def generate_random_ONFSM(num_states,
num_inputs,
num_outputs,
multiple_out_prob=0.1):
"""
Randomly generate an observable non-deterministic finite-state machine.
Args:
num_states: number of states
num_inputs: number of inputs
num_outputs: number of outputs
multiple_out_prob: probability that state will have multiple outputs (Default value = 0.1)
Returns:
randomly generated ONFSM
"""
inputs = [
random_string_generator(random.randint(1, 3))
for _ in range(num_inputs)
]
outputs = [
random_string_generator(random.randint(3, 7))
for _ in range(num_outputs)
]
states = []
for i in range(num_states):
state = OnfsmState(f's{i}')
states.append(state)
for state in states:
for i in inputs:
state_outputs = 1
if random.random() <= multiple_out_prob and num_outputs > 1:
state_outputs = random.randint(2, num_outputs)
random_out = random.sample(outputs, state_outputs)
for index in range(state_outputs):
state.transitions[i].append(
(random_out[index], random.choice(states)))
return Onfsm(states[0], states)
def generate_random_mdp(num_states,
len_input,
custom_outputs=None,
num_unique_outputs=None):
"""
Generates random MDP.
Args:
num_states: number of states
len_input: number of inputs
custom_outputs: user predefined outputs
num_unique_outputs: number of outputs
Returns:
random MDP
"""
num_unique_outputs = num_states if not num_unique_outputs else num_unique_outputs
outputs = [
random_string_generator(random.randint(3, 7))
for _ in range(num_unique_outputs)
]
outputs = custom_outputs if custom_outputs else outputs
while len(outputs) < num_states:
outputs.append(random.choice(outputs))
possible_probabilities = [1.0, 1.0, 1.0, 1.0, 0.8, 0.5, 0.9]
states = []
for i in range(num_states):
states.append(MdpState(f'q{i}', outputs.pop()))
for state in states:
for i in range(len_input):
prob = random.choice(possible_probabilities)
if prob == 1.:
state.transitions[i].append((random.choice(states), prob))
else:
new_states = list(states)
s1 = random.choice(new_states)
new_states.remove(s1)
state.transitions[i].append((s1, prob))
state.transitions[i].append(
(random.choice(new_states), round(1 - prob, 2)))
return Mdp(states[0], states), list(range(len_input))