Code analysis: Add editorconfig and fix code according to rules

README.md

@@ -55,7 +55,7 @@ However, several optimization methods are available for hyperparameter tuning:
 License
 -------
 
-SharpLearning is covered under the terms of the [MIT](LICENSE.md) license. You may therefore link to it and use it in both opensource and proprietary software projects.
+SharpLearning is covered under the terms of the [MIT](LICENSE) license. You may therefore link to it and use it in both opensource and proprietary software projects.
 
 Documentation
 -------------

SharpLearning.sln

@@ -64,6 +64,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ci", "ci", "{45CBAF1D-3116-
 EndProject
 Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "root", "root", "{D83436F7-2EE6-4D59-ACC3-355C973BCDAF}"
 	ProjectSection(SolutionItems) = preProject
+		.editorconfig = .editorconfig
 		.gitattributes = .gitattributes
 		.gitignore = .gitignore
 		CONTRIBUTING.md = CONTRIBUTING.md
@@ -74,12 +75,12 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "root", "root", "{D83436F7-2
 EndProject
 Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "src", "src", "{2204FA16-973A-48CF-A9FD-94FA72424BA4}"
 	ProjectSection(SolutionItems) = preProject
-		Directory.Build.props = Directory.Build.props
-		Directory.Build.targets = Directory.Build.targets
-		OutputBuildProject.props = OutputBuildProject.props
-		OutputBuildProps.props = OutputBuildProps.props
-		OutputBuildTargets.props = OutputBuildTargets.props
-		SourceLink.GitHub.props = SourceLink.GitHub.props
+		src\Directory.Build.props = src\Directory.Build.props
+		src\Directory.Build.targets = src\Directory.Build.targets
+		src\OutputBuildProject.props = src\OutputBuildProject.props
+		src\OutputBuildProps.props = src\OutputBuildProps.props
+		src\OutputBuildTargets.props = src\OutputBuildTargets.props
+		src\SourceLink.GitHub.props = src\SourceLink.GitHub.props
 	EndProjectSection
 EndProject
 Global

src/Directory.Build.props

@@ -29,13 +29,12 @@
 
     <GenerateDocumentationFile>true</GenerateDocumentationFile>
 
-    <!--Disabled for now-->
-    <!--<AnalysisLevel>latest</AnalysisLevel>
+    <AnalysisLevel>latest</AnalysisLevel>
     <EnforceCodeStyleInBuild>true</EnforceCodeStyleInBuild>
     <RunAnalyzersDuringBuild>true</RunAnalyzersDuringBuild>
     <EnableNETAnalyzers>true</EnableNETAnalyzers>
-    <CodeAnalysisTreatWarningsAsErrors>true</CodeAnalysisTreatWarningsAsErrors>-->
-    <NoWarn>CS1591</NoWarn>
+    <CodeAnalysisTreatWarningsAsErrors>true</CodeAnalysisTreatWarningsAsErrors>
+    <NoWarn>CS1591;CS1668</NoWarn>
 
   </PropertyGroup>
 

src/SharpLearning.AdaBoost.Test/AssemblyInitializeCultureTest.cs

@@ -2,19 +2,18 @@
 using System.Threading;
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 
-namespace SharpLearning.AdaBoost.Test
+namespace SharpLearning.AdaBoost.Test;
+
+[TestClass]
+public static class AssemblyInitializeCultureTest
 {
-    [TestClass]
-    public class AssemblyInitializeCultureTest
+    [AssemblyInitialize]
+    public static void AssemblyInitializeCultureTest_InvariantCulture(TestContext c)
     {
-        [AssemblyInitialize]
-        public static void AssemblyInitializeCultureTest_InvariantCulture(TestContext c)
-        {
-            CultureInfo culture = CultureInfo.InvariantCulture;
-            CultureInfo.DefaultThreadCurrentCulture = culture;
-            CultureInfo.DefaultThreadCurrentUICulture = culture;
-            Thread.CurrentThread.CurrentCulture = culture;
-            Thread.CurrentThread.CurrentUICulture = culture;
-        }
+        CultureInfo culture = CultureInfo.InvariantCulture;
+        CultureInfo.DefaultThreadCurrentCulture = culture;
+        CultureInfo.DefaultThreadCurrentUICulture = culture;
+        Thread.CurrentThread.CurrentCulture = culture;
+        Thread.CurrentThread.CurrentUICulture = culture;
     }
-}
+}

src/SharpLearning.AdaBoost.Test/DataSetUtilities.cs

@@ -2,28 +2,28 @@
 using SharpLearning.Containers.Matrices;
 using SharpLearning.InputOutput.Csv;
 
