def table_five():
"""Table 5: Effect of different epoch limits for 50 neurons"""
simulation_num = 5
print('\n** Running simulation {} **\n'.format(simulation_num))
table_header = [
'hidden_neurons', 'max_epochs', 'learning_rate', 'hits', 'mse'
]
args = INIT_ARGS.copy()
args.update({
'method': 'gdm',
'activation': 'purelin',
'learning_rate': 0.01,
'hidden_neurons': 50
})
for i in (100, 200, 300):
args['max_epochs'] = i
run_simulation(args, sim_num=simulation_num, header=table_header)
def table_two():
"""Table 2: Effect of the change in learning rate value"""
simulation_num = 2
print('\n** Running simulation {} **\n'.format(simulation_num))
table_header = [
'hidden_neurons', 'max_epochs', 'learning_rate', 'activation', 'hits',
'mse'
]
args = INIT_ARGS.copy()
args.update({
'method': 'gdm',
'activation': 'purelin',
'max_epochs': 100,
'learning_rate': 0.01
})
for i in (10, 30, 50):
args['hidden_neurons'] = i
run_simulation(args, sim_num=simulation_num, header=table_header)
def table_one():
"""Table 1: Training results for different neuron numbers"""
simulation_num = 1
print('\n** Running simulation {} **\n'.format(simulation_num))
table_header = [
'hidden_neurons', 'learning_rate', 'max_epochs', 'activation', 'hits',
'mse'
]
args = INIT_ARGS.copy()
args.update({
'method': 'gdm',
'activation': 'purelin',
'max_epochs': 100,
'learning_rate': 0.001
})
for i in (10, 30, 50):
args['hidden_neurons'] = i
run_simulation(args, sim_num=simulation_num, header=table_header)
def table_nine():
"""Table 9: Test results for various networks"""
simulation_num = 9
print('\n** Running simulation {} **\n'.format(simulation_num))
table_header = [
'hidden_neurons', 'method', 'learning_rate', 'max_epochs', 'hits',
'mse', 'write_training_results'
]
args = INIT_ARGS.copy()
args.update({
'hidden_neurons': 50,
'max_epochs': 200,
'write_training_results': True
})
for lrn_rate in (0.001, 0.01):
args['learning_rate'] = lrn_rate
for mthd in ('gdm', 'rp'):
args['method'] = mthd
run_simulation(args, sim_num=simulation_num, header=table_header)
def table_six():
"""Table 6: comparison of two methods for 10 neurons"""
simulation_num = 6
print('\n** Running simulation {} **\n'.format(simulation_num))
table_header = [
'hidden_neurons', 'method', 'learning_rate', 'max_epochs', 'hits',
'mse'
]
args = INIT_ARGS.copy()
args.update({
'activation': 'purelin',
'max_epochs': 100,
'hidden_neurons': 10
})
for mthd in ('gdm', 'rp'):
args['method'] = mthd
for lrn_rate in (0.01, 0.001):
args['learning_rate'] = lrn_rate
run_simulation(args, sim_num=simulation_num, header=table_header)
def table_eight():
"""Table 8: Effect of transfer function for resilient back-propagation method for 200 iteration"""
simulation_num = 8
print('\n** Running simulation {} **\n'.format(simulation_num))
table_header = [
'hidden_neurons', 'activation', 'method', 'max_epochs',
'learning_rate', 'hits', 'mse'
]
args = INIT_ARGS.copy()
args.update({'method': 'rp', 'max_epochs': 200})
for n_num in (10, 30, 50):
args['hidden_neurons'] = n_num
for act_fn in ('purelin', 'tansig'):
args['activation'] = act_fn
for lrn_rate in (0.01, 0.001):
args['learning_rate'] = lrn_rate
run_simulation(args,
sim_num=simulation_num,
header=table_header)