对于CorrelationinPython感兴趣的读者，本文将会是一篇不错的选择，并为您提供关于Connectionreseterror:(errno104)connectionresetbypeer
对于Correlation in Python感兴趣的读者，本文将会是一篇不错的选择，并为您提供关于Connectionreseterror: (errno 104) connection reset by peer: 在 AKS 中部署的 Python 脚本、Core Python | 2 - Core Python: Getting Started | 2.5 - Modularity | 2.5.4 - __name__、Core Python | 2 - Core Python: Getting Started | 2.5 - Modularity | 2.5.5 - The Python Execution Mod、eclipse 中通过Cygwin编译c++的python调用, “Python27/include/pyconfig.h:68:16: fatal error: io.h: No such file or directory”的有用信息。
本文目录一览：- Correlation in Python
- Connectionreseterror: (errno 104) connection reset by peer: 在 AKS 中部署的 Python 脚本
- Core Python | 2 - Core Python: Getting Started | 2.5 - Modularity | 2.5.4 - __name__
- Core Python | 2 - Core Python: Getting Started | 2.5 - Modularity | 2.5.5 - The Python Execution Mod
- eclipse 中通过Cygwin编译c++的python调用, “Python27/include/pyconfig.h:68:16: fatal error: io.h: No such file or directory”
Correlation in Python
http://benalexkeen.com/correlation-in-python/
Correlation in Python
Correlation values range between -1 and 1.
There are two key components of a correlation value:
- magnitude – The larger the magnitude (closer to 1 or -1), the stronger the correlation
- sign – If negative, there is an inverse correlation. If positive, there is a regular correlation.
Positive Correlation
Let’s take a look at a positive correlation. Numpy implements a corrcoef() function that returns a matrix of correlations of x with x, x with y, y with x and y with y. We’re interested in the values of correlation of x with y (so position (1, 0) or (0, 1)).
import numpy as np
np.random.seed(1) # 1000 random integers between 0 and 50 x = np.random.randint(0, 50, 1000) # Positive Correlation with some noise y = x + np.random.normal(0, 10, 1000) np.corrcoef(x, y) array([[ 1. , 0.81543901],
[ 0.81543901, 1. ]])This correlation is 0.815, a strong positive correlation, let’s take a look at a scatter chart.
import matplotlib
import matplotlib.pyplot as plt %matplotlib inline matplotlib.style.use(''ggplot'') plt.scatter(x, y) plt.show() Negative Correlation
What happens to our correlation figure if we invert the correlation such that an increase in x results in a decrease in y?
# 1000 random integers between 0 and 50
x = np.random.randint(0, 50, 1000) # Negative Correlation with some noise y = 100 - x + np.random.normal(0, 5, 1000) np.corrcoef(x, y) array([[ 1. , -0.94957116],
[-0.94957116, 1. ]])Our correlation is now negative and close to 1. Let’s take a look at what this looks like graphically:
plt.scatter(x, y) plt.show() No/Weak Correlatio
What if there is no correlation between x and y?
x = np.random.randint(0, 50, 1000) y = np.random.randint(0, 50, 1000) np.corrcoef(x, y) array([[ 1. , -0.00554681],
[-0.00554681, 1. ]])Here we see a very small value for the correlation between x and y, indicating no correlation.
Again, let’s plot this and take a look, we see there is no correlation between x and y:
plt.scatter(x, y) plt.show() Correlation Matrix
If we’re using pandas we can create a correlation matrix to view the correlations between different variables in a dataframe:
import pandas as pd
df = pd.DataFrame({''a'': np.random.randint(0, 50, 1000)}) df[''b''] = df[''a''] + np.random.normal(0, 10, 1000) # positively correlated with ''a'' df[''c''] = 100 - df[''a''] + np.random.normal(0, 5, 1000) # negatively correlated with ''a'' df[''d''] = np.random.randint(0, 50, 1000) # not correlated with ''a'' df.corr() | a | b | c | d | |
|---|---|---|---|---|
| a | 1.000000 | 0.825361 | -0.948845 | 0.009802 |
| b | 0.825361 | 1.000000 | -0.789391 | 0.011852 |
| c | -0.948845 | -0.789391 | 1.000000 | -0.003228 |
| d | 0.009802 | 0.011852 | -0.003228 | 1.000000 |
We can also view these correlations graphically as a scatter matrix:
pd.scatter_matrix(df, figsize=(6, 6)) plt.show() Or we can directly plot a correlation matrix plot:
plt.matshow(df.corr()) plt.xticks(range(len(df.columns)), df.columns) plt.yticks(range(len(df.columns)), df.columns) plt.colorbar() plt.show() 
Connectionreseterror: (errno 104) connection reset by peer: 在 AKS 中部署的 Python 脚本
如何解决Connectionreseterror: (errno 104) connection reset by peer: 在 AKS 中部署的 Python 脚本？
我最近在 AKS pod 中使用 docker 映像部署了一个 python 脚本。 执行请求的python代码：
response = requests.get("https://google.com")
Pod 部署文件：
apiVersion: v1
kind: Pod
Metadata:
name: test1
spec:
containers:
- name: test1
image: <linktodockerfile>
imagePullSecrets:
- name: test1
但是，无论我尝试向什么发出请求，我似乎都会收到“连接中止”，ConnectionResetError(104,''Connection reset by peer''))。
有没有人知道。我是 AKS 的新手，正在尝试进行最简单的测试
谢谢，
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。
小编邮箱:dio#foxmail.com (将#修改为@）
Core Python | 2 - Core Python: Getting Started | 2.5 - Modularity | 2.5.4 - __name__
With some background on functions in place and the terminal logical exactitude established, let's get back to organizing the code in our words module into function.
