mightu autosquid

tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

remap prefix to Control + a

Samuel's Python3 setup for Mac:

This document describes in which way, what packages, and from where are installed on my Macs to get a decent Python 3.x, scipy/numpy & Co setup.

Note, I switched to Python's 3.x pyvenv from virtualenv in order to handle my pip-installable packages. Also thanks to Anaconda, the whole setup is so simple now that I use this document just to remember which additional packages I like.

Install Homebrew

	# Mac OS X Lion introduced a new, iOS-like context menu when you press and hold a key
	# that enables you to choose a character from a menu of options. If you are on Lion
	# try it by pressing and holding down 'e' in any app that uses the default NSTextField
	# for input.
	#
	# It's a nice feature and continues the blending of Mac OS X and iOS features. However,
	# it's a nightmare to deal with in Sublime Text if you're running Vintage (Vim) mode,
	# as it means you cannot press and hold h/j/k/l to move through your file. You have
	# to repeatedly press the keys to navigate.

	#!/bin/bash
	#Heith Seewald 2012
	#Feel free to extend/modify to meet your needs.
	#Maya on Ubuntu v.1
	#This is the base installer... I’ll add more features in later versions.
	#if you have any issues, feel free email me at [email protected]

	#### Lets run a few checks to make sure things work as expected.
	#Make sure we’re running with root permissions.
	if [ `whoami` != root ]; then

	# Installing OpenCV python libs on mac to work with virtualenv
	# OpenCV 2.4.3
	# Python 2.7.3 installed with brew

	# assuming you have virtualenv, pip, and python installed via brew
	# assuming $WORKON_HOME is set to something like ~/.virtualenvs

	# using homebrew - make sure we're current
	brew update

	//copied from http://stackoverflow.com/questions/17424477/implementation-c14-make-integer-sequence

	template<int...> struct seq { using type = seq; };
	template<typename T1, typename T2> struct concat;
	template<int... I1, int... I2> struct concat<seq<I1...>, seq<I2...>>: seq<I1..., (sizeof...(I1) + I2)...> {};

	template<int N> struct gen_seq;
	template<int N> struct gen_seq: concat<typename gen_seq<N/2>::type, typename gen_seq<N-N/2>::type>::type {};
	template <> struct gen_seq<0>: seq<>{};
	template <> struct gen_seq<1>: seq<0>{};

	// generate [0..n-1]
	auto seq = [](size_t n) -> std::vector<size_t> {
	std::vector<size_t> v(n);
	for (size_t i=0; i<n; ++i) v[i] = i;
	return v;
	};
	auto index = seq(n);

	// n * n distance matrix
	std::vector<D> dists(n * n);

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

	# This is a simplified implementation of the LSTM language model (by Graham Neubig)
	#
	# LSTM Neural Networks for Language Modeling
	# Martin Sundermeyer, Ralf Schlüter, Hermann Ney
	# InterSpeech 2012
	#
	# The structure of the model is extremely simple. At every time step we

	https://www.gnu.org/licenses/gpl-3.0.en.html

	GNU GENERAL PUBLIC LICENSE v3