Removed a bunch of now-unused files
This commit is contained in:
Ozzie Gooen
parent
2da6f00211
commit
cecbe4fcc3
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@ -27,7 +27,6 @@
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"license": "MIT",
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"dependencies": {
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"@foretold/components": "0.0.6",
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"@foretold/guesstimator": "1.0.11",
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"@glennsl/bs-json": "^5.0.2",
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"antd": "3.17.0",
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"autoprefixer": "9.7.4",
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14
src/App.re
14
src/App.re
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@ -1,8 +1,6 @@
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type route =
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| Model(string)
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| DistBuilder
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| DistBuilder2
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| DistBuilder3
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| Drawer
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| Home
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| NotFound;
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@ -11,8 +9,6 @@ let routeToPath = route =>
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switch (route) {
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| Model(modelId) => "/m/" ++ modelId
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| DistBuilder => "/dist-builder"
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| DistBuilder2 => "/dist-builder2"
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| DistBuilder3 => "/dist-builder3"
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| Drawer => "/drawer"
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| Home => "/"
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| _ => "/"
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@ -75,12 +71,6 @@ module Menu = {
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<Item href={routeToPath(DistBuilder)} key="dist-builder">
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{"Dist Builder" |> R.ste}
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</Item>
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<Item href={routeToPath(DistBuilder2)} key="dist-builder-2">
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{"Dist Builder 2" |> R.ste}
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</Item>
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<Item href={routeToPath(DistBuilder3)} key="dist-builder-3">
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{"Dist Builder 3" |> R.ste}
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</Item>
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<Item href={routeToPath(Drawer)} key="drawer">
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{"Drawer" |> R.ste}
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</Item>
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@ -97,8 +87,6 @@ let make = () => {
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switch (url.path) {
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| ["m", modelId] => Model(modelId)
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| ["dist-builder"] => DistBuilder
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| ["dist-builder2"] => DistBuilder2
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| ["dist-builder3"] => DistBuilder3
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| ["drawer"] => Drawer
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| [] => Home
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| _ => NotFound
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@ -113,8 +101,6 @@ let make = () => {
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| None => <div> {"Page is not found" |> R.ste} </div>
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}
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| DistBuilder => <DistBuilder />
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| DistBuilder2 => <DistBuilder2 />
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| DistBuilder3 => <DistBuilder3 />
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| Drawer => <Drawer />
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| Home => <Home />
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| _ => <div> {"Page is not found" |> R.ste} </div>
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@ -1,105 +0,0 @@
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open BsReform;
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open Antd.Grid;
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module FormConfig = [%lenses type state = {guesstimatorString: string}];
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module Form = ReForm.Make(FormConfig);
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let schema = Form.Validation.Schema([||]);
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module FieldString = {
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[@react.component]
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let make = (~field, ~label) => {
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<Form.Field
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field
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render={({handleChange, error, value, validate}) =>
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<Antd.Form.Item label={label |> R.ste}>
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<Antd.Input
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value
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onChange={BsReform.Helpers.handleChange(handleChange)}
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onBlur={_ => validate()}
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/>
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</Antd.Form.Item>
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}
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/>;
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};
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};
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module Styles = {
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open Css;
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let dist = style([padding(em(1.))]);
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let spacer = style([marginTop(em(1.))]);
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};
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module DemoDist = {
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[@react.component]
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let make = (~guesstimatorString: string) => {
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let (ys, xs, isEmpty) =
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DistEditor.getPdfFromUserInput(guesstimatorString);
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let inside =
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isEmpty
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? "Nothing to show" |> R.ste
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: {
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let distPlus =
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DistPlus.make(
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~shape=
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Continuous(
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Continuous.make(`Linear, {xs, ys}, None),
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),
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~domain=Complete,
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~unit=UnspecifiedDistribution,
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~guesstimatorString=None,
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(),
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)
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|> DistPlus.T.normalize;
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<DistPlusPlot distPlus />;
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};
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<Antd.Card title={"Distribution" |> R.ste}>
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<div className=Styles.spacer />
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inside
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</Antd.Card>;
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};
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};
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[@react.component]
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let make = () => {
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let reform =
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Form.use(
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~validationStrategy=OnDemand,
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~schema,
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~onSubmit=({state}) => {None},
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~initialState={guesstimatorString: "lognormal(6.1, 1)"},
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(),
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);
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let demoDist =
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React.useMemo1(
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() => {
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<DemoDist
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guesstimatorString={reform.state.values.guesstimatorString}
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/>
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},
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[|reform.state.values.guesstimatorString|],
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);
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<div>
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<div className=Styles.spacer />
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demoDist
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<div className=Styles.spacer />
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<Antd.Card title={"Distribution Form" |> R.ste}>
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<Form.Provider value=reform>
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<Antd.Form>
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<Row _type=`flex>
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<Col span=12>
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<FieldString
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field=FormConfig.GuesstimatorString
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label="Guesstimator String"
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/>
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</Col>
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</Row>
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</Antd.Form>
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</Form.Provider>
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</Antd.Card>
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<div className=Styles.spacer />
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</div>;
