Merge branch 'Q-develop' into develop
This commit is contained in:
Umur Ozkul
commit
05886451b1
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|
@ -7,3 +7,5 @@ node_modules
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|||
packages/*/node_modules
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packages/website/.docusaurus
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packages/squiggle-lang/lib
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packages/squiggle-lang/.nyc_output/
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packages/squiggle-lang/coverage/
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||||
|
|
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|||
|
|
@ -11,7 +11,7 @@
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|||
"@types/lodash": "^4.14.182",
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||||
"@types/node": "^17.0.25",
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"@types/react": "^18.0.3",
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||||
"@types/react-dom": "^18.0.1",
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||||
"@types/react-dom": "^18.0.2",
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"antd": "^4.19.3",
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||||
"cross-env": "^7.0.3",
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||||
"lodash": "^4.17.21",
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||||
|
|
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|||
|
|
@ -6,7 +6,7 @@ import {
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errorValueToString,
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squiggleExpression,
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} from "@quri/squiggle-lang";
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import type { samplingParams, exportEnv } from "@quri/squiggle-lang";
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import type { samplingParams } from "@quri/squiggle-lang";
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import { NumberShower } from "./NumberShower";
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import { DistributionChart } from "./DistributionChart";
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import { ErrorBox } from "./ErrorBox";
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@ -129,9 +129,9 @@ export interface SquiggleChartProps {
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/** If the result is a function, how many points along the function it samples */
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diagramCount?: number;
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/** variables declared before this expression */
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environment?: exportEnv;
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environment?: unknown;
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/** When the environment changes */
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onEnvChange?(env: exportEnv): void;
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onEnvChange?(env: unknown): void;
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/** CSS width of the element */
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width?: number;
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height?: number;
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|
|
@ -155,7 +155,7 @@ export const SquiggleChart: React.FC<SquiggleChartProps> = ({
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sampleCount: sampleCount,
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xyPointLength: outputXYPoints,
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};
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let expressionResult = run(squiggleString, samplingInputs, environment);
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let expressionResult = run(squiggleString, samplingInputs);
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let internal: JSX.Element;
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if (expressionResult.tag === "Ok") {
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onEnvChange(environment);
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|
|
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@ -2,7 +2,6 @@ import * as React from "react";
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import * as ReactDOM from "react-dom";
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import { SquiggleChart } from "./SquiggleChart";
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import { CodeEditor } from "./CodeEditor";
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import type { exportEnv } from "@quri/squiggle-lang";
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import styled from "styled-components";
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export interface SquiggleEditorProps {
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|
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@ -21,9 +20,9 @@ export interface SquiggleEditorProps {
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/** If the result is a function, how many points along the function it samples */
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diagramCount?: number;
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/** The environment, other variables that were already declared */
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environment?: exportEnv;
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environment?: unknown;
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/** when the environment changes. Used again for notebook magic*/
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onEnvChange?(env: exportEnv): void;
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onEnvChange?(env: unknown): void;
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/** The width of the element */
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width: number;
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}
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|
|
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|
|
@ -67,7 +67,7 @@ describe("eval on distribution functions", () => {
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testEval("lognormal(10,2) / lognormal(5,2)", "Ok(Lognormal(5,2.8284271247461903))")
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testEval("lognormal(5, 2) / 2", "Ok(Lognormal(4.306852819440055,2))")
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testEval("2 / lognormal(5, 2)", "Ok(Lognormal(-4.306852819440055,2))")
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testEval("2 / normal(10, 2)", "Ok(Point Set Distribution)")
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testEval("2 / normal(10, 2)", "Ok(Sample Set Distribution)")
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testEval("normal(10, 2) / 2", "Ok(Normal(5,1))")
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})
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describe("truncate", () => {
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|
|
@ -77,21 +77,21 @@ describe("eval on distribution functions", () => {
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})
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describe("exp", () => {
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testEval("exp(normal(5,2))", "Ok(Point Set Distribution)")
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testEval("exp(normal(5,2))", "Ok(Sample Set Distribution)")
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})
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describe("pow", () => {
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testEval("pow(3, uniform(5,8))", "Ok(Point Set Distribution)")
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testEval("pow(uniform(5,8), 3)", "Ok(Point Set Distribution)")
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testEval("pow(3, uniform(5,8))", "Ok(Sample Set Distribution)")
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testEval("pow(uniform(5,8), 3)", "Ok(Sample Set Distribution)")
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testEval("pow(uniform(5,8), uniform(9, 10))", "Ok(Sample Set Distribution)")
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})
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|
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describe("log", () => {
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testEval("log(2, uniform(5,8))", "Ok(Point Set Distribution)")
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testEval("log(normal(5,2), 3)", "Ok(Point Set Distribution)")
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testEval("log(2, uniform(5,8))", "Ok(Sample Set Distribution)")
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testEval("log(normal(5,2), 3)", "Ok(Sample Set Distribution)")
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testEval("log(normal(5,2), normal(10,1))", "Ok(Sample Set Distribution)")
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testEval("log(uniform(5,8))", "Ok(Point Set Distribution)")
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testEval("log10(uniform(5,8))", "Ok(Point Set Distribution)")
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testEval("log(uniform(5,8))", "Ok(Sample Set Distribution)")
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testEval("log10(uniform(5,8))", "Ok(Sample Set Distribution)")
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})
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describe("dotLog", () => {
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|
|
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|
|
@ -20,12 +20,7 @@
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|||
],
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||||
"suffix": ".bs.js",
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||||
"namespace": true,
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||||
"bs-dependencies": [
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||||
"@glennsl/rescript-jest",
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"@glennsl/bs-json",
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"rationale",
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"bisect_ppx"
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||||
],
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"bs-dependencies": ["@glennsl/rescript-jest", "rationale", "bisect_ppx"],
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||||
"gentypeconfig": {
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"language": "typescript",
|
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"module": "commonjs",
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|
|
@ -37,7 +32,7 @@
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},
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"refmt": 3,
|
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"warnings": {
|
||||
"number": "+A-42-48-9-30-4-102-20-27-41"
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"number": "+A-42-48-9-30-4"
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},
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"ppx-flags": [
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["../../node_modules/bisect_ppx/ppx", "--exclude-files", ".*_test\\.res$$"]
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|
|
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|
|
@ -7,11 +7,10 @@
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"bundle": "webpack",
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"start": "rescript build -w -with-deps",
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"clean": "rescript clean",
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"test:reducer": "jest --testPathPattern '.*__tests__/Reducer.*'",
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"test:reducer": "jest __tests__/Reducer*/",
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"test": "jest",
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"test:ts": "jest __tests__/TS/",
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"test:rescript": "jest --modulePathIgnorePatterns=__tests__/TS/*",
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"test:reducer": "jest __tests__/Reducer*/",
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"test:watch": "jest --watchAll",
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"coverage:rescript": "rm -f *.coverage; yarn clean; BISECT_ENABLE=yes yarn build; yarn test:rescript; bisect-ppx-report html",
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"coverage:ts": "nyc --reporter=lcov yarn test:ts",
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|
|
@ -31,32 +30,30 @@
|
|||
"author": "Quantified Uncertainty Research Institute",
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"license": "MIT",
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||||
"dependencies": {
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||||
"@glennsl/bs-json": "^5.0.2",
|
||||
"bisect_ppx": "^2.7.1",
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||||
"jstat": "^1.9.5",
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"lodash": "4.17.21",
|
||||
"mathjs": "10.5.0",
|
||||
"pdfast": "^0.2.0",
|
||||
"rationale": "0.2.0",
|
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"rescript": "^9.1.4",
|
||||
"bisect_ppx": "^2.7.1"
|
||||
"rescript": "^9.1.4"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@glennsl/rescript-jest": "^0.9.0",
|
||||
"@istanbuljs/nyc-config-typescript": "^1.0.2",
|
||||
"@types/jest": "^27.4.0",
|
||||
"babel-plugin-transform-es2015-modules-commonjs": "^6.26.2",
|
||||
"docsify": "^4.12.2",
|
||||
"codecov": "3.8.3",
|
||||
"fast-check": "2.24.0",
|
||||
"gentype": "^4.3.0",
|
||||
"jest": "^27.5.1",
|
||||
"moduleserve": "0.9.1",
|
||||
"nyc": "^15.1.0",
|
||||
"ts-jest": "^27.1.4",
|
||||
"ts-loader": "^9.2.8",
|
||||
"fast-check": "2.24.0",
|
||||
"typescript": "^4.6.3",
|
||||
"webpack": "^5.72.0",
|
||||
"webpack-cli": "^4.9.2",
|
||||
"nyc": "^15.1.0",
|
||||
"@istanbuljs/nyc-config-typescript": "^1.0.2",
|
||||
"codecov": "3.8.3"
|
||||
"webpack-cli": "^4.9.2"
|
||||
},
|
||||
"source": "./src/js/index.ts",
|
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"main": "./dist/bundle.js",
|
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|
|
|
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|
|
@ -1,9 +1,4 @@
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|||
import * as _ from "lodash";
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||||
import type {
|
||||
exportEnv,
|
||||
exportDistribution,
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||||
} from "../rescript/ProgramEvaluator.gen";
|
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export type { exportEnv, exportDistribution };
|
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import {
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genericDist,
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samplingParams,
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|
|
@ -48,7 +43,6 @@ import {
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|||
Constructors_pointwiseLogarithm,
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Constructors_pointwisePower,
|
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} from "../rescript/Distributions/DistributionOperation/DistributionOperation.gen";
|
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import { pointSetDistFn } from "../rescript/OldInterpreter/DistPlus.bs";
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||||
export type { samplingParams, errorValue };
|
||||
|
||||
export let defaultSamplingInputs: samplingParams = {
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|
|
@ -98,8 +92,7 @@ export type squiggleExpression =
|
|||
| tagged<"record", { [key: string]: squiggleExpression }>;
|
||||
export function run(
|
||||
squiggleString: string,
|
||||
samplingInputs?: samplingParams,
|
||||
_environment?: exportEnv
|
||||
samplingInputs?: samplingParams
|
||||
): result<squiggleExpression, errorValue> {
|
||||
let si: samplingParams = samplingInputs
|
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? samplingInputs
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|
|
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|
|
@ -69,7 +69,7 @@ let toPointSet = (
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|||
~xyPointLength,
|
||||
~sampleCount,
|
||||
~xSelection: GenericDist_Types.Operation.pointsetXSelection=#ByWeight,
|
||||
unit,
|
||||
(),
|
||||
): result<PointSetTypes.pointSetDist, error> => {
|
||||
switch (t: t) {
|
||||
| PointSet(pointSet) => Ok(pointSet)
|
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|
|
@ -93,7 +93,7 @@ let toPointSet = (
|
|||
xyPointLength to be a bit longer than the eventual toSparkline downsampling. I chose 3
|
||||
fairly arbitrarily.
|
||||
*/
|
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let toSparkline = (t: t, ~sampleCount: int, ~bucketCount: int=20, unit): result<string, error> =>
|
||||
let toSparkline = (t: t, ~sampleCount: int, ~bucketCount: int=20, ()): result<string, error> =>
|
||||
t
|
||||
->toPointSet(~xSelection=#Linear, ~xyPointLength=bucketCount * 3, ~sampleCount, ())
|
||||
->E.R.bind(r =>
|
||||
|
|
@ -101,10 +101,16 @@ let toSparkline = (t: t, ~sampleCount: int, ~bucketCount: int=20, unit): result<
|
|||
)
|
||||
|
||||
module Truncate = {
|
||||
let trySymbolicSimplification = (leftCutoff, rightCutoff, t: t): option<t> =>
|
||||
let trySymbolicSimplification = (
|
||||
leftCutoff: option<float>,
|
||||
rightCutoff: option<float>,
|
||||
t: t,
|
||||
): option<t> =>
|
||||
switch (leftCutoff, rightCutoff, t) {
|
||||
| (None, None, _) => None
|
||||
| (lc, rc, Symbolic(#Uniform(u))) if lc < rc =>
|
||||
| (Some(lc), Some(rc), Symbolic(#Uniform(u))) if lc < rc =>
|
||||
Some(Symbolic(#Uniform(SymbolicDist.Uniform.truncate(Some(lc), Some(rc), u))))
|
||||
| (lc, rc, Symbolic(#Uniform(u))) =>
|
||||
Some(Symbolic(#Uniform(SymbolicDist.Uniform.truncate(lc, rc, u))))
|
||||
| _ => None
|
||||
}
|
||||
|
|
@ -158,7 +164,7 @@ module AlgebraicCombination = {
|
|||
|
||||
let runConvolution = (
|
||||
toPointSet: toPointSetFn,
|
||||
arithmeticOperation: GenericDist_Types.Operation.arithmeticOperation,
|
||||
arithmeticOperation: Operation.convolutionOperation,
|
||||
t1: t,
|
||||
t2: t,
|
||||
) =>
|
||||
|
|
@ -191,10 +197,23 @@ module AlgebraicCombination = {
|
|||
| _ => 1000
|
||||
}
|
||||
|
||||
let chooseConvolutionOrMonteCarlo = (t2: t, t1: t) =>
|
||||
expectedConvolutionCost(t1) * expectedConvolutionCost(t2) > 10000
|
||||
? #CalculateWithMonteCarlo
|
||||
: #CalculateWithConvolution
|
||||
type calculationMethod = MonteCarlo | Convolution(Operation.convolutionOperation)
|
||||
|
||||
let chooseConvolutionOrMonteCarlo = (
|
||||
op: Operation.algebraicOperation,
|
||||
t2: t,
|
||||
t1: t,
|
||||
): calculationMethod =>
|
||||
switch op {
|
||||
| #Divide
|
||||
| #Power
|
||||
| #Logarithm =>
|
||||
MonteCarlo
|
||||
| (#Add | #Subtract | #Multiply) as convOp =>
|
||||
expectedConvolutionCost(t1) * expectedConvolutionCost(t2) > 10000
|
||||
? MonteCarlo
|
||||
: Convolution(convOp)
|
||||
}
|
||||
|
||||
let run = (
|
||||
t1: t,
|
||||
|
|
@ -207,15 +226,10 @@ module AlgebraicCombination = {
|
|||
| Some(Ok(symbolicDist)) => Ok(Symbolic(symbolicDist))
|
||||
| Some(Error(e)) => Error(Other(e))
|
||||
| None =>
|
||||
switch chooseConvolutionOrMonteCarlo(t1, t2) {
|
||||
| #CalculateWithMonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
|
||||
| #CalculateWithConvolution =>
|
||||
runConvolution(
|
||||
toPointSetFn,
|
||||
arithmeticOperation,
|
||||
t1,
|
||||
t2,
|
||||
)->E.R2.fmap(r => DistributionTypes.PointSet(r))
|
||||
switch chooseConvolutionOrMonteCarlo(arithmeticOperation, t1, t2) {
|
||||
| MonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
|
||||
| Convolution(convOp) =>
|
||||
runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(r))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -96,36 +96,25 @@ let toDiscretePointMassesFromTriangulars = (
|
|||
}
|
||||
|
||||
let combineShapesContinuousContinuous = (
|
||||
op: Operation.algebraicOperation,
|
||||
op: Operation.convolutionOperation,
|
||||
s1: PointSetTypes.xyShape,
|
||||
s2: PointSetTypes.xyShape,
|
||||
): PointSetTypes.xyShape => {
|
||||
// if we add the two distributions, we should probably use normal filters.
|
||||
// if we multiply the two distributions, we should probably use lognormal filters.
