Algorithm Problem/Python

[python] ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค - ์™ธ๋ฒฝ ์ ๊ฒ€(2020 KAKAO BLIND RECRUITMENT)

deo2kim 2020. 9. 6. 08:30
๋ฐ˜์‘ํ˜•

๐Ÿค”๋ฌธ์ œ ํ•ด๊ฒฐ

Lv 3 | ์ •๋‹ต๋ฅ : 0.6%

์ฃผ์–ด์ง„ ๊ฐ’์˜ ํฌ๊ธฐ๊ฐ€ ํฌ์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ์™„์ „ํƒ์ƒ‰์œผ๋กœ ํ•ด๊ฒฐ์ด ๊ฐ€๋Šฅํ•˜๋‹ค.

 

๋จผ์ € ์นœ๊ตฌ๋ฅผ ์ˆœ์—ด๋กœ ๋งŒ๋“ ๋‹ค. ex) [1,2,3] ์ด๋ฉด [1,2,3], [1,3,2], [2,1,3], [2,3,1], [3,1,2], [3,2,1]

 

์™ธ๋ฒฝ์ด ์›ํ˜•์ด๋ฏ€๋กœ ์•ž๊ณผ ๋’ค๊ฐ€ ์ด์–ด์ ธ ์žˆ๋‹ค๊ณ  ์ƒ๊ฐํ•ด์•ผ ํ•œ๋‹ค.

์ทจ์•ฝ์ ๋„ ์ฒซ๋ฒˆ์งธ ์ทจ์•ฝ์ ์„ ๋จผ์ € ๊ฐˆ๊ฒƒ์ธ๊ฐ€ ๋‘๋ฒˆ์งธ ์ทจ์•ฝ์ ์„ ๋จผ์ € ๊ฐˆ๊ฒƒ์ธ๊ฐ€... ์ˆœ์„œ๋ฅผ ์ •ํ•ด์„œ ๊ฐ€์•ผํ•œ๋‹ค.

 

 - ์ฒซ๋ฒˆ ์งธ ํ…Œ์ŠคํŠธ์ผ€์ด์Šค์˜ ์ทจ์•ฝ์ ([1, 5, 6, 10])์„ ๋ฐฐ์—ด(n=12)๋กœ ๋‚˜ํƒ€๋‚ด๋ฉด [0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0]

  ์œ„์˜ ๊ฒฝ์šฐ๋Š” ์ฒซ๋ฒˆ์งธ ์ทจ์•ฝ์ (1)์ด ๊ฐ€์žฅ ๋จผ์ € ๋‚˜์˜ค๋Š” ๊ฒฝ์šฐ

 

 - ๋‹ค์Œ ์ทจ์•ฝ์  2๊ฐ€ ๊ฐ€์žฅ ๋จผ์ € ๋‚˜์˜ค๋Š” ๊ฒฝ์šฐ๋Š” 0,1์„ ๋งจ๋’ค๋กœ ์˜ฎ๊ฒจ์ฃผ๋ฉด ๋œ๋‹ค.

   [0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0]  => [0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1]

 - ๋‹ค์Œ ์ทจ์•ฝ์  3์ด ๊ฐ€์ • ๋จผ์ € ๋‚˜์˜ค๋Š” ๊ฒฝ์šฐ 0,0,0,1์„ ๋งจ ๋’ค๋กœ ์˜ฎ๊ฒจ์ค€๋‹ค.

   [0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1] => [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]

  ์ด๋Ÿฐ์‹์œผ๋กœ ์ทจ์•ฝ์  ํƒ์ƒ‰ ์ˆœ์„œ๋ฅผ ๋ฐ”๊ฟ”์ฃผ๋ฉด ๋œ๋‹ค.

 

๋งˆ์ง€๋ง‰์œผ๋กœ ์ทจ์•ฝ์ ์„ ํƒ์ƒ‰ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค.

์ ๊ฒ€์„ ์‹œ์ž‘ํ•  ์นœ๊ตฌ(dist์˜ ํ•˜๋‚˜)๋ฅผ ๊บผ๋‚ด๊ณ  ์ทจ์•ฝ์ ๋ฆฌ์ŠคํŠธ๋ฅผ ํƒ์ƒ‰ํ•œ๋‹ค.

