From 592a82b42373a57c5c9aa58de1f5c01c2ce7dafe Mon Sep 17 00:00:00 2001
From: Jeff Moser <jeff@moserware.com>
Date: Mon, 19 Apr 2010 07:39:24 -0400
Subject: [PATCH] Updated GaussianDistributionTests.cs based off feedback from
 nsp

---
 .../Numerics/GaussianDistributionTests.cs     | 52 +++++++++++++++++--
 1 file changed, 47 insertions(+), 5 deletions(-)

diff --git a/UnitTests/Numerics/GaussianDistributionTests.cs b/UnitTests/Numerics/GaussianDistributionTests.cs
index e0b0447..c1e3b12 100644
--- a/UnitTests/Numerics/GaussianDistributionTests.cs
+++ b/UnitTests/Numerics/GaussianDistributionTests.cs
@@ -9,6 +9,22 @@ namespace UnitTests.Numerics
     {
         private const double ErrorTolerance = 0.000001;
 
+        [Test]
+        public void CumulativeToTests()
+        {
+            // Verified with WolframAlpha
+            // (e.g. http://www.wolframalpha.com/input/?i=CDF%5BNormalDistribution%5B0%2C1%5D%2C+0.5%5D )
+            Assert.AreEqual(0.691462, GaussianDistribution.CumulativeTo(0.5), ErrorTolerance);            
+        }
+
+        [Test]
+        public void AtTests()
+        {
+            // Verified with WolframAlpha
+            // (e.g. http://www.wolframalpha.com/input/?i=PDF%5BNormalDistribution%5B0%2C1%5D%2C+0.5%5D )
+            Assert.AreEqual(0.352065, GaussianDistribution.At(0.5), ErrorTolerance);
+        }
+
         [Test]
         public void MultiplicationTests()
         {
@@ -56,13 +72,39 @@ namespace UnitTests.Numerics
         [Test]
         public void LogProductNormalizationTests()
         {
-            var m4s5 = new GaussianDistribution(4, 5);
-            var m6s7 = new GaussianDistribution(6, 7);
+            // Verified with Ralf Herbrich's F# implementation
+            var standardNormal = new GaussianDistribution(0, 1);
+            var lpn = GaussianDistribution.LogProductNormalization(standardNormal, standardNormal);
+            Assert.AreEqual(-1.2655121234846454, lpn, ErrorTolerance);
 
-            var product2 = m4s5 * m6s7;
-            var normConstant = 1.0 / (Math.Sqrt(2 * Math.PI) * product2.StandardDeviation);
-            var lpn = GaussianDistribution.LogProductNormalization(m4s5, m6s7);
+            var m1s2 = new GaussianDistribution(1, 2);
+            var m3s4 = new GaussianDistribution(3, 4);
+            var lpn2 = GaussianDistribution.LogProductNormalization(m1s2, m3s4);
+            Assert.AreEqual(-2.5168046699816684, lpn2, ErrorTolerance);
+        }
 
+        [Test]
+        public void LogRatioNormalizationTests()
+        {
+            // Verified with Ralf Herbrich's F# implementation            
+            var m1s2 = new GaussianDistribution(1, 2);
+            var m3s4 = new GaussianDistribution(3, 4);
+            var lrn = GaussianDistribution.LogRatioNormalization(m1s2, m3s4);
+            Assert.AreEqual(2.6157405972171204, lrn, ErrorTolerance);            
+        }
+
+        [Test]
+        public void AbsoluteDifferenceTests()
+        {
+            // Verified with Ralf Herbrich's F# implementation            
+            var standardNormal = new GaussianDistribution(0, 1);
+            var absDiff = GaussianDistribution.AbsoluteDifference(standardNormal, standardNormal);
+            Assert.AreEqual(0.0, absDiff, ErrorTolerance);
+
+            var m1s2 = new GaussianDistribution(1, 2);
+            var m3s4 = new GaussianDistribution(3, 4);
+            var absDiff2 = GaussianDistribution.AbsoluteDifference(m1s2, m3s4);
+            Assert.AreEqual(0.4330127018922193, absDiff2, ErrorTolerance);
         }
     }
 }
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