Minor formatting and name changes
This commit is contained in:
Ozzie Gooen
parent
502481e345
commit
acdd3dfe7a
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@ -1,161 +1,219 @@
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type algebraicOperation = [
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| `Add
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| `Multiply
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| `Subtract
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| `Divide
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];
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type algebraicOperation = [ | `Add | `Multiply | `Subtract | `Divide];
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type pointMassesWithMoments = {
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n: int,
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masses: array(float),
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means: array(float),
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variances: array(float)
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n: int,
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masses: array(float),
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means: array(float),
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variances: array(float),
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};
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let operationToFn: (algebraicOperation, float, float) => float =
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module Operation = {
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type t = algebraicOperation;
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let toFn: (t, float, float) => float =
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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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/* This function takes a continuous distribution and efficiently approximates it as
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point masses that have variances associated with them.
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We estimate the means and variances from overlapping triangular distributions which we imagine are making up the
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XYShape.
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We can then use the algebra of random variables to "convolve" the point masses and their variances,
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and finally reconstruct a new distribution from them, e.g. using a Fast Gauss Transform or Raykar et al. (2007). */
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let toDiscretePointMassesFromTriangulars = (~inverse=false, s: XYShape.T.t): pointMassesWithMoments => {
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// TODO: what if there is only one point in the distribution?
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let n = s |> XYShape.T.length;
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// first, double up the leftmost and rightmost points:
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let {xs, ys}: XYShape.T.t = s;
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let _ = Js.Array.unshift(xs[0], xs);
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let _ = Js.Array.unshift(ys[0], ys);
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let _ = Js.Array.push(xs[n - 1], xs);
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let _ = Js.Array.push(ys[n - 1], ys);
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let n = E.A.length(xs);
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// squares and neighbourly products of the xs
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let xsSq: array(float) = Belt.Array.makeUninitializedUnsafe(n);
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let xsProdN1: array(float) = Belt.Array.makeUninitializedUnsafe(n - 1);
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let xsProdN2: array(float) = Belt.Array.makeUninitializedUnsafe(n - 2);
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for (i in 0 to n - 1) {
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let _ = Belt.Array.set(xsSq, i, xs[i] *. xs[i]); ();
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let toString =
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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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};
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for (i in 0 to n - 2) {
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let _ = Belt.Array.set(xsProdN1, i, xs[i] *. xs[i + 1]); ();
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};
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for (i in 0 to n - 3) {
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let _ = Belt.Array.set(xsProdN2, i, xs[i] *. xs[i + 2]); ();
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};
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// means and variances
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let masses: array(float) = Belt.Array.makeUninitializedUnsafe(n - 2); // doesn't include the fake first and last points
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let means: array(float) = Belt.Array.makeUninitializedUnsafe(n - 2);
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let variances: array(float) = Belt.Array.makeUninitializedUnsafe(n - 2);
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if (inverse) {
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for (i in 1 to n - 2) {
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let _ = Belt.Array.set(masses, i - 1, (xs[i + 1] -. xs[i - 1]) *. ys[i] /. 2.);
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// this only works when the whole triange is either on the left or on the right of zero
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let a = xs[i - 1];
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let c = xs[i];
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let b = xs[i + 1];
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// These are the moments of the reciprocal of a triangular distribution, as symbolically integrated by Mathematica.
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// They're probably pretty close to invMean ~ 1/mean = 3/(a+b+c) and invVar. But I haven't worked out
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// the worst case error, so for now let's use these monster equations
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let inverseMean = 2. *. ((a *. log(a/.c) /. (a-.c)) +. ((b *. log(c/.b))/.(b-.c))) /. (a -. b);
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let inverseVar = 2. *. ((log(c/.a) /. (a-.c)) +. ((b *. log(b/.c))/.(b-.c))) /. (a -. b) -. inverseMean ** 2.;
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let _ = Belt.Array.set(means, i - 1, inverseMean);
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let _ = Belt.Array.set(variances, i - 1, inverseVar);
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/* This function takes a continuous distribution and efficiently approximates it as
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point masses that have variances associated with them.
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We estimate the means and variances from overlapping triangular distributions which we imagine are making up the
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XYShape.
