在与真实数据相同的图中绘制多项式回归 [英] plotting polynomial regression in same plot as the real data

问题描述

我有一些代码片段，可读取两个csv并使用matplotlib.pyplot对其进行绘制，并在相同的两个csv上执行多项式回归.我想要做的是在同一张图上同时绘制数据和多项式回归.

I have some snippets of code that read two csvs and plot them using matplotlib.pyplot and perform polynomial regression on the same two csvs. What I want to be able to do is plot both the data and my polynomial regression on the same graph.

import matplotlib.pyplot as plt
import csv
import numpy as np

datax=np.genfromtxt('Delta R.csv')
datay=np.genfromtxt('Example R.csv')


plt.title ('Test graph ')
plt.xlabel('x axis')
plt.ylabel('y axis ')

plt.plot(datax, datay,'o-')

plt.show()

第二段代码只是执行多项式回归

and my second piece of code performs simply polynomial regression

import matplotlib.pyplot as plt
import csv
import numpy as np

datax=np.genfromtxt('Delta R.csv')
datay=np.genfromtxt('Example R.csv')

z = np.polyfit(datax,datay,5) 

print z

任何人都可以帮助我在与原始数据相同的图中绘制此5阶polynomail回归的结果.谢谢GTPE

Can anybody help me plot the results of this 5th order polynomail regression in the same plot as the original data. Thanks GTPE

修改

tcaswell提供的代码非常有效.我唯一的问题是为什么多项式在某些地方这么有角度.这是我的示例图片:

Code provided by tcaswell works perfectly. My only question is why does the polynomial come out so angular in places. Here is my example image:

推荐答案

只需再次调用 plot :

datax=np.genfromtxt('Delta R.csv')
datay=np.genfromtxt('Example R.csv')

z = np.polyfit(datax,datay,5) 
p = np.poly1d(z)

plt.title ('Test graph ')
plt.xlabel('x axis')
plt.ylabel('y axis ')

plt.plot(datax, datay,'o-')
plt.plot(datax, p(datax), '-')

datax_os = np.linspace(np.min(datax), np.max(datax), 1024)
plt.plot(datax_os, f(datax_os)) # 'smoother' line

plt.show()

可以在

An alternate method using multiple arguements to plot can be seen in the np.polyfit documentation.

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