mickdelaney

[alias]
	co = checkout
	st = status
	ci = commit
	ad = add -A .
	pm = push origin master
	pl = pull origin master
[core]
	editor = code --wait
[pull]

mickdelaney

[user]
	name = someone
	email = someone@somewhere.com
[alias]
	st = status
	ci = commit
	ad = add -A .
	pm = push origin master
	pl = pull origin master
[core]

mickdelaney

<div ng-app="stepWizardApp" ng-controller="stepWizardCtrl">
  <ol class="step-wizard">
    <li ng-repeat="step in steps" ng-class="stepStyle($index)"ng-click="">
      <span class="number-circle" ng-bind="step.number"></span>
      <span class="step-name" ng-bind="step.name"></span>
    </li>
  </ol>
</div>

mickdelaney

upload-file angular-formly type

This is my upload-file type for what I use at work. We use angular-upload for the upload service to do the actual file uploading. We also have several abstractions and use ES6 that may confuse you a little bit (sorry about that). Hopefully this gets you started though.

mickdelaney

-- show running queries (pre 9.2)
SELECT procpid, age(query_start, clock_timestamp()), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(query_start, clock_timestamp()), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' 

mickdelaney

Host proxy-host
     Hostname host1
     port 22
     User username
     RequestTTY yes
     ForwardAgent yes
     IdentityFile /key-for-proxy.pem

Host target-host
     Hostname host2

mickdelaney

from pyspark.sql.types import StringType
from pyspark.sql.functions import udf

maturity_udf = udf(lambda age: "adult" if age >=18 else "child", StringType())

df = sqlContext.createDataFrame([{'name': 'Alice', 'age': 1}])
df.withColumn("maturity", maturity_udf(df.age))

mickdelaney

using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using Castle.MicroKernel.Registration;
using Castle.Windsor;
using MassTransit;
using MassTransit.Internals.Extensions;
using MassTransit.TestFramework;
using MassTransit.Pipeline;

mickdelaney

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

mickdelaney

Get-ChildItem .\ -include bin,obj -Recurse | foreach ($_) { remove-item $_.fullname -Force -Recurse }