def run_simulation(self): num_client = int(self.num_client.text()) num_severs = int(self.num_server.text()) iterations = int(self.num_iterations.text()) ml_algo_index = self.selectMLAlgorithm.currentIndex() method = 'log_reg' # default if ml_algo_index == 0: method = 'log_reg' elif ml_algo_index == 1: method = 'perceptron' else: method = 'mlp' random.seed(0) np.random.seed(0) initializer = Initializer( num_clients=num_client, iterations=iterations, num_servers=num_severs, method=method, simulation_output_view=self.simulation_output) # can use any amount of iterations less than config.ITERATIONS but the # initializer has only given each client config.ITERATIONS datasets for training. a = datetime.datetime.now() initializer.run_simulation(iterations, self.simulation_output, server_agent_name='server_agent0') b = datetime.datetime.now()
import random import warnings import datetime import config import numpy as np from initializer import Initializer if __name__ == '__main__': random.seed(0) np.random.seed(0) initializer = Initializer(num_clients=config.NUM_CLIENTS, iterations=config.ITERATIONS, num_servers=config.NUM_SERVERS) # can use any amount of iterations less than config.ITERATIONS but the # initializer has only given each client config.ITERATIONS datasets for training. a = datetime.datetime.now() initializer.run_simulation(config.ITERATIONS, server_agent_name='server_agent0') b = datetime.datetime.now()