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set.seed(100)
N <- 10000000
USER_NUM <- 25000
MOVIE_NUM <- 10000
dayOfWeek = rnorm(N, 6, 2)
partOfDay = c(1,2,3,3,4,4,4)
isRainOrSnow = c(0,0,0,1)
isRainOfSnowBefore = c(0,0,0,1)
temperature = c(0,1,2,3,4)
cameFrom = c(0,1,2,3,4,5,6,7)

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# read dataset from local file
data <- read.csv("/Users/kostya/Downloads/abalone.data.csv", header=F)

# set names for dataframe columns
colnames(data) <- c('Sex', 'Length', 'Diameter', 'Height', 'WholeWeight', 'ShuckedWeight',
                    'VisceraWeight', 'ShellWeight', 'Rings')

# split dataset into train and test seta
train.size <- floor(0.9 * nrow(data))

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library(ggplot2)

# read dataset from local file
abalone <- read.csv("/Users/kostya/Downloads/abalone.data.csv", header=F)

# set names for dataframe columns
colnames(abalone) <- c('Sex', 'Length', 'Diameter', 'Height', 'WholeWeight', 'ShuckedWeight',
                    'VisceraWeight', 'ShellWeight', 'Rings')

# plot histogram

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@Grab(group="org.seleniumhq.selenium", module="selenium-java", version="2.42.2")

import org.openqa.selenium.*;
import org.openqa.selenium.chrome.*;

System.setProperty("webdriver.chrome.driver", "/Users/kostya/Downloads/chromedriver");

ChromeOptions chromeOptions = new ChromeOptions();
chromeOptions.addArguments("--verbose", "--ignore-certificate-errors");
WebDriver driver = new ChromeDriver();

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library(rmr2)

rmr.options(backend = "local")
#hdfs.init()

lm.map = 
  function(., line) {
    keyval( line[[1]], paste(line[[2]], line[[3]], sep="|"))
  }

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# Spark scala project
mvn archetype:generate -B -DarchetypeGroupId=net.alchim31.maven -DarchetypeArtifactId=scala-archetype-simple -DarchetypeVersion=1.5  -DgroupId=org.apache.spark -DartifactId=<Project name> -Dversion=0.1-SNAPSHOT -Dpackage=org.apache.spark

# flink project
mvn archetype:generate                             \
    -DarchetypeGroupId=org.apache.flink            \
    -DarchetypeArtifactId=flink-quickstart-scala   \
    -DarchetypeVersion=0.9.1                       \
    -DgroupId=org.apache.flink.quickstart          \
    -DartifactId=flink-scala-project               \

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import sys
import random
import datetime

TNX_NUM =  int(sys.argv[1])
ACCOUNT_NUM =  int(sys.argv[2])
RATES_NUM = int(sys.argv[3])
PAR_ID = int(sys.argv[4])

def get_random(top_limit):

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import tensorflow as tf
import numpy as np

print(tf.__version__)

def calc_fx1(fx, x):
    return tf.gradients(fx, x)[0]

def calc_fx2(fx, x):
    return tf.gradients(tf.gradients(fx, x)[0], x)[0] 

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from aws_xray_sdk.core import xray_recorder

@xray_recorder.capture("get_balance")
def get_balance(user_acc_id):
  """ ... """
  ...