examples/learn_search_space/__init__.py
def shrink_ellips_space(hist_tasks, curr_task):
    """Shrink search space with ellipsoidal representation according to the historical knowledge"""
    hyper_param_dim = len(curr_task["space"])
    hist_opt, curr_bound = rep_task(hist_tasks, curr_task)
    matrix_a = np.zeros((hyper_param_dim, hyper_param_dim))
    vector_b = np.zeros((1, hyper_param_dim))

    # need to implement interior-points algorithm to solve the convex problem by CVXPY in future#

    return matrix_a, vector_b
Shrink search space with ellipsoidal representation according to the historical knowledge
Shrink search space with ellipsoidal representation according to the historical knowledge
Shrink search space with ellipsoidal representation according to the historical knowledge
def shrink_ellips_space(hist_tasks, curr_task):
    hyper_param_dim = len(curr_task["space"])
    hist_opt, curr_bound = rep_task(hist_tasks, curr_task)
    matrix_a = np.zeros((hyper_param_dim, hyper_param_dim))
    vector_b = np.zeros((1, hyper_param_dim))
    return matrix_a, vector_b
def shrink_ellips_space ( hist_tasks , curr_task ) : hyper_param_dim = len ( curr_task [ "space" ] ) hist_opt , curr_bound = rep_task ( hist_tasks , curr_task ) matrix_a = np . zeros ( ( hyper_param_dim , hyper_param_dim ) ) vector_b = np . zeros ( ( 1 , hyper_param_dim ) ) return matrix_a , vector_b
Shrink search space with ellipsoidal representation according to the historical knowledge
Shrink search space with ellipsoidal representation according to the historical knowledge
"""Shrink search space with ellipsoidal representation according to the historical knowledge""" # need to implement interior-points algorithm to solve the convex problem by CVXPY in future#
hist_tasks
curr_task