def logistic_regression():
N, dim = 3000, 3
# generic way to sample from distributions
data = pyro.sample('data', dist.Normal(0., 1.), sample_shape=(N, dim))
true_coefs = ops.arange(1., dim + 1.)
logits = ops.sum(true_coefs * data, axis=-1)
labels = pyro.sample('labels', dist.Bernoulli(logits=logits))
def model(x, y=None):
coefs = pyro.sample('coefs', dist.Normal(ops.zeros(dim),
ops.ones(dim)))
intercept = pyro.sample('intercept', dist.Normal(0., 1.))
logits = ops.sum(coefs * x, axis=-1) + intercept
return pyro.sample('obs', dist.Bernoulli(logits=logits), obs=y)
return {
'model': model,
'model_args': (data, ),
'model_kwargs': {
'y': labels
}
}
def model(x, y=None):
coefs = pyro.sample('coefs', dist.Normal(ops.zeros(dim),
ops.ones(dim)))
intercept = pyro.sample('intercept', dist.Normal(0., 1.))
logits = ops.sum(coefs * x, axis=-1) + intercept
return pyro.sample('obs', dist.Bernoulli(logits=logits), obs=y)
def model(data):
p = pyro.param("p", ops.tensor(0.5))
pyro.sample("x", dist.Bernoulli(p), obs=data)