def test_ones():
"""Tests ones"""
# Scalar
ones = ops.ones([1])
assert isinstance(ones, tf.Tensor)
assert ones.ndim == 1
assert ones.shape[0] == 1
assert ones.numpy() == 1.0
# 1D
ones = ops.ones([5])
assert isinstance(ones, tf.Tensor)
assert ones.ndim == 1
assert ones.shape[0] == 5
assert all(ones.numpy() == 1.0)
# 2D
ones = ops.ones([5, 4])
assert isinstance(ones, tf.Tensor)
assert ones.ndim == 2
assert ones.shape[0] == 5
assert ones.shape[1] == 4
assert np.all(ones.numpy() == 1.0)
# 3D
ones = ops.ones([5, 4, 3])
assert isinstance(ones, tf.Tensor)
assert ones.ndim == 3
assert ones.shape[0] == 5
assert ones.shape[1] == 4
assert ones.shape[2] == 3
assert np.all(ones.numpy() == 1.0)
def __init__(self,
k: int = 2,
shape: Union[int, List[int]] = [],
posterior=Dirichlet,
prior=None,
transform=None,
initializer={'concentration': pos_xavier},
var_transform={'concentration': O.softplus},
name='DirichletParameter'):
# Check type of k
if not isinstance(k, int):
raise TypeError('k must be an integer')
if k < 2:
raise ValueError('k must be >1')
# Make shape a list
if isinstance(shape, int):
shape = [shape]
# Create shape of underlying variable array
shape = shape + [k]
# Use a uniform prior
if prior is None:
prior = Dirichlet(O.ones(shape))
# Initialize the parameter
super().__init__(shape=shape,
posterior=posterior,
prior=prior,
transform=transform,
initializer=initializer,
var_transform=var_transform,
name=name)
def __init__(self,
k: int = 2,
shape: Union[int, List[int]] = [],
posterior=Categorical,
prior=None,
transform=None,
initializer={'probs': xavier},
var_transform={'probs': O.additive_logistic_transform},
name='CategoricalParameter'):
# Check type of k
if not isinstance(k, int):
raise TypeError('k must be an integer')
if k < 2:
raise ValueError('k must be >1')
# Make shape a list
if isinstance(shape, int):
shape = [shape]
# Use a uniform prior
if prior is None:
prior = Categorical(O.ones(shape) / float(k))
# Create shape of underlying variable array
shape = shape + [k - 1]
# Initialize the parameter
super().__init__(shape=shape,
posterior=posterior,
prior=prior,
transform=transform,
initializer=initializer,
var_transform=var_transform,
name=name)
# shape should correspond to the sample shape
self.shape = shape
def init(shape):
return val * O.ones(shape)