Fixed some indexing errors in convolution code
This commit is contained in:
parent
dc1ec1bb86
commit
d2e7e5f928
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@ -171,7 +171,8 @@ let make = () => {
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~schema,
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~onSubmit=({state}) => {None},
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~initialState={
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guesstimatorString: "mm(normal(-10, 2), uniform(18, 25), lognormal({mean: 10, stdev: 8}), triangular(31,40,50))",
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//guesstimatorString: "mm(normal(-10, 2), uniform(18, 25), lognormal({mean: 10, stdev: 8}), triangular(31,40,50))",
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guesstimatorString: "uniform(0, 1) + normal(1, 2)",
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domainType: "Complete",
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xPoint: "50.0",
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xPoint2: "60.0",
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@ -180,9 +181,9 @@ let make = () => {
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unitType: "UnspecifiedDistribution",
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zero: MomentRe.momentNow(),
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unit: "days",
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sampleCount: "30000",
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outputXYPoints: "10000",
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downsampleTo: "1000",
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sampleCount: "3000",
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outputXYPoints: "100",
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downsampleTo: "100",
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kernelWidth: "5",
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},
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(),
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@ -154,6 +154,7 @@ module Continuous = {
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// This is essentially like integrateWithTriangles, without the accumulation.
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let toDiscretePointMasses = (t: t): DistTypes.discreteShape => {
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let tl = t |> getShape |> XYShape.T.length;
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let pointMassesX: array(float) = Belt.Array.make(tl - 1, 0.0);
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let pointMassesY: array(float) = Belt.Array.make(tl - 1, 0.0);
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let {xs, ys}: XYShape.T.t = t |> getShape;
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for (x in 0 to E.A.length(xs) - 2) {
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@ -163,75 +164,24 @@ module Continuous = {
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x,
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(xs[x + 1] -. xs[x]) *. ((ys[x] +. ys[x + 1]) /. 2.),
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); // = dx * (1/2) * (avgY)
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let _ =
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Belt.Array.set(
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pointMassesX,
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x,
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(xs[x] +. xs[x + 1]) /. 2.,
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); // midpoints
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();
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};
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{
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xyShape: {
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xs,
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xs: pointMassesX,
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ys: pointMassesY,
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},
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knownIntegralSum: t.knownIntegralSum,
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};
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};
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/* Performs a discrete convolution between two continuous distributions A and B.
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* It is an extremely good idea to downsample the distributions beforehand,
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* because the number of samples in the convolution can be up to length(A) * length(B).
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*
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* Conventional convolution uses fn = (+.), but we also allow other operations to combine the xs.
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*
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* In practice, the convolution works by multiplying the ys for each possible combo of points of
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* the two shapes. This creates a new shape for each point of A. These new shapes are then combined
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* linearly. This may not always be the most efficient way, but it is probably the most robust for now.
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*
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* In the future, it may be possible to use a non-uniform fast Fourier transform instead (although only for addition).
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*/
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let convolveWithDiscrete = (fn, t1: t, t2: DistTypes.discreteShape) => {
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let t1s = t1 |> getShape;
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let t2s = t2.xyShape; // would like to use Discrete.getShape here, but current file structure doesn't allow for that
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let t1n = t1s |> XYShape.T.length;
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let t2n = t2s |> XYShape.T.length;
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let outXYShapes: array(array((float, float))) =
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Belt.Array.makeUninitializedUnsafe(t1n);
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for (i in 0 to t1n - 1) {
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// create a new distribution
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let dxyShape: array((float, float)) =
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Belt.Array.makeUninitializedUnsafe(t2n);
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for (j in 0 to t2n - 1) {
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let _ =
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Belt.Array.set(
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dxyShape,
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j,
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(fn(t1s.xs[i], t2s.xs[j]), t1s.ys[i] *. t2s.ys[j]),
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);
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();
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};
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let _ = Belt.Array.set(outXYShapes, i, dxyShape);
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();
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};
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let combinedIntegralSum =
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switch (t1.knownIntegralSum, t2.knownIntegralSum) {
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| (None, _)
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| (_, None) => None
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| (Some(s1), Some(s2)) => Some(s1 *. s2)
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};
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outXYShapes
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|> E.A.fmap(s => {
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let xyShape = XYShape.T.fromZippedArray(s);
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make(`Linear, xyShape, None);
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})
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|> reduce((+.))