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};
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@ -1,121 +0,0 @@
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open BsReform;
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open Antd.Grid;
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module FormConfig = [%lenses type state = {guesstimatorString: string}];
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module Form = ReForm.Make(FormConfig);
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let schema = Form.Validation.Schema([||]);
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module FieldString = {
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[@react.component]
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let make = (~field, ~label) => {
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<Form.Field
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field
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render={({handleChange, error, value, validate}) =>
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<Antd.Form.Item label={label |> R.ste}>
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<Antd.Input
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value
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onChange={BsReform.Helpers.handleChange(handleChange)}
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onBlur={_ => validate()}
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/>
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</Antd.Form.Item>
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}
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/>;
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};
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};
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module Styles = {
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open Css;
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let dist = style([padding(em(1.))]);
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let spacer = style([marginTop(em(1.))]);
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};
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module DemoDist = {
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[@react.component]
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let make = (~guesstimatorString: string) => {
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let parsed1 = MathJsParser.fromString(guesstimatorString);
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let shape =
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switch (parsed1) {
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| Ok(r) =>
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Some(
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ExpressionTree.toShape(
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10000,
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{sampleCount: 10000, outputXYPoints: 10000, kernelWidth: None},
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r,
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),
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)
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| _ => None
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};
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let str =
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switch (parsed1) {
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| Ok(r) => ExpressionTree.toString(r)
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| Error(e) => e
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};
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let inside =
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shape
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|> E.O.fmap(shape => {
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let distPlus =
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DistPlus.make(
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~shape,
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~domain=Complete,
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~unit=UnspecifiedDistribution,
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~guesstimatorString=None,
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(),
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)
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|> DistPlus.T.normalize;
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<DistPlusPlot distPlus />;
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})
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|> E.O.default(ReasonReact.null);
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<Antd.Card title={"Distribution" |> R.ste}>
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<div className=Styles.spacer />
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inside
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{str |> ReasonReact.string}
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</Antd.Card>;
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};
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};
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[@react.component]
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let make = () => {
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let reform =
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Form.use(
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~validationStrategy=OnDemand,
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~schema,
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~onSubmit=({state}) => {None},
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~initialState={guesstimatorString: "mm(1 to 100, 50 to 200, [.5,.5])"},
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(),
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);
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let demoDist =
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React.useMemo1(
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() => {
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<DemoDist
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guesstimatorString={reform.state.values.guesstimatorString}
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/>
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},
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[|reform.state.values.guesstimatorString|],
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);
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<div>
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<div className=Styles.spacer />
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demoDist
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<div className=Styles.spacer />
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<Antd.Card title={"Distribution Form" |> R.ste}>
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<Form.Provider value=reform>
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<Antd.Form>
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<Row _type=`flex>
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<Col span=12>
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<FieldString
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field=FormConfig.GuesstimatorString
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label="Guesstimator String"
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/>
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</Col>
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</Row>
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</Antd.Form>
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</Form.Provider>
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</Antd.Card>
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<div className=Styles.spacer />
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</div>;
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};
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@ -1,3 +0,0 @@
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[@bs.module "./main.js"]
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external getPdfFromUserInput: string => (array(float), array(float), bool) =
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"get_pdf_from_user_input";
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@ -1,247 +0,0 @@
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const _math = require("mathjs");
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const math = _math.create(_math.all);
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const jStat = require("jstat");
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/**
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* This module defines an abstract BinnedDistribution class, which
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* should be implemented for each distribution. You need to decide
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* how to bin the distribution (use _adabin unless there's a nicer
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* way for your distr) and how to choose the distribution's support.
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*/
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math.import({
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normal: jStat.normal,
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beta: jStat.beta,
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lognormal: jStat.lognormal,
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uniform: jStat.uniform
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});
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class BaseDistributionBinned {
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/**
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* @param args
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*/
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constructor(args) {
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this._set_props();
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this.max_bin_size = 0.005;
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this.min_bin_size = 0;
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this.increment = 0.0001;
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this.desired_delta = 0.001;
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this.start_bin_size = 0.0001;
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[this.params, this.pdf_func, this.sample] = this.get_params_and_pdf_func(
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args
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);
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[this.start_point, this.end_point] = this.get_bounds();
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[this.pdf_vals, this.divider_pts] = this.bin();
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}
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/**
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* this is hacky but class properties aren't always supported
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* @private
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*/
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_set_props() {
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throw new Error("NotImplementedError");
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}
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//Adaptive binning. Specify a desired change in density to get adjusted bin sizes.
