示例代码 以下是一个使用curve_fit进行二次曲线拟合的示例: importnumpyasnpimportmatplotlib.pyplotaspltfromscipy.optimizeimportcurve_fit# 定义模型函数defquadratic(x,a,b,c):returna*x**2+b*x+c# 生成模拟数据x_data=np.linspace(-10,10,100)y_data=quadratic(x_data,2,3,4)+np.random.normal(0,10...
plt.figure() plt.plot(control_points[:,0],control_points[:,1],'ro--',label='Control Points') plt.plot(curve_points[:,0],curve_points[:,1],'b-',label='Bezier Curve') plt.xlabel('X') plt.ylabel('Y') plt.title('Bezier Curve Fitting Example') plt.legend() plt.show() ``` ...
plt.figure() plt.plot(control_points[:,0],control_points[:,1],'ro--',label='Control Points') plt.plot(curve_points[:,0],curve_points[:,1],'b-',label='Bezier Curve') plt.xlabel('X') plt.ylabel('Y') plt.title('Bezier Curve Fitting Example') plt.legend() plt.show() ``` ...
weight)plt.plot(x,y,'r')plt.xlabel('Height')plt.ylabel('Weight')plt.title('Curve Fitting Example')plt.show()# 预测身高为185cm的体重predicted_weight=func(185,popt[0],popt[1])print(f"The predicted weight for a height of 185cm is{predicted_weight}kg.")...
(x_data), 100) y_fit = fit_func(x_fit, *params) # 绘制实际数据点和拟合曲线 plt.scatter(x_data, y_data, label='Data') plt.plot(x_fit, y_fit, color='red', label='Fitted curve') plt.legend() plt.xlabel('x') plt.ylabel('y') plt.title('Curve Fitting Example') plt.show(...
(b*x)# 生成模拟数据x=np.linspace(0,4,50)y=model_func(x,2.5,-1.3)+np.random.normal(size=x.size)# 拟合popt,pcov=curve_fit(model_func,x,y)# 绘图plt.scatter(x,y)plt.plot(x,model_func(x,*popt),'r-')plt.title('Curve Fitting Example')plt.xlabel('x')plt.ylabel('y')plt.show...
import numpy as np from scipy.optimize import curve_fit import matplotlib.pyplot as plt # 定义要拟合的函数 def func(x, a, b, c):return a * np.exp(b * x) + c # 生成一些模拟数据 x_data = np.array([1, 2, 3, 4, 5])y_data = np.array([2.1, 3.9, 8.1, 17.5, 36.8...
Running the example performs curve fitting and finds the optimal parameters to our objective function.First, the values of the parameters are reported.1 y = 0.48488 * x + 8.38067 Next, a plot is created showing the original data and the line that was fit to the data....
is a bit different than optimize.leastsq. Once you have the function y ou want to fit, you create a model object based on that function, a data object based on the data you want to fit, and then an odr object that does the fitting and gives the results. Here's a simple example: ...
Scipy Optimize Curve Fit example 看看上面的输出,以及生成的数据是什么样子的。 创建一个新函数*sin_func*并将此函数传递给方法curve_fit( )到*sin_func*,使用下面的代码生成数据。 # crating the sin function and fitting this to gnerated data using #curve_fit method def sin_func(X, a, b): retur...