def test_bool_gene_copy_constructor(self): g = BoolGene(True) c_copy = BoolGene(g) self.assertEqual(c_copy.data, True) # test copy does not depend on original g.data = False self.assertEqual(c_copy.data, True)
def __init__(self, fitness_calc):
Individual.__init__(self, fitness_calc)
self.length = 18 # number of parameters
num_hidden_layers = IntGene(
current=1, min=1, max=3) # the default tuple length is 1 (100,)
hidden_layer_sizes = TupleGene(current=(100, ),
size=num_hidden_layers,
gene_list=[
IntGene(100, 1, 200),
IntGene(0, 0, 200),
IntGene(0, 0, 200)
])
activation = ListGene(current='relu',
values=['relu', 'identity', 'logistic', 'tanh'])
solver = ListGene(current='adam', values=['adam', 'lbfgs', 'sgd'])
alpha = FloatGene(current=0.0001, std=0.01, min=0.00001, max=1)
batch_size = ListGene(current='auto', values=['auto', 10, 100, 1000])
learning_rate = ListGene(current='constant',
values=['constant', 'invscaling', 'adaptive'])
max_iter = IntGene(current=200, min=50, max=5000)
tol = FloatGene(current=1e-4, std=0.01, min=1e-6, max=1e-1)
learning_rate_init = FloatGene(current=0.001,
std=0.01,
min=0.0001,
max=1)
power_t = FloatGene(current=0.5, std=0.1, min=0.0001, max=1)
warm_start = BoolGene(current=False)
momentum = FloatGene(current=0.9, std=0.25, min=0, max=1)
nesterovs_momentum = BoolGene(current=True)
early_stopping = BoolGene(current=False)
validation_fraction = FloatGene(current=0.1, std=0.25, min=0, max=1)
beta_1 = FloatGene(current=0.9, std=0.25, min=0, max=1)
beta_2 = FloatGene(current=0.999, std=0.25, min=0, max=1)
epsilon = FloatGene(current=1e-8, std=1e-3, min=1e-10, max=1e-2)
self.genes = {'hidden_layer_sizes': hidden_layer_sizes, \
'activation': activation, \
'solver': solver, \
'alpha': alpha, \
'batch_size': batch_size, \
'learning_rate': learning_rate, \
'max_iter': max_iter, \
'tol': tol, \
'learning_rate_init': learning_rate_init, \
'power_t': power_t, \
'warm_start': warm_start, \
'momentum': momentum, \
'nesterovs_momentum': nesterovs_momentum, \
'early_stopping': early_stopping, \
'validation_fraction': validation_fraction, \
'beta_1': beta_1, \
'beta_2': beta_2, \
'epsilon': epsilon}
def test_bool_gene_mutation(self): g = BoolGene() self.assertFalse(g.data) g.mute() self.assertTrue(g.data) g = BoolGene(True) self.assertTrue(g.data) g.mute() self.assertFalse(g.data)