Remove Microsoft.ML prefix from namespaces in samples binary.

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/BootstrapSample.cs

@@ -1,7 +1,8 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class BootstrapSample
     {
@@ -12,7 +13,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
-            IEnumerable<SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample> enumerableOfData = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(5);
+            IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample> enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(5);
             var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Look at the original dataset
@@ -43,7 +44,7 @@ public static void Example()
             {
                 var resample = mlContext.Data.BootstrapSample(data, seed: i);
 
-                var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample>(resample, reuseRowObject: false);
+                var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample>(resample, reuseRowObject: false);
                 Console.WriteLine($"Label\tFeatures[0]");
                 foreach (var row in enumerable)
                 {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/Cache.cs

@@ -1,7 +1,8 @@
 using System;
+using Microsoft.ML;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class Cache
     {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/CrossValidationSplit.cs

@@ -1,8 +1,9 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using static Microsoft.ML.DataOperationsCatalog;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use CrossValidationSplit.

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/DataViewEnumerable.cs

@@ -1,8 +1,9 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use ShuffleRows.

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByColumn.cs

@@ -1,7 +1,8 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use FilterRowsByColumn.
@@ -15,7 +16,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
-            IEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData> enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
+            IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData> enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
             var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
@@ -42,7 +43,7 @@ public static void Example()
             var filteredData = mlContext.Data.FilterRowsByColumn(data, columnName: "Temperature", lowerBound: 34, upperBound: 37);
 
             // Look at the filtered data and observe that values outside [34,37) have been dropped.
-            var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
+            var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
             Console.WriteLine($"Date\tTemperature");
             foreach (var row in enumerable)
             {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByKeyColumnFraction.cs

@@ -1,9 +1,10 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     using MulticlassClassificationExample = DatasetUtils.MulticlassClassificationExample;
 

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByMissingValues.cs

@@ -1,7 +1,8 @@
 using System;
+using Microsoft.ML;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public class FilterRowsByMissingValues
     {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/ShuffleRows.cs

@@ -1,7 +1,8 @@
 using System;
+using Microsoft.ML;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use ShuffleRows.

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SkipRows.cs

@@ -1,5 +1,7 @@
 using System;
-namespace Microsoft.ML.Samples.Dynamic
+using Microsoft.ML;
+
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use Skip.
@@ -13,7 +15,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
-            var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
+            var enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
             var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
@@ -40,7 +42,7 @@ public static void Example()
             var filteredData = mlContext.Data.SkipRows(data, 5);
 
             // Look at the filtered data and observe that the first 5 rows have been dropped
-            var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
+            var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
             Console.WriteLine($"Date\tTemperature");
             foreach (var row in enumerable)
             {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TakeRows.cs

@@ -1,5 +1,7 @@
 using System;
-namespace Microsoft.ML.Samples.Dynamic
+using Microsoft.ML;
+
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use Take.
@@ -13,7 +15,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
-            var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
+            var enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
             var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
@@ -40,7 +42,7 @@ public static void Example()
             var filteredData = mlContext.Data.TakeRows(data, 5);
 
             // Look at the filtered data and observe that only the first 5 rows are in the resulting dataset.
-            var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
+            var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
             Console.WriteLine($"Date\tTemperature");
             foreach (var row in enumerable)
             {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TrainTestSplit.cs

@@ -1,11 +1,9 @@
 using System;
 using System.Collections.Generic;
-using System.Collections.Immutable;
-using System.Linq;
-using Microsoft.ML.Data;
+using Microsoft.ML;
 using static Microsoft.ML.DataOperationsCatalog;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use TrainTestSplit.

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -1,9 +1,10 @@
 using System;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.SamplesUtils;
 using Microsoft.ML.Trainers;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class FeatureContributionCalculationTransform
     {

docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

@@ -1,8 +1,9 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static partial class TransformSamples
     {
@@ -13,7 +14,7 @@ public static void Example()
             var ml = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert to IDataView.
-            IEnumerable<SamplesUtils.DatasetUtils.SampleSentimentData> data = SamplesUtils.DatasetUtils.GetSentimentData();
+            IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleSentimentData> data = Microsoft.ML.SamplesUtils.DatasetUtils.GetSentimentData();
             var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.

docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs

@@ -1,8 +1,9 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class NormalizerTransform
     {
@@ -13,7 +14,7 @@ public static void Example()
             var ml = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
-            IEnumerable<SamplesUtils.DatasetUtils.SampleInfertData> data = SamplesUtils.DatasetUtils.GetInfertData();
+            IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleInfertData> data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
             var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.

docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs

@@ -1,9 +1,10 @@
 using System;
 using System.Linq;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.OnnxRuntime;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class OnnxTransformExample
     {

