code: def get_centroids(data,k,labels,centroids,data_norms):
      
  
      D = data.shape[1]    
      for j in range(k):
          cluster_points = np.where(labels == j)
          cluster_total = len(cluster_points)
          if cluster_total == 0:
              _, temp = new_orthogonal_center(data,data_norms,centroids)
          else:
              temp = np.mean(data[cluster_points,:],axis=1)      
          centroids[j,:] = temp
      return centroids
docs: For each element in the dataset, choose the closest centroid
      
      Parameters
      ------------
      data: array-like, shape= (m_samples,n_samples)
      K: integer, number of K clusters  
      centroids: array-like, shape=(K, n_samples)     
      labels: array-like, shape (1,n_samples)
      returns
      -------------
      centroids: array-like, shape (K,n_samples)