b = CauchyVariable(loc=np.random.normal(0, 1, (dim, dim )),
scale=2 + np.random.normal(0, 1, (dim, dim ))**2,
name="b", learnable=True)
c = CauchyVariable(loc=a + b,
scale=1 + a**2,
name="c", learnable=True)
d = CauchyVariable(loc=c,
scale=1 + b**2,
name="d", learnable=True)
model = ProbabilisticModel([d])
#print(model.get_mean())
#print(model.get_variance())
#print(model.get_entropy())
n_samples = 5
ent_list = []
samp_ent_list = []
for itr in range(50):
q_sample = model._get_sample(n_samples)
entropy = sum([e.sum()/float(n_samples) for e in model._get_entropy(q_sample).values()])
ent_list.append(float(entropy))
sampled_entropy = -model.calculate_log_probability(q_sample).mean()
samp_ent_list.append(float(sampled_entropy))
print("Semi-analytic: {} +- {}".format(np.mean(ent_list), np.std(ent_list)))
print("Stochastic: {} +- {}".format(np.mean(samp_ent_list), np.std(samp_ent_list)))
scale=2 + np.random.normal(0, 1, (dim, dim))**2,
name="b",
learnable=True)
c = CauchyVariable(loc=a + b, scale=1 + a**2, name="c", learnable=True)
d = CauchyVariable(loc=c, scale=1 + b**2, name="d", learnable=True)
model = ProbabilisticModel([d])
#print(model.get_mean())
#print(model.get_variance())
#print(model.get_entropy())
n_samples = 5
ent_list = []
samp_ent_list = []
for itr in range(50):
q_sample = model._get_sample(n_samples)
entropy = sum([
e.sum() / float(n_samples)
for e in model._get_entropy(q_sample).values()
])
ent_list.append(float(entropy))
sampled_entropy = -model.calculate_log_probability(q_sample).mean()
samp_ent_list.append(float(sampled_entropy))
print("Semi-analytic: {} +- {}".format(np.mean(ent_list), np.std(ent_list)))
print("Stochastic: {} +- {}".format(np.mean(samp_ent_list),
np.std(samp_ent_list)))
