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TIL
  • MAIN
  • : TIL?
  • : WIL
  • : Plan
  • : Retrospective
    • 21Y
      • Wait a moment!
      • 9M 2W
      • 9M1W
      • 8M4W
      • 8M3W
      • 8M2W
      • 8M1W
      • 7M4W
      • 7M3W
      • 7M2W
      • 7M1W
      • 6M5W
      • 1H
    • ์ƒˆ์‚ฌ๋žŒ ๋˜๊ธฐ ํ”„๋กœ์ ํŠธ
      • 2ํšŒ์ฐจ
      • 1ํšŒ์ฐจ
  • TIL : ML
    • Paper Analysis
      • BERT
      • Transformer
    • Boostcamp 2st
      • [S]Data Viz
        • (4-3) Seaborn ์‹ฌํ™”
        • (4-2) Seaborn ๊ธฐ์ดˆ
        • (4-1) Seaborn ์†Œ๊ฐœ
        • (3-4) More Tips
        • (3-3) Facet ์‚ฌ์šฉํ•˜๊ธฐ
        • (3-2) Color ์‚ฌ์šฉํ•˜๊ธฐ
        • (3-1) Text ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-3) Scatter Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-2) Line Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-1) Bar Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (1-3) Python๊ณผ Matplotlib
        • (1-2) ์‹œ๊ฐํ™”์˜ ์š”์†Œ
        • (1-1) Welcome to Visualization (OT)
      • [P]MRC
        • (2๊ฐ•) Extraction-based MRC
        • (1๊ฐ•) MRC Intro & Python Basics
      • [P]KLUE
        • (5๊ฐ•) BERT ๊ธฐ๋ฐ˜ ๋‹จ์ผ ๋ฌธ์žฅ ๋ถ„๋ฅ˜ ๋ชจ๋ธ ํ•™์Šต
        • (4๊ฐ•) ํ•œ๊ตญ์–ด BERT ์–ธ์–ด ๋ชจ๋ธ ํ•™์Šต
        • [NLP] ๋ฌธ์žฅ ๋‚ด ๊ฐœ์ฒด๊ฐ„ ๊ด€๊ณ„ ์ถ”์ถœ
        • (3๊ฐ•) BERT ์–ธ์–ด๋ชจ๋ธ ์†Œ๊ฐœ
        • (2๊ฐ•) ์ž์—ฐ์–ด์˜ ์ „์ฒ˜๋ฆฌ
        • (1๊ฐ•) ์ธ๊ณต์ง€๋Šฅ๊ณผ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ
      • [U]Stage-CV
      • [U]Stage-NLP
        • 7W Retrospective
        • (10๊ฐ•) Advanced Self-supervised Pre-training Models
        • (09๊ฐ•) Self-supervised Pre-training Models
        • (08๊ฐ•) Transformer (2)
        • (07๊ฐ•) Transformer (1)
        • 6W Retrospective
        • (06๊ฐ•) Beam Search and BLEU score
        • (05๊ฐ•) Sequence to Sequence with Attention
        • (04๊ฐ•) LSTM and GRU
        • (03๊ฐ•) Recurrent Neural Network and Language Modeling
        • (02๊ฐ•) Word Embedding
        • (01๊ฐ•) Intro to NLP, Bag-of-Words
        • [ํ•„์ˆ˜ ๊ณผ์ œ 4] Preprocessing for NMT Model
        • [ํ•„์ˆ˜ ๊ณผ์ œ 3] Subword-level Language Model