|
||||
let t1m = toDiscretePointMassesFromTriangulars(s1)
|
||||
let t2m = switch op {
|
||||
| #Divide => toDiscretePointMassesFromTriangulars(~inverse=true, s2)
|
||||
| _ => toDiscretePointMassesFromTriangulars(~inverse=false, s2)
|
||||
}
|
||||
let t2m = toDiscretePointMassesFromTriangulars(~inverse=false, s2)
|
||||
|
||||
let combineMeansFn = switch op {
|
||||
| #Add => (m1, m2) => m1 +. m2
|
||||
| #Subtract => (m1, m2) => m1 -. m2
|
||||
| #Multiply => (m1, m2) => m1 *. m2
|
||||
| #Divide => (m1, mInv2) => m1 *. mInv2
|
||||
| #Power => (m1, mInv2) => m1 ** mInv2
|
||||
| #Logarithm => (m1, m2) => log(m1) /. log(m2)
|
||||
} // note: here, mInv2 = mean(1 / t2) ~= 1 / mean(t2)
|
||||
|
||||
// TODO: Variances are for exponentatiation or logarithms are almost totally made up and very likely very wrong.
|
||||
// converts the variances and means of the two inputs into the variance of the output
|
||||
let combineVariancesFn = switch op {
|
||||
| #Add => (v1, v2, _, _) => v1 +. v2
|
||||
| #Subtract => (v1, v2, _, _) => v1 +. v2
|
||||
| #Multiply => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
|
||||
| #Power => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
|
||||
| #Logarithm => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
|
||||
| #Divide => (v1, vInv2, m1, mInv2) => v1 *. vInv2 +. v1 *. mInv2 ** 2. +. vInv2 *. m1 ** 2.
|
||||
}
|
||||
|
||||
// TODO: If operating on two positive-domain distributions, we should take that into account
|
||||
|
|
@ -199,7 +188,7 @@ let toDiscretePointMassesFromDiscrete = (s: PointSetTypes.xyShape): pointMassesW
|
|||
}
|
||||
|
||||
let combineShapesContinuousDiscrete = (
|
||||
op: Operation.algebraicOperation,
|
||||
op: Operation.convolutionOperation,
|
||||
continuousShape: PointSetTypes.xyShape,
|
||||
discreteShape: PointSetTypes.xyShape,
|
||||
): PointSetTypes.xyShape => {
|
||||
|
|
@ -207,7 +196,7 @@ let combineShapesContinuousDiscrete = (
|
|||
let t2n = discreteShape |> XYShape.T.length
|
||||
|
||||
// each x pair is added/subtracted
|
||||
let fn = Operation.Algebraic.toFn(op)
|
||||
let fn = Operation.Convolution.toFn(op)
|
||||
|
||||
let outXYShapes: array<array<(float, float)>> = Belt.Array.makeUninitializedUnsafe(t2n)
|
||||
|
||||
|
|
@ -231,10 +220,7 @@ let combineShapesContinuousDiscrete = (
|
|||
Belt.Array.set(outXYShapes, j, dxyShape) |> ignore
|
||||
()
|
||||
}
|
||||
| #Multiply
|
||||
| #Power
|
||||
| #Logarithm
|
||||
| #Divide =>
|
||||
| #Multiply =>
|
||||
for j in 0 to t2n - 1 {
|
||||
// creates a new continuous shape for each one of the discrete points, and collects them in outXYShapes.
|
||||
let dxyShape: array<(float, float)> = Belt.Array.makeUninitializedUnsafe(t1n)
|
||||
|
|
|
|||
|
|
@ -87,7 +87,6 @@ let stepwiseToLinear = (t: t): t =>
|
|||
// Note: This results in a distribution with as many points as the sum of those in t1 and t2.
|
||||
let combinePointwise = (
|
||||
~integralSumCachesFn=(_, _) => None,
|
||||
~integralCachesFn: (t, t) => option<t>=(_, _) => None,
|
||||
~distributionType: PointSetTypes.distributionType=#PDF,
|
||||
fn: (float, float) => float,
|
||||
t1: PointSetTypes.continuousShape,
|
||||
|
|
@ -143,14 +142,9 @@ let updateIntegralCache = (integralCache, t: t): t => {...t, integralCache: inte
|
|||
|
||||
let reduce = (
|
||||
~integralSumCachesFn: (float, float) => option<float>=(_, _) => None,
|
||||
~integralCachesFn: (t, t) => option<t>=(_, _) => None,
|
||||
fn,
|
||||
continuousShapes,
|
||||
) =>
|
||||
continuousShapes |> E.A.fold_left(
|
||||
combinePointwise(~integralSumCachesFn, ~integralCachesFn, fn),
|
||||
empty,
|
||||
)
|
||||
) => continuousShapes |> E.A.fold_left(combinePointwise(~integralSumCachesFn, fn), empty)
|
||||
|
||||
let mapY = (~integralSumCacheFn=_ => None, ~integralCacheFn=_ => None, ~fn, t: t) =>
|
||||
make(
|
||||
|
|
@ -247,7 +241,7 @@ let downsampleEquallyOverX = (length, t): t =>
|
|||
/* This simply creates multiple copies of the continuous distribution, scaled and shifted according to
|
||||
each discrete data point, and then adds them all together. */
|
||||
let combineAlgebraicallyWithDiscrete = (
|
||||
op: Operation.algebraicOperation,
|
||||
op: Operation.convolutionOperation,
|
||||
t1: t,
|
||||
t2: PointSetTypes.discreteShape,
|
||||
) => {
|
||||
|
|
@ -269,8 +263,7 @@ let combineAlgebraicallyWithDiscrete = (
|
|||
)
|
||||
|
||||
let combinedIntegralSum = switch op {
|
||||
| #Multiply
|
||||
| #Divide =>
|
||||
| #Multiply =>
|
||||
Common.combineIntegralSums((a, b) => Some(a *. b), t1.integralSumCache, t2.integralSumCache)
|
||||
| _ => None
|
||||
}
|
||||
|
|
@ -280,7 +273,7 @@ let combineAlgebraicallyWithDiscrete = (
|
|||
}
|
||||
}
|
||||
|
||||
let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t) => {
|
||||
let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t) => {
|
||||
let s1 = t1 |> getShape
|
||||
let s2 = t2 |> getShape
|
||||
let t1n = s1 |> XYShape.T.length
|
||||
|
|
|
|||
|
|
@ -34,11 +34,6 @@ let lastY = (t: t) => t |> getShape |> XYShape.T.lastY
|
|||
|
||||
let combinePointwise = (
|
||||
~integralSumCachesFn=(_, _) => None,
|
||||
~integralCachesFn: (
|
||||
PointSetTypes.continuousShape,
|
||||
PointSetTypes.continuousShape,
|
||||
) => option<PointSetTypes.continuousShape>=(_, _) => None,
|
||||
fn,
|
||||
t1: PointSetTypes.discreteShape,
|
||||
t2: PointSetTypes.discreteShape,
|
||||
): PointSetTypes.discreteShape => {
|
||||
|
|
@ -62,16 +57,8 @@ let combinePointwise = (
|
|||
)
|
||||
}
|
||||
|
||||
let reduce = (
|
||||
~integralSumCachesFn=(_, _) => None,
|
||||
~integralCachesFn=(_, _) => None,
|
||||
fn,
|
||||
discreteShapes,
|
||||
): PointSetTypes.discreteShape =>
|
||||
discreteShapes |> E.A.fold_left(
|
||||
combinePointwise(~integralSumCachesFn, ~integralCachesFn, fn),
|
||||
empty,
|
||||
)
|
||||
let reduce = (~integralSumCachesFn=(_, _) => None, discreteShapes): PointSetTypes.discreteShape =>
|
||||
discreteShapes |> E.A.fold_left(combinePointwise(~integralSumCachesFn), empty)
|
||||
|
||||
let updateIntegralSumCache = (integralSumCache, t: t): t => {
|
||||
...t,
|
||||
|
|
@ -85,7 +72,7 @@ let updateIntegralCache = (integralCache, t: t): t => {
|
|||
|
||||
/* This multiples all of the data points together and creates a new discrete distribution from the results.
|
||||
Data points at the same xs get added together. It may be a good idea to downsample t1 and t2 before and/or the result after. */
|
||||
let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t => {
|
||||
let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {
|
||||
let t1s = t1 |> getShape
|
||||
let t2s = t2 |> getShape
|
||||
let t1n = t1s |> XYShape.T.length
|
||||
|
|
@ -97,7 +84,7 @@ let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t =
|
|||
t2.integralSumCache,
|
||||
)
|
||||
|
||||
let fn = Operation.Algebraic.toFn(op)
|
||||
let fn = Operation.Convolution.toFn(op)
|
||||
let xToYMap = E.FloatFloatMap.empty()
|
||||
|
||||
for i in 0 to t1n - 1 {
|
||||
|
|
|
|||
|
|
@ -164,12 +164,7 @@ module T = Dist({
|
|||
// This pipes all ys (continuous and discrete) through fn.
|
||||
// If mapY is a linear operation, we might be able to update the integralSumCaches as well;
|
||||
// if not, they'll be set to None.
|
||||
let mapY = (
|
||||
~integralSumCacheFn=previousIntegralSum => None,
|
||||
~integralCacheFn=previousIntegral => None,
|
||||
~fn,
|
||||
t: t,
|
||||
): t => {
|
||||
let mapY = (~integralSumCacheFn=_ => None, ~integralCacheFn=_ => None, ~fn, t: t): t => {
|
||||
let yMappedDiscrete: PointSetTypes.discreteShape =
|
||||
t.discrete
|
||||
|> Discrete.T.mapY(~fn)
|
||||
|
|
@ -226,7 +221,7 @@ module T = Dist({
|
|||
}
|
||||
})
|
||||
|
||||
let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t => {
|
||||
let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {
|
||||
// Discrete convolution can cause a huge increase in the number of samples,
|
||||
// so we'll first downsample.
|
||||
|
||||
|
|
@ -271,16 +266,13 @@ let combinePointwise = (
|
|||
t2: t,
|
||||
): t => {
|
||||
let reducedDiscrete =
|
||||
[t1, t2]
|
||||
|> E.A.fmap(toDiscrete)
|
||||
|> E.A.O.concatSomes
|
||||
|> Discrete.reduce(~integralSumCachesFn, ~integralCachesFn, fn)
|
||||
[t1, t2] |> E.A.fmap(toDiscrete) |> E.A.O.concatSomes |> Discrete.reduce(~integralSumCachesFn)
|
||||
|
||||
let reducedContinuous =
|
||||
[t1, t2]
|
||||
|> E.A.fmap(toContinuous)
|
||||
|> E.A.O.concatSomes
|
||||
|> Continuous.reduce(~integralSumCachesFn, ~integralCachesFn, fn)
|
||||
|> Continuous.reduce(~integralSumCachesFn, fn)
|
||||
|
||||
let combinedIntegralSum = Common.combineIntegralSums(
|
||||
integralSumCachesFn,
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ let toMixed = mapToAll((
|
|||
))
|
||||
|
||||
//TODO WARNING: The combineAlgebraicallyWithDiscrete will break for subtraction and division, like, discrete - continous
|
||||
let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t =>
|
||||
let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t =>
|
||||
switch (t1, t2) {
|
||||
| (Continuous(m1), Continuous(m2)) =>
|
||||
Continuous.combineAlgebraically(op, m1, m2) |> Continuous.T.toPointSetDist
|
||||
|
|
@ -59,13 +59,9 @@ let combinePointwise = (
|
|||
) =>
|
||||
switch (t1, t2) {
|
||||
| (Continuous(m1), Continuous(m2)) =>
|
||||
PointSetTypes.Continuous(
|
||||
Continuous.combinePointwise(~integralSumCachesFn, ~integralCachesFn, fn, m1, m2),
|
||||
)
|
||||
PointSetTypes.Continuous(Continuous.combinePointwise(~integralSumCachesFn, fn, m1, m2))
|
||||
| (Discrete(m1), Discrete(m2)) =>
|
||||
PointSetTypes.Discrete(
|
||||
Discrete.combinePointwise(~integralSumCachesFn, ~integralCachesFn, fn, m1, m2),
|
||||
)
|
||||
PointSetTypes.Discrete(Discrete.combinePointwise(~integralSumCachesFn, m1, m2))
|
||||
| (m1, m2) =>
|
||||
PointSetTypes.Mixed(
|
||||
Mixed.combinePointwise(~integralSumCachesFn, ~integralCachesFn, fn, toMixed(m1), toMixed(m2)),
|
||||
|
|
@ -134,11 +130,7 @@ module T = Dist({
|
|||
let integralYtoX = f =>
|
||||
mapToAll((Mixed.T.Integral.yToX(f), Discrete.T.Integral.yToX(f), Continuous.T.Integral.yToX(f)))
|
||||
let maxX = mapToAll((Mixed.T.maxX, Discrete.T.maxX, Continuous.T.maxX))
|
||||
let mapY = (
|
||||
~integralSumCacheFn=previousIntegralSum => None,
|
||||
~integralCacheFn=previousIntegral => None,
|
||||
~fn,
|
||||
) =>
|
||||
let mapY = (~integralSumCacheFn=_ => None, ~integralCacheFn=_ => None, ~fn) =>
|
||||
fmap((
|
||||
Mixed.T.mapY(~integralSumCacheFn, ~integralCacheFn, ~fn),
|
||||
Discrete.T.mapY(~integralSumCacheFn, ~integralCacheFn, ~fn),
|
||||
|
|
|
|||
|
|
@ -1,24 +0,0 @@
|
|||
open ASTTypes
|
||||
|
||||
let toString = ASTTypes.Node.toString
|
||||
|
||||
let envs = (samplingInputs, environment) => {
|
||||
samplingInputs: samplingInputs,
|
||||
environment: environment,
|
||||
evaluateNode: ASTEvaluator.toLeaf,
|
||||
}
|
||||
|
||||
let toLeaf = (samplingInputs, environment, node: node) =>
|
||||
ASTEvaluator.toLeaf(envs(samplingInputs, environment), node)
|
||||
|
||||
let toPointSetDist = (samplingInputs, environment, node: node) =>
|
||||
switch toLeaf(samplingInputs, environment, node) {
|
||||
| Ok(#RenderedDist(pointSetDist)) => Ok(pointSetDist)
|
||||
| Ok(_) => Error("Rendering failed.")
|
||||
| Error(e) => Error(e)
|
||||
}
|
||||
|
||||
let runFunction = (samplingInputs, environment, inputs, fn: ASTTypes.Function.t) => {
|
||||
let params = envs(samplingInputs, environment)
|
||||
ASTTypes.Function.run(params, inputs, fn)
|
||||
}
|
||||
|
|
@ -1,257 +0,0 @@
|
|||
open ASTTypes
|
||||
|
||||
type tResult = node => result<node, string>
|
||||
|
||||
/* Given two random variables A and B, this returns the distribution
|
||||
of a new variable that is the result of the operation on A and B.
|
||||
For instance, normal(0, 1) + normal(1, 1) -> normal(1, 2).
