Mark Saroufim msaroufim

Switch To Vim For Good

NOTE: This guide has moved to https://github.com/bpierre/switch-to-vim-for-good

This guide is coming from an email I used to send to newcomers to Vim. It is not intended to be a complete guide, it is about how I switched myself.

My decision to switch to Vim has been made a long time ago. Coming from TextMate 1, I wanted to learn an editor that is Open Source (so I don’t lose my time learning a tool that can be killed), cross platform (so I can use it everywhere), and powerful enough (so I won’t regret TextMate). For these reasons, Vim has always been the editor I wanted to learn, but it took me several years before I did it in a way that works for me. I tried to switch progressively, using the Janus Vim distribution for a few months, then got back to using TextMate 2 for a time, waiting for the next attempt… here is what finally worked for me.

Original gist with comments: https://gist.github.com/bpierre/0a0025d348b6001394e0

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

General rules

Follow standard conventions.
Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
Boy scout rule. Leave the campground cleaner than you found it.
Always find root cause. Always look for the root cause of a problem.

Design rules

Usually, located at /usr/local/cuda/bin

Non-Visual Profiler

$ nvprof python train_mnist.py

I prefer to use --print-gpu-trace.

	/*
	This example uses Scala. Please see the MLlib documentation for a Java example.

	Try running this code in the Spark shell. It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above.

	This example is paired with a blog post on LDA in Spark: http://databricks.com/blog
	Spark: http://spark.apache.org/
	*/

	import scala.collection.mutable

	#!/usr/bin/env python3
	# -- coding: utf-8 --

	import sys
	import math

	import numpy as np

	sys.path.append('xgboost/wrapper/')
	import xgboost as xgb

	"""A stripped-down MLP example, using Theano.

	Based on the tutorial here: http://deeplearning.net/tutorial/mlp.html

	This example trims away some complexities, and makes it easier to see how Theano works.

	Design changes:

	* Model compiled in a distinct function, so that symbolic variables are not in run-time scope.
	* No classes. Network shown by chained function calls.

	""" 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

	#!/bin/bash

	USER=${1:-sebble}

	STARS=$(curl -sI https://api.github.com/users/$USER/starred?per_page=1\|egrep '^Link'\|egrep -o 'page=[0-9]+'\|tail -1\|cut -c6-)
	PAGES=$((658/100+1))

	echo You have $STARS starred repositories.
	echo

	# coding: utf-8
	import re


	# Class WeightedGraph
	class WeightedGraph:
	def __init__(self, path):
	# open file to initialize the graph
	file = open(path, "r")
	p = re.compile("\d+")