-namespace SharpLearning.AdaBoost.Test
+namespace SharpLearning.AdaBoost.Test;
+
+public static class DataSetUtilities
 {
-    public static class DataSetUtilities
+    public static (F64Matrix observations, double[] targets) LoadAptitudeDataSet()
     {
-        public static (F64Matrix observations, double[] targets) LoadAptitudeDataSet()
-        {
-            var parser = new CsvParser(() => new StringReader(AptitudeData));
-            var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
-            var targets = parser.EnumerateRows("Pass").ToF64Vector();
-            return (observations, targets);
-        }
+        var parser = new CsvParser(() => new StringReader(AptitudeData));
+        var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
+        var targets = parser.EnumerateRows("Pass").ToF64Vector();
+        return (observations, targets);
+    }
 
-        public static (F64Matrix observations, double[] targets) LoadGlassDataSet()
-        {
-            var parser = new CsvParser(() => new StringReader(GlassData));
-            var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
-            var targets = parser.EnumerateRows("Target").ToF64Vector();
-            return (observations, targets);
-        }
+    public static (F64Matrix observations, double[] targets) LoadGlassDataSet()
+    {
+        var parser = new CsvParser(() => new StringReader(GlassData));
+        var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
+        var targets = parser.EnumerateRows("Target").ToF64Vector();
+        return (observations, targets);
+    }
 
-        public const string AptitudeData =
- @"AptitudeTestScore;PreviousExperience_month;Pass
+    public const string AptitudeData =
+@"AptitudeTestScore;PreviousExperience_month;Pass
 5;6;0
 1;15;0
 1;12;0
@@ -51,7 +51,7 @@ public static (F64Matrix observations, double[] targets) LoadGlassDataSet()
 1;8;0
 5;12;0";
 
-        public const string GlassData =
+    public const string GlassData =
 @"F1;F2;F3;F4;F5;F6;F7;F8;F10;Target
 1.52101;13.64;4.49;1.1;71.78;0.06;8.75;0;0;1
 1.51761;13.89;3.6;1.36;72.73;0.48;7.83;0;0;1
@@ -268,5 +268,4 @@ public static (F64Matrix observations, double[] targets) LoadGlassDataSet()
 1.51651;14.38;0;1.94;73.61;0;8.48;1.57;0;7
 1.51711;14.23;0;2.08;73.36;0;8.62;1.67;0;7";
 
-    }
-}
+}

src/SharpLearning.AdaBoost.Test/Learners/ClassificationAdaBoostLearnerTest.cs

@@ -5,83 +5,82 @@
 using SharpLearning.Containers.Extensions;
 using SharpLearning.Metrics.Classification;
 
-namespace SharpLearning.AdaBoost.Test.Learners
+namespace SharpLearning.AdaBoost.Test.Learners;
+
+[TestClass]
+public class ClassificationAdaBoostLearnerTest
 {
-    [TestClass]
-    public class ClassificationAdaBoostLearnerTest
+    [TestMethod]
+    public void ClassificationAdaBoostLearner_Learn_AptitudeData()
     {
-        [TestMethod]
-        public void ClassificationAdaBoostLearner_Learn_AptitudeData()
-        {
-            var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
+        var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
 
-            var sut = new ClassificationAdaBoostLearner(10);
-
-            var model = sut.Learn(observations, targets);
-            var predictions = model.Predict(observations);
+        var sut = new ClassificationAdaBoostLearner(10);
 
-            var evaluator = new TotalErrorClassificationMetric<double>();
-            var actual = evaluator.Error(targets, predictions);
+        var model = sut.Learn(observations, targets);
+        var predictions = model.Predict(observations);
 
-            Assert.AreEqual(0.038461538461538464, actual);
-        }
+        var evaluator = new TotalErrorClassificationMetric<double>();
+        var actual = evaluator.Error(targets, predictions);
 
-        [TestMethod]
-        public void ClassificationAdaBoostLearner_Learn_Glass()
-        {
-            var (observations, targets) = DataSetUtilities.LoadGlassDataSet();
+        Assert.AreEqual(0.038461538461538464, actual);
+    }
 
