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‎THIRD-PARTY-NOTICES.TXT

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@@ -42,4 +42,27 @@ PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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License notice for SharpZipLib
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------------------------------
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https://github.com/icsharpcode/SharpZipLib
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Copyright © 2000-2018 SharpZipLib Contributors
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Permission is hereby granted, free of charge, to any person obtaining a copy of this
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software and associated documentation files (the "Software"), to deal in the Software
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without restriction, including without limitation the rights to use, copy, modify, merge,
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publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons
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to whom the Software is furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all copies or
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substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
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INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
65+
PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE
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FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
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OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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DEALINGS IN THE SOFTWARE.

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

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@@ -1,7 +1,8 @@
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using System;
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using System.Collections.Generic;
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using Microsoft.ML;
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namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
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{
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public static class BootstrapSample
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{
@@ -12,7 +13,7 @@ public static void Example()
1213
var mlContext = new MLContext();
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1415
// Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
15-
IEnumerable<SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample> enumerableOfData = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(5);
16+
IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample> enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(5);
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var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
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// Look at the original dataset
@@ -43,7 +44,7 @@ public static void Example()
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{
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var resample = mlContext.Data.BootstrapSample(data, seed: i);
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46-
var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample>(resample, reuseRowObject: false);
47+
var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample>(resample, reuseRowObject: false);
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Console.WriteLine($"Label\tFeatures[0]");
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foreach (var row in enumerable)
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{

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

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

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

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

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

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

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

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using System;
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using System.Collections.Generic;
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using Microsoft.ML;
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namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
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{
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/// <summary>
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/// Sample class showing how to use FilterRowsByColumn.
@@ -15,7 +16,7 @@ public static void Example()
1516
var mlContext = new MLContext();
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1718
// Get a small dataset as an IEnumerable.
18-
IEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData> enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
19+
IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData> enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
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var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
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// Before we apply a filter, examine all the records in the dataset.
@@ -42,7 +43,7 @@ public static void Example()
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var filteredData = mlContext.Data.FilterRowsByColumn(data, columnName: "Temperature", lowerBound: 34, upperBound: 37);
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4445
// Look at the filtered data and observe that values outside [34,37) have been dropped.
45-
var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
46+
var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
4647
Console.WriteLine($"Date\tTemperature");
4748
foreach (var row in enumerable)
4849
{

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

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using System;
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using System.Collections.Generic;
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using Microsoft.ML;
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using Microsoft.ML.Data;
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using Microsoft.ML.SamplesUtils;
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6-
namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
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{
89
using MulticlassClassificationExample = DatasetUtils.MulticlassClassificationExample;
910

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

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using System;
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using Microsoft.ML;
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using Microsoft.ML.SamplesUtils;
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namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
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{
67
public class FilterRowsByMissingValues
78
{

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

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using System;
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using Microsoft.ML;
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using Microsoft.ML.SamplesUtils;
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4-
namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
56
{
67
/// <summary>
78
/// Sample class showing how to use ShuffleRows.

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

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using System;
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namespace Microsoft.ML.Samples.Dynamic
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using Microsoft.ML;
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4+
namespace Samples.Dynamic
35
{
46
/// <summary>
57
/// Sample class showing how to use Skip.
@@ -13,7 +15,7 @@ public static void Example()
1315
var mlContext = new MLContext();
1416

1517
// Get a small dataset as an IEnumerable.
16-
var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
18+
var enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
1719
var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
1820

1921
// Before we apply a filter, examine all the records in the dataset.
@@ -40,7 +42,7 @@ public static void Example()
4042
var filteredData = mlContext.Data.SkipRows(data, 5);
4143

4244
// Look at the filtered data and observe that the first 5 rows have been dropped
43-
var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
45+
var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
4446
Console.WriteLine($"Date\tTemperature");
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foreach (var row in enumerable)
4648
{

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

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using System;
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namespace Microsoft.ML.Samples.Dynamic
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using Microsoft.ML;
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4+
namespace Samples.Dynamic
35
{
46
/// <summary>
57
/// Sample class showing how to use Take.
@@ -13,7 +15,7 @@ public static void Example()
1315
var mlContext = new MLContext();
1416

1517
// Get a small dataset as an IEnumerable.
16-
var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
18+
var enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
1719
var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
1820

1921
// Before we apply a filter, examine all the records in the dataset.
@@ -40,7 +42,7 @@ public static void Example()
4042
var filteredData = mlContext.Data.TakeRows(data, 5);
4143

4244
// Look at the filtered data and observe that only the first 5 rows are in the resulting dataset.
43-
var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
45+
var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
4446
Console.WriteLine($"Date\tTemperature");
4547
foreach (var row in enumerable)
4648
{

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

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11
using System;
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using System.Collections.Generic;
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using System.Collections.Immutable;
4-
using System.Linq;
5-
using Microsoft.ML.Data;
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using Microsoft.ML;
64
using static Microsoft.ML.DataOperationsCatalog;
75

8-
namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
97
{
108
/// <summary>
119
/// Sample class showing how to use TrainTestSplit.

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

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11
using System;
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using Microsoft.ML;
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using Microsoft.ML.Data;
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using Microsoft.ML.SamplesUtils;
45
using Microsoft.ML.Trainers;
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6-
namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
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{
89
public static class FeatureContributionCalculationTransform
910
{

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

+3-2
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using System;
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using System.Collections.Generic;
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using Microsoft.ML;
34
using Microsoft.ML.Data;
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5-
namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
67
{
78
public static partial class TransformSamples
89
{
@@ -13,7 +14,7 @@ public static void Example()
1314
var ml = new MLContext();
1415

1516
// Get a small dataset as an IEnumerable and convert to IDataView.
16-
IEnumerable<SamplesUtils.DatasetUtils.SampleSentimentData> data = SamplesUtils.DatasetUtils.GetSentimentData();
17+
IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleSentimentData> data = Microsoft.ML.SamplesUtils.DatasetUtils.GetSentimentData();
1718
var trainData = ml.Data.LoadFromEnumerable(data);
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1920
// Preview of the data.

