Code analysis: Add editorconfig, activate code analysis, 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

@@ -5,7 +5,7 @@
 namespace SharpLearning.AdaBoost.Test
 {
     [TestClass]
-    public class AssemblyInitializeCultureTest
+    public static class AssemblyInitializeCultureTest
     {
         [AssemblyInitialize]
         public static void AssemblyInitializeCultureTest_InvariantCulture(TestContext c)

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

@@ -16,7 +16,7 @@ public void ClassificationAdaBoostLearner_Learn_AptitudeData()
             var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
 
             var sut = new ClassificationAdaBoostLearner(10);
-            
+
             var model = sut.Learn(observations, targets);
             var predictions = model.Predict(observations);
 
@@ -49,7 +49,7 @@ public void ClassificationAdaBoostLearner_Learn_AptitudeData_SequenceContainNoIt
             var indices = new int[] { 22, 6, 23, 12 };
 
             var sut = new ClassificationAdaBoostLearner(10);
-            
+
             var model = sut.Learn(observations, targets, indices);
             var predictions = model.Predict(observations);
             var indexedPredictions = predictions.GetIndices(indices);

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

@@ -16,7 +16,7 @@ public void RegressionAdaBoostLearner_Learn_AptitudeData_LinearLoss()
             var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
 
             var sut = new RegressionAdaBoostLearner(10);
-            
+
             var model = sut.Learn(observations, targets);
             var predictions = model.Predict(observations);
 

src/SharpLearning.AdaBoost.Test/Models/ClassificationAdaBoostModelTest.cs

@@ -40,7 +40,7 @@ public void ClassificationAdaBoostModel_Predict_Single()
         public void ClassificationAdaBoostModel_Precit_Multiple()
         {
             var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
-            
+
             var learner = new ClassificationAdaBoostLearner(10);
             var sut = learner.Learn(observations, targets);
 
@@ -72,7 +72,7 @@ public void ClassificationAdaBoostModel_PredictProbability_Single()
 
             Assert.AreEqual(0.038461538461538464, error, 0.0000001);
 
-            var expected = new ProbabilityPrediction[] {new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.553917222019051}, {1, 0.446082777980949}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.455270122123639}, {1, 0.544729877876361}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.590671208378385}, {1, 0.409328791621616}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.564961572849738}, {1, 0.435038427150263}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.455270122123639}, {1, 0.544729877876361}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.549970403132686}, {1, 0.450029596867314}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.417527839140627}, {1, 0.582472160859373}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.409988559960094}, {1, 0.590011440039906}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.630894242807786}, {1, 0.369105757192214}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.436954866525023}, {1, 0.563045133474978}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.461264944069783}, {1, 0.538735055930217}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.590671208378385}, {1, 0.409328791621616}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.549503146925505}, {1, 0.450496853074495}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.537653803214063}, {1, 0.462346196785938}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.37650723540928}, {1, 0.62349276459072}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.573579890413618}, {1, 0.426420109586382}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.549970403132686}, {1, 0.450029596867314}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.524371409810479}, {1, 0.475628590189522}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.436954866525023}, {1, 0.563045133474978}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.471117379964633}, {1, 0.528882620035367}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.630894242807786}, {1, 0.369105757192214}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.436954866525023}, {1, 0.563045133474978}, }),new ProbabilityPrediction(1, new Dictionary<double, double> {{0, 0.404976804073458}, {1, 0.595023195926542}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.573579890413618}, {1, 0.426420109586382}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.549970403132686}, {1, 0.450029596867314}, }),new ProbabilityPrediction(0, new Dictionary<double, double> {{0, 0.630894242807786}, {1, 0.369105757192214}, }),};
+            var expected = new ProbabilityPrediction[] { new(0, new Dictionary<double, double> { { 0, 0.553917222019051 }, { 1, 0.446082777980949 }, }), new(1, new Dictionary<double, double> { { 0, 0.455270122123639 }, { 1, 0.544729877876361 }, }), new(0, new Dictionary<double, double> { { 0, 0.590671208378385 }, { 1, 0.409328791621616 }, }), new(0, new Dictionary<double, double> { { 0, 0.564961572849738 }, { 1, 0.435038427150263 }, }), new(1, new Dictionary<double, double> { { 0, 0.455270122123639 }, { 1, 0.544729877876361 }, }), new(0, new Dictionary<double, double> { { 0, 0.549970403132686 }, { 1, 0.450029596867314 }, }), new(1, new Dictionary<double, double> { { 0, 0.417527839140627 }, { 1, 0.582472160859373 }, }), new(1, new Dictionary<double, double> { { 0, 0.409988559960094 }, { 1, 0.590011440039906 }, }), new(0, new Dictionary<double, double> { { 0, 0.630894242807786 }, { 1, 0.369105757192214 }, }), new(1, new Dictionary<double, double> { { 0, 0.436954866525023 }, { 1, 0.563045133474978 }, }), new(1, new Dictionary<double, double> { { 0, 0.461264944069783 }, { 1, 0.538735055930217 }, }), new(0, new Dictionary<double, double> { { 0, 0.590671208378385 }, { 1, 0.409328791621616 }, }), new(0, new Dictionary<double, double> { { 0, 0.549503146925505 }, { 1, 0.450496853074495 }, }), new(0, new Dictionary<double, double> { { 0, 0.537653803214063 }, { 1, 0.462346196785938 }, }), new(1, new Dictionary<double, double> { { 0, 0.37650723540928 }, { 1, 0.62349276459072 }, }), new(0, new Dictionary<double, double> { { 0, 0.573579890413618 }, { 1, 0.426420109586382 }, }), new(0, new Dictionary<double, double> { { 0, 0.549970403132686 }, { 1, 0.450029596867314 }, }), new(0, new Dictionary<double, double> { { 0, 0.524371409810479 }, { 1, 0.475628590189522 }, }), new(1, new Dictionary<double, double> { { 0, 0.436954866525023 }, { 1, 0.563045133474978 }, }), new(1, new Dictionary<double, double> { { 0, 0.471117379964633 }, { 1, 0.528882620035367 }, }), new(0, new Dictionary<double, double> { { 0, 0.630894242807786 }, { 1, 0.369105757192214 }, }), new(1, new Dictionary<double, double> { { 0, 0.436954866525023 }, { 1, 0.563045133474978 }, }), new(1, new Dictionary<double, double> { { 0, 0.404976804073458 }, { 1, 0.595023195926542 }, }), new(0, new Dictionary<double, double> { { 0, 0.573579890413618 }, { 1, 0.426420109586382 }, }), new(0, new Dictionary<double, double> { { 0, 0.549970403132686 }, { 1, 0.450029596867314 }, }), new(0, new Dictionary<double, double> { { 0, 0.630894242807786 }, { 1, 0.369105757192214 }, }), };
             CollectionAssert.AreEqual(expected, actual);
         }
 