|
||||
In general, this is implemented via convolution. */
|
||||
module AlgebraicCombination = {
|
||||
let tryAnalyticalSimplification = (operation, t1: node, t2: node) =>
|
||||
switch (operation, t1, t2) {
|
||||
| (operation, #SymbolicDist(d1), #SymbolicDist(d2)) =>
|
||||
switch SymbolicDist.T.tryAnalyticalSimplification(d1, d2, operation) {
|
||||
| #AnalyticalSolution(symbolicDist) => Ok(#SymbolicDist(symbolicDist))
|
||||
| #Error(er) => Error(er)
|
||||
| #NoSolution => Ok(#AlgebraicCombination(operation, t1, t2))
|
||||
}
|
||||
| _ => Ok(#AlgebraicCombination(operation, t1, t2))
|
||||
}
|
||||
|
||||
let combinationByRendering = (evaluationParams, algebraicOp, t1: node, t2: node): result<
|
||||
node,
|
||||
string,
|
||||
> =>
|
||||
E.R.merge(
|
||||
Node.ensureIsRenderedAndGetShape(evaluationParams, t1),
|
||||
Node.ensureIsRenderedAndGetShape(evaluationParams, t2),
|
||||
) |> E.R.fmap(((a, b)) => #RenderedDist(PointSetDist.combineAlgebraically(algebraicOp, a, b)))
|
||||
|
||||
let nodeScore: node => int = x =>
|
||||
switch x {
|
||||
| #SymbolicDist(#Float(_)) => 1
|
||||
| #SymbolicDist(_) => 1000
|
||||
| #RenderedDist(Discrete(m)) => m.xyShape |> XYShape.T.length
|
||||
| #RenderedDist(Mixed(_)) => 1000
|
||||
| #RenderedDist(Continuous(_)) => 1000
|
||||
| _ => 1000
|
||||
}
|
||||
|
||||
let choose = (t1: node, t2: node) =>
|
||||
nodeScore(t1) * nodeScore(t2) > 10000 ? #Sampling : #Analytical
|
||||
|
||||
let combine = (evaluationParams, algebraicOp, t1: node, t2: node): result<node, string> =>
|
||||
E.R.merge(
|
||||
ASTTypes.SamplingDistribution.renderIfIsNotSamplingDistribution(evaluationParams, t1),
|
||||
ASTTypes.SamplingDistribution.renderIfIsNotSamplingDistribution(evaluationParams, t2),
|
||||
) |> E.R.bind(_, ((a, b)) =>
|
||||
switch choose(a, b) {
|
||||
| #Sampling =>
|
||||
ASTTypes.SamplingDistribution.combineShapesUsingSampling(
|
||||
evaluationParams,
|
||||
algebraicOp,
|
||||
a,
|
||||
b,
|
||||
)
|
||||
| #Analytical => combinationByRendering(evaluationParams, algebraicOp, a, b)
|
||||
}
|
||||
)
|
||||
|
||||
let operationToLeaf = (
|
||||
evaluationParams: evaluationParams,
|
||||
algebraicOp: Operation.algebraicOperation,
|
||||
t1: node,
|
||||
t2: node,
|
||||
): result<node, string> =>
|
||||
algebraicOp
|
||||
|> tryAnalyticalSimplification(_, t1, t2)
|
||||
|> E.R.bind(_, x =>
|
||||
switch x {
|
||||
| #SymbolicDist(_) as t => Ok(t)
|
||||
| _ => combine(evaluationParams, algebraicOp, t1, t2)
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
module PointwiseCombination = {
|
||||
//TODO: This is crude and slow. It forces everything to be pointSetDist, even though much
|
||||
//of the process could happen on symbolic distributions without a conversion to be a pointSetDist.
|
||||
let pointwiseAdd = (evaluationParams: evaluationParams, t1: node, t2: node) =>
|
||||
switch (Node.render(evaluationParams, t1), Node.render(evaluationParams, t2)) {
|
||||
| (Ok(#RenderedDist(rs1)), Ok(#RenderedDist(rs2))) =>
|
||||
Ok(
|
||||
#RenderedDist(
|
||||
PointSetDist.combinePointwise(
|
||||
~integralSumCachesFn=(a, b) => Some(a +. b),
|
||||
~integralCachesFn=(a, b) => Some(
|
||||
Continuous.combinePointwise(~distributionType=#CDF, \"+.", a, b),
|
||||
),
|
||||
\"+.",
|
||||
rs1,
|
||||
rs2,
|
||||
),
|
||||
),
|
||||
)
|
||||
| (Error(e1), _) => Error(e1)
|
||||
| (_, Error(e2)) => Error(e2)
|
||||
| _ => Error("Pointwise combination: rendering failed.")
|
||||
}
|
||||
|
||||
let pointwiseCombine = (fn, evaluationParams: evaluationParams, t1: node, t2: node) =>
|
||||
switch // TODO: construct a function that we can easily sample from, to construct
|
||||
// a RenderedDist. Use the xMin and xMax of the rendered pointSetDists to tell the sampling function where to look.
|
||||
// TODO: This should work for symbolic distributions too!
|
||||
(Node.render(evaluationParams, t1), Node.render(evaluationParams, t2)) {
|
||||
| (Ok(#RenderedDist(rs1)), Ok(#RenderedDist(rs2))) =>
|
||||
Ok(#RenderedDist(PointSetDist.combinePointwise(fn, rs1, rs2)))
|
||||
| (Error(e1), _) => Error(e1)
|
||||
| (_, Error(e2)) => Error(e2)
|
||||
| _ => Error("Pointwise combination: rendering failed.")
|
||||
}
|
||||
|
||||
let operationToLeaf = (
|
||||
evaluationParams: evaluationParams,
|
||||
pointwiseOp: Operation.pointwiseOperation,
|
||||
t1: node,
|
||||
t2: node,
|
||||
) =>
|
||||
switch pointwiseOp {
|
||||
| #Add => pointwiseAdd(evaluationParams, t1, t2)
|
||||
| #Multiply => pointwiseCombine(\"*.", evaluationParams, t1, t2)
|
||||
| #Power => pointwiseCombine(\"**", evaluationParams, t1, t2)
|
||||
}
|
||||
}
|
||||
|
||||
module Truncate = {
|
||||
type simplificationResult = [
|
||||
| #Solution(ASTTypes.node)
|
||||
| #Error(string)
|
||||
| #NoSolution
|
||||
]
|
||||
|
||||
let trySimplification = (leftCutoff, rightCutoff, t): simplificationResult =>
|
||||
switch (leftCutoff, rightCutoff, t) {
|
||||
| (None, None, t) => #Solution(t)
|
||||
| (Some(lc), Some(rc), _) if lc > rc =>
|
||||
#Error("Left truncation bound must be smaller than right truncation bound.")
|
||||
| (lc, rc, #SymbolicDist(#Uniform(u))) =>
|
||||
#Solution(#SymbolicDist(#Uniform(SymbolicDist.Uniform.truncate(lc, rc, u))))
|
||||
| _ => #NoSolution
|
||||
}
|
||||
|
||||
let truncateAsShape = (evaluationParams: evaluationParams, leftCutoff, rightCutoff, t) =>
|
||||
switch // TODO: use named args for xMin/xMax in renderToShape; if we're lucky we can at least get the tail
|
||||
// of a distribution we otherwise wouldn't get at all
|
||||
Node.ensureIsRendered(evaluationParams, t) {
|
||||
| Ok(#RenderedDist(rs)) =>
|
||||
Ok(#RenderedDist(PointSetDist.T.truncate(leftCutoff, rightCutoff, rs)))
|
||||
| Error(e) => Error(e)
|
||||
| _ => Error("Could not truncate distribution.")
|
||||
}
|
||||
|
||||
let operationToLeaf = (
|
||||
evaluationParams,
|
||||
leftCutoff: option<float>,
|
||||
rightCutoff: option<float>,
|
||||
t: node,
|
||||
): result<node, string> =>
|
||||
t
|
||||
|> trySimplification(leftCutoff, rightCutoff)
|
||||
|> (
|
||||
x =>
|
||||
switch x {
|
||||
| #Solution(t) => Ok(t)
|
||||
| #Error(e) => Error(e)
|
||||
| #NoSolution => truncateAsShape(evaluationParams, leftCutoff, rightCutoff, t)
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
module Normalize = {
|
||||
let rec operationToLeaf = (evaluationParams, t: node): result<node, string> =>
|
||||
switch t {
|
||||
| #RenderedDist(s) => Ok(#RenderedDist(PointSetDist.T.normalize(s)))
|
||||
| #SymbolicDist(_) => Ok(t)
|
||||
| _ => ASTTypes.Node.evaluateAndRetry(evaluationParams, operationToLeaf, t)
|
||||
}
|
||||
}
|
||||
|
||||
module FunctionCall = {
|
||||
let _runHardcodedFunction = (name, evaluationParams, args) =>
|
||||
TypeSystem.Function.Ts.findByNameAndRun(HardcodedFunctions.all, name, evaluationParams, args)
|
||||
|
||||
let _runLocalFunction = (name, evaluationParams: evaluationParams, args) =>
|
||||
Environment.getFunction(evaluationParams.environment, name) |> E.R.bind(_, ((argNames, fn)) =>
|
||||
ASTTypes.Function.run(evaluationParams, args, (argNames, fn))
|
||||
)
|
||||
|
||||
let _runWithEvaluatedInputs = (
|
||||
evaluationParams: ASTTypes.evaluationParams,
|
||||
name,
|
||||
args: array<ASTTypes.node>,
|
||||
) =>
|
||||
_runHardcodedFunction(name, evaluationParams, args) |> E.O.default(
|
||||
_runLocalFunction(name, evaluationParams, args),
|
||||
)
|
||||
|
||||
// TODO: This forces things to be floats
|
||||
let run = (evaluationParams, name, args) =>
|
||||
args
|
||||
|> E.A.fmap(a => evaluationParams.evaluateNode(evaluationParams, a))
|
||||
|> E.A.R.firstErrorOrOpen
|
||||
|> E.R.bind(_, _runWithEvaluatedInputs(evaluationParams, name))
|
||||
}
|
||||
|
||||
module Render = {
|
||||
let rec operationToLeaf = (evaluationParams: evaluationParams, t: node): result<node, string> =>
|
||||
switch t {
|
||||
| #Function(_) => Error("Cannot render a function")
|
||||
| #SymbolicDist(d) =>
|
||||
Ok(
|
||||
#RenderedDist(
|
||||
SymbolicDist.T.toPointSetDist(evaluationParams.samplingInputs.pointSetDistLength, d),
|
||||
),
|
||||
)
|
||||
| #RenderedDist(_) as t => Ok(t) // already a rendered pointSetDist, we're done here
|
||||
| _ => ASTTypes.Node.evaluateAndRetry(evaluationParams, operationToLeaf, t)
|
||||
}
|
||||
}
|
||||
|
||||
/* This function recursively goes through the nodes of the parse tree,
|
||||
replacing each Operation node and its subtree with a Data node.
|
||||
Whenever possible, the replacement produces a new Symbolic Data node,
|
||||
but most often it will produce a RenderedDist.
|
||||
This function is used mainly to turn a parse tree into a single RenderedDist
|
||||
that can then be displayed to the user. */
|
||||
let rec toLeaf = (evaluationParams: ASTTypes.evaluationParams, node: node): result<node, string> =>
|
||||
switch node {
|
||||
// Leaf nodes just stay leaf nodes
|
||||
| #SymbolicDist(_)
|
||||
| #Function(_)
|
||||
| #RenderedDist(_) =>
|
||||
Ok(node)
|
||||
| #Array(args) =>
|
||||
args |> E.A.fmap(toLeaf(evaluationParams)) |> E.A.R.firstErrorOrOpen |> E.R.fmap(r => #Array(r))
|
||||
// Operations nevaluationParamsd to be turned into leaves
|
||||
| #AlgebraicCombination(algebraicOp, t1, t2) =>
|
||||
AlgebraicCombination.operationToLeaf(evaluationParams, algebraicOp, t1, t2)
|
||||
| #PointwiseCombination(pointwiseOp, t1, t2) =>
|
||||
PointwiseCombination.operationToLeaf(evaluationParams, pointwiseOp, t1, t2)
|
||||
| #Truncate(leftCutoff, rightCutoff, t) =>
|
||||
Truncate.operationToLeaf(evaluationParams, leftCutoff, rightCutoff, t)
|
||||
| #Normalize(t) => Normalize.operationToLeaf(evaluationParams, t)
|
||||
| #Render(t) => Render.operationToLeaf(evaluationParams, t)
|
||||
| #Hash(t) =>
|
||||
t
|
||||
|> E.A.fmap(((name: string, node: node)) =>
|
||||
toLeaf(evaluationParams, node) |> E.R.fmap(r => (name, r))
|
||||
)
|
||||
|> E.A.R.firstErrorOrOpen
|
||||
|> E.R.fmap(r => #Hash(r))
|
||||
| #Symbol(r) =>
|
||||
ASTTypes.Environment.get(evaluationParams.environment, r)
|
||||
|> E.O.toResult("Undeclared variable " ++ r)
|
||||
|> E.R.bind(_, toLeaf(evaluationParams))
|
||||
| #FunctionCall(name, args) =>
|
||||
FunctionCall.run(evaluationParams, name, args) |> E.R.bind(_, toLeaf(evaluationParams))
|
||||
}
|
||||
|
|
@ -1,233 +0,0 @@
|
|||
@genType
|
||||
type rec hash = array<(string, node)>
|
||||
and node = [
|
||||
| #SymbolicDist(SymbolicDistTypes.symbolicDist)
|
||||
| #RenderedDist(PointSetTypes.pointSetDist)
|
||||
| #Symbol(string)
|
||||
| #Hash(hash)
|
||||
| #Array(array<node>)
|
||||
| #Function(array<string>, node)
|
||||
| #AlgebraicCombination(Operation.algebraicOperation, node, node)
|
||||
| #PointwiseCombination(Operation.pointwiseOperation, node, node)
|
||||
| #Normalize(node)
|
||||
| #Render(node)
|
||||
| #Truncate(option<float>, option<float>, node)
|
||||
| #FunctionCall(string, array<node>)
|
||||
]
|
||||
|
||||
type statement = [
|
||||
| #Assignment(string, node)
|
||||
| #Expression(node)
|
||||
]
|
||||
type program = array<statement>
|
||||
|
||||
type environment = Belt.Map.String.t<node>
|
||||
|
||||
type rec evaluationParams = {
|
||||
samplingInputs: SamplingInputs.samplingInputs,
|
||||
environment: environment,
|
||||
evaluateNode: (evaluationParams, node) => Belt.Result.t<node, string>,
|
||||
}
|
||||
|
||||
module Environment = {
|
||||
type t = environment
|
||||
module MS = Belt.Map.String
|
||||
let fromArray = MS.fromArray
|
||||
let empty: t = []->fromArray
|
||||
let mergeKeepSecond = (a: t, b: t) =>
|
||||
MS.merge(a, b, (_, a, b) =>
|
||||
switch (a, b) {
|
||||
| (_, Some(b)) => Some(b)
|
||||
| (Some(a), _) => Some(a)
|
||||
| _ => None
|
||||
}
|
||||
)
|
||||
let update = (t, str, fn) => MS.update(t, str, fn)
|
||||
let get = (t: t, str) => MS.get(t, str)
|
||||
let getFunction = (t: t, str) =>
|
||||
switch get(t, str) {
|
||||
| Some(#Function(argNames, fn)) => Ok((argNames, fn))
|
||||
| _ => Error("Function " ++ (str ++ " not found"))
|
||||
}
|
||||
}
|
||||
|
||||
module Node = {
|
||||
let getFloat = (node: node) =>
|
||||
node |> (
|
||||
x =>
|
||||
switch x {
|
||||
| #RenderedDist(Discrete({xyShape: {xs: [x], ys: [1.0]}})) => Some(x)
|
||||
| #SymbolicDist(#Float(x)) => Some(x)
|
||||
| _ => None
|
||||
}
|
||||
)
|
||||
|
||||
let evaluate = (evaluationParams: evaluationParams) =>
|
||||
evaluationParams.evaluateNode(evaluationParams)
|
||||
|
||||
let evaluateAndRetry = (evaluationParams, fn, node) =>
|
||||
