用C#实现最小二乘法（用OxyPlot绘图）✨ - ToB企服应用市场:ToB评测及商务社交产业平台

NDArray? x, y;

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//使用NumSharp创建线性回归的数据集 x = np.arange(0, 10, 0.2); y = 2 * x + 3 + np.random.normal(0, 3, x.size);

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x = np.arange(0, 10, 0.2);

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np.random.normal(0, 3, x.size);

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// 初始化散点图数据 var scatterSeries = new ScatterSeries { MarkerType = MarkerType.Circle, MarkerSize = 5, MarkerFill = OxyColors.Blue };

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for (int i = 0; i < x.size; i++) { scatterSeries.Points.Add(new ScatterPoint(x[i], y[i])); }

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PlotModel? plotModel;

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// 创建 PlotModel plotModel = new PlotModel() { Title = "散点图" }; plotModel.Series.Add(scatterSeries); // 将 PlotModel 设置到 PlotView plotView1.Model = plotModel;

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double a = 0; double c = 0; double x_mean = x?.mean(); double y_mean = y?.mean(); //计算a和c for(int i = 0; i < x?.size; i++) { a += (x[i] - x_mean) * (y?[i] - y_mean); c += (x[i] - x_mean) * (x[i] - x_mean); } //计算斜率和截距 double m = a / c; double b = y_mean - m * x_mean; //拟合的直线 var y2 = m * x + b;

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double x_mean = x?.mean(); double y_mean = y?.mean();

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//画这条直线 var lineSeries = new LineSeries { Points = { new DataPoint(x?[0], y2[0]), new DataPoint(x?[-1], y2[-1]) }, Color = OxyColors.Red }; // 创建 PlotModel plotModel?.Series.Add(lineSeries); // 为图表添加标题 if (plotModel != null) { plotModel.Title = $"拟合的直线 y = {m:0.00}x + {b:0.00}"; } // 刷新 PlotView plotView1.InvalidatePlot(true);

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Points = { new DataPoint(x?[0], y2[0]), new DataPoint(x?[-1], y2[-1]) },

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using NumSharp; using OxyPlot.Series; using OxyPlot; namespace OlsRegressionDemoUsingWinform { public partial class Form1 : Form { NDArray? x, y; PlotModel? plotModel; public Form1() { InitializeComponent(); } private void button1_Click(object sender, EventArgs e) { //使用NumSharp创建线性回归的数据集 x = np.arange(0, 10, 0.2); y = 2 * x + 3 + np.random.normal(0, 3, x.size); // 初始化散点图数据 var scatterSeries = new ScatterSeries { MarkerType = MarkerType.Circle, MarkerSize = 5, MarkerFill = OxyColors.Blue }; for (int i = 0; i < x.size; i++) { scatterSeries.Points.Add(new ScatterPoint(x[i], y[i])); } // 创建 PlotModel plotModel = new PlotModel() { Title = "散点图" }; plotModel.Series.Add(scatterSeries); // 将 PlotModel 设置到 PlotView plotView1.Model = plotModel; } private void button2_Click(object sender, EventArgs e) { double a = 0; double c = 0; double x_mean = x?.mean(); double y_mean = y?.mean(); //计算a和c for(int i = 0; i < x?.size; i++) { a += (x[i] - x_mean) * (y?[i] - y_mean); c += (x[i] - x_mean) * (x[i] - x_mean); } //计算斜率和截距 double m = a / c; double b = y_mean - m * x_mean; //拟合的直线 var y2 = m * x + b; //画这条直线 var lineSeries = new LineSeries { Points = { new DataPoint(x?[0], y2[0]), new DataPoint(x?[-1], y2[-1]) }, Color = OxyColors.Red }; // 创建 PlotModel plotModel?.Series.Add(lineSeries); // 为图表添加标题 if (plotModel != null) { plotModel.Title = $"拟合的直线 y = {m:0.00}x + {b:0.00}"; } // 刷新 PlotView plotView1.InvalidatePlot(true); } } }

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import numpy as np import matplotlib.pyplot as plt # 用最小二乘法拟合 y = mx + b # 设置随机数种子以保证结果的可复现性 np.random.seed(0) # 生成一个在[0, 10]区间内均匀分布的100个数作为x x = np.linspace(0, 10, 100) # 生成y，y = 2x + 噪声，其中噪声是[0, 10)之间的随机整数 y = 2 * x + 5 + np.random.randint(0, 10, size=100) # 计算x和y的均值 x_mean = np.mean(x) y_mean = np.mean(y) a = 0 c = 0 for i in range(x.shape[0]): a += (x[i] - x_mean) * (y[i] - y_mean) c += (x[i] - x_mean) ** 2 # 计算斜率和截距 m = a / c b = y_mean - m * x_mean # 画这条直线 y2 = m * x + b plt.plot(x, y2, color='red') # 画数据点 plt.scatter(x, y) plt.xlabel('x') plt.ylabel('y') plt.title(f'y = {m:.2f}x + {b:.2f}') plt.show()

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