In [1]:

    

// You need to install 'nnet' and 'caret' packages if you do not have them
open RProvider.utils
open Deedle
open RDotNet
open RProvider
open RProvider.``base``
open RProvider.datasets
open RProvider.neuralnet
open RProvider.caret


    

In [2]:

    

// Load data from R to Deedle frame
let iris : Frame<int, string> = R.iris.GetValue()
 
// Observe iris data set
let features =
    iris
    |> Frame.filterCols (fun c _ -> c <> "Species")
    |> Frame.mapColValues (fun c -> c.As<double>())
let targets =
    R.as_factor(iris.Columns.["Species"].As<int>())
 
R.featurePlot(x = features, y = targets, plot = "pairs")


    

In [3]:

    

// Split data to training and testing sets (70% vs 30%)
let range = [1..iris.RowCount]
let trainingIdxs : int[] = R.sample(range, iris.RowCount*7/10).GetValue()
let testingIdxs  : int[] = R.setdiff(range, trainingIdxs).GetValue()
let trainingSet = iris.Rows.[trainingIdxs]
let testingSet  = iris.Rows.[testingIdxs]

// Train neural network
let nn =
    R.neuralnet(
        "Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width",
        data = trainingSet, hidden = R.c(3,2),
        err_fct = "ce", linear_output = true)
        
// Plot the resulting neural network with coefficients
R.plot_nn nn


    

In [5]:

    

// Split testing set into features and targets
let testingFeatures =
    testingSet
    |> Frame.filterCols (fun c _ -> c <> "Species")
    |> Frame.mapColValues (fun c -> c.As<double>())
let testingTargets =
    testingSet.Columns.["Species"].As<int>().Values
 
// Predict `Species` for testingFeatures with neural network
let prediction =
    R.compute(nn, testingFeatures)
     .AsList().["net.result"].AsVector()
    |> Seq.cast<double>
    |> Seq.map (round >> int)
 
// Calculate number of misclassified irises
let misclassified =
    Seq.zip prediction testingTargets
    |> Seq.filter (fun (a,b) -> a<>b)
    |> Seq.length
 
sprintf "Misclassified irises '%d' of '%d'" misclassified (testingSet.RowCount)


    

    
Out[5]:





"Misclassified irises '3' of '45'"