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs

@@ -2,8 +2,9 @@
 using System.Linq;
 using Microsoft.ML.Trainers;
 using Microsoft.ML.SamplesUtils;
+using Microsoft.ML;
 
-namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
+namespace Samples.Dynamic.PermutationFeatureImportance
 {
     public static class PfiHelper
     {

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIRegressionExample.cs

@@ -1,7 +1,8 @@
 using System;
 using System.Linq;
+using Microsoft.ML;
 
-namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
+namespace Samples.Dynamic.PermutationFeatureImportance
 {
     public static class PfiRegression
     {

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs

@@ -1,8 +1,9 @@
 using System;
 using System.Linq;
+using Microsoft.ML;
 using Microsoft.ML.Trainers;
 
-namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
+namespace Samples.Dynamic.PermutationFeatureImportance
 {
     public static class PfiBinaryClassification
     {

docs/samples/Microsoft.ML.Samples/Dynamic/ProjectionTransforms.cs

@@ -1,9 +1,10 @@
 using System;
 using System.Collections.Generic;
 using System.Linq;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class ProjectionTransforms
     {
@@ -14,7 +15,7 @@ public static void Example()
             var ml = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
-            IEnumerable<SamplesUtils.DatasetUtils.SampleVectorOfNumbersData> data = SamplesUtils.DatasetUtils.GetVectorOfNumbersData();
+            IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData> data = Microsoft.ML.SamplesUtils.DatasetUtils.GetVectorOfNumbersData();
             var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
@@ -37,13 +38,13 @@ public static void Example()
             };
 
             // A pipeline to project Features column into Random fourier space.
-            var rffPipeline = ml.Transforms.ApproximatedKernelMap(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), rank: 4);
+            var rffPipeline = ml.Transforms.ApproximatedKernelMap(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), rank: 4);
             // The transformed (projected) data.
             var transformedData = rffPipeline.Fit(trainData).Transform(trainData);
             // Getting the data of the newly created column, so we can preview it.
-            var randomFourier = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
+            var randomFourier = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
 
-            printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), randomFourier);
+            printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), randomFourier);
 
             // Features column obtained post-transformation.
             //
@@ -55,13 +56,15 @@ public static void Example()
             //0.165 0.117 -0.547  0.014
 
             // A pipeline to project Features column into L-p normalized vector.
-            var lpNormalizePipeline = ml.Transforms.NormalizeLpNorm(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), norm: Transforms.LpNormNormalizingEstimatorBase.NormFunction.L1);
+            var lpNormalizePipeline = ml.Transforms.NormalizeLpNorm(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features),
+                norm: Microsoft.ML.Transforms.LpNormNormalizingEstimatorBase.NormFunction.L1);
+
             // The transformed (projected) data.
             transformedData = lpNormalizePipeline.Fit(trainData).Transform(trainData);
             // Getting the data of the newly created column, so we can preview it.
-            var lpNormalize= transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
+            var lpNormalize= transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
 
-            printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), lpNormalize);
+            printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), lpNormalize);
 
             // Features column obtained post-transformation.
             //
@@ -73,13 +76,13 @@ public static void Example()
             // 0.133 0.156 0.178 0.200 0.000 0.022 0.044 0.067 0.089 0.111
 
             // A pipeline to project Features column into L-p normalized vector.
-            var gcNormalizePipeline = ml.Transforms.NormalizeGlobalContrast(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), ensureZeroMean:false);
+            var gcNormalizePipeline = ml.Transforms.NormalizeGlobalContrast(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), ensureZeroMean:false);
             // The transformed (projected) data.
             transformedData = gcNormalizePipeline.Fit(trainData).Transform(trainData);
             // Getting the data of the newly created column, so we can preview it.
-            var gcNormalize = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
+            var gcNormalize = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
 
-            printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), gcNormalize);
+            printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), gcNormalize);
 
             // Features column obtained post-transformation.
             //

docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/ImageClassification.cs

@@ -4,9 +4,10 @@
 using System.Net;
 using ICSharpCode.SharpZipLib.GZip;
 using ICSharpCode.SharpZipLib.Tar;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class ImageClassification
     {

docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs

@@ -1,8 +1,9 @@
 using System;
 using System.IO;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class TextClassification
     {
@@ -12,7 +13,7 @@ public static class TextClassification
         /// </summary>
         public static void Example()
         {
-            string modelLocation = SamplesUtils.DatasetUtils.DownloadTensorFlowSentimentModel();
+            string modelLocation = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadTensorFlowSentimentModel();
 
             var mlContext = new MLContext();
             var data = new[] { new IMDBSentiment() {