如何用python拟合非线性函数？

Question

我用 R 编写了以下代码来估计三个系数（a、b 和 c）：

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y <- c(120, 125, 158, 300, 350, 390, 2800, 5900, 7790)\nt <- 1:9\nfit <-  nls(y ~ a * (((b + c)^2/b) * exp(-(b + c) * t))/(1 + (c/b) *\n      exp(-(b + c) * t))^2, start = list(a = 17933, b = 0.01, c = 0.31))\n

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我得到这个结果

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> summary(fit )\n\nFormula: y ~ a * (((b + c)^2/b) * exp(-(b + c) * t))/(1 + (c/b) * exp(-(b + \n    c) * t))^2\n\nParameters:\n   Estimate Std. Error t value Pr(>|t|)    \na 2.501e+04  2.031e+03  12.312 1.75e-05 ***\nb 1.891e-05  1.383e-05   1.367    0.221    \nc 1.254e+00  1.052e-01  11.924 2.11e-05 ***\n---\nSignif. codes:  0 \xe2\x80\x98***\xe2\x80\x99 0.001 \xe2\x80\x98**\xe2\x80\x99 0.01 \xe2\x80\x98*\xe2\x80\x99 0.05 \xe2\x80\x98.\xe2\x80\x99 0.1 \xe2\x80\x98 \xe2\x80\x99 1\n\nResidual standard error: 248.8 on 6 degrees of freedom\n\nNumber of iterations to convergence: 33 \nAchieved convergence tolerance: 6.836e-06\n

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如何用Python做出同样的事情？

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Answer 1

您可以使用curve_fit，它会给出相同的结果：

import scipy.optimize as optimization
import numpy as np

y = np.array([120, 125, 158, 300, 350, 390, 2800, 5900, 7790])
t = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
start = np.array([17933, 0.01, 0.31])

def f(t,a,b,c):
    num = a*(np.exp(-t*(b+c))*np.power(b+c, 2)/b)
    denom = np.power(1+(c/b)*np.exp(-t*(b+c)), 2)
    return num/denom

print(optimization.curve_fit(f, t, y, start))
#(array([  2.50111448e+04,   1.89129922e-05,   1.25426156e+00]), array([[ 4.12657233e+06,   2.58151776e-02,  -2.00881091e+02],
#       [  2.58151776e-02,   1.91318685e-10,  -1.44733425e-06],
#       [ -2.00881091e+02,  -1.44733425e-06,   1.10654268e-02]]))