Python优化曲线拟合(关于现有答案)

2024-10-03 02:40:15 发布

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我想问一个关于用户对其他问题的回复的问题,但是由于某种原因,评论框没有出现。对不起,如果我做错了什么。在

无论如何,关于这个答复: https://stackoverflow.com/a/11507723/1950164

我有以下问题:如何使用此代码将不同的数据适应不同的函数?我有一个和他解决的问题相似的问题,希望我能拟合累积分布。所以我开始试着概括代码。我做了三个修改:

a)在计算直方图的行之后,我添加了

hist = numpy.cumsum(hist)

这将我们的分布转化为累积分布

b)我定义了一个新函数,而不是示例中的高斯函数

^{pr2}$

这就是高斯分布的累积值。在

c)最后,当然,我更改了曲线拟合线来调用我的函数:

coeff, var_matrix = curve_fit(myerf, bin_centres, hist, p0=p0)

这应该是一个微不足道的练习,除非它不起作用。程序现在返回以下错误消息:

bash-3.2$ python fitting.py
Traceback (most recent call last):
  File "fitting.py", line 27, in <module>
    coeff, var_matrix = curve_fit(myerf, bin_centres, hist, p0=p0)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/optimize/minpack.py", line 506, in curve_fit
    res = leastsq(func, p0, args=args, full_output=1, **kw)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/optimize/minpack.py", line 348, in leastsq
    m = _check_func('leastsq', 'func', func, x0, args, n)[0]
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/optimize/minpack.py", line 14, in _check_func
    res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/optimize/minpack.py", line 418, in _general_function
    return function(xdata, *params) - ydata
  File "fitting.py", line 22, in myerf
    return A/2. * (1+math.erf((x-mu)/(math.sqrt(2)*sigma)))
TypeError: only length-1 arrays can be converted to Python scalars

我做错什么了?在

另外:给我一个参考,它解释了函数的自变量中,*p是什么。在

谢谢!在

编辑:我试着用累积分布数据运行程序,但仍然调用高斯函数。这很管用,你只是不太适合。所以错误应该出在myerf功能的某个地方。在

编辑2:如果我试着用更简单的方法替换myerf函数的返回值,比如

return A + mu*x + sigma*x**2

那就行了。所以在回报中一定有一些东西没有做它应该做的。在

EDIT3:所以,我试着用scipy的error函数代替math中的error函数,现在可以用了。我不知道为什么它以前不起作用,但现在起作用了。所以代码是:

import matplotlib
matplotlib.use('Agg')
import numpy, math
import pylab as pl
from scipy.optimize import curve_fit
from scipy.special import erf

# Define some test data which is close to Gaussian
data = numpy.random.normal(size=10000)

hist, bin_edges = numpy.histogram(data, density=True)
bin_centres = (bin_edges[:-1] + bin_edges[1:])/2
hist = numpy.cumsum(hist)

def myerf(x, *p):
    A, mu, sigma = p
    return A/2. * (  1+erf(((x-mu)/(math.sqrt(2)*sigma)))  )

# p0 is the initial guess for the fitting coefficients (A, mu and sigma above)
p0 = [1., 0., 1.]

coeff, var_matrix = curve_fit(myerf, bin_centres, hist, p0=p0)

# Get the fitted curve
hist_fit = myerf(bin_centres, *coeff)

pl.plot(bin_centres, hist, label='Test data')
pl.plot(bin_centres, hist_fit, label='Fitted data')

# Finally, lets get the fitting parameters, i.e. the mean and standard deviation:
print 'Fitted mean = ', coeff[1]
print 'Fitted standard deviation = ', coeff[2]

pl.savefig('fitting.png')
pl.show()

Tags: 函数inpybinlinescipyhistfit
1条回答
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1楼 · 发布于 2024-10-03 02:40:15

math函数不同,numpy函数接受向量输入:

>>> import numpy, math
>>> numpy.exp([4,5])
array([  54.59815003,  148.4131591 ])
>>> math.exp([4,5])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: a float is required

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