geo_clustering
- class GeoCoordClusterer[source]
Bases:
abc.ABC- abstract fit_geo_coords(geo_coords: List[sensai.geoanalytics.geo_coords.GeoCoord])
- Parameters
geo_coords – the coordinates to be clustered
- abstract clusters_indices() Tuple[List[List[int]], List[int]]
- Returns
a tuple (clusters, outliers), where clusters is a list of point indices, one list for each cluster containing the indices of points within the cluster, and outliers is the list of indices of points not within clusters
- class GreedyAgglomerativeGeoCoordClusterer(max_min_distance_for_merge_m: float, max_distance_m: float, min_cluster_size: int, lcs: Optional[sensai.geoanalytics.local_coords.LocalCoordinateSystem] = None)[source]
Bases:
sensai.geoanalytics.geo_clustering.GeoCoordClusterer- __init__(max_min_distance_for_merge_m: float, max_distance_m: float, min_cluster_size: int, lcs: Optional[sensai.geoanalytics.local_coords.LocalCoordinateSystem] = None)
- Parameters
max_min_distance_for_merge_m – the maximum distance, in metres, for the minimum distance between two existing clusters for a merge to be admissible
max_distance_m – the maximum distance, in metres, between any two points for the points to be allowed to be in the same cluster
min_cluster_size – the minimum number of points any valid cluster must ultimately contain; the points in any smaller clusters shall be considered as outliers
lcs – the local coordinate system to use for clustering; if None, compute based on mean coordinates passed when fitting
- class Matrix(dim: int)
Bases:
object- UNSET_VALUE = inf
- __init__(dim: int)
- set(c1: int, c2: int, value: float)
- get(c1: int, c2: int) float
- class Cluster(point: sensai.geoanalytics.geo_clustering.GreedyAgglomerativeGeoCoordClusterer.LocalPoint, idx: int, clusterer: sensai.geoanalytics.geo_clustering.GreedyAgglomerativeGeoCoordClusterer)
Bases:
sensai.clustering.greedy_clustering.GreedyAgglomerativeClustering.Cluster- __init__(point: sensai.geoanalytics.geo_clustering.GreedyAgglomerativeGeoCoordClusterer.LocalPoint, idx: int, clusterer: sensai.geoanalytics.geo_clustering.GreedyAgglomerativeGeoCoordClusterer)
- merge_cost(other: sensai.geoanalytics.geo_clustering.GreedyAgglomerativeGeoCoordClusterer.Cluster)
Computes the cost of merging the given cluster with this cluster
- Returns
the (non-negative) merge cost or math.inf if a merge is inadmissible
- merge(other)
Merges the given cluster into this cluster”
- Parameters
other – the cluster that is to be merged into this cluster
- fit_geo_coords(geo_coords: List[sensai.geoanalytics.geo_coords.GeoCoord]) None
- Parameters
geo_coords – the coordinates to be clustered
- clusters_indices() Tuple[List[List[int]], List[int]]
- Returns
a tuple (clusters, outliers), where clusters is a list of point indices, one list for each cluster containing the indices of points within the cluster, and outliers is the list of indices of points not within clusters
- class MergeCandidateDeterminationStrategy(search_radius_m: float, parent: sensai.geoanalytics.geo_clustering.GreedyAgglomerativeGeoCoordClusterer)
-
- __init__(search_radius_m: float, parent: sensai.geoanalytics.geo_clustering.GreedyAgglomerativeGeoCoordClusterer)
- set_clusterer(clusterer: sensai.clustering.greedy_clustering.GreedyAgglomerativeClustering)
Initialises the clusterer the strategy is applied to :param clusterer: the clusterer
- iter_candidate_indices(wc: sensai.clustering.greedy_clustering.GreedyAgglomerativeClustering.WrappedCluster, initial: bool, merged_cluster_indices: Optional[Tuple[int, int]] = None) Iterator[int]
- Parameters
wc – the wrapped cluster: the cluster for which to determine the cluster indices that are to be considered for a potential merge
initial – whether we are computing the initial candidates (at the start of the clustering algorithm)
merged_cluster_indices – [for initial=False] the pair of cluster indices that were just joined to form the updated cluster wc
- Returns
an iterator of cluster indices that should be evaluated as potential merge partners for wc (it may contain the index of wc, which will be ignored)
- class SkLearnGeoCoordClusterer(clusterer, lcs: Optional[sensai.geoanalytics.local_coords.LocalCoordinateSystem] = None)[source]
Bases:
sensai.geoanalytics.geo_clustering.GeoCoordClusterer- __init__(clusterer, lcs: Optional[sensai.geoanalytics.local_coords.LocalCoordinateSystem] = None)
- Parameters
clusterer – a clusterer from sklearn.cluster
lcs – the local coordinate system to use for Euclidian conversion; if None, determine from data (using mean coordinate as centre)
- fit_geo_coords(geo_coords: List[sensai.geoanalytics.geo_coords.GeoCoord])
- Parameters
geo_coords – the coordinates to be clustered
- clusters_local_points() Tuple[List[List[Tuple[float, float]]], List[Tuple[float, float]]]
- Returns
a tuple (clusters, outliers), where clusters is a dictionary mapping from cluster index to the list of local points within the cluster and outliers is a list of local points not within clusters
- clusters_indices() Tuple[List[List[int]], List[int]]
- Returns
a tuple (clusters, outliers), where clusters is a list of point indices, one list for each cluster containing the indices of points within the cluster, and outliers is the list of indices of points not within clusters
- class DBSCANGeoCoordClusterer(eps, min_samples, lcs: Optional[sensai.geoanalytics.local_coords.LocalCoordinateSystem] = None, **kwargs)[source]
Bases:
sensai.geoanalytics.geo_clustering.SkLearnGeoCoordClusterer- __init__(eps, min_samples, lcs: Optional[sensai.geoanalytics.local_coords.LocalCoordinateSystem] = None, **kwargs)
- Parameters
eps – the maximum distance between two samples for one to be considered as in the neighbourhood of the other
min_samples – the minimum number of samples that must be within a neighbourhood for a cluster to be formed
lcs – the local coordinate system for conversion to a Euclidian space
kwargs – additional arguments to pass to DBSCAN (see https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)