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I’ve been trying to determine the silhouette scores for each object in an array, which contains 3 vectors that represent 3 different objects and cluster labels for them are [1, 0, 0].

import numpy as np
from sklearn_extra.cluster import KMedoids
from sklearn.metrics.pairwise import euclidean_distances


X2=np.array([[ 5.43840675, -1.05259078, -0.21793506,  8.56686818, -2.58056957,
        -0.07310339, -0.31181501,  0.02696586],
       [ 5.72318296, -0.99665473, -0.14540062,  8.32051008, -3.36201189,
        -0.04897565, -0.34271698, -0.0339766 ],
       [ 5.93081714, -1.52272427,  0.40706477,  8.56256569, -3.216366  ,
        -0.0108426 , -0.57434619, -0.18952662]])
        
 
model1 = KMedoids(n_clusters=2, random_state=0).fit(X2)
medoid=np.squeeze(X2[model1.medoid_indices_])
medoid

for i in range(len(X2)):
    print("object", i)
    s = []
    for j in range(len(medoid)):
        b = []
        print('S',j, ':')
        a1 = ( X2[i].reshape(-1, 1))
        b1 = (X2[model1.medoid_indices_][j].reshape(-1, 1))    
        a =euclidean_distances(a1, b1)
        print('a', a)
        
        for k in range(len(medoid)):
                if k != j:
                    a2 = ( X2[i].reshape(-1, 1))
                    b2 = (X2[model1.medoid_indices_][k].reshape(-1, 1))
                    b =euclidean_distances(a2, b2)
                    print('b', b)
        bmin=min(b)
        print('miminum b', min(b))
        print('bi-ai', bmin-a)
        print('max{a_i, b_i)', max(a, bmin))
        s.append((bmin-a)/(max(a, bmin)))
        print('SI', s)
            
        max_value = max(s)
        print("SI, max value:", max_value)

I want to calculate the silhouette score for each object by the following equation

S(i) = ( b(i) – a(i) ) / ( max { ( a(i), b(i) ) }

but my code is not working, any suggestions even with modified the code?

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