title: Scikit-learn: is semi-supervised Naive Bayes implementation available?
body: <p>I would like to use the implementation of Semi-supervised Naive Bayes (Bernoulli) of Scikit-learn. According to <a href="https://github.com/scikit-learn/scikit-learn/pull/430/files" rel="nofollow">this link in github</a>, there was some work and discussion about it one year ago (class SemisupervisedNB). On the other hand, there seems to be <a href="https://github.com/scikit-learn/scikit-learn/pull/107" rel="nofollow">another different implementation (function fit_semi?) which seems it was polished by another user afterwards</a>. However none of them are available in the current stable release. </p>
  
  <p>Could someone show me an example on how could I use one of these two implementations with the current release of scikit-learn in order to build a Semisupervised Naive Bayes ? Thanks.   </p>
  
  <p>P.S.: I am using scikit-learn classifiers from NLTK with the class SklearnClassifier </p>
  
  <p><strong>EDIT</strong></p>
  
  <p>I have tried the code of SemiSupervisedNB in my project changing the label for the unlabeled class from -1 to 2 (I am using SKlearnClassifier from NLTK and my unlabeled class gets the label 2). However, I am getting ValueError: array must not contain infs or NaNs when computing d (difference between current and previous params of the model) because the intercept arrays contain inf values... Any idea on how to solve this?</p>