์ทจ์•ฝ์ ๋ฆฌ์ŠคํŠธ์—์„œ 1์„ ๋งŒ๋‚˜๋ฉด ์ ๊ฒ€์‹œ์ž‘. 

์นœ๊ตฌ๊ฐ€ ์ ๊ฒ€ ๊ฐ€๋Šฅํ•œ ๊ธธ์ด๋งŒํผ ์ทจ์•ฝ์ ๋ฆฌ์ŠคํŠธ๋ฅผ ๊ฑด๋„ˆ๋›ฐ๊ณ  ๋‹ค๋ฅธ ์นœ๊ตฌ๋ฅผ ๋ถ€๋ฅธ๋‹ค.

 

์ข…๋ฃŒ ์กฐ๊ฑด

  • ์ ๊ฒ€ํ•˜๊ณ  ๊ฑด๋„ˆ๋›ฐ์—ˆ๋Š”๋ฐ ๋ฒฝ์ด ๋๋‚œ ๊ฒฝ์šฐ O
  • ๋ฒฝ์ด ๋๋‚˜์ง€ ์•Š์•„์„œ ์นœ๊ตฌ๋ฅผ ๋ณด๋ƒˆ๋Š”๋ฐ ๋”์ด์ƒ ์ทจ์•ฝ์ ์ด ์—†๋Š” ๊ฒฝ์šฐ O
  • ์นœ๊ตฌ๋ฅผ ๋‹ค ํˆฌ์ž…ํ–ˆ์ง€๋งŒ ์•„์ง ์ทจ์•ฝ์ ์ด ๋‚จ์•„์žˆ๋Š” ๊ฒฝ์šฐ X

๐Ÿ’จ ์™„์ „ํƒ์ƒ‰์œผ๋กœ ๋ง‰ ์ฝ”๋“œ๋ฅผ ์งฐ๋Š”๋ฐ ํ•ด๊ฒฐ๋๋‹ค. ์—ฌ๊ธฐ์„œ ์ข€ ๋” ๋‹ค๋“ฌ๊ณ  ์‹ถ์ง€๋งŒ ์ฝ”๋”ฉํ…Œ์ŠคํŠธ๊ฐ€ ์ฝ”์•ž์ด๋ผ ์™„๋ฒฝํ•˜๊ฒŒ ๋‹ค๋“ฌ์ง€๋Š” ๋ชปํ–ˆ๋‹ค. ์•„๋งˆ ์—ฌ๊ธฐ์„œ ์‹œ๊ฐ„์„ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ๋ถ€๋ถ„์ด ๋งŽ์ด ์žˆ์„ ๊ฒƒ์ด๋‹ค.( ํ•˜์ง€๋งŒ ์ด ์ฝ”๋“œ๋กœ๋„ ์ถฉ๋ถ„ํžˆ ํ†ต๊ณผ ๊ฐ€๋Šฅ )

 

๐Ÿ’ป์†Œ์Šค ์ฝ”๋“œ

from itertools import permutations


# ์™ธ๋ฒฝ์„ ์ ๊ฒ€ํ•  ์ˆœ์„œ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ
def lotation_wall(wall):
    for i in range(len(wall)):
        if wall[i] == 1:
            return wall[i+1:]+wall[:i+1]


# ์™ธ๋ฒฝ ์ˆ˜๋ฆฌ
def repair(dist_permu, wall):
    idx = 0  # ๋ฒฝ์˜ ์ธ๋ฑ์Šค
    for i in range(len(dist_permu)):
        
        dist = dist_permu[i]  # ์ ๊ฒ€ํ•  ์นœ๊ตฌ๋ฅผ ํ•˜๋‚˜ ๊บผ๋‚ด์„œ
        while idx < len(wall):
            if wall[idx] == 1:  # ์ทจ์•ฝ์ ์„ ๋งŒ๋‚˜๋ฉด
                idx += dist + 1  # ๋‹ค์Œ ์ทจ์•ฝ์ ์œผ๋กœ ๊ฑด๋„ˆ ๋›ด๋‹ค.

