ClusterPoints3D
Clusters 3D points using K Means Clustering method.
Syntax
C++
C#
Python
def ClusterPoints3D( inPoints: list[Point3D], /, *, inClusters: int = 2, inMaxIterations: int = 200, inSeed: int | None = 5489, inRunCount: int = 1 ) -> ( outClusters: list[list[Point3D] | None], outCentroids: list[Point3D | None], outDistanceSum: float )
Parameters
| Name | Type | Range | Default | Description | |
|---|---|---|---|---|---|
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inPoints | list[Point3D] | Array of points to cluster | ||
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inClusters | int | 2 - +![]() |
2 | Number of clusters to extract |
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inMaxIterations | int | 10 - 1000 | 200 | Maximal number of KMeans iterations |
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inSeed | int | None | 0 - +![]() |
5489 | Seed used to initialize random number generators |
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inRunCount | int | 1 - +![]() |
1 | Defines how many times the algorithm will be executed |
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outClusters | list[list[Point3D] | None] | Resulting Point3D clusters | ||
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outCentroids | list[Point3D | None] | Center of found clusters | ||
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outDistanceSum | float | Sum of distance squares from points in array to its respective cluster center |



