WoodProgrammer

import json
import urllib2
import requests
def  awake_db():
    payload = json.dumps({

        "Hostname":"",
        "User":"",
        "Memory":0,
        "Memoryswap":0,

WoodProgrammer

##### HEAD_COMMAND
#!/bin/bash

x=$1
i=0
while read line
do
        echo $line

        ((i++))

WoodProgrammer

import math

def distance(class_of_cluster,x1,x2,y1,y2):
	y_square = math.pow((y2-y1),2)
	x_square = math.pow((x2-x1),2)

	return class_of_cluster,math.sqrt(x_square+y_square)


def knn(data_set,target_data,neighbors_count):

WoodProgrammer

<script src="http://code.jquery.com/jquery-latest.min.js"
        type="text/javascript"></script>


<link href="https://cdn.datatables.net/1.10.15/css/jquery.dataTables.min.css" rel="stylesheet"/>
<script src="https://cdn.datatables.net/1.10.15/js/jquery.dataTables.min.js"></script>

<table class="table table-striped table-bordered datatable" id="p_history">
    <thead>
        <tr>

WoodProgrammer

# -*- coding: utf-8 -*-

from celery import Celery
from kombu import Exchange, Queue
import time

# Celery Config

hastane_app = Celery()
celeryconfig = {}

WoodProgrammer

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

WoodProgrammer

import requests,json
class Photo2Down: 


    def __init__(self,TOKEN):
        self.TOKEN = TOKEN 

    def download(self,file_id):

        file_meta_data = requests.get("https://api.telegram.org/bot{}/getFile?file_id={}".format(self.TOKEN,file_id)) ## resme ait meta data boyut,path vs

WoodProgrammer

import telegram
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
import requests,json
from photo2Down import Photo2Down
TOKEN = "YOUR_TOKEN"
updater = Updater(token=TOKEN)
dispatcher = updater.dispatcher

photo_wrapper = Photo2Down(TOKEN)
def Start(bot, update):

WoodProgrammer

#!/bin/bash
SWARM_STAT=''
docker swarm init 

echo "Docker-Compose  PATH "
read compose_path
echo "Service Name"
read service_name

if [ "$compose_path" == "" ]; then

WoodProgrammer

from mininet.node import CPULimitedHost
from mininet.topo import Topo
from mininet.net import Mininet
from mininet.log import setLogLevel, info
from mininet.node import RemoteController
from mininet.cli import CLI

#4 switches and 4 hosts. They are connected in a star shape.