XML documentation references cs code for examples

Microsoft.ML.sln

@@ -127,6 +127,10 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Benchmarks.Tes
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.TimeSeries.Tests", "test\Microsoft.ML.TimeSeries.Tests\Microsoft.ML.TimeSeries.Tests.csproj", "{4B101D58-E7E4-4877-A536-A9B41E2E82A3}"
 EndProject
+Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "samples", "samples", "{DA452A53-2E94-4433-B08C-041EDEC729E6}"
+EndProject
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Samples", "docs\samples\Microsoft.ML.Samples\Microsoft.ML.Samples.csproj", "{ECB71297-9DF1-48CE-B93A-CD969221F9B6}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -471,6 +475,14 @@ Global
 		{B6C83F04-A04B-4F00-9E68-1EC411F9317C}.Release|Any CPU.Build.0 = Release|Any CPU
 		{B6C83F04-A04B-4F00-9E68-1EC411F9317C}.Release-Intrinsics|Any CPU.ActiveCfg = Release-Intrinsics|Any CPU
 		{B6C83F04-A04B-4F00-9E68-1EC411F9317C}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
+		{ECB71297-9DF1-48CE-B93A-CD969221F9B6}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{ECB71297-9DF1-48CE-B93A-CD969221F9B6}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{ECB71297-9DF1-48CE-B93A-CD969221F9B6}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug-Intrinsics|Any CPU
+		{ECB71297-9DF1-48CE-B93A-CD969221F9B6}.Debug-Intrinsics|Any CPU.Build.0 = Debug-Intrinsics|Any CPU
+		{ECB71297-9DF1-48CE-B93A-CD969221F9B6}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{ECB71297-9DF1-48CE-B93A-CD969221F9B6}.Release|Any CPU.Build.0 = Release|Any CPU
+		{ECB71297-9DF1-48CE-B93A-CD969221F9B6}.Release-Intrinsics|Any CPU.ActiveCfg = Release-Intrinsics|Any CPU
+		{ECB71297-9DF1-48CE-B93A-CD969221F9B6}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
 		{4B101D58-E7E4-4877-A536-A9B41E2E82A3}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
 		{4B101D58-E7E4-4877-A536-A9B41E2E82A3}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{4B101D58-E7E4-4877-A536-A9B41E2E82A3}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug-Intrinsics|Any CPU
@@ -531,6 +543,7 @@ Global
 		{73DAAC82-D308-48CC-8FFE-3B037F8BBCCA} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
 		{49D03292-8AFE-4B82-823C-D047BF8420F7} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}
 		{B6C83F04-A04B-4F00-9E68-1EC411F9317C} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}
+		{ECB71297-9DF1-48CE-B93A-CD969221F9B6} = {DA452A53-2E94-4433-B08C-041EDEC729E6}
 		{4B101D58-E7E4-4877-A536-A9B41E2E82A3} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}
 	EndGlobalSection
 	GlobalSection(ExtensibilityGlobals) = postSolution

docs/samples/Microsoft.ML.Samples/DatasetCreator.cs

@@ -0,0 +1,60 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using System;
+using System.IO;
+using System.Text;
+
+namespace Microsoft.ML.Samples.StaticPipe
+{
+    public static class DatasetCreator
+    {
+        public static (string trainPath, string testPath) CreateRegressionDataset()
+        {
+            // creating a small sample dataset, and writting it to file
+            string trainDataPath = @"RegressionTrainDataset.txt";
+            string testDataPath = @"RegressionTestDataset.txt";
+
+            string header = "feature_a, feature_b, target";
+
+            int a = 0;
+            int b = 0;
+            int target = 0;
+
+            var csvTrain = new StringBuilder().AppendLine(header);
+            var csvTest = new StringBuilder().AppendLine(header);
+
+            Random rnd = new Random();
+            for (int i = 0; i < 1000; i++)
+            {
+                a = rnd.Next(i - 5, i + 5);
+                b = rnd.Next(0, 10);
+
+                target = 2*a + b;
+
+                if (i % 15 == 0)
+                    csvTest.AppendLine($"{a}, {b}, {target}");
+                else
+                    csvTrain.AppendLine($"{a}, {b} , {target}");
+            }
+
+
+            if (!File.Exists(trainDataPath))
+                File.WriteAllText(trainDataPath, csvTrain.ToString());
+            else
+            {
+                new Exception("Train dataset file already exists");
+            }
+
+            if (!File.Exists(testDataPath))
+                File.WriteAllText(testDataPath, csvTest.ToString());
+            else
+            {
+                new Exception("Test dataset file already exists");
+            }
+
+            return (trainDataPath, testDataPath);
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj

