Optimization of the OptiSim algorithm (For review)

[dhruvDev23]

This PR optimizes the algorithm() method of OptiSim class in the file selector/methods/distance.py.

Original Implementation

• In the Original implementation, a kd-tree was rebuild from all selected points on every iteration of the selection loop.
• For a Selection of k candidates, the kd-tree was being rebuilt k times, making the operation expensive as more points are selected.

After Optimization

• Removed the kd-tree rebuilt from the loop.
• Implemented a min_dists array that stores the minimum distances between each point and nearest selected point.
• Then Select and append the candidate farthest from its nearest selected point using min_dists array.
• Then update the min_dists array after selection of the new candidate.

All Test Cases Passed

Analysis of different input data shapes

Before Optimization

After Optimization