                # ๋งˆ์ง€๋ง‰ ์™ธ๋ฒฝ ์ ๊ฒ€
                if idx >= len(wall):  # ๊ฑด๋„ˆ ๋›ฐ์—ˆ๋Š”๋ฐ ์ทจ์•ฝ์ ์ด ๋” ์ด์ƒ ์—†์œผ๋ฉด ๋‹ค ์ ๊ฒ€ํ–ˆ์œผ๋ฏ€๋กœ ๋
                    return i + 1

                break  # ์ด๋ฒˆ ์นœ๊ตฌ๋Š” ์ ๊ฒ€์„ ๋งˆ์ณค์œผ๋ฏ€๋กœ ๋‹ค์Œ ์นœ๊ตฌ๊ฐ€ ์˜ค๊ธฐ ์œ„ํ•ด์„œ ์ ๊ฒ€ while ๋ฌธ์„ ๋๋‚ธ๋‹ค.

            idx += 1
        else:
            # ๋งˆ์ง€๋ง‰์ธ์ค„ ๋ชจ๋ฅด๊ณ  ์นœ๊ตฌํ•œ๋ช… ๋” ํˆฌ์ž… ์‹œ์ผฐ์„ ๋•Œ
            # ์นœ๊ตฌ๊ฐ€ ์ ๊ฒ€ํ•˜๋Ÿฌ ๋‚˜๊ฐ”๋Š”๋ฐ ๋” ์ด์ƒ ์ทจ์•ฝ์ ์ด ์—†์„ ๋•Œ
            return i

    else:
        # ์นœ๊ตฌ ๋‹ค ํˆฌ์ž…ํ–ˆ๋Š”๋ฐ ์ ๊ฒ€ ๋๋‚ด์ง€ ๋ชปํ•จ
        # ์ทจ์•ฝ์ ์ด ์•„์ง ๋‚จ์•˜์„ ๋•Œ
        return 10


def solution(n, weak, dist):
    # ์ทจ์•ฝ์  ๋‚˜์—ดํ•˜๊ธฐ
    wall = [0]*n
    for w in weak:
        wall[w] = 1

    # ๊ฒฐ๊ณผ ๊ฐ’๋“ค ์ €์žฅ
    result = []

    # ๊ณ ์น  ์ˆœ์„œ ์ •ํ•˜๊ธฐ
    length_dist = len(dist)
    for d in permutations(dist, length_dist):
        # ์ฒ˜์Œ ์ทจ์•ฝ ์  ์ˆœ์„œ๋กœ ํƒ์ƒ‰
        re_wall = wall
        result.append(repair(d, re_wall))

        # ์ทจ์•ฝ ์  ์ˆœ์„œ๋ฅผ ๋ฐ”๊ฟ”๊ฐ€๋ฉฐ ํƒ์ƒ‰
        for i in range(length_dist):
            re_wall = lotation_wall(re_wall)
            result.append(repair(d, re_wall))

    # ๊ฒฐ๊ณผ ์ค‘์—์„œ ๊ฐ€์žฅ ์ž‘์€ ๊ฐ’ ์„ ํƒ
    answer = min(result)
    # ๋งŒ์•ฝ ์ž‘์€ ๊ฐ’์ด 10(์™ธ๋ฒฝ์ ๊ฒ€์‹คํŒจ)๋ผ๋ฉด -1 ๋ฐ˜ํ™˜
    if answer == 10:
        answer = -1
    return answer
    
    

 

๐Ÿ“•๋ฌธ์ œ ํ™•์ธ

์ถœ์ฒ˜: ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค

๋งํฌ: https://programmers.co.kr/learn/courses/30/lessons/60062

 

์ฝ”๋”ฉํ…Œ์ŠคํŠธ ์—ฐ์Šต - ์™ธ๋ฒฝ ์ ๊ฒ€

๋ ˆ์Šคํ† ๋ž‘์„ ์šด์˜ํ•˜๊ณ  ์žˆ๋Š” ์Šค์นดํ”ผ๋Š” ๋ ˆ์Šคํ† ๋ž‘ ๋‚ด๋ถ€๊ฐ€ ๋„ˆ๋ฌด ๋‚ก์•„ ์นœ๊ตฌ๋“ค๊ณผ ํ•จ๊ป˜ ์ง์ ‘ ๋ฆฌ๋ชจ๋ธ๋ง ํ•˜๊ธฐ๋กœ ํ–ˆ์Šต๋‹ˆ๋‹ค. ๋ ˆ์Šคํ† ๋ž‘์ด ์žˆ๋Š” ๊ณณ์€ ์Šค๋…ธ์šฐํƒ€์šด์œผ๋กœ ๋งค์šฐ ์ถ”์šด ์ง€์—ญ์ด์–ด์„œ ๋‚ด๋ถ€ ๊ณต์‚ฌ๋ฅผ ํ•˜๋Š”

