Improve OptiSim selection method, documentation, and examples

Description:

1. Performance / Efficiency Issue

The current OptiSim selection method becomes very slow for large datasets during the setup phase because finding the optimal radius takes a lot of time.

Steps to reproduce:

Load a large dataset (e.g., 10,000+ points).
Call optisim_selection() or get_initial_selection().

Expected behavior: Selection completes quickly.
Actual behavior: Setup phase takes very long.

Possible solutions:

Use binary search for radius determination.
Use KDTree or BallTree for distance checks.
Use approximate or sampling-based methods for very large datasets.
2) Documentation Issue

README currently describes installation and citation, but lacks a clear example for using OptiSim method and its parameters.

Suggested solution:

Add a usage example with a code snippet showing optisim_selection() or get_initial_selection().

Proposed code snippet:

Example: OptiSim selection

from selector import optisim_selection
import numpy as np

Example dataset: 1000 points, each with 5 features

points = np.random.rand(1000, 5)

Select 50 diverse points using OptiSim

selected_points = optisim_selection(points, n_select=50)

print("Number of selected points:", len(selected_points))

Explanation:

points: Your dataset
n_select: Number of diverse points to select
optisim_selection(): OptiSim method call
Output: Selected points list and count
3) Testing Issue

Current tests do not cover large datasets to verify OptiSim performance and correctness.

Suggested solution:

Add unit tests for datasets of 5,000+ points to check runtime and correctness.
4)Web Server / Feature Issue

The web server allows selecting a diverse subset but does not provide options to tune OptiSim radius or optimization parameters.

Suggested solution:

Add an interactive slider or input for radius control so users can customize selection.