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|> updateKnownIntegralSum(combinedIntegralSum);
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};
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let convolve = (fn, t1: t, t2: t) =>
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convolveWithDiscrete(fn, t1, toDiscretePointMasses(t2));
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let mapY = (~knownIntegralSumFn=previousKnownIntegralSum => None, fn, t: t) => {
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let u = E.O.bind(_, knownIntegralSumFn);
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let yMapFn = shapeMap(XYShape.T.mapY(fn));
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@ -350,6 +300,69 @@ module Continuous = {
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XYShape.Analysis.getMeanOfSquaresContinuousShape,
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);
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});
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/* Performs a discrete convolution between two continuous distributions A and B.
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* It is an extremely good idea to downsample the distributions beforehand,
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* because the number of samples in the convolution can be up to length(A) * length(B).
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*
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* Conventional convolution uses fn = (+.), but we also allow other operations to combine the xs.
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*
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* In practice, the convolution works by multiplying the ys for each possible combo of points of
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* the two shapes. This creates a new shape for each point of A. These new shapes are then combined
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* linearly. This may not always be the most efficient way, but it is probably the most robust for now.
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*
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* In the future, it may be possible to use a non-uniform fast Fourier transform instead (although only for addition).
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*/
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let convolveWithDiscrete = (~downsample=false, fn, t1: t, t2: DistTypes.discreteShape) => {
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let t1s = t1 |> getShape;
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let t2s = t2.xyShape; // would like to use Discrete.getShape here, but current file structure doesn't allow for that
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let t1n = t1s |> XYShape.T.length;
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let t2n = t2s |> XYShape.T.length;
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let outXYShapes: array(array((float, float))) =
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Belt.Array.makeUninitializedUnsafe(t1n);
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for (i in 0 to t1n - 1) {
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// create a new distribution
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let dxyShape: array((float, float)) =
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Belt.Array.makeUninitializedUnsafe(t2n);
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for (j in 0 to t2n - 1) {
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let _ =
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Belt.Array.set(
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dxyShape,
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j,
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(fn(t1s.xs[i], t2s.xs[j]), t1s.ys[i] *. t2s.ys[j]),
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);
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();
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};
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let _ = Belt.Array.set(outXYShapes, i, dxyShape);
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();
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};
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let combinedIntegralSum = Common.combineIntegralSums((a, b) => Some(a *. b), t1.knownIntegralSum, t2.knownIntegralSum);
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outXYShapes
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|> E.A.fmap(s => {
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let xyShape = XYShape.T.fromZippedArray(s);
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make(`Linear, xyShape, None);
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})
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|> reduce((+.))
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|> updateKnownIntegralSum(combinedIntegralSum);
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};
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let convolve = (~downsample=false, fn, t1: t, t2: t) => {
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let downsampleIfTooLarge = (t: t) => {
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let sqtl = sqrt(float_of_int(t |> getShape |> XYShape.T.length));
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sqtl > 10. && downsample ? T.downsample(int_of_float(sqtl), t) : t;
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};
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let t1d = downsampleIfTooLarge(t1);
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let t2d = downsampleIfTooLarge(t2);
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convolveWithDiscrete(~downsample=false, fn, t1, toDiscretePointMasses(t2));
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};
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};
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module Discrete = {
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@ -489,17 +502,23 @@ module Discrete = {
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let downsample = (~cache=None, i, t: t): t => {
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// It's not clear how to downsample a set of discrete points in a meaningful way.
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// The best we can do is to clip off the smallest values.
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let clippedShape =
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t
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|> getShape
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|> XYShape.T.zip
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|> XYShape.Zipped.sortByY
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|> Belt.Array.reverse
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|> Belt.Array.slice(_, ~offset=0, ~len=i)
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|> XYShape.Zipped.sortByX
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|> XYShape.T.fromZippedArray;
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let currentLength = t |> getShape |> XYShape.T.length;
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make(clippedShape, None); // if someone needs the sum, they'll have to recompute it
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if (i < currentLength) {
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let clippedShape =
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t
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|> getShape
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|> XYShape.T.zip
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|> XYShape.Zipped.sortByY
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|> Belt.Array.reverse
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|> Belt.Array.slice(_, ~offset=0, ~len=i)
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|> XYShape.Zipped.sortByX
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|> XYShape.T.fromZippedArray;
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make(clippedShape, None); // if someone needs the sum, they'll have to recompute it
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} else {
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t;
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}
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};
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let truncate =
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@ -663,6 +682,8 @@ module Mixed = {
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Continuous.T.Integral.sum(~cache=None, continuous);
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let totalIntegralSum = discreteIntegralSum +. continuousIntegralSum;
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// TODO: figure out what to do when the totalIntegralSum is zero.
