module DigitRecognizerModule

open System
open System.IO

// type represents sample from dataset
type Sample = {Label:int; Vector:int[] }


// load CSV and return Sample array
let loadCSVLines(fn:string) = File.ReadAllLines(fn)
let lineToIntArr(line:string) = Array.map (fun s->s|>int) (line.Split(','))
let getData(fn:string) =
    loadCSVLines(fn)
    |> (fun x->x.[1..])
    |> Array.map (fun x->let arr=lineToIntArr x in {Label=arr.[0]; Vector=arr.[1..]})


// distance between two vectors
let dist (v1:int[], v2:int[]) =
    Array.zip v1 v2
    |> Array.map (fun x->((fst x-snd x)|>float)**2.0)
    |> Array.sum


// classify test sample by looking for most similar training sample
let classify(train:Sample[], test:Sample)=
    Array.map (fun x->dist(x.Vector, test.Vector)) train
    |> Array.zip [|0..(train|>Array.length)-1|]
    |> Array.fold (fun acc x->if (snd acc) > (snd x) then x else acc) (0, System.Double.MaxValue)
    |> fst
    |> (fun i->train.[i].Label)


// evaluate the digit classifier
let eval()=
        // load data
        let dir="../../../../"
        let test=getData(dir+"test.csv")
        let train=getData(dir+"train.csv")
        let len=test |> Array.length

        // classify samples from test set, calc accuracy
        let accuracy=(Array.map (fun i->if classify(train, test.[i])=test.[i].Label then 1.0 else 0.0) [|0..len-1|] )|> Array.sum |> (fun x->x/(len|>float))
        printfn "Accuracy: %f" accuracy