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/**
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* @returns {(number[]|[*])[]}
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* @private
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*/
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_adabin() {
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let point = this.start_point;
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let vals = [this.pdf_func(point)];
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let divider_pts = [point];
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let support = this.end_point - this.start_point;
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let bin_size = this.start_bin_size * support;
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while (point < this.end_point) {
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let val = this.pdf_func(point + bin_size);
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if (Math.abs(val - vals[vals.length - 1]) > this.desired_delta) {
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while (
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(Math.abs(val - vals[vals.length - 1]) > this.desired_delta) &
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(bin_size - this.increment * support > this.min_bin_size)
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) {
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bin_size -= this.increment;
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val = this.pdf_func(point + bin_size);
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}
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} else if (Math.abs(val - vals[vals.length - 1]) < this.desired_delta) {
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while (
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(Math.abs(val - vals[vals.length - 1]) < this.desired_delta) &
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(bin_size < this.max_bin_size)
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) {
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bin_size += this.increment;
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val = this.pdf_func(point + bin_size);
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}
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}
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point += bin_size;
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vals.push(val);
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divider_pts.push(point);
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}
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vals = vals.map((_, idx) => vals[idx] / 2 + vals[idx + 1] / 2);
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vals = vals.slice(0, -1);
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return [vals, divider_pts];
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}
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bin() {
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throw new Error("NotImplementedError");
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}
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get_bounds() {
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throw new Error("NotImplementedError");
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}
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/**
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* @param args
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* @returns {(any|(function(*=): *))[]}
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*/
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get_params_and_pdf_func(args) {
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let args_str = args.toString() + ")";
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let substr = this.name + ".pdf(x, " + args_str;
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let compiled = math.compile(substr);
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function pdf_func(x) {
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return compiled.evaluate({ x: x });
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}
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let mc_compiled = math.compile(this.name + ".sample(" + args_str);
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let kv_pairs = this.param_names.map((val, idx) => [val, args[idx]]);
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let params = Object.fromEntries(new Map(kv_pairs));
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return [params, pdf_func, mc_compiled.evaluate];
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}
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}
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class NormalDistributionBinned extends BaseDistributionBinned {
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/**
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* @private
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*/
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_set_props() {
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this.name = "normal";
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this.param_names = ["mean", "std"];
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}
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/**
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* @returns {(number|*)[]}
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*/
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get_bounds() {
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return [
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this.params.mean - 4 * this.params.std,
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this.params.mean + 4 * this.params.std
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];
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}
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/**
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* @returns {[[*], [*]]}
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*/
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bin() {
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return this._adabin(this.params.std);
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}
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}
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class UniformDistributionBinned extends BaseDistributionBinned {
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/**
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* @private
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*/
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_set_props() {
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this.name = "uniform";
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this.param_names = ["start_point", "end_point"];
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this.num_bins = 200;
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}
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/**
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* @returns {*[]}
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*/
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get_bounds() {
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return [this.params.start_point, this.params.end_point];
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}
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/**
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* @returns {(*[])[]}
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*/
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bin() {
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let divider_pts = evenly_spaced_grid(
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this.params.start_point,
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this.params.end_point,
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this.num_bins
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);
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let vals = divider_pts.map(x =>
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this.pdf_func(this.params.start_point / 2 + this.params.end_point / 2)
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);
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vals = vals.slice(0, -1);
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return [vals, divider_pts];
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}
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}
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class LogNormalDistributionBinned extends BaseDistributionBinned {
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/**
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* @private
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*/
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_set_props() {
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this.name = "lognormal";
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this.param_names = ["normal_mean", "normal_std"];
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this.n_bounds_samples = 10000;
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this.n_largest_bound_sample = 10;
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}
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/**
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* @param samples
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* @param n
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* @returns {any}
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* @private
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*/
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_nth_largest(samples, n) {
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var largest_buffer = Array(n).fill(-Infinity);
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for (const sample of samples) {
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if (sample > largest_buffer[n - 1]) {
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var i = n;
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while ((i > 0) & (sample > largest_buffer[i - 1])) {
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i -= 1;
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}
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largest_buffer[i] = sample;
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}
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}
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return largest_buffer[n - 1];
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}
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/**
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* @returns {(*|any)[]}
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*/
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get_bounds() {
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let samples = Array(this.n_bounds_samples)
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.fill(0)
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.map(() => this.sample());
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return [
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math.min(samples),
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this._nth_largest(samples, this.n_largest_bound_sample)
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];
|
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}
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/**
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* @returns {[[*], [*]]}
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*/
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bin() {
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return this._adabin();
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}
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}
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/**
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* @param start
|
||||
* @param stop
|
||||
* @param numel
|
||||
* @returns {*[]}
|
||||
*/
|
||||
function evenly_spaced_grid(start, stop, numel) {
|
||||
return Array(numel)
|
||||
.fill(0)
|
||||
.map((_, idx) => start + (idx / numel) * (stop - start));
|
||||
}
|
||||
|
||||
const distrs = {
|
||||
normal: NormalDistributionBinned,
|
||||
lognormal: LogNormalDistributionBinned,
|
||||
uniform: UniformDistributionBinned
|
||||
};
|
||||
|
||||
exports.distrs = distrs;
|
||||
|
|
@ -1,364 +0,0 @@
|
|||
const _math = require("mathjs");
|
||||
const bst = require("binary-search-tree");
|
||||
|
||||
const distrs = require("./distribution.js").distrs;
|
||||
const parse = require("./parse.js");
|
||||
const math = _math.create(_math.all);
|
||||
|
||||
const NUM_MC_SAMPLES = 3000;
|
||||
const OUTPUT_GRID_NUMEL = 3000;
|
||||
|
||||
|
||||
/**
|
||||
* The main algorithmic work is done by functions in this module.
