Yuji Kanagawa kngwyu

## foo.sh
#!/bin/sh

ProgName=$(basename $0)

sub_help(){
    echo "Usage: $ProgName <subcommand> [options]\n"
    echo "Subcommands:"
    echo "    bar   Do bar"
    echo "    baz   Run baz"
    echo ""

## bobp-python.md

      
              1 file
            
          
              570 forks
            
          
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              3238 stars
            
          
                sloria
                / bobp-python.md
            
            
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              July 21, 2024 04:44
            
              
                A "Best of the Best Practices" (BOBP) guide to developing in Python.
              
          
    The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.
In General

Values


"Build tools for others that you want to be built for you." - Kenneth Reitz
"Simplicity is alway better than functionality." - Pieter Hintjens


## gist:7360908

      
              1 file
            
          
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                rxaviers
                / gist:7360908
            
            
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              July 23, 2024 12:15
            
              
                Complete list of github markdown emoji markup
              
          
    People


 :bowtie:
😄 :smile:
😆 :laughing:


😊 :blush:
😃 :smiley:
☺️ :relaxed:


😏 :smirk:
😍 :heart_eyes:
😘 :kissing_heart:


😚 :kissing_closed_eyes:
😳 :flushed:
😌 :relieved:


😆 :satisfied:
😁 :grin:
😉 :wink:


😜 :stuck_out_tongue_winking_eye:
😝 :stuck_out_tongue_closed_eyes:
😀 :grinning:


😗 :kissing:
😙 :kissing_smiling_eyes:
😛 :stuck_out_tongue:


## ipython_notebook_in_git.md

      
              2 files
            
          
              46 forks
            
          
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              196 stars
            
          
                pbugnion
                /  ipython_notebook_in_git.md
            
            
              Last active
              October 22, 2023 12:25
            
              
                Keeping IPython notebooks under Git version control
              
          
    This gist lets you keep IPython notebooks in git repositories. It tells git to ignore prompt numbers and program outputs when checking that a file has changed.
To use the script, follow the instructions given in the script's docstring.
For further details, read this blogpost.
The procedure outlined here is inspired by this answer on Stack Overflow.

  
## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

## pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

## noglobal.py
# License:
#   I hereby state this snippet is below "threshold of originality" where applicable (public domain).
#
# Otherwise, since initially posted on Stackoverflow, use as:
#   CC-BY-SA 3.0 skyking, Glenn Maynard, Axel Huebl
#   http://stackoverflow.com/a/31047259/2719194
#   http://stackoverflow.com/a/4858123/2719194

import types

## nes.py
"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

## How to setup VirtualGL and TurboVNC on Ubuntu.md

      
              1 file
            
          
              24 forks
            
          
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                cyberang3l
                / How to setup VirtualGL and TurboVNC on Ubuntu.md
            
            
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              March 23, 2024 05:52
            
              
                Setup VirtualGL and TurboVNC on Ubuntu for OpenGL forwarding
              
          
    https://github.com/aancel/admin/wiki/VirtualGL-on-Ubuntu
https://virtualgl.org/About/Introduction
https://www.turbovnc.org
VirtualGL

When you use ssh with X forwarding, you might have noticed that you cannot execute programs that require 3D acceleration. That's where VirtualGL comes into play.

  
## config

[global_config]
  window_state = maximise
  handle_size = 0
  title_hide_sizetext = True
  title_transmit_fg_color = "#bd93f9"
  title_inactive_fg_color = "#f8f8f2"
  title_receive_bg_color = "#282a36"
  title_transmit_bg_color = "#282a36"
  title_receive_fg_color = "#8be9fd"
	#!/bin/sh

	ProgName=$(basename $0)

	sub_help(){
	echo "Usage: $ProgName <subcommand> [options]\n"
	echo "Subcommands:"
	echo " bar Do bar"
	echo " baz Run baz"
	echo ""
`:bowtie:`	😄 `:smile:`	😆 `:laughing:`
😊 `:blush:`	😃 `:smiley:`	☺️ `:relaxed:`
😏 `:smirk:`	😍 `:heart_eyes:`	😘 `:kissing_heart:`
😚 `:kissing_closed_eyes:`	😳 `:flushed:`	😌 `:relieved:`
😆 `:satisfied:`	😁 `:grin:`	😉 `:wink:`
😜 `:stuck_out_tongue_winking_eye:`	😝 `:stuck_out_tongue_closed_eyes:`	😀 `:grinning:`
😗 `:kissing:`	😙 `:kissing_smiling_eyes:`	😛 `:stuck_out_tongue:`
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)
	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward
	# License:
	# I hereby state this snippet is below "threshold of originality" where applicable (public domain).
	#
	# Otherwise, since initially posted on Stackoverflow, use as:
	# CC-BY-SA 3.0 skyking, Glenn Maynard, Axel Huebl
	# http://stackoverflow.com/a/31047259/2719194
	# http://stackoverflow.com/a/4858123/2719194

	import types
	"""
	A bare bones examples of optimizing a black-box function (f) using
	Natural Evolution Strategies (NES), where the parameter distribution is a
	gaussian of fixed standard deviation.
	"""

	import numpy as np
	np.random.seed(0)

	# the function we want to optimize

	[global_config]
	window_state = maximise
	handle_size = 0
	title_hide_sizetext = True
	title_transmit_fg_color = "#bd93f9"
	title_inactive_fg_color = "#f8f8f2"
	title_receive_bg_color = "#282a36"
	title_transmit_bg_color = "#282a36"
	title_receive_fg_color = "#8be9fd"