programmers.co.kr




๋ฌธ์ œ ์„ค๋ช…

๋ ˆ์Šคํ† ๋ž‘์„ ์šด์˜ํ•˜๊ณ  ์žˆ๋Š” ์Šค์นดํ”ผ๋Š” ๋ ˆ์Šคํ† ๋ž‘ ๋‚ด๋ถ€๊ฐ€ ๋„ˆ๋ฌด ๋‚ก์•„ ์นœ๊ตฌ๋“ค๊ณผ ํ•จ๊ป˜ ์ง์ ‘ ๋ฆฌ๋ชจ๋ธ๋ง ํ•˜๊ธฐ๋กœ ํ–ˆ์Šต๋‹ˆ๋‹ค. ๋ ˆ์Šคํ† ๋ž‘์ด ์žˆ๋Š” ๊ณณ์€ ์Šค๋…ธ์šฐํƒ€์šด์œผ๋กœ ๋งค์šฐ ์ถ”์šด ์ง€์—ญ์ด์–ด์„œ ๋‚ด๋ถ€ ๊ณต์‚ฌ๋ฅผ ํ•˜๋Š” ๋„์ค‘์— ์ฃผ๊ธฐ์ ์œผ๋กœ ์™ธ๋ฒฝ์˜ ์ƒํƒœ๋ฅผ ์ ๊ฒ€ํ•ด์•ผ ํ•  ํ•„์š”๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค.

๋ ˆ์Šคํ† ๋ž‘์˜ ๊ตฌ์กฐ๋Š” ์™„์ „ํžˆ ๋™๊ทธ๋ž€ ๋ชจ์–‘์ด๊ณ  ์™ธ๋ฒฝ์˜ ์ด ๋‘˜๋ ˆ๋Š” n๋ฏธํ„ฐ์ด๋ฉฐ, ์™ธ๋ฒฝ์˜ ๋ช‡๋ช‡ ์ง€์ ์€ ์ถ”์œ„๊ฐ€ ์‹ฌํ•  ๊ฒฝ์šฐ ์†์ƒ๋  ์ˆ˜๋„ ์žˆ๋Š” ์ทจ์•ฝํ•œ ์ง€์ ๋“ค์ด ์žˆ์Šต๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋‚ด๋ถ€ ๊ณต์‚ฌ ๋„์ค‘์—๋„ ์™ธ๋ฒฝ์˜ ์ทจ์•ฝ ์ง€์ ๋“ค์ด ์†์ƒ๋˜์ง€ ์•Š์•˜๋Š” ์ง€, ์ฃผ๊ธฐ์ ์œผ๋กœ ์นœ๊ตฌ๋“ค์„ ๋ณด๋‚ด์„œ ์ ๊ฒ€์„ ํ•˜๊ธฐ๋กœ ํ–ˆ์Šต๋‹ˆ๋‹ค. ๋‹ค๋งŒ, ๋น ๋ฅธ ๊ณต์‚ฌ ์ง„ํ–‰์„ ์œ„ํ•ด ์ ๊ฒ€ ์‹œ๊ฐ„์„ 1์‹œ๊ฐ„์œผ๋กœ ์ œํ•œํ–ˆ์Šต๋‹ˆ๋‹ค. ์นœ๊ตฌ๋“ค์ด 1์‹œ๊ฐ„ ๋™์•ˆ ์ด๋™ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฑฐ๋ฆฌ๋Š” ์ œ๊ฐ๊ฐ์ด๊ธฐ ๋•Œ๋ฌธ์—, ์ตœ์†Œํ•œ์˜ ์นœ๊ตฌ๋“ค์„ ํˆฌ์ž…ํ•ด ์ทจ์•ฝ ์ง€์ ์„ ์ ๊ฒ€ํ•˜๊ณ  ๋‚˜๋จธ์ง€ ์นœ๊ตฌ๋“ค์€ ๋‚ด๋ถ€ ๊ณต์‚ฌ๋ฅผ ๋•๋„๋ก ํ•˜๋ ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. ํŽธ์˜ ์ƒ ๋ ˆ์Šคํ† ๋ž‘์˜ ์ •๋ถ ๋ฐฉํ–ฅ ์ง€์ ์„ 0์œผ๋กœ ๋‚˜ํƒ€๋‚ด๋ฉฐ, ์ทจ์•ฝ ์ง€์ ์˜ ์œ„์น˜๋Š” ์ •๋ถ ๋ฐฉํ–ฅ ์ง€์ ์œผ๋กœ๋ถ€ํ„ฐ ์‹œ๊ณ„ ๋ฐฉํ–ฅ์œผ๋กœ ๋–จ์–ด์ง„ ๊ฑฐ๋ฆฌ๋กœ ๋‚˜ํƒ€๋ƒ…๋‹ˆ๋‹ค. ๋˜, ์นœ๊ตฌ๋“ค์€ ์ถœ๋ฐœ ์ง€์ ๋ถ€ํ„ฐ ์‹œ๊ณ„, ํ˜น์€ ๋ฐ˜์‹œ๊ณ„ ๋ฐฉํ–ฅ์œผ๋กœ ์™ธ๋ฒฝ์„ ๋”ฐ๋ผ์„œ๋งŒ ์ด๋™ํ•ฉ๋‹ˆ๋‹ค.