-            var sut = new ClassificationAdaBoostLearner(10, 1, 5);
+    [TestMethod]
+    public void ClassificationAdaBoostLearner_Learn_Glass()
+    {
+        var (observations, targets) = DataSetUtilities.LoadGlassDataSet();
 
-            var model = sut.Learn(observations, targets);
-            var predictions = model.Predict(observations);
+        var sut = new ClassificationAdaBoostLearner(10, 1, 5);
 
-            var evaluator = new TotalErrorClassificationMetric<double>();
-            var actual = evaluator.Error(targets, predictions);
+        var model = sut.Learn(observations, targets);
+        var predictions = model.Predict(observations);
 
-            Assert.AreEqual(0.0, actual);
-        }
+        var evaluator = new TotalErrorClassificationMetric<double>();
+        var actual = evaluator.Error(targets, predictions);
 
-        [TestMethod]
-        public void ClassificationAdaBoostLearner_Learn_AptitudeData_SequenceContainNoItemIssue_Solved()
-        {
-            var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
-            var indices = new int[] { 22, 6, 23, 12 };
+        Assert.AreEqual(0.0, actual);
+    }
 
-            var sut = new ClassificationAdaBoostLearner(10);
-
-            var model = sut.Learn(observations, targets, indices);
-            var predictions = model.Predict(observations);
-            var indexedPredictions = predictions.GetIndices(indices);
-            var indexedTargets = targets.GetIndices(indices);
+    [TestMethod]
+    public void ClassificationAdaBoostLearner_Learn_AptitudeData_SequenceContainNoItemIssue_Solved()
+    {
+        var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
+        var indices = new int[] { 22, 6, 23, 12 };
 
-            var evaluator = new TotalErrorClassificationMetric<double>();
-            var actual = evaluator.Error(indexedTargets, indexedPredictions);
+        var sut = new ClassificationAdaBoostLearner(10);
 
-            Assert.AreEqual(0.0, actual);
-        }
+        var model = sut.Learn(observations, targets, indices);
+        var predictions = model.Predict(observations);
+        var indexedPredictions = predictions.GetIndices(indices);
+        var indexedTargets = targets.GetIndices(indices);
 
-        [TestMethod]
-        public void ClassificationAdaBoostLearner_Learn_Glass_Indexed()
-        {
-            var (observations, targets) = DataSetUtilities.LoadGlassDataSet();
+        var evaluator = new TotalErrorClassificationMetric<double>();
+        var actual = evaluator.Error(indexedTargets, indexedPredictions);
+
+        Assert.AreEqual(0.0, actual);
+    }
+
+    [TestMethod]
+    public void ClassificationAdaBoostLearner_Learn_Glass_Indexed()
+    {
+        var (observations, targets) = DataSetUtilities.LoadGlassDataSet();
 
-            var sut = new ClassificationAdaBoostLearner(10, 1, 5);
+        var sut = new ClassificationAdaBoostLearner(10, 1, 5);
 
-            var indices = Enumerable.Range(0, targets.Length).ToArray();
-            indices.Shuffle(new Random(42));
-            indices = indices.Take((int)(targets.Length * 0.7))
-                .ToArray();
+        var indices = Enumerable.Range(0, targets.Length).ToArray();
+        indices.Shuffle(new Random(42));
+        indices = indices.Take((int)(targets.Length * 0.7))
+            .ToArray();
 
-            var model = sut.Learn(observations, targets, indices);
-            var predictions = model.Predict(observations);
-            var indexedPredictions = predictions.GetIndices(indices);
-            var indexedTargets = targets.GetIndices(indices);
+        var model = sut.Learn(observations, targets, indices);
+        var predictions = model.Predict(observations);
+        var indexedPredictions = predictions.GetIndices(indices);
+        var indexedTargets = targets.GetIndices(indices);
 
-            var evaluator = new TotalErrorClassificationMetric<double>();
-            var actual = evaluator.Error(indexedTargets, indexedPredictions);
+        var evaluator = new TotalErrorClassificationMetric<double>();
+        var actual = evaluator.Error(indexedTargets, indexedPredictions);
 
-            Assert.AreEqual(0.0, actual);
-        }
+        Assert.AreEqual(0.0, actual);
     }
 }