⚠️ Code, comments and documentation are written in Spanish as part of my university coursework.
Collection of exercises, simulations, lab practices and evidences from my Adaptive Systems Programming and Lab courses at UANL, covering self-adjustment, cellular automata, fuzzy systems, neural networks, swarm intelligence and complex networks — implemented in Python and Jupyter Notebook.
| # | Topic | Description |
|---|---|---|
| 01 | Auto-Adjustment | Self-adjusting simulations including a sentinel light controller, auto-irrigation system and adaptive transportation mode selector |
| 02 | Cellular Automata | 1D binary cellular automaton in Python with configurable rules (0–255) and a Java-based simulation as a runnable .jar |
| 03 | Fuzzy Systems | Fuzzy logic design and implementation with skfuzzy, including a Mario Kart race classifier and individual/team evidence PDFs |
| 04 | Neural Networks | MLP on Parkinson's dataset, CNN models for MNIST digit recognition, K-means from scratch and with sklearn, and individual evidence PDF |
| # | Practice | Description |
|---|---|---|
| 01 | ACO | Ant Colony Optimization applied to the Travelling Salesman Problem, with manual two-ant iteration and 10-run parameter analysis |
| 02 | Flood It | 14×14 color board game implemented in Python with tkinter, reading configurations from a .txt file |
| 03 | Fireworks | Particle explosion simulation with pygame, including original buggy template, corrected version and team analysis |
| 04 | K-Means | Guided K-means exercises and programming using Iris, E. coli and Glass datasets, with step-by-step guides |
| 05 | Complex Networks | Directed social network analysis with adjacency matrix, centrality and degree distribution in Python and Jupyter |
This project is licensed under the MIT License.