Added class for different clustering methods.

fdc/clustering.py

@@ -1,5 +1,7 @@
 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
@@ -20,3 +22,49 @@ def aglo_clustering(number_of_clusters, affinity, linkage
 
     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
+