package biocaml
The OCaml Bioinformatics Library
Install
Dune Dependency
Authors
Maintainers
Sources
biocaml-0.11.2.tbz
sha256=fae219e66db06f81f3fd7d9e44717ccf2d6d85701adb12004ab4ae6d3359dd2d
sha512=f6abd60dac2e02777be81ce3b5acdc0db23b3fa06731f5b2d0b32e6ecc9305fe64f407bbd95a3a9488b14d0a7ac7c41c73a7e18c329a8f18febfc8fd50eccbc6
doc/src/biocaml.unix/math.ml.html
Source file math.ml
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exception ValueError of string let row m i = if i < Array.length m then Array.copy m.(i) else failwith (sprintf "invalid row index %d" i) let column m i = if Array.for_all ~f:(fun row -> i < Array.length row) m then Array.init (Array.length m) ~f:(fun j -> m.(j).(i)) else failwith (sprintf "invalid column index %d" i) let is_rectangular a = let dimension = Array.length a in Array.for_all a ~f:(fun suba -> Array.length suba = dimension) let transpose a = if not (is_rectangular a) then invalid_arg "Math.transpose: not-rectangular"; let n_rows = Array.length a in if Array.length a = 0 || Array.length a.(0) = 0 then [| |] else let n_cols = Array.length a.(0) in let ans = Array.make_matrix ~dimx:n_cols ~dimy:n_rows a.(0).(0) in for i = 0 to n_rows - 1 do for j = 0 to Array.length a.(i) - 1 do ans.(j).(i) <- a.(i).(j) done done; ans let log ?base x = match base with | None -> Stdlib.log x | Some b -> (Stdlib.log x) /. (Stdlib.log b) let log10 = Stdlib.log10 let log2 = log ~base:2.0 let even x = (x mod 2) = 0 let odd x = (x mod 2) <> 0 let min a = Option.value_exn ~message:"Math.min: empty" (Array.reduce a ~f:Poly.min) let max a = Option.value_exn ~message:"Math.max: empty" (Array.reduce a ~f:Poly.max) let prange add step lo hi = let rec f acc x = if Poly.(x > hi) then List.rev acc else let next = add x step in f (x::acc) next in f [] lo let range_ints = prange (+) let range_floats = prange (+.) let range step first last = assert Float.(step > 0.0); let n = (((last -. first) /. step) |> Float.abs |> Float.round_up |> Int.of_float) + 1 in let a = Array.create ~len:n 0.0 in let (op,comp) = Float.(if first <= last then ((+.),(<=)) else ((-.),(>=))) in Array.iteri ~f:(fun i _ -> a.(i) <- op first (Float.of_int i *. step)) a; if comp a.(n-1) last then a else Array.sub a ~pos:0 ~len:(n-1) let mean a = let n = Array.length a in assert (n > 0); (Array.fold ~f:(+.) ~init:0. a) /. (Float.of_int n) let variance a = let n = Array.length a in assert (n > 1); let avrg = mean a in let f v = let diff = v -. avrg in diff *. diff in let a = Array.map ~f a in (Array.fold ~f:(+.) ~init:0. a) /. (Float.of_int (n - 1)) let rms a = Array.map ~f:(fun x -> x *. x) a |> mean |> sqrt let stdv x = variance x |> sqrt let median a = let n = Array.length a in assert (n > 0); let a = Array.copy a in Array.sort ~compare:Stdlib.compare a; if odd n then a.((n+1)/2 - 1) else let m = (n+1)/2 in (a.(m-1) +. a.(m)) /. 2.0 let pseudomedian a = let n = Array.length a in assert (n > 0); if n = 1 then a.(0) else let nn = n*(n-1)/2 in let averages = Array.create ~len:nn 0.0 in let idx = ref 0 in for i = 0 to n-2 do for j = i+1 to n-1 do averages.(!idx) <- (a.(i) +. a.(j)) /. 2.0; incr idx done done; median averages let mad a = assert (Array.length a > 0); let med = median a in let a = Array.map ~f:(fun v -> Float.abs (v -. med)) a in median a let quantile_normalization aa = assert (is_rectangular aa); if Array.length aa = 0 || Array.length aa.(0) = 0 || Array.length aa.(0) = 1 then Array.copy aa else let num_expts = Float.of_int (Array.length aa.(0)) in let num_pts = Array.length aa in let comp1 (a,_) (b,_) = Stdlib.compare a b in let comp2 (_,a) (_,b) = Stdlib.compare a b in let aa = transpose aa in let aa = Array.map ~f:(Array.mapi ~f:(fun a b -> (a, b))) aa in (Array.iter ~f:(Array.sort ~compare:comp2)) aa; let avg i = (Array.fold ~f:(fun sum expt -> snd expt.