scipy.curve_fit() 返回多行 [英] scipy.curve_fit() returns multiple lines

问题描述

我是 Python 新手，并试图使用以下代码拟合数据集分布.实际数据是一个列表，包含两列——预测市场价格和实际市场价格.我试图使用 scipy.curve_fit() 但它给了我在同一个地方绘制的多条线.任何帮助表示赞赏.

I am new to python and was trying to fit dataset distribution using the following code. The actual data is a list that contains two columns- predicted market price and actual market price. And I was trying to use scipy.curve_fit() but it gave me many lines plotted at the same place. Any help is appreciated.

# import the necessary modules and define a func.
from scipy.optimize import curve_fit
from matplotlib import pyplot as plt

def func(x, a, b, c):
    return a * x** b + c

# my data
pred_data = [3.0,1.0,1.0,7.0,6.0,1.0,7.0,4.0,9.0,3.0,5.0,5.0,2.0,6.0,8.0]
actu_data =[ 3.84,1.55,1.15,7.56,6.64,1.09,7.12,4.17,9.45,3.12,5.37,5.65,1.92,6.27,7.63]
popt, pcov = curve_fit(func, pred_data, actu_data)

#adjusting y 
yaj = func(pred_data, popt[0],popt[1], popt[2])

# plot the data
plt.plot(pred_data,actu_data, 'ro', label = 'Data')
plt.plot(pred_data,yaj,'b--', label = 'Best fit')

plt.legend()
plt.show()

推荐答案

Scipy 不会产生多行，奇怪的输出是由你将未排序的数据呈现给 matplotlib 的方式引起的.对您的 x 值进行排序，您将获得所需的输出:

Scipy doesn't produce multiple lines, the strange output is caused by the way you present your unsorted data to matplotlib. Sort your x-values and you get the desired output:

from scipy.optimize import curve_fit
from matplotlib import pyplot as plt

def func(x, a, b, c):
    return a * x** b + c

# my data
pred_data = [3.0,1.0,1.0,7.0,6.0,1.0,7.0,4.0,9.0,3.0,5.0,5.0,2.0,6.0,8.0]
actu_data =[ 3.84,1.55,1.15,7.56,6.64,1.09,7.12,4.17,9.45,3.12,5.37,5.65,1.92,6.27,7.63]
popt, pcov = curve_fit(func, pred_data, actu_data)

#adjusting y 
yaj = func(sorted(pred_data), *popt)

# plot the data
plt.plot(pred_data,actu_data, 'ro', label = 'Data')
plt.plot(sorted(pred_data),yaj,'b--', label = 'Best fit')

plt.legend()
plt.show()

当然更好的方法是为您的 x 值定义一个均匀间隔的高分辨率数组，并计算该数组的拟合以更平滑地表示您的拟合函数:

A better way is of course to define an evenly-spaced high resolution array for your x-values and calculate the fit for this array to have a smoother representation of your fit function:

from scipy.optimize import curve_fit
import numpy as np
from matplotlib import pyplot as plt

def func(x, a, b, c):
    return a * x** b + c

# my data
pred_data = [3.0,1.0,1.0,7.0,6.0,1.0,7.0,4.0,9.0,3.0,5.0,5.0,2.0,6.0,8.0]
actu_data =[ 3.84,1.55,1.15,7.56,6.64,1.09,7.12,4.17,9.45,3.12,5.37,5.65,1.92,6.27,7.63]
popt, pcov = curve_fit(func, pred_data, actu_data)

xaj = np.linspace(min(pred_data), max(pred_data), 1000)
yaj = func(xaj, *popt)

# plot the data
plt.plot(pred_data,actu_data, 'ro', label = 'Data')
plt.plot(xaj, yaj,'b--', label = 'Best fit')

plt.legend()
plt.show()

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