def test_arguments():
"""
args passing during initialization.
"""
l = msd.Cauchy()
assert isinstance(l, msd.Distribution)
l = msd.Cauchy([3.0], [4.0], dtype=dtype.float32)
assert isinstance(l, msd.Distribution)
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.c = msd.Cauchy(np.array([3.0]),
np.array([[2.0], [4.0]]),
seed=seed,
dtype=dtype.float32)
self.shape = shape
def __init__(self):
super(KL, self).__init__()
self.c = msd.Cauchy(np.array([3.]),
np.array([4.]),
dtype=dtype.float32)
def __init__(self):
super(LogProb, self).__init__()
self.c = msd.Cauchy(np.array([3.0]),
np.array([[2.0], [4.0]]),
dtype=dtype.float32)
def __init__(self):
super(CrossEntropy, self).__init__()
self.c = msd.Cauchy(np.array([3.]),
np.array([[2.0], [4.0]]),
dtype=dtype.float32)
def __init__(self):
super(Basics, self).__init__()
self.c = msd.Cauchy(np.array([3.0]),
np.array([2.0, 4.0]),
dtype=dtype.float32)
def __init__(self):
super(CauchyProb1, self).__init__()
self.cauchy = msd.Cauchy()
def __init__(self):
super(CauchyProb, self).__init__()
self.cauchy = msd.Cauchy(3.0, 4.0, dtype=dtype.float32)
def test_scale():
with pytest.raises(ValueError):
msd.Cauchy(0., 0.)
with pytest.raises(ValueError):
msd.Cauchy(0., -1.)
def test_name():
with pytest.raises(TypeError):
msd.Cauchy(0., 1., name=1.0)
def test_seed():
with pytest.raises(TypeError):
msd.Cauchy(0., 1., seed='seed')
def test_type():
with pytest.raises(TypeError):
msd.Cauchy(0., 1., dtype=dtype.int32)
def test_cauchy_shape_errpr():
"""
Invalid shapes.
"""
with pytest.raises(ValueError):
msd.Cauchy([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
def __init__(self):
super(CauchyConstruct, self).__init__()
self.cauchy = msd.Cauchy(3.0, 4.0)
self.cauchy1 = msd.Cauchy()
def __init__(self):
super(KL, self).__init__()
self.cauchy = msd.Cauchy(3.0, 4.0)
self.cauchy1 = msd.Cauchy()
def __init__(self, loc, scale, seed=10, dtype=ms.float32, name='Cauchy'):
super().__init__()
self.b = msd.Cauchy(loc, scale, seed, dtype, name)