(i) +. sum) ~init:0.0 aa) /. num_expts in let norms = Array.init num_pts ~f:avg in let aa = Array.map ~f:(Array.mapi ~f:(fun i (idx,_) -> idx, norms.(i))) aa in Array.iter ~f:(Array.sort ~compare:comp1) aa; transpose (Array.map ~f:(Array.map ~f:snd) aa) let histogram (type t) ?(cmp=Stdlib.compare) arr = let module M = struct include Map.Make(struct type t_ = t (* required only because OCaml doesn't have type non-rec definitions *) type t = t_ let compare = cmp let sexp_of_t _ = assert false let t_of_sexp _ = assert false end) end in let f (mp : int M.t) (a:t) = match M.find mp a with | Some e -> M.set mp ~key:a ~data:(e + 1) | None -> M.set mp ~key:a ~data:1 in let mp = Array.fold ~f ~init:M.empty arr in let ans = M.fold ~f:(fun ~key ~data ans -> (key,data)::ans) mp ~init:[] in Array.of_list (List.rev ans) let prediction_values tp tn fp fn = let tp = Float.of_int tp in let tn = Float.of_int tn in let fp = Float.of_int fp in let fn = Float.of_int fn in let sensitivity = tp /. (tp +. fn) in let specificity = tn /. (fp +. tn) in let pos_prediction_accuracy = tp /. (tp +. fp) in let neg_prediction_accuracy = tn /. (tn +. fn) in sensitivity, specificity, pos_prediction_accuracy, neg_prediction_accuracy let pearson (a1:float array) (a2:float array) = let a1avg,a2avg = (mean a1),(mean a2) in let a1sd,a2sd = (stdv a1),(stdv a2) in let a1,a2 = (Array.to_list a1), (Array.to_list a2) in let f acc e1 e2 = (((e1 -. a1avg) /. a1sd) *. ((e2 -. a2avg) /. a2sd)) +. acc in (List.fold2_exn ~f ~init:0. a1 a2) /. (Float.of_int ((List.length a1) - 1)) let rank arr = let arr = Array.copy arr in let arr = Array.mapi ~f:(fun i a -> a,i) arr in Array.sort ~compare:(fun (a,_) (b,_) -> Stdlib.compare a b) arr; let g _ il ans = let count = List.length il in let n = count + (List.length ans) in let hi = Float.of_int n in let lo = Float.of_int (n - count + 1) in let rank = (hi +. lo) /. 2. in (List.map ~f:(fun i -> rank,i) il) @ ans in let f (prev, il, ans) (x,i) = (* prev is the value that was equal *) let count = List.length il in (* il is list of original indices in reverse for items that were equal *) if count = 0 then (* ans is list of ranks and original index pairs in reverse *) x, [i], ans else if Poly.(x = prev) then x, i::il, ans else x, [i], g prev il ans in let prev,il,ans = Array.fold ~f ~init:(0.,[],[]) arr in let ans = g prev il ans in let ans = List.sort ~compare:(fun (_,a) (_,b) -> Stdlib.compare a b) ans in Array.of_list (List.map ~f:fst ans) let spearman (arr1:float array) (arr2: float array) = let arr1,arr2 = rank arr1, rank arr2 in pearson arr1 arr2 let cnd x = (* Modified from C++ code by David Koppstein. Found from www.sitmo.com/doc/Calculating_the_Cumulative_Normal_Distribution *) let b1,b2,b3,b4,b5,p,c = 0.319381530, -0.356563782, 1.781477937, -1.821255978, 1.330274429, 0.2316419, 0.39894228 in if Float.(x >= 0.) then let t = 1. /. (1. +. (p *. x)) in (1. -. (c *. (exp (-.x *. x /. 2.)) *. t *. (t *. (t *. (t *. ((t *. b5) +. b4) +. b3) +. b2) +. b1 ))) else let t = 1. /. (1. -. p *. x) in c *. (exp (-.x *. x /. 2.)) *. t *. (t *. (t *. (t *. ((t *. b5) +. b4) +. b3) +. b2) +. b1 ) let ltqnorm p = (* Modified from python code by David Koppstein. Original comments follow below. First version was written in perl, by Peter J. Acklam, 2000-07-19. Second version was ported to python, by Dan Field, 2004-05-03. *) if Float.(p <= 0. || p >= 1.) then raise (ValueError ("Argument to ltqnorm " ^ (Float.to_string p) ^ " must be in open interval (0,1)")) else (* Coefficients in rational approximations. *) let a = [|-3.969683028665376e+01; 2.209460984245205e+02; -2.759285104469687e+02; 1.383577518672690e+02; -3.066479806614716e+01; 2.506628277459239e+00|] in let b = [|-5.447609879822406e+01; 1.615858368580409e+02; -1.556989798598866e+02; 6.680131188771972e+01; -1.328068155288572e+01|] in let c = [|-7.784894002430293e-03; -3.223964580411365e-01; -2.400758277161838e+00; -2.549732539343734e+00; 4.374664141464968e+00; 2.938163982698783e+00|] in let d = [|7.784695709041462e-03; 3.224671290700398e-01; 2.445134137142996e+00; 3.754408661907416e+00|] in (* Define break-points. *) let plow = 0.02425 in let phigh = 1. -. plow in let f q = (((((c.(0)*.q+.c.(1))*.q+.c.(2))*.q+.c.(3))*.q+.c.(4))*.q+.c.(5)) /. ((((d.(0)*.q+.d.(1))*.q+.d.(2))*.q+.d.(3))*.q+.1.) in (* Rational approximation for lower region: *) if Float.(p < plow) then let q = sqrt ((-2.) *. (log p)) in f q (* Rational approximation for upper region: *) else if Float.(phigh < p) then let q = sqrt ((-2.) *. (log (1. -. p))) in f q (* Rational approximation for central region: *) else let q = p -. 0.5 in let r = q *. q in (((((a.(0)*.r+.a.(1))*.r+.a.(2))*.r+.a.(3))*.r+.a.(4))*.r+.a.(5))*.q /. (((((b.(0)*.r+.b.(1))*.r+.b.(2))*.r+.b.(3))*.r+.b.(4))*.r+.1.) let wilcoxon_rank_sum_to_z arr1 arr2 = let l1,l2 = (Array.length arr1),(Array.length arr2) in let ranked = rank (Array.append arr1 arr2) in let arr1 = Array.sub ranked ~pos:0 ~len:l1 in let l1,l2 = (Float.of_int l1), (Float.of_int l2) in let sum1 = let f acc elem = elem +. acc in Array.fold ~f ~init:0. arr1 in let expectation = (l1 *. (l1 +. l2 +. 1.)) /. 2. in let var = (l1 *. l2 *. ((l1 +. l2 +. 1.) /. 12.)) in (sum1 -. expectation) /. (sqrt var) let wilcoxon_rank_sum_to_p arr1 arr2 = (* assumes a two-tailed distribution *) let z = wilcoxon_rank_sum_to_z arr1 arr2 in 2. *. (1. -. (cnd (Float.abs z))) let wilcoxon_rank_sum ?(alpha=0.05) arr1 arr2 = Float.(wilcoxon_rank_sum_to_p arr1 arr2 < alpha) let idxsort (cmp : 'a -> 'a -> int) (a : 'a array) : int array = let a = Array.mapi a ~f:(fun i b -> (i, b)) in Array.sort ~compare:(fun a b -> cmp (snd a) (snd b)) a; Array.map ~f:fst a let find_regions ?(max_gap=0) pred a = if max_gap < 0 then failwith ("max gap must be non-negative but is " ^ (string_of_int max_gap)); let size = Array.length a in let ans = ref [] in (* Add region built up thus far, if any, to ans. * curr_index is one beyond what will be considered for inclusion in region *) let add_region curr_index start_index currGap = if start_index >= 0 then let finish_index = curr_index - currGap - 1 in ans := (start_index,finish_index)::!ans in (* i is current array index. * start_index is index of a region that has started to be built, -1 if none started yet. * currGap is number of previous contiguous items failing pred *) let rec loop i start_index currGap = if i = size then add_region i start_index currGap else ( if pred a.(i) then if start_index >= 0 then loop (i+1) start_index 0 else loop (i+1) i 0 else ( if currGap >= max_gap then (add_region i start_index currGap; loop (i+1) (-1) (currGap+1)) else loop (i+1) start_index (currGap+1) ) ) in loop 0 (-1) 0; Array.of_list (List.rev !ans) let find_min_window ?(init_direction="fwd") a pred i = let size = Array.length a in if size < 1 then [||] else let v = Range.make_unsafe 0 (size - 1) in let pred v = pred v.Range.lo v.Range.hi in let ans = Range.find_min_range ~init_direction v pred i in match ans with | None -> [||] | Some ans -> Array.sub a ~pos:ans.Range.lo ~len:(ans.Range.hi - ans.Range.lo + 1) let factorial n = if n < 2 then 1 else let rec aux acc n = if n < 2 then acc else aux (n * acc) (n - 1) in aux 1 n let epsilon f init fin = let rec aux acc n = if n = fin then acc else aux (acc +. (f n fin)) (n + 1) in aux 0. init let shuffle result = let result = Array.copy result in for i = Array.length result - 1 downto 0 do let other = Random.int (i + 1) and tmp = result.(i) in result.(i) <- result.(other); result.(other) <- tmp done; result
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