def test_arguments():
"""
Args passing during initialization.
"""
b = msd.Bernoulli()
assert isinstance(b, msd.Distribution)
b = msd.Bernoulli([0.1, 0.3, 0.5, 0.9], dtype=dtype.int32)
assert isinstance(b, msd.Distribution)
def test_prob():
"""
Invalid probability.
"""
with pytest.raises(ValueError):
msd.Bernoulli([-0.1], dtype=dtype.int32)
with pytest.raises(ValueError):
msd.Bernoulli([1.1], dtype=dtype.int32)
def __init__(self):
super(KL, self).__init__()
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
def __init__(self):
super(CrossEntropy, self).__init__()
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.b = msd.Bernoulli([0.7, 0.5], seed=seed, dtype=dtype.int32)
self.shape = shape
def __init__(self):
super(Basics, self).__init__()
self.b = msd.Bernoulli([0.3, 0.5, 0.7], dtype=dtype.int32)
def __init__(self):
super(BernoulliProb1, self).__init__()
self.b = msd.Bernoulli(dtype=dtype.int32)
def test_seed():
with pytest.raises(TypeError):
msd.Bernoulli([0.1], seed='seed')
def test_name():
with pytest.raises(TypeError):
msd.Bernoulli([0.1], name=1.0)
def test_type():
with pytest.raises(TypeError):
msd.Bernoulli([0.1], dtype=dtype.float32)
def __init__(self):
super(BernoulliConstruct, self).__init__()
self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
self.b1 = msd.Bernoulli(dtype=dtype.int32)
def __init__(self):
super(BernoulliKl, self).__init__()
self.b1 = msd.Bernoulli(0.7, dtype=dtype.int32)
self.b2 = msd.Bernoulli(dtype=dtype.int32)
def __init__(self):
super(BernoulliEntropy, self).__init__()
self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
def __init__(self):
super().__init__()
self.normal_dist = msd.Normal(dtype=mstype.float32)
self.bernoulli_dist = msd.Bernoulli(dtype=mstype.float32)
self.reduce_sum = P.ReduceSum(keep_dims=True)
