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pointwise-
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8deb796820 |
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@ -6,11 +6,12 @@ import {
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errorValueToString,
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errorValueToString,
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squiggleExpression,
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squiggleExpression,
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} from "@quri/squiggle-lang";
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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 { NumberShower } from "./NumberShower";
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import { DistributionChart } from "./DistributionChart";
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import { DistributionChart } from "./DistributionChart";
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import { ErrorBox } from "./ErrorBox";
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import { ErrorBox } from "./ErrorBox";
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import useSize from "@react-hook/size";
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import useSize from "@react-hook/size";
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type exportEnv = unknown;
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const variableBox = {
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const variableBox = {
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Component: styled.div`
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Component: styled.div`
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@ -2,9 +2,10 @@ import * as React from "react";
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import * as ReactDOM from "react-dom";
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import * as ReactDOM from "react-dom";
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import { SquiggleChart } from "./SquiggleChart";
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import { SquiggleChart } from "./SquiggleChart";
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import { CodeEditor } from "./CodeEditor";
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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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import styled from "styled-components";
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type exportEnv = unknown;
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export interface SquiggleEditorProps {
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export interface SquiggleEditorProps {
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/** The input string for squiggle */
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/** The input string for squiggle */
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initialSquiggleString?: string;
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initialSquiggleString?: string;
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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(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("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 / 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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testEval("normal(10, 2) / 2", "Ok(Normal(5,1))")
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})
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})
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describe("truncate", () => {
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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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})
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describe("exp", () => {
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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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})
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describe("pow", () => {
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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(3, uniform(5,8))", "Ok(Sample Set Distribution)")
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testEval("pow(uniform(5,8), 3)", "Ok(Point 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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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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describe("log", () => {
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testEval("log(2, uniform(5,8))", "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(Point 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(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("log(uniform(5,8))", "Ok(Sample Set Distribution)")
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testEval("log10(uniform(5,8))", "Ok(Point Set Distribution)")
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testEval("log10(uniform(5,8))", "Ok(Sample Set Distribution)")
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})
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})
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describe("dotLog", () => {
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describe("dotLog", () => {
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@ -1,9 +1,4 @@
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import * as _ from "lodash";
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import * as _ from "lodash";
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import type {
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exportEnv,
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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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import {
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genericDist,
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genericDist,
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samplingParams,
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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_pointwiseLogarithm,
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Constructors_pointwisePower,
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Constructors_pointwisePower,
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} from "../rescript/Distributions/DistributionOperation/DistributionOperation.gen";
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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 };
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export type { samplingParams, errorValue };
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export let defaultSamplingInputs: samplingParams = {
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export let defaultSamplingInputs: samplingParams = {
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@ -99,7 +93,7 @@ export type squiggleExpression =
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export function run(
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export function run(
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squiggleString: string,
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squiggleString: string,
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samplingInputs?: samplingParams,
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samplingInputs?: samplingParams,
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_environment?: exportEnv
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_environment?: unknown
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): result<squiggleExpression, errorValue> {
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): result<squiggleExpression, errorValue> {
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let si: samplingParams = samplingInputs
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let si: samplingParams = samplingInputs
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? samplingInputs
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? samplingInputs
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@ -158,7 +158,7 @@ module AlgebraicCombination = {
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let runConvolution = (
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let runConvolution = (
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toPointSet: toPointSetFn,
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toPointSet: toPointSetFn,
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arithmeticOperation: GenericDist_Types.Operation.arithmeticOperation,
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arithmeticOperation: Operation.convolutionOperation,
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t1: t,
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t1: t,
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t2: t,
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t2: t,
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) =>
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) =>
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@ -191,10 +191,23 @@ module AlgebraicCombination = {
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| _ => 1000
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| _ => 1000
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}
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}
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let chooseConvolutionOrMonteCarlo = (t2: t, t1: t) =>
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type calculationMethod = MonteCarlo | Convolution(Operation.convolutionOperation)
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expectedConvolutionCost(t1) * expectedConvolutionCost(t2) > 10000
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? #CalculateWithMonteCarlo
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let chooseConvolutionOrMonteCarlo = (
