OElesin

    def avgTime(message: String, f:  => Any) {
      var avg = 0L
      val c = 42
      1 to c foreach {
        _ =>
          val t0 = System.nanoTime()
          f
          val t1 = System.nanoTime()
          avg += t1 - t0
      }

OElesin

Squashing Git Commits

The easy and flexible way

This method avoids merge conflicts if you have periodically pulled master into your branch. It also gives you the opportunity to squash into more than 1 commit, or to re-arrange your code into completely different commits (e.g. if you ended up working on three different features but the commits were not consecutive).

Note: You cannot use this method if you intend to open a pull request to merge your feature branch. This method requires committing directly to master.

Switch to the master branch and make sure you are up to date:

OElesin

Sendy

Sendy is a self hosted email newsletter application that lets you send trackable emails via Amazon Simple Email Service (SES).

Heroku

You can deploy Sendy on Heroku using the following instructions (I assume you've already installed the heroku toolbelt).

1. On Heroku, create a new app.
2. Clone that app to your desktop

OElesin

/* Adapted from Tom Cunningham's 'Data Warehousing with MySql' (www.meansandends.com/mysql-data-warehouse) */

###### small-numbers table
DROP TABLE IF EXISTS numbers_small;
CREATE TABLE numbers_small (number INT);
INSERT INTO numbers_small VALUES (0),(1),(2),(3),(4),(5),(6),(7),(8),(9);

###### main numbers table
DROP TABLE IF EXISTS numbers;
CREATE TABLE numbers (number BIGINT);

OElesin

<?php
/* Caveat: I'm not a PHP programmer, so this may or may
 * not be the most idiomatic code...
 *
 * FPDF is a free PHP library for creating PDFs:
 * http://www.fpdf.org/
 */
require("fpdf.php");

class PDF extends FPDF {

OElesin

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

OElesin

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

OElesin

from __future__ import print_function

from pyspark import SparkContext, SparkConf
from pyspark.mllib.linalg import DenseVector, VectorUDT
from pyspark.sql import SQLContext

from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.sql.types import StructType, StructField, StringType, DoubleType, ArrayType

OElesin

/*
This example uses Scala.  Please see the MLlib documentation for a Java example.

Try running this code in the Spark shell.  It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above.

This example is paired with a blog post on LDA in Spark: http://databricks.com/blog
Spark: http://spark.apache.org/
*/

import scala.collection.mutable

OElesin

export SCALA_VERSION=scala-2.11.5
sudo wget http://www.scala-lang.org/files/archive/${SCALA_VERSION}.tgz
sudo echo "SCALA_HOME=/usr/local/scala/scala-2.11.5" > /etc/profile.d/scala.sh
sudo echo 'export SCALA_HOME' >> /etc/profile.d/scala.sh
sudo mkdir -p /usr/local/scala
sudo -s cp $SCALA_VERSION.tgz /usr/local/scala/
cd /usr/local/scala/
sudo -s tar xvf $SCALA_VERSION.tgz
sudo rm -f $SCALA_VERSION.tgz
sudo chown -R root:root /usr/local/scala