solution here

```{code-cell} ipython3

from numpy.random import uniform, randint

n = 1000 # number of agents (agents = 0, ..., n-1)

k = 10 # number of agents regarded as neighbors

require_same_type = 5 # want >= require_same_type neighbors of the same type

rng = np.random.default_rng()

def initialize_state():

locations = uniform(size=(n, 2))

types = randint(0, high=2, size=n) # label zero or one

locations = rng.uniform(size=(n, 2))

types = rng.integers(0, 2, size=n) # label zero or one

return locations, types

moved = False

while not is_happy(i, locations, types):

moved = True

locations[i, :] = uniform(), uniform()

locations[i, :] = rng.uniform(), rng.uniform()

return moved

def plot_distribution(locations, types, title, savepdf=False):

while current_iter <= max_iter:

# Choose a random agent and update them

i = randint(0, n)

i = rng.integers(0, n)

moved = update_agent(i, locations, types)

if flip_prob > 0:

# flip agent i's type with probability epsilon

U = uniform()

U = rng.uniform()

if U < flip_prob:

current_type = types[i]

types[i] = 0 if current_type == 1 else 1