def simulate(self, n_time_steps):
""" simulate the patient over the specified simulation length """
k = 0 # simulation time step
# while the patient is alive and simulation length is not yet reached
while self.stateMonitor.get_if_alive() and k < n_time_steps:
# find the transition probabilities to future states
trans_probs = self.params.probMatrix[
self.stateMonitor.currentState.value]
# create an empirical distribution
empirical_dist = RVGs.Empirical(probabilities=trans_probs)
# sample from the empirical distribution to get a new state
# (returns an integer from {0, 1, 2, ...})
new_state_index = empirical_dist.sample(rng=self.rng)
# update health state
self.stateMonitor.update(time_step=k,
new_state=P.HealthStates(new_state_index))
# increment time
k += 1
def __init__(self, transition_rate_matrix):
self._rateMatrix = transition_rate_matrix
self._expDists = []
self._empiricalDists = []
for i, row in enumerate(transition_rate_matrix):
# find sum of rates out of this state
rate_out = out_rate(row, i)
# if the rate is 0, put None as the exponential and empirical distributions
if rate_out > 0:
# create an exponential distribution with rate equal to sum of rates out of this state
self._expDists.append(RVG.Exponential(scale=1 / rate_out))
# find the transition rates to other states
# assume that the rate into this state is 0
rates = []
for j, v in enumerate(row):
if i == j:
rates.append(0)
else:
rates.append(v)
# calculate the probability of each event (prob_j = rate_j / (sum over j of rate_j)
probs = np.array(rates) / rate_out
# create an empirical distribution over the future states from this state
self._empiricalDists.append(RVG.Empirical(probs))
else: # if the sum of rates out of this state is 0
self._expDists.append(None)
self._empiricalDists.append(None)
def test_empirical(rnd, prob):
# empirical random variate generator
empirical_dist = RVGs.Empirical(prob)
# obtain samples
samples = get_samples(empirical_dist, rnd)
# report mean and variance
if type(prob) == list:
prob = np.array(prob)
outcome = np.array(range(len(prob)))
mean = sum(outcome * prob)
var = sum((outcome**2) * prob) - mean**2
print_test_results('Empirical', samples, expectation=mean, variance=var)
def simulate(self, n_time_steps):
t = 0
# while self.stateMonitor.get_if_alive() and t < n_time_steps:
while t < n_time_steps:
# find the transition probabilities to future states
trans_probs = self.params.probMatrix[
self.stateMonitor.currentState.value]
# create an empirical distribution
empirical_dist = RVGs.Empirical(probabilities=trans_probs)
# sample from the empirical distribution to get a new state
new_state_index = empirical_dist.sample(rng=self.rng)
# update health state
self.stateMonitor.update(time_step=t,
new_state=P.HealthStates(new_state_index))
# increment time
t += 1
