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41 lines
1.1 KiB
41 lines
1.1 KiB
11 months ago
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import numpy as np
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from scipy.optimize import leastsq
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import matplotlib.pyplot as plt
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# 定义待拟合的函数形式
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def func(x, coeffs):
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return coeffs[0] * np.sin(coeffs[1] * x) + coeffs[2]
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# 定义目标函数，即待拟合数据
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def target_func(x):
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return 2.5 * np.sin(0.7 * x) + 1.2
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# 生成带噪声的待拟合数据
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np.random.seed(0)
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x_data = np.linspace(-10, 10, 100)
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y_data = target_func(x_data) + np.random.normal(0, 0.5, 100)
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# 定义误差函数，即最小二乘法的目标函数
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def residuals(coeffs, y, x):
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return y - func(x, coeffs)
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# 初始参数的估计值
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initial_coeffs = [1, 1, 1]
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# 使用最小二乘法拟合函数
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result = leastsq(residuals, initial_coeffs, args=(y_data, x_data))
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best_coeffs = result[0]
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# 打印拟合得到的系数
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print("拟合得到的系数：", best_coeffs)
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# 绘制拟合结果
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x_fit = np.linspace(-10, 10, 100)
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y_fit = func(x_fit, best_coeffs)
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plt.plot(x_data, y_data, 'bo', label='原始数据')
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plt.plot(x_fit, y_fit, 'r-', label='拟合曲线')
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plt.legend()
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plt.xlabel('x')
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plt.ylabel('y')
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plt.show()
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