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var conn = new Mongo(),
    exercises

db = conn.getDB("educatopiadev")

exercises = db.exercises.find()

exercises.forEach(function (exercise) {
    db.exercises.update({_id: exercise._id}, { $set: { revisions: [] }});
})

flxw

module.exports = {
  config: {
    // default font size in pixels for all tabs
    fontSize: 12.5,

    // font family with optional fallbacks
    fontFamily: '"Meslo LG S for Powerline"',

    // terminal cursor background color and opacity (hex, rgb, hsl, hsv, hwb or cmyk)
    cursorColor: 'rgba(248,28,229,0.8)',

flxw

module.exports = {
  config: {
    // default font size in pixels for all tabs
    fontSize: 14,

    // font family with optional fallbacks
    fontFamily: '"Inconsolata for Powerline"',

    // terminal cursor background color and opacity (hex, rgb, hsl, hsv, hwb or cmyk)
    cursorColor: 'rgba(248,28,229,0.8)',

flxw

module.exports = {
  config: {
    // default font size in pixels for all tabs
    fontSize: 14,

    // font family with optional fallbacks
    fontFamily: '"Inconsolata for Powerline"',

    // terminal cursor background color and opacity (hex, rgb, hsl, hsv, hwb or cmyk)
    cursorColor: 'rgba(248,28,229,0.8)',

flxw

from PIL import Image
import multiprocessing

def test_screen(pic): #Prozess
    file_prefix = 'MA_HA1_drawing_'
    file_postfix = '.png'
    file_name = file_prefix + str(pic) + file_postfix

    im = Image.open(file_name)
    pix = im.load()

flxw

source('load_data.R')
d = read_and_preprocess_data_file('data/BADS_WS1718_known.csv')
library(dplyr)

# 1st idea (the image I previously shared on WhatsApp)
# relationship between total orders (item_count, not transaction count) and return count (again the item_count)
# Are users who order more "wiser" in terms of picking the right items than small-volume users?
orders_and_returns_per_user = d %>%
  group_by(user_id) %>%
  summarize(nreturn=sum(return), norder=n())

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source('load_data.R')
d = read_and_preprocess_data_file('data/BADS_WS1718_known.csv')
d = subset(d, select = -c(delivery_date)) # remove NAs

classdata = read_and_preprocess_data_file('data/BADS_WS1718_class.csv')
classdata = subset(classdata, select = -c(delivery_date)) # remove NAs

# train the final model with 632 bootstrapping
for (iter in 1:400) {
  # sample with replacement here - to understand why please refer to the book

flxw

source('load_data.R')

d = read_and_preprocess_data_file('data/BADS_WS1718_known.csv')
n = nrow(d)
ratio = sum(d$return) / n

set.seed(1)
accuracy = 100000
randomized_returns = sample(accuracy, n, replace=TRUE)/accuracy
randomized_returns = ifelse(randomized_returns > ratio, 1, 0)

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standardize <- function(x){
  mu <- mean(x)
  std <- sd(x)
  result <- (x - mu)/std

  return(result)
}

..static_user_statistics <- NULL
..read_and_preprocess_data_file = function(fp) {

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source('load_data.R')
# all you need to do
# now two variables are available in your environment:
# df_known - data frame with features from BADS_WS1718_known.csv
# df_class - data frame with features from BADS_WS1718_class.csv