

def __init__(self, X_lower, X_upper, d):
    '\n        Random EMbedding Bayesian Optimization [1] maps the original space\n        in a lower dimensional subspace via a random embedding matrix and\n        performs Bayesian Optimization only in this lower dimensional\n        subspace.\n\n        [1] Ziyu Wang and Masrour Zoghi and Frank Hutter and David Matheson\n            and Nando de Freitas\n            Bayesian Optimization in High Dimensions via Random Embeddings\n            In: Proceedings of the 23rd international joint conference\n            on Artificial Intelligence (IJCAI)\n\n        Parameters\n        ----------\n        X_lower : (D,) numpy array\n            The lower bound of the input space.\n        X_upper: (D,) numpy array\n            The upper bound of the input space.\n        d: int\n            Number of dimensions for the lower dimensional subspace\n        '
    self.d_orig = X_lower.shape[0]
    self.d = d
    self.A = (np.sqrt(self.d) * np.random.normal(loc=0.0, scale=1.0, size=(self.d_orig, self.d)))
    self.original_X_lower = X_lower
    self.original_X_upper = X_upper
    self.original_scaled_X_lower = ((- 1) * np.ones([self.d_orig]))
    self.original_scaled_X_upper = (1 * np.ones([self.d_orig]))
    super(REMBO, self).__init__(X_lower=((- np.sqrt(self.d)) * np.ones(self.d)), X_upper=(np.sqrt(self.d) * np.ones(self.d)), do_scaling=False)

dataset/ETHPy150Open automl/RoBO/robo/task/rembo.py REMBO.__init__/original