This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.
To capture the video (filesize: 19MB), using the free "QuickTime Player" application:
| # vim mode | |
| # also need to edit .inputrc | |
| set -o vi | |
| bind '"kj":vi-movement-mode' | |
| alias rm=rmtrash | |
| export CLICOLOR=1 | |
| export LSCOLORS=GxFxCxDxBxegedabagaced |
| """ 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 |
| import os | |
| from moviepy.editor import ImageSequenceClip | |
| def gif(filename, array, fps=10, scale=1.0): | |
| """Creates a gif given a stack of images using moviepy | |
| Notes | |
| ----- | |
| works with current Github version of moviepy (not the pip version) |
| # A simple cheat sheet of Spark Dataframe syntax | |
| # Current for Spark 1.6.1 | |
| # import statements | |
| from pyspark.sql import SQLContext | |
| from pyspark.sql.types import * | |
| from pyspark.sql.functions import * | |
| #creating dataframes | |
| df = sqlContext.createDataFrame([(1, 4), (2, 5), (3, 6)], ["A", "B"]) # from manual data |
| from numpy.linalg import solve | |
| class ExplicitMF(): | |
| def __init__(self, | |
| ratings, | |
| n_factors=40, | |
| item_reg=0.0, | |
| user_reg=0.0, | |
| verbose=False): | |
| """ |
| # Implements Create, Debug, and Delete Lattice Instance usable from within a Python app | |
| # | |
| import os, jsonr | |
| import requests | |
| # Note these are only a few variables, closer inspection of this Gist will show that there is alot of possibility here based on your app container. | |
| # needed to make requests | |
| auth_header = {'Authorization':'Basic YmJlcnRrYTprYXJtYTE5NzY='} |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |