Oml classification example

Classify_example.ml

# open Classify ;;
# let feature_map = function | `shortbread -> 0 | `lager -> 1 | `whiskey -> 2 | `porridge -> 3 | `football-> 4 ;;
val feature_map : [< `football | `lager | `porridge | `shortbread | `whiskey ] -> int = <fun>
# let data =
  [
    (`English, [`whiskey; `porridge; `football]);
    (`English, [`shortbread; `whiskey; `porridge]);
    (`English, [`shortbread; `lager; `football]);
    (`English, [`shortbread; `lager]);
    (`English, [`lager; `football]);
    (`English, [`porridge]);
    (`Scottish, [`shortbread; `porridge; `football]);
    (`Scottish, [`shortbread; `lager; `football]);
    (`Scottish, [`shortbread; `lager; `whiskey; `porridge]);
    (`Scottish, [`shortbread; `lager; `porridge]);
    (`Scottish, [`shortbread; `lager; `porridge; `football]);
    (`Scottish, [`shortbread; `whiskey; `porridge]);
    (`Scottish, [`shortbread; `whiskey])
  ] ;;
val data : ([> `English | `Scottish ] * [> `football | `lager | `porridge | `shortbread | `whiskey ] list) list =
...
# let to_feature l = l |> List.map feature_map |> Array.of_list ;;
val to_feature : [< `football | `lager | `porridge | `shortbread | `whiskey ] list -> int array = <fun>
# module NB = BinomialNaiveBayes(
  struct
    type feature = [ `shortbread | `lager | `whiskey | `porridge | `football ] list
    type clas = [ `English | `Scottish]
    let encoding = to_feature
    let size = 5
  end);;
module NB :
  sig
    type clas = [ `English | `Scottish ]
    type feature = [ `football | `lager | `porridge | `shortbread | `whiskey ] list
    type spec = Oml.Classify.binomial_spec
    val default : spec
    type t
    val eval : t -> feature -> clas Oml.Classify.probabilities
    type samples = (clas * feature) list
    val estimate : ?spec:spec -> ?classes:clas list -> samples -> t
    val class_probabilities : t -> clas -> float * (feature -> float array)
  end
# let naiveb = NB.estimate ~spec:{NB.default with bernoulli = true} data ;;
val naiveb : NB.t = <abstr> 
# let sample = [ `shortbread ; `whiskey; `porridge  ]  ;;
val sample : [> `porridge | `shortbread | `whiskey ] list = [`shortbread; `whiskey; `porridge]
# let result = NB.eval naiveb sample;;
val result : NB.clas Oml.Classify.probabilities = [(`English, 0.192372406057206957); (`Scottish, 0.807627593942793)]