gnb_10.kt

// Predict the label for the given sample.
private fun predictLabel( sample : FloatArray ) {
    val probArray = FloatArray( dataFrame.numClasses )
    for ( ( i , priorProb ) in priorProbabilities.values.withIndex()) {
        // We take the log probabilities so as to avoid underflow.
        var p = log10( priorProb )
        // While we take log, the product is transformed into a sum
        // log( a . b ) = log(a) + log(b)
        for ( j in 0 until dataFrame.numFeatures ) {
            p += featureDistributions[ j ].getLogProb( sample[ i ] )
        }
        probArray[ i ] = p
    }
    // Get the label with highest probability.
    val label = priorProbabilities.keys.toTypedArray()[ probArray.indexOf( probArray.max()!!) ]
    resultCallback?.onPredictionResult( label , probArray )
}