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: #CalculateWithConvolution
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op: Operation.algebraicOperation,
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t2: t,
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t1: t,
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): calculationMethod =>
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switch op {
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| #Divide
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| #Power
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| #Logarithm =>
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MonteCarlo
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| (#Add | #Subtract | #Multiply) as convOp =>
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expectedConvolutionCost(t1) * expectedConvolutionCost(t2) > 10000
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? MonteCarlo
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: Convolution(convOp)
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}
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let run = (
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let run = (
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t1: t,
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t1: t,
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@ -207,15 +220,10 @@ module AlgebraicCombination = {
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| Some(Ok(symbolicDist)) => Ok(Symbolic(symbolicDist))
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| Some(Ok(symbolicDist)) => Ok(Symbolic(symbolicDist))
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| Some(Error(e)) => Error(Other(e))
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| Some(Error(e)) => Error(Other(e))
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| None =>
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| None =>
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switch chooseConvolutionOrMonteCarlo(t1, t2) {
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switch chooseConvolutionOrMonteCarlo(arithmeticOperation, t1, t2) {
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| #CalculateWithMonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
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| MonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
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| #CalculateWithConvolution =>
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| Convolution(convOp) =>
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runConvolution(
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runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(r))
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toPointSetFn,
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arithmeticOperation,
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t1,
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t2,
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)->E.R2.fmap(r => DistributionTypes.PointSet(r))
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}
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}
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}
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}
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}
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}
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@ -247,9 +247,10 @@ let downsampleEquallyOverX = (length, t): t =>
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/* This simply creates multiple copies of the continuous distribution, scaled and shifted according to
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/* This simply creates multiple copies of the continuous distribution, scaled and shifted according to
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each discrete data point, and then adds them all together. */
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each discrete data point, and then adds them all together. */
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let combineAlgebraicallyWithDiscrete = (
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let combineAlgebraicallyWithDiscrete = (
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op: Operation.algebraicOperation,
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op: Operation.convolutionOperation,
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t1: t,
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t1: t,
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t2: PointSetTypes.discreteShape,
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t2: PointSetTypes.discreteShape,
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discreteFirst: bool,
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) => {
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) => {
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let t1s = t1 |> getShape
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let t1s = t1 |> getShape
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let t2s = t2.xyShape // TODO would like to use Discrete.getShape here, but current file structure doesn't allow for that
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let t2s = t2.xyShape // TODO would like to use Discrete.getShape here, but current file structure doesn't allow for that
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@ -262,15 +263,15 @@ let combineAlgebraicallyWithDiscrete = (
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| #Stepwise => stepwiseToLinear(t1)
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| #Stepwise => stepwiseToLinear(t1)
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}
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}
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|
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let combinedShape = AlgebraicShapeCombination.combineShapesContinuousDiscrete(
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let combinedShape = NumericShapeCombination.combineShapesContinuousDiscrete(
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op,
|
op,
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continuousAsLinear |> getShape,
|
continuousAsLinear |> getShape,
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t2s,
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t2s,
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|
discreteFirst,
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)
|
)
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|
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let combinedIntegralSum = switch op {
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let combinedIntegralSum = switch op {
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| #Multiply
|
| #Multiply =>
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| #Divide =>
|
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Common.combineIntegralSums((a, b) => Some(a *. b), t1.integralSumCache, t2.integralSumCache)
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Common.combineIntegralSums((a, b) => Some(a *. b), t1.integralSumCache, t2.integralSumCache)
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| _ => None
|
| _ => None
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}
|
}
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@ -280,7 +281,7 @@ let combineAlgebraicallyWithDiscrete = (
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}
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}
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}
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}
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|
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let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t) => {
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let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t) => {
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let s1 = t1 |> getShape
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let s1 = t1 |> getShape
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let s2 = t2 |> getShape
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let s2 = t2 |> getShape
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let t1n = s1 |> XYShape.T.length
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let t1n = s1 |> XYShape.T.length
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|
@ -288,7 +289,7 @@ let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t) => {
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if t1n == 0 || t2n == 0 {
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if t1n == 0 || t2n == 0 {
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empty
|
empty
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} else {
|
} else {
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let combinedShape = AlgebraicShapeCombination.combineShapesContinuousContinuous(op, s1, s2)
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let combinedShape = NumericShapeCombination.combineShapesContinuousContinuous(op, s1, s2)
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let combinedIntegralSum = Common.combineIntegralSums(
|
let combinedIntegralSum = Common.combineIntegralSums(
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(a, b) => Some(a *. b),
|
(a, b) => Some(a *. b),
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t1.integralSumCache,
|
t1.integralSumCache,
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|
|
|
@ -85,7 +85,7 @@ let updateIntegralCache = (integralCache, t: t): t => {
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|
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/* This multiples all of the data points together and creates a new discrete distribution from the results.
|
/* This multiples all of the data points together and creates a new discrete distribution from the results.
