Kaive Bayes

Sorry about the ridiculous name, this is a very simple Naive Bayes classifier written in Kotlin.

Techniques used

In order to be competitive, Kaive Bayes leverages the following techniques:

• Laplace Smoothing for eliminating zero probabilities
• log-sum trick to prevent number underflows
• Normalization
• Stopword purging

Serialization is to be added soon along with various optimization enhancements.

Usage

 var bae = BayesClassifier<String>()

    bae.train(Input("A great game", "sports"))
    bae.train(Input("The election was over", "notSports"))

    bae.train(Input("Very clean match", "sports"))
    bae.train(Input("A clean but forgettable game", "sports"))
    bae.train(Input("It was a close election", "notSports"))

    val message1 = "A very close game"
    var map = bae.predict(message1)
    println("RESULTS OF TEXT: $message1")
    println("------------------------------------------------------------------")
    println("Is about sports " + map["sports"])
    println("Is NOT about sports " + map["notSports"])
    println()

For more examples see the Main function