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Translate pmf to pdf for kde

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This commit is contained in:
Sam Nolan 2022-04-26 12:22:31 -04:00
parent d82d890e4d
commit e47deb8433
5 changed files with 57 additions and 20 deletions
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25
packages/squiggle-lang/__tests__/Distributions/Invariants/AlgebraicCombination_test.res
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@ -65,7 +65,7 @@ describe("(Algebraic) addition of distributions", () => {
| None => "algebraicAdd has"->expect->toBe("failed")
// This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
// sometimes it works with ~digits=2.
| Some(x) => x->expect->toBeSoCloseTo(0.01927225696028752, ~digits=1) // (uniformMean +. betaMean)
| Some(x) => x->expect->toBeSoCloseTo(9.78655777150074, ~digits=1) // (uniformMean +. betaMean)
}
})
test("beta(alpha=2, beta=5) + uniform(low=9, high=10)", () => {
@ -82,7 +82,7 @@ describe("(Algebraic) addition of distributions", () => {
| None => "algebraicAdd has"->expect->toBe("failed")
// This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
// sometimes it works with ~digits=2.
| Some(x) => x->expect->toBeSoCloseTo(0.019275414920485248, ~digits=1) // (uniformMean +. betaMean)
| Some(x) => x->expect->toBeSoCloseTo(9.786753454457116, ~digits=1) // (uniformMean +. betaMean)
}
})
})
@ -162,8 +162,8 @@ describe("(Algebraic) addition of distributions", () => {
switch received {
| None => "algebraicAdd has"->expect->toBe("failed")
// This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
// sometimes it works with ~digits=4.
| Some(x) => x->expect->toBeSoCloseTo(0.001978994877226945, ~digits=3)
// This value was calculated by a python script
| Some(x) => x->expect->toBeSoCloseTo(0.979023, ~digits=0)
}
})
test("(beta(alpha=2, beta=5) + uniform(low=9, high=10)).pdf(10)", () => {
@ -176,9 +176,8 @@ describe("(Algebraic) addition of distributions", () => {
->E.R.toExn("Expected float", _)
switch received {
| None => "algebraicAdd has"->expect->toBe("failed")
// This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
// sometimes it works with ~digits=4.
| Some(x) => x->expect->toBeSoCloseTo(0.001978994877226945, ~digits=3)
// This is nondeterministic.
| Some(x) => x->expect->toBeSoCloseTo(0.979023, ~digits=0)
}
})
})
@ -253,8 +252,8 @@ describe("(Algebraic) addition of distributions", () => {
switch received {
| None => "algebraicAdd has"->expect->toBe("failed")
// This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
// sometimes it works with ~digits=4.
| Some(x) => x->expect->toBeSoCloseTo(0.0013961779932477507, ~digits=3)
// The value was calculated externally using a python script
| Some(x) => x->expect->toBeSoCloseTo(0.71148, ~digits=1)
}
})
test("(beta(alpha=2, beta=5) + uniform(low=9, high=10)).cdf(10)", () => {
@ -268,8 +267,8 @@ describe("(Algebraic) addition of distributions", () => {
switch received {
| None => "algebraicAdd has"->expect->toBe("failed")
// This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
// sometimes it works with ~digits=4.
| Some(x) => x->expect->toBeSoCloseTo(0.001388898111625753, ~digits=3)
// The value was calculated externally using a python script
| Some(x) => x->expect->toBeSoCloseTo(0.71148, ~digits=1)
}
})
})
@ -346,7 +345,7 @@ describe("(Algebraic) addition of distributions", () => {
| None => "algebraicAdd has"->expect->toBe("failed")
// This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
// sometimes it works with ~digits=2.
| Some(x) => x->expect->toBeSoCloseTo(10.927078217530806, ~digits=0)
| Some(x) => x->expect->toBeSoCloseTo(9.179319623146968, ~digits=0)
}
})
test("(beta(alpha=2, beta=5) + uniform(low=9, high=10)).inv(2e-2)", () => {
@ -361,7 +360,7 @@ describe("(Algebraic) addition of distributions", () => {
| None => "algebraicAdd has"->expect->toBe("failed")
// This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad.
// sometimes it works with ~digits=2.
| Some(x) => x->expect->toBeSoCloseTo(10.915396627014363, ~digits=0)
| Some(x) => x->expect->toBeSoCloseTo(9.174267267465632, ~digits=0)
}
})
})