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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. */
|
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. */
|
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let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t => {
|
let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {
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let t1s = t1 |> getShape
|
let t1s = t1 |> getShape
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let t2s = t2 |> getShape
|
let t2s = t2 |> getShape
|
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let t1n = t1s |> XYShape.T.length
|
let t1n = t1s |> XYShape.T.length
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|
@ -97,7 +97,7 @@ let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t =
|
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t2.integralSumCache,
|
t2.integralSumCache,
|
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)
|
)
|
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|
|
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let fn = Operation.Algebraic.toFn(op)
|
let fn = Operation.Convolution.toFn(op)
|
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let xToYMap = E.FloatFloatMap.empty()
|
let xToYMap = E.FloatFloatMap.empty()
|
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|
|
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for i in 0 to t1n - 1 {
|
for i in 0 to t1n - 1 {
|
||||||
|
|
|
@ -226,7 +226,7 @@ module T = Dist({
|
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}
|
}
|
||||||
})
|
})
|
||||||
|
|
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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,
|
// Discrete convolution can cause a huge increase in the number of samples,
|
||||||
// so we'll first downsample.
|
// so we'll first downsample.
|
||||||
|
|
||||||
|
@ -242,8 +242,18 @@ let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t =
|
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// continuous (*) continuous => continuous, but also
|
// continuous (*) continuous => continuous, but also
|
||||||
// discrete (*) continuous => continuous (and vice versa). We have to take care of all combos and then combine them:
|
// discrete (*) continuous => continuous (and vice versa). We have to take care of all combos and then combine them:
|
||||||
let ccConvResult = Continuous.combineAlgebraically(op, t1.continuous, t2.continuous)
|
let ccConvResult = Continuous.combineAlgebraically(op, t1.continuous, t2.continuous)
|
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let dcConvResult = Continuous.combineAlgebraicallyWithDiscrete(op, t2.continuous, t1.discrete)
|
let dcConvResult = Continuous.combineAlgebraicallyWithDiscrete(
|
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let cdConvResult = Continuous.combineAlgebraicallyWithDiscrete(op, t1.continuous, t2.discrete)
|
op,
|
||||||
|
t2.continuous,
|
||||||
|
t1.discrete,
|
||||||
|
true,
|
||||||
|
)
|
||||||
|
let cdConvResult = Continuous.combineAlgebraicallyWithDiscrete(
|
||||||
|
op,
|
||||||
|
t1.continuous,
|
||||||
|
t2.discrete,
|
||||||
|
false,
|
||||||
|
)
|
||||||
let continuousConvResult = Continuous.reduce(\"+.", [ccConvResult, dcConvResult, cdConvResult])
|
let continuousConvResult = Continuous.reduce(\"+.", [ccConvResult, dcConvResult, cdConvResult])
|
||||||
|
|
||||||
// ... finally, discrete (*) discrete => discrete, obviously:
|
// ... finally, discrete (*) discrete => discrete, obviously:
|
||||||
|
|
|
@ -96,7 +96,7 @@ let toDiscretePointMassesFromTriangulars = (
|
||||||
}
|
}
|
||||||
|
|
||||||
let combineShapesContinuousContinuous = (
|
let combineShapesContinuousContinuous = (
|
||||||
op: Operation.algebraicOperation,
|
op: Operation.convolutionOperation,
|
||||||
s1: PointSetTypes.xyShape,
|
s1: PointSetTypes.xyShape,
|
||||||
s2: PointSetTypes.xyShape,
|
s2: PointSetTypes.xyShape,
|
||||||
): PointSetTypes.xyShape => {
|
): PointSetTypes.xyShape => {
|
||||||
|
@ -104,7 +104,6 @@ let combineShapesContinuousContinuous = (
|
||||||
// if we multiply the two distributions, we should probably use lognormal filters.
|
// if we multiply the two distributions, we should probably use lognormal filters.
