Formatter
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@ -63,9 +63,15 @@ describe("FunctionRegistry Library", () => {
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testEvalToBe("SampleSet.fromList([3,5,2,3,5,2,3,5,2,3,3,5])", "Ok(Sample Set Distribution)")
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testEvalToBe("SampleSet.fromList([3,5,2,3,5,2,3,5,2,3,3,5])", "Ok(Sample Set Distribution)")
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testEvalToBe("SampleSet.fromFn({|| sample(normal(5,2))})", "Ok(Sample Set Distribution)")
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testEvalToBe("SampleSet.min(SampleSet.fromDist(normal(50,2)), 2)", "Ok(Sample Set Distribution)")
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testEvalToBe(
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"SampleSet.min(SampleSet.fromDist(normal(50,2)), 2)",
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"Ok(Sample Set Distribution)",
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)
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testEvalToBe("mean(SampleSet.min(SampleSet.fromDist(normal(50,2)), 2))", "Ok(2)")
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testEvalToBe("SampleSet.max(SampleSet.fromDist(normal(50,2)), 10)", "Ok(Sample Set Distribution)")
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testEvalToBe(
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"SampleSet.max(SampleSet.fromDist(normal(50,2)), 10)",
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"Ok(Sample Set Distribution)",
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)
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testEvalToBe(
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"addOne(t)=t+1; SampleSet.toList(SampleSet.map(SampleSet.fromList([1,2,3,4,5,6]), addOne))",
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"Ok([2,3,4,5,6,7])",
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@ -31,7 +31,7 @@ let isSymbolic = (t: t) =>
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let sampleN = (t: t, n) =>
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switch t {
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| PointSet(r) => PointSetDist.T.sampleN(r,n)
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| PointSet(r) => PointSetDist.T.sampleN(r, n)
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| SampleSet(r) => SampleSetDist.sampleN(r, n)
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| Symbolic(r) => SymbolicDist.T.sampleN(n, r)
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}
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@ -271,24 +271,24 @@ module T = Dist({
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let variance = (t: t): float =>
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XYShape.Analysis.getVarianceDangerously(t, mean, Analysis.getMeanOfSquares)
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let doN = (n, fn) => {
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let items = Belt.Array.make(n, 0.0)
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for x in 0 to n - 1 {
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let _ = Belt.Array.set(items, x, fn())
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let doN = (n, fn) => {
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let items = Belt.Array.make(n, 0.0)
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for x in 0 to n - 1 {
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let _ = Belt.Array.set(items, x, fn())
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}
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items
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}
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items
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}
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let sample = (t: t): float => {
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let randomItem = Random.float(1.0)
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t |> integralYtoX(randomItem)
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}
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let sample = (t: t): float => {
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let randomItem = Random.float(1.0)
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t |> integralYtoX(randomItem)
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}
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let sampleN = (dist, n) => {
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let integralCache = integral(dist)
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let distWithUpdatedIntegralCache = updateIntegralCache(Some(integralCache), dist)
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doN(n, () => sample(distWithUpdatedIntegralCache))
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}
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let sampleN = (dist, n) => {
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let integralCache = integral(dist)
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let distWithUpdatedIntegralCache = updateIntegralCache(Some(integralCache), dist)
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doN(n, () => sample(distWithUpdatedIntegralCache))
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}
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})
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let isNormalized = (t: t): bool => {
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@ -224,8 +224,8 @@ module T = Dist({
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XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
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}
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let sampleN = (t: t, n): array<float> => {
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let normalized = t->normalize->getShape
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Stdlib.Random.sample(normalized.xs, {probs: normalized.ys, size: n})
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}
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let sampleN = (t: t, n): array<float> => {
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let normalized = t->normalize->getShape
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Stdlib.Random.sample(normalized.xs, {probs: normalized.ys, size: n})
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}
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})
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@ -270,9 +270,9 @@ module T = Dist({
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})
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}
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let discreteIntegralSum =({discrete}: t): float => Discrete.T.Integral.sum(discrete)
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let continuousIntegralSum =({continuous}: t): float => Continuous.T.Integral.sum(continuous)
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let integralSum =(t:t): float => discreteIntegralSum(t) +. continuousIntegralSum(t)
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let discreteIntegralSum = ({discrete}: t): float => Discrete.T.Integral.sum(discrete)
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let continuousIntegralSum = ({continuous}: t): float => Continuous.T.Integral.sum(continuous)
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let integralSum = (t: t): float => discreteIntegralSum(t) +. continuousIntegralSum(t)
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let mean = ({discrete, continuous} as t: t): float => {
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let discreteMean = Discrete.T.mean(discrete)
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@ -289,7 +289,7 @@ module T = Dist({
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let _integralSum = integralSum(t)
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let getMeanOfSquares = ({discrete, continuous}: t) => {
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let discreteMean = discrete |> Discrete.shapeMap(XYShape.T.square) |> Discrete.T.mean
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let continuousMean = continuous -> Continuous.Analysis.getMeanOfSquares
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let continuousMean = continuous->Continuous.Analysis.getMeanOfSquares
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(discreteMean *. discreteIntegralSum(t) +. continuousMean *. continuousIntegralSum(t)) /.
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integralSum(t)
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}
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@ -301,15 +301,17 @@ module T = Dist({
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}
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}
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let sampleN = (t: t, n:int): array<float> => {
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let discreteIntegralSum = discreteIntegralSum(t);
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let integralSum = integralSum(t);
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let discreteSampleLength:int = (Js.Int.toFloat(n) *. discreteIntegralSum /. integralSum) -> E.Float.toInt
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let continuousSampleLength = n - discreteSampleLength;
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let continuousSamples = t.continuous ->Continuous.T.normalize-> Continuous.T.sampleN( continuousSampleLength)
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let discreteSamples = t.discrete ->Discrete.T.normalize->Discrete.T.sampleN(discreteSampleLength)
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Js.log3("Samples", continuousSamples, discreteSamples);
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E.A.concat(discreteSamples, continuousSamples) -> E.A.shuffle
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let sampleN = (t: t, n: int): array<float> => {
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let discreteIntegralSum = discreteIntegralSum(t)
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let integralSum = integralSum(t)
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let discreteSampleLength: int =
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(Js.Int.toFloat(n) *. discreteIntegralSum /. integralSum)->E.Float.toInt
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let continuousSampleLength = n - discreteSampleLength
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let continuousSamples =
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t.continuous->Continuous.T.normalize->Continuous.T.sampleN(continuousSampleLength)
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let discreteSamples = t.discrete->Discrete.T.normalize->Discrete.T.sampleN(discreteSampleLength)
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Js.log3("Samples", continuousSamples, discreteSamples)
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E.A.concat(discreteSamples, continuousSamples)->E.A.shuffle
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}
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})
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@ -201,9 +201,9 @@ module T = Dist({
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let sampleN = (t: t, int): array<float> =>
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switch t {
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| Mixed(m) => Mixed.T.sampleN(m,int)
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| Discrete(m) => Discrete.T.sampleN(m,int)
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| Continuous(m) => Continuous.T.sampleN(m,int)
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| Mixed(m) => Mixed.T.sampleN(m, int)
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| Discrete(m) => Discrete.T.sampleN(m, int)
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| Continuous(m) => Continuous.T.sampleN(m, int)
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}
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})
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