clusterer¶
This module provides time series clustering functionality using complex invariant distance. For details see Steinmann et al (2020)
- ema_workbench.analysis.clusterer.apply_agglomerative_clustering(distances, n_clusters, linkage='average')¶
apply agglomerative clustering to the distances
- Parameters
distances (ndarray) –
n_clusters (int) –
linkage ({'average', 'complete', 'single'}) –
- Return type
1D ndarray with cluster assignment
- ema_workbench.analysis.clusterer.calculate_cid(data, condensed_form=False)¶
calculate the complex invariant distance between all rows
- Parameters
data (2d ndarray) –
condensed_form (bool, optional) –
- Returns
a 2D ndarray with the distances between all time series, or condensed form similar to scipy.spatial.distance.pdist¶
- Return type
distances
- ema_workbench.analysis.clusterer.plot_dendrogram(distances)¶
plot dendrogram for distances