removed \TwoScalars\ material

packages/squiggle-lang/__tests__/Distributions/Scoring/TwoScalars_test.res

@@ -1,90 +0,0 @@
-open Jest
-open Expect
-open GenericDist_Fixtures
-exception ScoreFailed
-
-describe("TwoScalars: scalar -> scalar -> score", () => {
-  test("score: infinity", () => {
-    let scalar1 = 1.0 // 100% of probability mass 1.0
-    let scalar2 = 2.0 // 100% of probability mass to 2.0
-    let score = PointSetDist_Scoring.TwoScalars.score(~estimate=scalar1, ~answer=scalar2)
-    switch score {
-    | Ok(x) => x->expect->toEqual(infinity)
-    | _ => raise(MixtureFailed)
-    }
-  })
-
-  test("score: 0.0", () => {
-    let scalar1 = 1.5 // 100% of probability mass 1.5
-    let scalar2 = 1.5 // 100% of probability mass to 1.5
-    let score = PointSetDist_Scoring.TwoScalars.score(~estimate=scalar1, ~answer=scalar2)
-    switch score {
-    | Ok(x) => x->expect->toEqual(0.0)
-    | _ => raise(MixtureFailed)
-    }
-  })
-
-  test("scoreWithPrior: minus infinity", () => {
-    let scalar1 = 1.5 // 100% of probability mass 1.5
-    let scalar2 = 1.5 // 100% of probability mass to 1.5
-    let scalar3 = 1.0 // 100% of probability mass to 1.0
-
-    let score = PointSetDist_Scoring.TwoScalars.scoreWithPrior(
-      ~estimate=scalar1,
-      ~answer=scalar2,
-      ~prior=scalar3,
-    )
-    switch score {
-    | Ok(x) => x->expect->toEqual(-.infinity)
-    | _ => raise(MixtureFailed)
-    }
-  })
-
-  test("scoreWithPrior: 0.0", () => {
-    let scalar1 = 1.5 // 100% of probability mass 1.5
-    let scalar2 = 1.5 // 100% of probability mass to 1.5
-    let scalar3 = 1.5 // 100% of probability mass to 1.5
-
-    let score = PointSetDist_Scoring.TwoScalars.scoreWithPrior(
-      ~estimate=scalar1,
-      ~answer=scalar2,
-      ~prior=scalar3,
-    )
-    switch score {
-    | Ok(x) => x->expect->toEqual(0.0)
-    | _ => raise(ScoreFailed)
-    }
-  })
-
-  test("scoreWithPrior: really dumb forecasters", () => {
-    let scalar1 = 1.0 // 100% of probability mass 1.0
-    let scalar2 = 1.5 // 100% of probability mass to 1.5
-    let scalar3 = 1.0 // 100% of probability mass to 1.0
-
-    let score = PointSetDist_Scoring.TwoScalars.scoreWithPrior(
-      ~estimate=scalar1,
-      ~answer=scalar2,
-      ~prior=scalar3,
-    )
-    switch score {
-    | Ok(x) => x->expect->toEqual(infinity -. infinity) // "Error: Really dumb forecasters"
-    | _ => raise(ScoreFailed)
-    }
-  })
-
-  test("scoreWithPrior: 0.0", () => {
-    let scalar1 = 1.0 // 100% of probability mass 1.0
-    let scalar2 = 1.0 // 100% of probability mass to 1.0
-    let scalar3 = 1.0 // 100% of probability mass to 1.0
-
-    let score = PointSetDist_Scoring.TwoScalars.scoreWithPrior(
-      ~estimate=scalar1,
-      ~answer=scalar2,
-      ~prior=scalar3,
-    )
-    switch score {
-    | Ok(x) => x->expect->toEqual(0.0)
-    | _ => raise(ScoreFailed)
-    }
-  })
-})

packages/squiggle-lang/src/rescript/Distributions/GenericDist.res

@@ -190,14 +190,7 @@ module Score = {
     | (Score_Scalar(_), Score_Dist(_), None) => NotYetImplemented->Error
     | (Score_Scalar(_), Score_Dist(_), Some(Ok(PSScalar(_)))) => NotYetImplemented->Error
     | (Score_Scalar(_), _, Some(Ok(PSDist(_)))) => DistributionTypes.Unreachable->Error
-    | (Score_Scalar(esti'), Score_Scalar(answ'), None) =>
+    | (Score_Scalar(_), Score_Scalar(_), _) => NotYetImplemented->Error
-      {estimate: esti', answer: answ', prior: None}
-      ->PointSetDist_Scoring.ScalarEstimateScalarAnswer
-      ->Ok
-    | (Score_Scalar(esti'), Score_Scalar(answ'), Some(Ok(PSScalar(prior'')))) =>
-      {estimate: esti', answer: answ', prior: prior''->Some}
-      ->PointSetDist_Scoring.ScalarEstimateScalarAnswer
-      ->Ok
     | (_, _, Some(Error(err))) => err->Error
     }
   }

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist_Scoring.res

@@ -6,7 +6,6 @@ type abstractScoreArgs<'a, 'b> = {estimate: 'a, answer: 'b, prior: option<'a>}
 type scoreArgs =
   | DistEstimateDistAnswer(abstractScoreArgs<pointSetDist, pointSetDist>)
   | DistEstimateScalarAnswer(abstractScoreArgs<pointSetDist, scalar>)
-  | ScalarEstimateScalarAnswer(abstractScoreArgs<scalar, scalar>)
 
 let logFn = Js.Math.log // base e
 let minusScaledLogOfQuotient = (~esti, ~answ): result<float, Operation.Error.t> => {
@@ -130,23 +129,6 @@ module WithScalarAnswer = {
   }
 }
 
-module TwoScalars = {
-  // You will almost never want to use this.
-  let score = (~estimate: scalar, ~answer: scalar) => {
-    if Js.Math.abs_float(estimate -. answer) < MagicNumbers.Epsilon.ten {
-      0.0->Ok
-    } else {
-      infinity->Ok // - log(0)
-    }
-  }
-
-  let scoreWithPrior = (~estimate: scalar, ~answer: scalar, ~prior: scalar) => {
-    E.R.merge(score(~estimate, ~answer), score(~estimate=prior, ~answer))->E.R2.fmap(((s1, s2)) =>
-      s1 -. s2
-    ) // This will presently NaN if both are wrong: infinity-infinity.
-  }
-}
-
 let twoGenericDistsToTwoPointSetDists = (~toPointSetFn, estimate, answer): result<
   (pointSetDist, pointSetDist),
   'e,
@@ -165,8 +147,4 @@ let logScore = (args: scoreArgs, ~combineFn, ~integrateFn, ~toMixedFn): result<
     WithScalarAnswer.score(~estimate, ~answer)
   | DistEstimateScalarAnswer({estimate, answer, prior: Some(prior)}) =>
     WithScalarAnswer.scoreWithPrior(~estimate, ~answer, ~prior)
-  | ScalarEstimateScalarAnswer({estimate, answer, prior: None}) =>
-    TwoScalars.score(~estimate, ~answer)
-  | ScalarEstimateScalarAnswer({estimate, answer, prior: Some(prior)}) =>
-    TwoScalars.scoreWithPrior(~estimate, ~answer, ~prior)
   }