def estimate_pvalue_SI(underlying: Network,
theta: float,
outcome: dict,
initials: set,
immunes: set = set(),
tmax=1000.0,
dt=1,
n_iterations=10000):
est_p = Simulator.estimate_SI(underlying=underlying,
theta=theta,
outcome=outcome,
initials=initials,
tmax=tmax,
dt=dt)
n_less = 0
for i in range(n_iterations):
result = Simulator.simulate_SI(underlying=underlying,
theta=theta,
infected=initials,
immunes=immunes,
tmax=tmax,
dt=dt)
if result['p'] <= est_p:
n_less += 1
return n_less / n_iterations
def test_SI(ntw: Network, true_theta: float):
result = Simulator.simulate_SI(underlying=ntw,
theta=true_theta,
infected={1},
tmax=300,
dt=1)
print(result)
th = 0.0
est_th = th
best_pest = 0
ml_pval = 0
thetas = []
ps = []
total_pest = 0
while th <= 1.0:
pest = Simulator.estimate_SI(underlying=ntw,
outcome=result['outcome'],
theta=th,
initials={1},
tmax=300,
dt=1)
# print(str(th) + ' ' + str(pest))
if pest > best_pest:
best_pest = pest
est_th = th
thetas.append(th)
ps.append(pest)
total_pest += pest
th += 0.01
pval = de.estimate_pvalue_SI(underlying=ntw,
outcome=result['outcome'],
theta=est_th,
initials={1},
tmax=300,
dt=1,
n_iterations=1000)
print('True theta: ' + str(true_theta))
print('Max liklehood estimation: ' + str(est_th) + ' p-val ' + str(pval))
psnorm = stat.normalize_series(ps)
confide = stat.confidential_from_p_series(psnorm, 0.95)
print('0.95 confidential interval: (' + str(thetas[confide[0]]) + ', ' +
str(thetas[confide[1]]) + ')')
pyplot.plot(thetas, psnorm)
pyplot.show()