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Fix discrete+continuous integration issue by cleaning up PointwiseCombination.combine

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This commit is contained in:
Sebastian Kosch 2020-07-13 16:32:45 -07:00
parent f8060458be
commit 49e379c8aa
6 changed files with 105 additions and 43 deletions
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2
src/components/charts/DistPlusPlot.re
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@ -287,7 +287,7 @@ module IntegralChart = {
};
let maxX = {
distPlus |> DistPlus.T.Integral.yToX(~cache=None, 0.99);
distPlus |> DistPlus.T.Integral.yToX(~cache=None, 0.99999);
};
let timeScale = distPlus.unit |> DistTypes.DistributionUnit.toJson;
<DistributionPlot

6
src/distPlus/distribution/AlgebraicShapeCombination.re
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@ -304,5 +304,9 @@ let combineShapesContinuousDiscrete =
outXYShapes
|> E.A.fmap(XYShape.T.fromZippedArray)
|> E.A.fold_left(XYShape.PointwiseCombination.combineLinear((+.)), XYShape.T.empty);
|> E.A.fold_left(
XYShape.PointwiseCombination.combine((+.),
XYShape.XtoY.linearBetweenPointsExtrapolateZero,
XYShape.XtoY.linearBetweenPointsExtrapolateZero),
XYShape.T.empty);
};

6
src/distPlus/distribution/Continuous.re
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@ -43,8 +43,10 @@ let combinePointwise =
make(
`Linear,
XYShape.PointwiseCombination.combineLinear(
~fn=(+.),
XYShape.PointwiseCombination.combine(
(+.),
XYShape.XtoY.linearBetweenPointsExtrapolateZero,
XYShape.XtoY.linearBetweenPointsExtrapolateZero,
t1.xyShape,
t2.xyShape,
),

24
src/distPlus/distribution/Discrete.re
View File

@ -33,9 +33,9 @@ let combinePointwise =
make(
XYShape.PointwiseCombination.combine(
~xsSelection=ALL_XS,
~xToYSelection=XYShape.XtoY.stepwiseIfAtX,
~fn=(a, b) => fn(E.O.default(0.0, a), E.O.default(0.0, b)), // stepwiseIfAtX returns option(float), so this fn needs to handle None
(+.),
XYShape.XtoY.assumeZeroBetweenPoints,
XYShape.XtoY.assumeZeroBetweenPoints,
t1.xyShape,
t2.xyShape,
),
@ -113,12 +113,18 @@ module T =
if (t |> getShape |> XYShape.T.length > 0) {
switch (cache) {
| Some(c) => c
| None =>
Continuous.make(
`Stepwise,
XYShape.T.accumulateYs((+.), getShape(t)),
None,
)
| None => {
let ts = getShape(t);
// The first xy of this integral should always be the zero, to ensure nice plotting
let firstX = ts |> XYShape.T.minX;
let prependedZeroPoint: XYShape.T.t = {xs: [|firstX -. epsilon_float|], ys: [|0.|]};
let integralShape =
ts
|> XYShape.T.concat(prependedZeroPoint)
|> XYShape.T.accumulateYs((+.));
Continuous.make(`Stepwise, integralShape, None);
}
};
} else {
Continuous.make(

8
src/distPlus/distribution/Mixed.re
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@ -147,15 +147,17 @@ module T =
switch (cache) {
| Some(cache) => cache
| None =>
// note: if the underlying shapes aren't normalized, then these integrals won't be either!
// note: if the underlying shapes aren't normalized, then these integrals won't be either -- but that's the way it should be.
let continuousIntegral =
Continuous.T.Integral.get(~cache=None, continuous);
let discreteIntegral = Discrete.T.Integral.get(~cache=None, discrete);
Continuous.make(
`Linear,
XYShape.PointwiseCombination.combineLinear(
~fn=(+.),
XYShape.PointwiseCombination.combine(
(+.),
XYShape.XtoY.linearBetweenPointsExtrapolateFlat,
XYShape.XtoY.stepwiseBetweenPointsExtrapolateFlat,
Continuous.getShape(continuousIntegral),
Continuous.getShape(discreteIntegral),
),