์™ธ๋ฒฝ์˜ ๊ธธ์ด n, ์ทจ์•ฝ ์ง€์ ์˜ ์œ„์น˜๊ฐ€ ๋‹ด๊ธด ๋ฐฐ์—ด weak, ๊ฐ ์นœ๊ตฌ๊ฐ€ 1์‹œ๊ฐ„ ๋™์•ˆ ์ด๋™ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฑฐ๋ฆฌ๊ฐ€ ๋‹ด๊ธด ๋ฐฐ์—ด dist๊ฐ€ ๋งค๊ฐœ๋ณ€์ˆ˜๋กœ ์ฃผ์–ด์งˆ ๋•Œ, ์ทจ์•ฝ ์ง€์ ์„ ์ ๊ฒ€ํ•˜๊ธฐ ์œ„ํ•ด ๋ณด๋‚ด์•ผ ํ•˜๋Š” ์นœ๊ตฌ ์ˆ˜์˜ ์ตœ์†Œ๊ฐ’์„ return ํ•˜๋„๋ก solution ํ•จ์ˆ˜๋ฅผ ์™„์„ฑํ•ด์ฃผ์„ธ์š”.

์ œํ•œ์‚ฌํ•ญ

  • n์€ 1 ์ด์ƒ 200 ์ดํ•˜์ธ ์ž์—ฐ์ˆ˜์ž…๋‹ˆ๋‹ค.
  • weak์˜ ๊ธธ์ด๋Š” 1 ์ด์ƒ 15 ์ดํ•˜์ž…๋‹ˆ๋‹ค.
    • ์„œ๋กœ ๋‹ค๋ฅธ ๋‘ ์ทจ์•ฝ์ ์˜ ์œ„์น˜๊ฐ€ ๊ฐ™์€ ๊ฒฝ์šฐ๋Š” ์ฃผ์–ด์ง€์ง€ ์•Š์Šต๋‹ˆ๋‹ค.
    • ์ทจ์•ฝ ์ง€์ ์˜ ์œ„์น˜๋Š” ์˜ค๋ฆ„์ฐจ์ˆœ์œผ๋กœ ์ •๋ ฌ๋˜์–ด ์ฃผ์–ด์ง‘๋‹ˆ๋‹ค.
    • weak์˜ ์›์†Œ๋Š” 0 ์ด์ƒ n - 1 ์ดํ•˜์ธ ์ •์ˆ˜์ž…๋‹ˆ๋‹ค.
  • dist์˜ ๊ธธ์ด๋Š” 1 ์ด์ƒ 8 ์ดํ•˜์ž…๋‹ˆ๋‹ค.
    • dist์˜ ์›์†Œ๋Š” 1 ์ด์ƒ 100 ์ดํ•˜์ธ ์ž์—ฐ์ˆ˜์ž…๋‹ˆ๋‹ค.
  • ์นœ๊ตฌ๋“ค์„ ๋ชจ๋‘ ํˆฌ์ž…ํ•ด๋„ ์ทจ์•ฝ ์ง€์ ์„ ์ „๋ถ€ ์ ๊ฒ€ํ•  ์ˆ˜ ์—†๋Š” ๊ฒฝ์šฐ์—๋Š” -1์„ return ํ•ด์ฃผ์„ธ์š”.

