clustering_base
- class EuclideanClusterer(noise_label=- 1, min_cluster_size: Optional[int] = None, max_cluster_size: Optional[int] = None)[source]
Bases:
sensai.util.cache.PickleLoadSaveMixin,abc.ABCBase class for all clustering algorithms. Supports noise clusters and relabelling of identified clusters as noise based on their size.
- Parameters
noise_label – label that is associated with the noise cluster or None
min_cluster_size – if not None, clusters below this size will be labeled as noise
max_cluster_size – if not None, clusters above this size will be labeled as noise
- __init__(noise_label=- 1, min_cluster_size: Optional[int] = None, max_cluster_size: Optional[int] = None)
- class Cluster(datapoints: numpy.ndarray, identifier: Union[int, str])
Bases:
object- __init__(datapoints: numpy.ndarray, identifier: Union[int, str])
- centroid()
- radius()
- summary_dict()
- Returns
dictionary containing coarse information about the cluster (e.g. num_members and centroid)
- clusters(condition: Optional[Callable[[sensai.clustering.clustering_base.EuclideanClusterer.Cluster], bool]] = None) Iterable[sensai.clustering.clustering_base.EuclideanClusterer.Cluster]
- Parameters
condition – if provided, only clusters fulfilling the condition will be included
- Returns
generator of clusters
- noise_cluster()
- summary_df(condition: Optional[Callable[[sensai.clustering.clustering_base.EuclideanClusterer.Cluster], bool]] = None)
- Parameters
condition – if provided, only clusters fulfilling the condition will be included
- Returns
pandas DataFrame containing coarse information about the clusters
- fit(data: numpy.ndarray) None
- property is_fitted
- property datapoints: numpy.ndarray
- property labels: numpy.ndarray
- property cluster_identifiers: Set[int]
- get_cluster(cluster_id: int) sensai.clustering.clustering_base.EuclideanClusterer.Cluster
- property num_clusters: int