- works with all types of tokens, not just text
- allows purging of low-frequency tokens (for performance)
- uses log probabilities to avoid underflow
- allows prior distribution on classes to be assumed uniform
- customizable constant value for Laplacian smoothing
- allows for multiple categories
- optional binarized mode
{:ok, nbayes} = NaiveBayes.new
tokenize = fn s ->
s |> String.downcase |> String.replace(~r/[^a-z ]/, "") |> String.split(~r/\s+/)
end
nbayes |> NaiveBayes.train( tokenize.("You need to buy some Viagra"), "SPAM" )
nbayes |> NaiveBayes.train( tokenize.("This is not spam, just a letter to Bob."), "HAM" )
nbayes |> NaiveBayes.train( tokenize.("Hey Oasic, Do you offer consulting?"), "HAM" )
nbayes |> NaiveBayes.train( tokenize.("You should buy this stock"), "SPAM" )
results = nbayes |> NaiveBayes.classify( tokenize.("Now is the time to buy Viagra cheaply and discreetly") )
IO.inspect results
# => %{"HAM" => 0.4832633319857435, "SPAM" => 0.5167366680142564}See the docs or test/naive_bayes_test.ex for more examples.
def deps do
[{:naive_bayes, "~> 0.1.3"}]
end