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We can then use the algebra of random variables to "convolve" the point masses and their variances,
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and finally reconstruct a new distribution from them, e.g. using a Fast Gauss Transform or Raykar et al. (2007). */
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let toDiscretePointMassesFromTriangulars =
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(~inverse=false, s: XYShape.T.t): pointMassesWithMoments => {
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// TODO: what if there is only one point in the distribution?
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let n = s |> XYShape.T.length;
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// first, double up the leftmost and rightmost points:
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let {xs, ys}: XYShape.T.t = s;
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let _ = Js.Array.unshift(xs[0], xs);
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let _ = Js.Array.unshift(ys[0], ys);
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let _ = Js.Array.push(xs[n - 1], xs);
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let _ = Js.Array.push(ys[n - 1], ys);
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let n = E.A.length(xs);
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// squares and neighbourly products of the xs
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let xsSq: array(float) = Belt.Array.makeUninitializedUnsafe(n);
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let xsProdN1: array(float) = Belt.Array.makeUninitializedUnsafe(n - 1);
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let xsProdN2: array(float) = Belt.Array.makeUninitializedUnsafe(n - 2);
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for (i in 0 to n - 1) {
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let _ = Belt.Array.set(xsSq, i, xs[i] *. xs[i]);
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();
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};
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{n: n - 2, masses, means, variances};
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} else {
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for (i in 1 to n - 2) {
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let _ = Belt.Array.set(masses, i - 1, (xs[i + 1] -. xs[i - 1]) *. ys[i] /. 2.);
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let _ = Belt.Array.set(means, i - 1, (xs[i - 1] +. xs[i] +. xs[i + 1]) /. 3.);
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let _ = Belt.Array.set(variances, i - 1,
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(xsSq[i-1] +. xsSq[i] +. xsSq[i+1] -. xsProdN1[i-1] -. xsProdN1[i] -. xsProdN2[i-1]) /. 18.);
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};
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for (i in 0 to n - 2) {
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let _ = Belt.Array.set(xsProdN1, i, xs[i] *. xs[i + 1]);
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();
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};
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{n: n - 2, masses, means, variances};
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};
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};
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};
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for (i in 0 to n - 3) {
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let _ = Belt.Array.set(xsProdN2, i, xs[i] *. xs[i + 2]);
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();
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};
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// means and variances
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let masses: array(float) = Belt.Array.makeUninitializedUnsafe(n - 2); // doesn't include the fake first and last points
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let means: array(float) = Belt.Array.makeUninitializedUnsafe(n - 2);
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let variances: array(float) = Belt.Array.makeUninitializedUnsafe(n - 2);
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if (inverse) {
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for (i in 1 to n - 2) {
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let _ =
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Belt.Array.set(
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masses,
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i - 1,
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(xs[i + 1] -. xs[i - 1]) *. ys[i] /. 2.,
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);
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let combineShapesContinuousContinuous = (op: algebraicOperation, s1: DistTypes.xyShape, s2: DistTypes.xyShape): DistTypes.xyShape => {
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let t1n = s1 |> XYShape.T.length;
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let t2n = s2 |> XYShape.T.length;
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// this only works when the whole triange is either on the left or on the right of zero
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let a = xs[i - 1];
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let c = xs[i];
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let b = xs[i + 1];
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// if we add the two distributions, we should probably use normal filters.
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// if we multiply the two distributions, we should probably use lognormal filters.
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let t1m = toDiscretePointMassesFromTriangulars(s1);
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let t2m = toDiscretePointMassesFromTriangulars(s2);
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// These are the moments of the reciprocal of a triangular distribution, as symbolically integrated by Mathematica.
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// They're probably pretty close to invMean ~ 1/mean = 3/(a+b+c) and invVar. But I haven't worked out
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// the worst case error, so for now let's use these monster equations
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let inverseMean =
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2.
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*. (a *. log(a /. c) /. (a -. c) +. b *. log(c /. b) /. (b -. c))
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/. (a -. b);
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let inverseVar =
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2.
