dbscan-core-point — Ola case

Ola-inspired location-clustering service must identify dense groups of nearby pickup points while treating sparse points differently. You need to determine whether each point is a DBSCAN core point under the supplied radius and minimum-neighbor rules.

For every point, decide whether it is a DBSCAN core point: it is one if at least min_samples points (itself included) lie within Euclidean distance eps of it, boundary included.

Implement solve(X,eps,min_samples).

Returns. Return a boolean NumPy array with one entry per point.

Examples

Example 1

Input:

solve([[0, 0], [0, 1], [5, 5]], 1.1, 2)

Output:

[True, True, False]

Example 2

Input:

solve([[0.0], [1.0], [2.0], [3.0]], 1.0, 3)

Output:

[False, True, True, False]