        • [ํ•„์ˆ˜ ๊ณผ์ œ2] RNN-based Language Model
        • [์„ ํƒ ๊ณผ์ œ] BERT Fine-tuning with transformers
        • [ํ•„์ˆ˜ ๊ณผ์ œ] Data Preprocessing
      • Mask Wear Image Classification
        • 5W Retrospective
        • Report_Level1_6
        • Performance | Review
        • DAY 11 : HardVoting | MultiLabelClassification
        • DAY 10 : Cutmix
        • DAY 9 : Loss Function
        • DAY 8 : Baseline
        • DAY 7 : Class Imbalance | Stratification
        • DAY 6 : Error Fix
        • DAY 5 : Facenet | Save
        • DAY 4 : VIT | F1_Loss | LrScheduler
        • DAY 3 : DataSet/Lodaer | EfficientNet
        • DAY 2 : Labeling
        • DAY 1 : EDA
        • 2_EDA Analysis
      • [P]Stage-1
        • 4W Retrospective
        • (10๊ฐ•) Experiment Toolkits & Tips
        • (9๊ฐ•) Ensemble
        • (8๊ฐ•) Training & Inference 2
        • (7๊ฐ•) Training & Inference 1
        • (6๊ฐ•) Model 2
        • (5๊ฐ•) Model 1
        • (4๊ฐ•) Data Generation
        • (3๊ฐ•) Dataset
        • (2๊ฐ•) Image Classification & EDA
        • (1๊ฐ•) Competition with AI Stages!
      • [U]Stage-3
        • 3W Retrospective
        • PyTorch
          • (10๊ฐ•) PyTorch Troubleshooting
          • (09๊ฐ•) Hyperparameter Tuning
          • (08๊ฐ•) Multi-GPU ํ•™์Šต
          • (07๊ฐ•) Monitoring tools for PyTorch
          • (06๊ฐ•) ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ
          • (05๊ฐ•) Dataset & Dataloader
          • (04๊ฐ•) AutoGrad & Optimizer
          • (03๊ฐ•) PyTorch ํ”„๋กœ์ ํŠธ ๊ตฌ์กฐ ์ดํ•ดํ•˜๊ธฐ
          • (02๊ฐ•) PyTorch Basics
          • (01๊ฐ•) Introduction to PyTorch
      • [U]Stage-2
        • 2W Retrospective
        • DL Basic
          • (10๊ฐ•) Generative Models 2
          • (09๊ฐ•) Generative Models 1
          • (08๊ฐ•) Sequential Models - Transformer
          • (07๊ฐ•) Sequential Models - RNN
          • (06๊ฐ•) Computer Vision Applications
          • (05๊ฐ•) Modern CNN - 1x1 convolution์˜ ์ค‘์š”์„ฑ
          • (04๊ฐ•) Convolution์€ ๋ฌด์—‡์ธ๊ฐ€?
          • (03๊ฐ•) Optimization
          • (02๊ฐ•) ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ - MLP (Multi-Layer Perceptron)
