def test_ga_optimise(self):
# simple population processer function
# that just assigns fitness based on the sum of
# a set of parameters
def cpf(target, pop):
print "cpf called!"
fs_and_ps = []
for ind in range(0, len(pop)):
# fitness is a sum of the parameter settings
# so higher parameter values should be selected for...
f_and_p = {"fitness":sum(pop[ind]), "params":pop[ind]}
fs_and_ps.append(f_and_p)
return fs_and_ps
iterations = 10
fs = ga.ga_optimise(compute_population_fitnesses = cpf,
target = [[0.1], [0.1]],
param_count = ga.CONST_DX_PARAM_COUNT,
iterations = iterations,
pop_size = 1000,
crossovers = 3,
mutation_rate = 0.5
)
self.assertEquals(len(fs), iterations)
self.assertTrue(max(fs) <= ga.CONST_DX_PARAM_COUNT)
def ga_optimise(synth, param_count, target, output_dir, iterations = 10, pop_size = 500): fs = ga.ga_optimise(compute_population_fitnesses = ga.compute_population_fitnesses, target = target, synth = synth, param_count = param_count, iterations = iterations, pop_size = pop_size, crossovers = param_count / 5, mutation_rate = 0.5, log = True, data_folder = output_dir) return fs
def ga_optimise(synth,
param_count,
target,
output_dir,
iterations=10,
pop_size=500):
fs = ga.ga_optimise(
compute_population_fitnesses=ga.compute_population_fitnesses,
target=target,
synth=synth,
param_count=param_count,
iterations=iterations,
pop_size=pop_size,
crossovers=param_count / 5,
mutation_rate=0.5,
log=True,
data_folder=output_dir)
return fs
def test_ga_optimise(self):
# simple population processer function
# that just assigns fitness based on the sum of
# a set of parameters
def cpf(target, pop):
print "cpf called!"
fs_and_ps = []
for ind in range(0, len(pop)):
# fitness is a sum of the parameter settings
# so higher parameter values should be selected for...
f_and_p = {"fitness": sum(pop[ind]), "params": pop[ind]}
fs_and_ps.append(f_and_p)
return fs_and_ps
iterations = 10
fs = ga.ga_optimise(compute_population_fitnesses=cpf,
target=[[0.1], [0.1]],
param_count=ga.CONST_DX_PARAM_COUNT,
iterations=iterations,
pop_size=1000,
crossovers=3,
mutation_rate=0.5)
self.assertEquals(len(fs), iterations)
self.assertTrue(max(fs) <= ga.CONST_DX_PARAM_COUNT)