@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <TargetFramework>netcoreapp2.1</TargetFramework>
+    <OutputType>Exe</OutputType>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.ML.StandardLearners\Microsoft.ML.StandardLearners.csproj" />
+
+    <NativeAssemblyReference Include="CpuMathNative" />
+
+    <ProjectReference Include="..\..\..\src\Microsoft.ML.Analyzer\Microsoft.ML.Analyzer.csproj">
+      <ReferenceOutputAssembly>false</ReferenceOutputAssembly>
+      <OutputItemType>Analyzer</OutputItemType>
+    </ProjectReference>
+
+  </ItemGroup>
+
+</Project>

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -0,0 +1,16 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using Microsoft.ML.Samples.StaticPipe;
+
+namespace Microsoft.ML.Samples
+{
+    internal static class Program
+    {
+        static void Main(string[] args)
+        {
+            Trainers.SdcaRegression();
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Trainers.cs

@@ -0,0 +1,72 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using Microsoft.ML.Runtime.Data;
+using Microsoft.ML.Runtime.Learners;
+using Microsoft.ML.StaticPipe;
+using System;
+
+namespace Microsoft.ML.Samples.StaticPipe
+{
+    public static class Trainers
+    { 
+        public static void SdcaRegression()
+        {
+            var (trainDataPath, testDataPath) = DatasetCreator.CreateRegressionDataset();
+
+            //creating the ML.Net IHostEnvironment object, needed for the pipeline
+            var env = new LocalEnvironment(seed: 0);
+
+            // creating the ML context, based on the task performed.
+            var regressionContext = new RegressionContext(env);
+
+            // Creating a data reader, based on the format of the data
+            var reader = TextLoader.CreateReader(env, c => (
+                     label: c.LoadFloat(2),
+                     features: c.LoadFloat(0, 1)
+                 ),
+                separator: ',', hasHeader: true);
+
+            // Read the data
+            var trainData = reader.Read(new MultiFileSource(trainDataPath));
+
+            // The predictor that gets produced out of training
+            LinearRegressionPredictor pred = null;
+
+            // Create the estimator
+            var learningPipeline = reader.MakeNewEstimator()
+                .Append(r => (r.label, score: regressionContext.Trainers.Sdca(
+                                            r.label,
+                                            r.features,
+                                            l1Threshold: 0f,
+                                            maxIterations: 100,
+                                        onFit: p => pred = p)
+                                )
+                        );
+
+            // fit this pipeline to the training data
+            var model = learningPipeline.Fit(trainData);
+
+            // check the weights that the model learned
+            VBuffer<float> weights = default;
+            pred.GetFeatureWeights(ref weights);
+
+            Console.WriteLine($"weight 0 - {weights.Values[0]}");
+            Console.WriteLine($"weight 1 - {weights.Values[1]}");
+
+            // test the model we just trained, using the test file. 
+            var testData = reader.Read(new MultiFileSource(testDataPath));
+            var data = model.Transform(testData);
+
+            //Evaluate how the model is doing on the test data
+            var metrics = regressionContext.Evaluate(data, r => r.label, r => r.score);
+
+            Console.WriteLine($"L1 - {metrics.L1}");
+            Console.WriteLine($"L2 - {metrics.L2}");
+            Console.WriteLine($"LossFunction - {metrics.LossFn}");
+            Console.WriteLine($"RMS - {metrics.Rms}");
+            Console.WriteLine($"RSquared - {metrics.RSquared}");
+        }
+    }
+}

src/Microsoft.ML.StandardLearners/Standard/SdcaStatic.cs

@@ -33,6 +33,12 @@ public static partial class RegressionTrainers
         /// the linear model that was trained.  Note that this action cannot change the result in any way; it is only a way for the caller to
         /// be informed about what was learnt.</param>
         /// <returns>The predicted output.</returns>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        ///  [!code-csharp[SDCA](../../../docs/samples/Microsoft.ML.Samples.StaticPipe/Trainers.cs?range=5-8,12-70) "The SDCA regression example."]
+        /// ]]></format>
+        /// </example>
         public static Scalar<float> Sdca(this RegressionContext.RegressionTrainers ctx,
             Scalar<float> label, Vector<float> features, Scalar<float> weights = null,
             float? l2Const = null,