New functions use new syntax
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@ -259,6 +259,7 @@ module FloatFromDist = {
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};
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};
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// TODO: This forces things to be floats
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let callableFunction = (evaluationParams, name, args) => {
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let b =
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args
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@ -267,7 +268,7 @@ let callableFunction = (evaluationParams, name, args) => {
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|> E.R.bind(_, Render.toFloat)
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)
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|> E.A.R.firstErrorOrOpen;
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b |> E.R.bind(_, Functions.fnn("normal"));
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b |> E.R.bind(_, Functions.fnn(name));
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};
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module Render = {
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@ -8,20 +8,75 @@ let twoFloats = (fn, n1: node, n2: node): result(node, string) =>
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| _ => Error("Variables have wrong type")
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};
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let threeFloats = (fn, n1: node, n2: node, n3: node): result(node, string) =>
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switch (n1, n2, n3) {
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| (
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`SymbolicDist(`Float(a)),
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`SymbolicDist(`Float(b)),
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`SymbolicDist(`Float(c)),
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) =>
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fn(a, b, c)
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| _ => Error("Variables have wrong type")
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};
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let twoFloatsToOkSym = fn => twoFloats((f1, f2) => fn(f1, f2) |> toOkSym);
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let threeFloats = fn => threeFloats((f1, f2, f3) => fn(f1, f2, f3));
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let apply2 = (fn, args): result(node, string) =>
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switch (args) {
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| [|a, b|] => fn(a, b)
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| _ => Error("Needs 2 args")
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};
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let apply3 = (fn, args: array(node)): result(node, string) =>
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switch (args) {
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| [|a, b, c|] => fn(a, b, c)
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| _ => Error("Needs 3 args")
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};
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let to_: array(node) => result(node, string) =
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fun
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| [|`SymbolicDist(`Float(low)), `SymbolicDist(`Float(high))|]
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when low <= 0.0 && low < high => {
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Ok(`SymbolicDist(SymbolicDist.Normal.from90PercentCI(low, high)));
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}
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| [|`SymbolicDist(`Float(low)), `SymbolicDist(`Float(high))|]
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when low < high => {
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Ok(`SymbolicDist(SymbolicDist.Lognormal.from90PercentCI(low, high)));
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}
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| [|`SymbolicDist(`Float(_)), `SymbolicDist(_)|] =>
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Error("Low value must be less than high value.")
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| _ => Error("Requires 2 variables");
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let fnn = (name, args: array(node)) => {
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switch (name) {
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| "normal" => apply2(twoFloatsToOkSym(SymbolicDist.Normal.make), args)
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| "uniform" => apply2(twoFloatsToOkSym(SymbolicDist.Uniform.make), args)
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| "beta" => apply2(twoFloatsToOkSym(SymbolicDist.Beta.make), args)
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| "cauchy" => apply2(twoFloatsToOkSym(SymbolicDist.Cauchy.make), args)
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| "lognormal" => apply2(twoFloatsToOkSym(SymbolicDist.Lognormal.make), args)
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| "lognormalFromMeanAndStdDev" => apply2(twoFloatsToOkSym(SymbolicDist.Lognormal.fromMeanAndStdev), args)
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| "exponential" =>
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switch (args) {
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| [|
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`SymbolicDist(`Float(a)),
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|] =>
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Ok(`SymbolicDist(SymbolicDist.Exponential.make(a)));
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| _ => Error("Needs 3 valid arguments")
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}
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| "triangular" =>
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switch (args) {
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| [|
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`SymbolicDist(`Float(a)),
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`SymbolicDist(`Float(b)),
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`SymbolicDist(`Float(c)),
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|] =>
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SymbolicDist.Triangular.make(a, b, c)
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|> E.R.fmap(r => `SymbolicDist(r))
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| _ => Error("Needs 3 valid arguments")
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}
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| "to" => to_(args)
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| _ => Error("Function not found")
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};
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};
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@ -93,56 +93,26 @@ module MathAdtToDistDst = {
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);
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};
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let lognormal:
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array(arg) => result(ExpressionTypes.ExpressionTree.node, string) =
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fun
