scipy curve_fit不喜欢数学模块 [英] scipy curve_fit doesn't like math module
问题描述
在尝试使用scipy.optimize curve_fit
创建示例时,我发现scipy似乎与Python的math
模块不兼容.虽然功能f1
正常工作,但f2
会引发错误消息.
While trying to create an example with scipy.optimize curve_fit
I found that scipy seems to be incompatible with Python's math
module. While function f1
works fine, f2
throws an error message.
from scipy.optimize import curve_fit
from math import sin, pi, log, exp, floor, fabs, pow
x_axis = np.asarray([pi * i / 6 for i in range(-6, 7)])
y_axis = np.asarray([sin(i) for i in x_axis])
def f1(x, m, n):
return m * x + n
coeff1, mat = curve_fit(f1, x_axis, y_axis)
print(coeff1)
def f2(x, m, n):
return m * sin(x) + n
coeff2, mat = curve_fit(f2, x_axis, y_axis)
print(coeff2)
完整的追溯是
Traceback (most recent call last):
File "/Documents/Programming/Eclipse/PythonDevFiles/so_test.py", line 49, in <module>
coeff2, mat = curve_fit(f2, x_axis, y_axis)
File "/usr/local/lib/python3.5/dist-packages/scipy/optimize/minpack.py", line 742, in curve_fit
res = leastsq(func, p0, Dfun=jac, full_output=1, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/scipy/optimize/minpack.py", line 377, in leastsq
shape, dtype = _check_func('leastsq', 'func', func, x0, args, n)
File "/usr/local/lib/python3.5/dist-packages/scipy/optimize/minpack.py", line 26, in _check_func
res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
File "/usr/local/lib/python3.5/dist-packages/scipy/optimize/minpack.py", line 454, in func_wrapped
return func(xdata, *params) - ydata
File "/Documents/Programming/Eclipse/PythonDevFiles/so_test.py", line 47, in f2
return m * sin(x) + n
TypeError: only length-1 arrays can be converted to Python scalars
出现错误消息,并带有列表和numpy
数组作为输入.它会影响我测试过的所有math
函数(请参阅import中的函数),并且必须与数学模块如何处理输入数据有关. pow()
函数最明显-如果我不从math
导入此函数,则curve_fit
与pow()
一起正常工作.
The error message appears with lists and numpy
arrays as input alike. It affects all math
functions, I tested (see functions in import) and must have something to do with, how the math module manipulates input data. This is most obvious with pow()
function - if I don't import this function from math
, curve_fit
works properly with pow()
.
一个显而易见的问题-为什么会发生这种情况?math
函数如何与curve_fit
一起使用?
The obvious question - why does this happen and how can math
functions be used with curve_fit
?
P.S .:请不要讨论,不应将样本数据线性拟合.选择它只是为了说明问题.
P.S.: Please don't discuss, that one shouldn't fit the sample data with a linear fit. This was just chosen to illustrate the problem.
推荐答案
小心numpy数组,对数组进行操作和对标量进行操作!
Be careful with numpy-arrays, operations working on arrays and operations working on scalars!
Scipy优化假定输入(初始点)是一维数组,在其他情况下通常会出错(例如,列表变成数组,并且如果您假设要在列表上工作,则事情会变得很混乱;那种)问题在StackOverflow上很常见,调试起来并不容易做到;代码交互很有帮助!).
Scipy optimize assumes the input (initial-point) to be a 1d-array and often things go wrong in other cases (a list for example becomes an array and if you assumed to work on lists, things go havoc; those kind of problems are common here on StackOverflow and debugging is not that easy to do by the eye; code-interaction helps!).
import numpy as np
import math
x = np.ones(1)
np.sin(x)
> array([0.84147098])
math.sin(x)
> 0.8414709848078965 # this only works as numpy has dedicated support
# as indicated by the error-msg below!
x = np.ones(2)
np.sin(x)
> array([0.84147098, 0.84147098])
math.sin(x)
> TypeError: only size-1 arrays can be converted to Python scalars
老实说:这是对numpy的基本了解,应该在使用scipy的某些敏感函数时理解.
To be honest: this is part of a very basic understanding of numpy and should be understood when using scipy's somewhat sensitive functions.
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