Enhanced Visualization for Activity Selection Algorithms

• Day: 2024-04-17
• Time: 03:00 to 05:30
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Visualization, Dynamic Programming, Activity Selection, Matplotlib, Algorithm

Description

Session Goal

The session aimed to enhance the visualization capabilities of various activity selection algorithms, including GreedySelector, BacktrackingSelector, and DynamicProgrammingSelector, using Python’s [[matplotlib]].

Key Activities

• Modified the visualizar method to separate selected and unselected activities using distinct colors and adjusted y-axis coordinates.
• Integrated intermediate visualizations in GreedySelector to display the state of activities after each decision.
• Adapted the visualizar method for BacktrackingSelector to graphically represent selected and evaluated activities.
• Implemented visualizations in DynamicProgrammingSelector to illustrate memoization table filling and solution reconstruction.
• Reviewed and clarified the reconstruction logic of the dynamic programming algorithm for activity selection.

Achievements

• Successfully integrated visualization techniques across different selection algorithms, improving the understanding of decision-making processes.
• Clarified the decision-making and reconstruction processes in dynamic programming, identifying and correcting potential errors.

Pending Tasks

• Further testing and validation of the visualization methods in different algorithmic contexts to ensure robustness and accuracy.
• Exploration of additional visualization libraries or techniques that could enhance the current implementations.

Evidence

• source_file=2024-04-17.sessions.jsonl, line_number=0, event_count=0, session_id=0024897803b8fe2942b54d40f9e6636b3c467be02e10bac4088f271aff0e0ffc
• event_ids: []