📅 2023-09-11 — Session: Development and Optimization of Graph Timing Experiments
🕒 17:30–18:50
🏷️ Labels: Graph Theory, Timing Experiment, Python, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to develop and optimize functions for generating and analyzing graph structures, specifically focusing on timing experiments for different graph representations.
Key Activities
- Matrix Generation: Developed functions to generate random adjacency matrices and graph representations, including adjacency matrix, edge set, and neighborhood set.
- Class Implementations: Implemented and modified Python classes
GraphWithEdgeSetandGraphWithNeighborhoodSetto handle graph data structures efficiently. - Experiment Planning: Created a detailed plan for conducting timing experiments on random networks, focusing on the execution time of graph operations.
- Function Encapsulation: Encapsulated the timing experiment logic into a
timing_experiment()function, allowing flexible testing of graph methods. - Error Handling: Identified and addressed several errors in graph initialization and function argument passing, improving robustness.
Achievements
- Successfully implemented and tested graph classes and functions, allowing for comprehensive timing experiments.
- Developed a flexible framework for analyzing execution time across different graph operations and representations.
- Identified and resolved critical errors in graph manipulation and function design.
Pending Tasks
- Further optimize the
timing_experiment()function to handle larger datasets efficiently. - Continue debugging and refining error handling in graph operations, particularly in edge removal and adjacency checks.