node |> evaluationParams.evaluateNode(evaluationParams) |> E.R.bind(_, fn(evaluationParams))
|
||||
|
||||
let rec toString: node => string = x =>
|
||||
switch x {
|
||||
| #SymbolicDist(d) => SymbolicDist.T.toString(d)
|
||||
| #RenderedDist(_) => "[renderedShape]"
|
||||
| #AlgebraicCombination(op, t1, t2) =>
|
||||
Operation.Algebraic.format(op, toString(t1), toString(t2))
|
||||
| #PointwiseCombination(op, t1, t2) =>
|
||||
Operation.Pointwise.format(op, toString(t1), toString(t2))
|
||||
| #Normalize(t) => "normalize(k" ++ (toString(t) ++ ")")
|
||||
| #Truncate(lc, rc, t) => Operation.Truncate.toString(lc, rc, toString(t))
|
||||
| #Render(t) => toString(t)
|
||||
| #Symbol(t) => "Symbol: " ++ t
|
||||
| #FunctionCall(name, args) =>
|
||||
"[Function call: (" ++
|
||||
(name ++
|
||||
((args |> E.A.fmap(toString) |> Js.String.concatMany(_, ",")) ++ ")]"))
|
||||
| #Function(args, internal) =>
|
||||
"[Function: (" ++ ((args |> Js.String.concatMany(_, ",")) ++ (toString(internal) ++ ")]"))
|
||||
| #Array(a) => "[" ++ ((a |> E.A.fmap(toString) |> Js.String.concatMany(_, ",")) ++ "]")
|
||||
| #Hash(h) =>
|
||||
"{" ++
|
||||
((h
|
||||
|> E.A.fmap(((name, value)) => name ++ (":" ++ toString(value)))
|
||||
|> Js.String.concatMany(_, ",")) ++
|
||||
"}")
|
||||
}
|
||||
|
||||
let render = (evaluationParams: evaluationParams, r) => #Render(r) |> evaluate(evaluationParams)
|
||||
|
||||
let ensureIsRendered = (params, t) =>
|
||||
switch t {
|
||||
| #RenderedDist(_) => Ok(t)
|
||||
| _ =>
|
||||
switch render(params, t) {
|
||||
| Ok(#RenderedDist(r)) => Ok(#RenderedDist(r))
|
||||
| Ok(_) => Error("Did not render as requested")
|
||||
| Error(e) => Error(e)
|
||||
}
|
||||
}
|
||||
|
||||
let ensureIsRenderedAndGetShape = (params, t) =>
|
||||
switch ensureIsRendered(params, t) {
|
||||
| Ok(#RenderedDist(r)) => Ok(r)
|
||||
| Ok(_) => Error("Did not render as requested")
|
||||
| Error(e) => Error(e)
|
||||
}
|
||||
|
||||
let toPointSetDist = (item: node) =>
|
||||
switch item {
|
||||
| #RenderedDist(r) => Some(r)
|
||||
| _ => None
|
||||
}
|
||||
|
||||
let _toFloat = (t: PointSetTypes.pointSetDist) =>
|
||||
switch t {
|
||||
| Discrete({xyShape: {xs: [x], ys: [1.0]}}) => Some(#SymbolicDist(#Float(x)))
|
||||
| _ => None
|
||||
}
|
||||
|
||||
let toFloat = (item: node): result<node, string> =>
|
||||
item |> toPointSetDist |> E.O.bind(_, _toFloat) |> E.O.toResult("Not valid shape")
|
||||
}
|
||||
|
||||
module Function = {
|
||||
type t = (array<string>, node)
|
||||
let fromNode: node => option<t> = node =>
|
||||
switch node {
|
||||
| #Function(r) => Some(r)
|
||||
| _ => None
|
||||
}
|
||||
let argumentNames = ((a, _): t) => a
|
||||
let internals = ((_, b): t) => b
|
||||
let run = (evaluationParams: evaluationParams, args: array<node>, t: t) =>
|
||||
if E.A.length(args) == E.A.length(argumentNames(t)) {
|
||||
let newEnvironment = Belt.Array.zip(argumentNames(t), args) |> Environment.fromArray
|
||||
let newEvaluationParams: evaluationParams = {
|
||||
samplingInputs: evaluationParams.samplingInputs,
|
||||
environment: Environment.mergeKeepSecond(evaluationParams.environment, newEnvironment),
|
||||
evaluateNode: evaluationParams.evaluateNode,
|
||||
}
|
||||
evaluationParams.evaluateNode(newEvaluationParams, internals(t))
|
||||
} else {
|
||||
Error("Wrong number of variables")
|
||||
}
|
||||
}
|
||||
|
||||
module SamplingDistribution = {
|
||||
type t = [
|
||||
| #SymbolicDist(SymbolicDistTypes.symbolicDist)
|
||||
| #RenderedDist(PointSetTypes.pointSetDist)
|
||||
]
|
||||
|
||||
let isSamplingDistribution: node => bool = x =>
|
||||
switch x {
|
||||
| #SymbolicDist(_) => true
|
||||
| #RenderedDist(_) => true
|
||||
| _ => false
|
||||
}
|
||||
|
||||
let fromNode: node => result<t, string> = x =>
|
||||
switch x {
|
||||
| #SymbolicDist(n) => Ok(#SymbolicDist(n))
|
||||
| #RenderedDist(n) => Ok(#RenderedDist(n))
|
||||
| _ => Error("Not valid type")
|
||||
}
|
||||
|
||||
let renderIfIsNotSamplingDistribution = (params, t): result<node, string> =>
|
||||
!isSamplingDistribution(t)
|
||||
? switch Node.render(params, t) {
|
||||
| Ok(r) => Ok(r)
|
||||
| Error(e) => Error(e)
|
||||
}
|
||||
: Ok(t)
|
||||
|
||||
let map = (~renderedDistFn, ~symbolicDistFn, node: node) =>
|
||||
node |> (
|
||||
x =>
|
||||
switch x {
|
||||
| #RenderedDist(r) => Some(renderedDistFn(r))
|
||||
| #SymbolicDist(s) => Some(symbolicDistFn(s))
|
||||
| _ => None
|
||||
}
|
||||
)
|
||||
|
||||
let sampleN = n =>
|
||||
map(~renderedDistFn=PointSetDist.sampleNRendered(n), ~symbolicDistFn=SymbolicDist.T.sampleN(n))
|
||||
|
||||
let getCombinationSamples = (n, algebraicOp, t1: node, t2: node) =>
|
||||
switch (sampleN(n, t1), sampleN(n, t2)) {
|
||||
| (Some(a), Some(b)) =>
|
||||
Some(
|
||||
Belt.Array.zip(a, b) |> E.A.fmap(((a, b)) => Operation.Algebraic.toFn(algebraicOp, a, b)),
|
||||
)
|
||||
| _ => None
|
||||
}
|
||||
|
||||
let combineShapesUsingSampling = (
|
||||
evaluationParams: evaluationParams,
|
||||
algebraicOp,
|
||||
t1: node,
|
||||
t2: node,
|
||||
) => {
|
||||
let i1 = renderIfIsNotSamplingDistribution(evaluationParams, t1)
|
||||
let i2 = renderIfIsNotSamplingDistribution(evaluationParams, t2)
|
||||
E.R.merge(i1, i2) |> E.R.bind(_, ((a, b)) => {
|
||||
let samples =
|
||||
getCombinationSamples(
|
||||
evaluationParams.samplingInputs.sampleCount,
|
||||
algebraicOp,
|
||||
a,
|
||||
b,
|
||||
) |> E.O.toResult("Could not get samples")
|
||||
|
||||
let sampleSetDist = samples->E.R.bind(SampleSetDist.make)
|
||||
|
||||
let pointSetDist =
|
||||
sampleSetDist->E.R.bind(r =>
|
||||
SampleSetDist.toPointSetDist(~samplingInputs=evaluationParams.samplingInputs, ~samples=r)
|
||||
)
|
||||
pointSetDist |> E.R.fmap(r => #Normalize(#RenderedDist(r)))
|
||||
})
|
||||
}
|
||||
}
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
open PointSetTypes
|
||||
|
||||
@genType
|
||||
type t = PointSetTypes.distPlus
|
||||
|
||||
let pointSetDistIntegral = pointSetDist => PointSetDist.T.Integral.get(pointSetDist)
|
||||
let make = (~pointSetDist, ~squiggleString, ()): t => {
|
||||
let integral = pointSetDistIntegral(pointSetDist)
|
||||
{pointSetDist: pointSetDist, integralCache: integral, squiggleString: squiggleString}
|
||||
}
|
||||
|
||||
let update = (~pointSetDist=?, ~integralCache=?, ~squiggleString=?, t: t) => {
|
||||
pointSetDist: E.O.default(t.pointSetDist, pointSetDist),
|
||||
integralCache: E.O.default(t.integralCache, integralCache),
|
||||
squiggleString: E.O.default(t.squiggleString, squiggleString),
|
||||
}
|
||||
|
||||
let updateShape = (pointSetDist, t) => {
|
||||
let integralCache = pointSetDistIntegral(pointSetDist)
|
||||
update(~pointSetDist, ~integralCache, t)
|
||||
}
|
||||
|
||||
let toPointSetDist = ({pointSetDist, _}: t) => pointSetDist
|
||||
|
||||
let pointSetDistFn = (fn, {pointSetDist}: t) => fn(pointSetDist)
|
||||
|
||||
module T = Distributions.Dist({
|
||||
type t = PointSetTypes.distPlus
|
||||
type integral = PointSetTypes.distPlus
|
||||
let toPointSetDist = toPointSetDist
|
||||
let toContinuous = pointSetDistFn(PointSetDist.T.toContinuous)
|
||||
let toDiscrete = pointSetDistFn(PointSetDist.T.toDiscrete)
|
||||
|
||||
let normalize = (t: t): t => {
|
||||
let normalizedShape = t |> toPointSetDist |> PointSetDist.T.normalize
|
||||
t |> updateShape(normalizedShape)
|
||||
}
|
||||
|
||||
let truncate = (leftCutoff, rightCutoff, t: t): t => {
|
||||
let truncatedShape = t |> toPointSetDist |> PointSetDist.T.truncate(leftCutoff, rightCutoff)
|
||||
|
||||
t |> updateShape(truncatedShape)
|
||||
}
|
||||
|
||||
let xToY = (f, t: t) => t |> toPointSetDist |> PointSetDist.T.xToY(f)
|
||||
|
||||
let minX = pointSetDistFn(PointSetDist.T.minX)
|
||||
let maxX = pointSetDistFn(PointSetDist.T.maxX)
|
||||
let toDiscreteProbabilityMassFraction = pointSetDistFn(
|
||||
PointSetDist.T.toDiscreteProbabilityMassFraction,
|
||||
)
|
||||
|
||||
// This bit is kind of awkward, could probably use rethinking.
|
||||
let integral = (t: t) => updateShape(Continuous(t.integralCache), t)
|
||||
|
||||
let updateIntegralCache = (integralCache: option<PointSetTypes.continuousShape>, t) =>
|
||||
update(~integralCache=E.O.default(t.integralCache, integralCache), t)
|
||||
|
||||
let downsample = (i, t): t => updateShape(t |> toPointSetDist |> PointSetDist.T.downsample(i), t)
|
||||
// todo: adjust for limit, maybe?
|
||||
let mapY = (
|
||||
~integralSumCacheFn=previousIntegralSum => None,
|
||||
~integralCacheFn=previousIntegralCache => None,
|
||||
~fn,
|
||||
{pointSetDist, _} as t: t,
|
||||
): t => PointSetDist.T.mapY(~integralSumCacheFn, ~fn, pointSetDist) |> updateShape(_, t)
|
||||
|
||||
// get the total of everything
|
||||
let integralEndY = (t: t) => {
|
||||
PointSetDist.T.Integral.sum(toPointSetDist(t))
|
||||
}
|
||||
|
||||
// TODO: Fix this below, obviously. Adjust for limits
|
||||
let integralXtoY = (f, t: t) => {
|
||||
PointSetDist.T.Integral.xToY(f, toPointSetDist(t))
|
||||
}
|
||||
|
||||
// TODO: This part is broken when there is a limit, if this is supposed to be taken into account.
|
||||
let integralYtoX = (f, t: t) => {
|
||||
PointSetDist.T.Integral.yToX(f, toPointSetDist(t))
|
||||
}
|
||||
|
||||
let mean = (t: t) => {
|
||||
PointSetDist.T.mean(t.pointSetDist)
|
||||
}
|
||||
let variance = (t: t) => PointSetDist.T.variance(t.pointSetDist)
|
||||
})
|
||||
|
|
@ -1,240 +0,0 @@
|
|||
open TypeSystem
|
||||
|
||||
let wrongInputsError = (r: array<typedValue>) => {
|
||||
let inputs = r |> E.A.fmap(TypedValue.toString) |> Js.String.concatMany(_, ",")
|
||||
Js.log3("Inputs were", inputs, r)
|
||||
Error("Wrong inputs. The inputs were:" ++ inputs)
|
||||
}
|
||||
|
||||
let to_: (float, float) => result<node, string> = (low, high) =>
|
||||
switch (low, high) {
|
||||
| (low, high) if low <= 0.0 && low < high =>
|
||||
Ok(#SymbolicDist(SymbolicDist.Normal.from90PercentCI(low, high)))
|
||||
| (low, high) if low < high =>
|
||||
Ok(#SymbolicDist(SymbolicDist.Lognormal.from90PercentCI(low, high)))
|
||||
| (_, _) => Error("Low value must be less than high value.")
|
||||
}
|
||||
|
||||
let makeSymbolicFromTwoFloats = (name, fn) =>
|
||||
Function.T.make(
|
||||
~name,
|
||||
~outputType=#SamplingDistribution,
|
||||
~inputTypes=[#Float, #Float],
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#Float(a), #Float(b)] => fn(a, b) |> E.R.fmap(r => #SymbolicDist(r))
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
)
|
||||
|
||||
let makeSymbolicFromOneFloat = (name, fn) =>
|
||||
Function.T.make(
|
||||
~name,
|
||||
~outputType=#SamplingDistribution,
|
||||
~inputTypes=[#Float],
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#Float(a)] => fn(a) |> E.R.fmap(r => #SymbolicDist(r))
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
)
|
||||
|
||||
let makeDistFloat = (name, fn) =>
|
||||
Function.T.make(
|
||||
~name,
|
||||
~outputType=#SamplingDistribution,
|
||||
~inputTypes=[#SamplingDistribution, #Float],
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#SamplingDist(a), #Float(b)] => fn(a, b)
|
||||
| [#RenderedDist(a), #Float(b)] => fn(#RenderedDist(a), b)
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
)
|
||||
|
||||
let makeRenderedDistFloat = (name, fn) =>
|
||||
Function.T.make(
|
||||
~name,
|
||||
~outputType=#RenderedDistribution,
|
||||
~inputTypes=[#RenderedDistribution, #Float],
|
||||
~shouldCoerceTypes=true,
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#RenderedDist(a), #Float(b)] => fn(a, b)
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
)
|
||||
|
||||
let makeDist = (name, fn) =>
|
||||
Function.T.make(
|
||||
~name,
|
||||
~outputType=#SamplingDistribution,
|
||||
~inputTypes=[#SamplingDistribution],
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#SamplingDist(a)] => fn(a)
|
||||
| [#RenderedDist(a)] => fn(#RenderedDist(a))
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
)
|
||||
|
||||
let floatFromDist = (
|
||||
distToFloatOp: Operation.distToFloatOperation,
|
||||
t: TypeSystem.samplingDist,
|
||||
): result<node, string> =>
|
||||
switch t {
|
||||
| #SymbolicDist(s) =>
|
||||
SymbolicDist.T.operate(distToFloatOp, s) |> E.R.bind(_, v => Ok(#SymbolicDist(#Float(v))))
|
||||
| #RenderedDist(rs) =>
|
||||
PointSetDist.operate(distToFloatOp, rs) |> (v => Ok(#SymbolicDist(#Float(v))))
|
||||
}
|
||||
|
||||
let verticalScaling = (scaleOp, rs, scaleBy) => {
|
||||
// scaleBy has to be a single float, otherwise we'll return an error.
