39 lines
1.6 KiB
Plaintext
39 lines
1.6 KiB
Plaintext
open Jest
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open Expect
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open TestHelpers
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describe("Scale logarithm", () => {
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/* These tests may not be important, because scalelog isn't normalized
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The first one may be failing for a number of reasons.
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*/
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Skip.test("mean of the base e scalar logarithm of an exponential(10)", () => {
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let rate = 10.0
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let scalelog = DistributionOperation.Constructors.scaleLogarithm(
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~env,
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mkExponential(rate),
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MagicNumbers.Math.e,
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)
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let meanResult = E.R2.bind(DistributionOperation.Constructors.mean(~env), scalelog)
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// expected value of log of exponential distribution.
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let meanAnalytical = Js.Math.log(rate) +. 1.0
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switch meanResult {
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| Ok(meanValue) => meanValue->expect->toBeCloseTo(meanAnalytical)
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| Error(err) => err->expect->toBe(DistributionTypes.OperationError(DivisionByZeroError))
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}
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})
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let low = 10.0
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let high = 100.0
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let scalelog = DistributionOperation.Constructors.scaleLogarithm(~env, mkUniform(low, high), 2.0)
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test("mean of the base 2 scalar logarithm of a uniform(10, 100)", () => {
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//For uniform pdf `_ => 1 / (b - a)`, the expected value of log of uniform is `integral from a to b of x * log(1 / (b -a)) dx`
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let meanResult = E.R2.bind(DistributionOperation.Constructors.mean(~env), scalelog)
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let meanAnalytical = -.Js.Math.log2(high -. low) /. 2.0 *. (high ** 2.0 -. low ** 2.0) // -. Js.Math.log2(high -. low)
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switch meanResult {
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| Ok(meanValue) => meanValue->expect->toBeCloseTo(meanAnalytical)
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| Error(err) => err->expect->toEqual(DistributionTypes.OperationError(NegativeInfinityError))
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}
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})
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})
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