message);
}
return array;
}
public static double compute_error_for_line_given_points(double b,double m,NDArray array)
{
double totalError = 0;
for(int i = 0;i < array.shape;i++)
{
double x = array;
double y = array;
totalError += Math.Pow((y - (m*x+b)),2);
}
return totalError / array.shape;
}
public static double[] step_gradient(double b_current,double m_current,NDArray array,double learningRate)
{
double[] args = new double;
double b_gradient = 0;
double m_gradient = 0;
double N = array.shape;
for (int i = 0; i < array.shape; i++)
{
double x = array;
double y = array;
b_gradient += -(2 / N) * (y - ((m_current * x) + b_current));
m_gradient += -(2 / N) * x * (y - ((m_current * x) + b_current));
}
double new_b = b_current - (learningRate * b_gradient);
double new_m = m_current - (learningRate * m_gradient);
args = new_b;
args = new_m;
return args;
}
public static List<double> gradient_descent_runner(NDArray array, double starting_b, double starting_m, double learningRate,double num_iterations)
{
double[] args = new double;
List<double> argsList = new List<double>();
args = starting_b;
args = starting_m;
for(int i = 0 ; i < num_iterations; i++)
{
args = step_gradient(args, args, array, learningRate);
argsList.AddRange(args);
}
return argsList;
}
}
}
</code></pre>
<p>然后得到的图片如下所示：</p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/image-20240113202345301.png"></p>
<p>在以上代码中需要注意的地方：</p>
<pre><code class="language-csharp">var x1 = array[$":", 0];
var y1 = array[$":", 1];
</code></pre>
<p>是在使用NumSharp中的切片，x1表示所有行的第一列，y1表示所有行的第二列。</p>
<p>当然我们不满足于只是保存图片，在控制台应用程序中，再添加一个 ScottPlot.WinForms包：</p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/image-20240113202751162.png"></p>
<p>右键控制台项目选择属性，将目标OS改为Windows：</p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/image-20240113212334704.png"></p>
<p>将上述代码中的</p>
<pre><code class="language-csharp">myPlot.SaveFig("图片.png");
</code></pre>
<p>修改为：</p>
<pre><code class="language-csharp"> var viewer = new ScottPlot.FormsPlotViewer(myPlot);
viewer.ShowDialog();
</code></pre>
<p>再次运行结果如下：</p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/image-20240113203022718.png"></p>
<h3 id="winform进行可视化">winform进行可视化</h3>
<p>我也想像Python代码中那样画动图，因此做了个winform程序进行演示。</p>
<p>首先创建一个winform，添加ScottPlot.WinForms包，然后从工具箱中添加FormsPlot这个控件：</p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/image-20240113205227384.png"></p>
<p>有两种方法实现，第一种方法用了定时器：</p>
<pre><code class="language-csharp">using NumSharp;
namespace WinFormDemo
{
public partial class Form1 : Form
{
System.Windows.Forms.Timer updateTimer = new System.Windows.Forms.Timer();
int num_iterations;
int count = 0;
NDArray? x1, y1, b_each, m_each;
public Form1()
{
InitializeComponent();
}
private void button1_Click(object sender, EventArgs e)
{
StartLinearRegression();
}
public void StartLinearRegression()
{
//创建double类型的列表
List<double> Array = new List<double>();
List<double> ArgsList = new List<double>();
// 指定CSV文件的路径
string filePath = "你的data.csv路径";
// 调用ReadCsv方法读取CSV文件数据
Array = ReadCsv(filePath);
var array = np.array(Array).reshape(100, 2);
double learning_rate = 0.0001;
double initial_b = 0;
double initial_m = 0;
num_iterations = 10;
ArgsList = gradient_descent_runner(array, initial_b, initial_m, learning_rate, num_iterations);
x1 = array[$":", 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.ToArray<double>(), y1.ToArray<double>(), markerSize: 5);
//formsPlot1.Plot.AddScatter(x1.ToArray<double>(), y2.ToArray<double>(), markerSize: 0);
formsPlot1.Render();
}
static List<double> ReadCsv(string filePath)
{
List<double> array = new List<double>();
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; i++)
{
double x = array;
double y = array;
totalError += Math.Pow((y - (m * x + b)), 2);
}
return totalError / array.shape;
}
public static double[] step_gradient(double b_current, double m_current, NDArray array, double learningRate)
{
double[] args = new double;
double b_gradient = 0;
double m_gradient = 0;
double N = array.shape;
for (int i = 0; i < array.shape; i++)
{
double x = array;
double y = array;
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 = new_b;
args = new_m;
return args;
}
public static List<double> gradient_descent_runner(NDArray array, double starting_b, double starting_m, double learningRate, double num_iterations)
{
