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()
