linearregression

This is a quick sample on how I have used websocket_client to test Erlang WebSocket-based applications. To
taste it you will need to have a copy of websocket_client from my fork, available at:

https://github.com/leandrosilva/erlang_websocket/blob/master/src/websocket_client.erl

The websocket_client module was made by Dave Bryson on erlang_websocket project:

https://github.com/davebryson/erlang_websocket

Enjoy!

linearregression

-module(et).

% $ erl -make ; erl -sname eu -noshell -eval 'eunit:test(".", []).'  -s init stop

-include_lib("eunit/include/eunit.hrl").

pattern1_test_no()->
    {inparallel,
     [
      {setup, local,

linearregression

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

linearregression

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *

linearregression

//==================================================================
// SPARK INSTRUMENTATION
//==================================================================

import com.codahale.metrics.{MetricRegistry, Meter, Gauge}
import org.apache.spark.{SparkEnv, Accumulator}
import org.apache.spark.metrics.source.Source
import org.joda.time.DateTime
import scala.collection.mutable

linearregression

# alteRyx_install_packages.R
#  > code to install packages via R Developer tool in Alteryx(R) module
#  > note call to custom Alteryx-R function wrte.Alteryx()

altx.repo <- getOption("repos")
altx.repo["CRAN"] <- "http://cran.rstudio.com"  # set your primary repo if you haven't already
options(repos = altx.repo)
#write.Alteryx(getOption("repos"), 1)  # DEBUG
install.packages("XML")

linearregression

This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown) 
using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
This combination of tools provides an exciting improvement in usability for 
[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
Specifically, this post 
(1) discusses getting started with R Markdown and `knitr` in  Rstudio 0.96;
(2) provides a basic example of producing console output and plots using R Markdown;
(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and 
(5) discusses the implications of R Markdown.

linearregression

On master node

wget http://archive.cloudera.com/cdh5/one-click-install/redhat/6/x86_64/cloudera-cdh-5-0.x86_64.rpm
sudo yum --nogpgcheck localinstall cloudera-cdh-5-0.x86_64.rpm
sudo yum clean all
sudo yum install hadoop-hdfs-namenode
sudo yum install R git
sudo yum install spark-core spark-master spark-python

cd

linearregression

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

linearregression

XVFB=/usr/bin/Xvfb
XVFBARGS=":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset"
PIDFILE=/var/run/xvfb.pid
case "$1" in
  start)
    echo -n "Starting virtual X frame buffer: Xvfb"
    start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS
    echo "."
    ;;
  stop)