netologist

#!/bin/sh
if [ "$1" == "" ]; then
  echo "Usage: $0 <PROJECT_ROOT> <GITHUB_REPO>"
  exit 1
fi
if [ "$2" == "" ]; then
  echo "Usage: $0 <PROJECT_ROOT> <GITHUB_REPO>"
  exit 1
fi

netologist

package main

import (
	"fmt"
	"regexp"
)

// Turkish Alpabetic Numeric "(),\-./"
func IsAlphaNumeric(text string) (ok bool) {
	ok, _ = regexp.MatchString("^([\\(\\)\\,\\\\\\-\\./ a-zA-ZğüşıöçĞÜŞİÖÇ0-9]+)$", text)

netologist

const items = [1,52,14,54,[10,1,3,5],14,6];

const flatten = (arr) => {
  const flat = [].concat(...arr)
  return flat.some(Array.isArray) ? flatten(flat) : flat;
}

const splitAt = (arr, predicate) => {
  var selected = [];
  var others = [];

netologist

# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048

netologist

include $(GOROOT)/src/Make.inc

GOFMT=gofmt -spaces=true -tabindent=false -tabwidth=4

all:
	$(GC) jsontest.go 
	$(LD) -o jsontest.out  jsontest.$O

format:
	$(GOFMT) -w jsontest.go

netologist

Sublime Text 2 – Useful Shortcuts (Mac OS X)

General

⌘T	go to file
⌘⌃P	go to project
⌘R	go to methods
⌃G	go to line
⌘KB	toggle side bar
⌘⇧P	command prompt

netologist

/* This is intentionally blank just and exists to secure a decent gist name */

netologist

function toSeconds( time ) {
    var parts = time.split(':');
    return (+parts[0]) * 60 * 60 + (+parts[1]) * 60 + (+parts[2]); 
}

function toHHMMSS(sec) {
    var sec_num = parseInt(sec, 10); // don't forget the second parm
    var hours   = Math.floor(sec_num / 3600);
    var minutes = Math.floor((sec_num - (hours * 3600)) / 60);
    var seconds = sec_num - (hours * 3600) - (minutes * 60);

netologist

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

netologist

import com.typesafe.config.ConfigFactory

import scala.slick.jdbc.JdbcBackend.Database

class BaseDAO {
  lazy val conf = ConfigFactory.load()
  lazy val uri = conf.getString(s"spray.oauth2.datasource.uri")
  lazy val user = conf.getString(s"spray.oauth2.datasource.username")
  lazy val password = conf.getString(s"spray.oauth2.datasource.password")
  lazy val driver = conf.getString(s"spray.oauth2.datasource.driver")