🧠 Algorithms Solutions

A collection of algorithm implementations in Go and Python. This repository contains solutions to various algorithmic problems including search algorithms, optimization problems, and AI/ML challenges. You can find more details and theory for the course in this github - https://github.com/ElitsaY/AI_FMI_course

🚀 Quick Start

Prerequisites

• Go (1.21+) - for Go solutions
• Python (3.8+) - for Python solutions

Installation

1. Clone the repository:

git clone <repository-url>
cd algorithms-solutions

2. Install Python dependencies (handled automatically by the Makefile for temp venvs; for persistent venvs they are installed during make venv <task>). Consolidated dependencies live in the root requirements.txt.

pip install -r requirements.txt

📋 Available Commands

Discovery & Help

make              # Show help menu
make help         # Show detailed help
make ls           # List all available tasks

Running Tasks

make run <task-name>          # Auto-detects language and runs
make run frog-leap-puzzle     # Example: Go
make run iris                 # Example: Python
make run decision-tree        # Example: Python (ID3)
make run neural-networks      # Example: Python (MLP, interactive)

Building Tasks (Go)

make build <task-name>
make build n-queens

Testing Tasks

make test <task-name>
make test tsp

Cleaning

make clean <task-name>        # Clean specific task
make clean-venvs              # Remove Python venvs
make clean-all                # Remove ALL binaries and .judge artifacts

🐍 Python Virtual Environments

• Temporary venv (auto): make py <task> — creates, installs dependencies from root requirements.txt, runs, then cleans up.
• Persistent venv: make venv <task> then make run <task> — installs from root requirements.txt, reuses environment.

🏗️ Creating New Tasks

make new <task-name>
make new my-algorithm

📁 Project Structure (high level)

algorithms-solutions/
├── Makefile
├── README.md
├── requirements.txt
├── venv-run.sh
├── python-with-venv.sh
│
├── frog-leap-puzzle/
│   └── go/
├── n-queens/
│   └── go/
├── n-puzzle/
│   └── go/
├── tsp/
│   └── go/
├── iris/
│   └── python/
├── tic-tac-toe/
│   └── python/
├── naive-bayes-classifier/
│   └── python/
├── decision-tree/
│   └── python/
└── algorithms/
    ├── beam-search/
    ├── dfs-and-bfs/
    ├── dijkstra/
    ├── genetic/
    └── minmax/

📚 Study Guide Coverage (Data Mining)

Task READMEs include theory, pseudocode, complexity, and exam tips:

• Frog Leap Puzzle — BFS/DFS/A* state modeling
• N-Queens — Backtracking and CSP
• N-Puzzle — A* and admissible heuristics (Manhattan)
• TSP — Exact vs heuristic (Nearest Neighbor, 2-Opt)
• Iris — KNN, scaling, cross-validation
• Naive Bayes Classifier — MAP rule, smoothing, variants
• Tic-Tac-Toe — Minimax and alpha-beta pruning
• Decision Tree — ID3 (entropy, information gain), pruning (REP)
• Neural Networks — MLP activations (sigmoid/tanh), backpropagation, XOR learnability

🔧 Troubleshooting

• Missing Python module: ensure dependencies are installed via make venv <task> or pip install -r requirements.txt.
• Judge artifacts: use make clean <task> or make clean-all.
• Go build issues: initialize/tidy modules inside <task>/go/.

📜 License

See individual task directories for specific licensing information.