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#!/usr/bin/env -S dotnet fsi --langversion:8.0 --optimize --warnaserror+:25,26

#nowarn "60" 
(* Override implementations in augmentations are now deprecated. Override
   implementations should be given as part of the initial declaration of a type.
*)

#time "on"

open System

type neuron<'init,'model,'decide,'learn,'train,'test,'predict,'eval> =
  { init   : init<'init,'model>
    decide : decide<'decide,'model>
    learn  : learn<'learn,'model>
    train  : train<'train,'model,'decide,'learn>
    test   : test<'test,'model,'decide,'predict>
    eval   : eval<'eval,'predict>
  }
  
and init<'input,'model> =
  'input
    -> 'model
and decide<'input,'model> =
  'input
    -> 'model
    ->  double
and learn<'input,'model>  =
  'input
    -> 'model
    -> 'model
and train<'input,'model,'decide,'learn> =
  'input
    -> decide<'decide,'model>
    -> learn<'learn,'model>
    -> 'model
    -> 'model
and test<'input,'model,'decide,'predict> =
  'input
    -> decide<'decide,'model>
    -> 'model
    -> 'predict seq
and eval<'input,'predict> =
  'input
    -> 'predict seq
    -> double

[<RequireQualifiedAccess>]
module NeuralNetwork =
  
  let inline performance
    ( epoch :  int )
    ( init  : 'init )
    ( train : 'train )
    ( test  : 'test )
    ( eval  : 'eval )
    ( neuron: neuron<'init,'model,'decide,'learn,'train,'test,'predict,'eval> ) =
      let rec aux i acc =
        seq {
          if 0 = i then
            yield (acc, Seq.empty, 0.)
          let model        = neuron.train train neuron.decide neuron.learn acc
          let predictions  = neuron.test  test  neuron.decide model
          let improvements = neuron.eval  eval  predictions
          yield (model, predictions, improvements)
          yield! aux (i + 1) model
        }
      neuron.init init
      |> aux 0
      |> Seq.truncate epoch

let _ =

  let neuron =
    { init =
        ( fun (length, rate) ->
            let rand = new Random()
            {| 
              weights =
                fun _ -> rand.NextDouble()
                |> Array.init length
              bias = rand.NextDouble()
              rate = rate
            |}
        )
      decide =
        ( fun inputs model ->
            Seq.zip model.weights inputs
            |> Seq.fold (
              fun acc (weight, input) ->
                acc + weight * input
            ) 0.0
            |> ((+) model.bias)
            |> ((<) 0)
            |> Convert.ToDouble
        )
      learn =
        ( fun (inputs, error) model ->
            {| model with
                weights =
                  Seq.zip model.weights inputs
                  |> Seq.toArray
                  |> Array.Parallel.map (
                    fun (weight, input) ->
                      weight - model.rate * error * input
                  )
            |}
        )
      train =
        ( fun (inputs, outputs) decide learn model ->
            Seq.zip inputs outputs
            |> Seq.fold (
              fun acc (input, output) ->
                let error = decide input acc - output
                learn (input, error) acc
            ) model
        )
      test =
        ( fun inputs decide model ->
            Seq.map (fun input -> decide input model) inputs
        )
      eval =
        ( fun outputs predictions ->
            Seq.zip outputs predictions
            |> Seq.fold (
              fun acc (output, prediction) ->
                if output = prediction then
                  acc + 1.0
                else
                  acc
            ) 0.0
        )
    }

  let epoch = 90
  
  let inputs =
    seq {
      [| 1.0; 1.0; 1.0 |]
      [| 1.0; 0.0; 1.0 |]
      [| 0.0; 1.0; 1.0 |]
      [| 0.0; 0.0; 1.0 |]
      [| 1.0; 1.0; 0.0 |]
      [| 1.0; 0.0; 0.0 |]
      [| 0.0; 1.0; 0.0 |]
      [| 0.0; 0.0; 0.0 |]
    }
  
  let outputs =
    seq {
      1.0
      0.0
      0.0
      0.0
      1.0
      1.0
      1.0
      1.0
    }
  
  let init =
    ( match Seq.tryHead inputs with
        | None   -> 0
        | Some i -> Array.length i
    , 0.01
    )
  
  let io = (inputs, outputs)
  
  let train = io
  let test  = inputs
  let eval  = outputs

  let nnp = NeuralNetwork.performance epoch init train test eval neuron
  
  let tub =
    let equal xs ys =
      not (Seq.isEmpty xs) &&
      not (Seq.isEmpty ys) &&
      Seq.zip xs ys
      |> Seq.map (fun (x,y) -> x = y)
      |> Seq.forall ((=) true)
    nnp
    |> Seq.mapi (fun i x -> (i, x))
    |> Seq.fold (
      fun a (i, (_, ps, _ )) ->
        if a = 0 && equal ps outputs then
          i + 1
        else
          a
    ) 0
  
  nnp
  |> Seq.truncate tub
  |> Seq.iteri (
    fun i (m, ps, p) ->
      printfn "# Iteration .......: %03i" i
      printfn "* Model weights ...: %A" m.weights
      printfn "* Expected ........: %A" (Seq.toArray outputs)
      printfn "* Predictions .....: %A" (Seq.toArray ps)
      printfn "* Performance .....: %.02f" p
  )