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| [|Value(mu), Value(sigma)|] =>
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Ok(`SymbolicDist(`Lognormal({mu, sigma})))
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| [|Object(o)|] => {
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let g = Js.Dict.get(o);
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switch (g("mean"), g("stdev"), g("mu"), g("sigma")) {
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| (Some(Value(mean)), Some(Value(stdev)), _, _) =>
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Ok(
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`SymbolicDist(
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SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev),
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),
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)
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| (_, _, Some(Value(mu)), Some(Value(sigma))) =>
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Ok(`SymbolicDist(`Lognormal({mu, sigma})))
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| _ => Error("Lognormal distribution would need mean and stdev")
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};
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}
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| _ => Error("Wrong number of variables in lognormal distribution");
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let to_: array(arg) => result(ExpressionTypes.ExpressionTree.node, string) =
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fun
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| [|Value(low), Value(high)|] when low <= 0.0 && low < high => {
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Ok(`SymbolicDist(SymbolicDist.Normal.from90PercentCI(low, high)));
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}
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| [|Value(low), Value(high)|] when low < high => {
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let lognormal = (args, parseArgs, nodeParser) =>
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switch (args) {
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| [|Object(o)|] =>
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let g = s =>
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Js.Dict.get(o, s) |> E.O.toResult("") |> E.R.bind(_, nodeParser);
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switch (g("mean"), g("stdev"), g("mu"), g("sigma")) {
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| (Ok(mean), Ok(stdev), _, _) =>
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Ok(
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`SymbolicDist(SymbolicDist.Lognormal.from90PercentCI(low, high)),
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);
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}
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| [|Value(_), Value(_)|] =>
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Error("Low value must be less than high value.")
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| _ => Error("Wrong number of variables in lognormal distribution");
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let exponential:
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array(arg) => result(ExpressionTypes.ExpressionTree.node, string) =
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fun
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| [|Value(rate)|] => Ok(`SymbolicDist(`Exponential({rate: rate})))
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| _ => Error("Wrong number of variables in Exponential distribution");
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let triangular:
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array(arg) => result(ExpressionTypes.ExpressionTree.node, string) =
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fun
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| [|Value(low), Value(medium), Value(high)|]
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when low < medium && medium < high =>
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Ok(`SymbolicDist(`Triangular({low, medium, high})))
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| [|Value(_), Value(_), Value(_)|] =>
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Error("Triangular values must be increasing order")
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| _ => Error("Wrong number of variables in triangle distribution");
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`CallableFunction(("lognormalFromMeanAndStdDev", [|mean, stdev|])),
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)
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| (_, _, Ok(mu), Ok(sigma)) =>
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Ok(`CallableFunction(("lognormal", [|mu, sigma|])))
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| _ => Error("Lognormal distribution needs either mean and stdev or mu and sigma")
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};
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| _ =>
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parseArgs()
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|> E.R.fmap((args: array(ExpressionTypes.ExpressionTree.node)) =>
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`CallableFunction(("lognormal", args))
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)
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};
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let multiModal =
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(
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@ -150,15 +120,6 @@ module MathAdtToDistDst = {
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weights: option(array(float)),
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) => {
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let weights = weights |> E.O.default([||]);
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/*let dists: =
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args
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|> E.A.fmap(
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fun
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| Ok(a) => a
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| Error(e) => Error(e)
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);*/
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let firstWithError = args |> Belt.Array.getBy(_, Belt.Result.isError);
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let withoutErrors = args |> E.A.fmap(E.R.toOption) |> E.A.O.concatSomes;
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@ -249,17 +210,22 @@ module MathAdtToDistDst = {
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};
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let functionParser = (nodeParser, name, args) => {
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let parseArgs = () =>
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args |> E.A.fmap(nodeParser) |> E.A.R.firstErrorOrOpen;
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let parseArray = ags =>
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ags |> E.A.fmap(nodeParser) |> E.A.R.firstErrorOrOpen;
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let parseArgs = () => parseArray(args);
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switch (name) {
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| "normal" | "uniform" | "beta" | "caucy" =>