          • (01๊ฐ•) ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ณธ ์šฉ์–ด ์„ค๋ช… - Historical Review
        • Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] Multi-headed Attention Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] LSTM Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] CNN Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] Optimization Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] MLP Assignment
      • [U]Stage-1
        • 1W Retrospective
        • AI Math
          • (AI Math 10๊ฐ•) RNN ์ฒซ๊ฑธ์Œ
          • (AI Math 9๊ฐ•) CNN ์ฒซ๊ฑธ์Œ
          • (AI Math 8๊ฐ•) ๋ฒ ์ด์ฆˆ ํ†ต๊ณ„ํ•™ ๋ง›๋ณด๊ธฐ
          • (AI Math 7๊ฐ•) ํ†ต๊ณ„ํ•™ ๋ง›๋ณด๊ธฐ
          • (AI Math 6๊ฐ•) ํ™•๋ฅ ๋ก  ๋ง›๋ณด๊ธฐ
          • (AI Math 5๊ฐ•) ๋”ฅ๋Ÿฌ๋‹ ํ•™์Šต๋ฐฉ๋ฒ• ์ดํ•ดํ•˜๊ธฐ
          • (AI Math 4๊ฐ•) ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ• - ๋งค์šด๋ง›
          • (AI Math 3๊ฐ•) ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ• - ์ˆœํ•œ๋ง›
          • (AI Math 2๊ฐ•) ํ–‰๋ ฌ์ด ๋ญ์˜ˆ์š”?
          • (AI Math 1๊ฐ•) ๋ฒกํ„ฐ๊ฐ€ ๋ญ์˜ˆ์š”?
        • Python
          • (Python 7-2๊ฐ•) pandas II
          • (Python 7-1๊ฐ•) pandas I
          • (Python 6๊ฐ•) numpy
          • (Python 5-2๊ฐ•) Python data handling
          • (Python 5-1๊ฐ•) File / Exception / Log Handling
          • (Python 4-2๊ฐ•) Module and Project
          • (Python 4-1๊ฐ•) Python Object Oriented Programming
          • (Python 3-2๊ฐ•) Pythonic code
          • (Python 3-1๊ฐ•) Python Data Structure
          • (Python 2-4๊ฐ•) String and advanced function concept
          • (Python 2-3๊ฐ•) Conditionals and Loops
          • (Python 2-2๊ฐ•) Function and Console I/O
          • (Python 2-1๊ฐ•) Variables
          • (Python 1-3๊ฐ•) ํŒŒ์ด์ฌ ์ฝ”๋”ฉ ํ™˜๊ฒฝ
          • (Python 1-2๊ฐ•) ํŒŒ์ด์ฌ ๊ฐœ์š”
          • (Python 1-1๊ฐ•) Basic computer class for newbies
        • Assignment
          • [์„ ํƒ ๊ณผ์ œ 3] Maximum Likelihood Estimate
          • [์„ ํƒ ๊ณผ์ œ 2] Backpropagation
          • [์„ ํƒ ๊ณผ์ œ 1] Gradient Descent
          • [ํ•„์ˆ˜ ๊ณผ์ œ 5] Morsecode
