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- import numpy as np
- from sklearn.cluster import AgglomerativeClustering
- from sklearn.cluster import KMeans
- from sklearn.cluster import DBSCAN
- 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
- class Clustering:
- def __init__(self,high_dim,low_dim,visual):
- self.high_dim=high_dim
- self.low_dim=low_dim
- self.visual=visual
- def Agglomerative(self,number_of_clusters, affinity, linkage):
- self.number_of_clusters=number_of_clusters
- self.affinity=affinity
- self.linkage=linkage
- ag_cluster = AgglomerativeClustering(n_clusters=number_of_clusters, affinity=affinity, linkage=linkage)
- clusters = ag_cluster.fit_predict(self.high_dim)
- (values, counts) = np.unique(clusters, return_counts=True)
- self.low_dim['Cluster'] = clusters
-
- if self.visual:
- plotCluster(self.low_dim, clusterName="Cluster", xName="UMAP_0", yName="UMAP_1", stroke=3)
- return self.low_dim.Cluster.to_list(), counts
-
-
- def DBSCAN(self,eps,min_samples):
- self.eps=eps
- self.min_samples=min_samples
- dbscan = DBSCAN(eps=eps, min_samples = min_samples)
- clusters = dbscan.fit_predict(self.high_dim)
- (values, counts) = np.unique(clusters, return_counts=True)
- self.low_dim['Cluster'] = clusters
-
- if self.visual:
- plotCluster(self.low_dim, clusterName="Cluster", xName="UMAP_0", yName="UMAP_1", stroke=3)
- return self.low_dim.Cluster.to_list(), counts
-
- def K_means(self,no_of_clusters):
- self.no_of_clusters=no_of_clusters
- kmeans = KMeans(n_clusters=no_of_clusters)
- clusters = kmeans.fit_predict(self.high_dim)
- (values, counts) = np.unique(clusters, return_counts=True)
- self.low_dim['Cluster'] = clusters
-
- if self.visual:
- plotCluster(self.low_dim, clusterName="Cluster", xName="UMAP_0", yName="UMAP_1", stroke=3)
- return self.low_dim.Cluster.to_list(), counts
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