Whichever one you use, you'll probably need to edit the source to change the probability formula from (n_hits / n_total) to ((n_hits + 1) / (n_total + 2)). That's the correct formula based on an even distribution of probabilities (which is close enough to the actual distribution in most situations for this to be a huge improvement). I can never find a reference for this when I search for one, but you can verify it ex…
Sounds to me that you are describing Laplace Smoothing in a Naive Bayes classifier. This is a pretty standard technique for avoiding the problem that you are seeing where the probability comes out as 100%/0% because of a lack of information in the model.
This is important, because while adding a single pseudocount to each column will prevent zero divisions, it's probably not reflective of the true distribution of values. If instead, you add pseudocounts using a Dirichlet where the parameters are set based on some prior knowledge, you can often improve the performance of the classifier (especially in low-count situations), without biasing the results unfairly.