          • [ํ•„์ˆ˜ ๊ณผ์ œ 4] Baseball
          • [ํ•„์ˆ˜ ๊ณผ์ œ 3] Text Processing 2
          • [ํ•„์ˆ˜ ๊ณผ์ œ 2] Text Processing 1
          • [ํ•„์ˆ˜ ๊ณผ์ œ 1] Basic Math
    • ๋”ฅ๋Ÿฌ๋‹ CNN ์™„๋ฒฝ ๊ฐ€์ด๋“œ - Fundamental ํŽธ
      • ์ข…ํ•ฉ ์‹ค์Šต 2 - ์บ๊ธ€ Plant Pathology(๋‚˜๋ฌด์žŽ ๋ณ‘ ์ง„๋‹จ) ๊ฒฝ์—ฐ ๋Œ€ํšŒ
      • ์ข…ํ•ฉ ์‹ค์Šต 1 - 120์ข…์˜ Dog Breed Identification ๋ชจ๋ธ ์ตœ์ ํ™”
      • ์‚ฌ์ „ ํ›ˆ๋ จ ๋ชจ๋ธ์˜ ๋ฏธ์„ธ ์กฐ์ • ํ•™์Šต๊ณผ ๋‹ค์–‘ํ•œ Learning Rate Scheduler์˜ ์ ์šฉ
      • Advanced CNN ๋ชจ๋ธ ํŒŒํ—ค์น˜๊ธฐ - ResNet ์ƒ์„ธ์™€ EfficientNet ๊ฐœ์š”
      • Advanced CNN ๋ชจ๋ธ ํŒŒํ—ค์น˜๊ธฐ - AlexNet, VGGNet, GoogLeNet
      • Albumentation์„ ์ด์šฉํ•œ Augmentation๊ธฐ๋ฒ•๊ณผ Keras Sequence ํ™œ์šฉํ•˜๊ธฐ
      • ์‚ฌ์ „ ํ›ˆ๋ จ CNN ๋ชจ๋ธ์˜ ํ™œ์šฉ๊ณผ Keras Generator ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ดํ•ด
      • ๋ฐ์ดํ„ฐ ์ฆ๊ฐ•์˜ ์ดํ•ด - Keras ImageDataGenerator ํ™œ์šฉ
      • CNN ๋ชจ๋ธ ๊ตฌํ˜„ ๋ฐ ์„ฑ๋Šฅ ํ–ฅ์ƒ ๊ธฐ๋ณธ ๊ธฐ๋ฒ• ์ ์šฉํ•˜๊ธฐ
    • AI School 1st
    • ํ˜„์—… ์‹ค๋ฌด์ž์—๊ฒŒ ๋ฐฐ์šฐ๋Š” Kaggle ๋จธ์‹ ๋Ÿฌ๋‹ ์ž…๋ฌธ
    • ํŒŒ์ด์ฌ ๋”ฅ๋Ÿฌ๋‹ ํŒŒ์ดํ† ์น˜
  • TIL : Python & Math
    • Do It! ์žฅ๊ณ +๋ถ€ํŠธ์ŠคํŠธ๋žฉ: ํŒŒ์ด์ฌ ์›น๊ฐœ๋ฐœ์˜ ์ •์„
      • Relations - ๋‹ค๋Œ€๋‹ค ๊ด€๊ณ„
      • Relations - ๋‹ค๋Œ€์ผ ๊ด€๊ณ„
      • ํ…œํ”Œ๋ฆฟ ํŒŒ์ผ ๋ชจ๋“ˆํ™” ํ•˜๊ธฐ
      • TDD (Test Driven Development)
      • template tags & ์กฐ๊ฑด๋ฌธ
      • ์ •์  ํŒŒ์ผ(static files) & ๋ฏธ๋””์–ด ํŒŒ์ผ(media files)
      • FBV (Function Based View)์™€ CBV (Class Based View)
      • Django ์ž…๋ฌธํ•˜๊ธฐ
      • ๋ถ€ํŠธ์ŠคํŠธ๋žฉ
      • ํ”„๋ก ํŠธ์—”๋“œ ๊ธฐ์ดˆ๋‹ค์ง€๊ธฐ (HTML, CSS, JS)
      • ๋“ค์–ด๊ฐ€๊ธฐ + ํ™˜๊ฒฝ์„ค์ •
    • Algorithm
      • Programmers
        • Level1
          • ์†Œ์ˆ˜ ๋งŒ๋“ค๊ธฐ
          • ์ˆซ์ž ๋ฌธ์ž์—ด๊ณผ ์˜๋‹จ์–ด
          • ์ž์—ฐ์ˆ˜ ๋’ค์ง‘์–ด ๋ฐฐ์—ด๋กœ ๋งŒ๋“ค๊ธฐ
          • ์ •์ˆ˜ ๋‚ด๋ฆผ์ฐจ์ˆœ์œผ๋กœ ๋ฐฐ์น˜ํ•˜๊ธฐ
          • ์ •์ˆ˜ ์ œ๊ณฑ๊ทผ ํŒ๋ณ„
          • ์ œ์ผ ์ž‘์€ ์ˆ˜ ์ œ๊ฑฐํ•˜๊ธฐ
          • ์ง์‚ฌ๊ฐํ˜• ๋ณ„์ฐ๊ธฐ
          • ์ง์ˆ˜์™€ ํ™€์ˆ˜
          • ์ฒด์œก๋ณต
          • ์ตœ๋Œ€๊ณต์•ฝ์ˆ˜์™€ ์ตœ์†Œ๊ณต๋ฐฐ์ˆ˜
          • ์ฝœ๋ผ์ธ  ์ถ”์ธก
          • ํฌ๋ ˆ์ธ ์ธํ˜•๋ฝ‘๊ธฐ ๊ฒŒ์ž„
          • ํ‚คํŒจ๋“œ ๋ˆ„๋ฅด๊ธฐ