|
||||||
let t1m = toDiscretePointMassesFromTriangulars(s1)
|
let t1m = toDiscretePointMassesFromTriangulars(s1)
|
||||||
let t2m = switch op {
|
let t2m = switch op {
|
||||||
| #Divide => toDiscretePointMassesFromTriangulars(~inverse=true, s2)
|
|
||||||
| _ => toDiscretePointMassesFromTriangulars(~inverse=false, s2)
|
| _ => toDiscretePointMassesFromTriangulars(~inverse=false, s2)
|
||||||
}
|
}
|
||||||
|
|
||||||
|
@ -112,9 +111,6 @@ let combineShapesContinuousContinuous = (
|
||||||
| #Add => (m1, m2) => m1 +. m2
|
| #Add => (m1, m2) => m1 +. m2
|
||||||
| #Subtract => (m1, m2) => m1 -. m2
|
| #Subtract => (m1, m2) => m1 -. m2
|
||||||
| #Multiply => (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)
|
} // 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.
|
// TODO: Variances are for exponentatiation or logarithms are almost totally made up and very likely very wrong.
|
||||||
|
@ -123,9 +119,6 @@ let combineShapesContinuousContinuous = (
|
||||||
| #Add => (v1, v2, _, _) => v1 +. v2
|
| #Add => (v1, v2, _, _) => v1 +. v2
|
||||||
| #Subtract => (v1, v2, _, _) => v1 +. v2
|
| #Subtract => (v1, v2, _, _) => v1 +. v2
|
||||||
| #Multiply => (v1, v2, m1, m2) => v1 *. v2 +. v1 *. m2 ** 2. +. v2 *. m1 ** 2.
|
| #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
|
// TODO: If operating on two positive-domain distributions, we should take that into account
|
||||||
|
@ -199,59 +192,28 @@ let toDiscretePointMassesFromDiscrete = (s: PointSetTypes.xyShape): pointMassesW
|
||||||
}
|
}
|
||||||
|
|
||||||
let combineShapesContinuousDiscrete = (
|
let combineShapesContinuousDiscrete = (
|
||||||
op: Operation.algebraicOperation,
|
op: Operation.convolutionOperation,
|
||||||
continuousShape: PointSetTypes.xyShape,
|
continuousShape: PointSetTypes.xyShape,
|
||||||
discreteShape: PointSetTypes.xyShape,
|
discreteShape: PointSetTypes.xyShape,
|
||||||
|
discreteFirst: bool,
|
||||||
): PointSetTypes.xyShape => {
|
): PointSetTypes.xyShape => {
|
||||||
let t1n = continuousShape |> XYShape.T.length
|
|
||||||
let t2n = discreteShape |> XYShape.T.length
|
|
||||||
|
|
||||||
// each x pair is added/subtracted
|
// each x pair is added/subtracted
|
||||||
let fn = Operation.Algebraic.toFn(op)
|
let opFunc = Operation.Convolution.toFn(op)
|
||||||
|
let fn = discreteFirst ? (a, b) => opFunc(b, a) : opFunc
|
||||||
|
|
||||||
let outXYShapes: array<array<(float, float)>> = Belt.Array.makeUninitializedUnsafe(t2n)
|
let discretePoints = Belt.Array.zip(discreteShape.xs, discreteShape.ys)
|
||||||
|
let continuousPoints = Belt.Array.zip(continuousShape.xs, continuousShape.ys)
|
||||||
|
|
||||||
switch op {
|
let outXYShapes = switch op {
|
||||||
| #Add
|
| #Add
|
||||||
| #Subtract =>
|
| #Subtract =>
|
||||||
for j in 0 to t2n - 1 {
|
discretePoints->E.A2.fmap(((dx, dy)) =>
|
||||||
// creates a new continuous shape for each one of the discrete points, and collects them in outXYShapes.