|
||||
* It also contains the main function, taking the user's string
|
||||
* and returning pdf values and x's.
|
||||
*/
|
||||
|
||||
/**
|
||||
* @param start
|
||||
* @param stop
|
||||
* @param numel
|
||||
* @returns {*[]}
|
||||
*/
|
||||
function evenly_spaced_grid(start, stop, numel) {
|
||||
return Array(numel)
|
||||
.fill(0)
|
||||
.map((_, idx) => start + (idx / numel) * (stop - start));
|
||||
}
|
||||
|
||||
/**
|
||||
* Takes an array of strings like "normal(0, 1)" and
|
||||
* returns the corresponding distribution objects
|
||||
* @param substrings
|
||||
* @returns {*}
|
||||
*/
|
||||
function get_distributions(substrings) {
|
||||
let names_and_args = substrings.map(parse.get_distr_name_and_args);
|
||||
let pdfs = names_and_args.map(x => new distrs[x[0]](x[1]));
|
||||
return pdfs;
|
||||
}
|
||||
|
||||
/**
|
||||
* update the binary search tree with bin points of
|
||||
* deterministic_pdf transformed by tansform func
|
||||
* (transfrom func can be a stocahstic func with parameters
|
||||
* sampled from mc_distrs)
|
||||
*
|
||||
* @param transform_func
|
||||
* @param deterministic_pdf
|
||||
* @param mc_distrs
|
||||
* @param track_idx
|
||||
* @param num_mc_samples
|
||||
* @param bst_pts_and_idxs
|
||||
* @returns {(number)[]}
|
||||
*/
|
||||
function update_transformed_divider_points_bst(
|
||||
transform_func,
|
||||
deterministic_pdf,
|
||||
mc_distrs,
|
||||
track_idx,
|
||||
num_mc_samples,
|
||||
bst_pts_and_idxs
|
||||
) {
|
||||
var transformed_pts = [];
|
||||
var pdf_inner_idxs = [];
|
||||
var factors = [];
|
||||
var start_pt = Infinity;
|
||||
var end_pt = -Infinity;
|
||||
let use_mc = mc_distrs.length > 0;
|
||||
var num_outer_iters = use_mc ? num_mc_samples : 1;
|
||||
|
||||
for (let sample_idx = 0; sample_idx < num_outer_iters; ++sample_idx) {
|
||||
var this_transformed_pts = deterministic_pdf.divider_pts;
|
||||
if (use_mc) {
|
||||
let samples = mc_distrs.map(x => x.sample());
|
||||
this_transformed_pts = this_transformed_pts.map(x =>
|
||||
transform_func([x].concat(samples))
|
||||
);
|
||||
} else {
|
||||
this_transformed_pts = this_transformed_pts.map(x => transform_func([x]));
|
||||
}
|
||||
var this_transformed_pts_paired = [];
|
||||
for (let tp_idx = 0; tp_idx < this_transformed_pts.length - 1; tp_idx++) {
|
||||
let sorted = [
|
||||
this_transformed_pts[tp_idx],
|
||||
this_transformed_pts[tp_idx + 1]
|
||||
].sort((a, b) => a - b);
|
||||
if (sorted[0] < start_pt) {
|
||||
start_pt = sorted[0];
|
||||
}
|
||||
if (sorted[1] > end_pt) {
|
||||
end_pt = sorted[1];
|
||||
}
|
||||
this_transformed_pts_paired.push(sorted);
|
||||
}
|
||||
|
||||
transformed_pts = transformed_pts.concat(this_transformed_pts_paired);
|
||||
|
||||
pdf_inner_idxs = pdf_inner_idxs.concat([
|
||||
...Array(this_transformed_pts_paired.length).keys()
|
||||
]);
|
||||
var this_factors = [];
|
||||
for (let idx = 0; idx < this_transformed_pts_paired.length; idx++) {
|
||||
this_factors.push(
|
||||
(deterministic_pdf.divider_pts[idx + 1] -
|
||||
deterministic_pdf.divider_pts[idx]) /
|
||||
(this_transformed_pts_paired[idx][1] -
|
||||
this_transformed_pts_paired[idx][0])
