def getweight( city : Vec, node : Vec, net : List[Vec], k): #print(city) numerator = fi( math.fabs( city.distance( node ) ), k) denominator = 0 for each in net: denominator += fi( math.fabs( city.distance( each )), k) return numerator / denominator
clusters = {}
# Count the number of iterations until convergence
iterations = 0
# Will be used to determine if the algorithm is complete
convergence = False
# Initialize the means to random points in the general range of our dataset
means = [Vec(randrange(minX, maxX), randrange(minY, maxY)) for x in range(k)]
while convergence is False:
iterations += 1
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