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*. (log(c /. a) /. (a -. c) +. b *. log(b /. c) /. (b -. c))
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/. (a -. b)
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-. inverseMean
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** 2.;
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let combineMeansFn = switch (op) {
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| `Add => (m1, m2) => m1 +. m2
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| `Subtract => (m1, m2) => m1 -. m2
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| `Multiply => (m1, m2) => m1 *. m2
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| `Divide => (m1, mInv2) => m1 *. mInv2
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}; // note: here, mInv2 = mean(1 / t2) ~= 1 / mean(t2)
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let _ = Belt.Array.set(means, i - 1, inverseMean);
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// converts the variances and means of the two inputs into the variance of the output
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let combineVariancesFn = switch (op) {
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| `Add => (v1, v2, m1, m2) => v1 +. v2
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| `Subtract => (v1, v2, m1, m2) => v1 +. v2
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| `Multiply => (v1, v2, m1, m2) => (v1 *. v2) +. (v1 *. m1**2.) +. (v2 *. m1**2.)
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| `Divide => (v1, vInv2, m1, mInv2) => (v1 *. vInv2) +. (v1 *. mInv2**2.) +. (vInv2 *. m1**2.)
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};
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let outputMinX: ref(float) = ref(infinity);
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let outputMaxX: ref(float) = ref(neg_infinity);
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let masses: array(float) = Belt.Array.makeUninitializedUnsafe(t1m.n * t2m.n);
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let means: array(float) = Belt.Array.makeUninitializedUnsafe(t1m.n * t2m.n);
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let variances: array(float) = Belt.Array.makeUninitializedUnsafe(t1m.n * t2m.n);
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// then convolve the two sets of pointMassesWithMoments
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for (i in 0 to t1m.n - 1) {
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for (j in 0 to t2m.n - 1) {
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let k = i * t2m.n + j;
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let _ = Belt.Array.set(masses, k, t1m.masses[i] *. t2m.masses[j]);
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let mean = combineMeansFn(t1m.means[i], t2m.means[j]);
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let variance = combineVariancesFn(t1m.variances[i], t2m.variances[j], t1m.means[i], t2m.means[j]);
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let _ = Belt.Array.set(means, k, mean);
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let _ = Belt.Array.set(variances, k, variance);
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// update bounds
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let minX = mean -. variance *. 1.644854;
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let maxX = mean +. variance *. 1.644854;
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if (minX < outputMinX^) {
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outputMinX := minX;
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}
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if (maxX > outputMaxX^) {
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outputMaxX := maxX;
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}
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};
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};
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// we now want to create a set of target points. For now, let's just evenly distribute 200 points between
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// between the outputMinX and outputMaxX
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let outputXs: array(float) = E.A.Floats.range(outputMinX^, outputMaxX^, 200);
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let outputYs: array(float) = Belt.Array.make(200, 0.0);
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// now, for each of the outputYs, accumulate from a Gaussian kernel over each input point.
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for (i in 0 to E.A.length(outputXs) - 1) {
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let x = outputXs[i];
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for (j in 0 to E.A.length(masses) - 1) {
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let dx = outputXs[i] -. means[j];
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let contribution = masses[j] *. exp(-.(dx**2.) /. (2. *. variances[j]));
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let _ = Belt.Array.set(outputYs, i, outputYs[i] +. contribution);
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();
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};
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let _ = Belt.Array.set(variances, i - 1, inverseVar);
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();
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};
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{xs: outputXs, ys: outputYs};
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{n: n - 2, masses, means, variances};
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} else {
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for (i in 1 to n - 2) {
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let _ =
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Belt.Array.set(
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masses,
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i - 1,
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(xs[i + 1] -. xs[i - 1]) *. ys[i] /. 2.,
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);
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let _ =
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Belt.Array.set(means, i - 1, (xs[i - 1] +. xs[i] +. xs[i + 1]) /. 3.);
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let _ =
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Belt.Array.set(
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variances,
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i - 1,
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(
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xsSq[i - 1]
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+. xsSq[i]
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+. xsSq[i + 1]
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-. xsProdN1[i - 1]
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-. xsProdN1[i]
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-. xsProdN2[i - 1]
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)
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/. 18.,
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);
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();
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};
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{n: n - 2, masses, means, variances};
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};
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};
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let combineShapesContinuousContinuous =
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(op: algebraicOperation, s1: DistTypes.xyShape, s2: DistTypes.xyShape)
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: DistTypes.xyShape => {
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let t1n = s1 |> XYShape.T.length;
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let t2n = s2 |> XYShape.T.length;
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// if we add the two distributions, we should probably use normal filters.