|
||||
let fn = (secondary, main) => Operation.Scale.toFn(scaleOp, main, secondary)
|
||||
let integralSumCacheFn = Operation.Scale.toIntegralSumCacheFn(scaleOp)
|
||||
let integralCacheFn = Operation.Scale.toIntegralCacheFn(scaleOp)
|
||||
Ok(
|
||||
#RenderedDist(
|
||||
PointSetDist.T.mapY(
|
||||
~integralSumCacheFn=integralSumCacheFn(scaleBy),
|
||||
~integralCacheFn=integralCacheFn(scaleBy),
|
||||
~fn=fn(scaleBy),
|
||||
rs,
|
||||
),
|
||||
),
|
||||
)
|
||||
}
|
||||
|
||||
module Multimodal = {
|
||||
let getByNameResult = Hash.getByNameResult
|
||||
|
||||
let _paramsToDistsAndWeights = (r: array<typedValue>) =>
|
||||
switch r {
|
||||
| [#Hash(r)] =>
|
||||
let dists =
|
||||
getByNameResult(r, "dists")
|
||||
->E.R.bind(TypeSystem.TypedValue.toArray)
|
||||
->E.R.bind(r => r |> E.A.fmap(TypeSystem.TypedValue.toDist) |> E.A.R.firstErrorOrOpen)
|
||||
let weights =
|
||||
getByNameResult(r, "weights")
|
||||
->E.R.bind(TypeSystem.TypedValue.toArray)
|
||||
->E.R.bind(r => r |> E.A.fmap(TypeSystem.TypedValue.toFloat) |> E.A.R.firstErrorOrOpen)
|
||||
|
||||
E.R.merge(dists, weights)->E.R.bind(((a, b)) =>
|
||||
E.A.length(b) > E.A.length(a)
|
||||
? Error("Too many weights provided")
|
||||
: Ok(
|
||||
E.A.zipMaxLength(a, b) |> E.A.fmap(((a, b)) => (
|
||||
a |> E.O.toExn(""),
|
||||
b |> E.O.default(1.0),
|
||||
)),
|
||||
)
|
||||
)
|
||||
| _ => Error("Needs items")
|
||||
}
|
||||
let _runner: array<typedValue> => result<node, string> = r => {
|
||||
let paramsToDistsAndWeights =
|
||||
_paramsToDistsAndWeights(r) |> E.R.fmap(
|
||||
E.A.fmap(((dist, weight)) =>
|
||||
#FunctionCall("scaleMultiply", [dist, #SymbolicDist(#Float(weight))])
|
||||
),
|
||||
)
|
||||
let pointwiseSum: result<node, string> =
|
||||
paramsToDistsAndWeights->E.R.bind(E.R.errorIfCondition(E.A.isEmpty, "Needs one input"))
|
||||
|> E.R.fmap(r =>
|
||||
r
|
||||
|> Js.Array.sliceFrom(1)
|
||||
|> E.A.fold_left((acc, x) => #PointwiseCombination(#Add, acc, x), E.A.unsafe_get(r, 0))
|
||||
)
|
||||
pointwiseSum
|
||||
}
|
||||
|
||||
let _function = Function.T.make(
|
||||
~name="multimodal",
|
||||
~outputType=#SamplingDistribution,
|
||||
~inputTypes=[#Hash([("dists", #Array(#SamplingDistribution)), ("weights", #Array(#Float))])],
|
||||
~run=_runner,
|
||||
(),
|
||||
)
|
||||
}
|
||||
|
||||
let all = [
|
||||
makeSymbolicFromTwoFloats("normal", SymbolicDist.Normal.make),
|
||||
makeSymbolicFromTwoFloats("uniform", SymbolicDist.Uniform.make),
|
||||
makeSymbolicFromTwoFloats("beta", SymbolicDist.Beta.make),
|
||||
makeSymbolicFromTwoFloats("lognormal", SymbolicDist.Lognormal.make),
|
||||
makeSymbolicFromTwoFloats("lognormalFromMeanAndStdDev", SymbolicDist.Lognormal.fromMeanAndStdev),
|
||||
makeSymbolicFromOneFloat("exponential", SymbolicDist.Exponential.make),
|
||||
Function.T.make(
|
||||
~name="to",
|
||||
~outputType=#SamplingDistribution,
|
||||
~inputTypes=[#Float, #Float],
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#Float(a), #Float(b)] => to_(a, b)
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
),
|
||||
Function.T.make(
|
||||
~name="triangular",
|
||||
~outputType=#SamplingDistribution,
|
||||
~inputTypes=[#Float, #Float, #Float],
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#Float(a), #Float(b), #Float(c)] =>
|
||||
SymbolicDist.Triangular.make(a, b, c) |> E.R.fmap(r => #SymbolicDist(r))
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
),
|
||||
Function.T.make(
|
||||
~name="log",
|
||||
~outputType=#Float,
|
||||
~inputTypes=[#Float],
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#Float(a)] => Ok(#SymbolicDist(#Float(Js.Math.log(a))))
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
),
|
||||
makeDistFloat("pdf", (dist, float) => floatFromDist(#Pdf(float), dist)),
|
||||
makeDistFloat("inv", (dist, float) => floatFromDist(#Inv(float), dist)),
|
||||
makeDistFloat("cdf", (dist, float) => floatFromDist(#Cdf(float), dist)),
|
||||
makeDist("mean", dist => floatFromDist(#Mean, dist)),
|
||||
makeDist("sample", dist => floatFromDist(#Sample, dist)),
|
||||
Function.T.make(
|
||||
~name="render",
|
||||
~outputType=#RenderedDistribution,
|
||||
~inputTypes=[#RenderedDistribution],
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#RenderedDist(c)] => Ok(#RenderedDist(c))
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
),
|
||||
Function.T.make(
|
||||
~name="normalize",
|
||||
~outputType=#SamplingDistribution,
|
||||
~inputTypes=[#SamplingDistribution],
|
||||
~run=x =>
|
||||
switch x {
|
||||
| [#SamplingDist(#SymbolicDist(c))] => Ok(#SymbolicDist(c))
|
||||
| [#SamplingDist(#RenderedDist(c))] => Ok(#RenderedDist(PointSetDist.T.normalize(c)))
|
||||
| e => wrongInputsError(e)
|
||||
},
|
||||
(),
|
||||
),
|
||||
makeRenderedDistFloat("scaleExp", (dist, float) => verticalScaling(#Power, dist, float)),
|
||||
makeRenderedDistFloat("scaleMultiply", (dist, float) => verticalScaling(#Multiply, dist, float)),
|
||||
makeRenderedDistFloat("scaleLog", (dist, float) => verticalScaling(#Logarithm, dist, float)),
|
||||
Multimodal._function,
|
||||
]
|
||||
|
|
@ -1,196 +0,0 @@
|
|||
type node = ASTTypes.node
|
||||
let getFloat = ASTTypes.Node.getFloat
|
||||
|
||||
type samplingDist = [
|
||||
| #SymbolicDist(SymbolicDistTypes.symbolicDist)
|
||||
| #RenderedDist(PointSetTypes.pointSetDist)
|
||||
]
|
||||
|
||||
type rec hashType = array<(string, _type)>
|
||||
and _type = [
|
||||
| #Float
|
||||
| #SamplingDistribution
|
||||
| #RenderedDistribution
|
||||
| #Array(_type)
|
||||
| #Hash(hashType)
|
||||
]
|
||||
|
||||
type rec hashTypedValue = array<(string, typedValue)>
|
||||
and typedValue = [
|
||||
| #Float(float)
|
||||
| #RenderedDist(PointSetTypes.pointSetDist)
|
||||
| #SamplingDist(samplingDist)
|
||||
| #Array(array<typedValue>)
|
||||
| #Hash(hashTypedValue)
|
||||
]
|
||||
|
||||
type _function = {
|
||||
name: string,
|
||||
inputTypes: array<_type>,
|
||||
outputType: _type,
|
||||
run: array<typedValue> => result<node, string>,
|
||||
shouldCoerceTypes: bool,
|
||||
}
|
||||
|
||||
type functions = array<_function>
|
||||
type inputNodes = array<node>
|
||||
|
||||
module TypedValue = {
|
||||
let rec toString: typedValue => string = x =>
|
||||
switch x {
|
||||
| #SamplingDist(_) => "[sampling dist]"
|
||||
| #RenderedDist(_) => "[rendered PointSetDist]"
|
||||
| #Float(f) => "Float: " ++ Js.Float.toString(f)
|
||||
| #Array(a) => "[" ++ ((a |> E.A.fmap(toString) |> Js.String.concatMany(_, ",")) ++ "]")
|
||||
| #Hash(v) =>
|
||||
"{" ++
|
||||
((v
|
||||
|> E.A.fmap(((name, value)) => name ++ (":" ++ toString(value)))
|
||||
|> Js.String.concatMany(_, ",")) ++
|
||||
"}")
|
||||
}
|
||||
|
||||
let rec fromNode = (node: node): result<typedValue, string> =>
|
||||
switch node {
|
||||
| #SymbolicDist(#Float(r)) => Ok(#Float(r))
|
||||
| #SymbolicDist(s) => Ok(#SamplingDist(#SymbolicDist(s)))
|
||||
| #RenderedDist(s) => Ok(#RenderedDist(s))
|
||||
| #Array(r) => r |> E.A.fmap(fromNode) |> E.A.R.firstErrorOrOpen |> E.R.fmap(r => #Array(r))
|
||||
| #Hash(hash) =>
|
||||
hash
|
||||
|> E.A.fmap(((name, t)) => fromNode(t) |> E.R.fmap(r => (name, r)))
|
||||
|> E.A.R.firstErrorOrOpen
|
||||
|> E.R.fmap(r => #Hash(r))
|
||||
| e => Error("Wrong type: " ++ ASTTypes.Node.toString(e))
|
||||
}
|
||||
|
||||
// todo: Arrays and hashes
|
||||
let rec fromNodeWithTypeCoercion = (evaluationParams, _type: _type, node) =>
|
||||
switch (_type, node) {
|
||||
| (#Float, _) =>
|
||||
switch getFloat(node) {
|
||||
| Some(a) => Ok(#Float(a))
|
||||
| _ => Error("Type Error: Expected float.")
|
||||
}
|
||||
| (#SamplingDistribution, _) =>
|
||||
ASTTypes.SamplingDistribution.renderIfIsNotSamplingDistribution(
|
||||
evaluationParams,
|
||||
node,
|
||||
) |> E.R.bind(_, fromNode)
|
||||
| (#RenderedDistribution, _) =>
|
||||
ASTTypes.Node.render(evaluationParams, node) |> E.R.bind(_, fromNode)
|
||||
| (#Array(_type), #Array(b)) =>
|
||||
b
|
||||
|> E.A.fmap(fromNodeWithTypeCoercion(evaluationParams, _type))
|
||||
|> E.A.R.firstErrorOrOpen
|
||||
|> E.R.fmap(r => #Array(r))
|
||||
| (#Hash(named), #Hash(r)) =>
|
||||
let keyValues =
|
||||
named |> E.A.fmap(((name, intendedType)) => (name, intendedType, Hash.getByName(r, name)))
|
||||
let typedHash =
|
||||
keyValues
|
||||
|> E.A.fmap(((name, intendedType, optionNode)) =>
|
||||
switch optionNode {
|
||||
| Some(node) =>
|
||||
fromNodeWithTypeCoercion(evaluationParams, intendedType, node) |> E.R.fmap(node => (
|
||||
name,
|
||||
node,
|
||||
))
|
||||
| None => Error("Hash parameter not present in hash.")
|
||||
}
|
||||
)
|
||||
|> E.A.R.firstErrorOrOpen
|
||||
|> E.R.fmap(r => #Hash(r))
|
||||
typedHash
|
||||
| _ => Error("fromNodeWithTypeCoercion error, sorry.")
|
||||
}
|
||||
|
||||
let toFloat: typedValue => result<float, string> = x =>
|
||||
switch x {
|
||||
| #Float(x) => Ok(x)
|
||||
| _ => Error("Not a float")
|
||||
}
|
||||
|
||||
let toArray: typedValue => result<array<'a>, string> = x =>
|
||||
switch x {
|
||||
| #Array(x) => Ok(x)
|
||||
| _ => Error("Not an array")
|
||||
}
|
||||
|
||||
let toNamed: typedValue => result<hashTypedValue, string> = x =>
|
||||
switch x {
|
||||
| #Hash(x) => Ok(x)
|
||||
| _ => Error("Not a named item")
|
||||
}
|
||||
|
||||
let toDist: typedValue => result<node, string> = x =>
|
||||
switch x {
|
||||
| #SamplingDist(#SymbolicDist(c)) => Ok(#SymbolicDist(c))
|
||||
| #SamplingDist(#RenderedDist(c)) => Ok(#RenderedDist(c))
|
||||
| #RenderedDist(c) => Ok(#RenderedDist(c))
|
||||
| #Float(x) => Ok(#SymbolicDist(#Float(x)))
|
||||
| x => Error("Cannot be converted into a distribution: " ++ toString(x))
|
||||
}
|
||||
}
|
||||
|
||||
module Function = {
|
||||
type t = _function
|
||||
type ts = functions
|
||||
|
||||
module T = {
|
||||
let make = (~name, ~inputTypes, ~outputType, ~run, ~shouldCoerceTypes=true, _): t => {
|
||||
name: name,
|
||||
inputTypes: inputTypes,
|
||||
outputType: outputType,
|
||||
run: run,
|
||||
shouldCoerceTypes: shouldCoerceTypes,
|
||||
}
|
||||
|
||||
let _inputLengthCheck = (inputNodes: inputNodes, t: t) => {
|
||||
let expectedLength = E.A.length(t.inputTypes)
|
||||
let actualLength = E.A.length(inputNodes)
|
||||
expectedLength == actualLength
|
||||
? Ok(inputNodes)
|
||||
: Error(
|
||||
"Wrong number of inputs. Expected" ++
|
||||
((expectedLength |> E.I.toString) ++
|
||||
(". Got:" ++ (actualLength |> E.I.toString))),
|
||||
)
|
||||
}
|
||||
|
||||
let _coerceInputNodes = (evaluationParams, inputTypes, shouldCoerce, inputNodes) =>
|
||||
Belt.Array.zip(inputTypes, inputNodes)
|
||||
|> E.A.fmap(((def, input)) =>
|
||||
shouldCoerce
|
||||
? TypedValue.fromNodeWithTypeCoercion(evaluationParams, def, input)
|
||||
: TypedValue.fromNode(input)
|
||||
)
|
||||
|> E.A.R.firstErrorOrOpen
|
||||
|
||||
let inputsToTypedValues = (
|
||||
evaluationParams: ASTTypes.evaluationParams,
|
||||
inputNodes: inputNodes,
|
||||
t: t,
|
||||
) =>
|
||||
_inputLengthCheck(inputNodes, t)->E.R.bind(
|
||||
_coerceInputNodes(evaluationParams, t.inputTypes, t.shouldCoerceTypes),
|
||||
)
|
||||
|
||||
let run = (evaluationParams: ASTTypes.evaluationParams, inputNodes: inputNodes, t: t) =>
|
||||
inputsToTypedValues(evaluationParams, inputNodes, t)->E.R.bind(t.run)
|
||||
|> (
|
||||
x =>
|
||||
switch x {
|
||||
| Ok(i) => Ok(i)
|
||||
| Error(r) => Error("Function " ++ (t.name ++ (" error: " ++ r)))
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
module Ts = {
|
||||
let findByName = (ts: ts, n: string) => ts |> Belt.Array.getBy(_, ({name}) => name == n)
|
||||
|
||||
let findByNameAndRun = (ts: ts, n: string, evaluationParams, inputTypes) =>
|
||||
findByName(ts, n) |> E.O.fmap(T.run(evaluationParams, inputTypes))
|
||||
}
|
||||
}
|
||||
|
|
@ -1,290 +0,0 @@
|
|||
module MathJsonToMathJsAdt = {
|
||||
type rec arg =
|
||||
| Symbol(string)
|
||||
| Value(float)
|
||||
| Fn(fn)
|
||||
| Array(array<arg>)
|
||||
| Blocks(array<arg>)
|
||||
| Object(Js.Dict.t<arg>)
|
||||
| Assignment(arg, arg)
|
||||
| FunctionAssignment(fnAssignment)
|
||||
and fn = {
|
||||
name: string,
|
||||
args: array<arg>,
|
||||
}
|
||||
and fnAssignment = {
|
||||
name: string,
|
||||
args: array<string>,
|
||||
expression: arg,
|
||||
}
|
||||
|
||||
let rec run = (j: Js.Json.t) => {
|
||||
open Json.Decode
|
||||
switch field("mathjs", string, j) {
|
||||
| "FunctionNode" =>
|
||||
let args = j |> field("args", array(run))
|
||||
let name = j |> optional(field("fn", field("name", string)))
|
||||
name |> E.O.fmap(name => Fn({name: name, args: args |> E.A.O.concatSomes}))
|
||||
| "OperatorNode" =>
|
||||
let args = j |> field("args", array(run))
|
||||
Some(
|
||||
Fn({
|
||||
name: j |> field("fn", string),
|
||||
args: args |> E.A.O.concatSomes,
|
||||
}),
|
||||
)
|
||||
| "ConstantNode" => optional(field("value", Json.Decode.float), j) |> E.O.fmap(r => Value(r))
|
||||
| "ParenthesisNode" => j |> field("content", run)
|
||||
| "ObjectNode" =>
|
||||
let properties = j |> field("properties", dict(run))
|
||||
Js.Dict.entries(properties)
|
||||
|> E.A.fmap(((key, value)) => value |> E.O.fmap(v => (key, v)))
|
||||
|> E.A.O.concatSomes
|
||||
|> Js.Dict.fromArray
|
||||
|> (r => Some(Object(r)))
|
||||
| "ArrayNode" =>
|
||||
let items = field("items", array(run), j)
|
||||
Some(Array(items |> E.A.O.concatSomes))
|
||||
| "SymbolNode" => Some(Symbol(field("name", string, j)))
|
||||
| "AssignmentNode" =>
|
||||
let object_ = j |> field("object", run)
|
||||
let value_ = j |> field("value", run)
|
||||
switch (object_, value_) {
|
||||
| (Some(o), Some(v)) => Some(Assignment(o, v))
|
||||
| _ => None
|
||||
}
|
||||
| "BlockNode" =>
|
||||
let block = r => r |> field("node", run)
|
||||
let args = j |> field("blocks", array(block)) |> E.A.O.concatSomes
|
||||
Some(Blocks(args))
|
||||
| "FunctionAssignmentNode" =>
|
||||
let name = j |> field("name", string)
|
||||
let args = j |> field("params", array(field("name", string)))
|
||||
let expression = j |> field("expr", run)
|
||||
expression |> E.O.fmap(expression => FunctionAssignment({
|
||||
name: name,
|
||||
args: args,
|
||||
expression: expression,
|
||||
}))
|
||||
| n =>
|
||||
Js.log3("Couldn't parse mathjs node", j, n)
|
||||
None
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
module MathAdtToDistDst = {
|
||||
open MathJsonToMathJsAdt
|
||||
|
||||
let handleSymbol = sym => Ok(#Symbol(sym))
|
||||
|
||||
// TODO: This only works on the top level, which needs to be refactored. Also, I think functions don't need to be done like this anymore.
