Deniz Balcı SirmaXX

## client.py
import asyncio
import websockets
import json

async def hello():
    async with websockets.connect('ws://localhost:8001/ws') as websocket:
        message = {"user_id_foreginkey": 0, "message": "sson123", "game_room_foreignkey": 0, "game_date": "2023-03-29"}
        await websocket.send(json.dumps(message))
        response = await websocket.recv()
        print(response)

## table.txt
+---------------------------------------------+------------------------------------------------+
| Avantajları                                 | Dezavantajları                                 |
+---------------------------------------------+------------------------------------------------+
| Kolay konfigürasyon                         | Arayüze sahip olmaması(konsol üzerinden        |
|                                             | sistemimizi yapılandıracağız.)                 |
+---------------------------------------------+------------------------------------------------+
| Birçok farklı yapıyı bir arada kullanabilme | Daha fazla ram kullanımı                       |
+---------------------------------------------+------------------------------------------------+
| Daha az disk alanı kullanımı                | Disk yönetimini daha zorlu (dockerda volumelar |
|                                             | yani sanal diskler kullanılıyor)               |

## hideorremove.html
<!DOCTYPE html>
<html>
<body>
<head>
  <style>
    #demo1{
      background-color: tomato;
      color: aliceblue;
      width: 150px;
      height: 150px;

## index.php
<?php

// update job
$data_file='Json_Folder/products.json';

$id =15;
$data = file_get_contents($data_file);
$json =  json_decode($data, true);

foreach($json as &$a){

## upsetWithoutlibrary.R

values <- c(1:5)
# define area for the histogram
par(fig=c(0.1,0.7,0.3,0.9))
barplot(values)
# define area for the boxplot
par(fig=c(0.8,1,0,1), new=TRUE)
stripchart(values, method="jitter")
# define area for the stripchart
par(fig=c(0.1,0.67,0.1,0.25), new=TRUE)

## maewhile.R
startTime <- Sys.time()

y<-c(10,8,6,4,2)
x<-c(5,4,3,2,1)
n<-length(y)

toplam=0

i <- 1
while (i <= n) {

## maefor.R

startTime <- Sys.time()

y<-c(10,8,6,4,2)
x<-c(5,4,3,2,1)
n<-length(y)

toplam=0
for (i in 1:n) {
  diff=y[i]-x[i]

## Factory_Log.py


class Log:
    global date_time
    now = datetime.now()
    date_time = now.strftime("%m/%d/%Y, %H:%M:%S")


    def __init__(self,severity,message,user):
       self.severity=severity

## solution.R
---
title: "R Notebook"
output: html_notebook
---


# Chapter 10  Canonical correlation Analysis  (CH10 p 539)

Chapter is related to Partitioning the covariance matrix (p73 ch2) .(ABbrevation :kısaltma )

## chapter8.Rmd

# Multivariate Statisttics 7.Week Lecture Notes

# Multivariate Multiple Regression Analysis

You can find this topic in the chapter 8 in the book.

1. Multiple Regression

$$  Y = X\beta +\varepsilon ~~\to \text{Regression Model} $$
	import asyncio
	import websockets
	import json

	async def hello():
	async with websockets.connect('ws://localhost:8001/ws') as websocket:
	message = {"user_id_foreginkey": 0, "message": "sson123", "game_room_foreignkey": 0, "game_date": "2023-03-29"}
	await websocket.send(json.dumps(message))
	response = await websocket.recv()
	print(response)
	+---------------------------------------------+------------------------------------------------+
	\| Avantajları \| Dezavantajları \|
	+---------------------------------------------+------------------------------------------------+
	\| Kolay konfigürasyon \| Arayüze sahip olmaması(konsol üzerinden \|
	\| \| sistemimizi yapılandıracağız.) \|
	+---------------------------------------------+------------------------------------------------+
	\| Birçok farklı yapıyı bir arada kullanabilme \| Daha fazla ram kullanımı \|
	+---------------------------------------------+------------------------------------------------+
	\| Daha az disk alanı kullanımı \| Disk yönetimini daha zorlu (dockerda volumelar \|
	\| \| yani sanal diskler kullanılıyor) \|
	<!DOCTYPE html>
	<html>
	<body>
	<head>
	<style>
	#demo1{
	background-color: tomato;
	color: aliceblue;
	width: 150px;
	height: 150px;
	<?php

	// update job
	$data_file='Json_Folder/products.json';

	$id =15;
	$data = file_get_contents($data_file);
	$json = json_decode($data, true);

	foreach($json as &$a){

	values <- c(1:5)
	# define area for the histogram
	par(fig=c(0.1,0.7,0.3,0.9))
	barplot(values)
	# define area for the boxplot
	par(fig=c(0.8,1,0,1), new=TRUE)
	stripchart(values, method="jitter")
	# define area for the stripchart
	par(fig=c(0.1,0.67,0.1,0.25), new=TRUE)
	startTime <- Sys.time()

	y<-c(10,8,6,4,2)
	x<-c(5,4,3,2,1)
	n<-length(y)

	toplam=0

	i <- 1
	while (i <= n) {


	class Log:
	global date_time
	now = datetime.now()
	date_time = now.strftime("%m/%d/%Y, %H:%M:%S")


	def __init__(self,severity,message,user):
	self.severity=severity
	---
	title: "R Notebook"
	output: html_notebook
	---


	# Chapter 10 Canonical correlation Analysis (CH10 p 539)

	Chapter is related to Partitioning the covariance matrix (p73 ch2) .(ABbrevation :kısaltma )

	# Multivariate Statisttics 7.Week Lecture Notes

	# Multivariate Multiple Regression Analysis

	You can find this topic in the chapter 8 in the book.

	1. Multiple Regression

	$$ Y = X\beta +\varepsilon ~~\to \text{Regression Model} $$