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# Simulando probabilidades
# Teste
simula_eleicoes <- function(sd) {
ns <- seq(0.25, 0.75, 0.001)
v <- purrr::map_dbl(ns,  ~ mean(
  ifelse(
    rnorm(n = 100000, mean = .x, sd = sd) > .5
    , 1, 0 )
    )
  )

duarteguilherme

import os
from itertools import repeat
import requests
import json
from multiprocessing.dummy import Pool as ThreadPool

def le_json():
    """ Carrega arquivo de dados json para leitura
    """
    if os.path.isfile('./dados.json') is False:

duarteguilherme

library(tidyverse)


# Colliders

x1 <- rnorm(1000)
y <- 1.2 + 1.6 * x1 + rnorm(1000)
z <- 0.6 + 1.3*x1 + 1.5*y + rnorm(1000)

duarteguilherme

from PIL import Image, ImageFilter
import os
import requests
import numpy as np
import hashlib
import pickle

def classifica_arquivo():
    url = 'http://www.cmc.pr.gov.br/wspl/jcaptcha.jpg'
    content = requests.get(url)

duarteguilherme

from keras.models import Model, Input,Sequential
from keras.layers import Dense
from keras.optimizers import Adam # not important as there's no training here.
import numpy as np

from sklearn.datasets import load_digits
import numpy as np


digits = load_digits()

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import requests
from bs4 import BeautifulSoup
import json
import sys

class Crawler:
    def __init__(self, uf):
        self.url = "http://www.cnj.jus.br/bnmp/rest/pesquisar"
        self.uf = uf
        input("teste uf: " + uf + " pode ir?")

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import urllib.request 
import json

jsondata = json.loads(urllib.request.urlopen('https://reddit.com/.json').read())


# Dicionario guarda numero de cada estrutura de dados
count = { 'n_dicts': 0, 'n_lists': 0 } 

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lookback <- 50
model <- keras_model_sequential()
model %>%
  layer_lstm(units = lookback,input_shape=c(1,lookback), activation = "relu") %>% 
  layer_dense(units = 1, activation = "linear")



# try using different optimizers and different optimizer configs
model %>% compile(

duarteguilherme

Exercises for practicing tidyverse

Tidyverse is a set of packaged created by Hadley Wickham that changed the way R looks and works.

With these exercises, we intend to employ public datasets in order to challenge people.

Solving harder exercises, one can get better at tidy packages.

It's gonna be comprehensive of packages such as dplyr, purrr, tidyr, ggplot2, and lots of others

List of datasets included in packages.

duarteguilherme

• Encontrar o ponto de acesso para o banco da Digesto
• Achar lista de envolvidos no banco da Digesto