code: import numpy as np
  from numpy import linalg as LA
  
  
  def calc_minimizer_sog(point, p, sigma_sq, store_x0, matrix_test, store_c):
      """
      Finds the nearest local minimizer for point using the Sum of Gaussians
      function.
  
      Parameters
      ----------
      point : 1-D array with shape (d, )
              A point used to evaluate the function.
      p : integer
          Number of local minima.
      sigma_sq: float or integer
                Value of sigma squared.
      store_x0 : 2-D arrays with shape (p, d).
      matrix_test : 3-D arrays with shape (p, d, d).
      store_c : 3-D arrays with shape (p, ).
  
      Returns
      -------
      np.argmin(dist) : integer
                        Position of the local minimizer which produces the
                        smallest distance between point and all p local
                        minimizers.
      """
      dist = np.zeros((p))
      for i in range(p):
          dist[i] = LA.norm(point - store_x0[i])
      assert(np.min(dist) < 0.25)
      return np.argmin(dist)
  
apis: numpy.argmin numpy.linalg.norm numpy.min numpy.zeros