2023-10-14 18:59:56 +00:00
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(* Constants *)
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let pi = acos (-1.)
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2023-10-14 19:12:42 +00:00
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let normal_95_ci_length = 1.6448536269514722
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2023-10-14 18:59:56 +00:00
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2023-10-14 19:50:25 +00:00
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(* Array manipulation helpers *)
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let sumFloats xs = List.fold_left(fun acc x -> acc +. x) 0.0 xs
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2023-10-14 19:57:21 +00:00
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let normalizeXs xs =
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let sum_xs = sumFloats xs in
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2023-10-14 19:50:25 +00:00
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List.map(fun x -> x /. sum_xs) xs
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let cumsumXs xs =
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let _, cum_sum = List.fold_left(fun (sum, ys) x ->
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let new_sum = sum +. x in
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new_sum, ys @ [new_sum]
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) (0.0, []) xs in
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cum_sum
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2023-10-14 18:59:56 +00:00
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(* Basic samplers *)
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let sampleZeroToOne () : float = Random.float 1.0
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let sampleStandardNormal (): float =
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let u1 = sampleZeroToOne () in
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let u2 = sampleZeroToOne () in
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let z = sqrt(-2.0 *. log(u1)) *. sin(2.0 *. pi *. u2) in
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z
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2023-10-14 19:12:42 +00:00
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let sampleNormal mean std = mean +. std *. (sampleStandardNormal ())
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let sampleLognormal logmean logstd = exp(sampleNormal logmean logstd)
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let sampleTo low high =
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let loglow = log(low) in
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let loghigh = log(high) in
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let logmean = (loglow +. loghigh) /. 2.0 in
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let logstd = (loghigh -. loglow) /. (2.0 -. normal_95_ci_length ) in
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sampleLognormal logmean logstd
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2023-10-14 19:50:25 +00:00
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let mixture (samplers: (unit -> float) list) (weights: float list) =
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match (List.length samplers == List.length weights) with
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| false -> None
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2023-10-14 19:57:21 +00:00
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| true ->
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let normalized_weights = normalizeXs weights in
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let cumsummed_normalized_weights = cumsumXs normalized_weights in
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Some(1.0)
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2023-10-14 19:50:25 +00:00
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2023-10-14 18:59:56 +00:00
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let () =
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Random.init 1;
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Printf.printf "%f\n" (sampleZeroToOne());
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Printf.printf "%f\n" (sampleZeroToOne());
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