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- import numpy as np
- from sklearn.cluster import AgglomerativeClustering
- from fdc.visualize import plotCluster
- def aglo_clustering(number_of_clusters, affinity, linkage
- , five_d_embedding, two_d_embedding
- , visual=False):
- np.random.seed(42)
- ag_cluster = AgglomerativeClustering(
- n_clusters=number_of_clusters
- , affinity=affinity
- , linkage=linkage
- )
- clusters = ag_cluster.fit_predict(five_d_embedding)
- (values, counts) = np.unique(clusters, return_counts=True)
- two_d_embedding['Cluster'] = clusters
-
- if visual:
- plotCluster(two_d_embedding, clusterName="Cluster", xName="UMAP_0", yName="UMAP_1", stroke=3)
- return two_d_embedding.Cluster.to_list(), counts
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