6

我有实验数据：

xdata = [85,86,87,88,89,90,91,91.75,93,96,100,101,102,103,104,105,106,107.25,108.25,109,109.75,111,112,112.75,114,115.25,116,116.75,118,119.25,120,121,122,122.5,123.5,125.25,126,126.75,127.75,129.25,130.25,131,131.75,133,134.25,135,136,137,138,139,140,141,142,143,144,144.75,146,146.75,148,149.25,150,150.5,152,153.25,154,155,156.75,158,159,159.75,161,162,162.5,164,165,166]

ydata = [0.2,0.21,0.18,0.21,0.19,0.2,0.21,0.2,0.18,0.204,0.208,0.2,0.21,0.25,0.2,0.19,0.216,0.22,0.224,0.26,0.229,0.237,0.22,0.246,0.25,0.264,0.29,0.274,0.29,0.3,0.27,0.32,0.38,0.348,0.372,0.398,0.35,0.42,0.444,0.48,0.496,0.55,0.51,0.54,0.57,0.51,0.605,0.57,0.65,0.642,0.6,0.66,0.7,0.688,0.69,0.705,0.67,0.717,0.69,0.728,0.75,0.736,0.73,0.744,0.72,0.76,0.752,0.74,0.76,0.7546,0.77,0.74,0.758,0.74,0.78,0.76]

和公式f(x) = m1 + m2 / (1 + e ^ (-m3*(x - m4)))。我需要m1, m2, m3, m4用最小二乘法找到，其中 0.05 < m1 < 0.3 0.3 < m2 < 0.8 0.05 < m3 < 0.5 100 < m4 < 200。

我使用curve_fit，我的功能是：

def f(xdata, m1, m2, m3, m4):
    if m1 > 0.05 and m1 < 0.3 and \
       m2 > 0.3 and m2 < 0.8 and \
       m3 > 0.05 and m3 < 0.5 and \
       m4 > 100 and m4 < 200:
          return m1 + (m2 * 1. / (1 + e ** (-m3 * (x - m4))))
    return (abs(m1) + abs(m2) + abs(m3) + abs(m4)) * 1e14 # some large number

但是程序返回错误：RuntimeError: Optimal parameters not found: Number of calls to function has reached maxfev = 1000.

该怎么办？

import numpy as np
from scipy.optimize import curve_fit
from math import e

xdata = np.array([85,86,87,88,89,90,91,91.75,93,96,100,101,102,103,104,105,106,107.25,108.25,109,109.75,111,112,112.75,114,115.25,116,116.75,118,119.25,120,121,122,122.5,123.5,125.25,126,126.75,127.75,129.25,130.25,131,131.75,133,134.25,135,136,137,138,139,140,141,142,143,144,144.75,146,146.75,148,149.25,150,150.5,152,153.25,154,155,156.75,158,159,159.75,161,162,162.5,164,165,166])`
ydata = np.array([0.2,0.21,0.18,0.21,0.19,0.2,0.21,0.2,0.18,0.204,0.208,0.2,0.21,0.25,0.2,0.19,0.216,0.22,0.224,0.26,0.229,0.237,0.22,0.246,0.25,0.264,0.29,0.274,0.29,0.3,0.27,0.32,0.38,0.348,0.372,0.398,0.35,0.42,0.444,0.48,0.496,0.55,0.51,0.54,0.57,0.51,0.605,0.57,0.65,0.642,0.6,0.66,0.7,0.688,0.69,0.705,0.67,0.717,0.69,0.728,0.75,0.736,0.73,0.744,0.72,0.76,0.752,0.74,0.76,0.7546,0.77,0.74,0.758,0.74,0.78,0.76])

def f(xdata, m1, m2, m3, m4):
    if m1 > 0.05 and m1 < 0.3 and \
       m2 > 0.3 and m2 < 0.8 and \
       m3 > 0.05 and m3 < 0.5 and \
       m4 > 100 and m4 < 200:
        return m1 + (m2 * 1. / (1 + e ** (-m3 * (x - m4))))
    return (abs(m1) + abs(m2) + abs(m3) + abs(m4)) * 1e14

print curve_fit(f, xdata, ydata)
4

2 回答 2

4

将初始参数设置为有用的值：

curve_fit(f, xdata, ydata, p0=(0.1, 0.5, 0.1, 150)))

