clusters = {i : [] for i in range(0,k)}
for v in data:
cluster = -1
distance = float('inf')
for i in range(len(means)):
d = Vec.distance(v, means[i])
if d < distance:
distance = d
cluster = i
clusters[cluster].append(v)
# Create a list to store the new means
new_means = []
for key in clusters:
# Sum the vectors in each cluster
summed = Vec.zero()
cluster = clusters[key]
for v in cluster:
summed += v
# Find the mean of all the vectors in each cluster
count = len(cluster)
if count > 0:
new_means.append(summed / Vec(count, count))
else:
new_means.append(means[key])
# If the new means are the same as the old, the algorithm has converged
convergence = means == new_means
means = new_means
