def __init__(self,
x0,
sigma0,
opts=None):
self.x0 = x0
self.sigma0 = sigma0
self.popsize = opts['popsize']
self.elite_ratio = opts['elite_ratio']
self.elite_size = max(1, int(self.elite_ratio*self.popsize))
self.seed = opts['seed'] if 'seed' in opts else np.random.randint(1, 2**32)
self.np_random = np.random.RandomState(self.seed)
self.noise_scheduler = LinearSchedule(*opts['noise_scheduler_args'])
self.iter = 0
# initialize mean and std
self.x = np.asarray(x0).astype(np.float32)
self.shape = self.x.shape
if np.isscalar(sigma0):
self.sigma = np.full(self.shape, sigma0, dtype=np.float32)
else:
self.sigma = np.asarray(sigma0).astype(np.float32)
self.xbest = None
self.fbest = None
def __init__(self, x0, sigma0, opts=None):
self.x0 = x0
self.sigma0 = sigma0
self.popsize = opts['popsize']
self.sigma_scheduler = LinearSchedule(*opts['sigma_scheduler_args'])
self.lr = opts['lr']
self.lr_decay = opts['lr_decay']
self.min_lr = opts['min_lr']
self.antithetic = opts['antithetic']
if self.antithetic:
assert self.popsize % 2 == 0, 'popsize must be even for antithetic sampling. '
self.rank_transform = opts['rank_transform']
self.seed = opts['seed'] if 'seed' in opts else np.random.randint(1, 2**32)
self.np_random = np.random.RandomState(self.seed)
self.iter = 0
# initialize mean and std
self.x = torch.from_numpy(np.asarray(x0)).float()
self.x.requires_grad = True
self.shape = tuple(self.x.shape)
if np.isscalar(sigma0):
self.sigma = np.full(self.shape, sigma0, dtype=np.float32)
else:
self.sigma = np.asarray(sigma0).astype(np.float32)
self.optimizer = optim.Adam([self.x], lr=self.lr)
self.lr_scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=self.lr_decay)
self.xbest = None
self.fbest = None
def test_linear_schedule():
with pytest.raises(AssertionError):
LinearSchedule(1.0, 0.1, 0, 0)
with pytest.raises(AssertionError):
LinearSchedule(1.0, 0.1, -1, 0)
with pytest.raises(AssertionError):
LinearSchedule(1.0, 0.1, 10, -1)
with pytest.raises(AssertionError):
LinearSchedule(1.0, 0.1, 10, 0)(-1)
# increasing: without warmup start
scheduler = LinearSchedule(initial=0.5, final=2.0, N=3, start=0)
assert scheduler(0) == 0.5
assert scheduler(1) == 1.0
assert scheduler(2) == 1.5
assert scheduler(3) == 2.0
assert all([scheduler(i) == 2.0] for i in [4, 5, 6, 7, 8])
assert all([scheduler(i) == scheduler.get_current() for i in range(10)])
# increasing: with warmup start
scheduler = LinearSchedule(initial=0.5, final=2.0, N=3, start=2)
assert all([scheduler(i) == 0.5] for i in [0, 1, 2])
assert scheduler(3) == 1.0
assert scheduler(4) == 1.5
assert scheduler(5) == 2.0
assert all([scheduler(i) == 2.0 for i in [6, 7, 8]])
assert all([scheduler(i) == scheduler.get_current() for i in range(10)])
# decreasing: without warmup start
scheduler = LinearSchedule(initial=1.0, final=0.1, N=3, start=0)
assert scheduler(0) == 1.0
assert scheduler(1) == 0.7
assert scheduler(2) == 0.4
assert scheduler(3) == 0.1
assert all([scheduler(i) == 0.1 for i in [4, 5, 6]])
assert all([scheduler(i) == scheduler.get_current() for i in range(10)])
# decreasing: with warmup start
scheduler = LinearSchedule(initial=1.0, final=0.1, N=3, start=2)
assert all([scheduler(i) for i in [0, 1, 2]])
assert scheduler(3) == 0.7
assert scheduler(4) == 0.4
assert scheduler(5) == 0.1
assert all([scheduler(i) == 0.1 for i in [6, 7, 8]])
assert all([scheduler(i) == scheduler.get_current() for i in range(10)])