|
||||
);
|
||||
}
|
||||
factors = factors.concat(this_factors);
|
||||
}
|
||||
for (let i = 0; i < transformed_pts.length; ++i) {
|
||||
bst_pts_and_idxs.insert(transformed_pts[i][0], {
|
||||
start: transformed_pts[i][0],
|
||||
end: transformed_pts[i][1],
|
||||
idx: [track_idx, pdf_inner_idxs[i]],
|
||||
factor: factors[i] / num_outer_iters
|
||||
});
|
||||
}
|
||||
return [start_pt, end_pt];
|
||||
}
|
||||
|
||||
/**
|
||||
* Take the binary search tree with transformed bin points,
|
||||
* and an array of pdf values associated with the bins,
|
||||
* and return a pdf over an evenly spaced grid
|
||||
*
|
||||
* @param pdf_vals
|
||||
* @param bst_pts_and_idxs
|
||||
* @param output_grid
|
||||
* @returns {[]}
|
||||
*/
|
||||
function get_final_pdf(pdf_vals, bst_pts_and_idxs, output_grid) {
|
||||
var offset = output_grid[1] / 2 - output_grid[0] / 2;
|
||||
var active_intervals = new Map();
|
||||
var active_endpoints = new bst.AVLTree();
|
||||
var final_pdf_vals = [];
|
||||
|
||||
for (
|
||||
let out_grid_idx = 0;
|
||||
out_grid_idx < output_grid.length;
|
||||
++out_grid_idx
|
||||
) {
|
||||
let startpoints_within_bin = bst_pts_and_idxs.betweenBounds({
|
||||
$gte: output_grid[out_grid_idx] - offset,
|
||||
$lt: output_grid[out_grid_idx] + offset
|
||||
});
|
||||
for (let interval of startpoints_within_bin) {
|
||||
active_intervals.set(interval.idx, [
|
||||
interval.start,
|
||||
interval.end,
|
||||
interval.factor
|
||||
]);
|
||||
active_endpoints.insert(interval.end, interval.idx);
|
||||
}
|
||||
var contrib = 0;
|
||||
for (let [pdf_idx, bounds_and_ratio] of active_intervals.entries()) {
|
||||
let overlap_start = Math.max(
|
||||
output_grid[out_grid_idx] - offset,
|
||||
bounds_and_ratio[0]
|
||||
);
|
||||
let overlap_end = Math.min(
|
||||
output_grid[out_grid_idx] + offset,
|
||||
bounds_and_ratio[1]
|
||||
);
|
||||
let interval_size = bounds_and_ratio[1] - bounds_and_ratio[0];
|
||||
let contrib_frac =
|
||||
interval_size === 0
|
||||
? 0
|
||||
: (overlap_end - overlap_start) * bounds_and_ratio[2];
|
||||
let t = contrib_frac * pdf_vals[pdf_idx[0]][pdf_idx[1]];
|
||||
contrib += t;
|
||||
}
|
||||
final_pdf_vals.push(contrib);
|
||||
let endpoints_within_bin = active_endpoints.betweenBounds({
|
||||
$gte: output_grid[out_grid_idx] - offset,
|
||||
$lt: output_grid[out_grid_idx] + offset
|
||||
});
|
||||
for (let interval_idx of endpoints_within_bin) {
|
||||
active_intervals.delete(interval_idx);
|
||||
}
|
||||
}
|
||||
|
||||
return final_pdf_vals;
|
||||
}
|
||||
|
||||
/**
|
||||
* @param {string} str
|
||||
* @param {string} char
|
||||
* @returns {number}
|
||||
*/
|
||||
function get_count_of_chars(str, char) {
|
||||
return str.split(char).length - 1;
|
||||
}
|
||||
|
||||
/**
|
||||
* Entrypoint. Pass user input strings to this function,
|
||||
* get the corresponding pdf values and input points back.
|
||||
* If the pdf requires monte carlo (it contains a between-distr function)
|
||||
* we first determing which distr to have deterministic
|
||||
* and which to sample from. This is decided based on which
|
||||
* choice gives the least variance.
