๐Ÿ’ป Computer Science

    [ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค] ์ฃผ์‹ ๊ฐ€๊ฒฉ_์Šคํƒ/ํ (C++)

    [ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค] ์ฃผ์‹ ๊ฐ€๊ฒฉ_์Šคํƒ/ํ (C++)

    ๋ฌธ์ œ ์„ค๋ช… ์ดˆ ๋‹จ์œ„๋กœ ๊ธฐ๋ก๋œ ์ฃผ์‹๊ฐ€๊ฒฉ์ด ๋‹ด๊ธด ๋ฐฐ์—ด prices๊ฐ€ ๋งค๊ฐœ๋ณ€์ˆ˜๋กœ ์ฃผ์–ด์งˆ ๋•Œ, ๊ฐ€๊ฒฉ์ด ๋–จ์–ด์ง€์ง€ ์•Š์€ ๊ธฐ๊ฐ„์€ ๋ช‡ ์ดˆ์ธ์ง€๋ฅผ return ํ•˜๋„๋ก solution ํ•จ์ˆ˜๋ฅผ ์™„์„ฑํ•˜์„ธ์š”. ์ œํ•œ ์‚ฌํ•ญ prices์˜ ๊ฐ ๊ฐ€๊ฒฉ์€ 1 ์ด์ƒ 10,000 ์ดํ•˜์ธ ์ž์—ฐ์ˆ˜์ž…๋‹ˆ๋‹ค. prices์˜ ๊ธธ์ด๋Š” 2 ์ด์ƒ 100,000 ์ดํ•˜์ž…๋‹ˆ๋‹ค. ์ž…์ถœ๋ ฅ ์˜ˆ prices return [1,2,3,2,3] [4,3,1,1,0] ์ž…์ถœ๋ ฅ ์˜ˆ ์„ค๋ช… 1์ดˆ ์‹œ์ ์˜ โ‚ฉ1์€ ๋๊นŒ์ง€ ๊ฐ€๊ฒฉ์ด ๋–จ์–ด์ง€์ง€ ์•Š์•˜์Šต๋‹ˆ๋‹ค. 2์ดˆ ์‹œ์ ์˜ โ‚ฉ2์€ ๋๊นŒ์ง€ ๊ฐ€๊ฒฉ์ด ๋–จ์–ด์ง€์ง€ ์•Š์•˜์Šต๋‹ˆ๋‹ค. 3์ดˆ ์‹œ์ ์˜ โ‚ฉ3์€ 1์ดˆ๋’ค์— ๊ฐ€๊ฒฉ์ด ๋–จ์–ด์ง‘๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ 1์ดˆ๊ฐ„ ๊ฐ€๊ฒฉ์ด ๋–จ์–ด์ง€์ง€ ์•Š์€ ๊ฒƒ์œผ๋กœ ๋ด…๋‹ˆ๋‹ค. 4์ดˆ ์‹œ์ ์˜ โ‚ฉ2์€ 1์ดˆ๊ฐ„ ๊ฐ€๊ฒฉ์ด ๋–จ์–ด์ง€์ง€ ์•Š์•˜์Šต๋‹ˆ๋‹ค. 5์ดˆ ์‹œ์ ์˜ โ‚ฉ3์€ 0์ดˆ๊ฐ„ ๊ฐ€๊ฒฉ..

    [ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค] ๋‘ ๊ฐœ ๋ฝ‘์•„์„œ ๋”ํ•˜๊ธฐ_์›”๊ฐ„ ์ฝ”๋“œ ์ฑŒ๋ฆฐ์ง€ ์‹œ์ฆŒ1 (C++)

    [ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค] ๋‘ ๊ฐœ ๋ฝ‘์•„์„œ ๋”ํ•˜๊ธฐ_์›”๊ฐ„ ์ฝ”๋“œ ์ฑŒ๋ฆฐ์ง€ ์‹œ์ฆŒ1 (C++)

