class AfterSimulationWeightedRandomWalkTestSuite(unittest.TestCase):
def setUp(self):
self.simu = Multi_Agent_Simulation(100,
50,
2,
0.1,
1,
"weighted",
_printing=True)
self.simu.setup()
self.simu.run()
def tearDown(self):
pass
#self.simu.dispose()
#self.simu = None
def test_calculation_cum_weight(self):
pass
def test_calculation_exit_probabilities(self):
for agent in self.simu.agents:
self.assertAlmostEqual(
sum(tip.exit_probability_multiple_agents[agent]
for tip in agent.tips), 1)
def setUp(self):
self.simu = Multi_Agent_Simulation(100,
50,
2,
0.1,
1,
"weighted",
_printing=True)
class SimulationSetupAndRunTestSuite(unittest.TestCase):
def setUp(self):
self.simu = Multi_Agent_Simulation(100,
50,
2,
0.1,
1,
"weighted",
_printing=True)
def tearDown(self):
pass
#self.simu.dispose()
#self.simu = None
def test_simulation_setup(self):
self.simu.setup()
# Test to be written
pass
def test_simulation_run(self):
self.simu.setup()
self.simu.run()
# Test to be written
pass
class AfterSimulationWeightedRandomWalkTestSuite(unittest.TestCase):
def setUp(self):
self.simu = Multi_Agent_Simulation(100, 10, 2, 0.005, 1, distances,
"weighted")
self.simu.setup()
self.simu.run()
def tearDown(self):
pass
#self.simu.dispose()
#self.simu = None
def test_calculation_cum_weight(self):
for transaction in self.simu.DG.nodes:
self.assertEqual(
transaction.cum_weight,
len(list(nx.ancestors(self.simu.DG, transaction))) + 1)
def test_calculation_exit_probabilities(self):
for agent in self.simu.agents:
self.assertEqual(
sum(tip.exit_probability_multiple_agents[agent]
for tip in agent.tips), 1)
def test_calculation_conf_conf(self):
self.simu.calc_confirmation_confidence_multiple_agents()
def simulation(data):
distance = 50000
distances = [[0, distance, distance], [distance, 0, distance],
[distance, distance, 0]]
simu = Multi_Agent_Simulation(300, 2, 2, 0.005, 1, distances, "weighted")
simu.setup()
simu.run()
csv_export(simu)
return (data, simu.measure_partitioning())
class SimulationSetupAndRunTestSuite(unittest.TestCase):
def setUp(self):
self.simu = Multi_Agent_Simulation(100, 10, 2, 0.005, 1, distances,
"weighted")
def tearDown(self):
pass
#self.simu.dispose()
#self.simu = None
def test_simulation_setup(self):
self.simu.setup()
# Test to be written
def test_simulation_run(self):
self.simu.setup()
self.simu.run()
def simulation(data):
number_of_agents = 10
distances = [[0.0, 10.0, 30.0, 20.0, 10.0, 10.0, 40.0, 10.0, 20.0, 20.0],
[10.0, 0.0, 40.0, 30.0, 20.0, 20.0, 50.0, 20.0, 30.0, 30.0],
[30.0, 40.0, 0.0, 30.0, 40.0, 20.0, 10.0, 40.0, 30.0, 10.0],
[20.0, 30.0, 30.0, 0.0, 30.0, 10.0, 40.0, 30.0, 20.0, 20.0],
[10.0, 20.0, 40.0, 30.0, 0.0, 20.0, 50.0, 20.0, 30.0, 30.0],
[10.0, 20.0, 20.0, 10.0, 20.0, 0.0, 30.0, 20.0, 10.0, 10.0],
[40.0, 50.0, 10.0, 40.0, 50.0, 30.0, 0.0, 50.0, 40.0, 20.0],
[10.0, 20.0, 40.0, 30.0, 20.0, 20.0, 50.0, 0.0, 10.0, 30.0],
[20.0, 30.0, 30.0, 20.0, 30.0, 10.0, 40.0, 10.0, 0.0, 20.0],
[20.0, 30.0, 10.0, 20.0, 30.0, 10.0, 20.0, 30.0, 20.0, 0.0]]
simu = Multi_Agent_Simulation(10000, 50, number_of_agents, 0.1, distances,
"weighted")
simu.setup()
simu.run()
averages = []
print("Done with one simu")
return (data, simu.record_attachment_probabilities)
# SIMULATION: MULTI AGENT
#############################################################################
#Parameters: no_of_transactions, lambda, no_of_agents, alpha, distance, tip_selection_algo
# latency (default value 1), agent_choice (default vlaue uniform distribution, printing)
#Tip selection algorithms: Choose among "random", "weighted", "unweighted" as input
start_time = timeit.default_timer()
runs = 1
for i in range(runs):
simu2 = Multi_Agent_Simulation(800,
50,
2,
0.1,
1,
"weighted",
_printing=True)
simu2.setup()
simu2.run()
# csv_export(simu2)
print("TOTAL simulation time: " +
str(np.round(timeit.default_timer() - start_time, 3)) + " seconds\n")
#############################################################################
# PLOTTING
#############################################################################
# print_graph(simu2)
netsize = 20
lam_m = 0
alpha = 0.001
txs = 1200
dir_name = './SimuData/2021-06'
suffix = '.csv'
for lam in my_lambda:
timestr = time.strftime("%Y%m%d-%H%M")
base_name = '{}lam_{}_txs_{}_tsa_{}_size_{}' \
.format(timestr, lam, txs, tsa, netsize)
simu2 = Multi_Agent_Simulation(_no_of_transactions=txs,
_lambda=lam,
_no_of_agents=netsize,
_alpha=alpha,
_distance=1,
_tip_selection_algo=tsa,
_latency=1,
_agent_choice=None,
_printing=True,
_lambda_m=lam_m)
simu2.setup()
simu2.run()
file_name = os.path.join(dir_name, base_name + suffix)
csv_export(simu2, file_name)
print("TOTAL simulation time: " +
str(np.round(timeit.default_timer() - start_time, 3)) + " seconds\n")
#############################################################################
# PLOTTING
#############################################################################
#############################################################################
# SIMULATION: MULTI AGENT
#############################################################################
#Parameters: no_of_transactions, lambda, no_of_agents, alpha, distance, tip_selection_algo
# latency (default value 1), agent_choice (default vlaue uniform distribution, printing)
#Tip selection algorithms: Choose among "random", "weighted", "unweighted" as input
start_time = timeit.default_timer()
runs = 100
data = []
for i in range(runs):
run_data = []
simu2 = Multi_Agent_Simulation(_no_of_transactions=50, _lambda=2, _no_of_agents=2, \
_alpha=0.5, _distance=1, _tip_selection_algos=["selfish", "weighted"], \
_conflict_coefficient=0.1, _conflicts=False, _printing=False)
simu2.setup()
simu2.run()
# print(simu2.get_heaviest_chain())
# print_confirmation_confidences_all_agents_with_tangle(simu2)
attachment = simu2.attachment_probabilities
data.append(attachment)
data = np.mean(data, axis=0)
print("OVERALL ATTACHMENT:", data)
# csv_export(simu2)
print("TOTAL simulation time: " +
str(np.round(timeit.default_timer() - start_time, 3)) + " seconds\n")
def setUp(self):
self.simu = Multi_Agent_Simulation(100, 10, 2, 0.005, 1, distances,
"weighted")
self.simu.setup()
self.simu.run()