def test_ccmaes():
x = np.zeros(1)
opt = CCMAESOptimizer(x, random_state=0)
opt.init(1, 1)
r = eval_loop(x, opt, n_evals=1000)
assert_less(-1e-7, r.max())
return r
def test_linear_contextual_sphere():
random_state = np.random.RandomState(0)
n_params = 3
n_context_dims = 2
obj = LinearContextualSphere(random_state, n_params, n_context_dims)
opt = CCMAESOptimizer(context_features="affine", random_state=random_state,
log_to_stdout=False)
opt.init(n_params, n_context_dims)
params = np.empty(n_params)
for i in range(1000):
context = random_state.rand(n_context_dims) * 2.0 - 1.0
opt.set_context(context)
opt.get_next_parameters(params)
opt.set_evaluation_feedback([obj.feedback(params, context)])
policy = opt.best_policy()
mean_reward = evaluate(policy, obj)
assert_greater(mean_reward, -1e-4)
def test_cmaes_no_initial_params():
opt = CCMAESOptimizer()
opt.init(10, 5)
params = np.empty(10)
opt.set_context(np.zeros(5))
opt.get_next_parameters(params)
def test_cmaes_diagonal_cov():
opt = CCMAESOptimizer(covariance=np.zeros(10))
opt.init(10, 5)
params = np.empty(10)
opt.set_context(np.zeros(5))
opt.get_next_parameters(params)