问题描述
我正在尝试将简单函数适合python中两个独立数据数组。我知道我需要将独立变量的数据打包到一个数组中，但是在尝试拟合时，传递变量的方式似乎仍然存在问题。 （以前有几篇与此相关的文章，但并没有太大帮助。）
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.)
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
我读取的错误，
raise TypeError('Improper input: N=%s must not exceed M=%s' % (n, m))
TypeError: Improper input: N=4 must not exceed M=3
M 的长度是多少？ N 是 p0 的长度吗？我在这里做什么错了？
What is M the length of? Is N the length of p0? What am I doing wrong here?
推荐答案
N和M在。 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，这很有意义。
N and M are defined in the help for 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.
此代码对我有用：
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
我添加了更多数据。我还更改了 fitFunc ，使其以仅作为单个x的函数进行扫描的形式编写-装配工将处理对所有数据点的调用。您发布的代码还引用了 x_3d [2，：] ，这会导致错误。
I have included more data. I have also changed fitFunc to 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.
这篇关于在Python中拟合多元curve_fit的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！