def main():
name = 'plot-function-generator'
try:
with open('bin/path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation"
)
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
with open("bin/logging.yaml") as f:
l_dict = yaml.load(f)
log_output_file = os.path.join(paths.results_path, l_dict['handlers']['file']['filename'])
l_dict['handlers']['file']['filename'] = log_output_file
logging.config.dictConfig(l_dict)
print("All output can be found in file ", log_output_file)
print("Change the values in logging.yaml to control log level and destination")
print("e.g. change the handler to console for the loggers you're interesting in to get output to stdout")
fg_params = [GaussianParameters(sigma=[[1.5, .1], [.1, .3]], mean=[-1., -1.]),
GaussianParameters(sigma=[[.25, .3], [.3, 1.]], mean=[1., 1.]),
GaussianParameters(sigma=[[.5, .25], [.25, 1.3]], mean=[2., -2.])]
FunctionGenerator(fg_params, dims=2, noise=True).plot()
FunctionGenerator([PermutationParameters(beta=0.005)], dims=2).plot()
FunctionGenerator([EasomParameters()], dims=3).plot()
FunctionGenerator([LangermannParameters(A='default', c='default')], dims=2).plot()
FunctionGenerator([MichalewiczParameters(m='default')], dims=2).plot()
FunctionGenerator([ShekelParameters(A='default', c='default')], dims=2).plot()
fg_params = [ShekelParameters(A=[[8, 5]], c=[0.08]),
LangermannParameters(A='default', c='default')]
FunctionGenerator(fg_params, dims=2).plot()
FunctionGenerator([RastriginParameters()], dims=2).plot()
FunctionGenerator([RosenbrockParameters()], dims=2).plot()
FunctionGenerator([ChasmParameters()], dims=2).plot()
FunctionGenerator([AckleyParameters()], dims=2).plot()
def _create_ackley10d(self, noise, mu, sigma):
return FunctionGenerator([AckleyParameters()],
dims=10, noise=noise, mu=mu, sigma=sigma)
def _create_chasm(self, noise, mu, sigma):
return FunctionGenerator([ChasmParameters()],
dims=2, noise=noise, mu=mu, sigma=sigma)
def _create_rastrigin10d(self, noise, mu, sigma):
return FunctionGenerator([RastriginParameters()],
dims=10, noise=noise, mu=mu, sigma=sigma)
def _create_rosenbrock10d(self, noise, mu, sigma):
return FunctionGenerator([RosenbrockParameters()],
dims=10, noise=noise, mu=mu, sigma=sigma)
def _create_langermann(self, noise, mu, sigma):
return FunctionGenerator([LangermannParameters(A='default', c='default')],
dims=2, noise=noise, mu=mu, sigma=sigma)
def _create_3gaussians2d(self, noise, mu, sigma):
fg_params = [GaussianParameters(sigma=[[1.5, .1], [.1, .3]], mean=[-1., -1.]),
GaussianParameters(sigma=[[.25, .3], [.3, 1.]], mean=[1., 1.]),
GaussianParameters(sigma=[[.5, .25], [.25, 1.3]], mean=[2., -2.])]
return FunctionGenerator(fg_params, dims=2, noise=noise, mu=mu, sigma=sigma)
def _create_easom10d(self, noise, mu, sigma):
return FunctionGenerator([EasomParameters()],
dims=10, noise=noise, mu=mu, sigma=sigma)
def _create_permutation2d(self, noise, mu, sigma):
return FunctionGenerator([PermutationParameters(beta=0.005)],
dims=2, noise=noise, mu=mu, sigma=sigma)
def _create_michalewicz2d(self, noise, mu, sigma):
return FunctionGenerator([MichalewiczParameters(m='default')],
dims=2, noise=noise, mu=mu, sigma=sigma)
def _create_shekel2d(self, noise, mu, sigma):
return FunctionGenerator([ShekelParameters(A='default', c='default')],
dims=2, noise=noise, mu=mu, sigma=sigma)
def _create_gauss2d(self, noise, mu, sigma):
return FunctionGenerator([GaussianParameters(sigma=[[1.5, .1], [.1, .3]], mean=[-1., -1.])],
dims=2, noise=noise, mu=mu, sigma=sigma)