22
packages/squiggle-lang/__tests__/Distributions/SampleSetDist_ToPointSet_test.res Normal file
View File

@ -0,0 +1,22 @@
open Jest
open Expect
describe("Converting from a sample set distribution", () => {
test("Should be normalized", () => {
let outputXYShape = SampleSetDist_ToPointSet.Internals.KDE.normalSampling(
[1., 2., 3., 3., 4., 5., 5., 5., 6., 8., 9., 9.],
50,
2,
)
let c: PointSetTypes.continuousShape = {
xyShape: outputXYShape,
interpolation: #Linear,
integralSumCache: None,
integralCache: None,
}
let fullShape = Continuous.updateIntegralCache(Some(Continuous.T.integral(c)), c)
let endY = Continuous.T.integralEndY(fullShape)
expect(endY)->toBeCloseTo(1.)
})
})

14
packages/squiggle-lang/__tests__/TS/JS_test.ts
View File

@ -46,6 +46,8 @@ describe("Distribution", () => {
//It's important that sampleCount is less than 9. If it's more, than that will create randomness
//Also, note, the value should be created using makeSampleSetDist() later on.
let env = { sampleCount: 8, xyPointLength: 100 };
let dist1Samples = [3, 4, 5, 6, 6, 7, 10, 15, 30];
let dist1SampleCount = dist1Samples.length;
let dist = new Distribution(
{ tag: "SampleSet", value: [3, 4, 5, 6, 6, 7, 10, 15, 30] },
env
@ -56,16 +58,18 @@ describe("Distribution", () => {
);
test("mean", () => {
expect(dist.mean().value).toBeCloseTo(3.737);
expect(dist.mean().value).toBeCloseTo(8.704375514292865);
});
test("pdf", () => {
expect(dist.pdf(5.0).value).toBeCloseTo(0.0431);
expect(dist.pdf(5.0).value).toBeCloseTo(0.052007455285386944, 1);
});
test("cdf", () => {
expect(dist.cdf(5.0).value).toBeCloseTo(0.155);
expect(dist.cdf(5.0).value).toBeCloseTo(
dist1Samples.filter((x) => x <= 5).length / dist1SampleCount
);
});
test("inv", () => {
expect(dist.inv(0.5).value).toBeCloseTo(9.458);
expect(dist.inv(0.5).value).toBeCloseTo(6);
});
test("toPointSet", () => {
expect(
@ -87,6 +91,6 @@ describe("Distribution", () => {
resultMap(dist.pointwiseAdd(dist2), (r: Distribution) =>
r.toSparkline(20)
).value
).toEqual(Ok("▁▂▅██▅▅▅▆▇█▆▅▃▃▂▂▁▁▁"));
).toEqual(Ok("▁▂▅██▅▅▅▆▆▇▅▄▃▃▂▂▁▁▁"));
});
});

4
packages/squiggle-lang/__tests__/TS/SampleSet_test.ts
View File

@ -46,7 +46,9 @@ describe("cumulative density function", () => {
);
});
test("at the highest number in the sample is close to 1", () => {
// This may not be true due to KDE estimating there to be mass above the
// highest value. These tests fail
test.skip("at the highest number in the sample is close to 1", () => {
fc.assert(
fc.property(arrayGen(), (xs_) => {
let xs = Array.from(xs_);

12
packages/squiggle-lang/src/rescript/Distributions/SampleSetDist/KdeLibrary.js
View File

@ -15,8 +15,18 @@ const samplesToContinuousPdf = (
if (_.isFinite(max)) {
_samples = _.filter(_samples, (r) => r < max);
}
// The pdf that's created from this function is not a pdf but a pmf. y values
// being probability mass and not density.
// This is awkward, because our code assumes later that y is a density
let pdf = pdfast.create(_samples, { size, width });
return { xs: pdf.map((r) => r.x), ys: pdf.map((r) => r.y) };
// To convert this to a density, we need to find the step size. This is kept
// constant for all y values
let stepSize = pdf[1].x - pdf[0].x;
// We then adjust the y values to density
return { xs: pdf.map((r) => r.x), ys: pdf.map((r) => r.y / stepSize) };
};
module.exports = {