numl Machine Learning

This is the getting started sample for the numl machine learning library available at http://numl.net/ written in F#.

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#r """..\packages\numl.0.7.5\lib\net40\numl.dll"""

open numl
open numl.Model
open numl.Supervised.DecisionTree

type Outlook =
    | Sunny = 0
    | Overcast = 1
    | Rainy = 2

type Temperature =
    | Low = 0
    | High = 1

type Tennis =
    {
        [<Feature>] Outlook : Outlook
        [<Feature>] Temperature : Temperature
        [<Feature>] Windy : bool
        [<Label>] Play : bool
    }

    static member New outlook temperature windy play =
        box {
            Outlook = outlook
            Temperature = temperature
            Windy = windy
            Play = play
        }

let data =
    [
        Tennis.New Outlook.Sunny Temperature.Low true true
        Tennis.New Outlook.Sunny Temperature.High true false
        Tennis.New Outlook.Sunny Temperature.High false false
        Tennis.New Outlook.Overcast Temperature.Low true true
        Tennis.New Outlook.Overcast Temperature.High false true
        Tennis.New Outlook.Overcast Temperature.Low false true
        Tennis.New Outlook.Rainy Temperature.Low true false
        Tennis.New Outlook.Rainy Temperature.Low false true
    ]

let descriptor = Descriptor.Create<Tennis>()
let generator = DecisionTreeGenerator descriptor
generator.SetHint false
let model = Learner.Learn(data, 0.8, 1000, generator)

printfn "%A" model

//Learning Model:
//  Generator numl.Supervised.DecisionTree.DecisionTreeGenerator
//  Model:
//	[Outlook, 0,3380]
//	 |- Sunny
//	 |	[Temperature, 1,0000]
//	 |	 |- Low
//	 |	 |	 +(True, 1)
//	 |	 |- High
//	 |	 |	 +(False, -1)
//	 |- Overcast
//	 |	 +(True, 1)
//	 |- Rainy
//	 |	[Windy, 1,0000]
//	 |	 |- False
//	 |	 |	 +(True, 1)
//	 |	 |- True
//	 |	 |	 +(False, -1)
//
//  Accuracy: 100,00 %

namespace numl

namespace numl.Model

namespace numl.Supervised

namespace numl.Supervised.DecisionTree

type Outlook =
  | Sunny = 0
  | Overcast = 1
  | Rainy = 2

Full name: Script.Outlook

Outlook.Sunny: Outlook = 0

Outlook.Overcast: Outlook = 1

Outlook.Rainy: Outlook = 2

type Temperature =
| Low = 0
| High = 1

Full name: Script.Temperature

Temperature.Low: Temperature = 0

Temperature.High: Temperature = 1

type Tennis =
  {Outlook: Outlook;
   Temperature: Temperature;
   Windy: bool;
   Play: bool;}
  static member New : outlook:Outlook -> temperature:Temperature -> windy:bool -> play:bool -> obj

Full name: Script.Tennis

Multiple items
type FeatureAttribute =
inherit NumlAttribute
new : unit -> FeatureAttribute

Full name: numl.Model.FeatureAttribute

--------------------
FeatureAttribute() : unit

Multiple items
Tennis.Outlook: Outlook

--------------------
type Outlook =
  | Sunny = 0
  | Overcast = 1
  | Rainy = 2

Full name: Script.Outlook

Multiple items
Tennis.Temperature: Temperature

--------------------
type Temperature =
| Low = 0
| High = 1

Full name: Script.Temperature

Tennis.Windy: bool

type bool = System.Boolean

Full name: Microsoft.FSharp.Core.bool

Multiple items
type LabelAttribute =
  inherit NumlAttribute
  new : unit -> LabelAttribute
  member GenerateProperty : property:PropertyInfo -> Property

Full name: numl.Model.LabelAttribute

--------------------
LabelAttribute() : unit

Tennis.Play: bool

static member Tennis.New : outlook:Outlook -> temperature:Temperature -> windy:bool -> play:bool -> obj