|
continuousPoints->E.A2.fmap(((cx, cy)) => (fn(cx, dx), cy *. dy))
|
||||||
let dxyShape: array<(float, float)> = Belt.Array.makeUninitializedUnsafe(t1n)
|
)
|
||||||
for i in 0 to t1n - 1 {
|
| #Multiply =>
|
||||||
Belt.Array.set(
|
discretePoints->E.A2.fmap(((dx, dy)) =>
|
||||||
dxyShape,
|
continuousPoints->E.A2.fmap(((cx, cy)) => (fn(cx, dx), cy *. dy /. dx))
|
||||||
i,
|
)
|
||||||
(
|
|
||||||
fn(continuousShape.xs[i], discreteShape.xs[j]),
|
|
||||||
continuousShape.ys[i] *. discreteShape.ys[j],
|
|
||||||
),
|
|
||||||
) |> ignore
|
|
||||||
()
|
|
||||||
}
|
|
||||||
Belt.Array.set(outXYShapes, j, dxyShape) |> ignore
|
|
||||||
()
|
|
||||||
}
|
|
||||||
| #Multiply
|
|
||||||
| #Power
|
|
||||||
| #Logarithm
|
|
||||||
| #Divide =>
|
|
||||||
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)
|
|
||||||
for i in 0 to t1n - 1 {
|
|
||||||
Belt.Array.set(
|
|
||||||
dxyShape,
|
|
||||||
i,
|
|
||||||
(
|
|
||||||
fn(continuousShape.xs[i], discreteShape.xs[j]),
|
|
||||||
continuousShape.ys[i] *. discreteShape.ys[j] /. discreteShape.xs[j],
|
|
||||||
),
|
|
||||||
) |> ignore
|
|
||||||
()
|
|
||||||
}
|
|
||||||
Belt.Array.set(outXYShapes, j, dxyShape) |> ignore
|
|
||||||
()
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
|
|
||||||
outXYShapes
|
outXYShapes
|
|
@ -0,0 +1,187 @@
|
||||||
|
// Types
|
||||||
|
|
||||||
|
let emptyXYShape: PointSetTypes.xyShape = {xs: [], ys: []}
|
||||||
|
exception LogicallyInconsistent(string)
|
||||||
|
|
||||||
|
// Helpers
|
||||||
|
|
||||||
|
let getArithmeticComplementOfDistributionForSubstraction = (
|
||||||
|
dist: PointSetTypes.xyShape,
|
||||||
|
): PointSetTypes.xyShape => {
|
||||||
|
let newXs = Belt.Array.map(dist.xs, x => -.x)
|
||||||
|
{xs: newXs, ys: dist.ys}
|
||||||
|
}
|
||||||
|
|
||||||
|
let getApproximatePdfOfContinuousDistributionAtPoint = (
|
||||||
|
dist: PointSetTypes.xyShape,
|
||||||
|
point: float,
|
||||||
|
): float => {
|
||||||
|
let closestFromBelowIndex = E.A.reducei(dist.xs, None, (accumulator, item, index) =>
|
||||||
|
item < point ? Some(index) : accumulator
|
||||||
|
) // This could be improved by taking advantage of the fact that these are ordered
|
||||||
|
let closestFromAboveIndexOption = Belt.Array.getIndexBy(dist.xs, item => item > point)
|
||||||
|
|
||||||
|
let weightedMean = (
|
||||||
|
point: float,
|
||||||
|
closestFromBelow: float,
|
||||||
|
closestFromAbove: float,
|
||||||
|
valueclosestFromBelow,
|
||||||
|
valueclosestFromAbove,
|
||||||
|
): float => {
|
||||||
|
let distance = closestFromAbove -. closestFromBelow
|
||||||
|
let w1 = (point -. closestFromBelow) /. distance
|
||||||
|
let w2 = (closestFromAbove -. point) /. distance
|
||||||
|
let result = w1 *. valueclosestFromAbove +. w2 *. valueclosestFromBelow
|
||||||
|
result
|
||||||
|
}
|
||||||
|
|
||||||
|
let result = switch (closestFromBelowIndex, closestFromAboveIndexOption) {
|
||||||
|
| (None, None) =>
|
||||||
|
raise(
|
||||||
|
LogicallyInconsistent(
|
||||||
|
"Logically inconsistent option in NumericShapeCombination2.res. Possibly caused by empty distribution",
|
||||||
|
), // to do: give an error type
|
||||||
|
) // all are smaller, and all are larger
|
||||||
|
| (None, Some(i)) => 0.0 // none are smaller, all are larger
|
||||||
|
| (Some(i), None) => 0.0 // all are smaller, none are larger
|
||||||
|
| (Some(i), Some(j)) => weightedMean(point, dist.xs[i], dist.xs[j], dist.ys[i], dist.ys[j]) // there is a lowerBound and an upperBound.