double[] args = new double;
List<double> argsList = new List<double>();
args = starting_b;
args = starting_m;
for (int i = 0; i < num_iterations; i++)
{
args = step_gradient(args, args, 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?;
double m = m_each?;
var y2 = m * x1 + b;
formsPlot1.Plot.Clear();
formsPlot1.Plot.AddScatterPoints(x1?.ToArray<double>(), y1?.ToArray<double>(), markerSize: 5);
formsPlot1.Plot.AddScatter(x1?.ToArray<double>(), y2.ToArray<double>(), 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)
{
}
}
}
</code></pre>
<p>简单介绍一下思路，首先创建<code>List<double> argsList</code>用来保存每次迭代生成的参数b、m，然后用</p>
<pre><code class="language-csharp"> var argsArr = np.array(ArgsList).reshape(num_iterations, 2);
</code></pre>
<p>将<code>argsList</code>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。</p>
<pre><code class="language-csharp"> b_each = argsArr[$":", 0];
m_each = argsArr[$":", 1];
</code></pre>
<p><code>argsArr[$":", 0]</code>表示每一行中第一列的值，也就是每一个b，<code>argsArr[$":", 1]</code>表示每一行中第二列的值。</p>
<pre><code class="language-csharp"> double b = b_each[-1];
double m = m_each[-1];
</code></pre>
<p><code>b_each[-1]</code>用了NumSharp的功能表示<code>b_each</code>最后一个元素。</p>
<p>实现效果如下所示：</p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/winform%E7%BB%98%E5%9B%BE%E6%95%88%E6%9E%9C1.gif"></p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/image-20240113205549690.png"></p>
<p>另一种方法可以通过异步实现：</p>
<pre><code class="language-csharp">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
List<double> Array = new List<double>();
List<double> ArgsList = new List<double>();
// 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.ToArray<double>(), y1.ToArray<double>(), markerSize: 5);
formsPlot1.Render();
}
static List<double> ReadCsv(string filePath)
{
List<double> array = new List<double>();
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; i++)
{
double x = array;
double y = array;
totalError += Math.Pow((y - (m * x + b)), 2);
}
return totalError / array.shape;
}
public static double[] step_gradient(double b_current, double m_current, NDArray array, double learningRate)
{
double[] args = new double;
double b_gradient = 0;
double m_gradient = 0;
double N = array.shape;
for (int i = 0; i < array.shape; i++)
{
double x = array;
double y = array;
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 = new_b;
args = new_m;
return args;
}
public static List<double> gradient_descent_runner(NDArray array, double starting_b, double starting_m, double learningRate, double num_iterations)
{
double[] args = new double;
List<double> argsList = new List<double>();
args = starting_b;
args = starting_m;
for (int i = 0; i < num_iterations; i++)
{
args = step_gradient(args, args, 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?;
double m = m_each?;
var y2 = m * x1 + b;
formsPlot1.Plot.Clear();
formsPlot1.Plot.AddScatterPoints(x1?.ToArray<double>(), y1?.ToArray<double>(), markerSize: 5);
formsPlot1.Plot.AddScatter(x1?.ToArray<double>(), y2.ToArray<double>(), markerSize: 0);
formsPlot1.Plot.Title($"第{i + 1}次迭代：y = {m:0.00}x + {b:0.00}");
formsPlot1.Render();
await Task.Delay(1000);
}
}
private async void button2_Click(object sender, EventArgs e)
{
await UpdateGraph();
}
}
}
</code></pre>
<p>点击更新按钮开始执行异步任务：</p>
<pre><code class="language-csharp"> private async void button2_Click(object sender, EventArgs e)
{
await UpdateGraph();
}
</code></pre>
<pre><code class="language-csharp"> public async Task UpdateGraph()
{
for (int i = 0; i < num_iterations; i++)
{
double b = b_each?;
double m = m_each?;
var y2 = m * x1 + b;
formsPlot1.Plot.Clear();
formsPlot1.Plot.AddScatterPoints(x1?.ToArray<double>(), y1?.ToArray<double>(), markerSize: 5);
formsPlot1.Plot.AddScatter(x1?.ToArray<double>(), y2.ToArray<double>(), markerSize: 0);
formsPlot1.Plot.Title($"第{i + 1}次迭代：y = {m:0.00}x + {b:0.00}");
formsPlot1.Render();
await Task.Delay(1000);
}
</code></pre>
<p>实现效果如下：</p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/winform%E7%BB%98%E5%9B%BE%E6%95%88%E6%9E%9C2.gif"></p>
<p><img src="https://mingupupup.oss-cn-wuhan-lr.aliyuncs.com/imgs/image-20240113210320131.png"></p>
<h2 id="总结">总结</h2>
<p>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。</p><br><br>
来源：https://www.cnblogs.com/mingupupu/p/17963079
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