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## keybase.md

      
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              aantonop
            / keybase.md
          
          
            Created Sep 22, 2018
          
        
      View keybase.md
  
    
    Keybase proof
I hereby claim:

I am aantonop on github.
I am aantonop (https://keybase.io/aantonop) on keybase.
I have a public key ASBrgyjIGUGoCIINvfYXSj1X4F6y40UCVfUIOv8fTDAYAgo

To claim this, I am signing this object:

  
## rendered.html
<ul class="list-group">
   <li class="list-group-item">Style encapsulation and abstraction</li>
   <li class="list-group-item">Component wrapping</li>
   <li class="list-group-item">High order component</li>
</ul>

## FizzBuzz.cpp
#include<iostream>
int main(){
	using namespace std;
	for(int i=1; i<101; i++){
			if(i%3==0){
				cout<<"Fizz";
			}
			if(i%5==0){
				cout<<"Buzz";
			}

## parse_method.rb
def self.parse(string)
  commands = string.split("\n")
  rover = new(*commands.delete_at(0).split(' '))
  output = ''
  commands.each_slice(2) do |deploy_coords, instructions|
    rover.deploy(*deploy_coords.split(' '))
    output << rover.process(instructions) + "\n"
  end
  output
 end

## apply_log.py
import redbaron
import re


logfunctions = ['info', 'warning', 'error', 'critical', 'exception']


#If you dont use the logger.info('text',...) format, change this code
def should_handle(node):
    parent = node.parent

## index.html
<!DOCTYPE html>
<html lang="en" >

<head>
  <meta charset="UTF-8">
  <title>A Pen by  Pablo Mariño</title>


## command_where_to_turn.rb
def process(commands)
  commands.each_char do |character|
    case character
      when 'L' then turn_left
      when 'R' then turn_right
      when 'M' then move_forward
    end
  end

  [@x, @y, @face].join(' ')

## CountVectorizer_example2.py
from sklearn.feature_extraction.text import CountVectorizer

train_X = ["John likes to watch movies",
           "Mary likes movies too",
           "Joe only likes horror movies and action movies"]

vectorizer = CountVectorizer(token_pattern=r'\b\w+\b') # take a word as a token.
train_vector = vectorizer.fit_transform(train_X) # Learn the vocabulary dictionary and return term-document matrix.
token_set = vectorizer.get_feature_names() # the vocabulary dictionary: ['action', 'and', 'horror', 'joe', 'john', 'likes', 'mary', 'movies', 'only', 'to', 'too', 'watch']

## demoError.sol
pragma solidity ^0.4.0;

contract demoError{

    function demoAssert(){
        assert(1==2);
    }

    function demoThrow(){
        throw;

## hangman2.ts
function hangman2(secretWords: string, ...letters: string[]) {
  return secretWords.split('').every(char => letters.includes(char))
}