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

+3-2
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using System;
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using System.Collections.Generic;
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using Microsoft.ML;
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using Microsoft.ML.Data;
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5-
namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
67
{
78
public static class NormalizerTransform
89
{
@@ -13,7 +14,7 @@ public static void Example()
1314
var ml = new MLContext();
1415

1516
// Get a small dataset as an IEnumerable and convert it to an IDataView.
16-
IEnumerable<SamplesUtils.DatasetUtils.SampleInfertData> data = SamplesUtils.DatasetUtils.GetInfertData();
17+
IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleInfertData> data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
1718
var trainData = ml.Data.LoadFromEnumerable(data);
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1920
// Preview of the data.

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

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using System;
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using System.Linq;
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using Microsoft.ML;
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using Microsoft.ML.Data;
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using Microsoft.ML.OnnxRuntime;
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6-
namespace Microsoft.ML.Samples.Dynamic
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namespace Samples.Dynamic
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{
89
public static class OnnxTransformExample
910
{

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

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using System.Linq;
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using Microsoft.ML.Trainers;
44
using Microsoft.ML.SamplesUtils;
5+
using Microsoft.ML;
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6-
namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
7+
namespace Samples.Dynamic.PermutationFeatureImportance
78
{
89
public static class PfiHelper
910
{

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

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using System;
22
using System.Linq;
3+
using Microsoft.ML;
34

4-
namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
5+
namespace Samples.Dynamic.PermutationFeatureImportance
56
{
67
public static class PfiRegression
78
{

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

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11
using System;
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using System.Linq;
3+
using Microsoft.ML;
34
using Microsoft.ML.Trainers;
45

5-
namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
6+
namespace Samples.Dynamic.PermutationFeatureImportance
67
{
78
public static class PfiBinaryClassification
89
{

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

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using System;
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using System.Collections.Generic;
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using System.Linq;
4+
using Microsoft.ML;
45
using Microsoft.ML.Data;
56

6-
namespace Microsoft.ML.Samples.Dynamic
7+
namespace Samples.Dynamic
78
{
89
public static class ProjectionTransforms
910
{
@@ -14,7 +15,7 @@ public static void Example()
1415
var ml = new MLContext();
1516

1617
// Get a small dataset as an IEnumerable and convert it to an IDataView.
17-
IEnumerable<SamplesUtils.DatasetUtils.SampleVectorOfNumbersData> data = SamplesUtils.DatasetUtils.GetVectorOfNumbersData();
18+
IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData> data = Microsoft.ML.SamplesUtils.DatasetUtils.GetVectorOfNumbersData();
1819
var trainData = ml.Data.LoadFromEnumerable(data);
1920

2021
// Preview of the data.
@@ -37,13 +38,13 @@ public static void Example()
3738
};
3839

3940
// A pipeline to project Features column into Random fourier space.
40-
var rffPipeline = ml.Transforms.ApproximatedKernelMap(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), rank: 4);
41+
var rffPipeline = ml.Transforms.ApproximatedKernelMap(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), rank: 4);
4142
// The transformed (projected) data.
4243
var transformedData = rffPipeline.Fit(trainData).Transform(trainData);
4344
// Getting the data of the newly created column, so we can preview it.
44-
var randomFourier = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
45+
var randomFourier = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
4546

46-
printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), randomFourier);
47+
printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), randomFourier);
4748

4849
// Features column obtained post-transformation.
4950
//
@@ -55,13 +56,15 @@ public static void Example()
5556
//0.165 0.117 -0.547 0.014
5657

5758
// A pipeline to project Features column into L-p normalized vector.
58-
var lpNormalizePipeline = ml.Transforms.NormalizeLpNorm(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), norm: Transforms.LpNormNormalizingEstimatorBase.NormFunction.L1);
59+
var lpNormalizePipeline = ml.Transforms.NormalizeLpNorm(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features),
60+
norm: Microsoft.ML.Transforms.LpNormNormalizingEstimatorBase.NormFunction.L1);
61+
5962
// The transformed (projected) data.
6063
transformedData = lpNormalizePipeline.Fit(trainData).Transform(trainData);
6164
// Getting the data of the newly created column, so we can preview it.
62-
var lpNormalize= transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
65+
var lpNormalize= transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
6366

64-
printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), lpNormalize);
67+
printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), lpNormalize);
6568

6669
// Features column obtained post-transformation.
6770
//
@@ -73,13 +76,13 @@ public static void Example()
7376
// 0.133 0.156 0.178 0.200 0.000 0.022 0.044 0.067 0.089 0.111
7477

7578
// A pipeline to project Features column into L-p normalized vector.
76-
var gcNormalizePipeline = ml.Transforms.NormalizeGlobalContrast(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), ensureZeroMean:false);
79+
var gcNormalizePipeline = ml.Transforms.NormalizeGlobalContrast(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), ensureZeroMean:false);
7780
// The transformed (projected) data.
7881
transformedData = gcNormalizePipeline.Fit(trainData).Transform(trainData);
7982
// Getting the data of the newly created column, so we can preview it.
80-
var gcNormalize = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
83+
var gcNormalize = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
8184

82-
printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), gcNormalize);
85+
printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), gcNormalize);
8386

8487
// Features column obtained post-transformation.
8588
//

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