def __init__(self, path=None): super().__init__(path) ones = np.ones(self.config.dimension) self._dist_initial = np.sqrt(np.log(1 / self.config.initial_value) / 4) self._x0 = self._dist_initial * ones / np.sqrt(self.config.dimension) self._max_value = 1.0 self._domain = ContinuousDomain(-ones, ones)
def __init__(self, path=None): super().__init__(path) self._max_value = self.config.s theta = np.random.normal(size=self.config.dimension) self._theta = theta / np.linalg.norm(theta) * self.config.s self._domain = ContinuousDomain(-2 * np.ones(self.config.dimension), 2 * np.ones(self.config.dimension))
def __init__(self, path=None): super().__init__(path) self._max_value = 1.03162842 d = self.config.dimension if d <= 2: raise Exception( "Need dimension at least 3 to create embedded version of Camelback" ) self._x0 = np.array([0.5, 0.2] + [0.] * (d - 2)) self._domain = ContinuousDomain(np.array([-2, -1] + [-1] * (d - 2)), np.array([2, 1] + [1] * (d - 2)))
def test_norm_denorm_same(self): self.init() for i in range(20): self.X = (np.random.random((self.dim,)) - 0.5) * 2. assert (self.X <= 1.0).all() assert (self.X >= -1.0).all() domain = ContinuousDomain(np.asarray([-5] * self.dim), np.asarray([2] * self.dim)) X_norm = rembo_algorithm.normalize(self.X, domain) X_denorm = rembo_algorithm.denormalize(X_norm, domain) assert np.isclose(self.X, X_denorm).all(), ("Not quite the same values after norm+denorm! ", (self.X, X_denorm))
def __init__(self, path=None): super().__init__(path) self._x0 = np.array([0., 0.]) self._max_value = 1. self._domain = ContinuousDomain(np.array([-0.5, -0.5]), np.array([0.5, 0.5]))
def __init__(self, path=None): super().__init__(path) self._x0 = np.array([0.5, 0.2]) self._x0 = np.array([-0.12977758051079197, 0.2632096107305229]) self._max_value = 1.03162842 self._domain = ContinuousDomain(np.array([-2, -1]), np.array([2, 1]))
def __init__(self, path=None): super().__init__(path) self._x = np.array([1.]) self._max_value = 1.0026469 self._domain = ContinuousDomain(np.array([-1]), np.array([2]))
def __init__(self, path=None): super().__init__(path) self._x = np.array([0.1]) self._max_value = 1.1 # at 0.5 self._domain = ContinuousDomain(np.array([0.]), np.array([1]))
def __init__(self, path=None): super().__init__(path) self._x = np.array([15]) self._max_value = 1.25375424 # determined using scipy.minimze self._domain = ContinuousDomain(np.array([-20]), np.array([20]))
def __init__(self, path=None): super().__init__(path) ones = np.ones(self.config.dimension) self._x0 = 0.5 * ones / np.sqrt(self.config.dimension) self._max_value = 1.0 self._domain = ContinuousDomain(-ones, ones)