def forward(self):
mu = self.marsaglia(self.prior_mean, self.prior_stddev)
likelihood = Normal(mu, self.likelihood_stddev)
pyprob.tag(mu, name='mu')
pyprob.observe(likelihood, name='obs0')
pyprob.observe(likelihood, name='obs1')
return mu
def forward(self):
uniform = Uniform(0, 1)
val = pyprob.sample(uniform)
val = pyprob.sample(uniform)
val = pyprob.sample(uniform, control=False)
val = pyprob.sample(uniform, control=False)
val = pyprob.sample(uniform, control=False)
pyprob.tag(value=val, name='val')
pyprob.observe(uniform, 0.5)
pyprob.observe(uniform, 0.5)
pyprob.observe(uniform, 0.5)
pyprob.observe(uniform, 0.5)
return val
def marsaglia(self, mean, stddev):
uniform = Uniform(-1, 1)
s = 1
i = 0
while True:
# x = pyprob.sample(uniform, replace=self.replace)
# y = pyprob.sample(uniform, replace=self.replace)
x = pyprob.sample(uniform)
y = pyprob.sample(uniform)
s = x * x + y * y
i += 1
if float(s) < 1:
pyprob.tag(x, name='x_accepted')
pyprob.tag(y, name='y_accepted')
pyprob.tag(s, name='s_accepted')
break
else:
pyprob.tag(x, name='x_rejected')
pyprob.tag(y, name='y_rejected')
pyprob.tag(s, name='s_rejected')
pyprob.tag(i, name='iterations')
return mean + stddev * (x * torch.sqrt(-2 * torch.log(s) / s))