    ๋ฌธ์ œ ์„ค๋ช… ์ •์ˆ˜ ๋ฐฐ์—ด numbers๊ฐ€ ์ฃผ์–ด์ง‘๋‹ˆ๋‹ค. numbers์—์„œ ์„œ๋กœ ๋‹ค๋ฅธ ์ธ๋ฑ์Šค์— ์žˆ๋Š” ๋‘ ๊ฐœ์˜ ์ˆ˜๋ฅผ ๋ฝ‘์•„ ๋”ํ•ด์„œ ๋งŒ๋“ค ์ˆ˜ ์žˆ๋Š” ๋ชจ๋“  ์ˆ˜๋ฅผ ๋ฐฐ์—ด์— ์˜ค๋ฆ„์ฐจ์ˆœ์œผ๋กœ ๋‹ด์•„ return ํ•˜๋„๋ก solution ํ•จ์ˆ˜๋ฅผ ์™„์„ฑํ•ด์ฃผ์„ธ์š”. ์ œํ•œ ์‚ฌํ•ญ numbers์˜ ๊ธธ์ด๋Š” 2 ์ด์ƒ 100 ์ดํ•˜์ž…๋‹ˆ๋‹ค. numbers์˜ ๋ชจ๋“  ์ˆ˜๋Š” 0 ์ด์ƒ 100 ์ดํ•˜์ž…๋‹ˆ๋‹ค. ์ž…์ถœ๋ ฅ ์˜ˆ numbers return [2,1,3,4,1] [2,3,4,5,6,7,] [5,0,2,7] [2,5,7,9,12] ์ž…์ถœ๋ ฅ ์˜ˆ ์„ค๋ช… ์ž…์ถœ๋ ฅ ์˜ˆ #1 2 = 1 + 1 ์ž…๋‹ˆ๋‹ค. (1์ด numbers์— ๋‘ ๊ฐœ ์žˆ์Šต๋‹ˆ๋‹ค.) 3 = 2 + 1 ์ž…๋‹ˆ๋‹ค. 4 = 1 + 3 ์ž…๋‹ˆ๋‹ค. 5 = 1 + 4 = 2 + 3 ์ž…๋‹ˆ๋‹ค. 6 = 2 + 4 ์ž…๋‹ˆ๋‹ค. 7 = 3 + 4..

    [ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค] k ๋ฒˆ์งธ ์ˆ˜_์ •๋ ฌ (C++)

    [ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค] k ๋ฒˆ์งธ ์ˆ˜_์ •๋ ฌ (C++)

    ๋ฌธ์ œ ์„ค๋ช… ๋ฐฐ์—ด array์˜ i๋ฒˆ์งธ ์ˆซ์ž๋ถ€ํ„ฐ j๋ฒˆ์งธ ์ˆซ์ž๊นŒ์ง€ ์ž๋ฅด๊ณ  ์ •๋ ฌํ–ˆ์„ ๋•Œ, k๋ฒˆ์งธ์— ์žˆ๋Š” ์ˆ˜๋ฅผ ๊ตฌํ•˜๋ ค ํ•ฉ๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด array๊ฐ€ [1, 5, 2, 6, 3, 7, 4], i = 2, j = 5, k = 3์ด๋ผ๋ฉด array์˜ 2๋ฒˆ์งธ๋ถ€ํ„ฐ 5๋ฒˆ์งธ๊นŒ์ง€ ์ž๋ฅด๋ฉด [5, 2, 6, 3]์ž…๋‹ˆ๋‹ค. 1์—์„œ ๋‚˜์˜จ ๋ฐฐ์—ด์„ ์ •๋ ฌํ•˜๋ฉด [2, 3, 5, 6]์ž…๋‹ˆ๋‹ค. 2์—์„œ ๋‚˜์˜จ ๋ฐฐ์—ด์˜ 3๋ฒˆ์งธ ์ˆซ์ž๋Š” 5์ž…๋‹ˆ๋‹ค. ๋ฐฐ์—ด array, [i, j, k]๋ฅผ ์›์†Œ๋กœ ๊ฐ€์ง„ 2์ฐจ์› ๋ฐฐ์—ด commands๊ฐ€ ๋งค๊ฐœ๋ณ€์ˆ˜๋กœ ์ฃผ์–ด์งˆ ๋•Œ, commands์˜ ๋ชจ๋“  ์›์†Œ์— ๋Œ€ํ•ด ์•ž์„œ ์„ค๋ช…ํ•œ ์—ฐ์‚ฐ์„ ์ ์šฉํ–ˆ์„ ๋•Œ ๋‚˜์˜จ ๊ฒฐ๊ณผ๋ฅผ ๋ฐฐ์—ด์— ๋‹ด์•„ return ํ•˜๋„๋ก solution ํ•จ์ˆ˜๋ฅผ ์ž‘์„ฑํ•ด์ฃผ์„ธ์š”. ์ œํ•œ ์‚ฌํ•ญ array์˜ ๊ธธ์ด๋Š” 1 ์ด์ƒ 100 ์ดํ•˜์ž…๋‹ˆ๋‹ค. ..