          • ํ‰๊ท  ๊ตฌํ•˜๊ธฐ
          • ํฐ์ผ“๋ชฌ
          • ํ•˜์ƒค๋“œ ์ˆ˜
          • ํ•ธ๋“œํฐ ๋ฒˆํ˜ธ ๊ฐ€๋ฆฌ๊ธฐ
          • ํ–‰๋ ฌ์˜ ๋ง์…ˆ
        • Level2
          • ์ˆซ์ž์˜ ํ‘œํ˜„
          • ์ˆœ์œ„ ๊ฒ€์ƒ‰
          • ์ˆ˜์‹ ์ตœ๋Œ€ํ™”
          • ์†Œ์ˆ˜ ์ฐพ๊ธฐ
          • ์†Œ์ˆ˜ ๋งŒ๋“ค๊ธฐ
          • ์‚ผ๊ฐ ๋‹ฌํŒฝ์ด
          • ๋ฌธ์ž์—ด ์••์ถ•
          • ๋ฉ”๋‰ด ๋ฆฌ๋‰ด์–ผ
          • ๋” ๋งต๊ฒŒ
          • ๋•…๋”ฐ๋จน๊ธฐ
          • ๋ฉ€์ฉกํ•œ ์‚ฌ๊ฐํ˜•
          • ๊ด„ํ˜ธ ํšŒ์ „ํ•˜๊ธฐ
          • ๊ด„ํ˜ธ ๋ณ€ํ™˜
          • ๊ตฌ๋ช…๋ณดํŠธ
          • ๊ธฐ๋Šฅ ๊ฐœ๋ฐœ
          • ๋‰ด์Šค ํด๋Ÿฌ์Šคํ„ฐ๋ง
          • ๋‹ค๋ฆฌ๋ฅผ ์ง€๋‚˜๋Š” ํŠธ๋Ÿญ
          • ๋‹ค์Œ ํฐ ์ˆซ์ž
          • ๊ฒŒ์ž„ ๋งต ์ตœ๋‹จ๊ฑฐ๋ฆฌ
          • ๊ฑฐ๋ฆฌ๋‘๊ธฐ ํ™•์ธํ•˜๊ธฐ
          • ๊ฐ€์žฅ ํฐ ์ •์‚ฌ๊ฐํ˜• ์ฐพ๊ธฐ
          • H-Index
          • JadenCase ๋ฌธ์ž์—ด ๋งŒ๋“ค๊ธฐ
          • N๊ฐœ์˜ ์ตœ์†Œ๊ณต๋ฐฐ์ˆ˜
          • N์ง„์ˆ˜ ๊ฒŒ์ž„
          • ๊ฐ€์žฅ ํฐ ์ˆ˜
          • 124 ๋‚˜๋ผ์˜ ์ˆซ์ž
          • 2๊ฐœ ์ดํ•˜๋กœ ๋‹ค๋ฅธ ๋น„ํŠธ
          • [3์ฐจ] ํŒŒ์ผ๋ช… ์ •๋ ฌ
          • [3์ฐจ] ์••์ถ•
          • ์ค„ ์„œ๋Š” ๋ฐฉ๋ฒ•
          • [3์ฐจ] ๋ฐฉ๊ธˆ ๊ทธ๊ณก
          • ๊ฑฐ๋ฆฌ๋‘๊ธฐ ํ™•์ธํ•˜๊ธฐ
        • Level3
          • ๋งค์นญ ์ ์ˆ˜
          • ์™ธ๋ฒฝ ์ ๊ฒ€
          • ๊ธฐ์ง€๊ตญ ์„ค์น˜
          • ์ˆซ์ž ๊ฒŒ์ž„
          • 110 ์˜ฎ๊ธฐ๊ธฐ
          • ๊ด‘๊ณ  ์ œ๊ฑฐ
          • ๊ธธ ์ฐพ๊ธฐ ๊ฒŒ์ž„
          • ์…”ํ‹€๋ฒ„์Šค
          • ๋‹จ์†์นด๋ฉ”๋ผ
          • ํ‘œ ํŽธ์ง‘
          • N-Queen
          • ์ง•๊ฒ€๋‹ค๋ฆฌ ๊ฑด๋„ˆ๊ธฐ
          • ์ตœ๊ณ ์˜ ์ง‘ํ•ฉ
          • ํ•ฉ์Šน ํƒ์‹œ ์š”๊ธˆ
          • ๊ฑฐ์Šค๋ฆ„๋ˆ
          • ํ•˜๋…ธ์ด์˜ ํƒ‘
          • ๋ฉ€๋ฆฌ ๋›ฐ๊ธฐ
          • ๋ชจ๋‘ 0์œผ๋กœ ๋งŒ๋“ค๊ธฐ
        • Level4
    • Head First Python
    • ๋ฐ์ดํ„ฐ ๋ถ„์„์„ ์œ„ํ•œ SQL
    • ๋‹จ ๋‘ ์žฅ์˜ ๋ฌธ์„œ๋กœ ๋ฐ์ดํ„ฐ ๋ถ„์„๊ณผ ์‹œ๊ฐํ™” ๋ฝ€๊ฐœ๊ธฐ
    • Linear Algebra(Khan Academy)
    • ์ธ๊ณต์ง€๋Šฅ์„ ์œ„ํ•œ ์„ ํ˜•๋Œ€์ˆ˜
    • Statistics110
  • TIL : etc
    • [๋”ฐ๋ฐฐ๋Ÿฐ] Kubernetes
    • [๋”ฐ๋ฐฐ๋Ÿฐ] Docker
      • 2. ๋„์ปค ์„ค์น˜ ์‹ค์Šต 1 - ํ•™์ŠตํŽธ(์ค€๋น„๋ฌผ/์‹ค์Šต ์œ ํ˜• ์†Œ๊ฐœ)
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  • River Crossing : ์ตœ์ข… ์ฝ”๋“œ