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// if we multiply the two distributions, we should probably use lognormal filters.
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let t1m = toDiscretePointMassesFromTriangulars(s1);
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let t2m = toDiscretePointMassesFromTriangulars(s2);
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let combineMeansFn =
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switch (op) {
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| `Add => ((m1, m2) => m1 +. m2)
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| `Subtract => ((m1, m2) => m1 -. m2)
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| `Multiply => ((m1, m2) => m1 *. m2)
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| `Divide => ((m1, mInv2) => m1 *. mInv2)
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}; // note: here, mInv2 = mean(1 / t2) ~= 1 / mean(t2)
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// converts the variances and means of the two inputs into the variance of the output
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let combineVariancesFn =
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switch (op) {
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| `Add => ((v1, v2, m1, m2) => v1 +. v2)
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| `Subtract => ((v1, v2, m1, m2) => v1 +. v2)
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| `Multiply => (
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(v1, v2, m1, m2) => v1 *. v2 +. v1 *. m1 ** 2. +. v2 *. m1 ** 2.
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)
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| `Divide => (
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(v1, vInv2, m1, mInv2) =>
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v1 *. vInv2 +. v1 *. mInv2 ** 2. +. vInv2 *. m1 ** 2.
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)
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};
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let outputMinX: ref(float) = ref(infinity);
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let outputMaxX: ref(float) = ref(neg_infinity);
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let masses: array(float) =
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Belt.Array.makeUninitializedUnsafe(t1m.n * t2m.n);
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let means: array(float) =
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Belt.Array.makeUninitializedUnsafe(t1m.n * t2m.n);
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let variances: array(float) =
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Belt.Array.makeUninitializedUnsafe(t1m.n * t2m.n);
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// then convolve the two sets of pointMassesWithMoments
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for (i in 0 to t1m.n - 1) {
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for (j in 0 to t2m.n - 1) {
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let k = i * t2m.n + j;
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let _ = Belt.Array.set(masses, k, t1m.masses[i] *. t2m.masses[j]);
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let mean = combineMeansFn(t1m.means[i], t2m.means[j]);
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let variance =
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combineVariancesFn(
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t1m.variances[i],
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t2m.variances[j],
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t1m.means[i],
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t2m.means[j],
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);
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let _ = Belt.Array.set(means, k, mean);
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let _ = Belt.Array.set(variances, k, variance);
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// update bounds
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let minX = mean -. variance *. 1.644854;
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let maxX = mean +. variance *. 1.644854;
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if (minX < outputMinX^) {
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outputMinX := minX;
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};
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if (maxX > outputMaxX^) {
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outputMaxX := maxX;
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};
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};
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};
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// we now want to create a set of target points. For now, let's just evenly distribute 200 points between
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// between the outputMinX and outputMaxX
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let outputXs: array(float) =
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E.A.Floats.range(outputMinX^, outputMaxX^, 200);
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let outputYs: array(float) = Belt.Array.make(200, 0.0);
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// now, for each of the outputYs, accumulate from a Gaussian kernel over each input point.
