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_dat
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.")...
(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...
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: ...
...对压缩后的字节数据进行解压缩,通过zlib.decompress()方法解压缩字节数据,再将字节数据解码为Unicode字符串 在这里我们将Python之禅进行压缩和解压缩处理 import zlib...import this def main(): python_zen = this.s # 获取Python之禅的Unicode字符串 com_bytes = zlib.compress...example06.py The Zen ...
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...
importmatplotlib.pyplotasplt##同理,绘图库fromscipy.optimizeimportcurve_fit##同理,导入拟合函数。至此学了两种导入方法。x=np.array([1,2,3,4,5,6,7,8])##定义x的numpy数组,这样可以直接整体运算y=np.array([1.1,1.9,3.1,3.8,5.12,6.2,6.89,8.1])##定义yplt.figure(figsize=(10,10))## 定义...