code: def test_GGM1(verbose=0):
      shape = 1
      scale = 1
      mean = 0
      var = 1
      G = GGM(shape, scale, mean, var)
      sx = 1000
      x = -2.5 + nr.randn(sx)
      G.estimate(x)
      b = np.absolute(G.mean + 2.5) < 0.5
      if verbose:
          # G.parameters()
          print(x.max())
      assert b
  
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 1 for calling a global function with example usage: test_GGM1(verbose) without return types