|
||||||
|
}
|
||||||
|
|
||||||
|
result
|
||||||
|
}
|
||||||
|
|
||||||
|
// Inner functions
|
||||||
|
|
||||||
|
let addContinuousContinuous = (
|
||||||
|
s1: PointSetTypes.xyShape,
|
||||||
|
s2: PointSetTypes.xyShape,
|
||||||
|
): PointSetTypes.xyShape => {
|
||||||
|
// Assumption: xyShapes are ordered on the x coordinate.
|
||||||
|
|
||||||
|
// Get some needed variables
|
||||||
|
let len1 = XYShape.T.length(s1)
|
||||||
|
let mins1xs = s1.xs[0] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a < b ? a : b)
|
||||||
|
let maxs1xs = s1.xs[len1 - 1] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a > b ? a : b)
|
||||||
|
|
||||||
|
let len2 = XYShape.T.length(s2)
|
||||||
|
let mins2xs = s2.xs[0] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a < b ? a : b)
|
||||||
|
let maxs2xs = s2.xs[len1 - 1] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a > b ? a : b)
|
||||||
|
|
||||||
|
let lowerBound = mins1xs +. mins2xs
|
||||||
|
let upperBound = maxs1xs +. maxs2xs
|
||||||
|
let numIntervals = 2 * Js.Math.max_int(len1, len2) // 5000
|
||||||
|
let epsilon = (upperBound -. lowerBound) /. Belt.Int.toFloat(numIntervals) // Js.Math.pow_float(~base=2.0, ~exp=-16.0)
|
||||||
|
|
||||||
|
let newXs: array<float> = Belt.Array.makeUninitializedUnsafe(numIntervals)
|
||||||
|
let newYs: array<float> = Belt.Array.makeUninitializedUnsafe(numIntervals)
|
||||||
|
|
||||||
|
let getApproximatePdfOfS1AtPoint = x => getApproximatePdfOfContinuousDistributionAtPoint(s1, x)
|
||||||
|
let getApproximatePdfOfS2AtPoint = x => getApproximatePdfOfContinuousDistributionAtPoint(s2, x)
|
||||||
|
let float = x => Belt.Int.toFloat(x)
|
||||||
|
// Compute the integral numerically.
|
||||||
|
// I wouldn't worry too much about the O(n^3). At 5000 samples, this takes on the order of 25 million operations
|
||||||
|
// The AMD Ryzen 7 processor in my computer can do around 300K million operations per second.
|
||||||
|
// src: https://wikiless.org/wiki/Instructions_per_second?lang=en#Thousand_instructions_per_second_(TIPS/kIPS)
|
||||||
|
for i in 0 to numIntervals - 1 {
|
||||||
|
// where are the x points in the resulting distribution
|
||||||
|
let z = lowerBound +. float(i) *. epsilon
|
||||||
|
newXs[i] = z
|
||||||
|
newYs[i] = 0.0
|
||||||
|
for j in 0 to numIntervals - 1 {
|
||||||
|
// how fine-grained do we want our approximation of the integral to be.
|
||||||
|
let x = lowerBound +. float(j) *. epsilon
|
||||||
|
let deltaYi = getApproximatePdfOfS1AtPoint(x) *. getApproximatePdfOfS2AtPoint(z -. x)
|
||||||
|
newYs[i] = newYs[i] +. deltaYi
|
||||||
|
}
|
||||||
|
}
|
||||||
|
// This could be improved by, for instance, choosing the location of the xs strategically
|
||||||
|
// for example, such that each of them is "equidistant" in a cdf, that is, such that the
|
||||||
|
// cdf increases by constant amounts from one point to another.
|
||||||
|
{xs: newXs, ys: newYs}
|
||||||
|
}
|
||||||
|
|
||||||
|
let multiplyContinuousContinuous = (
|
||||||
|
s1: PointSetTypes.xyShape,
|
||||||
|
s2: PointSetTypes.xyShape,
|
||||||
|
): PointSetTypes.xyShape => {
|
||||||
|
// Assumption: xyShapes are ordered on the x coordinate.
