new_contents: import numpy as np
  from milk.unsupervised import gaussianmixture
  
  def _sq(x):
      return x*x
  def test_gm():
      np.random.seed(22)
      centroids = np.repeat(np.arange(4), 4).reshape((4,4))
      fmatrix = np.concatenate([(np.random.randn(12,4)+c) for c in centroids])
      assignments = np.repeat(np.arange(4), 12)
      rss = sum(np.sum(_sq(fmatrix[i*12:(i+1)*12]-i)) for i in xrange(4))
      assert np.abs(gaussianmixture.residual_sum_squares(fmatrix, assignments, centroids) - rss) < 1.e-12
      assert gaussianmixture.BIC(fmatrix, assignments, centroids) > 0
      assert gaussianmixture.AIC(fmatrix, assignments, centroids) > 0
  
subject: Test gaussiamixture