Abhinav Moudgil amoudgl

## dance.py
import numpy as np
import librosa
import time
import os
import pygame

steps =[
"""
 o
\ /

## trajectory_visualization_v2.ipynb

      
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                ronghanghu
                / trajectory_visualization_v2.ipynb
            
            
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              June 11, 2024 18:29
            
              
                Speaker-Follower visualization
              
          
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## tensorflow-agents-ppo-minitaur.py
def minitaur_config():
  # General
  algorithm = ppo.PPOAlgorithm
  num_agents = 10
  eval_episodes = 30
  use_gpu = False
  # Environment
  env = 'MinitaurBulletEnv-v0'
  max_length = 1000
  steps = 1e7  # 10M

## data_loader.py
"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

## download.sh
#!/bin/bash

baseurl="http://cvlab.hanyang.ac.kr/tracker_benchmark"
wget "$baseurl/datasets.html"
cat datasets.html | grep '\.zip' | sed -e 's/\.zip".*/.zip/' | sed -e s'/.*"//' >files.txt
cat files.txt | xargs -n 1 -P 8 -I {} wget -c "$baseurl/{}"

## dummyfig.tex
%% This part goes in preamble
\newcommand{\dummyfig}[1]{
  \centering
  \fbox{
    \begin{minipage}[c][0.33\textheight][c]{0.5\textwidth}
      \centering{#1}
    \end{minipage}
  }
}

## tmux-cheatsheet.markdown

      
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                MohamedAlaa
                / tmux-cheatsheet.markdown
            
            
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              June 29, 2024 08:12
            
              
                tmux shortcuts & cheatsheet
              
          
    tmux shortcuts & cheatsheet

start new:
tmux

start new with session name:
tmux new -s myname
	import numpy as np
	import librosa
	import time
	import os
	import pygame

	steps =[
	"""
	o
	\ /
	def minitaur_config():
	# General
	algorithm = ppo.PPOAlgorithm
	num_agents = 10
	eval_episodes = 30
	use_gpu = False
	# Environment
	env = 'MinitaurBulletEnv-v0'
	max_length = 1000
	steps = 1e7 # 10M
	"""
	Create train, valid, test iterators for CIFAR-10 [1].
	Easily extended to MNIST, CIFAR-100 and Imagenet.

	[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
	"""

	import torch
	import numpy as np
	#!/bin/bash

	baseurl="http://cvlab.hanyang.ac.kr/tracker_benchmark"
	wget "$baseurl/datasets.html"
	cat datasets.html \| grep '\.zip' \| sed -e 's/\.zip"./.zip/' \| sed -e s'/."//' >files.txt
	cat files.txt \| xargs -n 1 -P 8 -I {} wget -c "$baseurl/{}"
	%% This part goes in preamble
	\newcommand{\dummyfig}[1]{
	\centering
	\fbox{
	\begin{minipage}[c][0.33\textheight][c]{0.5\textwidth}
	\centering{#1}
	\end{minipage}
	}
	}