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for (i in 0 to E.A.length(outputXs) - 1) {
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let x = outputXs[i];
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for (j in 0 to E.A.length(masses) - 1) {
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let dx = outputXs[i] -. means[j];
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let contribution =
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masses[j] *. exp(-. (dx ** 2.) /. (2. *. variances[j]));
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let _ = Belt.Array.set(outputYs, i, outputYs[i] +. contribution);
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();
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};
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();
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};
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{xs: outputXs, ys: outputYs};
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};
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@ -285,7 +285,7 @@ module Continuous = {
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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 fn = AlgebraicCombinations.operationToFn(op);
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let fn = AlgebraicCombinations.Operation.toFn(op);
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let outXYShapes: array(array((float, float))) =
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Belt.Array.makeUninitializedUnsafe(t2n);
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@ -402,7 +402,7 @@ module Discrete = {
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t2.knownIntegralSum,
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);
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let fn = AlgebraicCombinations.operationToFn(op);
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let fn = AlgebraicCombinations.Operation.toFn(op);
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let xToYMap = E.FloatFloatMap.empty();
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for (i in 0 to t1n - 1) {
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|
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@ -33,68 +33,4 @@ let buildSimple = (~continuous: option(DistTypes.continuousShape), ~discrete: op
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);
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Some(Mixed(mixedDist));
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};
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};
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// TODO: Delete, only being used in tests
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/*let build = (~continuous, ~discrete, ~assumptions) =>
|
||||
switch (assumptions) {
|
||||
| {
|
||||
continuous: ADDS_TO_CORRECT_PROBABILITY,
|
||||
discrete: ADDS_TO_CORRECT_PROBABILITY,
|
||||
discreteProbabilityMass: Some(r),
|
||||
} =>
|
||||
// TODO: Fix this, it's wrong :(
|
||||
Some(
|
||||
Distributions.Mixed.make(
|
||||
~continuous,
|
||||
~discrete,
|
||||
~discreteProbabilityMassFraction=r,
|
||||
),
|
||||
)
|
||||
|
||||
| {
|
||||
continuous: ADDS_TO_1,
|
||||
discrete: ADDS_TO_1,
|
||||
discreteProbabilityMass: Some(r),
|
||||
} =>
|
||||
Some(
|
||||
Distributions.Mixed.make(
|
||||
~continuous,
|
||||
~discrete,
|
||||
~discreteProbabilityMassFraction=r,
|
||||
),
|
||||
)
|
||||
|
||||
| {
|
||||
continuous: ADDS_TO_1,
|
||||
discrete: ADDS_TO_1,
|
||||
discreteProbabilityMass: None,
|
||||
} =>
|
||||
None
|
||||
|
||||
| {
|
||||
continuous: ADDS_TO_CORRECT_PROBABILITY,
|
||||
discrete: ADDS_TO_1,
|
||||
discreteProbabilityMass: None,
|
||||
} =>
|
||||
None
|
||||
|
||||
| {
|
||||
continuous: ADDS_TO_1,
|
||||
discrete: ADDS_TO_CORRECT_PROBABILITY,
|
||||
discreteProbabilityMass: None,
|
||||
} =>
|
||||
let discreteProbabilityMassFraction =
|
||||
Distributions.Discrete.T.Integral.sum(~cache=None, discrete);
|
||||
let discrete =
|
||||
Distributions.Discrete.T.scaleToIntegralSum(~intendedSum=1.0, discrete);
|
||||
Some(
|
||||
Distributions.Mixed.make(
|
||||
~continuous,
|
||||
~discrete,
|
||||
~discreteProbabilityMassFraction,
|
||||
),
|
||||
);
|
||||
| _ => None
|
||||
};*/
|
||||
};
|
||||
|
|
@ -36,7 +36,6 @@ type continuousShape = {
|
|||
cdf: DistTypes.continuousShape,
|
||||
};
|
||||
|
||||
|
||||
type dist = [
|
||||
| `Normal(normal)
|
||||
| `Beta(beta)
|
||||
|
|
@ -54,6 +53,7 @@ module ContinuousShape = {
|
|||
let make = (pdf, cdf): t => {pdf, cdf};
|
||||
let pdf = (x, t: t) =>
|
||||
Distributions.Continuous.T.xToY(x, t.pdf).continuous;
|
||||
// TODO: pdf and inv are currently the same, this seems broken.
|
||||
let inv = (p, t: t) =>
|
||||
Distributions.Continuous.T.xToY(p, t.pdf).continuous;
|
||||
// TODO: Fix the sampling, to have it work correctly.
|
||||
|
|
@ -77,7 +77,7 @@ module Cauchy = {
|
|||
let pdf = (x, t: t) => Jstat.cauchy##pdf(x, t.local, t.scale);
|
||||
let inv = (p, t: t) => Jstat.cauchy##inv(p, t.local, t.scale);
|
||||
let sample = (t: t) => Jstat.cauchy##sample(t.local, t.scale);
|
||||
let mean = (t: t) => Error("Cauchy distributions have no mean value.")
|
||||
let mean = (_: t) => Error("Cauchy distributions have no mean value.");
|
||||
let toString = ({local, scale}: t) => {j|Cauchy($local, $scale)|j};
|
||||
};
|
||||
|
||||
|
|
@ -117,8 +117,10 @@ module Normal = {
|
|||
|
||||
// TODO: is this useful here at all? would need the integral as well ...
