code: def test_em_gmm_multi():
      # Playing with various initializations on the same data
  
      # generate some data
      dim = 2
      x = np.concatenate(
          (nr.randn(1000, dim), 3 + 2 * nr.randn(100, dim))
      )
  
      # estimate different GMMs of that data
      maxiter, delta, ninit, k = 100, 1.0e-4, 5, 2
  
      lgmm = GMM(k, dim)
      bgmm = lgmm.initialize_and_estimate(
          x, niter=maxiter, delta=delta, ninit=ninit
      )
      bic = bgmm.evidence(x)
  
      assert np.isfinite(bic)
  
file_path: nipy/nipy/algorithms/clustering/tests/test_gmm.py
imports: import numpy
  from ..gmm import GMM, best_fitting_GMM
label_desc: Use image node_id 11 for calling a global function with example usage: test_em_gmm_multi() without return types