| gitc() { | |
| git fetch --prune | |
| for branch in `git branch -vv | grep ': gone]' | awk '{print $1}'`; | |
| do | |
| git branch -D $branch | |
| done; | |
| git branch --merged master | grep -v '^*' | grep -v '^ master$' | xargs git branch -d | |
| } |
| " Rob Volk's .vimrc, completely based off of " This is Rob Volk's .vimrc, completely based off of Gary Bernhardt's .vimrc file | |
| " vim:set ts=2 sts=2 sw=2 expandtab: | |
| autocmd! | |
| call pathogen#infect('bundle/{}') | |
| call plug#begin('~/.vim/plugged') | |
| Plug 'prettier/vim-prettier', { | |
| \ 'do': 'yarn install', |
| function upgrade-vim { | |
| brew update | |
| brew upgrade vim | |
| cd ~/.vim/bundle | |
| for i in `ls`; do | |
| cd "$i" | |
| git pull | |
| cd .. | |
| done |
| function scrabble { | |
| node -p -e "const s = '$1'; let o = ''; for (c of s) o += ':scrabble-' + c + ': '" | |
| } |
| load "#{Rails.root}/lib/my_file.rb" |
| // ES6 Fetch docs | |
| // https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API/Using_Fetch | |
| fetch('https://some.url.com') | |
| .then(response => { | |
| if (response.ok) { | |
| return Promise.resolve(response); | |
| } | |
| else { | |
| return Promise.reject(new Error('Failed to load')); |
| function hasValues(hash, fields) { | |
| return fields.map(field => { | |
| return hash[field] != null; | |
| }).reduce((x, y) => { return x || y }); | |
| } | |
| hash = { name: "robert paulson", affiliation: "fightclub" }; | |
| hasValues(hash, [ "name", "email" ]); // => true | |
| hasValues(hash, [ "email" ]); // => false |
| food = ["bacon", "eggs", "cheese", "eggs", "bacon", "bacon", "bacon"] | |
| food.inject(Hash.new(0)) {|h, k| h[k] += 1; h }.sort_by {|k,v| -1 * v} | |
| # => [["bacon", 4], ["eggs", 2], ["cheese", 1]] |
| # From Rails console, run the following and you'll see raw Moped queries for every Mongoid query! | |
| Moped.logger = Logger.new(STDOUT) |
This is a small programming problem to test your technical ability and coding style.
Write a simple script to generate a set of n-grams from a string of text. N-grams are a contiguous sequence of n words from a string of text, and have many applications from full-text search indexes to machine learning.
You'll generate a set of every permutation of contiguous n-grams from a string of text, from 1-gram to n-grams where n is the number of words in the string