python tutorial

目录

• Beginning Python
  • • 1.matplotlib:example of figures
    • 2.font Chinese
    • 3.print method
    • 4.if-else
    • 5.compare two numbers
    • 6.Parameters of legend
    • 7.Machine Learning
    • 8.SVM
    • Tool of Deep Learning
    • Tool of Reinforcement Learning
    • Python3.5 link
    • PyCharm
    • PiP command
    • image augmentation
    • Deep Reinforcement Learning
    • Attention Matrix Visualization
    • beam Search
    • path
    • jieba library
    • numpy list array
    • books and links for python
    • Python environment conda
    • combinations and permutations
    • file copy and delete
    • Random

Beginning Python

1.matplotlib:example of figures

https://matplotlib.org/gallery/index.html

2.font Chinese

import matplotlib.font_manager as fm
import matplotlib.pyplot as plt

plt.title("折线图进阶",fontproperties="simhei")

or

plt.rcParams['font.sans-serif']=['SimHei']

3.print method

print(int(str(i)+str(j)+str(k)),end=' ')

4.if-else

min = x if x<y else y

5.compare two numbers

0.4-0.3 == 0.1 #deviation
math.isclose(0.4-0.3,0.1) 

6.Parameters of legend

plt.legend(loc='best')
plt.legend(loc="upper right",fontsize=8,borderaxespad=0.3)

7.Machine Learning

https://scikit-learn.org/stable/index.html

8.SVM

LibSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/

SVM-Light: http://svmlight.joachims.org/

Liblinear:https://www.csie.ntu.edu.tw/~cjlin/liblinear/

valid:

http://ntur.lib.ntu.edu.tw/bitstream/246246/20060927122847351581/1/libsvm.pdf

Tool of Deep Learning

1. PaddlePaddle: https://github.com/PaddlePaddle/Paddle

   https://paddlepaddle.org

2. Caffe(image): https://github.com/BVLC/caffe

3. Tensorflow(C++): https://github.com/tensorflow/tensorflow

4. keras(Python): https://github.com/fchollet/keras

Tool of Reinforcement Learning

1. PARL: https://github.com/paddlepaddle/parl

   DQN, DDPG, PPO, A2C

Python3.5 link

https://pan.baidu.com/s/1t_mBGiwre7eGTio-0MRqmQ

4efy

PyCharm

1. link: https://pan.baidu.com/s/1t_mBGiwre7eGTio-0MRqmQ
2. code: 4efy

PiP command

upgrade pip : python -m pip install --upgrade pip

image augmentation

https://github.com/aleju/imgaug

Deep Reinforcement Learning

Attention Matrix Visualization

1. https://medium.com/datalogue/attention-in-keras-1892773a4f22
2. https://github.com/zhaocq-nlp/Attention-Visualization
3. https://github.com/Aurelius84/SPWE

beam Search

relation between words --decoder

path

(r’D:\python\image.jpg') \ recognization

jieba library

install:

• easy_install jieba or pip install jieba or pip3 install jieba
• https://pypi.python.org/pypi/jieba/ python setup.py install
• put the content of jieba to site-packages
• import jiebe

numpy list array

​ install: python -m pip intall --user numpy

books and links for python

Python environment conda

1.Create a new environment:

​ conda create --name <env_name> <package_name>

​ example:

​ conda create --name python3 python3.7

2.List all envs

​ conda info --envs

3.activate/del environment

​ conda activate python3

​ conda deactivate

​ conda remove --name python3 --all

4. Tsinghua tuna

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/

conda config --set show_channel_urls yes

​ conda install numpy

​ conda remove numpy

​ conda remove --name <env_name> <package_name>

• version

conda search --full-name pandas

• conda list

combinations and permutations

from itertools import *

characters = '1234'
for item in combinations(characters,3):
	print(item,end=' ')
print('\n'+'='*20)
for item in permutations(characters,3):
	print(item,end=' ')

file copy and delete

shutil

Random

import random
random.choice('a','b','c')
random.sample(range(100),10) # sampling without replacement
random.random() # random float 0-1
random.randrange(6) # random integer chosen from range(6)