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# from : https://stackoverflow.com/questions/36587211/easiest-way-to-read-csv-files-with-multiprocessing-in-pandas

import os
import pandas as pd

from multiprocessing import Pool
from glob import glob

data_files = glob(os.path.join(data_dir, "*.xlsx"))

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// From : https://leetcode.com/problems/two-sum

pub struct Inputs {
    pub nums: Vec<i32>,
    pub target: i32,
}

pub trait Solution {
    fn two_sum(&self) -> Vec<i32>;
    fn result_solution(&self) -> String {

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//use std::cmp;

/*
impl Solution {
    pub fn two_sum(nums: Vec<i32>, target: i32) -> Vec<i32> {
        
    }
}
*/

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library("shiny")
library("ggplot2")
library("dplyr")

# reference : 
# https://shiny.rstudio.com/gallery/plot-interaction-basic.html
# https://shiny.rstudio.com/gallery/plot-interaction-zoom.html

data <- mtcars %>% mutate(id = 1:n())

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# coding: utf-8

# From : https://gist.github.com/haven-jeon/6b508f4547418ab26f6e56b7a831dd9a#file-word2vec-ipynb

# !git clone https://github.com/haven-jeon/KoWordSpacing
# !bunzip2 KoWordSpacing/input.txt.bz2
# # 임베딩 평가 데이터
# !git clone https://github.com/SungjoonPark/KoreanWordVectors

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package main

import (
        "fmt"
        "github.com/kniren/gota/dataframe"
        tf "github.com/tensorflow/tensorflow/tensorflow/go"
        "github.com/tensorflow/tensorflow/tensorflow/go/op"
        "os"
)

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// From : https://www.edwith.org/datascience/lecture/33888/
// edwith에서 한글자막을 달아놓은 MIT의 Data Science 강좌중 첫번째 챕터입니다.
// 위 링크에서 파이썬 버전의 코드를 받아보실 수 있습니다.
// 칼로리가 제한될 때, 음식을 선택하는 문제를 탐욕 알고리즘으로 푸는 문제입니다.
// 파이썬 코드를 Golang으로 변환했습니다.

package main

import (
	"flag"

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# 스타워즈 시리즈들의 유사도를 등장인물들을 통해 계산하고 시각화 하는 문제
# From : https://github.com/R-Korea/weekly_R_quiz/blob/master/201808/1.movie_similarity/movie_similarity_quiz.Rmd
library(dplyr)
library(data.table)
library(ggplot2)
library(scales)

rm(list=ls())

raw.data <- 

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import tensorflow as tf
from tensorflow import layers
from tensorflow.contrib.layers import xavier_initializer

import gym
import numpy as np

from collections import deque
from collections import namedtuple

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# References:

import argparse

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as T