rleonid

open Lacaml.D

let capture_failure_of_list = function
  | [] -> Mat.empty
  | (h :: _) as lst ->
    let m = List.length lst in
    let n = List.length h in
    let mat = Mat.create m n in
    let rec loop i = function
      | [] -> mat

rleonid

# let pred =
    Array.init 100 (fun r ->
      let a = n1 () in
      let b = n1 () in
      let c = a +. b in (* Makes the third column/predictor redundant and our data collinear! *)
      [|a;b;c|]) ;;
val pred : float array array =
  [|[|2.1000; 0.9001; 3.0001|]; [|1.7343; 2.9934; 4.7277|]; [|2.7644; 4.0290; 6.7935|]; 
    [|0.7972; 2.6754; 3.4726|]; [|0.6089; 2.7250; 3.3340|]; [|0.7870; 2.7721; 3.5592|]; 
   ...|]

rleonid

data
|> List.filter (fun (s,_,_) -> s = "HLA-A-3101") 
|> List.map (fun (_,a,b) -> (a,b))
|> Array.of_list
|> (fun arr -> [|0;9;18;27;36|] 
      |> Array.map (fun s -> Array.sub arr s 9 
          |> Array.to_list
          |> List.sort (fun (_,v1) (_,v2) -> compare v2 v1)
          |> Array.of_list)
      |> transpose)

rleonid

# #require "oml" ;;
# #show_module Oml ;;
module Oml :
  sig
    module Util : sig  end
    module Vectors : sig  end
    module Matrices : sig  end
    module Continued_fraction : sig  end
    module Functions : sig  end
    module Distributions : sig  end

rleonid

# let shorter_float_printer fr = Format.fprintf fr "%0.4f"  ;;
val shorter_float_printer : Format.formatter -> float -> unit = <fun>
# #install_printer shorter_float_printer ;;

rleonid

# let n1 = Sampling.normal ~mean:2. ~std:1. () ;;
val n1 : float Oml.Sampling.generator = <fun>
# let n2 = Sampling.normal ~mean:2. ~std:10. () ;;
val n2 : float Oml.Sampling.generator = <fun>
# let data =
  Array.init 10000 (fun i -> if i mod 10 = 0 then n2 () else n1 ()) ;;
val data : float array
= [|4.9157; 0.5018; 1.9781; 3.1645; 2.6965; 2.8426; ... |]

rleonid

# Descriptive.unbiased_summary data ;;
- : Descriptive.summary =
   {Descriptive.size = 10000;
    min = -28.4799;
    max = 36.8804;
    mean = 1.9916;
    std = 3.3391;
    var = 11.1497;
    skew = (-0.2382, `Negative);
    kurtosis = (23.2732, `Fat)

rleonid

# let data_skew ftf =
  Array.init 10000 (fun i -> if i mod ftf = 0 then n2 () else n1())
  |> Descriptive.unbiased_summary
  |> fun s -> s.Descriptive.skew
  |> snd ;;
val data_skew : int -> Oml.Descriptive.skew_classification = <fun>
# let count_big_skew =
  Array.fold_left (fun c s ->
      if s = `Positive || s = `Negative then c + 1 else c) 0 ;;
val count_big_skew : _[> `Negative | `Positive ] array -> int = <fun>

rleonid

# Inference.mean_t_test 2.0 Inference.Two_sided data ;;
- : Oml.Inference.test = {Oml.Inference.degrees_of_freedom = 9999.0000; statistic = -0.2509; standard_error = 0.0334; prob_by_chance = 0.8019}

rleonid

# let pred = Array.init 100 (fun r -> Array.init 3 (fun _ -> n1 ())) ;;
val pred : float array array =
[|[|2.8291; 2.0174; 2.3971|];
  [|1.5963; 2.0713; 2.3183|];
  [|1.5755; 0.5216; 2.8682|];
  [|1.9523; 0.4255; 3.6062|];
  [|2.5397; 3.8247; 3.4417|]; ...|]
# let resp = Array.map (fun arr -> 2. +. 3. *. arr.(0) +. 4. *. arr.(1) +. 5. *. arr.(2)) pred ;;
val resp : float array =
[|30.5424; 26.6658; 23.1534; 27.5899; 33.7047; 21.5728; 16.6898; 20.5841; 17.1497; ...|]