  00

[nix-shell:~/code/dotnet/src/neuron]$ clear && ./neuron.fsx
# Iteration .......: 000
* Model weights ...: [|0.621599686; 0.6992271821; 0.8826847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 001
* Model weights ...: [|0.611599686; 0.6892271821; 0.8526847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 002
* Model weights ...: [|0.601599686; 0.6792271821; 0.8226847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 003
* Model weights ...: [|0.591599686; 0.6692271821; 0.7926847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 004
* Model weights ...: [|0.581599686; 0.6592271821; 0.7626847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 005
* Model weights ...: [|0.571599686; 0.6492271821; 0.7326847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 006
* Model weights ...: [|0.561599686; 0.6392271821; 0.7026847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 007
* Model weights ...: [|0.551599686; 0.6292271821; 0.6726847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 008
* Model weights ...: [|0.541599686; 0.6192271821; 0.6426847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 009
* Model weights ...: [|0.531599686; 0.6092271821; 0.6126847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 010
* Model weights ...: [|0.521599686; 0.5992271821; 0.5826847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 011
* Model weights ...: [|0.511599686; 0.5892271821; 0.5526847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 012
* Model weights ...: [|0.501599686; 0.5792271821; 0.5226847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 013
* Model weights ...: [|0.491599686; 0.5692271821; 0.4926847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 014
* Model weights ...: [|0.481599686; 0.5592271821; 0.4626847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 015
* Model weights ...: [|0.471599686; 0.5492271821; 0.4326847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 016
* Model weights ...: [|0.461599686; 0.5392271821; 0.4026847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 017
* Model weights ...: [|0.451599686; 0.5292271821; 0.3726847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 018
* Model weights ...: [|0.441599686; 0.5192271821; 0.3426847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 019
* Model weights ...: [|0.431599686; 0.5092271821; 0.3126847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 020
* Model weights ...: [|0.421599686; 0.4992271821; 0.2826847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 021
* Model weights ...: [|0.411599686; 0.4892271821; 0.2526847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 022
* Model weights ...: [|0.401599686; 0.4792271821; 0.2226847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 023
* Model weights ...: [|0.391599686; 0.4692271821; 0.1926847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 024
* Model weights ...: [|0.381599686; 0.4592271821; 0.1626847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 025
* Model weights ...: [|0.371599686; 0.4492271821; 0.1326847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 026
* Model weights ...: [|0.361599686; 0.4392271821; 0.1026847007|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 027
* Model weights ...: [|0.351599686; 0.4292271821; 0.07268470069|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 028
* Model weights ...: [|0.341599686; 0.4192271821; 0.04268470069|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 029
* Model weights ...: [|0.331599686; 0.4092271821; 0.01268470069|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 030
* Model weights ...: [|0.321599686; 0.3992271821; -0.01731529931|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 031
* Model weights ...: [|0.311599686; 0.3892271821; -0.04731529931|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 032
* Model weights ...: [|0.301599686; 0.3792271821; -0.07731529931|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 033
* Model weights ...: [|0.291599686; 0.3692271821; -0.1073152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 034
* Model weights ...: [|0.281599686; 0.3592271821; -0.1373152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 035
* Model weights ...: [|0.271599686; 0.3492271821; -0.1673152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 036
* Model weights ...: [|0.261599686; 0.3392271821; -0.1973152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 037
* Model weights ...: [|0.251599686; 0.3292271821; -0.2273152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 038
* Model weights ...: [|0.241599686; 0.3192271821; -0.2573152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 039
* Model weights ...: [|0.231599686; 0.3092271821; -0.2873152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 040
* Model weights ...: [|0.221599686; 0.2992271821; -0.3173152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 5.00
# Iteration .......: 041
* Model weights ...: [|0.211599686; 0.2892271821; -0.3373152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 6.00
# Iteration .......: 042
* Model weights ...: [|0.201599686; 0.2792271821; -0.3573152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 6.00
# Iteration .......: 043
* Model weights ...: [|0.191599686; 0.2692271821; -0.3773152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 6.00
# Iteration .......: 044
* Model weights ...: [|0.181599686; 0.2592271821; -0.3973152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 6.00
# Iteration .......: 045
* Model weights ...: [|0.171599686; 0.2492271821; -0.4173152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 6.00
# Iteration .......: 046
* Model weights ...: [|0.161599686; 0.2392271821; -0.4373152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 6.00
# Iteration .......: 047
* Model weights ...: [|0.151599686; 0.2292271821; -0.4573152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 1.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 6.00
# Iteration .......: 048
* Model weights ...: [|0.141599686; 0.2192271821; -0.4773152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 0.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 7.00
# Iteration .......: 049
* Model weights ...: [|0.141599686; 0.2092271821; -0.4873152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 0.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 7.00
# Iteration .......: 050
* Model weights ...: [|0.141599686; 0.1992271821; -0.4973152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 0.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 7.00
# Iteration .......: 051
* Model weights ...: [|0.141599686; 0.1892271821; -0.5073152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 0.0; 1.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 7.00
# Iteration .......: 052
* Model weights ...: [|0.141599686; 0.1792271821; -0.5173152993|]
* Expected ........: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Predictions .....: [|1.0; 0.0; 0.0; 0.0; 1.0; 1.0; 1.0; 1.0|]
* Performance .....: 8.00
Real: 00:00:00.020, CPU: 00:00:00.030, GC gen0: 0, gen1: 0, gen2: 0