Full name: Script.Tennis.New

val outlook : Outlook

val temperature : Temperature

val windy : bool

val play : bool

val box : value:'T -> obj

Full name: Microsoft.FSharp.Core.Operators.box

val data : obj list

Full name: Script.data

static member Tennis.New : outlook:Outlook -> temperature:Temperature -> windy:bool -> play:bool -> obj

val descriptor : Descriptor

Full name: Script.descriptor

Multiple items
type Descriptor =
  new : unit -> Descriptor
  member At : i:int -> Property
  member ColumnAt : i:int -> string
  member Convert : item:obj -> IEnumerable<float> + 2 overloads
  member Features : Property[] with get, set
  member GetColumns : unit -> IEnumerable<string>
  member GetSchema : unit -> XmlSchema
  member Item : int -> Property
  member Item : string -> Property
  member Label : Property with get, set
  ...

Full name: numl.Model.Descriptor

--------------------
type Descriptor<'T> =
  inherit Descriptor
  new : unit -> Descriptor<'T>
  member Learn : property:Expression<Func<'T, obj>> -> Descriptor<'T>
  member With : property:Expression<Func<'T, obj>> -> Descriptor<'T>
  member WithDateTime : property:Expression<Func<'T, DateTime>> * features:DateTimeFeature -> Descriptor<'T> + 1 overload
  member WithEnumerable : property:Expression<Func<'T, IEnumerable>> * length:int -> Descriptor<'T>
  member WithString : property:Expression<Func<'T, string>> * splitType:StringSplitType * ?separator:string * ?asEnum:bool * ?exclusions:string -> Descriptor<'T>

Full name: numl.Model.Descriptor<_>

--------------------
Descriptor() : unit

--------------------
Descriptor() : unit

Descriptor.Create<'T (requires reference type)>() : Descriptor
Descriptor.Create(t: System.Type) : Descriptor

val generator : DecisionTreeGenerator

Full name: Script.generator

Multiple items
type DecisionTreeGenerator =
  inherit Generator
  new : descriptor:Descriptor -> DecisionTreeGenerator + 1 overload
  member Depth : int with get, set
  member Generate : x:Matrix * y:Vector -> IModel
  member Hint : float with get, set
  member Impurity : Impurity
  member ImpurityType : Type with get, set
  member SetHint : o:obj -> unit
  member Width : int with get, set

Full name: numl.Supervised.DecisionTree.DecisionTreeGenerator

--------------------
DecisionTreeGenerator(descriptor: Descriptor) : unit
DecisionTreeGenerator(?depth: int, ?width: int, ?descriptor: Descriptor, ?impurityType: System.Type, ?hint: float) : unit

DecisionTreeGenerator.SetHint(o: obj) : unit

val model : LearningModel

Full name: Script.model

type Learner =
static member Best : models:IEnumerable<LearningModel> -> LearningModel
static member Learn : examples:IEnumerable<obj> * trainingPercentage:float * repeat:int * [<ParamArray>] generators:IGenerator[] -> LearningModel[] + 3 overloads

Full name: numl.Learner

Learner.Learn<'T>(examples: System.Collections.Generic.IEnumerable<'T>, trainingPercentage: float, repeat: int, generator: Supervised.IGenerator) : LearningModel
Learner.Learn(examples: System.Collections.Generic.IEnumerable<obj>, trainingPercentage: float, repeat: int, generator: Supervised.IGenerator) : LearningModel
Learner.Learn(examples: System.Data.DataTable, trainingPercentage: float, repeat: int, generator: Supervised.IGenerator) : LearningModel
Learner.Learn(examples: System.Collections.Generic.IEnumerable<obj>, trainingPercentage: float, repeat: int, [<System.ParamArray>] generators: Supervised.IGenerator []) : LearningModel []

val printfn : format:Printf.TextWriterFormat<'T> -> 'T

Full name: Microsoft.FSharp.Core.ExtraTopLevelOperators.printfn

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More information

Link:	http://fssnip.net/lz
Posted:	10 years ago
Author:	Taha Hachana
Tags:	numl , machine , learning