gnb_2.kt

// Class to implement Gaussian Naive Bayes
class GaussianNB( private var dataFrame : DataFrame ) {

    ...

    // Prior probabilities stored in a HashMap of form ( column_name , prior_prob )
    private var priorProbabilities : HashMap<String,Float>

    ...

    // Compute the prior probabilities.
    // These probabilities are p( class=some_class ) which are calculated as
    // p( class=apple ) = num_samples_label_as_apple / num_samples_in_ds
    private fun computePriorProbabilities( labels : Array<String> ) : HashMap<String,Float> {
        // Get the count ( freq ) of each unique class in labels.
        val labelCountMap = labels.groupingBy { it }.eachCount()
        // The prior probabilties are stored in a HashMap of form ( column_name , prob )
        val out = HashMap<String,Float>()
        for ( ( label , count ) in labelCountMap ) {
            // Append the prob with key=column_name
            out[ label ] = count.toFloat() / dataFrame.numSamples.toFloat()
        }
        return out
    
    ...

}