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Blog 2019/7/18

"Hello, world" in Clojure compiled with GraalVM running in Docker

Create a new minimal Clojure project using leiningen:

Binding to C Libraries with Nim

Auto-deploying built products to gh-pages with Travis

This is a set up for projects which want to check in only their source files, but have their gh-pages branch automatically updated with some compiled output every time they push.

Create a compile script

You want a script that does a local compile to e.g. an out/ directory. Let's call this compile.sh for our purposes, but for your project it might be npm build or gulp make-docs or anything similar.

The out/ directory should contain everything you want deployed to gh-pages. That almost always includes an index.html.

Code for Keras plays catch blog post

Train

python qlearn.py

Test

Generate figures

graph-tool on Ubuntu 14.04

New: See ansible-role-graph-tool for an automated way to install graph-tool and dependencies on many Ubuntu/Debian distros with Ansible

Here are the steps to install graph-tool on Ubuntu 14.04, including all the prerequisites:

sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
sudo apt-get update

INSTALLATION OF GRAPH-TOOL FROM SOURCE

System

OS : Ubuntu 14.04.1 LTS Desktop 64-bit (http://www.ubuntu.com/download/alternative-downloads#external)
Python Distribution : Anaconda Python (https://store.continuum.io/cshop/anaconda/)
Hardware : Lenovo Thinkpad L440

	## install Catalyst proprietary
	sudo ntfsfix /dev/sda2
	sudo cp /etc/X11/xorg.conf /etc/X11/xorg.conf.BAK
	sudo apt-get remove --purge fglrx*
	sudo apt-get install linux-headers-generic
	sudo apt-get install fglrx xvba-va-driver libva-glx1 libva-egl1 vainfo
	sudo amdconfig --initial

	## install build essentials
	sudo apt-get install cmake

	""" 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