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์˜คํ† ๋งˆํƒ€์™€ ์ปดํŒŒ์ผ๋Ÿฌ

River Crossing : ์ตœ์ข… ์ฝ”๋“œ

#
#   data : 2020.12.02
#   author : sangmandu at Dankook Univ.
#   program : river crossing
#   language : python
#

#
#   H : Human,  W : Wolf,   S : Sheep,  C : Cabbage
#   1 ... W eat S when there are only W and S
#   2 ... S eat C when there are only S and C
#   3 ... boat can be accommodate in up to 2 elements and Human essential
#   4 ... objective is that all element moves left to right
#

#
#   two lists that left and right
#   prior : keep from generating duplicate
#   dir : direction left to right or vice versa
#   record : the procedure to success(or failure)
#
#   failure     1) left check issue : collision with rule 1 or 2
#               2) left check issue : collision with rule 1 or 2
#               3) duplicate case : prevent that infinite loop
#   

# initialization
_left = ['H', 'W', 'S', 'C']
_prior = ''
_right = []
_dir = '>'
_record = []

# check rule 1 and 2
# when breaking rule then True else False
def checkIssue(rand):
    if len(rand) == 2 and 'S' in rand:
        if 'W' in rand or 'C' in rand:
            return True
    return False

stack = [(_left, _prior, _right, _dir, _record)]
while stack:
    left, prior, right, dir, record = stack.pop(0)
    print()
    print("selected element : ", left, prior, right, dir)
    print("and remained stack : ", stack)
    if not left:
        _record.append(record)
        continue

    if dir == ">":
        for ele in left:
            if ele == 'H':
                continue
            print(f"ele is {ele}")
            l, p, r, rec = left[:], prior[:], right[:], record[:]
            if ele == prior:
                print("ele == prior")
                continue
            l.remove(ele)
            l.remove('H')
            if checkIssue(l):
                print("left check issue")
                continue
            r.append(ele)
            r.append('H')
            if checkIssue(r):
                print("right check issue")
                continue
            memo = f"{ele} and human move from left : {left} to right : {right}"
            if memo in rec:
                print("duplicate issue")
                continue
            rec.append(memo)
            stack.append((l, ele, r, '<', rec))
            print(f"stack append {(l, ele, r, '<', rec)}")
    else:   # dir == "<"
        for ele in right:
            if ele == 'H':
                continue
            print(f"ele is {ele}")
            l, p, r, rec = left[:], prior[:], right[:], record[:]
            if ele == prior:
                print("ele == prior")
                continue
            r.remove(ele)
            r.remove('H')
            if checkIssue(r):
                print("right check issue")
                continue
            l.append(ele)
            l.append('H')
            if checkIssue(l):
                print("left check issue")
                continue
            memo = f"{ele} and human move to left : {left} from right : {right} "
            if memo in rec:
                print("duplicate issue")
                continue
            rec.append(memo)
            stack.append((l, ele, r, '>', rec))
            print(f"stack append {(l, ele, r, '>', rec)}")
        record.append(f"only human moves to left : {left} from right : {right} ")
        right.remove('H')
        left.append('H')
        if checkIssue(right):
            print("right check issue when human only on board")
            continue
        stack.append((left, '', right, '>', record))
        print(f"stack append that only human {(l, '', r, '>', rec)}")

print()
for idx, record in enumerate(_record, 1):
    print(f"#{idx} success case")
    for rec in record:
        print(rec)
    print()

