def clustering(NC, ref_imgs, img_infos):
clusters, centroids = K.KMeans_clustering(ref_imgs, NC, img_infos)
np.save(clusters, 'clusters' + NC + '.npy')
np.save(centroids, 'centroids' + NC + '.npy')
j = test_files[i]
imgs.append(ref_imgs[j])
input_infos.append(img_infos[j])
ref_imgs = np.delete(ref_imgs, (j), axis=0)
img_infos = np.delete(img_infos, (j), axis=0)
distance_matrix = np.delete(distance_matrix, (j), axis=0)
distance_matrix = np.delete(distance_matrix, (j), axis=1)
# loading clusters and centroids
clusters2, centroids2 = np.load('clusters2.npy').item(), np.genfromtxt(
'centroids2.csv', delimiter=',')
centroids2 = centroids2.astype(int)
clusters3, centroids3 = np.load('clusters3.npy').item(), np.genfromtxt(
'centroids3.csv', delimiter=',')
centroids3 = centroids3.astype(int)
clusters11, centroids11 = K.KMeans_clustering(ref_imgs, 1, img_infos)
for max_iter in range(
1, 200, 1
): # We test the accuracy of each algorithm having different values for maximum iterations
print(max_iter)
#Testing the ML-ANN algorithm
NC = 1 # number of clusters
weights = np.zeros(NC) # weights for each cluster
CDistances = np.zeros(NC) # dictances between clusters and input image
distances = {}
for i in range(0, NC):
distances[str(i)] = []
clusters, centroids = copy.deepcopy(clusters11), copy.deepcopy(
centroids11) # clustering reference images by K-Means algorithm
