Fix issue 374 in AiDotNet

tests/AiDotNet.Tests/UnitTests/FitDetectors/BayesianFitDetectorTests.cs

@@ -0,0 +1,131 @@
+using AiDotNet.FitDetectors;
+using AiDotNet.LinearAlgebra;
+using AiDotNet.Models;
+using AiDotNet.Models.Options;
+using Xunit;
+
+namespace AiDotNetTests.UnitTests.FitDetectors
+{
+    public class BayesianFitDetectorTests
+    {
+        private ModelEvaluationData<double, Matrix<double>, Vector<double>> CreateMockEvaluationData(
+            Vector<double> predicted, Vector<double> actual)
+        {
+            return new ModelEvaluationData<double, Matrix<double>, Vector<double>>
+            {
+                ModelStats = new ModelStats<double, Matrix<double>, Vector<double>>
+                {
+                    Predicted = predicted,
+                    Actual = actual
+                }
+            };
+        }
+
+        [Fact]
+        public void Constructor_WithDefaultOptions_CreatesInstance()
+        {
+            // Act
+            var detector = new BayesianFitDetector<double, Matrix<double>, Vector<double>>();
+
+            // Assert
+            Assert.NotNull(detector);
+        }
+
+        [Fact]
+        public void Constructor_WithCustomOptions_CreatesInstance()
+        {
+            // Arrange
+            var options = new BayesianFitDetectorOptions
+            {
+                PriorStrength = 0.5,
+                CredibleIntervalLevel = 0.9
+            };
+
+            // Act
+            var detector = new BayesianFitDetector<double, Matrix<double>, Vector<double>>(options);
+
+            // Assert
+            Assert.NotNull(detector);
+        }
+
+        [Fact]
+        public void DetectFit_WithValidData_ReturnsResult()
+        {
+            // Arrange
+            var detector = new BayesianFitDetector<double, Matrix<double>, Vector<double>>();
+            var predicted = new Vector<double>(new double[] { 1.0, 2.0, 3.0, 4.0, 5.0 });
+            var actual = new Vector<double>(new double[] { 1.1, 1.9, 3.1, 3.9, 5.1 });
+
+            var evaluationData = CreateMockEvaluationData(predicted, actual);
+
+            // Act
+            var result = detector.DetectFit(evaluationData);
+
+            // Assert
+            Assert.NotNull(result);
+            Assert.NotNull(result.FitType);
+            Assert.NotEmpty(result.Recommendations);
+        }
+
+        [Fact]
+        public void DetectFit_CalculatesConfidenceLevel()
+        {
+            // Arrange
+            var detector = new BayesianFitDetector<double, Matrix<double>, Vector<double>>();
+            var predicted = new Vector<double>(new double[] { 1.0, 2.0, 3.0, 4.0, 5.0 });
+            var actual = new Vector<double>(new double[] { 1.0, 2.0, 3.0, 4.0, 5.0 });
+
+            var evaluationData = CreateMockEvaluationData(predicted, actual);
+
+            // Act
+            var result = detector.DetectFit(evaluationData);
+
+            // Assert
+            Assert.NotNull(result);
+            Assert.NotNull(result.ConfidenceLevel);
+            Assert.True(result.ConfidenceLevel >= 0.0);
+            Assert.True(result.ConfidenceLevel <= 1.0);
+        }
+
+        [Fact]
+        public void DetectFit_GeneratesRecommendations()
+        {
+            // Arrange
+            var detector = new BayesianFitDetector<double, Matrix<double>, Vector<double>>();
+            var predicted = new Vector<double>(new double[] { 1.0, 2.0, 3.0, 4.0, 5.0 });
+            var actual = new Vector<double>(new double[] { 1.2, 1.8, 3.2, 3.8, 5.2 });
+
+            var evaluationData = CreateMockEvaluationData(predicted, actual);
+
+            // Act
+            var result = detector.DetectFit(evaluationData);
+
+            // Assert
+            Assert.NotNull(result);
+            Assert.NotEmpty(result.Recommendations);
+            Assert.All(result.Recommendations, r => Assert.False(string.IsNullOrWhiteSpace(r)));
+        }
+
+        [Fact]
+        public void DetectFit_ResultContainsAllRequiredFields()
+        {
+            // Arrange
+            var detector = new BayesianFitDetector<double, Matrix<double>, Vector<double>>();
+            var predicted = new Vector<double>(new double[] { 1.0, 2.0, 3.0, 4.0, 5.0 });
+            var actual = new Vector<double>(new double[] { 1.0, 2.0, 3.0, 4.0, 5.0 });
+
+            var evaluationData = CreateMockEvaluationData(predicted, actual);
+
+            // Act
+            var result = detector.DetectFit(evaluationData);
+
+            // Assert
+            Assert.NotNull(result);
+            Assert.NotNull(result.FitType);
+            Assert.NotNull(result.ConfidenceLevel);
+            Assert.NotNull(result.Recommendations);
+            Assert.NotEmpty(result.Recommendations);
+            Assert.NotNull(result.AdditionalInfo);
+        }
+    }
+}