|
||||
*
|
||||
* @param user_input_string
|
||||
* @returns {([]|*[])[]}
|
||||
*/
|
||||
function get_pdf_from_user_input(user_input_string) {
|
||||
try {
|
||||
const count_opened_bracket = get_count_of_chars(user_input_string, '(');
|
||||
const count_closed_bracket = get_count_of_chars(user_input_string, ')');
|
||||
if (count_opened_bracket !== count_closed_bracket) {
|
||||
throw new Error('Count of brackets are not equal.');
|
||||
}
|
||||
|
||||
let parsed = parse.parse_initial_string(user_input_string);
|
||||
let mm_args = parse.separate_mm_args(parsed.mm_args_string);
|
||||
const is_mm = mm_args.distrs.length > 0;
|
||||
if (!parsed.outer_string) {
|
||||
throw new Error('Parse string is empty.');
|
||||
}
|
||||
|
||||
let tree = new bst.AVLTree();
|
||||
let possible_start_pts = [];
|
||||
let possible_end_pts = [];
|
||||
let all_vals = [];
|
||||
let weights = is_mm ? math.compile(mm_args.weights).evaluate()._data : [1];
|
||||
let weights_sum = weights.reduce((a, b) => a + b);
|
||||
weights = weights.map(x => x / weights_sum);
|
||||
let n_iters = is_mm ? mm_args.distrs.length : 1;
|
||||
|
||||
for (let i = 0; i < n_iters; ++i) {
|
||||
let distr_string = is_mm ? mm_args.distrs[i] : parsed.outer_string;
|
||||
var [deterministic_pdf, mc_distrs] = choose_pdf_func(distr_string);
|
||||
var grid_transform = get_grid_transform(distr_string);
|
||||
var [start_pt, end_pt] = update_transformed_divider_points_bst(
|
||||
grid_transform,
|
||||
deterministic_pdf,
|
||||
mc_distrs,
|
||||
i,
|
||||
NUM_MC_SAMPLES,
|
||||
tree
|
||||
);
|
||||
possible_start_pts.push(start_pt);
|
||||
possible_end_pts.push(end_pt);
|
||||
all_vals.push(deterministic_pdf.pdf_vals.map(x => x * weights[i]));
|
||||
}
|
||||
|
||||
start_pt = Math.min(...possible_start_pts);
|
||||
end_pt = Math.max(...possible_end_pts);
|
||||
|
||||
let output_grid = evenly_spaced_grid(start_pt, end_pt, OUTPUT_GRID_NUMEL);
|
||||
let final_pdf_vals = get_final_pdf(all_vals, tree, output_grid);
|
||||
|
||||
return [final_pdf_vals, output_grid, false];
|
||||
} catch (e) {
|
||||
return [[], [], true];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @param vals
|
||||
* @returns {number}
|
||||
*/
|
||||
function variance(vals) {
|
||||
var vari = 0;
|
||||
for (let i = 0; i < vals[0].length; ++i) {
|
||||
let mean = 0;
|
||||
let this_vari = 0;
|
||||
for (let val of vals) {
|
||||
mean += val[i] / vals.length;
|
||||
}
|
||||
for (let val of vals) {
|
||||
this_vari += (val[i] - mean) ** 2;
|
||||
}
|
||||
vari += this_vari;
|
||||
}
|
||||
return vari;
|
||||
}
|
||||
|
||||
/**
|
||||
* @param array
|
||||
* @param idx
|
||||
* @returns {*[]}
|
||||
*/
|
||||
function pluck_from_array(array, idx) {
|
||||
return [array[idx], array.slice(0, idx).concat(array.slice(idx + 1))];
|
||||
}
|
||||
|
||||
/**
|
||||
* If distr_string requires MC, try all possible
|
||||
* choices for the deterministic distribution,
|
||||
* and pick the one with the least variance.
|
||||
* It's much better to sample from a normal than a lognormal.
|
||||
*
|
||||
* @param distr_string
|
||||
* @returns {(*|*[])[]|*[]}
|
||||
*/
|
||||
function choose_pdf_func(distr_string) {
|
||||
var variances = [];
|
||||
let transform_func = get_grid_transform(distr_string);
|
||||
let substrings = parse.get_distr_substrings(distr_string);
|
||||
var pdfs = get_distributions(substrings);
|
||||
if (pdfs.length === 1) {
|
||||
return [pdfs[0], []];
|
||||
}
|
||||
var start_pt = 0;
|
||||
var end_pt = 0;
|
||||
for (let i = 0; i < pdfs.length; ++i) {
|
||||
var outputs = [];
|
||||
for (let j = 0; j < 20; ++j) {
|
||||
let tree = new bst.AVLTree();
|
||||
let [deterministic_pdf, mc_distrs] = pluck_from_array(pdfs, i);
|
||||
let [this_start_pt, this_end_pt] = update_transformed_divider_points_bst(
|
||||
transform_func,
|
||||
deterministic_pdf,
|
||||
mc_distrs,
|
||||
0,
|
||||
10,
|
||||
tree
|
||||
);
|
||||
[start_pt, end_pt] =
|
||||
j === 0 ? [this_start_pt, this_end_pt] : [start_pt, end_pt];
|
||||
var output_grid = evenly_spaced_grid(start_pt, end_pt, 100);
|
||||
let final_pdf_vals = get_final_pdf(
|
||||
[deterministic_pdf.pdf_vals],
|
||||
tree,
|
||||
output_grid
|
||||
);
|
||||
outputs.push(final_pdf_vals);
|
||||
}
|
||||
variances.push(variance(outputs));
|
||||
}
|
||||
let best_variance = Math.min(...variances);
|
||||
let best_idx = variances
|
||||
.map((val, idx) => [val, idx])
|
||||
.filter(x => x[0] === best_variance)[0][1];
|
||||
let mc_distrs = pdfs.slice(0, best_idx).concat(pdfs.slice(best_idx + 1));
|
||||
return [pdfs[best_idx], mc_distrs];
|
||||
}
|
||||
|
||||
/**
|
||||
* @param distr_string
|
||||
* @returns {function(*): *}
|
||||
*/
|
||||
function get_grid_transform(distr_string) {
|
||||
let substrings = parse.get_distr_substrings(distr_string);
|
||||
let arg_strings = [];
|
||||
for (let i = 0; i < substrings.length; ++i) {
|
||||
distr_string = distr_string.replace(substrings[i], "x_" + i.toString());
|
||||
arg_strings.push("x_" + i.toString());
|
||||
}
|
||||
let compiled = math.compile(distr_string);
|
||||
|
||||
function grid_transform(x) {
|
||||
let kv_pairs = arg_strings.map((val, idx) => [val, x[idx]]);
|
||||
let args_obj = Object.fromEntries(new Map(kv_pairs));
|
||||
return compiled.evaluate(args_obj);
|
||||
}
|
||||
|
||||
return grid_transform;
|
||||
}
|
||||
|
||||
exports.get_pdf_from_user_input = get_pdf_from_user_input;
|
||||
|
|
@ -1,139 +0,0 @@
|
|||
const _math = require("mathjs");
|
||||
const math = _math.create(_math.all);
|
||||
|
||||
// Functions for parsing/processing user input strings are here
|
||||
|
||||
// @todo: Do not use objects.