102
src/distPlus/distribution/XYShape.re
View File

@ -32,6 +32,11 @@ module T = {
let accumulateYs = (fn, p: t) => {
fromArray((p.xs, E.A.accumulate(fn, p.ys)));
};
let concat = (t1: t, t2: t) => {
let cxs = Array.concat([t1.xs, t2.xs]);
let cys = Array.concat([t1.ys, t2.ys]);
{xs: cxs, ys: cys};
};
let fromZippedArray = (pairs: array((float, float))): t =>
pairs |> Belt.Array.unzip |> fromArray;
let equallyDividedXs = (t: t, newLength) => {
@ -137,6 +142,61 @@ module XtoY = {
};
n;
};
/* The extrapolators below can be used e.g. with PointwiseCombination.combine */
let linearBetweenPointsExtrapolateFlat = (t: T.t, leftIndex: int, x: float) => {
if (leftIndex < 0) {
t.ys[0];
} else if (leftIndex >= T.length(t) - 1) {
T.lastY(t);
} else {
let x1 = t.xs[leftIndex];
let x2 = t.xs[leftIndex + 1];
let y1 = t.ys[leftIndex];
let y2 = t.ys[leftIndex + 1];
let fraction = (x -. x1) /. (x2 -. x1);
y1 *. (1. -. fraction) +. y2 *. fraction;
}
};
let linearBetweenPointsExtrapolateZero = (t: T.t, leftIndex: int, x: float) => {
if (leftIndex < 0) {
0.0
} else if (leftIndex >= T.length(t) - 1) {
0.0
} else {
let x1 = t.xs[leftIndex];
let x2 = t.xs[leftIndex + 1];
let y1 = t.ys[leftIndex];
let y2 = t.ys[leftIndex + 1];
let fraction = (x -. x1) /. (x2 -. x1);
y1 *. (1. -. fraction) +. y2 *. fraction;
}
};
let stepwiseBetweenPointsExtrapolateFlat = (t: T.t, leftIndex: int, x: float) => {
if (leftIndex < 0) {
t.ys[0];
} else if (leftIndex >= T.length(t) - 1) {
T.lastY(t);
} else {
t.ys[leftIndex];
}
};
let stepwiseBetweenPointsExtrapolateZero = (t: T.t, leftIndex: int, x: float) => {
if (leftIndex < 0) {
0.0
} else if (leftIndex >= T.length(t) - 1) {
0.0
} else {
t.ys[leftIndex];
}
};
let assumeZeroBetweenPoints = (t: T.t, leftIndex: int, x: float) => {
0.0;
};
};
module XsConversion = {
@ -171,24 +231,22 @@ module Zipped = {
};
module PointwiseCombination = {
type xsSelection =
| ALL_XS
| XS_EVENLY_DIVIDED(int);
let combineLinear = [%raw {| // : (float => float => float, T.t, T.t) => T.t
// t1Interpolator and t2Interpolator are functions from XYShape.XtoY, e.g. linearBetweenPointsExtrapolateFlat.
let combine = [%raw {| // : (float => float => float, T.t, T.t, bool) => T.t
// This function combines two xyShapes by looping through both of them simultaneously.
// It always moves on to the next smallest x, whether that's in the first or second input's xs,
// and interpolates the value on the other side, thus accumulating xs and ys.
// In this implementation (unlike in XtoY.linear above), values outside of a shape's xs are considered 0.0 (instead of firstY or lastY).
// This is written in raw JS because this can still be a bottleneck, and using refs for the i and j indices is quite painful.
function(fn, t1, t2) {
function(fn, t1Interpolator, t2Interpolator, t1, t2) {
let t1n = t1.xs.length;
let t2n = t2.xs.length;
let outX = [];
let outY = [];
let i = -1;
let j = -1;
while (i <= t1n - 1 && j <= t2n - 1) {
let x, ya, yb;
if (j == t2n - 1 && i < t1n - 1 ||
@ -198,8 +256,7 @@ module PointwiseCombination = {
x = t1.xs[i];
ya = t1.ys[i];
let bFraction = (x - (t2.xs[j] || x)) / ((t2.xs[j+1] || x) - (t2.xs[j] || 0));
yb = (t2.ys[j] || 0) * (1-bFraction) + (t2.ys[j+1] || 0) * bFraction;
yb = t2Interpolator(t2, j, x);
} else if (i == t1n - 1 && j < t2n - 1 ||
t1.xs[i+1] > t2.xs[j+1]) { // if b has to catch up to a, or if a is already done
j++;
@ -207,8 +264,7 @@ module PointwiseCombination = {
x = t2.xs[j];
yb = t2.ys[j];
let aFraction = (x - (t1.xs[i] || x)) / ((t1.xs[i+1] || x) - (t1.xs[i] || 0));
ya = (t1.ys[i] || 0) * (1-aFraction) + (t1.ys[i+1] || 0) * aFraction;
ya = t1Interpolator(t1, i, x);
} else if (i < t1n - 1 && j < t2n && t1.xs[i+1] === t2.xs[j+1]) { // if they happen to be equal, move both ahead
i++;
j++;
@ -227,15 +283,16 @@ module PointwiseCombination = {
outX.push(x);
outY.push(fn(ya, yb));
}
return {xs: outX, ys: outY};
}
|}];
let combine =
let combineEvenXs =
(
~xToYSelection: (float, T.t) => 'a,
~xsSelection=ALL_XS,
~fn,
~xToYSelection,
sampleCount,
t1: T.t,
t2: T.t,
) => {
@ -245,24 +302,15 @@ module PointwiseCombination = {
| (0, _) => t2
| (_, 0) => t1
| (_, _) => {
let allXs =
switch (xsSelection) {
| ALL_XS => Ts.allXs([|t1, t2|])
| XS_EVENLY_DIVIDED(sampleCount) =>
Ts.equallyDividedXs([|t1, t2|], sampleCount)
};
let allXs = Ts.equallyDividedXs([|t1, t2|], sampleCount);
let allYs =
allXs |> E.A.fmap(x => fn(xToYSelection(x, t1), xToYSelection(x, t2)));
let allYs = allXs |> E.A.fmap(x => fn(xToYSelection(x, t1), xToYSelection(x, t2)));
T.fromArrays(allXs, allYs);
}
}
};
let combineStepwise = combine(~xToYSelection=XtoY.stepwiseIncremental);
let combineIfAtX = combine(~xToYSelection=XtoY.stepwiseIfAtX);
// TODO: I'd bet this is pretty slow. Maybe it would be faster to intersperse Xs and Ys separately.
let intersperse = (t1: T.t, t2: T.t) => {
E.A.intersperse(T.zip(t1), T.zip(t2)) |> T.fromZippedArray;
@ -355,10 +403,10 @@ let pointLogScore = (prediction, answer) =>
};
let logScorePoint = (sampleCount, t1, t2) =>
PointwiseCombination.combine(
~xsSelection=XS_EVENLY_DIVIDED(sampleCount),
~xToYSelection=XtoY.linear,
PointwiseCombination.combineEvenXs(
~fn=pointLogScore,
~xToYSelection=XtoY.linear,
sampleCount,
t1,
t2,
)