Leonid PershinandClaude Opus 4.8
d0104df304
CI / build-test (push) Successful in 1m28s
A small AI helper computing how well a value matches a preferred optimum: a Gaussian bell in [0,1], 1 at the optimum, e^-0.5 one tolerance away, over an arbitrary input scale (unlike ResponseCurve's monotonic [0,1] shaping). The building block for environment-suitability growth. Covered by tests. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
52 lines
1.3 KiB
C#
52 lines
1.3 KiB
C#
using MrGameEng.AI;
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using Xunit;
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namespace MrGameEng.AI.Tests;
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public class SuitabilityTests
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{
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[Fact]
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public void Gaussian_PeaksAtOptimum()
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{
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Assert.Equal(1f, Suitability.Gaussian(18f, optimum: 18f, tolerance: 5f), 5);
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}
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[Fact]
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public void Gaussian_IsSymmetricAroundOptimum()
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{
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var below = Suitability.Gaussian(13f, optimum: 18f, tolerance: 5f);
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var above = Suitability.Gaussian(23f, optimum: 18f, tolerance: 5f);
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Assert.Equal(below, above, 5);
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}
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[Fact]
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public void Gaussian_AtOneToleranceAway_IsAboutPoint607()
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{
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Assert.Equal(MathF.Exp(-0.5f), Suitability.Gaussian(23f, 18f, 5f), 5);
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}
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[Fact]
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public void Gaussian_FallsOffWithDistance()
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{
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var near = Suitability.Gaussian(20f, 18f, 5f);
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var far = Suitability.Gaussian(30f, 18f, 5f);
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Assert.True(far < near && far > 0f);
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}
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[Fact]
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public void Gaussian_StaysWithinUnitRange()
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{
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for (var v = -50f; v <= 50f; v += 1f)
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{
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Assert.InRange(Suitability.Gaussian(v, 0f, 7f), 0f, 1f);
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}
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}
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[Fact]
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public void Gaussian_NonPositiveTolerance_IsExactMatch()
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{
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Assert.Equal(1f, Suitability.Gaussian(5f, 5f, 0f));
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Assert.Equal(0f, Suitability.Gaussian(6f, 5f, 0f));
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}
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}
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