I am trying out the Gaussian mixture models in the package. I tried to model a mixture with two Components, G(1000,500^2), and G(2000,600^2). The following is the code:

data = np.random.normal(1000,500,1000)
data2 = np.random.normal(2000,600,1000)
data = list(data) + list(data2)
model = mixture.DPGMM(n_components=10,alpha=10,n_iter=10000)
model.fit(data)
print model.means_

And I got the following means of the components.
[[ 0.13436485]
[ 0.13199086]
[ 0.11750537]
[ 0.10560644]
[ 0.12162311]
[ 0.00204134]
[ 0.12058521]
[ 0.11997703]
[ 0.11944384]
[ 0.11890694]]

It seems the model does not fit properly to the data. Is it a bug or I have got something wrong in the application of the model?

Thanks.
Fan