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Example classes should be static (#2548)
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‎docs/samples/Microsoft.ML.Samples/Dynamic/Calibrator.cs

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/// <summary>
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/// This example first trains a StochasticDualCoordinateAscentBinary Classifier and then convert its output to probability via training a calibrator.
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/// </summary>
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public class CalibratorExample
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public static class Calibrator
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{
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public static void Calibration()
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public static void Example()
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{
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// Downloading the dataset from github.com/dotnet/machinelearning.
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// This will create a sentiment.tsv file in the filesystem.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public static class BootstrapSample
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public static class Bootstrap
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{
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public static void Example()
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{

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class FastTreeRegressionExample
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public static class FastTreeRegression
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{
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public static void FastTreeRegression()
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public static void Example()
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{
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// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
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// as well as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class FeatureContributionCalculationTransform
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public static class FeatureContributionCalculationTransform
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{
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public static void Example()
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{

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class FeatureSelectionTransformExample
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public static class FeatureSelectionTransform
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{
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public static void FeatureSelectionTransform()
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public static void Example()
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{
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// Downloading a classification dataset from github.com/dotnet/machinelearning.
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// It will be stored in the same path as the executable

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

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using Microsoft.ML.Data;
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namespace Microsoft.ML.Samples.Dynamic
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{
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public class FFM_BinaryClassificationExample
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public static class FFMBinaryClassification
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{
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public static void FFM_BinaryClassification()
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public static void Example()
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{
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// Downloading the dataset from github.com/dotnet/machinelearning.
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// This will create a sentiment.tsv file in the filesystem.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class GeneralizedAdditiveModels_RegressionExample
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public static class GeneralizedAdditiveModelsRegression
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{
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public static void RunExample()
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public partial class TransformSamples
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public static partial class TransformSamples
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{
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class ChangePointPrediction
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{

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public partial class TransformSamples
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public static partial class TransformSamples
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{
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class IidSpikeData
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{

‎docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/ConvertToGrayScale.cs

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class ConvertToGrayscaleExample
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public static class ConvertToGrayscale
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{
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// Sample that loads images from the file system, and converts them to grayscale.
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public static void ConvertToGrayscale()
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public static void Example()
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{
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var mlContext = new MLContext();
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‎docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/ExtractPixels.cs

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class ExtractPixelsExample
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public static class ExtractPixels
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{
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// Sample that loads the images from the file system, resizes them (ExtractPixels requires a resizing operation), and extracts the
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// values of the pixels as a vector.
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public static void ExtractPixels()
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public static void Example()
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{
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var mlContext = new MLContext();
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‎docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/LoadImage.cs

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class LoadImageExample
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public static class LoadImage
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{
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// Loads the images of the imagesFolder into an IDataView.
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public static void LoadImage()
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public static void Example()
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{
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var mlContext = new MLContext();
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‎docs/samples/Microsoft.ML.Samples/Dynamic/ImageAnalytics/ResizeImage.cs

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class ResizeImageExample
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public static class ResizeImage
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{
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// Example on how to load the images from the file system, and resize them.
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public static void ResizeImage()
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public static void Example()
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{
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var mlContext = new MLContext();
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‎docs/samples/Microsoft.ML.Samples/Dynamic/KMeans.cs

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class KMeans_example
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public class KMeans
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{
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public static void KMeans()
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public static void Example()
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{
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// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
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// as well as the source of randomness.