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let downsampledDiscrete =
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Discrete.T.downsample(
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int_of_float(
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@ -811,17 +832,16 @@ module Mixed = {
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};
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});
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let convolve = (fn: (float, float) => float, t1: t, t2: t): t => {
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let convolve = (~downsample=false, fn: (float, float) => float, t1: t, t2: t): t => {
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// Discrete convolution can cause a huge increase in the number of samples,
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// so we'll first downsample.
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// An alternative (to be explored in the future) may be to first perform the full convolution and then to downsample the result;
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// to use non-uniform fast Fourier transforms (for addition only), add web workers or gpu.js, etc. ...
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// TODO: make this optional or customizable
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let downsampleIfTooLarge = (t: t) => {
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let sqtl = sqrt(float_of_int(totalLength(t)));
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sqtl > 10. ? T.downsample(int_of_float(sqtl), t) : t;
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sqtl > 10. && downsample ? T.downsample(int_of_float(sqtl), t) : t;
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};
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let t1d = downsampleIfTooLarge(t1);
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@ -830,11 +850,11 @@ module Mixed = {
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// continuous (*) continuous => continuous, but also
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// discrete (*) continuous => continuous (and vice versa). We have to take care of all combos and then combine them:
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let ccConvResult =
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Continuous.convolve(fn, t1d.continuous, t2d.continuous);
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Continuous.convolve(~downsample=false, fn, t1d.continuous, t2d.continuous);
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let dcConvResult =
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Continuous.convolveWithDiscrete(fn, t2d.continuous, t1d.discrete);
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Continuous.convolveWithDiscrete(~downsample=false, fn, t2d.continuous, t1d.discrete);
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let cdConvResult =
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Continuous.convolveWithDiscrete(fn, t1d.continuous, t2d.discrete);
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Continuous.convolveWithDiscrete(~downsample=false, fn, t1d.continuous, t2d.discrete);
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let continuousConvResult =
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Continuous.reduce((+.), [|ccConvResult, dcConvResult, cdConvResult|]);
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@ -870,7 +890,13 @@ module Shape = {
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));
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let convolve = (fn, t1: t, t2: t): t => {
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Mixed(Mixed.convolve(fn, toMixed(t1), toMixed(t2)));
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switch ((t1, t2)) {
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| (Continuous(m1), Continuous(m2)) => DistTypes.Continuous(Continuous.convolve(~downsample=true, fn, m1, m2))
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| (Discrete(m1), Discrete(m2)) => DistTypes.Discrete(Discrete.convolve(fn, m1, m2))
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| (m1, m2) => {
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DistTypes.Mixed(Mixed.convolve(~downsample=true, fn, toMixed(m1), toMixed(m2)))
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}
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};
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};
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let combine = (~knownIntegralSumsFn=(_, _) => None, fn, t1: t, t2: t) =>
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@ -9,7 +9,7 @@ let interpolate =
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};
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// TODO: Make sure that shapes cannot be empty.
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let extImp = E.O.toExt("Should not be possible");
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let extImp = E.O.toExt("Tried to perform an operation on an empty XYShape.");
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module T = {
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type t = xyShape;
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@ -283,11 +283,11 @@ module GenericDistFunctions = {
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(~xSelection: [ | `Linear | `ByWeight]=`Linear, dist: dist, n) => {
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switch (xSelection, dist) {
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| (`Linear, _) => E.A.Floats.range(min(dist), max(dist), n)
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| (`ByWeight, `Uniform(n)) =>
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/* | (`ByWeight, `Uniform(n)) =>
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// In `ByWeight mode, uniform distributions get special treatment because we need two x's
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// on either side for proper rendering (just left and right of the discontinuities).