|
||||
const DISTR_REGEXS = [
|
||||
/beta\(/g,
|
||||
/(log)?normal\(/g,
|
||||
/multimodal\(/g,
|
||||
/mm\(/g,
|
||||
/uniform\(/g
|
||||
];
|
||||
|
||||
/**
|
||||
* @param user_input_string
|
||||
* @returns {{mm_args_string: string, outer_string: string}}
|
||||
*/
|
||||
function parse_initial_string(user_input_string) {
|
||||
let outer_output_string = "";
|
||||
let mm_args_string = "";
|
||||
let idx = 0;
|
||||
|
||||
while (idx < user_input_string.length) {
|
||||
if (
|
||||
user_input_string.substring(idx - 11, idx) === "multimodal(" ||
|
||||
user_input_string.substring(idx - 3, idx) === "mm("
|
||||
) {
|
||||
let num_open_brackets = 1;
|
||||
while (num_open_brackets > 0 && idx < user_input_string.length) {
|
||||
mm_args_string += user_input_string[idx];
|
||||
idx += 1;
|
||||
if (user_input_string[idx] === ")") {
|
||||
num_open_brackets -= 1;
|
||||
} else if (user_input_string[idx] === "(") {
|
||||
num_open_brackets += 1;
|
||||
}
|
||||
}
|
||||
outer_output_string += ")";
|
||||
idx += 1;
|
||||
} else {
|
||||
outer_output_string += user_input_string[idx];
|
||||
idx += 1;
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
outer_string: outer_output_string,
|
||||
mm_args_string: mm_args_string
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* @param mm_args_string
|
||||
* @returns {{distrs: [], weights: string}}
|
||||
*/
|
||||
function separate_mm_args(mm_args_string) {
|
||||
if (mm_args_string.endsWith(",")) {
|
||||
mm_args_string = mm_args_string.slice(0, -1);
|
||||
}
|
||||
let args_array = [];
|
||||
let num_open_brackets = 0;
|
||||
let arg_substring = "";
|
||||
for (let char of mm_args_string) {
|
||||
if (num_open_brackets === 0 && char === ",") {
|
||||
args_array.push(arg_substring.trim());
|
||||
arg_substring = "";
|
||||
} else {
|
||||
if (char === ")" || char === "]") {
|
||||
num_open_brackets -= 1;
|
||||
} else if (char === "(" || char === "[") {
|
||||
num_open_brackets += 1;
|
||||
}
|
||||
arg_substring += char;
|
||||
}
|
||||
}
|
||||
return {
|
||||
distrs: args_array,
|
||||
weights: arg_substring.trim()
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* @param distr_string
|
||||
* @returns {[]}
|
||||
*/
|
||||
function get_distr_substrings(distr_string) {
|
||||
let substrings = [];
|
||||
for (let regex of DISTR_REGEXS) {
|
||||
let matches = distr_string.matchAll(regex);
|
||||
for (let match of matches) {
|
||||
let idx = match.index + match[0].length;
|
||||
let num_open_brackets = 1;
|
||||
let distr_substring = "";
|
||||
while (num_open_brackets !== 0 && idx < distr_string.length) {
|
||||
distr_substring += distr_string[idx];
|
||||
if (distr_string[idx] === "(") {
|
||||
num_open_brackets += 1;
|
||||
} else if (distr_string[idx] === ")") {
|
||||
num_open_brackets -= 1;
|
||||
}
|
||||
idx += 1;
|
||||
}
|
||||
substrings.push((match[0] + distr_substring).trim());
|
||||
}
|
||||
}
|
||||
|
||||
return substrings;
|
||||
}
|
||||
|
||||
/**
|
||||
* @param substr
|
||||
* @returns {(string|*)[]}
|
||||
*/
|
||||
function get_distr_name_and_args(substr) {
|
||||
let distr_name = "";
|
||||
let args_str = "";
|
||||
let args_flag = false;
|
||||
for (let char of substr) {
|
||||
if (!args_flag && char !== "(") {
|
||||
distr_name += char;
|
||||
}
|
||||