์ž…์ถœ๋ ฅ ์˜ˆ

nweakdistresult

12 [1, 5, 6, 10] [1, 2, 3, 4] 2
12 [1, 3, 4, 9, 10] [3, 5, 7] 1

์ž…์ถœ๋ ฅ ์˜ˆ์— ๋Œ€ํ•œ ์„ค๋ช…

์ž…์ถœ๋ ฅ ์˜ˆ #1

์›ํ˜• ๋ ˆ์Šคํ† ๋ž‘์—์„œ ์™ธ๋ฒฝ์˜ ์ทจ์•ฝ ์ง€์ ์˜ ์œ„์น˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค.

์นœ๊ตฌ๋“ค์„ ํˆฌ์ž…ํ•˜๋Š” ์˜ˆ์‹œ ์ค‘ ํ•˜๋‚˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค.

  • 4m๋ฅผ ์ด๋™ํ•  ์ˆ˜ ์žˆ๋Š” ์นœ๊ตฌ๋Š” 10m ์ง€์ ์—์„œ ์ถœ๋ฐœํ•ด ์‹œ๊ณ„๋ฐฉํ–ฅ์œผ๋กœ ๋Œ์•„ 1m ์œ„์น˜์— ์žˆ๋Š” ์ทจ์•ฝ ์ง€์ ์—์„œ ์™ธ๋ฒฝ ์ ๊ฒ€์„ ๋งˆ์นฉ๋‹ˆ๋‹ค.
  • 2m๋ฅผ ์ด๋™ํ•  ์ˆ˜ ์žˆ๋Š” ์นœ๊ตฌ๋Š” 4.5m ์ง€์ ์—์„œ ์ถœ๋ฐœํ•ด 6.5m ์ง€์ ์—์„œ ์™ธ๋ฒฝ ์ ๊ฒ€์„ ๋งˆ์นฉ๋‹ˆ๋‹ค.

๊ทธ ์™ธ์— ์—ฌ๋Ÿฌ ๋ฐฉ๋ฒ•๋“ค์ด ์žˆ์ง€๋งŒ, ๋‘ ๋ช…๋ณด๋‹ค ์ ์€ ์นœ๊ตฌ๋ฅผ ํˆฌ์ž…ํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์—†์Šต๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์นœ๊ตฌ๋ฅผ ์ตœ์†Œ ๋‘ ๋ช… ํˆฌ์ž…ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

์ž…์ถœ๋ ฅ ์˜ˆ #2

์›ํ˜• ๋ ˆ์Šคํ† ๋ž‘์—์„œ ์™ธ๋ฒฝ์˜ ์ทจ์•ฝ ์ง€์ ์˜ ์œ„์น˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค.

7m๋ฅผ ์ด๋™ํ•  ์ˆ˜ ์žˆ๋Š” ์นœ๊ตฌ๊ฐ€ 4m ์ง€์ ์—์„œ ์ถœ๋ฐœํ•ด ๋ฐ˜์‹œ๊ณ„ ๋ฐฉํ–ฅ์œผ๋กœ ์ ๊ฒ€์„ ๋Œ๋ฉด ๋ชจ๋“  ์ทจ์•ฝ ์ง€์ ์„ ์ ๊ฒ€ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์นœ๊ตฌ๋ฅผ ์ตœ์†Œ ํ•œ ๋ช… ํˆฌ์ž…ํ•˜๋ฉด ๋ฉ๋‹ˆ๋‹ค.

๋ฐ˜์‘ํ˜•