|
||||
let pointwiseProduct = (n1: t, n2: t) => {
|
||||
let mean = (n1.mean *. n2.stdev**2. +. n2.mean *. n1.stdev**2.) /. (n1.stdev**2. +. n2.stdev**2.);
|
||||
let stdev = 1. /. ((1. /. n1.stdev**2.) +. (1. /. n2.stdev**2.));
|
||||
let mean =
|
||||
(n1.mean *. n2.stdev ** 2. +. n2.mean *. n1.stdev ** 2.)
|
||||
/. (n1.stdev ** 2. +. n2.stdev ** 2.);
|
||||
let stdev = 1. /. (1. /. n1.stdev ** 2. +. 1. /. n2.stdev ** 2.);
|
||||
`Normal({mean, stdev});
|
||||
};
|
||||
};
|
||||
|
|
@ -162,12 +164,12 @@ module Lognormal = {
|
|||
let multiply = (l1, l2) => {
|
||||
let mu = l1.mu +. l2.mu;
|
||||
let sigma = l1.sigma +. l2.sigma;
|
||||
`Lognormal({mu, sigma})
|
||||
`Lognormal({mu, sigma});
|
||||
};
|
||||
let divide = (l1, l2) => {
|
||||
let mu = l1.mu -. l2.mu;
|
||||
let sigma = l1.sigma +. l2.sigma;
|
||||
`Lognormal({mu, sigma})
|
||||
`Lognormal({mu, sigma});
|
||||
};
|
||||
};
|
||||
|
||||
|
|
@ -277,21 +279,20 @@ module GenericDistFunctions = {
|
|||
| `Beta(n) => Beta.mean(n)
|
||||
| `ContinuousShape(n) => ContinuousShape.mean(n)
|
||||
| `Uniform(n) => Uniform.mean(n)
|
||||
| `Float(n) => Float.mean(n)
|
||||
| `Float(n) => Float.mean(n);
|
||||
|
||||
let interpolateXs =
|
||||
(~xSelection: [ | `Linear | `ByWeight]=`Linear, dist: dist, n) => {
|
||||
(~xSelection: [ | `Linear | `ByWeight]=`Linear, dist: dist, n) => {
|
||||
switch (xSelection, dist) {
|
||||
| (`Linear, _) => E.A.Floats.range(min(dist), max(dist), n)
|
||||
/* | (`ByWeight, `Uniform(n)) =>
|
||||
// In `ByWeight mode, uniform distributions get special treatment because we need two x's
|
||||
// on either side for proper rendering (just left and right of the discontinuities).
|
||||
let dx = 0.00001 *. (n.high -. n.low);
|
||||
[|n.low -. dx, n.low +. dx, n.high -. dx, n.high +. dx|]; */
|
||||
/* | (`ByWeight, `Uniform(n)) =>
|
||||
// In `ByWeight mode, uniform distributions get special treatment because we need two x's
|
||||
// on either side for proper rendering (just left and right of the discontinuities).
|
||||
let dx = 0.00001 *. (n.high -. n.low);
|
||||
[|n.low -. dx, n.low +. dx, n.high -. dx, n.high +. dx|]; */
|
||||
| (`ByWeight, _) =>
|
||||
let ys = E.A.Floats.range(minCdfValue, maxCdfValue, n);
|
||||
ys |> E.A.fmap(y => inv(y, dist));
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
/* This module represents a tree node. */
|
||||
|
||||
// todo: Symbolic already has an arbitrary continuousShape option. It seems messy to have both.
