reinvanoyen

Add Git Branch Name to Terminal Prompt (zsh)

Updated for MacOS with zsh

• Catalina
• Big Sur
• Monterey
• Ventura
• Sonoma

brunofbrito

#!/bin/zsh
echo Time to install all apps at once!
read -p "Press any key to continue... " -n1 -s
echo  '\n'

echo Installing Homebrew...
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

echo Installing Oh My Zsh...
sh -c "$(curl -fsSL https://raw.githubusercontent.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"

morcefaster

Preface

Hello everyone. With Ceb drama getting so much traction, I decided to do what any rational person would do in this situation - download all public matches of all professional dota 2 players and then scan the all-chat for any racism or naughty naughty words, so the j͇͕͙ͣu͒͆s̼̠͍̖̮̳ͮ̃t̫̙̯͎ͬ̇̊̄iͨć̼͓ͬͨ͑ͣe͉̜̫̱̠̘̋̒ͅ can be upheld once and for all.

There are several nuances that stood in my way.

1. Many professionals' pubs are kept private.
2. Most of the trashtalk happens in ally chat, which isn't saved.
3. Some of the trashtalk happens over mic... which again, isn't saved.
4. Some of the trashtalk happens on smurf accounts, which I did not include here.

yelizariev

Allows to create TODOs for a small group of users.

Tasks can have on of the following states:

• TODO -- to be done
• DONE -- done
• CANCELED -- nothing was done and not going to be done
• WAITING -- cannot be started and waits for something

Technical specification

ivan-mitb

# load READY.DAT (56 cols)
from dataload import load_object
x_train, x_test, y_train, y_test = load_object('ready.dat')
x_train.max(axis=0)
del x_test, y_test      # we don't need the test set for now

# under-sample the big classes to make the set manageable
from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler(ratio={'normal':50000, 'dos':50000}, random_state=4129)
x_train, y_train = rus.fit_sample(x_train, y_train.attack_type)

maxim5

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import print_function

__author__ = 'maxim'

import numpy as np
import gensim
import string

rspeer

kastnerkyle

# Original code from tinrtgu on Kaggle under WTFPL license
# Relicensed to BSD 3-clause (it does say do what you want...)
# Authors: Kyle Kastner
# License: BSD 3-clause

# Reference links:
# Adaptive learning: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41159.pdf
# Criteo scalable response prediction: http://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf
# Vowpal Wabbit (hashing trick): https://github.com/JohnLangford/vowpal_wabbit/
# Hashing Trick: http://arxiv.org/pdf/0902.2206.pdf