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let dx = 0.00001 *. (n.high -. n.low);
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[|n.low -. dx, n.low +. dx, n.high -. dx, n.high +. dx|];
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[|n.low -. dx, n.low +. dx, n.high -. dx, n.high +. dx|]; */
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| (`ByWeight, _) =>
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let ys = E.A.Floats.range(minCdfValue, maxCdfValue, n);
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ys |> E.A.fmap(y => inv(y, dist));
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@ -55,6 +55,52 @@ module TreeNode = {
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type t = treeNode;
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type simplifier = treeNode => result(treeNode, string);
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let rec toString = (t: t): string => {
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let stringFromStandardOperation =
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fun
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| `Add => " + "
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| `Subtract => " - "
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| `Multiply => " * "
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| `Divide => " / "
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| `Exponentiate => "^";
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let stringFromPointwiseOperation =
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fun
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| `Add => " .+ "
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| `Multiply => " .* ";
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let stringFromFloatFromDistOperation =
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fun
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| `Pdf(f) => "pdf(x=$f, "
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| `Inv(f) => "inv(c=$f, "
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| `Sample => "sample("
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| `Mean => "mean(";
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switch (t) {
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| `DistData(`Symbolic(d)) =>
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SymbolicDist.GenericDistFunctions.toString(d)
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| `DistData(`RenderedShape(s)) => "[shape]"
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| `Operation(`StandardOperation(op, t1, t2)) =>
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toString(t1) ++ stringFromStandardOperation(op) ++ toString(t2)
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| `Operation(`PointwiseOperation(op, t1, t2)) =>
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toString(t1) ++ stringFromPointwiseOperation(op) ++ toString(t2)
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| `Operation(`ScaleOperation(_scaleOp, t, scaleBy)) =>
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toString(t) ++ " @ " ++ toString(scaleBy)
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| `Operation(`Normalize(t)) => "normalize(" ++ toString(t) ++ ")"
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| `Operation(`FloatFromDist(floatFromDistOp, t)) => stringFromFloatFromDistOperation(floatFromDistOp) ++ toString(t) ++ ")"
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| `Operation(`Truncate(lc, rc, t)) =>
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"truncate("
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++ toString(t)
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++ ", "
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++ E.O.dimap(Js.Float.toString, () => "-inf", lc)
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++ ", "
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++ E.O.dimap(Js.Float.toString, () => "inf", rc)
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++ ")"
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| `Operation(`Render(t)) => toString(t)
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};
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};
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/* The following modules encapsulate everything we can do with
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* different kinds of operations. */
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@ -482,52 +528,6 @@ module TreeNode = {
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| `Operation(op) => operationToDistData(sampleCount, op)
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};
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};
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let rec toString = (t: t): string => {
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let stringFromStandardOperation =
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fun
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| `Add => " + "
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| `Subtract => " - "
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| `Multiply => " * "
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| `Divide => " / "
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| `Exponentiate => "^";
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let stringFromPointwiseOperation =
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fun
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| `Add => " .+ "
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| `Multiply => " .* ";
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let stringFromFloatFromDistOperation =
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fun
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| `Pdf(f) => "pdf(x=$f, "
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| `Inv(f) => "inv(c=$f, "
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| `Sample => "sample("
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| `Mean => "mean(";
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switch (t) {
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| `DistData(`Symbolic(d)) =>
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SymbolicDist.GenericDistFunctions.toString(d)
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| `DistData(`RenderedShape(s)) => "[shape]"
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| `Operation(`StandardOperation(op, t1, t2)) =>
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toString(t1) ++ stringFromStandardOperation(op) ++ toString(t2)
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| `Operation(`PointwiseOperation(op, t1, t2)) =>
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toString(t1) ++ stringFromPointwiseOperation(op) ++ toString(t2)
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| `Operation(`ScaleOperation(_scaleOp, t, scaleBy)) =>
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toString(t) ++ " @ " ++ toString(scaleBy)
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| `Operation(`Normalize(t)) => "normalize(" ++ toString(t) ++ ")"
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| `Operation(`FloatFromDist(floatFromDistOp, t)) => stringFromFloatFromDistOperation(floatFromDistOp) ++ toString(t) ++ ")"
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| `Operation(`Truncate(lc, rc, t)) =>
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"truncate("
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++ toString(t)
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++ ", "
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++ E.O.dimap(Js.Float.toString, () => "-inf", lc)
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++ ", "
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++ E.O.dimap(Js.Float.toString, () => "inf", rc)
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++ ")"
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| `Operation(`Render(t)) => toString(t)
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};
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};
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};
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let toShape = (sampleCount: int, treeNode: treeNode) => {
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@ -539,7 +539,7 @@ let toShape = (sampleCount: int, treeNode: treeNode) => {
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let continuous = Distributions.Shape.T.toContinuous(rs);
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let discrete = Distributions.Shape.T.toDiscrete(rs);
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let shape = MixedShapeBuilder.buildSimple(~continuous, ~discrete);
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shape |> E.O.toExt("");
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shape |> E.O.toExt("Could not build final shape.");
|
||||
| Ok(_) => E.O.toExn("Rendering failed.", None)
|
||||
| Error(message) => E.O.toExn("No shape found, error: " ++ message, None)
|
||||
};
|
||||
|
|
Loading…
Reference in New Issue
Block a user