Mingzhou ZHUANG mingzhou

Download CMake from: https://cmake.org/download/

wget https://cmake.org/files/v3.12/cmake-3.12.3.tar.gz

Compile from source and install

tar zxvf cmake-3.*

This gist contains out.tex, a tex file that adds a PDF outline ("bookmarks") to the freely available pdf file of the book

The Elements of Statistical Learning (2nd ed), by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

https://web.stanford.edu/~hastie/ElemStatLearn/

The bookmarks allow to navigate the contents of the book while reading it on a screen.

Usage

In this gist I would like to describe an idea for GraphQL subscriptions. It was inspired by conversations about subscriptions in the GraphQL slack channel and different GH issues, like #89 and #411.

Conceptual Model

At the moment GraphQL allows 2 types of queries:

query
mutation

Reference implementation also adds the third type: subscription. It does not have any semantics yet, so here I would like to propose one possible semantics interpretation and the reasoning behind it.

Develop locally with Elasticsearch on OSX using Docker

Docker

Docker does not run natively on OSX, only Linux. Docker Machine was created to add a Linux VM environment to run Docker containers on OSX. Install using Homebrew:

brew install docker
brew install docker-machine
docker-machine create

	<#
	- BIOS of host machine also needs to be configured to allow hardware virtualization
	- Windows 10 Pro or otherwise is needed; Windows 10 Home Edition CANNOT get WSL
	- This gist WSLv2, but can use WSLv1 instead. I needed v1 as I run Windows 10 in a VM in Virtualbox.
	- WSLv2 has been giving me problems in Virtualbox 6.1, but WSLv1 works properly.
	- vbox has issues with the GUI settings when it comes to nested virtualization on certain systems,
	so run the following if needing to give a VM this enabled setting:

	VBoxManage modifyvm <vm-name> --nested-hw-virt on
	#>

	import graphql

	# build_executable schema
	#
	# accepts schema_definition (string) and resolvers (object) in style of graphql-tools
	# returns a schema ready for execution

	def build_executable_schema(schema_definition, resolvers):
	ast = graphql.parse(schema_definition)
	schema = graphql.build_ast_schema(ast)

	"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3"""
	import tensorflow as tf

	from tensorflow.examples.tutorials.mnist import input_data as mnist_data
	from tensorflow.contrib import slim
	from tensorflow.contrib.learn import ModeKeys
	from tensorflow.contrib.learn import learn_runner


	# Show debugging output

	import java.time.Instant
	import java.time.format.DateTimeFormatter
	import java.util.concurrent.ConcurrentLinkedQueue

	import sangria.ast._
	import sangria.execution._
	import sangria.schema.Context
	import sangria.marshalling.queryAst._
	import sangria.renderer.SchemaRenderer

	/*
	* Functional Programming in JavaScript
	* Chapter 01
	* Magical -run- function in support of Listing 1.1
	* Author: Luis Atencio
	*/
	// -run- with two functions
	var run2 = function(f, g) {
	return function(x) {
	return f(g(x));