if (args_flag && char !== ")") {
|
||||
args_str += char;
|
||||
}
|
||||
if (char === "(") {
|
||||
args_str += "[";
|
||||
args_flag = true;
|
||||
}
|
||||
}
|
||||
args_str += "]";
|
||||
let args = math.compile(args_str).evaluate()._data;
|
||||
return [distr_name, args];
|
||||
}
|
||||
|
||||
exports.get_distr_name_and_args = get_distr_name_and_args;
|
||||
exports.get_distr_substrings = get_distr_substrings;
|
||||
exports.separate_mm_args = separate_mm_args;
|
||||
exports.parse_initial_string = parse_initial_string;
|
||||
|
|
@ -40,16 +40,5 @@ let run =
|
|||
(inputs: RenderTypes.ShapeRenderer.Combined.inputs)
|
||||
: RenderTypes.ShapeRenderer.Combined.outputs => {
|
||||
let symbolic = runSymbolic(inputs);
|
||||
let sampling =
|
||||
switch (symbolic) {
|
||||
| Ok(_) => None
|
||||
| Error(_) =>
|
||||
Samples.T.fromGuesstimatorString(
|
||||
~guesstimatorString=inputs.guesstimatorString,
|
||||
~samplingInputs=inputs.samplingInputs,
|
||||
(),
|
||||
)
|
||||
};
|
||||
Js.log3("IS SOME?", symbolic |> E.R.toOption |> E.O.isSome, symbolic);
|
||||
{symbolic: Some(symbolic), sampling};
|
||||
{symbolic: Some(symbolic), sampling: None};
|
||||
};
|
||||
|
|
|
|||
|
|
@ -1,13 +0,0 @@
|
|||
[@bs.deriving abstract]
|
||||
type discrete = {
|
||||
xs: array(float),
|
||||
ys: array(float),
|
||||
};
|
||||
|
||||
let jsToDistDiscrete = (d: discrete): DistTypes.discreteShape => {xyShape: {
|
||||
xs: xsGet(d),
|
||||
ys: ysGet(d),
|
||||
}, knownIntegralSum: None};
|
||||
|
||||
[@bs.module "./GuesstimatorLibrary.js"]
|
||||
external stringToSamples: (string, int) => array(float) = "stringToSamples";
|
||||
|
|
@ -1,37 +0,0 @@
|
|||
const _ = require("lodash");
|
||||
const {
|
||||
Guesstimator
|
||||
} = require('@foretold/guesstimator/src');
|
||||
|
||||
const stringToSamples = (
|
||||
text,
|
||||
sampleCount,
|
||||
inputs = [],
|
||||
) => {
|
||||
const [_error, {
|
||||
parsedInput,
|
||||
parsedError
|
||||
}] = Guesstimator.parse({
|
||||
text: "=" + text
|
||||
});
|
||||
|
||||
const guesstimator = new Guesstimator({
|
||||
parsedInput
|
||||
});
|
||||
const {
|
||||
values,
|
||||
errors
|
||||
} = guesstimator.sample(
|
||||
sampleCount,
|
||||
inputs,
|
||||
);
|
||||
if (errors.length > 0) {
|
||||
return []
|
||||
} else {
|
||||
return _.filter(values, _.isFinite)
|
||||
}
|
||||
};
|
||||
|
||||
module.exports = {
|
||||
stringToSamples,
|
||||
};
|
||||
|
|
@ -175,26 +175,4 @@ module T = {
|
|||
RenderTypes.ShapeRenderer.Sampling.Inputs.toF(samplingInputs);
|
||||
toShape(~samples, ~samplingInputs, ());
|
||||
};
|
||||
|
||||
let fromGuesstimatorString =
|
||||
(
|
||||
~guesstimatorString,
|
||||
~samplingInputs=RenderTypes.ShapeRenderer.Sampling.Inputs.empty,
|
||||
(),
|
||||
) => {
|
||||
let hasValidSamples =
|
||||
Guesstimator.stringToSamples(guesstimatorString, 10) |> E.A.length > 0;
|
||||
let _samplingInputs =
|
||||
RenderTypes.ShapeRenderer.Sampling.Inputs.toF(samplingInputs);
|
||||
switch (hasValidSamples) {
|
||||
| false => None
|
||||
| true =>
|
||||
let samples =
|
||||
Guesstimator.stringToSamples(
|
||||
guesstimatorString,
|
||||
_samplingInputs.sampleCount,
|
||||
);
|
||||
Some(fromSamples(~samplingInputs, samples));
|
||||
};
|
||||
};
|
||||
};
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user