Clustering

Clustering module: ezdxf.math.clustering

ezdxf.math.clustering.average_cluster_radius(clusters: list[list[Vec2 | Vec3]]) → float

Returns the average cluster radius.

ezdxf.math.clustering.average_intra_cluster_distance(clusters: list[list[Vec2 | Vec3]]) → float

Returns the average point-to-point intra cluster distance.

ezdxf.math.clustering.dbscan(points: list[Vec2 | Vec3], *, radius: float, min_points: int = 4, rtree: RTree | None = None, max_node_size: int = 5) → list[list[Vec2 | Vec3]]

DBSCAN clustering.

https://en.wikipedia.org/wiki/DBSCAN

Parameters:

• points – list of points to cluster

• radius – radius of the dense regions

• min_points – minimum number of points that needs to be within the radius for a point to be a core point (must be >= 2)

• rtree – optional RTree

• max_node_size – max node size for internally created RTree

Returns:

list of clusters, each cluster is a list of points

ezdxf.math.clustering.k_means(points: list[Vec2 | Vec3], k: int, max_iter: int = 10) → list[list[Vec2 | Vec3]]

K-means clustering.

https://en.wikipedia.org/wiki/K-means_clustering

Parameters:

• points – list of points to cluster

• k – number of clusters

• max_iter – max iterations

Returns:

list of clusters, each cluster is a list of points