Update the Unit test for KNN

Author: ngtduc693

Algorithms.Tests/MachineLearning/KNearestNeighborsTests.cs

@@ -2,86 +2,85 @@
 using Algorithms.MachineLearning;
 using System;
 
-namespace Algorithms.Tests.MachineLearning
+namespace Algorithms.Tests.MachineLearning;
+
+[TestFixture]
+public class KNearestNeighborsTests
 {
-    [TestFixture]
-    public class KNearestNeighborsTests
+    [Test]
+    public void Constructor_InvalidK_ThrowsException()
     {
-        [Test]
-        public void Constructor_InvalidK_ThrowsException()
-        {
-            Assert.Throws<ArgumentOutOfRangeException>(() => new KNearestNeighbors<string>(0));
-        }
+        Assert.Throws<ArgumentOutOfRangeException>(() => new KNearestNeighbors<string>(0));
+    }
 
-        [Test]
-        public void AddSample_NullFeatures_ThrowsException()
-        {
-            var knn = new KNearestNeighbors<string>(3);
-            double[]? features = null;
-            Assert.Throws<ArgumentNullException>(() => knn.AddSample(features!, "A"));
-        }
+    [Test]
+    public void AddSample_NullFeatures_ThrowsException()
+    {
+        var knn = new KNearestNeighbors<string>(3);
+        double[]? features = null;
+        Assert.Throws<ArgumentNullException>(() => knn.AddSample(features!, "A"));
+    }
 
-        [Test]
-        public void Predict_NoTrainingData_ThrowsException()
-        {
-            var knn = new KNearestNeighbors<string>(1);
-            Assert.Throws<InvalidOperationException>(() => knn.Predict(new double[] { 1.0 }));
-        }
+    [Test]
+    public void Predict_NoTrainingData_ThrowsException()
+    {
+        var knn = new KNearestNeighbors<string>(1);
+        Assert.Throws<InvalidOperationException>(() => knn.Predict(new double[] { 1.0 }));
+    }
 
-        [Test]
-        public void Predict_NullFeatures_ThrowsException()
-        {
-            var knn = new KNearestNeighbors<string>(1);
-            knn.AddSample(new double[] { 1.0 }, "A");
-            double[]? features = null;
-            Assert.Throws<ArgumentNullException>(() => knn.Predict(features!));
-        }
+    [Test]
+    public void Predict_NullFeatures_ThrowsException()
+    {
+        var knn = new KNearestNeighbors<string>(1);
+        knn.AddSample(new double[] { 1.0 }, "A");
+        double[]? features = null;
+        Assert.Throws<ArgumentNullException>(() => knn.Predict(features!));
+    }
 
-        [Test]
-        public void EuclideanDistance_DifferentLengths_ThrowsException()
-        {
-            Assert.Throws<ArgumentException>(() => KNearestNeighbors<string>.EuclideanDistance(new double[] { 1.0 }, new double[] { 1.0, 2.0 }));
-        }
+    [Test]
+    public void EuclideanDistance_DifferentLengths_ThrowsException()
+    {
+        Assert.Throws<ArgumentException>(() => KNearestNeighbors<string>.EuclideanDistance(new double[] { 1.0 }, new double[] { 1.0, 2.0 }));
+    }
 
-        [Test]
-        public void EuclideanDistance_CorrectResult()
-        {
-            double[] a = { 1.0, 2.0 };
-            double[] b = { 4.0, 6.0 };
-            double expected = 5.0;
-            double actual = KNearestNeighbors<string>.EuclideanDistance(a, b);
-            Assert.That(actual, Is.EqualTo(expected).Within(1e-9));
-        }
+    [Test]
+    public void EuclideanDistance_CorrectResult()
+    {
+        double[] a = { 1.0, 2.0 };
+        double[] b = { 4.0, 6.0 };
+        double expected = 5.0;
+        double actual = KNearestNeighbors<string>.EuclideanDistance(a, b);
+        Assert.That(actual, Is.EqualTo(expected).Within(1e-9));
+    }
 
-        [Test]
-        public void Predict_SingleNeighbor_CorrectLabel()
-        {
-            var knn = new KNearestNeighbors<string>(1);
-            knn.AddSample(new double[] { 1.0, 2.0 }, "A");
-            knn.AddSample(new double[] { 3.0, 4.0 }, "B");
-            var label = knn.Predict(new double[] { 1.1, 2.1 });
-            Assert.That(label, Is.EqualTo("A"));
-        }
+    [Test]
+    public void Predict_SingleNeighbor_CorrectLabel()
+    {
+        var knn = new KNearestNeighbors<string>(1);
+        knn.AddSample(new double[] { 1.0, 2.0 }, "A");
+        knn.AddSample(new double[] { 3.0, 4.0 }, "B");
+        var label = knn.Predict(new double[] { 1.1, 2.1 });
+        Assert.That(label, Is.EqualTo("A"));
+    }
 
-        [Test]
-        public void Predict_MajorityVote_CorrectLabel()
-        {
-            var knn = new KNearestNeighbors<string>(3);
-            knn.AddSample(new double[] { 0.0, 0.0 }, "A");
-            knn.AddSample(new double[] { 0.1, 0.1 }, "A");
-            knn.AddSample(new double[] { 1.0, 1.0 }, "B");
-            var label = knn.Predict(new double[] { 0.05, 0.05 });
-            Assert.That(label, Is.EqualTo("A"));
-        }
+    [Test]
+    public void Predict_MajorityVote_CorrectLabel()
+    {
+        var knn = new KNearestNeighbors<string>(3);
+        knn.AddSample(new double[] { 0.0, 0.0 }, "A");
+        knn.AddSample(new double[] { 0.1, 0.1 }, "A");
+        knn.AddSample(new double[] { 1.0, 1.0 }, "B");
+        var label = knn.Predict(new double[] { 0.05, 0.05 });
+        Assert.That(label, Is.EqualTo("A"));
+    }
 
-        [Test]
-        public void Predict_TieBreaker_ReturnsConsistentLabel()
-        {
-            var knn = new KNearestNeighbors<string>(2);
-            knn.AddSample(new double[] { 0.0, 0.0 }, "A");
-            knn.AddSample(new double[] { 1.0, 1.0 }, "B");
-            var label = knn.Predict(new double[] { 0.5, 0.5 });
-            Assert.That(label, Is.EqualTo("B"));
-        }
+    [Test]
+    public void Predict_TieBreaker_ReturnsConsistentLabel()
+    {
+        var knn = new KNearestNeighbors<string>(2);
+        knn.AddSample(new double[] { 0.0, 0.0 }, "A");
+        knn.AddSample(new double[] { 1.0, 1.0 }, "B");
+        var label = knn.Predict(new double[] { 0.5, 0.5 });
+        Assert.That(label, Is.EqualTo("B"));
     }
 }