์ค‘๊ฐ„ ์ฝ”๋“œ์™€๋Š” ๋น„์Šทํ•˜๋ฉด์„œ ์ข€ ๋‹ค๋ฅด๋‹ค. (ํ•œ๋ฒˆ ๊ฐˆ์•„ ์—Ž์–ด๊ฐ€์ง€๊ณ ...) ์žฌ๊ท€ ํ•จ์ˆ˜๋ณด๋‹ค๋Š” while / stack ๋ฐฉ์‹์ด ๋ณ€์ˆ˜ ๊ด€๋ฆฌ๊ฐ€ ํŽธํ•œ ๊ฒƒ ๊ฐ™๋‹ค. ํ•จ์ˆ˜ ๋‚ด์—์„œ ๋ฐ˜๋ณต์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ๋ณ€์ˆ˜๋Š” l = left[:] ์™€ ๊ฐ™์ด ๊นŠ์€ ๋ณต์‚ฌ๋ฅผ ํ•˜๋ ค๊ณ  ํ•ด๋„ ์ž˜ ์•ˆ๋œ๋‹ค. ๊ฒฐ๊ตญ copy.deepcopy๋ฅผ ์จ์•ผ ๋˜๋Š”๋ฐ, ๊ตณ์ด ์ด๋ ‡๊ฒŒ? ๋ผ๋Š” ์ƒ๊ฐ์ด ๋„ˆ๋ฌด ๋งŽ์ด ๋“ ๋‹ค. ํ•จ์ˆ˜ ๋‚ด์—์„œ๋Š” ์žฌ๊ท€ํ•จ์ˆ˜ ๋˜ํ•œ ๋™์ผํ•œ ๋ณ€์ˆ˜๋ฅผ ์‚ฌ์šฉํ•ด์„œ ๊ทธ๋Ÿฐ๊ฒŒ ์•„๋‹๊นŒ ๋ผ๋Š” ์ถ”์ธก์€ ์žˆ์ง€๋งŒ ํ™•์‹คํ•œ ์ด์œ ๋Š” ๋ชจ๋ฅด๊ฒ ๋‹ค.

100์ค„ ์กฐ๊ธˆ ๋„˜๊ฒŒ ์ž‘์„ฑ๋œ ์ฝ”๋“œ. ์ข€ ๋” ํ•จ์ˆ˜ํ™”ํ•˜๊ณ  ํ•จ์ถ•ํ•˜์—ฌ ์ฝ”๋“œ๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ๊ฒ ์ง€๋งŒ ์• ์ดˆ์— ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ ์ž‘์•„์„œ ๊ทธ๋Ÿด ํ•„์š”๊ฐ€ ์—†๋‹ค. ์ข€ ๋” ์ง๊ด€์ ์ด์–ด์„œ ์„ค๋ช…ํ•˜๊ฑฐ๋‚˜ ๋ณผ ๋•Œ๋Š” ์ข‹์„ ์ˆ˜ ์žˆ๋‹ค. ์กฐ๊ธˆ ๋” ์ˆ˜์ •ํ•˜์ž๋ฉด prior ๋ณ€์ˆ˜๋ฅผ ์—†์• ๊ณ  duplicate case์— ์ถ”๊ฐ€ํ•  ์ˆ˜ ์žˆ๊ฒ ๋‹ค.

