def setUp(self):
state = np.random.RandomState(0)
X = generate_dummy_dataset_alltypes(state, N_ROWS, DIM_REG, DIM_BOOL, DIM_ORD, DIM_CAT).X
self.data = X
self.model = Vae(config)
self.model.compile()
def setUp(self):
state = np.random.RandomState(0)
X = generate_dummy_dataset_alltypes(state, N_ROWS, DIM_REG, DIM_BOOL, DIM_ORD, DIM_CAT).X
temps = state.random((N_ROWS, 1))
self.data = (X, temps)
self.network = StackedGmvaeNet(config)
def setUp(self):
state = np.random.RandomState(0)
X = generate_dummy_dataset_alltypes(state, N_ROWS, DIM_REG, DIM_BOOL,
DIM_ORD, DIM_CAT).X
temps = state.random((N_ROWS, 1))
self.data = ((X, X), (X, X), ([X, temps], X))
self.networks = [GeneralisedAutoencoderNet(c) for c in CONFIGS]
def setUp(self):
state = np.random.RandomState(0)
X = generate_dummy_dataset_alltypes(state, N_ROWS, DIM_REG, DIM_BOOL,
DIM_ORD, DIM_CAT).X
temps = state.random((N_ROWS, 1))
self.data = X
self.network = VaeNet(config, dtype=tf.dtypes.float64)
def setUp(self):
state = np.random.RandomState(0)
X = generate_dummy_dataset_alltypes(state, N_ROWS, DIM_REG, DIM_BOOL,
DIM_ORD, DIM_CAT).X
temps = state.random((N_ROWS, 1))
self.data = ((X, X), (X, X), (X, X), ([X, temps], X))
self.models = [AdversarialAutoencoder(c) for c in CONFIGS]
for m in self.models:
m.compile()
def setUp(self):
state = np.random.RandomState(0)
X = generate_dummy_dataset_alltypes(state, N_ROWS, DIM_REG, DIM_BOOL,
DIM_ORD, DIM_CAT).X
temps = state.random((N_ROWS, 1))
self.data = X
self.models = (Gmvae(config0), Gmvae(config1))
for model in self.models:
model.compile()
def setUp(self):
state = np.random.RandomState(0)
X_all = generate_dummy_dataset_alltypes(state, N_ROWS, DIM_REG,
DIM_BOOL, DIM_ORD, DIM_CAT)
X = X_all.X
# categories
y = (state.random((N_ROWS, NCATS)) > 0.5).astype(float)
self.data = (X, y, X_all)
self.network = MarginalGmVaeNet(config)
def setUp(self):
state = np.random.RandomState(0)
X = generate_dummy_dataset_alltypes(state, N_ROWS, DIM_REG, DIM_BOOL,
DIM_ORD, DIM_CAT).X
temps = state.random((N_ROWS, 1))
self.data = ((X, X), (X, X), ([X, temps], X))
self.networks = [AdversarialAuoencoderNet(c) for c in CONFIGS]
self.lossnets = [
AdverasrialAutoencoderLossNet(c, net)
for net, c in zip(self.networks, CONFIGS)
]
