parameters['num_layers'] = 3
parameters['iterations'] = 2
parameters['batch_size'] = 128
parameters['module_name'] = 'gru' # Other options: 'lstm' or 'lstmLN'
parameters['z_dim'] = len(dataX[0][0,:])
#%% Experiments
# Output Initialization
Discriminative_Score = list()
Predictive_Score = list()
# Each Iteration
for it in range(Iteration):
# Synthetic Data Generation
dataX_hat = tgan(dataX,dataXs, parameters)
print('Finish Synthetic Data Generation')
#%% Performance Metrics
# 1. Discriminative Score
Acc = list()
for tt in range(Sub_Iteration):
Temp_Disc = discriminative_score_metrics (dataX, dataX_hat)
Acc.append(Temp_Disc)
Discriminative_Score.append(np.mean(Acc))
print('generated discriminative score')
# 2. Predictive Performance
MAE_All = list()
def fit(self, filename, logger=''):
self.dataX_hat = tgan(self.dataX, self.parameters,
self.noise_generator, logger, filename)
print('Finish Synthetic Data Generation')