|
||||
module MathAdtCleaner = {
|
||||
let transformWithSymbol = (f: float, s: string) =>
|
||||
switch s {
|
||||
| "K" => Some(f *. 1000.)
|
||||
| "M" => Some(f *. 1000000.)
|
||||
| "B" => Some(f *. 1000000000.)
|
||||
| "T" => Some(f *. 1000000000000.)
|
||||
| _ => None
|
||||
}
|
||||
let rec run = x =>
|
||||
switch x {
|
||||
| Fn({name: "multiply", args: [Value(f), Symbol(s)]}) as doNothing =>
|
||||
transformWithSymbol(f, s) |> E.O.fmap(r => Value(r)) |> E.O.default(doNothing)
|
||||
| Fn({name: "unaryMinus", args: [Value(f)]}) => Value(-1.0 *. f)
|
||||
| Fn({name, args}) => Fn({name: name, args: args |> E.A.fmap(run)})
|
||||
| Array(args) => Array(args |> E.A.fmap(run))
|
||||
| Symbol(s) => Symbol(s)
|
||||
| Value(v) => Value(v)
|
||||
| Blocks(args) => Blocks(args |> E.A.fmap(run))
|
||||
| Assignment(a, b) => Assignment(a, run(b))
|
||||
| FunctionAssignment(a) => FunctionAssignment(a)
|
||||
| Object(v) =>
|
||||
Object(
|
||||
v
|
||||
|> Js.Dict.entries
|
||||
|> E.A.fmap(((key, value)) => (key, run(value)))
|
||||
|> Js.Dict.fromArray,
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
let lognormal = (args, parseArgs, nodeParser) =>
|
||||
switch args {
|
||||
| [Object(o)] =>
|
||||
let g = s =>
|
||||
Js.Dict.get(o, s) |> E.O.toResult("Variable was empty") |> E.R.bind(_, nodeParser)
|
||||
switch (g("mean"), g("stdev"), g("mu"), g("sigma")) {
|
||||
| (Ok(mean), Ok(stdev), _, _) =>
|
||||
Ok(#FunctionCall("lognormalFromMeanAndStdDev", [mean, stdev]))
|
||||
| (_, _, Ok(mu), Ok(sigma)) => Ok(#FunctionCall("lognormal", [mu, sigma]))
|
||||
| _ => Error("Lognormal distribution needs either mean and stdev or mu and sigma")
|
||||
}
|
||||
| _ => parseArgs() |> E.R.fmap((args: array<ASTTypes.node>) => #FunctionCall("lognormal", args))
|
||||
}
|
||||
|
||||
// Error("Dotwise exponentiation needs two operands")
|
||||
let operationParser = (name: string, args: result<array<ASTTypes.node>, string>): result<
|
||||
ASTTypes.node,
|
||||
string,
|
||||
> => {
|
||||
let toOkAlgebraic = r => Ok(#AlgebraicCombination(r))
|
||||
let toOkPointwise = r => Ok(#PointwiseCombination(r))
|
||||
let toOkTruncate = r => Ok(#Truncate(r))
|
||||
args |> E.R.bind(_, args =>
|
||||
switch (name, args) {
|
||||
| ("add", [l, r]) => toOkAlgebraic((#Add, l, r))
|
||||
| ("add", _) => Error("Addition needs two operands")
|
||||
| ("unaryMinus", [l]) => toOkAlgebraic((#Multiply, #SymbolicDist(#Float(-1.0)), l))
|
||||
| ("subtract", [l, r]) => toOkAlgebraic((#Subtract, l, r))
|
||||
| ("subtract", _) => Error("Subtraction needs two operands")
|
||||
| ("multiply", [l, r]) => toOkAlgebraic((#Multiply, l, r))
|
||||
| ("multiply", _) => Error("Multiplication needs two operands")
|
||||
| ("pow", [l, r]) => toOkAlgebraic((#Power, l, r))
|
||||
| ("pow", _) => Error("Exponentiation needs two operands")
|
||||
| ("dotMultiply", [l, r]) => toOkPointwise((#Multiply, l, r))
|
||||
| ("dotMultiply", _) => Error("Dotwise multiplication needs two operands")
|
||||
| ("dotPow", [l, r]) => toOkPointwise((#Power, l, r))
|
||||
| ("dotPow", _) => Error("Dotwise exponentiation needs two operands")
|
||||
| ("rightLogShift", [l, r]) => toOkPointwise((#Add, l, r))
|
||||
| ("rightLogShift", _) => Error("Dotwise addition needs two operands")
|
||||
| ("divide", [l, r]) => toOkAlgebraic((#Divide, l, r))
|
||||
| ("divide", _) => Error("Division needs two operands")
|
||||
| ("leftTruncate", [d, #SymbolicDist(#Float(lc))]) => toOkTruncate((Some(lc), None, d))
|
||||
| ("leftTruncate", _) =>
|
||||
Error("leftTruncate needs two arguments: the expression and the cutoff")
|
||||
| ("rightTruncate", [d, #SymbolicDist(#Float(rc))]) => toOkTruncate((None, Some(rc), d))
|
||||
| ("rightTruncate", _) =>
|
||||
Error("rightTruncate needs two arguments: the expression and the cutoff")
|
||||
| ("truncate", [d, #SymbolicDist(#Float(lc)), #SymbolicDist(#Float(rc))]) =>
|
||||
toOkTruncate((Some(lc), Some(rc), d))
|
||||
| ("truncate", _) => Error("truncate needs three arguments: the expression and both cutoffs")
|
||||
| _ => Error("This type not currently supported")
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
let functionParser = (
|
||||
nodeParser: MathJsonToMathJsAdt.arg => Belt.Result.t<ASTTypes.node, string>,
|
||||
name: string,
|
||||
args: array<MathJsonToMathJsAdt.arg>,
|
||||
): result<ASTTypes.node, string> => {
|
||||
let parseArray = ags => ags |> E.A.fmap(nodeParser) |> E.A.R.firstErrorOrOpen
|
||||
let parseArgs = () => parseArray(args)
|
||||
switch name {
|
||||
| "lognormal" => lognormal(args, parseArgs, nodeParser)
|
||||
| "multimodal"
|
||||
| "add"
|
||||
| "subtract"
|
||||
| "multiply"
|
||||
| "unaryMinus"
|
||||
| "dotMultiply"
|
||||
| "dotPow"
|
||||
| "rightLogShift"
|
||||
| "divide"
|
||||
| "pow"
|
||||
| "leftTruncate"
|
||||
| "rightTruncate"
|
||||
| "truncate" =>
|
||||
operationParser(name, parseArgs())
|
||||
| "mm" =>
|
||||
let weights =
|
||||
args
|
||||
|> E.A.last
|
||||
|> E.O.bind(_, x =>
|
||||
switch x {
|
||||
| Array(values) => Some(parseArray(values))
|
||||
| _ => None
|
||||
}
|
||||
)
|
||||
let possibleDists = E.O.isSome(weights)
|
||||
? Belt.Array.slice(args, ~offset=0, ~len=E.A.length(args) - 1)
|
||||
: args
|
||||
let dists = parseArray(possibleDists)
|
||||
switch (weights, dists) {
|
||||
| (Some(Error(r)), _) => Error(r)
|
||||
| (_, Error(r)) => Error(r)
|
||||
| (None, Ok(dists)) =>
|
||||
let hash: ASTTypes.node = #FunctionCall(
|
||||
"multimodal",
|
||||
[#Hash([("dists", #Array(dists)), ("weights", #Array([]))])],
|
||||
)
|
||||
Ok(hash)
|
||||
| (Some(Ok(weights)), Ok(dists)) =>
|
||||
let hash: ASTTypes.node = #FunctionCall(
|
||||
"multimodal",
|
||||
[#Hash([("dists", #Array(dists)), ("weights", #Array(weights))])],
|
||||
)
|
||||
Ok(hash)
|
||||
}
|
||||
| name => parseArgs() |> E.R.fmap((args: array<ASTTypes.node>) => #FunctionCall(name, args))
|
||||
}
|
||||
}
|
||||
|
||||
let rec nodeParser: MathJsonToMathJsAdt.arg => result<ASTTypes.node, string> = x =>
|
||||
switch x {
|
||||
| Value(f) => Ok(#SymbolicDist(#Float(f)))
|
||||
| Symbol(sym) => Ok(#Symbol(sym))
|
||||
| Fn({name, args}) => functionParser(nodeParser, name, args)
|
||||
| _ => Error("This type not currently supported")
|
||||
}
|
||||
|
||||
// | FunctionAssignment({name, args, expression}) => {
|
||||
// let evaluatedExpression = run(expression);
|
||||
// `Function(_ => Ok(evaluatedExpression));
|
||||
// }
|
||||
let rec topLevel = (r): result<ASTTypes.program, string> =>
|
||||
switch r {
|
||||
| FunctionAssignment({name, args, expression}) =>
|
||||
switch nodeParser(expression) {
|
||||
| Ok(r) => Ok([#Assignment(name, #Function(args, r))])
|
||||
| Error(r) => Error(r)
|
||||
}
|
||||
| Value(_) as r => nodeParser(r) |> E.R.fmap(r => [#Expression(r)])
|
||||
| Fn(_) as r => nodeParser(r) |> E.R.fmap(r => [#Expression(r)])
|
||||
| Array(_) => Error("Array not valid as top level")
|
||||
| Symbol(s) => handleSymbol(s) |> E.R.fmap(r => [#Expression(r)])
|
||||
| Object(_) => Error("Object not valid as top level")
|
||||
| Assignment(name, value) =>
|
||||
switch name {
|
||||
| Symbol(symbol) => nodeParser(value) |> E.R.fmap(r => [#Assignment(symbol, r)])
|
||||
| _ => Error("Symbol not a string")
|
||||
}
|
||||
| Blocks(blocks) =>
|
||||
blocks |> E.A.fmap(b => topLevel(b)) |> E.A.R.firstErrorOrOpen |> E.R.fmap(E.A.concatMany)
|
||||
}
|
||||
|
||||
let run = (r): result<ASTTypes.program, string> => r |> MathAdtCleaner.run |> topLevel
|
||||
}
|
||||
|
||||
/* The MathJs parser doesn't support '.+' syntax, but we want it because it
|
||||
would make sense with '.*'. Our workaround is to change this to >>>, which is
|
||||
logShift in mathJS. We don't expect to use logShift anytime soon, so this tradeoff
|
||||
seems fine.
|
||||
*/
|
||||
let pointwiseToRightLogShift = Js.String.replaceByRe(%re("/\.\+/g"), ">>>")
|
||||
|
||||
let fromString2 = str => {
|
||||
/* We feed the user-typed string into Mathjs.parseMath,
|
||||
which returns a JSON with (hopefully) a single-element array.
|
||||
This array element is the top-level node of a nested-object tree
|
||||
representing the functions/arguments/values/etc. in the string.
|
||||
|
||||
The function MathJsonToMathJsAdt then recursively unpacks this JSON into a typed data structure we can use.
|
||||
Inside of this function, MathAdtToDistDst is called whenever a distribution function is encountered.
|
||||
*/
|
||||
let mathJsToJson = str |> pointwiseToRightLogShift |> Mathjs.parseMath
|
||||
|
||||
let mathJsParse = E.R.bind(mathJsToJson, r =>
|
||||
switch MathJsonToMathJsAdt.run(r) {
|
||||
| Some(r) => Ok(r)
|
||||
| None => Error("MathJsParse Error")
|
||||
}
|
||||
)
|
||||
|
||||
let value = E.R.bind(mathJsParse, MathAdtToDistDst.run)
|
||||
value
|
||||
}
|
||||
|
||||
let fromString = str => fromString2(str)
|
||||
|
|
@ -1,185 +0,0 @@
|
|||
// TODO: This setup is more confusing than it should be, there's more work to do in cleanup here.
