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) ]