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// Convert the given String[] to a Float[]
// Note: The `CSVReader` returns a row as String[] where each String is a number. We parse this String and convert it to
// a float.
fun convertStringArrayToFloatArray( strArray : Array<String> ) : FloatArray {
    val out = strArray.map { si -> si.toFloat() }.toFloatArray()
    return out
}

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// Set data from `rawData` to `featureColumnData`
private fun populateColumns() {
    // Create an empty ArrayList to store the labels
    val labels = ArrayList<String>()

    // Iterate through `rawData` starting from index=1 ( as index=0 refers to the column names )
    for ( strSample in rawData.subList( 1 , rawData.size ) ) {

        // Append the label which is the last element of `strSample`.
        labels.add( strSample[ numFeatures ] )

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...

// Number of features in the dataset. This number equals ( num_cols - 1 ) where num_cols is the number of
// columns in the CSV file.
// ( Note: We assume that the file has the labels column as the last column ).
var numFeatures = 0

// Number of samples in the dataset. This number equals ( num_rows - 1 ) where num_rows is the number of
// rows in the CSV file.
// Note: We assume that the CSV file has its first row as the column names.

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 // Get column names from `rawData`.
private fun getColumnNames() : Array<String> {
    val columnNames = rawData[ 0 ]
    return columnNames
}

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// Helper class to load data from given CSV file and transform it into a Array<FeatureColumn>
class DataFrame( context: Context , assetsFileName : String ) {

    // HashMap which stores the CSV data in the form ( Column_Name , Float[] ). Where Float[] holds
    // the feature value for all samples.
    private var featureColumnData = HashMap<String,ArrayList<Float>>()

    // Variable to store the parsed CSV file from `CSVReader`.
    private var rawData : List<Array<String>>

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// 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>

    ...

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// Holds data for a particular feature.
class FeatureColumn( var name : String , var data : FloatArray ) {

    // Mean of given `data`
    var featureMean : Float

    // Variance of given `data`
    var featureVariance : Float

    // Standard deviation of given `data`

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// Predict a class for the given sample using the Random Forest.
fun predict( x : HashMap<String,String> ) : String {
    // Create an empty array to store class labels.
    val treeOutputs = Array( NUM_TREES ) { "" }
    for ( i in 0 until NUM_TREES ) {
        // Store the output of each DecisionTree in our forest.
        treeOutputs[ i ] = forest[i].predict( x )
        println( "Prediction ${i+1} DecisionTree is ${treeOutputs[i]}")
    }
    // Get the majority label, which is our final prediction for the given sample.

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...

// The forest represented as an array of DecisionTree objects.
private var forest : ArrayList<DecisionTree> = ArrayList()

init {
  
   ...
  
    // Initialize the forest

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// Return samples ( in form of DataFrame object ) given their indices.
fun getEntries( indices : IntArray ) : DataFrame {
    val dataFrame = DataFrame()
    data.map { column ->
        // `column` represent a Map -> ( String , ArrayList<String> )
        // column.key -> Name of the column as in the training datasets.
        // column.value -> ArrayList<String> containing the column's data.
        val columnData = ArrayList<String>()
        val values = column.value
        // Add the feature values corresponding to each index in `indices`.