Initialized logScore and logScoreAgainstImproperPrior

Value: [1e-5 to 6e-3]

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

@@ -277,13 +277,19 @@ module T = Dist({
       prediction.xyShape,
       answer.xyShape,
     )
-    let xyShapeToContinuous: XYShape.xyShape => t = xyShape => {
+    newShape->E.R2.fmap(x => x->make->integralEndY)
-      xyShape: xyShape,
-      interpolation: #Linear,
-      integralSumCache: None,
-      integralCache: None,
   }
-    newShape->E.R2.fmap(x => x->xyShapeToContinuous->integralEndY)
+  let logScore = (prior: t, prediction: t, answer: float) => {
+    let newShape = XYShape.PointwiseCombination.combineAlongSupportOfSecondArgument(
+      PointSetDist_Scoring.LogScore.integrand(~answer),
+      prior.xyShape,
+      prediction.xyShape,
+    )
+    newShape->E.R2.fmap(x => x->make->integralEndY)
+  }
+  let logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
+    let prior = make({xs: prediction.xyShape.xs, ys: E.A.fmap(_ => 1.0, prediction.xyShape.xs)})
+    logScore(prior, prediction, answer)
   }
 })
 

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

@@ -229,4 +229,10 @@ module T = Dist({
       answer,
     )->E.R2.fmap(integralEndY)
   }
+  let logScore = (prior: t, prediction: t, answer: float) => {
+    Error(Operation.NotYetImplemented)
+  }
+  let logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
+    Error(Operation.NotYetImplemented)
+  }
 })

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

@@ -34,6 +34,8 @@ module type dist = {
   let mean: t => float
   let variance: t => float
   let klDivergence: (t, t) => result<float, Operation.Error.t>
+  let logScore: (t, t, float) => result<float, Operation.Error.t>
+  let logScoreAgainstImproperPrior: (t, float) => result<float, Operation.Error.t>
 }
 
 module Dist = (T: dist) => {
@@ -57,6 +59,8 @@ module Dist = (T: dist) => {
   let variance = T.variance
   let integralEndY = T.integralEndY
   let klDivergence = T.klDivergence
+  let logScore = T.logScore
+  let logScoreAgainstImproperPrior = T.logScoreAgainstImproperPrior
 
   let updateIntegralCache = T.updateIntegralCache
 

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

@@ -306,6 +306,12 @@ module T = Dist({
     let klContinuousPart = Continuous.T.klDivergence(prediction.continuous, answer.continuous)
     E.R.merge(klDiscretePart, klContinuousPart)->E.R2.fmap(t => fst(t) +. snd(t))
   }
+  let logScore = (prior: t, prediction: t, answer: float) => {
+    Error(Operation.NotYetImplemented)
+  }
+  let logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
+    Error(Operation.NotYetImplemented)
+  }
 })
 
 let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {

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

@@ -196,12 +196,21 @@ module T = Dist({
     | Continuous(m) => Continuous.T.variance(m)
     }
 
-  let klDivergence = (t1: t, t2: t) =>
+  let klDivergence = (prediction: t, answer: t) =>
-    switch (t1, t2) {
+    switch (prediction, answer) {
     | (Continuous(t1), Continuous(t2)) => Continuous.T.klDivergence(t1, t2)
     | (Discrete(t1), Discrete(t2)) => Discrete.T.klDivergence(t1, t2)
-    | (Mixed(t1), Mixed(t2)) => Mixed.T.klDivergence(t1, t2)
+    | (m1, m2) => Mixed.T.klDivergence(m1->toMixed, m2->toMixed)
-    | _ => Error(NotYetImplemented)
+    }
+
+  let logScore = (prior: t, prediction: t, answer: float) => {
+    switch (prior, prediction) {
+    | (Continuous(t1), Continuous(t2)) => Continuous.T.logScore(t1, t2, answer)
+    | _ => Error(Operation.NotYetImplemented)
+    }
+  }
+  let logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
+    Error(Operation.NotYetImplemented)
   }
 })
 

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

@@ -14,3 +14,20 @@ module KLDivergence = {
       quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answerElement *. logFn(quot))
     }
 }
+
+/*
+
+*/
+module LogScore = {
+  let logFn = Js.Math.log
+  let integrand = (priorElement: float, predictionElement: float, ~answer: float) => {
+    if answer == 0.0 {
+      Ok(0.0)
+    } else if predictionElement == 0.0 {
+      Ok(infinity)
+    } else {
+      let quot = predictionElement /. priorElement
+      quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answer *. logFn(quot /. answer))
+    }
+  }
+}