AStupidBear AStupidBear

## tmux-cheatsheet.markdown

      
              1 file
            
          
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                MohamedAlaa
                / tmux-cheatsheet.markdown
            
            
              Last active
              May 1, 2024 03:34
            
              
                tmux shortcuts & cheatsheet
              
          
    tmux shortcuts & cheatsheet

start new:
tmux

start new with session name:
tmux new -s myname


## tmux_local_install.sh
#!/bin/bash

# Script for installing tmux on systems where you don't have root access.
# tmux will be installed in $HOME/local/bin.
# It's assumed that wget and a C/C++ compiler are installed.

# exit on error
set -e

TMUX_VERSION=1.8

## 词性标记.md

      
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                luw2007
                / 词性标记.md
            
            
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              March 18, 2024 06:36
            
              
                词性标记： 包含 ICTPOS3.0词性标记集、ICTCLAS 汉语词性标注集、jieba 字典中出现的词性、simhash 中可以忽略的部分词性
              
          
    词的分类


实词：名词、动词、形容词、状态词、区别词、数词、量词、代词
虚词：副词、介词、连词、助词、拟声词、叹词。

ICTPOS3.0词性标记集

n 名词

nr 人名

  
## gist:4582705
"""making a dataframe"""
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))

"""quick way to create an interesting data frame to try things out"""
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd'])

"""convert a dictionary into a DataFrame"""
"""make the keys into columns"""
df = pd.DataFrame(dic, index=[0])

## bench_rocsgd.py
import itertools

import numpy as np

from sklearn.linear_model import SGDClassifier, SGDRanking
from sklearn import metrics
from minirank.compat import RankSVM as MinirankSVM
from scipy import stats


## CodeLLDB_CentOS.md

      
              1 file
            
          
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              3 comments
            
          
              7 stars
            
          
                JaySon-Huang
                / CodeLLDB_CentOS.md
            
            
              Last active
              December 14, 2023 18:23
            
              
                Get VSCode CodeLLDB plugin work on CentOS 7
              
          
    I want to debug with CodeLLDB on CentOS 7, but get an error like this:
/home/xxx/.vscode/extensions/vadimcn.vscode-lldb-1.6.1/adapter/codelldb: /lib64/libc.so.6: version `GLIBC_2.18' not found (required by /home/xxx/.vscode/extensions/vadimcn.vscode-lldb-1.6.1/adapter/codelldb)

LLDB requires a higher version of glibc.
I get it work by the following steps:

  
## h5map.ipynb

      
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                maartenbreddels
                / h5map.ipynb
            
            
              Created
              October 25, 2017 12:25
            
              
                memory mapping hdf5 continuous data
              
          
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## build-tensorflow-from-source.md

      
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                Brainiarc7
                / build-tensorflow-from-source.md
            
            
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              July 29, 2023 21:28
            
              
                Build Tensorflow from source, for better performance on Ubuntu.
              
          
    Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:
TensorFlow is now distributed under an Apache v2 open source license on GitHub.
On Ubuntu 16.04LTS+:
Step 1. Install NVIDIA CUDA:
To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

  
## parquet-benchmark-20170210.py
import gc
import os
import time

import numpy as np
import pandas as pd
from pyarrow.compat import guid
import pyarrow as pa
import pyarrow.parquet as pq
import snappy

## wechat.json
{
  "schema_version": 1,
  "name": "WeChat",
  "description": "微信公众号全文 RSS",
  "source_url": false,
  "guid": "dd67102f09869c2228f8ed903a32d063",
  "tag_fg_color": "#333333",
  "tag_bg_color": "#66ff66",
  "icon": "leaf",
  "exported_at": "2019-01-12T10:56:41Z",
	#!/bin/bash

	# Script for installing tmux on systems where you don't have root access.
	# tmux will be installed in $HOME/local/bin.
	# It's assumed that wget and a C/C++ compiler are installed.

	# exit on error
	set -e

	TMUX_VERSION=1.8
	"""making a dataframe"""
	df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))

	"""quick way to create an interesting data frame to try things out"""
	df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd'])

	"""convert a dictionary into a DataFrame"""
	"""make the keys into columns"""
	df = pd.DataFrame(dic, index=[0])
	import itertools

	import numpy as np

	from sklearn.linear_model import SGDClassifier, SGDRanking
	from sklearn import metrics
	from minirank.compat import RankSVM as MinirankSVM
	from scipy import stats
	import gc
	import os
	import time

	import numpy as np
	import pandas as pd
	from pyarrow.compat import guid
	import pyarrow as pa
	import pyarrow.parquet as pq
	import snappy
	{
	"schema_version": 1,
	"name": "WeChat",
	"description": "微信公众号全文 RSS",
	"source_url": false,
	"guid": "dd67102f09869c2228f8ed903a32d063",
	"tag_fg_color": "#333333",
	"tag_bg_color": "#66ff66",
	"icon": "leaf",
	"exported_at": "2019-01-12T10:56:41Z",