Enrique Correa isseu

## feriados.csv

          
            2011-01-01
            Año Nuevo

            
              2011-04-22
              Viernes Santo

            
              2011-04-23
              Sábado Santo

            
              2011-05-01
              Día Nacional Del Trabajo

            
              2011-05-21
              Día De Las Glorias Navales

            
              2011-05-31
              Centenario De La Fundación De Puerto Natales

            
              2011-06-27
              San Pedro Y San Pablo

            
              2011-07-16
              Virgen Del Carmen

            
              2011-08-15
              Asunción De La Virgen

            
              2011-08-20
              Nacimiento Del Prócer De La Independencia

## Bitbucket Pipeline Rails.yml
# This is a sample build configuration for Ruby.
# Check our guides at https://confluence.atlassian.com/x/VYk8Lw for more examples.
# Only use spaces to indent your .yml configuration.
# -----
# You can specify a custom docker image from Docker Hub as your build environment.
image: ruby:2.2.4

clone:
  depth: 3

## CartPole-v0-Keras.py
from __future__ import division
import gym
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import sgd
import os
import random
from os.path import isfile
from collections import deque

## SqueezeNet TFLearn.py
self.network = input_data(shape = [None, SIZE_FACE, SIZE_FACE, 1])
self.network = conv_2d(self.network, 96, 3, strides = 3, activation = 'relu')
self.network = max_pool_2d(self.network, 3, strides = 2)

# Fire 1
fire2_squeeze = conv_2d(self.network, 16, 1, activation = 'relu')
fire2_expand1 = conv_2d(fire2_squeeze, 64, 1, activation = 'relu')
fire2_expand2 = conv_2d(fire2_squeeze, 64, 3, activation = 'relu')
self.network = merge([fire2_expand1, fire2_expand2], mode = 'concat', axis = 1)

## CartPole-v0-TFLearn.py
from __future__ import division
import gym
import numpy as np
import tflearn
from tflearn.data_utils import to_categorical
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn import lstm, embedding
from tflearn.layers.estimator import regression
import random
from collections import deque

## Update Heroku Letsencrypt
./letsencrypt-auto certonly -d www.domain.com -d domain.com --manual

We put information in /public/.well-known/acme-challenge.

We update certs in Heroku

sudo heroku certs:update /etc/letsencrypt/live/domain.com/fullchain.pem /etc/letsencrypt/live/domain.com/privkey.pem

## check_K_palindrome.rb
# Edit-Distance algorithm

def edit_distance_alg(string1, string2)
	lenString1 = string1.length
	lenString2 = string2.length
	if([lenString1, lenString2].min == 0)
		return [lenString1, lenString2].max
	end
	return [
		edit_distance_alg(string1[0..-2],  string2) + 1,

## crawler-ramos-uc.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Credits isseu
import urllib2
import re
from titlecase import titlecase
import unicodedata

cursos = []
data = urllib2.urlopen("http://catalogo.uc.cl/index.php?Itemid=55").read().replace("\n", "")

## dolar_chile.php
<?php
/**
* Código para obtener el precio del dolar desde la bolsa electrónica de santiago
*/
class BolsaElectronicaDolarAPI
{
	private $dominio = "http://www.bolchile.cl";
	private $url = "/portlets/Dolar2Portlet/XmlApiGraf?idioma=es&intervalo=60&periodo=HOY&mayorA10=true";
	private $ultimos_cuanto = 30;