๊ฐ„๋‹จํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ธ๋ฐ ์ƒ๊ฐ๋ณด๋‹ค ๊ณ ๋ฏผ์ด ๊ธธ์—ˆ๋‹ค. ์ฃผ๋œ ํฌ์ธํŠธ๋Š” left to right์ธ์ง€ ๋˜๋Š” ๋ฐ˜๋Œ€์ธ์ง€๋ฅผ ํ™•์ธํ•˜๊ณ  ์ด์— ๋Œ€ํ•ด ๊ฐ left(or right)์—์„œ ๋‹ค์‹œ ๋ฐ˜๋Œ€ํŽธ์œผ๋กœ ๊ฑด๋„ˆ๊ฐˆ ์š”์†Œ๋ฅผ ๊ณ ๋ฅธ๋‹ค. ์ด ๋•Œ prior๋Š” ์ด์ „์— ๊ฐ€์ ธ์˜จ ์š”์†Œ๋ฅผ ๊ธฐ์–ตํ•˜์—ฌ ๋‹ค์‹œ ๊ณ ๋ฅด์ง€ ์•Š๋„๋ก ํ•œ๋‹ค. checkIssue๋Š” ์ „๋‹ฌ๋œ ๋ฆฌ์ŠคํŠธ์—์„œ rule์ด ์ง€์ผœ์ง€๋Š”์ง€ ํ™•์ธํ•˜์—ฌ ์ง€์ผœ์ง€์ง€ ์•Š์•˜์„ ๊ฒฝ์šฐ True๋ฅผ ๋ฐ˜ํ™˜ํ•œ๋‹ค. (๋ฃฐ ํ™•์ธ์„ ์œ„ํ•ด if๋ฌธ ์“ธ ๋•Œ not์„ ์“ฐ๊ธฐ๊ฐ€ ์ง€์ €๋ถ„ ํ•œ๊ฒƒ ๊ฐ™์•„ True๋ฅผ ๋ฐ˜ํ™˜ํ•˜๋„๋ก ํ–ˆ๋‹ค...)

๊ฐ case๋ฅผ BFS ๋ฐฉ์‹์œผ๋กœ ์ ‘๊ทผํ•˜๋ฉฐ ๊ฐ ์ผ€์ด์Šค๋งˆ๋‹ค ์‹คํŒจํ•  ๊ฒฝ์šฐ ์‹คํŒจ ์ด์œ ๊ฐ€ ์ถœ๋ ฅ๋œ๋‹ค. ์‹คํŒจํ–ˆ์„ ๊ฒฝ์šฐ์—๋Š” ๊ทธ ๊ณผ์ •์€ ์ผ๋ถ€๋กœ ์ถœ๋ ฅํ•˜์ง€ ์•Š๋„๋ก ํ–ˆ๋‹ค. (์•ˆ๊ถ๊ธˆํ•ด์„œ?) ๋˜ํ•œ ์„ฑ๊ณตํ•  ๊ฒฝ์šฐ๋ฅผ ๋งˆ์ง€๋ง‰์— ์ •๋ฆฌํ•ด์„œ ๋‹ค์‹œ ์ถœ๋ ฅํ•˜๋„๋ก ํ–ˆ๋‹ค. ์‹ค์ œ๋กœ๋„ 2๊ฐ€์ง€์˜ ๊ฒฝ์šฐ์˜ ์ˆ˜๊ฐ€ ์กด์žฌํ•˜๋Š”๋ฐ, ํ”„๋กœ๊ทธ๋žจ ๋˜ํ•œ ๋‘ ๊ฐ€์ง€์˜ ์„ฑ๊ณต ์ผ€์ด์Šค๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค. ๊ฒฐ๊ณผ 150์ค„ ์ •๋„ ์ถœ๋ ฅ๋œ๋‹ค.

BFS๋ฐฉ์‹์œผ๋กœ ์ถœ๋ ฅํ•˜๋Š” ๋ชจ์Šต. ๊ฐ ์‹คํŒจ ์ผ€์ด์Šค์˜ ์ด์œ ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค.

๋‹ค์Œ์€ ์„ฑ๊ณต์ ์œผ๋กœ ์ถœ๋ ฅ๋˜๋Š” ๋ชจ์Šต.

http://lg-sl.net/product/scilab/sciencestorylist/ALMA/readSciencestoryList.mvc?sciencestoryListId=ALMA2020010003lg-sl.net