def test_no_arguments():
"""
No args passed in during initialization.
"""
n = nn.Normal()
assert isinstance(n, nn.Distribution)
b = nn.Bernoulli()
assert isinstance(b, nn.Distribution)
def test_with_arguments():
"""
Args passed in during initialization.
"""
n = nn.Normal([3.0], [4.0], dtype=dtype.float32)
assert isinstance(n, nn.Distribution)
b = nn.Bernoulli([0.3, 0.5], dtype=dtype.int32)
assert isinstance(b, nn.Distribution)
def test_basics():
"""
Test mean/standard deviation and probs.
"""
basics = Net3()
mean, sd = basics()
expect_mean = [0.5, 0.5]
assert (mean.asnumpy() == expect_mean).all()
assert (sd.asnumpy() == expect_mean).all()
b = nn.Bernoulli([0.7, 0.5], dtype=dtype.int32)
probs = b.probs()
expect_probs = [0.7, 0.5]
tol = 1e-6
assert (np.abs(probs.asnumpy() - expect_probs) < tol).all()
def __init__(self, shape, seed=0):
super(Net4, self).__init__()
self.b = nn.Bernoulli([0.7, 0.5], seed=seed, dtype=dtype.int32)
self.shape = shape
def __init__(self):
super(Net3, self).__init__()
self.b = nn.Bernoulli([0.5, 0.5], dtype=dtype.int32)
def __init__(self):
super(Net2, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
def __init__(self):
super(NormalBernoulli, self).__init__()
self.n = nn.Normal(3.0, 4.0, dtype=dtype.float32)
self.b = nn.Bernoulli(0.5, dtype=dtype.int32)
def __init__(self):
super(BernoulliKlNoArgs, self).__init__()
self.b = nn.Bernoulli(dtype=dtype.int32)
def __init__(self):
super(BernoulliLogProb2, self).__init__()
self.bernoulli = nn.Bernoulli(0.5)
def __init__(self):
super(BernoulliProb1, self).__init__()
self.bernoulli = nn.Bernoulli()
def __init__(self):
super(BernoulliLogProb, self).__init__()
self.bernoulli = nn.Bernoulli(0.5, dtype=dtype.int32)