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| "normal"
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| "uniform"
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| "beta"
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| "triangular"
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| "to"
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| "exponential"
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| "cauchy" =>
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parseArgs()
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|> E.R.fmap(
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(
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args: array(ExpressionTypes.ExpressionTree.node),
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) =>
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|> E.R.fmap((args: array(ExpressionTypes.ExpressionTree.node)) =>
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`CallableFunction((name, args))
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)
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| "lognormal" => lognormal(args, parseArgs, nodeParser)
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| "mm" =>
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let weights =
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args
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@ -2,8 +2,12 @@ open SymbolicTypes;
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module Exponential = {
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type t = exponential;
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let make = (rate): symbolicDist =>
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`Exponential({rate});
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let make = (rate:float): symbolicDist =>
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`Exponential(
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{
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rate:rate
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},
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);
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let pdf = (x, t: t) => Jstat.exponential##pdf(x, t.rate);
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let cdf = (x, t: t) => Jstat.exponential##cdf(x, t.rate);
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let inv = (p, t: t) => Jstat.exponential##inv(p, t.rate);
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@ -14,8 +18,7 @@ module Exponential = {
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module Cauchy = {
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type t = cauchy;
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let make = (local, scale): symbolicDist =>
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`Cauchy({local,scale});
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let make = (local, scale): symbolicDist => `Cauchy({local, scale});
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let pdf = (x, t: t) => Jstat.cauchy##pdf(x, t.local, t.scale);
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let cdf = (x, t: t) => Jstat.cauchy##cdf(x, t.local, t.scale);
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let inv = (p, t: t) => Jstat.cauchy##inv(p, t.local, t.scale);
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@ -26,8 +29,10 @@ module Cauchy = {
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module Triangular = {
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type t = triangular;
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let make = (low, medium, high): symbolicDist =>
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`Triangular({low, medium, high});
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let make = (low, medium, high): result(symbolicDist, string) =>
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low < medium && medium < high
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? Ok(`Triangular({low, medium, high}))
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: Error("Triangular values must be increasing order");
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let pdf = (x, t: t) => Jstat.triangular##pdf(x, t.low, t.high, t.medium);
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let cdf = (x, t: t) => Jstat.triangular##cdf(x, t.low, t.high, t.medium);
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let inv = (p, t: t) => Jstat.triangular##inv(p, t.low, t.high, t.medium);
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@ -82,7 +87,7 @@ module Normal = {
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module Beta = {
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type t = beta;
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let make = (alpha, beta) => `Beta({alpha, beta})
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let make = (alpha, beta) => `Beta({alpha, beta});
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let pdf = (x, t: t) => Jstat.beta##pdf(x, t.alpha, t.beta);
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let cdf = (x, t: t) => Jstat.beta##cdf(x, t.alpha, t.beta);
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let inv = (p, t: t) => Jstat.beta##inv(p, t.alpha, t.beta);
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@ -93,7 +98,7 @@ module Beta = {
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module Lognormal = {
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type t = lognormal;
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let make = (mu, sigma) => `Lognormal({mu, sigma})
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let make = (mu, sigma) => `Lognormal({mu, sigma});
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let pdf = (x, t: t) => Jstat.lognormal##pdf(x, t.mu, t.sigma);
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let cdf = (x, t: t) => Jstat.lognormal##cdf(x, t.mu, t.sigma);
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let inv = (p, t: t) => Jstat.lognormal##inv(p, t.mu, t.sigma);
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@ -140,7 +145,7 @@ module Lognormal = {
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module Uniform = {
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type t = uniform;
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let make = (low, high) => `Uniform({low, high})
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let make = (low, high) => `Uniform({low, high});
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let pdf = (x, t: t) => Jstat.uniform##pdf(x, t.low, t.high);
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let cdf = (x, t: t) => Jstat.uniform##cdf(x, t.low, t.high);
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let inv = (p, t: t) => Jstat.uniform##inv(p, t.low, t.high);
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@ -156,7 +161,7 @@ module Uniform = {
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module Float = {
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type t = float;
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let make = t => `Float(t)
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let make = t => `Float(t);
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let pdf = (x, t: t) => x == t ? 1.0 : 0.0;
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let cdf = (x, t: t) => x >= t ? 1.0 : 0.0;
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let inv = (p, t: t) => p < t ? 0.0 : 1.0;
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