大家好，我想用曲线拟合在python中进行非线性回归，这是我的代码：
#fit a fourth degree polynomial to the economic data
from numpy import arange
from scipy.optimize import curve_fit
from matplotlib import pyplot
import math
x = [17.47,20.71,21.08,18.08,17.12,14.16,14.06,12.44,11.86,11.19,10.65]
y = [5,35,65,95,125,155,185,215,245,275,305]
# define the true objective function
def objective(x, a, b, c, d, e):
return ((a)-((b)*(x/3-5)))+((c)*(x/305)**2)-((d)*(math.log(305))-math.log(x))+((e)*(math.log(305)-(math.log(x))**2))
popt, _ = curve_fit(objective, x, y)
# summarize the parameter values
a, b, c, d, e = popt
# plot input vs output
pyplot.scatter(x, y)
# define a sequence of inputs between the smallest and largest known inputs
x_line = arange(min(x), max(x), 1)
# calculate the output for the range
y_line = objective(x_line, a, b, c, d, e)
# create a line plot for the mapping function
pyplot.plot(x_line, y_line, '--', color='red')
pyplot.show()
这是我的错误：
追溯（最近一次通话）：文件“ C：\ Users \ Fahmi \ PycharmProjects \ pythonProject \ main.py”，行16，在popt中，_ = curve_fit（objective，x，y）文件“ C：\ Users \ Fahmi \ PycharmProjects \ pythonProject \ venv \ lib \ site-packages \ scipy \ optimize \ minpack.py“，第784行，位于curve_fit res = minimumsq（func，p0，Dfun = jac，full_output = 1，** kwargs）文件” C ：\ Users \ Fahmi \ PycharmProjects \ pythonProject \ venv \ lib \ site-packages \ scipy \ optimize \ minpack.py”，第410行，infinitesq形状，dtype = _check_func（'leastsq'，'func'，func，x0， args，n）文件“ C：\ Users \ Fahmi \ PycharmProjects \ pythonProject \ venv \ lib \ site-packages \ scipy \ optimize \ minpack.py”，第24行，在_check_func res = atleast_1d（thefunc（（（x0 [：numinputs]，）+ args）））文件“ C：\ Users \ Fahmi \ PycharmProjects \ pythonProject \ venv \ lib \ site-packages \ scipy \ optimize \ minpack.py”，行484，在func_wrapped返回中func（xdata，params）-ydata文件“ C：\ Users \ Fahmi \ PycharmProjects \ pythonProject \ main.py”，第13行，目标返回（（a）-（（b）（x / 3-5））） +（（c）（x / 305）** 2）-（（d）（math.log（305））-math.log（x））+（（e）（math.log（305）-（math .log（x））** 2））TypeError：只能将size-1数组转换为Python标量
之前感谢
这是数学库的已知问题。只需使用numpy即可解决问题，因为numpy函数支持标量和数组。
#fit a fourth degree polynomial to the economic data
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
x = [17.47,20.71,21.08,18.08,17.12,14.16,14.06,12.44,11.86,11.19,10.65]
y = [5,35,65,95,125,155,185,215,245,275,305]
# define the true objective function
def objective(x, a, b, c, d, e):
return ((a)-((b)*(x/3-5)))+((c)*(x/305)**2)-((d)*(np.log(305))-np.log(x))+((e)*(np.log(305)-(np.log(x))**2))
popt, _ = curve_fit(objective, x, y)
# summarize the parameter values
a, b, c, d, e = popt
# plot input vs output
plt.scatter(x, y)
# define a sequence of inputs between the smallest and largest known inputs
x_line = np.arange(np.min(x), np.max(x), 1)
# calculate the output for the range
y_line = objective(x_line, a, b, c, d, e)
# create a line plot for the mapping function
plt.plot(x_line, y_line, '--', color='red')
plt.show()
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我来说两句