ryanbekabe

## README.md

      
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                ryanbekabe
                / README.md
            
            
              Created
              July 20, 2020 00:43
                — forked from nichtich/README.md
            
              
                How to automatically deploy from GitHub
              
          
    Deploy your site with git

This gist assumes:

you have an online remote repository (github / bitbucket etc.)
you have a local git repo
and a cloud server (Rackspace cloud / Amazon EC2 etc)

your (PHP) scripts are served from /var/www/html/
your webpages are executed by Apache


the Apache user is named www-data (may be apache on other systems)


## emoji.php
<?php

// MySQL schema
/*
CREATE TABLE `test` (
  `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
  `text` text,
  `blob` blob,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=latin1;

## vhost.py
#! /usr/bin/python3.5
#Ubuntu 16.04.6 LTS
#Apache/2.4.18 (Ubuntu)
#Source: https://gist.githubusercontent.com/fideloper/2710970/raw/cbe308f44bfd299a3f410bb1a06aa67a2753763a/vhost.py
#Modif: 12:43 11/05/2020 - bekabeipa@gmail.com - hanyajasa.com
#Example: python3.5 vhost.py -d /var/www/html/lab -s lab.bekabe.my.id

from sys import argv
from os.path import exists
from os import makedirs

## permute-stl.cpp
//
// An example of how to generate permutations
// using STL algorithms library
//
#include <iostream>
#include <algorithm>
using namespace std;

int main () {
  const int N = 8;

## python_sjt_coroutines.py
from time import sleep

__author__ = "Sahand Saba"


def nobody():
    while True:
        yield False


## log_loss.py
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
import numpy as np

x = np.array([-2.2, -1.4, -.8, .2, .4, .8, 1.2, 2.2, 2.9, 4.6])
y = np.array([0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])

logr = LogisticRegression(solver='lbfgs')
logr.fit(x.reshape(-1, 1), y)

## tea.py
# coding: utf-8
"""
Implementation of the Tiny Encryption Algorithm (TEA) for Python
https://en.wikipedia.org/wiki/Tiny_Encryption_Algorithm

Example Usage:

import tea

# The key must be 16 characters

## dna.py
sample = ['GTA','GGG','CAC']

def read_dna(dna_file):
  dna_data = ""
  with open(dna_file, "r") as f:
    for line in f:
      dna_data += line
  return dna_data

def dna_codons(dna):

## rsa.py
'''
620031587
Net-Centric Computing Assignment
Part A - RSA Encryption
'''

import random


'''

## rsa_p36.py
"""
Implementation of RSA cryptography
using samples of large numbers
"""
import random
import sys
import math

from random import randrange
	<?php

	// MySQL schema
	/*
	CREATE TABLE `test` (
	`id` int(11) unsigned NOT NULL AUTO_INCREMENT,
	`text` text,
	`blob` blob,
	PRIMARY KEY (`id`)
	) ENGINE=InnoDB DEFAULT CHARSET=latin1;
	#! /usr/bin/python3.5
	#Ubuntu 16.04.6 LTS
	#Apache/2.4.18 (Ubuntu)
	#Source: https://gist.githubusercontent.com/fideloper/2710970/raw/cbe308f44bfd299a3f410bb1a06aa67a2753763a/vhost.py
	#Modif: 12:43 11/05/2020 - bekabeipa@gmail.com - hanyajasa.com
	#Example: python3.5 vhost.py -d /var/www/html/lab -s lab.bekabe.my.id

	from sys import argv
	from os.path import exists
	from os import makedirs
	//
	// An example of how to generate permutations
	// using STL algorithms library
	//
	#include <iostream>
	#include <algorithm>
	using namespace std;

	int main () {
	const int N = 8;
	from time import sleep

	__author__ = "Sahand Saba"


	def nobody():
	while True:
	yield False
	from sklearn.linear_model import LogisticRegression
	from sklearn.metrics import log_loss
	import numpy as np

	x = np.array([-2.2, -1.4, -.8, .2, .4, .8, 1.2, 2.2, 2.9, 4.6])
	y = np.array([0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])

	logr = LogisticRegression(solver='lbfgs')
	logr.fit(x.reshape(-1, 1), y)
	# coding: utf-8
	"""
	Implementation of the Tiny Encryption Algorithm (TEA) for Python
	https://en.wikipedia.org/wiki/Tiny_Encryption_Algorithm

	Example Usage:

	import tea

	# The key must be 16 characters
	sample = ['GTA','GGG','CAC']

	def read_dna(dna_file):
	dna_data = ""
	with open(dna_file, "r") as f:
	for line in f:
	dna_data += line
	return dna_data

	def dna_codons(dna):
	'''
	620031587
	Net-Centric Computing Assignment
	Part A - RSA Encryption
	'''

	import random


	'''
	"""
	Implementation of RSA cryptography
	using samples of large numbers
	"""
	import random
	import sys
	import math

	from random import randrange