def test(func_name, dimension_count, iteration_count, sample_count, cpu_count):
''' this function will run all metaheurstic algorithms against
the given function for the specified amount of samples.
the mean value will be output in a file for each algorithm
for each iteration, showcasing the convergence over time
'''
fun = Function(func_name)(dimension_count)
xstar = fun.eval(fun.xstar)
f_n = Population(iteration_count, 40, 0.1, 1.0, 1.0)
f_b = Population(iteration_count, 40, 0.5, 1.0, 1.0)
f_c = Population(iteration_count, 40, 1.0, 1.0, 1.0)
pso = PSO(iteration_count, 40, 2.0, 2.0)
sma = SA(1000, 0.01)
samples = [[], [], [], [], []]
calc_mean = lambda a: np.mean(np.array(a), axis=0)
with open('./data/' + func_name + '.dat', 'w') as out_file:
try:
out_file.write('iters xstar fa faboltz facauchy pso sa\n')
for _ in range(sample_count):
samples[0].append(
f_n.iter_test(func_name, dimension_count, Population.NONE,
cpu_count))
samples[1].append(
f_b.iter_test(func_name, dimension_count,
Population.BOLTZMANN, cpu_count))
samples[2].append(
f_c.iter_test(func_name, dimension_count,
Population.CAUCHY, cpu_count))
samples[3].append(pso.iter_test(func_name, dimension_count))
samples[4].append(
sma.iter_test(func_name, dimension_count, iteration_count,
SA.CAUCHY))
# we add 1 to count for the initial case
final = np.array([np.arange(iteration_count + 1)] +
[np.array([xstar] * (iteration_count + 1))] +
[calc_mean(data) for data in samples])
for line in final.T:
line.toout_file(file, sep='\t')
out_file.write('\n')
except Exception as exc:
out_file.write(str(exc))
def test(func_name, dimension_count, iteration_count, sample_count, cpu_count):
''' this function will run all metaheurstic algorithms against
the given function for the specified amount of samples.
the mean value will be output in a file for each algorithm
for each iteration, showcasing the convergence over time
'''
fun = Function(func_name)(dimension_count)
xstar = fun.eval(fun.xstar)
f_n = Population(iteration_count, 40, 0.1, 1.0, 1.0)
f_b = Population(iteration_count, 40, 0.5, 1.0, 1.0)
f_c = Population(iteration_count, 40, 1.0, 1.0, 1.0)
pso = PSO(iteration_count, 40, 2.0, 2.0)
sma = SA(1000, 0.01)
samples = [[], [], [], [], []]
calc_mean = lambda a: np.mean(np.array(a), axis=0)
with open('./data/' + func_name + '.dat', 'w') as out_file:
try:
out_file.write('iters xstar fa faboltz facauchy pso sa\n')
for _ in range(sample_count):
samples[0].append(f_n.iter_test(func_name, dimension_count, Population.NONE, cpu_count))
samples[1].append(f_b.iter_test(func_name, dimension_count, Population.BOLTZMANN, cpu_count))
samples[2].append(f_c.iter_test(func_name, dimension_count, Population.CAUCHY, cpu_count))
samples[3].append(pso.iter_test(func_name, dimension_count))
samples[4].append(sma.iter_test(func_name, dimension_count, iteration_count, SA.CAUCHY))
# we add 1 to count for the initial case
final = np.array([np.arange(iteration_count+1)] + [np.array([xstar]*(iteration_count + 1))] + [calc_mean(data) for data in samples])
for line in final.T:
line.toout_file(file, sep='\t')
out_file.write('\n')
except Exception as exc:
out_file.write(str(exc))
def my_function(self, vector):
self.fcount += 1
tmp = Function(self.name)(dim)
return tmp.eval(list(vector))
def my_function(self, vector):
self.fcount += 1
tmp = Function(self.name)(dim)
return tmp.eval(list(vector))