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

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using System.Collections.Generic;
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using Microsoft.ML.Data;
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using Microsoft.ML.Transforms.Conversions;
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using Microsoft.ML.Transforms.Text;
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namespace Microsoft.ML.Samples.Dynamic
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{
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public class KeyToValueValueToKeyExample
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public class KeyToValueValueToKey
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{
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public static void KeyToValueValueToKey()
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public static void Example()
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{
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// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
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// as well as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class LdaTransformExample
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public static class LdaTransform
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{
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public static void LdaTransform()
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public static void Example()
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{
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// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
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// as well as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class LogisticRegressionExample
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public static class LogisticRegressionExample
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{
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public static void LogisticRegression()
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public static void Example()
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{
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var ml = new MLContext();
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‎docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

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namespace Microsoft.ML.Samples.Dynamic
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{
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public partial class NgramTransformSamples
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public static partial class TransformSamples
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{
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public static void NgramTransform()
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{

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class NormalizerExample
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public static class NormalizerTransform
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{
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public static void Normalizer()
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public static void Example()
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{
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// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
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// as well as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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class OnnxTransformExample
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public static class OnnxTransformExample
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{
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/// <summary>
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/// Example use of OnnxEstimator in an ML.NET pipeline
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/// </summary>
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public static void OnnxTransformSample()
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public static void Example()
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{
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// Download the squeeznet image model from ONNX model zoo, version 1.2
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// https://github.com/onnx/models/tree/master/squeezenet

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

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using System;
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using System.Linq;
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using Microsoft.Data.DataView;
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using Microsoft.ML.Data;
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using Microsoft.ML.Trainers;
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using Microsoft.ML.SamplesUtils;
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using Microsoft.ML.Trainers.HalLearners;
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namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
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{
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public class PfiHelper
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public static class PfiHelper
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{
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public static IDataView GetHousingRegressionIDataView(MLContext mlContext, out string labelName, out string[] featureNames, bool binaryPrediction = false)
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{

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

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namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
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{
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public class PfiRegressionExample
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public static class PfiRegression
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{
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public static void RunExample()
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
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{
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public class PfiBinaryClassificationExample
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public static class PfiBinaryClassification
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{
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public static void RunExample()
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class ProjectionTransformsExample
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public static class ProjectionTransforms
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{
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public static void ProjectionTransforms()
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public static void Example()
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{
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// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
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// as well as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class SDCARegressionExample
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public static class SDCARegression
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{
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public static void SDCARegression()
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public static void Example()
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{
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// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
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// as well as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public partial class TransformSamples
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public static partial class TransformSamples
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{
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class SsaChangePointData
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{

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public static partial class TransformSamples
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{
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class SsaSpikeData
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{

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

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class StopWordRemoverTransformExample
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public static class StopWordRemoverTransform
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{
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public static void StopWordRemoverTransform()
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public static void Example()
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{
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// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
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// as well as the source of randomness.

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

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namespace Microsoft.ML.Samples.Dynamic.TensorFlow
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{
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class ImageClassification
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public static class ImageClassification
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{
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/// <summary>
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/// Example use of the TensorFlow image model in a ML.NET pipeline.
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/// </summary>
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public static void ScoringWithImageClassificationModelSample()
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public static void Example()
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{
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// Download the ResNet 101 model from the location below.
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// https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/resnet_v2_101.tgz

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

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using System;
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using System.IO;
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using System.Linq;
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using Microsoft.ML.Data;
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using Microsoft.ML.Transforms.TensorFlow;
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namespace Microsoft.ML.Samples.Dynamic.TensorFlow
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{
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class TextClassification
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public static class TextClassification
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{
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public const int MaxSentenceLenth = 600;
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/// <summary>
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/// Example use of the TensorFlow sentiment classification model.
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/// </summary>
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public static void ScoringWithTextClassificationModelSample()
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public static void Example()
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{
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string modelLocation = SamplesUtils.DatasetUtils.DownloadTensorFlowSentimentModel();
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‎docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class TextTransformExample
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public static class TextTransform
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{
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public static void TextTransform()
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public static void Example()
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{
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// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
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// as well as the source of randomness.

‎docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCALogisticRegression.cs

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class SDCALogisticRegression
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public static class SDCALogisticRegression
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{
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public static void Example()
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{

‎docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCASupportVectorMachine.cs

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namespace Microsoft.ML.Samples.Dynamic
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{
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public class SDCASupportVectorMachine
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public static class SDCASupportVectorMachine
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{
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public static void Example()
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{

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