code: def emptyclusKmeans():
      # Connect to a pre-existing cluster
      # connect to localhost:54321
  
      # Log.info("Importing ozone.csv data...\n")
      ozone_sci = np.loadtxt(
          pyunit_utils.locate("smalldata/glm_test/ozone.csv"),
          delimiter=",",
          skiprows=1,
      )
      ozone_h2o = h2o.import_file(
          path=pyunit_utils.locate("smalldata/glm_test/ozone.csv")
      )
  
      ncent = 10
      nempty = random.randint(1, old_div(ncent, 2))
      initial_centers = [
          [41, 190, 67, 7.4],
          [36, 118, 72, 8],
          [12, 149, 74, 12.6],
          [18, 313, 62, 11.5],
          [23, 299, 65, 8.6],
          [19, 99, 59, 13.8],
          [8, 19, 61, 20.1],
          [16, 256, 69, 9.7],
          [11, 290, 66, 9.2],
          [14, 274, 68, 10.9],
      ]
      for i in random.sample(list(range(ncent - 1)), nempty):
          initial_centers[i] = [
              100 * i for z in range(1, len(initial_centers[0]) + 1)
          ]
  
      initial_centers_sci = np.asarray(initial_centers)
  
      initial_centers_h2o = h2o.H2OFrame(initial_centers)
  
      # Log.info("Initial cluster centers:")
      print("H2O initial centers:")
      initial_centers_h2o.show()
      print("scikit initial centers:")
      print(initial_centers_sci)
  
      # H2O can handle empty clusters and so can scikit
      # Log.info("Check that H2O can handle badly initialized centers")
      km_sci = KMeans(
          n_clusters=ncent, init=initial_centers_sci, n_init=1
      )
      km_sci.fit(preprocessing.scale(ozone_sci))
      print("scikit final centers")
      print(km_sci.cluster_centers_)
  
      km_h2o = H2OKMeansEstimator(
          k=ncent, user_points=initial_centers_h2o, standardize=True
      )
      km_h2o.train(
          x=list(range(ozone_h2o.ncol)), training_frame=ozone_h2o
      )
      print("H2O final centers")
      print(km_h2o.centers())
  
file_path: h2o-3/h2o-py/tests/testdir_algos/kmeans/pyunit_emptyclusKmeans.py
imports: from builtins import zip
  from builtins import range
  from past.utils import old_div
  import sys
  import h2o
  from tests import pyunit_utils
  import random
  import numpy
  from sklearn import preprocessing
  from sklearn.cluster import KMeans
  from h2o.estimators.kmeans import H2OKMeansEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: emptyclusKmeans() without return types