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import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from typing import Any
def find_cluster_centroids(embeddings, max_k=10) -> Any:
inertia = []
cluster_centroids = []
K = range(1, max_k+1)
for k in K:
kmeans = KMeans(n_clusters=k, random_state=0)
kmeans.fit(embeddings)
inertia.append(kmeans.inertia_)
cluster_centroids.append({"k": k, "centroids": kmeans.cluster_centers_})
diffs = [inertia[i] - inertia[i+1] for i in range(len(inertia)-1)]
optimal_centroids = cluster_centroids[diffs.index(max(diffs)) + 1]['centroids']
return optimal_centroids
def find_closest_centroid(centroids: list, normed_face_embedding) -> list:
try:
centroids = np.array(centroids)
normed_face_embedding = np.array(normed_face_embedding)
similarities = np.dot(centroids, normed_face_embedding)
closest_centroid_index = np.argmax(similarities)
return closest_centroid_index, centroids[closest_centroid_index]
except ValueError:
return None |