tweak: Cleanup
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@ -54,7 +54,14 @@ module Internals = {
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let binomial = ((n, k, p)) => choose((n, k)) *. pow(p, k) *. pow(1.0 -. p, n -. k)
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// Integral helper functions
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let applyFunctionAtPoint = (
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let castFloatToInternalNumber = x => ReducerInterface_InternalExpressionValue.IEvNumber(x)
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let castArrayOfFloatsToInternalArrayOfInternals = xs => ReducerInterface_InternalExpressionValue.IEvArray(
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Belt.Array.map(xs, x => castFloatToInternalNumber(x)),
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)
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/* Helper functions. May be useful in 3 months when coming back to this code.
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@dead let applyFunctionAtPoint = (
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aLambda,
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internalNumber: internalExpressionValue,
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environment,
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@ -68,15 +75,11 @@ module Internals = {
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)
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result
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}
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let castFloatToInternalNumber = x => ReducerInterface_InternalExpressionValue.IEvNumber(x)
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let castArrayOfFloatsToInternalArrayOfInternals = xs => ReducerInterface_InternalExpressionValue.IEvArray(
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Belt.Array.map(xs, x => castFloatToInternalNumber(x)),
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)
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@dead
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let applyFunctionAtFloat = (aLambda, point, environment, reducer) =>
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@dead let applyFunctionAtFloat = (aLambda, point, environment, reducer) =>
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// reason for existence: might be an useful template to have for calculating diminishing marginal returns later on
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applyFunctionAtPoint(aLambda, castFloatToInternalNumber(point), environment, reducer)
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// integrate function itself
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*/
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let integrateFunctionBetweenWithNumIntegrationPoints = (
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aLambda,
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min: float,
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@ -171,6 +174,9 @@ module Internals = {
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}
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result
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}
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// Diminishing returns
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// Helpers
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type diminishingReturnsAccumulatorInner = {
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optimalAllocations: array<float>,
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currentMarginalReturns: result<array<float>, string>,
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@ -183,7 +189,9 @@ module Internals = {
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}
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})
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type diminishingReturnsAccumulator = result<diminishingReturnsAccumulatorInner, string>
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let diminishingMarginalReturnsForTwoFunctions = (
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/* Simple function. May be useful for remembering how this works when I come back to this code weeks or months from now.
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@dead let diminishingMarginalReturnsForTwoFunctions = (
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// left alive for now because I know it works.
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lambda1,
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lambda2,
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funds,
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@ -277,48 +285,7 @@ module Internals = {
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| Error(b) => Error(b)
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}
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optimalAllocationResult
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}
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/*
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let diminishingMarginalReturnsForManyFunctionsSkeleton = (
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lambdas,
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funds,
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approximateIncrement,
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environment,
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reducer,
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) => {
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let applyFunctionAtFloatToFloatOption = (lambda, point: float) => {
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// Defined here so that it has access to environment, reducer
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let pointAsInternalExpression = castFloatToInternalNumber(point)
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let resultAsInternalExpression = Reducer_Expression_Lambda.doLambdaCall(
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lambda,
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list{pointAsInternalExpression},
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environment,
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reducer,
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)
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let result = switch resultAsInternalExpression {
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| Ok(IEvNumber(x)) => Ok(x)
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| Error(_) =>
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Error(
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"Error 1 in Danger.diminishingMarginalReturnsForManyFunctions. It's possible that your function doesn't return a number, try definining auxiliaryFunction(x) = mean(yourFunction(x)) and integrate auxiliaryFunction instead",
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)
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| _ => Error("Error 2 in Danger.diminishingMarginalReturnsForManyFunctions")
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}
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result
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}
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let answer =
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E.A.fmap(
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lambda => applyFunctionAtFloatToFloatOption(lambda, 0.0),
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lambdas,
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)->E.A.R.firstErrorOrOpen
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let result = switch answer {
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| Ok(xs) => xs->castArrayOfFloatsToInternalArrayOfInternals->Ok
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| Error(b) => Error(b)
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}
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// let result = [0.0, 0.0]->castArrayOfFloatsToInternalArrayOfInternals->Ok
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result
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}*/
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let diminishingMarginalReturnsForManyFunctions = (
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lambdas,
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funds,
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@ -350,11 +317,13 @@ module Internals = {
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}
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result
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}
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let numDivisions = Js.Math.round(funds /. approximateIncrement)
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let numDivisionsInt = Belt.Float.toInt(numDivisions)
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let increment = funds /. numDivisions
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let arrayOfIncrements = Belt.Array.makeBy(numDivisionsInt, _ => increment)
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let numLambdas = E.A.length(lambdas)
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let initAccumulator: diminishingReturnsAccumulator = Ok({
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optimalAllocations: Belt.Array.makeBy(numLambdas, _ => 0.0),
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currentMarginalReturns: E.A.fmap(
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@ -362,6 +331,7 @@ module Internals = {
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lambdas,
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)->E.A.R.firstErrorOrOpen,
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})
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let optimalAllocationEndAccumulator = E.A.reduce(arrayOfIncrements, initAccumulator, (
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acc,
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newIncrement,
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@ -402,13 +372,16 @@ module Internals = {
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| Error(b) => Error(b)
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}
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})
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let optimalAllocationResult = switch optimalAllocationEndAccumulator {
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| Ok(inner) => Ok(castArrayOfFloatsToInternalArrayOfInternals(inner.optimalAllocations))
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| Error(b) => Error(b)
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}
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optimalAllocationResult
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//let result = [0.0, 0.0]->castArrayOfFloatsToInternalArrayOfInternals->Ok
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// let result = [0.0, 0.0]->castArrayOfFloatsToInternalArrayOfInternals->Ok
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// result
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// ^ useful for debugging.
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}
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}
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@ -454,6 +427,7 @@ let library = [
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(),
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),
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// Helper functions building up to the integral
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/* Initial functions that helped me build understanding, may help when coming back to the code weeks or months from now.
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Function.make(
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~name="applyFunctionAtZero",
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~nameSpace,
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@ -503,6 +477,7 @@ let library = [
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],
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(),
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),
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*/
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// Integral in terms of function, min, max, num points
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// Note that execution time will be more predictable, because it
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// will only depend on num points and the complexity of the function
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@ -584,6 +559,10 @@ let library = [
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],
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(),
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),
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// Diminishing marginal return functions
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// There are functions diminishingMarginalReturnsForFunctions2 through diminishingMarginalReturnsForFunctions7
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// Because of this bug: <https://github.com/quantified-uncertainty/squiggle/issues/1090>
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// As soon as that is fixed, I will destroy this monstrosity.
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Function.make(
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~name="diminishingMarginalReturnsForFunctions2",
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~nameSpace,
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@ -835,7 +814,7 @@ let library = [
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],
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(),
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),
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// The following will compile, but not work, because of this bug: <https://github.com/quantified-uncertainty/squiggle/issues/558> Instead, I am creating different functions for different numbers of inputs
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// The following will compile, but not work, because of this bug: <https://github.com/quantified-uncertainty/squiggle/issues/558> Instead, I am creating different functions for different numbers of inputs above.
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/*
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Function.make(
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~name="diminishingMarginalReturnsForManyFunctions",
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