We'll move all the code except the import statement, into a function called fetch words. You do that simply by adding the def statement and indenting the code below it by one extra level. Save the module, start a fresh Python REPL, and import your module again with import words. The module imports, but Now the words are not fetched until we call the fetch_words function with words.fetch_words. This use of the dot is called qualifying the function name with the module name. Alternatively, we can import are specific function using a different form of the import statement, from words import fetch_words. Having imported the fetch_words function directly into our REPL session, which is itself a module, we can invoke fetch_words using its unqualified name. So far, so good.
But what happens when we try to run our module directly from the operating system shell? Exit from the REPL with Ctrl+D from Mac or Linux, or Ctrl+Z for Windows, and run Python, passing the module file name. No words are printed, that's because all the module does Now is define a function and then exit. The function is never called.
What we'd prefer is that the module actually print something when we execute it. To make a module from which we can usefully import functions into the REPL and which can be run as a script, we need to learn a new Python idiom. As we mentioned earlier, one of the specially named variables is called dunder name, and it gives us the means to detect whether our module has been run as a script or imported into another module or the REPL. To see how, add print dunder name at the end of your module, outside of the fetch_words function. Let's import the modified words module back into the REPL with import words. We can see that when imported, dunder name does indeed evaluate to the module's name. As a brief aside, if you import the module again in the same REPL, the print statement will not be executed. Module code is only executed once on first import. Now let's try running the module as a script from the operating system shell with Python 3 words.py. Now the special dunder name variable is equal to the string dunder main, which is also delimited by double underscores. That is, Python sets the value of dunder name differently, depending on how our module is being used. The key idea we're introducing here is that our module can use this behavior to decide how it should behave. We replaced the print statement with an if statement, which tests the value of dunder name. If dunder name is equal to the string dunder main, we execute our function. On the other hand, if dunder name is not equal to dunder main, the module kNows it's being imported into another module, not executed, and so only defines the fetch_words function without executing it. We can Now safely import our module without unduly executing our function, and we can usefully run our module as a script.
于是，我们可以用这个知识来限定某些代码的执行，如果是直接运行其所在的py文件，则代码执行，如果是其他py文件导入当前py文件并运行，则代码不执行
Core Python | 2 - Core Python: Getting Started | 2.5 - Modularity | 2.5.5 - The Python Execution Mod
It's important to understand the Python execution model and precisely when function deFinitions and other important events occur when a module is imported or executed. Here, we show execution of our Python module as it's imported in a graphical debugging environment. We step through the top‑level statements in the module. What's important to realize here is that the def used for the fetch_words function isn't merely a declaration. It's actually a statement, which when executed in sequence with the other top‑level model scope code, causes the code within the function to be bound to the name of the function. When modules are imported or run, all of the top‑level statements are run, and this is the means by which the function within the module namespace are defined. We are sometimes asked about the difference between Python modules, Python scripts, and Python programs. Any .py file constitutes a Python module. But as we've seen, modules can be written for convenient import, convenient execution, or using the if dunder name = dunder main idiom, both. We strongly recommend making even simple scripts importable since it eases development and testing so much if you can access your code from within the REPL. Likewise, even modules, which are only ever meant to be imported in production settings, benefit from having executable test code. For this reason, nearly all modules we create have this form of defining one or more importable functions with a postscript to facilitate execution. Whether you consider a module to be a Python script or Python program is a matter of context and usage. It's certainly wrong to consider Python to be merely a scripting tool, in the vein of Windows batch files or UNIX Shell scripts, as many large and complex applications are built exclusively with python.
- def不仅仅是一个declaration声明，更是一条statement语句。它将其中的python代码于函数名绑定在一起
- 一个py文件就是一个模块，这个模块包含类或函数。你写python，要尽量将代码包装成函数和类，方便各种import
- 一个py文件也可看作是一个脚本，在系统命令行中运行
- python不仅仅是脚本语言，很多大型程序都是用python构建的
eclipse 中通过Cygwin编译c++的python调用, “Python27/include/pyconfig.h:68:16: fatal error: io.h: No such file or directory”
错误提示：
Python27/include/pyconfig.h:68:16: fatal error: io.h: No such file or directory
#include <io.h>
^
/cygdrive/d/android-ndk-r9/build/core/build-binary.mk:348: recipe for target
背景：
android用Cygwin编译c++, c++中有引用python.h
具体：
我在cpp有对python的调用, include了 "python.h", vc2012中编译没有问题, 调试中也可以调用到python的方法, 但是因为我是用cocos2dx框架开发的手机游戏, 要生成apk文件, 在eclipse平台通过Cygwin编译却提示 引用了python.h的cpp文件找不到, 如上的错误...
我尝试了
在eclipse->Properties->C/C++ General->Paths and Sysbols->includes的assembly中
和
在环境变量path中
把"D:\Program Files (x86)\Microsoft Visual Studio 11.0\VC\include"添加
还是编译通不过..
非常肯定大神帮忙看看啊..感激不尽..
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