def setUp(self):
"""Setup Tests"""
# Setup up variable and truth configuration
eugene.Config.VAR['x'] = np.linspace(0, 8.0 * np.pi, 1024)
eugene.Config.TRUTH = eugene.Config.VAR['x'] * np.sin(eugene.Config.VAR['x']) + eugene.Config.VAR['x']/2.0 + 1.61
def error_and_complexity(gene_expression, scale):
"""user fitness function, weighted combination of error and complexity"""
weights = np.array([0.95, 0.025, 0.025])
scaled_gene_expression = 1.0 / (gene_expression / scale)
return np.dot(scaled_gene_expression, weights)
# Setup Populations
self.P1 = Population(
init_population_size=100,
objective_function=error_and_complexity,
max_generations=100,
init_tree_size=2,
target=eugene.Config.TRUTH,
pruning=False
)
class PopulationTests(unittest.TestCase):
"""
Test the Population Module.
"""
def setUp(self):
"""Setup Tests"""
# Setup up variable and truth configuration
eugene.Config.VAR['x'] = np.linspace(0, 8.0 * np.pi, 1024)
eugene.Config.TRUTH = eugene.Config.VAR['x'] * np.sin(eugene.Config.VAR['x']) + eugene.Config.VAR['x']/2.0 + 1.61
def error_and_complexity(gene_expression, scale):
"""user fitness function, weighted combination of error and complexity"""
weights = np.array([0.95, 0.025, 0.025])
scaled_gene_expression = 1.0 / (gene_expression / scale)
return np.dot(scaled_gene_expression, weights)
# Setup Populations
self.P1 = Population(
init_population_size=100,
objective_function=error_and_complexity,
max_generations=100,
init_tree_size=2,
target=eugene.Config.TRUTH,
pruning=False
)
def test_29_initialize_population(self):
"""Initialize a Random Population"""
self.P1.initialize()
def test_30_initialize_population(self):
"""Initialize a seeded Population"""
inds = [Individual(Tree(
Node('n_add',
Node('n_pow',
Node('x'),
Node(2.0)
),
Node('n_pow',
Node('y'),
Node(2.0)
)
)
))]
self.P1.initialize(inds)
def test_31_display_population(self):
"""Display Population Info"""
self.P1.initialize()
self.P1.describe()
def test_32_run_population_roulette(self):
"""Run Population for 10 generations with roulette selection"""
self.P1.initialize()
self.P1.run(10)
def test_33_run_population_pruning(self):
"""Run Population for 10 generations with pruning"""
self.P1.pruning = True
self.P1.initialize()
self.P1.run(10)
def test_34_run_population_stochastic(self):
"""Run Population for 10 generations with stochastic selection"""
self.P1.selectmethod = 'stochastic'
self.P1.initialize()
self.P1.run(10)
def test_35_run_population_tournament(self):
"""Run Population for 10 generations with tournament selection"""
self.P1.selectmethod = 'tournament'
self.P1.initialize()
self.P1.run(10)
def test_36_run_population_rank_roulette(self):
"""Run Population for 10 generations with rank roulette selection"""
self.P1.selectmethod = 'rank_roulette'
self.P1.initialize()
self.P1.run(10)
from eugene.Population import Population
# Setup up variable and truth configuration
eugene.Config.VAR['x'] = np.linspace(0, 8.0 * np.pi, 1024)
eugene.Config.TRUTH = eugene.Config.VAR['x'] * np.sin(eugene.Config.VAR['x']) + eugene.Config.VAR['x']/2.0 + 1.61
# @profile
def error_and_complexity(gene_expression, scale):
"""user fitness function, weighted combination of error and complexity"""
weights = np.array([0.95, 0.025, 0.025])
scaled_gene_expression = 1.0 / (gene_expression / scale)
return np.dot(scaled_gene_expression, weights)
# Setup Population
P = Population(
init_population_size=1000,
objective_function=error_and_complexity,
max_generations=100,
init_tree_size=2,
target=eugene.Config.TRUTH,
pruning=False
)
# Initialize Population
P.initialize()
# Run the Population
P.run(20)