@@ -90,7 +90,7 @@ public void ClassificationAdaBoostModel_PredictProbability_Multiple()
 
             Assert.AreEqual(0.038461538461538464, error, 0.0000001);
 
-            var expected = new ProbabilityPrediction[] { new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.553917222019051 }, { 1, 0.446082777980949 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.455270122123639 }, { 1, 0.544729877876361 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.590671208378385 }, { 1, 0.409328791621616 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.564961572849738 }, { 1, 0.435038427150263 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.455270122123639 }, { 1, 0.544729877876361 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.549970403132686 }, { 1, 0.450029596867314 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.417527839140627 }, { 1, 0.582472160859373 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.409988559960094 }, { 1, 0.590011440039906 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.630894242807786 }, { 1, 0.369105757192214 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.436954866525023 }, { 1, 0.563045133474978 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.461264944069783 }, { 1, 0.538735055930217 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.590671208378385 }, { 1, 0.409328791621616 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.549503146925505 }, { 1, 0.450496853074495 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.537653803214063 }, { 1, 0.462346196785938 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.37650723540928 }, { 1, 0.62349276459072 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.573579890413618 }, { 1, 0.426420109586382 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.549970403132686 }, { 1, 0.450029596867314 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.524371409810479 }, { 1, 0.475628590189522 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.436954866525023 }, { 1, 0.563045133474978 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.471117379964633 }, { 1, 0.528882620035367 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.630894242807786 }, { 1, 0.369105757192214 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.436954866525023 }, { 1, 0.563045133474978 }, }), new ProbabilityPrediction(1, new Dictionary<double, double> { { 0, 0.404976804073458 }, { 1, 0.595023195926542 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.573579890413618 }, { 1, 0.426420109586382 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.549970403132686 }, { 1, 0.450029596867314 }, }), new ProbabilityPrediction(0, new Dictionary<double, double> { { 0, 0.630894242807786 }, { 1, 0.369105757192214 }, }), };
+            var expected = new ProbabilityPrediction[] { new(0, new Dictionary<double, double> { { 0, 0.553917222019051 }, { 1, 0.446082777980949 }, }), new(1, new Dictionary<double, double> { { 0, 0.455270122123639 }, { 1, 0.544729877876361 }, }), new(0, new Dictionary<double, double> { { 0, 0.590671208378385 }, { 1, 0.409328791621616 }, }), new(0, new Dictionary<double, double> { { 0, 0.564961572849738 }, { 1, 0.435038427150263 }, }), new(1, new Dictionary<double, double> { { 0, 0.455270122123639 }, { 1, 0.544729877876361 }, }), new(0, new Dictionary<double, double> { { 0, 0.549970403132686 }, { 1, 0.450029596867314 }, }), new(1, new Dictionary<double, double> { { 0, 0.417527839140627 }, { 1, 0.582472160859373 }, }), new(1, new Dictionary<double, double> { { 0, 0.409988559960094 }, { 1, 0.590011440039906 }, }), new(0, new Dictionary<double, double> { { 0, 0.630894242807786 }, { 1, 0.369105757192214 }, }), new(1, new Dictionary<double, double> { { 0, 0.436954866525023 }, { 1, 0.563045133474978 }, }), new(1, new Dictionary<double, double> { { 0, 0.461264944069783 }, { 1, 0.538735055930217 }, }), new(0, new Dictionary<double, double> { { 0, 0.590671208378385 }, { 1, 0.409328791621616 }, }), new(0, new Dictionary<double, double> { { 0, 0.549503146925505 }, { 1, 0.450496853074495 }, }), new(0, new Dictionary<double, double> { { 0, 0.537653803214063 }, { 1, 0.462346196785938 }, }), new(1, new Dictionary<double, double> { { 0, 0.37650723540928 }, { 1, 0.62349276459072 }, }), new(0, new Dictionary<double, double> { { 0, 0.573579890413618 }, { 1, 0.426420109586382 }, }), new(0, new Dictionary<double, double> { { 0, 0.549970403132686 }, { 1, 0.450029596867314 }, }), new(0, new Dictionary<double, double> { { 0, 0.524371409810479 }, { 1, 0.475628590189522 }, }), new(1, new Dictionary<double, double> { { 0, 0.436954866525023 }, { 1, 0.563045133474978 }, }), new(1, new Dictionary<double, double> { { 0, 0.471117379964633 }, { 1, 0.528882620035367 }, }), new(0, new Dictionary<double, double> { { 0, 0.630894242807786 }, { 1, 0.369105757192214 }, }), new(1, new Dictionary<double, double> { { 0, 0.436954866525023 }, { 1, 0.563045133474978 }, }), new(1, new Dictionary<double, double> { { 0, 0.404976804073458 }, { 1, 0.595023195926542 }, }), new(0, new Dictionary<double, double> { { 0, 0.573579890413618 }, { 1, 0.426420109586382 }, }), new(0, new Dictionary<double, double> { { 0, 0.549970403132686 }, { 1, 0.450029596867314 }, }), new(0, new Dictionary<double, double> { { 0, 0.630894242807786 }, { 1, 0.369105757192214 }, }), };
             CollectionAssert.AreEqual(expected, actual);
         }
 
