在python中拟合多元curve_fit
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fitting multivariate curve_fit in python
提问by user3133865
I'm trying to fit a simple function to two arrays of independent data in python. I understand that I need to bunch the data for my independent variables into one array, but something still seems to be wrong with the way I'm passing variables when I try to do the fit. (There are a couple previous posts related to this one, but they haven't been much help.)
我正在尝试将一个简单的函数拟合到 python 中的两个独立数据数组。我知道我需要将自变量的数据集中到一个数组中，但是当我尝试进行拟合时，我传递变量的方式似乎仍然有问题。（之前有几篇与此相关的帖子，但它们并没有太大帮助。）
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def fitFunc(x_3d, a, b, c, d):
return a + b*x_3d[0,:] + c*x_3d[1,:] + d*x_3d[0,:]*x_3d[1,:]
x_3d = np.array([[1,2,3],[4,5,6]])
p0 = [5.11, 3.9, 5.3, 2]
fitParams, fitCovariances = curve_fit(fitFunc, x_3d[:2,:], x_3d[2,:], p0)
print ' fit coefficients:\n', fitParams
The error I get reads,
我读到的错误，
raise TypeError('Improper input: N=%s must not exceed M=%s' % (n, m))
TypeError: Improper input: N=4 must not exceed M=3
What is Mthe length of? Is Nthe length of p0? What am I doing wrong here?
什么是M长度？是N的长度p0？我在这里做错了什么？
采纳答案by chthonicdaemon
N and M are defined in the helpfor the function. N is the number of data points and M is the number of parameters. Your error therefore basically means you need at least as many data points as you have parameters, which makes perfect sense.
N 和 M在函数的帮助中定义。N 是数据点的数量，M 是参数的数量。因此，您的错误基本上意味着您至少需要与参数一样多的数据点，这是完全有道理的。
This code works for me:
这段代码对我有用：
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def fitFunc(x, a, b, c, d):
return a + b*x[0] + c*x[1] + d*x[0]*x[1]
x_3d = np.array([[1,2,3,4,6],[4,5,6,7,8]])
p0 = [5.11, 3.9, 5.3, 2]
fitParams, fitCovariances = curve_fit(fitFunc, x_3d, x_3d[1,:], p0)
print ' fit coefficients:\n', fitParams
I have included more data. I have also changed fitFuncto be written in a form that scans as only being a function of a single x - the fitter will handle calling this for all the data points. The code as you posted also referenced x_3d[2,:], which was causing an error.
我已经包含了更多的数据。我也更改fitFunc为以扫描为仅作为单个 x 的函数的形式编写 - 钳工将处理对所有数据点的调用。您发布的代码也引用了x_3d[2,:]，这导致了错误。
回答by Chris Young
The default curve_fitmethod needs you to have fewer parameters for the fitted function fitFuncthan data points. I had the same problem fitting a function that took 15 parameters in total and I had only 13 data points. The solution is to use another method (e.g. dogboxor trf).
默认curve_fit方法需要拟合函数的参数fitFunc少于数据点。我在拟合一个总共有 15 个参数的函数时遇到了同样的问题，我只有 13 个数据点。解决方案是使用另一种方法（例如dogbox或trf）。