|
||||||
|
|
||||||
|
// Get some needed variables
|
||||||
|
let len1 = XYShape.T.length(s1)
|
||||||
|
let mins1xs = s1.xs[0] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a < b ? a : b)
|
||||||
|
let maxs1xs = s1.xs[len1 - 1] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a > b ? a : b)
|
||||||
|
|
||||||
|
let len2 = XYShape.T.length(s2)
|
||||||
|
let mins2xs = s2.xs[0] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a < b ? a : b)
|
||||||
|
let maxs2xs = s2.xs[len1 - 1] // Belt.Array.reduce(s1.xs, s1.xs[0], (a, b) => a > b ? a : b)
|
||||||
|
|
||||||
|
let lowerBound = mins1xs *. mins2xs
|
||||||
|
let upperBound = maxs1xs *. maxs2xs
|
||||||
|
let numIntervals = 2 * Js.Math.max_int(len1, len2) // 5000
|
||||||
|
let epsilon = (upperBound -. lowerBound) /. Belt.Int.toFloat(numIntervals) // Js.Math.pow_float(~base=2.0, ~exp=-16.0)
|
||||||
|
let epsilonForIgnoreInIntegral = Js.Math.pow_float(~base=2.0, ~exp=-16.0)
|
||||||
|
|
||||||
|
let newXs: array<float> = Belt.Array.makeUninitializedUnsafe(numIntervals)
|
||||||
|
let newYs: array<float> = Belt.Array.makeUninitializedUnsafe(numIntervals)
|
||||||
|
|
||||||
|
let getApproximatePdfOfS1AtPoint = x => getApproximatePdfOfContinuousDistributionAtPoint(s1, x)
|
||||||
|
let getApproximatePdfOfS2AtPoint = x => getApproximatePdfOfContinuousDistributionAtPoint(s2, x)
|
||||||
|
let float = x => Belt.Int.toFloat(x)
|
||||||
|
// Compute the integral numerically.
|
||||||
|
// I wouldn't worry too much about the O(n^3). At 5000 samples, this takes on the order of 25 million operations
|
||||||
|
// The AMD Ryzen 7 processor in my computer can do around 300K million operations per second.
|
||||||
|
// src: https://wikiless.org/wiki/Instructions_per_second?lang=en#Thousand_instructions_per_second_(TIPS/kIPS)
|
||||||
|
for i in 0 to numIntervals - 1 {
|
||||||
|
// where are the x points in the resulting distribution
|
||||||
|
let z = lowerBound +. float(i) *. epsilon
|
||||||
|
newXs[i] = z
|
||||||
|
newYs[i] = 0.0
|
||||||
|
for j in 0 to numIntervals - 1 {
|
||||||
|
// how fine-grained do we want our approximation of the integral to be.
|
||||||
|
let x = lowerBound +. float(j) *. epsilon
|
||||||
|
let absX = Js.Math.abs_float(x)
|
||||||
|
if absX > epsilonForIgnoreInIntegral {
|
||||||
|
let deltaYi =
|
||||||
|
getApproximatePdfOfS1AtPoint(x) *. getApproximatePdfOfS2AtPoint(z /. x) *. (1.0 /. x)
|
||||||
|
newYs[i] = newYs[i] +. deltaYi
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
// This could be improved by, for instance, choosing the location of the xs strategically
|
||||||
|
// for example, such that each of them is "equidistant" in a cdf, that is, such that the
|
||||||
|
// cdf increases by constant amounts from one point to another.
|
||||||
|
{xs: newXs, ys: newYs}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Main function
|
||||||
|
|
||||||
|
let combineShapesContinuousContinuous = (
|
||||||
|
op: Operation.algebraicOperation,
|
||||||
|
s1: PointSetTypes.xyShape,
|
||||||
|
s2: PointSetTypes.xyShape,
|
||||||
|
): PointSetTypes.xyShape => {
|
||||||
|
let result = switch op {
|
||||||
|
| #Add => addContinuousContinuous(s1, s2)
|
||||||
|
| #Subtract =>
|
||||||
|
addContinuousContinuous(s1, getArithmeticComplementOfDistributionForSubstraction(s2))
|
||||||
|
| #Multiply => emptyXYShape
|
||||||
|
| #Divide => emptyXYShape
|
||||||
|
| #Power => emptyXYShape
|
||||||
|
| #Logarithm => emptyXYShape
|
||||||
|
}
|
||||||
|
result
|
||||||
|
}
|