|
||||
type distData = [
|
||||
| `Symbolic(SymbolicDist.dist)
|
||||
| `RenderedShape(DistTypes.shape)
|
||||
|
|
@ -46,7 +47,7 @@ and operation = [
|
|||
|
||||
module TreeNode = {
|
||||
type t = treeNode;
|
||||
type simplifier = treeNode => result(treeNode, string);
|
||||
type tResult = treeNode => result(treeNode, string);
|
||||
|
||||
let rec toString = (t: t): string => {
|
||||
let stringFromAlgebraicCombination =
|
||||
|
|
@ -63,16 +64,15 @@ module TreeNode = {
|
|||
|
||||
let stringFromFloatFromDistOperation =
|
||||
fun
|
||||
| `Pdf(f) => "pdf(x=$f, "
|
||||
| `Inv(f) => "inv(c=$f, "
|
||||
| `Pdf(f) => {j|pdf(x=$f, |j}
|
||||
| `Inv(f) => {j|inv(x=$f, |j}
|
||||
| `Sample => "sample("
|
||||
| `Mean => "mean(";
|
||||
|
||||
|
||||
switch (t) {
|
||||
| `DistData(`Symbolic(d)) =>
|
||||
SymbolicDist.GenericDistFunctions.toString(d)
|
||||
| `DistData(`RenderedShape(s)) => "[shape]"
|
||||
| `DistData(`RenderedShape(_)) => "[shape]"
|
||||
| `Operation(`AlgebraicCombination(op, t1, t2)) =>
|
||||
toString(t1) ++ stringFromAlgebraicCombination(op) ++ toString(t2)
|
||||
| `Operation(`PointwiseCombination(op, t1, t2)) =>
|
||||
|
|
@ -102,12 +102,12 @@ module TreeNode = {
|
|||
In general, this is implemented via convolution. */
|
||||
module AlgebraicCombination = {
|
||||
let simplify = (algebraicOp, t1: t, t2: t): result(treeNode, string) => {
|
||||
let tryCombiningFloats: simplifier =
|
||||
let tryCombiningFloats: tResult =
|
||||
fun
|
||||
| `Operation(
|
||||
`AlgebraicCombination(
|
||||
`Divide,
|
||||
`DistData(`Symbolic(`Float(v1))),
|
||||
`DistData(`Symbolic(`Float(_))),
|
||||
`DistData(`Symbolic(`Float(0.))),
|
||||
),
|
||||
) =>
|
||||
|
|
@ -119,12 +119,12 @@ module TreeNode = {
|
|||
`DistData(`Symbolic(`Float(v2))),
|
||||
),
|
||||
) => {
|
||||
let func = AlgebraicCombinations.operationToFn(algebraicOp);
|
||||
let func = AlgebraicCombinations.Operation.toFn(algebraicOp);
|
||||
Ok(`DistData(`Symbolic(`Float(func(v1, v2)))));
|
||||
}
|
||||
| t => Ok(t);
|
||||
|
||||
let tryCombiningNormals: simplifier =
|
||||
let tryCombiningNormals: tResult =
|
||||
fun
|
||||
| `Operation(
|
||||
`AlgebraicCombination(
|
||||
|
|
@ -144,7 +144,7 @@ module TreeNode = {
|
|||
Ok(`DistData(`Symbolic(SymbolicDist.Normal.subtract(n1, n2))))
|
||||
| t => Ok(t);
|
||||
|
||||
let tryCombiningLognormals: simplifier =
|
||||
let tryCombiningLognormals: tResult =
|
||||
fun
|
||||
| `Operation(
|
||||
`AlgebraicCombination(
|
||||
|
|
@ -281,13 +281,13 @@ module TreeNode = {
|
|||
|
||||
module Truncate = {
|
||||
module Simplify = {
|
||||
let tryTruncatingNothing: simplifier =
|
||||
let tryTruncatingNothing: tResult =
|
||||
fun
|
||||
| `Operation(`Truncate(None, None, `DistData(d))) =>
|
||||
Ok(`DistData(d))
|
||||
| t => Ok(t);
|
||||
|
||||
let tryTruncatingUniform: simplifier =
|
||||
let tryTruncatingUniform: tResult =
|
||||
fun
|
||||
| `Operation(`Truncate(lc, rc, `DistData(`Symbolic(`Uniform(u))))) => {
|
||||
// just create a new Uniform distribution
|
||||
|
|
@ -508,7 +508,7 @@ module TreeNode = {
|
|||
but most often it will produce a RenderedShape.
|
||||
This function is used mainly to turn a parse tree into a single RenderedShape
|
||||
that can then be displayed to the user. */
|
||||
let rec toDistData = (treeNode: t, sampleCount: int): result(t, string) => {
|
||||
let toDistData = (treeNode: t, sampleCount: int): result(t, string) => {
|
||||
switch (treeNode) {
|
||||
| `DistData(d) => Ok(`DistData(d))
|
||||
| `Operation(op) => operationToDistData(sampleCount, op)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user