Automatically encode categorical data

[JedS6391]

At the moment, there is no built-in way to encode categorical features/targets which means the burden is on the user to manually construct their data set in a way that will work with the system.

One option could be that presented in PR #47, where the labels (categories) are encoded into a vector. For example, the Iris data set is as follows:

sepal_length	sepal_width	petal_length	petal_width	species
5.1	3.5	1.4	0.2	Iris-setosa
7.0	3.2	4.7	1.4	Iris-versicolor
6.3	3.3	6.0	2.5	Iris-virginica

Which is transformed into the encoding:

sepal_length	sepal_width	petal_length	petal_width	species_being_Iris-setosa	species_being_Iris-versicolor	species_being_Iris-virginica
5.1	3.5	1.4	0.2	1.0	0.0	0.0
7.0	3.2	4.7	1.4	0.0	1.0	0.0
6.3	3.3	6.0	2.5	0.0	0.0	1.0

Another option would to be provide a parsing function that can do this automatically, similarly to what is done in lgp.examples.Iris:

val targetLabels = setOf("Iris-setosa", "Iris-versicolor", "Iris-virginica")
val featureIndices = 0..3
val targetIndex = 4

val datasetLoader = CsvDatasetLoader(
        reader = BufferedReader(
            // Load from the resource file.
            InputStreamReader(this.datasetStream)
        ),
        featureParseFunction = { header: Header, row: Row ->
            val features = row.zip(header)
                              .slice(featureIndices)
                              .map { (featureValue, featureName) ->

                Feature(
                        name = featureName,
                        value = featureValue.toDouble()
                )
            }

            Sample(features)
        },
        targetParseFunction = { _: Header, row: Row ->
            val target = row[targetIndex]

            // ["Iris-setosa", "Iris-versicolor", "Iris-virginica"] -> [0.0, 1.0, 2.0]
            Targets.Single(targetLabels.indexOf(target).toDouble())
        }
)