46 lines
1.1 KiB
Python
46 lines
1.1 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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# 自定义函数 e指数形式
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def func(x, a, b, c):
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return a * np.sqrt(x) * (b * np.square(x) + c)
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# 定义x、y散点坐标
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x = [20, 30, 40, 50, 60, 70]
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x = np.array(x)
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num = [453, 482, 503, 508, 498, 479]
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y = np.array(num)
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def get_curve_fit(x, y):
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# 非线性最小二乘法拟合
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popt, pcov = curve_fit(func, x, y)
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# 获取popt里面是拟合系数
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# print(popt)
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a = popt[0]
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b = popt[1]
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c = popt[2]
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yvals = func(x, a, b, c) # 拟合y值
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# print('popt:', popt)
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# print('系数a:', a)
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# print('系数b:', b)
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# print('系数c:', c)
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# print('系数pcov:', pcov)
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# print('系数yvals:', yvals)
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return yvals
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yvals = get_curve_fit(x, y)
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print(yvals)
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# 绘图
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plot1 = plt.plot(x, y, 's', label='original values')
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plot2 = plt.plot(x, yvals, 'r', label='polyfit values')
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plt.xlabel('x')
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plt.ylabel('y')
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plt.legend(loc=4) # 指定legend的位置右下角
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plt.title('curve_fit')
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plt.show()
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