@@ -99,14 +99,14 @@ public void ClassificationAdaBoostModel_GetVariableImportance()
         {
             var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
 
-            var featureNameToIndex = new Dictionary<string, int> { { "AptitudeTestScore", 0 }, 
+            var featureNameToIndex = new Dictionary<string, int> { { "AptitudeTestScore", 0 },
                 { "PreviousExperience_month", 1 } };
 
             var learner = new ClassificationAdaBoostLearner(10, 1, 3);
             var sut = learner.Learn(observations, targets);
 
             var actual = sut.GetVariableImportance(featureNameToIndex);
-            var expected = new Dictionary<string, double> { { "PreviousExperience_month", 100.0 }, 
+            var expected = new Dictionary<string, double> { { "PreviousExperience_month", 100.0 },
                 { "AptitudeTestScore", 24.0268096428771 } };
 
             Assert.AreEqual(expected.Count, actual.Count);
@@ -169,8 +169,7 @@ public void ClassificationAdaBoostModel_Load()
             Assert.AreEqual(0.19230769230769232, error, 0.0000001);
         }
 
-
-        void Write(ProbabilityPrediction[] predictions)
+        static void Write(ProbabilityPrediction[] predictions)
         {
             var value = "new ProbabilityPrediction[] {";
             foreach (var item in predictions)

src/SharpLearning.AdaBoost.Test/Models/RegressionAdaBoostModelTest.cs

@@ -55,14 +55,14 @@ public void RegressionAdaBoostModel_GetVariableImportance()
         {
             var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
 
-            var featureNameToIndex = new Dictionary<string, int> { { "AptitudeTestScore", 0 }, 
+            var featureNameToIndex = new Dictionary<string, int> { { "AptitudeTestScore", 0 },
                 { "PreviousExperience_month", 1 } };
 
             var learner = new RegressionAdaBoostLearner(10);
             var sut = learner.Learn(observations, targets);
 
             var actual = sut.GetVariableImportance(featureNameToIndex);
-            var expected = new Dictionary<string, double> { { "PreviousExperience_month", 100.0 }, 
+            var expected = new Dictionary<string, double> { { "PreviousExperience_month", 100.0 },
                 { "AptitudeTestScore", 33.8004886838701 } };
 
             Assert.AreEqual(expected.Count, actual.Count);

src/SharpLearning.AdaBoost.Test/WeightedRandomSamplerTest.cs

@@ -15,7 +15,7 @@ public void WeightedRandomSampler_Sample_Weight_10()
 
             var actual = new int[indices.Length];
             sut.Sample(indices, weights, actual);
-            
+
             var expected = new int[] { 2, 5, 6, 7, 7, 8, 8, 8, 9, 9 };
             CollectionAssert.AreEqual(expected, actual);
         }