|
||||
module Inputs = {
|
||||
module SamplingInputs = {
|
||||
type t = {
|
||||
sampleCount: option<int>,
|
||||
outputXYPoints: option<int>,
|
||||
kernelWidth: option<float>,
|
||||
pointDistLength: option<int>,
|
||||
}
|
||||
}
|
||||
let defaultRecommendedLength = 100
|
||||
let defaultShouldDownsample = true
|
||||
|
||||
type inputs = {
|
||||
squiggleString: string,
|
||||
samplingInputs: SamplingInputs.t,
|
||||
environment: ASTTypes.environment,
|
||||
}
|
||||
|
||||
let empty: SamplingInputs.t = {
|
||||
sampleCount: None,
|
||||
outputXYPoints: None,
|
||||
kernelWidth: None,
|
||||
pointDistLength: None,
|
||||
}
|
||||
|
||||
let make = (
|
||||
~samplingInputs=empty,
|
||||
~squiggleString,
|
||||
~environment=ASTTypes.Environment.empty,
|
||||
(),
|
||||
): inputs => {
|
||||
samplingInputs: samplingInputs,
|
||||
squiggleString: squiggleString,
|
||||
environment: environment,
|
||||
}
|
||||
}
|
||||
|
||||
type exportDistribution = [
|
||||
| #DistPlus(DistPlus.t)
|
||||
| #Float(float)
|
||||
| #Function(float => Belt.Result.t<DistPlus.t, string>)
|
||||
]
|
||||
|
||||
type exportEnv = array<(string, ASTTypes.node)>
|
||||
|
||||
type exportType = {
|
||||
environment: exportEnv,
|
||||
exports: array<exportDistribution>,
|
||||
}
|
||||
|
||||
module Internals = {
|
||||
let addVariable = (
|
||||
{samplingInputs, squiggleString, environment}: Inputs.inputs,
|
||||
str,
|
||||
node,
|
||||
): Inputs.inputs => {
|
||||
samplingInputs: samplingInputs,
|
||||
squiggleString: squiggleString,
|
||||
environment: ASTTypes.Environment.update(environment, str, _ => Some(node)),
|
||||
}
|
||||
|
||||
type outputs = {
|
||||
graph: ASTTypes.node,
|
||||
pointSetDist: PointSetTypes.pointSetDist,
|
||||
}
|
||||
let makeOutputs = (graph, shape): outputs => {graph: graph, pointSetDist: shape}
|
||||
|
||||
let makeInputs = (inputs: Inputs.inputs): SamplingInputs.samplingInputs => {
|
||||
sampleCount: inputs.samplingInputs.sampleCount |> E.O.default(10000),
|
||||
outputXYPoints: inputs.samplingInputs.outputXYPoints |> E.O.default(10000),
|
||||
kernelWidth: inputs.samplingInputs.kernelWidth,
|
||||
pointSetDistLength: inputs.samplingInputs.pointDistLength |> E.O.default(10000),
|
||||
}
|
||||
|
||||
let runNode = (inputs, node) => AST.toLeaf(makeInputs(inputs), inputs.environment, node)
|
||||
|
||||
let renderIfNeeded = (inputs: Inputs.inputs, node: ASTTypes.node): result<
|
||||
ASTTypes.node,
|
||||
string,
|
||||
> =>
|
||||
node |> (
|
||||
x =>
|
||||
switch x {
|
||||
| #Normalize(_) as n
|
||||
| #SymbolicDist(_) as n =>
|
||||
#Render(n)
|
||||
|> runNode(inputs)
|
||||
|> (
|
||||
x =>
|
||||
switch x {
|
||||
| Ok(#RenderedDist(_)) as r => r
|
||||
| Error(r) => Error(r)
|
||||
| _ => Error("Didn't render, but intended to")
|
||||
}
|
||||
)
|
||||
|
||||
| n => Ok(n)
|
||||
}
|
||||
)
|
||||
|
||||
let outputToDistPlus = (inputs: Inputs.inputs, pointSetDist: PointSetTypes.pointSetDist) =>
|
||||
DistPlus.make(~pointSetDist, ~squiggleString=Some(inputs.squiggleString), ())
|
||||
|
||||
let rec returnDist = (
|
||||
functionInfo: (array<string>, ASTTypes.node),
|
||||
inputs: Inputs.inputs,
|
||||
env: ASTTypes.environment,
|
||||
) => {
|
||||
(input: float) => {
|
||||
let foo: Inputs.inputs = {...inputs, environment: env}
|
||||
evaluateFunction(foo, functionInfo, [#SymbolicDist(#Float(input))]) |> E.R.bind(_, a =>
|
||||
switch a {
|
||||
| #DistPlus(d) => Ok(DistPlus.T.normalize(d))
|
||||
| n =>
|
||||
Js.log2("Error here", n)
|
||||
Error("wrong type")
|
||||
}
|
||||
)
|
||||
}
|
||||
}
|
||||
// TODO: Consider using ExpressionTypes.ExpressionTree.getFloat or similar in this function
|
||||
and coersionToExportedTypes = (inputs, env: ASTTypes.environment, ex: ASTTypes.node): result<
|
||||
exportDistribution,
|
||||
string,
|
||||
> =>
|
||||
ex
|
||||
|> renderIfNeeded(inputs)
|
||||
|> E.R.bind(_, x =>
|
||||
switch x {
|
||||
| #RenderedDist(Discrete({xyShape: {xs: [x], ys: [1.0]}})) => Ok(#Float(x))
|
||||
| #SymbolicDist(#Float(x)) => Ok(#Float(x))
|
||||
| #RenderedDist(n) => Ok(#DistPlus(outputToDistPlus(inputs, n)))
|
||||
| #Function(n) => Ok(#Function(returnDist(n, inputs, env)))
|
||||
| n => Error("Didn't output a rendered distribution. Format:" ++ AST.toString(n))
|
||||
}
|
||||
)
|
||||
|
||||
and evaluateFunction = (inputs: Inputs.inputs, fn: (array<string>, ASTTypes.node), fnInputs) => {
|
||||
let output = AST.runFunction(makeInputs(inputs), inputs.environment, fnInputs, fn)
|
||||
output |> E.R.bind(_, coersionToExportedTypes(inputs, inputs.environment))
|
||||
}
|
||||
|
||||
let runProgram = (inputs: Inputs.inputs, p: ASTTypes.program) => {
|
||||
let ins = ref(inputs)
|
||||
p
|
||||
|> E.A.fmap(x =>
|
||||
switch x {
|
||||
| #Assignment(name, node) =>
|
||||
ins := addVariable(ins.contents, name, node)
|
||||
None
|
||||
| #Expression(node) => Some(runNode(ins.contents, node))
|
||||
}
|
||||
)
|
||||
|> E.A.O.concatSomes
|
||||
|> E.A.R.firstErrorOrOpen
|
||||
|> E.R.bind(_, d =>
|
||||
d
|
||||
|> E.A.fmap(x => coersionToExportedTypes(inputs, ins.contents.environment, x))
|
||||
|> E.A.R.firstErrorOrOpen
|
||||
)
|
||||
|> E.R.fmap(ex => {
|
||||
environment: Belt.Map.String.toArray(ins.contents.environment),
|
||||
exports: ex,
|
||||
})
|
||||
}
|
||||
|
||||
let inputsToLeaf = (inputs: Inputs.inputs) =>
|
||||
Parser.fromString(inputs.squiggleString) |> E.R.bind(_, g => runProgram(inputs, g))
|
||||
}
|
||||
|
||||
@genType
|
||||
let runAll: (string, Inputs.SamplingInputs.t, exportEnv) => result<exportType, string> = (
|
||||
squiggleString,
|
||||
samplingInputs,
|
||||
environment,
|
||||
) => {
|
||||
let inputs = Inputs.make(
|
||||
~samplingInputs,
|
||||
~squiggleString,
|
||||
~environment=Belt.Map.String.fromArray(environment),
|
||||
(),
|
||||
)
|
||||
Internals.inputsToLeaf(inputs)
|
||||
}
|
||||
|
|
@ -15,7 +15,7 @@ type t = expression
|
|||
*/
|
||||
let rec toString = expression =>
|
||||
switch expression {
|
||||
| T.EBindings(bindings) => "$$bound"
|
||||
| T.EBindings(_) => "$$bound"
|
||||
| T.EList(aList) =>
|
||||
`(${Belt.List.map(aList, aValue => toString(aValue))
|
||||
->Extra.List.interperse(" ")
|
||||
|
|
@ -119,7 +119,7 @@ let reduceExpression = (expression: t, bindings: T.bindings): result<expressionV
|
|||
|
||||
let rec seekMacros = (expression: t, bindings: T.bindings): result<t, 'e> =>
|
||||
switch expression {
|
||||
| T.EValue(value) => expression->Ok
|
||||
| T.EValue(_) => expression->Ok
|
||||
| T.EList(list) => {
|
||||
let racc: result<list<t>, 'e> = list->Belt.List.reduceReverse(Ok(list{}), (
|
||||
racc,
|
||||
|
|
@ -156,7 +156,7 @@ let reduceExpression = (expression: t, bindings: T.bindings): result<expressionV
|
|||
)
|
||||
racc->Result.flatMap(acc => acc->reduceValueList)
|
||||
}
|
||||
| T.EBindings(bindings) => RETodo("Cannot return bindings")->Error
|
||||
| T.EBindings(_) => RETodo("Cannot return bindings")->Error
|
||||
}
|
||||
|
||||
let rExpandedExpression: result<t, 'e> = expression->seekMacros(bindings)
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ type answer = {"value": unit}
|
|||
Rescript cannot type cast on basic values passed on their own.
|
||||
This is why we call evalua inside Javascript and wrap the result in an Object
|
||||
*/
|
||||
let eval__ = %raw(`function (expr) { return {value: Mathjs.evaluate(expr)}; }`)
|
||||
let eval__: string => 'a = %raw(`function (expr) { return {value: Mathjs.evaluate(expr)}; }`)
|
||||
|
||||
/*
|
||||
Call MathJs evaluate and return as a variant
|
||||
|
|
|
|||
|
|
@ -126,7 +126,7 @@ module Helpers = {
|
|||
| Error(err) => GenDistError(ArgumentError(err))
|
||||
}
|
||||
}
|
||||
| Some(EvDistribution(b)) =>
|
||||
| Some(EvDistribution(_)) =>
|
||||
switch parseDistributionArray(args) {
|
||||
| Ok(distributions) => mixtureWithDefaultWeights(distributions)
|
||||
| Error(err) => GenDistError(ArgumentError(err))
|
||||
|
|
@ -226,7 +226,7 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall): option<
|
|||
Helpers.twoDiststoDistFn(Algebraic, "log", a, GenericDist.fromFloat(10.0))->Some
|
||||
| ("unaryMinus", [EvDistribution(a)]) =>
|
||||
Helpers.twoDiststoDistFn(Algebraic, "multiply", a, GenericDist.fromFloat(-1.0))->Some
|
||||
| (("add" | "multiply" | "subtract" | "divide" | "pow" | "log") as arithmetic, [a, b] as args) =>
|
||||
| (("add" | "multiply" | "subtract" | "divide" | "pow" | "log") as arithmetic, [_, _] as args) =>
|
||||
Helpers.catchAndConvertTwoArgsToDists(args)->E.O2.fmap(((fst, snd)) =>
|
||||
Helpers.twoDiststoDistFn(Algebraic, arithmetic, fst, snd)
|
||||
)
|
||||
|
|
@ -237,7 +237,7 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall): option<
|
|||
| "dotDivide"
|
||||
| "dotPow"
|
||||
| "dotLog") as arithmetic,
|
||||
[a, b] as args,
|
||||
[_, _] as args,
|
||||
) =>
|
||||
Helpers.catchAndConvertTwoArgsToDists(args)->E.O2.fmap(((fst, snd)) =>
|
||||
Helpers.twoDiststoDistFn(Pointwise, arithmetic, fst, snd)
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ module Int = {
|
|||
}
|
||||
/* Utils */
|
||||
module U = {
|
||||
let isEqual = (a, b) => a == b
|
||||
let isEqual = \"=="
|
||||
let toA = a => [a]
|
||||
let id = e => e
|
||||
}
|
||||
|
|
@ -340,8 +340,6 @@ module A = {
|
|||
let reduce = Belt.Array.reduce
|
||||
let reducei = Belt.Array.reduceWithIndex
|
||||
let isEmpty = r => length(r) < 1
|
||||
let min = a => get(a, 0) |> O.fmap(first => Belt.Array.reduce(a, first, (i, j) => i < j ? i : j))
|
||||
let max = a => get(a, 0) |> O.fmap(first => Belt.Array.reduce(a, first, (i, j) => i > j ? i : j))
|
||||
let stableSortBy = Belt.SortArray.stableSortBy
|
||||
let toRanges = (a: array<'a>) =>
|
||||
switch a |> Belt.Array.length {
|
||||
|
|
@ -448,8 +446,11 @@ module A = {
|
|||
| (Some(min), Some(max)) => Some(max -. min)
|
||||
| _ => None
|
||||
}
|
||||
|
||||
let floatCompare: (float, float) => int = compare
|
||||
|
||||
let binarySearchFirstElementGreaterIndex = (ar: array<'a>, el: 'a) => {
|
||||
let el = Belt.SortArray.binarySearchBy(ar, el, compare)
|
||||
let el = Belt.SortArray.binarySearchBy(ar, el, floatCompare)
|
||||
let el = el < 0 ? el * -1 - 1 : el
|
||||
switch el {
|
||||
| e if e >= length(ar) => #overMax
|
||||
|
|
@ -460,13 +461,13 @@ module A = {
|
|||
|
||||
let concat = (t1: array<'a>, t2: array<'a>) => {
|
||||
let ts = Belt.Array.concat(t1, t2)
|
||||
ts |> Array.fast_sort(compare)
|
||||
ts |> Array.fast_sort(floatCompare)
|
||||
ts
|
||||
}
|
||||
|
||||
let concatMany = (t1: array<array<'a>>) => {
|
||||
let ts = Belt.Array.concatMany(t1)
|
||||
ts |> Array.fast_sort(compare)
|
||||
ts |> Array.fast_sort(floatCompare)
|
||||
ts
|
||||
}
|
||||
|
||||
|
|
@ -525,6 +526,9 @@ module A = {
|
|||
let diff = (max -. min) /. Belt.Float.fromInt(n - 1)
|
||||
Belt.Array.makeBy(n, i => min +. Belt.Float.fromInt(i) *. diff)
|
||||
}
|
||||
|
||||
let min = Js.Math.minMany_float
|
||||
let max = Js.Math.maxMany_float
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -9,6 +9,13 @@ type algebraicOperation = [
|
|||
| #Power
|
||||
| #Logarithm
|
||||
]
|
||||
|
||||
type convolutionOperation = [
|
||||
| #Add
|
||||
| #Multiply
|
||||
| #Subtract
|
||||
]
|
||||
|
||||
@genType
|
||||
type pointwiseOperation = [#Add | #Multiply | #Power]
|
||||
type scaleOperation = [#Multiply | #Power | #Logarithm | #Divide]
|
||||
|
|
@ -20,6 +27,16 @@ type distToFloatOperation = [
|
|||
| #Sample
|
||||
]
|
||||
|
||||
module Convolution = {
|
||||
type t = convolutionOperation
|
||||
let toFn: (t, float, float) => float = x =>
|
||||
switch x {
|
||||
| #Add => \"+."
|
||||
| #Subtract => \"-."
|
||||
| #Multiply => \"*."
|
||||
}
|
||||
}
|
||||
|
||||
module Algebraic = {
|
||||
type t = algebraicOperation
|
||||
let toFn: (t, float, float) => float = x =>
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ let create = (relativeHeights: array<float>, ~maximum=?, ()) => {
|
|||
if E.A.length(relativeHeights) === 0 {
|
||||
""
|
||||
} else {
|
||||
let maximum = maximum->E.O2.default(E.A.max(relativeHeights)->E.O2.toExn(""))
|
||||
let maximum = maximum->E.O2.default(E.A.Floats.max(relativeHeights))
|
||||
|
||||
relativeHeights
|
||||
->E.A2.fmap(_heightToTickIndex(maximum))
|
||||
|
|
|
|||
|
|
@ -60,8 +60,8 @@ module T = {
|
|||
|
||||
module Ts = {
|
||||
type t = T.ts
|
||||
let minX = (t: t) => t |> E.A.fmap(T.minX) |> E.A.min |> extImp
|
||||
let maxX = (t: t) => t |> E.A.fmap(T.maxX) |> E.A.max |> extImp
|
||||
let minX = (t: t) => t |> E.A.fmap(T.minX) |> E.A.Floats.min
|
||||
let maxX = (t: t) => t |> E.A.fmap(T.maxX) |> E.A.Floats.max
|
||||
let equallyDividedXs = (t: t, newLength) => E.A.Floats.range(minX(t), maxX(t), newLength)
|
||||
let allXs = (t: t) => t |> E.A.fmap(T.xs) |> E.A.Sorted.concatMany
|
||||
}
|
||||
|
|
@ -199,7 +199,7 @@ module XtoY = {
|
|||
|
||||
/* Returns a between-points-interpolating function that can be used with PointwiseCombination.combine.
