code: def test_GGGM0(verbose=0, seed=1):
      G = GGGM()
      sx = 1000
      # x = np.array([float(st.t.rvs(dof)) for i in range(sx)])
      if seed:
          nr = np.random.RandomState([seed])
      else:
          import numpy.random as nr
      x = nr.randn(sx)
      G.init(x)
      G.estimate(x)
      if verbose:
          G.parameters()
      assert np.absolute(G.mean) < 0.3
  
file_path: nipy/nipy/algorithms/clustering/tests/test_ggm.py
imports: import numpy
  import numpy.random
  import scipy.stats
  from ..ggmixture import GGGM, GGM, Gamma
label_desc: Use image node_id 3 for calling a global function with example usage: test_GGGM0(verbose, seed) without return types