data_map.add_random_location()
# Add 5 random drop zones
for i in range(5):
data_map.add_random_dropoff_zone()
averages = [0.0] * 6
num_iterations = 100
print 'Running simulated annealing and k-means clustering %d times' % num_iterations
for i in range(0, num_iterations):
print 'Pass %d' % (i + 1)
# Show initial map cost
SA_final = SimulatedAnnealing(3)
initial_map_cost = SA_final.cost(data_map)
print 'Initial map cost: %d' % initial_map_cost
averages[0] += initial_map_cost
# Plot initial map
plot = Plot()
plot.plot(data_map, False)
# Run simulated annealing experiments
for i in range(1, 5):
print 'Running simulated annealing for type %d:' % i
SA = SimulatedAnnealing(i)
new_map = SA.anneal(data_map)
cost = SA_final.cost(new_map)