|
||||
For discrete distributions, the probability density between points is zero, so we just return zero here. */
|
||||
let discreteInterpolator: interpolator = (t: T.t, leftIndex: int, x: float) => 0.0
|
||||
let discreteInterpolator: interpolator = (_: T.t, _: int, _: float) => 0.0
|
||||
}
|
||||
|
||||
module XsConversion = {
|
||||
|
|
@ -220,8 +220,8 @@ module XsConversion = {
|
|||
|
||||
module Zipped = {
|
||||
type zipped = array<(float, float)>
|
||||
let compareYs = ((_, y1), (_, y2)) => y1 > y2 ? 1 : 0
|
||||
let compareXs = ((x1, _), (x2, _)) => x1 > x2 ? 1 : 0
|
||||
let compareYs = ((_, y1): (float, float), (_, y2): (float, float)) => y1 > y2 ? 1 : 0
|
||||
let compareXs = ((x1, _): (float, float), (x2, _): (float, float)) => x1 > x2 ? 1 : 0
|
||||
let sortByY = (t: zipped) => t |> E.A.stableSortBy(_, compareYs)
|
||||
let sortByX = (t: zipped) => t |> E.A.stableSortBy(_, compareXs)
|
||||
let filterByX = (testFn: float => bool, t: zipped) => t |> E.A.filter(((x, _)) => testFn(x))
|
||||
|
|
@ -229,7 +229,7 @@ module Zipped = {
|
|||
|
||||
module PointwiseCombination = {
|
||||
// t1Interpolator and t2Interpolator are functions from XYShape.XtoY, e.g. linearBetweenPointsExtrapolateFlat.
|
||||
let combine = %raw(` // : (float => float => float, T.t, T.t, bool) => T.t
|
||||
let combine: ((float, float) => float, interpolator, T.t, T.t) => T.t = %raw(`
|
||||
// This function combines two xyShapes by looping through both of them simultaneously.
|
||||
// It always moves on to the next smallest x, whether that's in the first or second input's xs,
|
||||
// and interpolates the value on the other side, thus accumulating xs and ys.
|
||||
|
|
|
|||
65
yarn.lock
65
yarn.lock
|
|
@ -1928,11 +1928,6 @@
|
|||
resolved "https://registry.yarnpkg.com/@gar/promisify/-/promisify-1.1.3.tgz#555193ab2e3bb3b6adc3d551c9c030d9e860daf6"
|
||||
integrity sha512-k2Ty1JcVojjJFwrg/ThKi2ujJ7XNLYaFGNB/bWT9wGR+oSMJHMa5w+CUq6p/pVrKeNNgA7pCqEcjSnHVoqJQFw==
|
||||
|
||||
"@glennsl/bs-json@^5.0.2":
|
||||
version "5.0.4"
|
||||
resolved "https://registry.yarnpkg.com/@glennsl/bs-json/-/bs-json-5.0.4.tgz#8a80906f3b5e04d78dc06722e5987ff6499c89a8"
|
||||
integrity sha512-Th9DetZjRlMZrb74kgGJ44oWcoFyOTE884WlSuXft0Cd+J09vHRxiB7eVyK7Gthb4cSevsBBJDHYAbGGL25wPw==
|
||||
|
||||
"@glennsl/rescript-jest@^0.9.0":
|
||||
version "0.9.1"
|
||||
resolved "https://registry.yarnpkg.com/@glennsl/rescript-jest/-/rescript-jest-0.9.1.tgz#a85a6f0e4c3b79010b5a917c3652aa70d374e4d1"
|
||||
|
|
@ -4041,10 +4036,10 @@
|
|||
resolved "https://registry.yarnpkg.com/@types/range-parser/-/range-parser-1.2.4.tgz#cd667bcfdd025213aafb7ca5915a932590acdcdc"
|
||||
integrity sha512-EEhsLsD6UsDM1yFhAvy0Cjr6VwmpMWqFBCb9w07wVugF7w9nfajxLuVmngTIpgS6svCnm6Vaw+MZhoDCKnOfsw==
|
||||
|
||||
"@types/react-dom@^18.0.0", "@types/react-dom@^18.0.1":
|
||||
version "18.0.1"
|
||||
resolved "https://registry.yarnpkg.com/@types/react-dom/-/react-dom-18.0.1.tgz#cb3cc10ea91141b12c71001fede1017acfbce4db"
|
||||
integrity sha512-jCwTXvHtRLiyVvKm9aEdHXs8rflVOGd5Sl913JZrPshfXjn8NYsTNZOz70bCsA31IR0TOqwi3ad+X4tSCBoMTw==
|
||||
"@types/react-dom@^18.0.0", "@types/react-dom@^18.0.2":
|
||||
version "18.0.2"
|
||||
resolved "https://registry.yarnpkg.com/@types/react-dom/-/react-dom-18.0.2.tgz#2d6b46557aa30257e87e67a6d952146d15979d79"
|
||||
integrity sha512-UxeS+Wtj5bvLRREz9tIgsK4ntCuLDo0EcAcACgw3E+9wE8ePDr9uQpq53MfcyxyIS55xJ+0B6mDS8c4qkkHLBg==
|
||||
dependencies:
|
||||
"@types/react" "*"
|
||||
|
||||
|
|
@ -4082,9 +4077,9 @@
|
|||
"@types/react" "*"
|
||||
|
||||
"@types/react@*", "@types/react@^16.9.19", "@types/react@^18.0.1", "@types/react@^18.0.3":
|
||||
version "18.0.5"
|
||||
resolved "https://registry.yarnpkg.com/@types/react/-/react-18.0.5.tgz#1a4d4b705ae6af5aed369dec22800b20f89f5301"
|
||||
integrity sha512-UPxNGInDCIKlfqBrm8LDXYWNfLHwIdisWcsH5GpMyGjhEDLFgTtlRBaoWuCua9HcyuE0rMkmAeZ3FXV1pYLIYQ==
|
||||
version "18.0.6"
|
||||
resolved "https://registry.yarnpkg.com/@types/react/-/react-18.0.6.tgz#30206c3830af6ce8639b91ace5868bc2d3d1d96c"
|
||||
integrity sha512-bPqwzJRzKtfI0mVYr5R+1o9BOE8UEXefwc1LwcBtfnaAn6OoqMhLa/91VA8aeWfDPJt1kHvYKI8RHcQybZLHHA==
|
||||
dependencies:
|
||||
"@types/prop-types" "*"
|
||||
"@types/scheduler" "*"
|
||||
|
|
@ -7566,20 +7561,6 @@ dns-packet@^5.2.2:
|
|||
dependencies:
|
||||
"@leichtgewicht/ip-codec" "^2.0.1"
|
||||
|
||||
docsify@^4.12.2:
|
||||
version "4.12.2"
|
||||
resolved "https://registry.yarnpkg.com/docsify/-/docsify-4.12.2.tgz#749115d2ff7d358780ea865e01f4a0162423d67f"
|
||||
integrity sha512-hpRez5upcvkYigT2zD8P5kH5t9HpSWL8yn/ZU/g04/WfAfxVNW6CPUVOOF1EsQUDxTRuyNTFOb6uUv+tPij3tg==
|
||||
dependencies:
|
||||
dompurify "^2.3.1"
|
||||
marked "^1.2.9"
|
||||
medium-zoom "^1.0.6"
|
||||
opencollective-postinstall "^2.0.2"
|
||||
prismjs "^1.23.0"
|
||||
strip-indent "^3.0.0"
|
||||
tinydate "^1.3.0"
|
||||
tweezer.js "^1.4.0"
|
||||
|
||||
doctrine@^2.1.0:
|
||||
version "2.1.0"
|
||||
resolved "https://registry.yarnpkg.com/doctrine/-/doctrine-2.1.0.tgz#5cd01fc101621b42c4cd7f5d1a66243716d3f39d"
|
||||
|
|
@ -7677,11 +7658,6 @@ domhandler@^4.0.0, domhandler@^4.2.0, domhandler@^4.3.1:
|
|||
dependencies:
|
||||
domelementtype "^2.2.0"
|
||||
|
||||
dompurify@^2.3.1:
|
||||
version "2.3.6"
|
||||
resolved "https://registry.yarnpkg.com/dompurify/-/dompurify-2.3.6.tgz#2e019d7d7617aacac07cbbe3d88ae3ad354cf875"
|
||||
integrity sha512-OFP2u/3T1R5CEgWCEONuJ1a5+MFKnOYpkywpUSxv/dj1LeBT1erK+JwM7zK0ROy2BRhqVCf0LRw/kHqKuMkVGg==
|
||||
|
||||
domutils@1.5.1:
|
||||
version "1.5.1"
|
||||
resolved "https://registry.yarnpkg.com/domutils/-/domutils-1.5.1.tgz#dcd8488a26f563d61079e48c9f7b7e32373682cf"
|
||||
|
|
@ -11579,11 +11555,6 @@ markdown-to-jsx@^7.1.3:
|
|||
resolved "https://registry.yarnpkg.com/markdown-to-jsx/-/markdown-to-jsx-7.1.7.tgz#a5f22102fb12241c8cea1ca6a4050bb76b23a25d"
|
||||
integrity sha512-VI3TyyHlGkO8uFle0IOibzpO1c1iJDcXcS/zBrQrXQQvJ2tpdwVzVZ7XdKsyRz1NdRmre4dqQkMZzUHaKIG/1w==
|
||||
|
||||
marked@^1.2.9:
|
||||
version "1.2.9"
|
||||
resolved "https://registry.yarnpkg.com/marked/-/marked-1.2.9.tgz#53786f8b05d4c01a2a5a76b7d1ec9943d29d72dc"
|
||||
integrity sha512-H8lIX2SvyitGX+TRdtS06m1jHMijKN/XjfH6Ooii9fvxMlh8QdqBfBDkGUpMWH2kQNrtixjzYUa3SH8ROTgRRw==
|
||||
|
||||
mathjs@10.5.0:
|
||||
version "10.5.0"
|
||||
resolved "https://registry.yarnpkg.com/mathjs/-/mathjs-10.5.0.tgz#f81d0518fe7b4b2a0b85e1125b8ecfc364fb0292"
|
||||
|
|
@ -11666,11 +11637,6 @@ media-typer@0.3.0:
|
|||
resolved "https://registry.yarnpkg.com/media-typer/-/media-typer-0.3.0.tgz#8710d7af0aa626f8fffa1ce00168545263255748"
|
||||
integrity sha1-hxDXrwqmJvj/+hzgAWhUUmMlV0g=
|
||||
|
||||
medium-zoom@^1.0.6:
|
||||
version "1.0.6"
|
||||
resolved "https://registry.yarnpkg.com/medium-zoom/-/medium-zoom-1.0.6.tgz#9247f21ca9313d8bbe9420aca153a410df08d027"
|
||||
integrity sha512-UdiUWfvz9fZMg1pzf4dcuqA0W079o0mpqbTnOz5ip4VGYX96QjmbM+OgOU/0uOzAytxC0Ny4z+VcYQnhdifimg==
|
||||
|
||||
mem@^8.1.1:
|
||||
version "8.1.1"
|
||||
resolved "https://registry.yarnpkg.com/mem/-/mem-8.1.1.tgz#cf118b357c65ab7b7e0817bdf00c8062297c0122"
|
||||
|
|
@ -12421,11 +12387,6 @@ open@^8.0.9, open@^8.4.0:
|
|||
is-docker "^2.1.1"
|
||||
is-wsl "^2.2.0"
|
||||
|
||||
opencollective-postinstall@^2.0.2:
|
||||
version "2.0.3"
|
||||
resolved "https://registry.yarnpkg.com/opencollective-postinstall/-/opencollective-postinstall-2.0.3.tgz#7a0fff978f6dbfa4d006238fbac98ed4198c3259"
|
||||
integrity sha512-8AV/sCtuzUeTo8gQK5qDZzARrulB3egtLzFgteqB2tcT4Mw7B8Kt7JcDHmltjz6FOAHsvTevk70gZEbhM4ZS9Q==
|
||||
|
||||
opener@^1.5.2:
|
||||
version "1.5.2"
|
||||
resolved "https://registry.yarnpkg.com/opener/-/opener-1.5.2.tgz#5d37e1f35077b9dcac4301372271afdeb2a13598"
|
||||
|
|
@ -13595,7 +13556,7 @@ prism-react-renderer@^1.2.1, prism-react-renderer@^1.3.1:
|
|||
resolved "https://registry.yarnpkg.com/prism-react-renderer/-/prism-react-renderer-1.3.1.tgz#88fc9d0df6bed06ca2b9097421349f8c2f24e30d"
|
||||
integrity sha512-xUeDMEz074d0zc5y6rxiMp/dlC7C+5IDDlaEUlcBOFE2wddz7hz5PNupb087mPwTt7T9BrFmewObfCBuf/LKwQ==
|
||||
|
||||
prismjs@^1.21.0, prismjs@^1.23.0, prismjs@^1.27.0, prismjs@~1.27.0:
|
||||
prismjs@^1.21.0, prismjs@^1.27.0, prismjs@~1.27.0:
|
||||
version "1.27.0"
|
||||
resolved "https://registry.yarnpkg.com/prismjs/-/prismjs-1.27.0.tgz#bb6ee3138a0b438a3653dd4d6ce0cc6510a45057"
|
||||
integrity sha512-t13BGPUlFDR7wRB5kQDG4jjl7XeuH6jbJGt11JHPL96qwsEHNX2+68tFXqc1/k+/jALsbSWJKUOT/hcYAZ5LkA==
|
||||
|
|
@ -16479,11 +16440,6 @@ tiny-warning@^1.0.0, tiny-warning@^1.0.3:
|
|||
resolved "https://registry.yarnpkg.com/tiny-warning/-/tiny-warning-1.0.3.tgz#94a30db453df4c643d0fd566060d60a875d84754"
|
||||
integrity sha512-lBN9zLN/oAf68o3zNXYrdCt1kP8WsiGW8Oo2ka41b2IM5JL/S1CTyX1rW0mb/zSuJun0ZUrDxx4sqvYS2FWzPA==
|
||||
|
||||
tinydate@^1.3.0:
|
||||
version "1.3.0"
|
||||
resolved "https://registry.yarnpkg.com/tinydate/-/tinydate-1.3.0.tgz#e6ca8e5a22b51bb4ea1c3a2a4fd1352dbd4c57fb"
|
||||
integrity sha512-7cR8rLy2QhYHpsBDBVYnnWXm8uRTr38RoZakFSW7Bs7PzfMPNZthuMLkwqZv7MTu8lhQ91cOFYS5a7iFj2oR3w==
|
||||
|
||||
tmpl@1.0.5:
|
||||
version "1.0.5"
|
||||
resolved "https://registry.yarnpkg.com/tmpl/-/tmpl-1.0.5.tgz#8683e0b902bb9c20c4f726e3c0b69f36518c07cc"
|
||||
|
|
@ -16686,11 +16642,6 @@ tty-browserify@0.0.0:
|
|||
resolved "https://registry.yarnpkg.com/tty-browserify/-/tty-browserify-0.0.0.tgz#a157ba402da24e9bf957f9aa69d524eed42901a6"
|
||||
integrity sha1-oVe6QC2iTpv5V/mqadUk7tQpAaY=
|
||||
|
||||
tweezer.js@^1.4.0:
|
||||
version "1.5.0"
|
||||
resolved "https://registry.yarnpkg.com/tweezer.js/-/tweezer.js-1.5.0.tgz#ca50ac5215022203fd3be4d28617e8e2305f5c0c"
|
||||
integrity sha512-aSiJz7rGWNAQq7hjMK9ZYDuEawXupcCWgl3woQQSoDP2Oh8O4srWb/uO1PzzHIsrPEOqrjJ2sUb9FERfzuBabQ==
|
||||
|
||||
type-check@^0.4.0, type-check@~0.4.0:
|
||||
version "0.4.0"
|
||||
resolved "https://registry.yarnpkg.com/type-check/-/type-check-0.4.0.tgz#07b8203bfa7056c0657050e3ccd2c37730bab8f1"
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user