    [ํ”„๋กœ๊ทธ๋ž˜๋จธ์Šค] ๋ชจ์˜๊ณ ์‚ฌ_์™„์ „ํƒ์ƒ‰ (C++)

    ๋ฌธ์ œ ์„ค๋ช… ์ˆ˜ํฌ์ž๋Š” ์ˆ˜ํ•™์„ ํฌ๊ธฐํ•œ ์‚ฌ๋žŒ์˜ ์ค€๋ง์ž…๋‹ˆ๋‹ค. ์ˆ˜ํฌ์ž ์‚ผ์ธ๋ฐฉ์€ ๋ชจ์˜๊ณ ์‚ฌ์— ์ˆ˜ํ•™ ๋ฌธ์ œ๋ฅผ ์ „๋ถ€ ์ฐ์œผ๋ ค ํ•ฉ๋‹ˆ๋‹ค. ์ˆ˜ํฌ์ž๋Š” 1๋ฒˆ ๋ฌธ์ œ๋ถ€ํ„ฐ ๋งˆ์ง€๋ง‰ ๋ฌธ์ œ๊นŒ์ง€ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์ฐ์Šต๋‹ˆ๋‹ค. 1๋ฒˆ ์ˆ˜ํฌ์ž๊ฐ€ ์ฐ๋Š” ๋ฐฉ์‹: 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, ... 2๋ฒˆ ์ˆ˜ํฌ์ž๊ฐ€ ์ฐ๋Š” ๋ฐฉ์‹: 2, 1, 2, 3, 2, 4, 2, 5, 2, 1, 2, 3, 2, 4, 2, 5, ... 3๋ฒˆ ์ˆ˜ํฌ์ž๊ฐ€ ์ฐ๋Š” ๋ฐฉ์‹: 3, 3, 1, 1, 2, 2, 4, 4, 5, 5, 3, 3, 1, 1, 2, 2, 4, 4, 5, 5, ... 1๋ฒˆ ๋ฌธ์ œ๋ถ€ํ„ฐ ๋งˆ์ง€๋ง‰ ๋ฌธ์ œ๊นŒ์ง€์˜ ์ •๋‹ต์ด ์ˆœ์„œ๋Œ€๋กœ ๋“ค์€ ๋ฐฐ์—ด answers๊ฐ€ ์ฃผ์–ด์กŒ์„ ๋•Œ, ๊ฐ€์žฅ ๋งŽ์€ ๋ฌธ์ œ๋ฅผ ๋งžํžŒ ์‚ฌ๋žŒ์ด ๋ˆ„๊ตฌ์ธ์ง€ ๋ฐฐ์—ด์— ๋‹ด์•„ return ํ•˜๋„๋ก solution ํ•จ์ˆ˜๋ฅผ ์ž‘..

    Parkinsons Telemonitoring Using Deep Learning

    Parkinsons Telemonitoring Using Deep Learning

    Introduction This article is about the term project I carried out in the machine learning course. I chose the data set of parkinsons telemonitoring. This dataset was created by Atanasios Tsanas and Max Little of Oxford University in collaboration with 10 U.S. medical centers and Intel Corporation. Originally, this study used various linear and nonlinear regression methods to predict clinicians' ..