def test_bernoulli_vector_default_output_layer():
"""
BernoulliVector's default output layer is compatible with its required
output space
"""
mlp = MLP(layers=[Linear(layer_name="h", dim=5, irange=0.01, max_col_norm=0.01)])
conditional = BernoulliVector(mlp=mlp, name="conditional")
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
def test_bernoulli_vector_conditional_expectation():
"""
BernoulliVector.conditional_expectation doesn't crash
"""
mlp = MLP(layers=[Linear(layer_name="h", dim=5, irange=0.01, max_col_norm=0.01)])
conditional = BernoulliVector(mlp=mlp, name="conditional")
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix("mu")
conditional.conditional_expectation([mu])
def test_bernoulli_vector_sample_from_conditional():
"""
BernoulliVector.sample_from_conditional works when num_samples is provided
"""
mlp = MLP(layers=[Linear(layer_name="h", dim=5, irange=0.01, max_col_norm=0.01)])
conditional = BernoulliVector(mlp=mlp, name="conditional")
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix("mu")
conditional.sample_from_conditional([mu], num_samples=2)
def test_bernoulli_vector_default_output_layer():
"""
BernoulliVector's default output layer is compatible with its required
output space
"""
mlp = MLP(layers=[Linear(layer_name='h', dim=5, irange=0.01,
max_col_norm=0.01)])
conditional = BernoulliVector(mlp=mlp, name='conditional')
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
def test_bernoulli_vector_conditional_expectation():
"""
BernoulliVector.conditional_expectation doesn't crash
"""
mlp = MLP(layers=[Linear(layer_name='h', dim=5, irange=0.01,
max_col_norm=0.01)])
conditional = BernoulliVector(mlp=mlp, name='conditional')
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix('mu')
conditional.conditional_expectation([mu])
def test_bernoulli_vector_sample_from_conditional():
"""
BernoulliVector.sample_from_conditional works when num_samples is provided
"""
mlp = MLP(layers=[Linear(layer_name='h', dim=5, irange=0.01,
max_col_norm=0.01)])
conditional = BernoulliVector(mlp=mlp, name='conditional')
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix('mu')
conditional.sample_from_conditional([mu], num_samples=2)
def test_bernoulli_vector_reparametrization_trick():
"""
BernoulliVector.sample_from_conditional raises an error when asked to
sample using the reparametrization trick
"""
mlp = MLP(layers=[Linear(layer_name="h", dim=5, irange=0.01, max_col_norm=0.01)])
conditional = BernoulliVector(mlp=mlp, name="conditional")
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix("mu")
epsilon = T.tensor3("epsilon")
conditional.sample_from_conditional([mu], epsilon=epsilon)
def test_bernoulli_vector_reparametrization_trick():
"""
BernoulliVector.sample_from_conditional raises an error when asked to
sample using the reparametrization trick
"""
mlp = MLP(layers=[Linear(layer_name='h', dim=5, irange=0.01,
max_col_norm=0.01)])
conditional = BernoulliVector(mlp=mlp, name='conditional')
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix('mu')
epsilon = T.tensor3('epsilon')
conditional.sample_from_conditional([mu], epsilon=epsilon)