||||||
|
|
||||||
|
// Not sure I understand how to combine continuous and discrete distribution
|
||||||
|
|
||||||
|
let combineShapesContinuousDiscrete = (
|
||||||
|
op: Operation.algebraicOperation,
|
||||||
|
continuousShape: PointSetTypes.xyShape,
|
||||||
|
discreteShape: PointSetTypes.xyShape,
|
||||||
|
): PointSetTypes.xyShape => emptyXYShape
|
||||||
|
|
||||||
|
let combineShapesDiscreteContinuous = (
|
||||||
|
op: Operation.algebraicOperation,
|
||||||
|
discreteShape: PointSetTypes.xyShape,
|
||||||
|
continuousShape: PointSetTypes.xyShape,
|
||||||
|
): PointSetTypes.xyShape => emptyXYShape
|
|
@ -34,14 +34,14 @@ let toMixed = mapToAll((
|
||||||
),
|
),
|
||||||
))
|
))
|
||||||
|
|
||||||
//TODO WARNING: The combineAlgebraicallyWithDiscrete will break for subtraction and division, like, discrete - continous
|
let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t =>
|
||||||
let combineAlgebraically = (op: Operation.algebraicOperation, t1: t, t2: t): t =>
|
|
||||||
switch (t1, t2) {
|
switch (t1, t2) {
|
||||||
| (Continuous(m1), Continuous(m2)) =>
|
| (Continuous(m1), Continuous(m2)) =>
|
||||||
Continuous.combineAlgebraically(op, m1, m2) |> Continuous.T.toPointSetDist
|
Continuous.combineAlgebraically(op, m1, m2) |> Continuous.T.toPointSetDist
|
||||||
| (Continuous(m1), Discrete(m2))
|
| (Discrete(m1), Continuous(m2)) =>
|
||||||
| (Discrete(m2), Continuous(m1)) =>
|
Continuous.combineAlgebraicallyWithDiscrete(op, m2, m1, true) |> Continuous.T.toPointSetDist
|
||||||
Continuous.combineAlgebraicallyWithDiscrete(op, m1, m2) |> Continuous.T.toPointSetDist
|
| (Continuous(m1), Discrete(m2)) =>
|
||||||
|
Continuous.combineAlgebraicallyWithDiscrete(op, m1, m2, false) |> Continuous.T.toPointSetDist
|
||||||
| (Discrete(m1), Discrete(m2)) =>
|
| (Discrete(m1), Discrete(m2)) =>
|
||||||
Discrete.combineAlgebraically(op, m1, m2) |> Discrete.T.toPointSetDist
|
Discrete.combineAlgebraically(op, m1, m2) |> Discrete.T.toPointSetDist
|
||||||
| (m1, m2) => Mixed.combineAlgebraically(op, toMixed(m1), toMixed(m2)) |> Mixed.T.toPointSetDist
|
| (m1, m2) => Mixed.combineAlgebraically(op, toMixed(m1), toMixed(m2)) |> Mixed.T.toPointSetDist
|
||||||
|
|
|
@ -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)
|
|
||||||
}
|
|
|
@ -9,6 +9,13 @@ type algebraicOperation = [
|
||||||
| #Power
|
| #Power
|
||||||
| #Logarithm
|
| #Logarithm
|
||||||
]
|
]
|
||||||
|
|
||||||
|
type convolutionOperation = [
|
||||||
|
| #Add
|
||||||
|
| #Multiply
|
||||||
|
| #Subtract
|
||||||
|
]
|
||||||
|
|
||||||
@genType
|
@genType
|
||||||
type pointwiseOperation = [#Add | #Multiply | #Power]
|
type pointwiseOperation = [#Add | #Multiply | #Power]
|
||||||
type scaleOperation = [#Multiply | #Power | #Logarithm | #Divide]
|
type scaleOperation = [#Multiply | #Power | #Logarithm | #Divide]
|
||||||
|
@ -20,6 +27,16 @@ type distToFloatOperation = [
|
||||||
| #Sample
|
| #Sample
|
||||||
]
|
]
|
||||||
|
|
||||||
|
module Convolution = {
|
||||||
|
type t = convolutionOperation
|
||||||
|
let toFn: (t, float, float) => float = x =>
|
||||||
|
switch x {
|
||||||
|
| #Add => \"+."
|
||||||
|
| #Subtract => \"-."
|
||||||
|
| #Multiply => \"*."
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
module Algebraic = {
|
module Algebraic = {
|
||||||
type t = algebraicOperation
|
type t = algebraicOperation
|
||||||
let toFn: (t, float, float) => float = x =>
|
let toFn: (t, float, float) => float = x =>
|
||||||
|
|
Loading…
Reference in New Issue
Block a user