Simple example using BernoulliNB (naive bayes classifier) scikit-learn in python - cannot explain classification

Using scikit-learn 0.10

Why does the following trivial code snippet:

from sklearn.naive_bayes import *

import sklearn
from sklearn.naive_bayes import *

print sklearn.__version__

X = np.array([ [1, 1, 1, 1, 1], 
               [0, 0, 0, 0, 0] ])
print "X: ", X
Y = np.array([ 1, 2 ])
print "Y: ", Y

clf = BernoulliNB()
clf.fit(X, Y)
print "Prediction:", clf.predict( [0, 0, 0, 0, 0] )    

Print out an answer of "1" ? Having trained the model on [0,0,0,0,0] => 2 I was expecting "2" as the answer.

And why does replacing Y with

Y = np.array([ 3, 2 ])

Give a different class "2" as an answer (the correct one) ? Isn't this just a class label?

Can someone shed some light on this?