另外，在你的函数中使用xdata而不是：xf

return m1 + (m2 * 1. / (1 + e ** (-m3 * (xdata - m4))))

这是我修改后的程序：

def f(xdata, m1, m2, m3, m4):
    if (0.05 < m1 < 0.3 and
        0.3 < m2 < 0.8 and
        0.05 < m3 < 0.5 and
        100 < m4 < 200):
        return m1 + (m2 * 1. / (1 + e ** (-m3 * (xdata - m4))))
    return 1e38

print(curve_fit(f, xdata, ydata, p0=(0.1, 0.5, 0.1, 150)))

结果：

(array([   0.19567035,    0.56792559,    0.13434829,  129.98915877]), 
 array([[  2.94622909e-05,  -3.96126279e-05,   1.99236054e-05,
          7.48438125e-04],
       [ -3.96126279e-05,   9.24145662e-05,  -4.62302643e-05,
          5.04671621e-04],
       [  1.99236054e-05,  -4.62302643e-05,   3.77364832e-05,
         -2.43866126e-04],
       [  7.48438125e-04,   5.04671621e-04,  -2.43866126e-04,
          1.34700612e-01]]))
于 2015-11-11T13:10:03.243 回答
3

或者，您也可以使用lmfit，它可以让您轻松设置边界并避免if函数中的“丑陋”语句。您获取的参数如下：

m1:   0.19567033 +/- 0.005427 (2.77%) (init= 0.1)
m2:   0.56792558 +/- 0.009613 (1.69%) (init= 0.5)
m3:   0.13434829 +/- 0.006143 (4.57%) (init= 0.2)
m4:   129.989156 +/- 0.367009 (0.28%) (init= 150)

您获得的输出如下所示：

在此处输入图像描述

这是带有几条注释的完整代码；如果您还有其他问题，请告诉我：

from lmfit import minimize, Parameters, Parameter, report_fit
import numpy as np

xdata = np.array([85,86,87,88,89,90,91,91.75,93,96,100,101,102,103,104,105,106,107.25,108.25,109,109.75,111,112,112.75,114,115.25,116,116.75,118,119.25,120,121,122,122.5,123.5,125.25,126,126.75,127.75,129.25,130.25,131,131.75,133,134.25,135,136,137,138,139,140,141,142,143,144,144.75,146,146.75,148,149.25,150,150.5,152,153.25,154,155,156.75,158,159,159.75,161,162,162.5,164,165,166])
ydata = np.array([0.2,0.21,0.18,0.21,0.19,0.2,0.21,0.2,0.18,0.204,0.208,0.2,0.21,0.25,0.2,0.19,0.216,0.22,0.224,0.26,0.229,0.237,0.22,0.246,0.25,0.264,0.29,0.274,0.29,0.3,0.27,0.32,0.38,0.348,0.372,0.398,0.35,0.42,0.444,0.48,0.496,0.55,0.51,0.54,0.57,0.51,0.605,0.57,0.65,0.642,0.6,0.66,0.7,0.688,0.69,0.705,0.67,0.717,0.69,0.728,0.75,0.736,0.73,0.744,0.72,0.76,0.752,0.74,0.76,0.7546,0.77,0.74,0.758,0.74,0.78,0.76])

def fit_fc(params, x, data):  
    m1 = params['m1'].value
    m2 = params['m2'].value
    m3 = params['m3'].value
    m4 = params['m4'].value

    model = m1 + (m2 * 1. / (1 + np.exp(-m3 * (x - m4))))
    return model - data #that's what you want to minimize

# create a set of Parameters
# 'value' is the initial condition
# 'min' and 'max' define your boundaries
params = Parameters()
params.add('m1', value= 0.1, min=0.05, max=0.3) 
params.add('m2', value= 0.5, min=0.3, max=0.8)
params.add('m3', value= 0.2, min=0.05, max=0.5) 
params.add('m4', value= 150.0, min=100, max=200) 

# do fit, here with leastsq model
result = minimize(fit_fc, params, args=(xdata, ydata))

# calculate final result
final = ydata + result.residual

# write error report
report_fit(params)

#plot results
try:
    import pylab
    pylab.plot(xdata, ydata, 'k+')
    pylab.plot(xdata, final, 'r')
    pylab.show()
except:
    pass
于 2015-11-12T10:03:22.923 回答