diff --git a/docs/api-reference/algo-details-fastforest.md b/docs/api-reference/algo-details-fastforest.md index fb9998d93d..ac3b7bee59 100644 --- a/docs/api-reference/algo-details-fastforest.md +++ b/docs/api-reference/algo-details-fastforest.md @@ -28,4 +28,6 @@ For more see: * [Quantile regression forest](http://jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf) * [From Stumps to Trees to - Forests](https://blogs.technet.microsoft.com/machinelearning/2014/09/10/from-stumps-to-trees-to-forests/) \ No newline at end of file + Forests](https://blogs.technet.microsoft.com/machinelearning/2014/09/10/from-stumps-to-trees-to-forests/) + +Check the See Also section for links to examples of the usage. \ No newline at end of file diff --git a/docs/api-reference/algo-details-fasttree.md b/docs/api-reference/algo-details-fasttree.md index dd14727d22..54985d89d2 100644 --- a/docs/api-reference/algo-details-fasttree.md +++ b/docs/api-reference/algo-details-fasttree.md @@ -35,4 +35,6 @@ For more information see: * [Wikipedia: Gradient boosting (Gradient tree boosting).](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) * [Greedy function approximation: A gradient boosting -machine.](https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451) \ No newline at end of file +machine.](https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451) + +Check the See Also section for links to examples of the usage. \ No newline at end of file diff --git a/docs/api-reference/algo-details-gam.md b/docs/api-reference/algo-details-gam.md index 30222459b9..5d079dc687 100644 --- a/docs/api-reference/algo-details-gam.md +++ b/docs/api-reference/algo-details-gam.md @@ -15,4 +15,6 @@ the average prediction over the training set, and the shape functions are normalized to represent the deviation from the average prediction. This results in models that are easily interpreted simply by inspecting the intercept and the shape functions. See the sample below for an example of how to train a GAM model -and inspect and interpret the results. \ No newline at end of file +and inspect and interpret the results. + +Check the See Also section for links to examples of the usage. \ No newline at end of file diff --git a/docs/api-reference/algo-details-lightgbm.md b/docs/api-reference/algo-details-lightgbm.md index a965c4a942..522b5c6267 100644 --- a/docs/api-reference/algo-details-lightgbm.md +++ b/docs/api-reference/algo-details-lightgbm.md @@ -3,3 +3,5 @@ LightGBM is an open source implementation of gradient boosting decision tree. For implementation details, please see [LightGBM's official documentation](https://lightgbm.readthedocs.io/en/latest/index.html) or this [paper](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf). + +Check the See Also section for links to examples of the usage. \ No newline at end of file diff --git a/docs/api-reference/algo-details-sdca.md b/docs/api-reference/algo-details-sdca.md index f70f666c4f..8832a4629c 100644 --- a/docs/api-reference/algo-details-sdca.md +++ b/docs/api-reference/algo-details-sdca.md @@ -57,4 +57,6 @@ For more information, see: * [Scaling Up Stochastic Dual Coordinate Ascent.](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/main-3.pdf) * [Stochastic Dual Coordinate Ascent Methods for Regularized Loss - Minimization.](http://www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf) \ No newline at end of file + Minimization.](http://www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf) + +Check the See Also section for links to examples of the usage. \ No newline at end of file diff --git a/docs/api-reference/algo-details-sgd.md b/docs/api-reference/algo-details-sgd.md index d008e590ae..b2fe81c90c 100644 --- a/docs/api-reference/algo-details-sgd.md +++ b/docs/api-reference/algo-details-sgd.md @@ -6,4 +6,6 @@ Hogwild Stochastic Gradient Descent for binary classification that supports multi-threading without any locking. If the associated optimization problem is sparse, Hogwild Stochastic Gradient Descent achieves a nearly optimal rate of convergence. For more details about Hogwild Stochastic Gradient Descent can be -found [here](http://arxiv.org/pdf/1106.5730v2.pdf). \ No newline at end of file +found [here](http://arxiv.org/pdf/1106.5730v2.pdf). + +Check the See Also section for links to examples of the usage. \ No newline at end of file diff --git a/src/Microsoft.ML.Data/Transforms/ColumnConcatenatingEstimator.cs b/src/Microsoft.ML.Data/Transforms/ColumnConcatenatingEstimator.cs index 123bbad3c9..02146d1c4e 100644 --- a/src/Microsoft.ML.Data/Transforms/ColumnConcatenatingEstimator.cs +++ b/src/Microsoft.ML.Data/Transforms/ColumnConcatenatingEstimator.cs @@ -31,7 +31,7 @@ namespace Microsoft.ML.Transforms /// If the input columns' data type is a vector the output column data type remains the same. However, the size of /// the vector will be the sum of the sizes of the input vectors. /// - /// Check the See Also section for links to examples of the usage. + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Data/Transforms/ColumnCopying.cs b/src/Microsoft.ML.Data/Transforms/ColumnCopying.cs index 79e8a66cc0..bc681c5dbb 100644 --- a/src/Microsoft.ML.Data/Transforms/ColumnCopying.cs +++ b/src/Microsoft.ML.Data/Transforms/ColumnCopying.cs @@ -45,7 +45,7 @@ namespace Microsoft.ML.Transforms /// /// The resulting [ColumnCopyingTransformer](xref:Microsoft.ML.Transforms.ColumnCopyingTransformer) creates a new column, named as specified in the output column name parameters, and /// copies the data from the input column to this new column. - /// Check the See Also section for links to examples of the usage. + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Data/Transforms/ColumnSelecting.cs b/src/Microsoft.ML.Data/Transforms/ColumnSelecting.cs index 607f546f67..346025ca94 100644 --- a/src/Microsoft.ML.Data/Transforms/ColumnSelecting.cs +++ b/src/Microsoft.ML.Data/Transforms/ColumnSelecting.cs @@ -53,7 +53,7 @@ namespace Microsoft.ML.Transforms /// In the case of serialization, every column in the schema will be written out. If there are columns /// that should not be saved, this estimator can be used to remove them. /// - /// Check the See Also section for links to examples of the usage. + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Data/Transforms/FeatureContributionCalculationTransformer.cs b/src/Microsoft.ML.Data/Transforms/FeatureContributionCalculationTransformer.cs index 5b80958a6f..e8c07e1b8b 100644 --- a/src/Microsoft.ML.Data/Transforms/FeatureContributionCalculationTransformer.cs +++ b/src/Microsoft.ML.Data/Transforms/FeatureContributionCalculationTransformer.cs @@ -284,6 +284,8 @@ private Delegate GetValueGetter(DataViewRow input, int colSrc) /// while keeping the other features constant. The contribution of feature F1 for the given example is the difference between the original score /// and the score obtained by taking the opposite decision at the node corresponding to feature F1. This algorithm extends naturally to models with /// many decision trees. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Data/Transforms/Hashing.cs b/src/Microsoft.ML.Data/Transforms/Hashing.cs index 9c99596c81..6919f865ae 100644 --- a/src/Microsoft.ML.Data/Transforms/Hashing.cs +++ b/src/Microsoft.ML.Data/Transforms/Hashing.cs @@ -1115,6 +1115,7 @@ public override void Process() /// | Input column data type | Vector or scalars of numeric, boolean, [text](xref:Microsoft.ML.Data.TextDataViewType), [DateTime](xref: System.DateTime) and [key](xref:Microsoft.ML.Data.KeyDataViewType) type. | /// | Output column data type | Vector or scalar [key](xref:Microsoft.ML.Data.KeyDataViewType) type. | /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Data/Transforms/KeyToValue.cs b/src/Microsoft.ML.Data/Transforms/KeyToValue.cs index 5ff2c88f67..a6db9f0051 100644 --- a/src/Microsoft.ML.Data/Transforms/KeyToValue.cs +++ b/src/Microsoft.ML.Data/Transforms/KeyToValue.cs @@ -512,6 +512,7 @@ public override JToken SavePfa(BoundPfaContext ctx, JToken srcToken) /// | Input column data type | [key](xref:Microsoft.ML.Data.KeyDataViewType) type. | /// | Output column data type | Type of the original data, prior to converting to [key](xref:Microsoft.ML.Data.KeyDataViewType) type. | /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Data/Transforms/KeyToVector.cs b/src/Microsoft.ML.Data/Transforms/KeyToVector.cs index 35dfb69614..01fc39048c 100644 --- a/src/Microsoft.ML.Data/Transforms/KeyToVector.cs +++ b/src/Microsoft.ML.Data/Transforms/KeyToVector.cs @@ -737,6 +737,8 @@ private bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string src /// /// It iterates over keys in data, and for each key it produces vector of key cardinality filled with zeros except position of key value in which it put's `1.0`. /// For vector of keys it can either produce vector of counts for each key or concatenate them together into one vector. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Data/Transforms/Normalizer.cs b/src/Microsoft.ML.Data/Transforms/Normalizer.cs index ae52b8c13f..7f2776b5a6 100644 --- a/src/Microsoft.ML.Data/Transforms/Normalizer.cs +++ b/src/Microsoft.ML.Data/Transforms/Normalizer.cs @@ -60,6 +60,8 @@ namespace Microsoft.ML.Transforms /// * [NormalizeLogMeanVariance](xref:Microsoft.ML.NormalizationCatalog.NormalizeLogMeanVariance(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int64,System.Boolean)) /// * [NormalizeBinning](xref:Microsoft.ML.NormalizationCatalog.NormalizeBinning(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int64,System.Boolean,System.Int32)) /// * [NormalizeSupervisedBinning](xref:Microsoft.ML.NormalizationCatalog.NormalizeSupervisedBinning(Microsoft.ML.TransformsCatalog,System.String,System.String,System.String,System.Int64,System.Boolean,System.Int32,System.Int32)) + /// + /// Check the above links for usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Data/Transforms/TypeConverting.cs b/src/Microsoft.ML.Data/Transforms/TypeConverting.cs index bdb79e3c75..d86f1219db 100644 --- a/src/Microsoft.ML.Data/Transforms/TypeConverting.cs +++ b/src/Microsoft.ML.Data/Transforms/TypeConverting.cs @@ -526,6 +526,7 @@ private bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, string srcVariableName, /// | Input column data type | Vector or primitive numeric, boolean, [text](xref:Microsoft.ML.Data.TextDataViewType), [System.DateTime](xref:System.DateTime) and [key](xref:Microsoft.ML.Data.KeyDataViewType) type. | /// | Output column data type | Vector or primitive numeric, boolean, [text](xref:Microsoft.ML.Data.TextDataViewType), [System.DateTime](xref:System.DateTime) and [key](xref:Microsoft.ML.Data.KeyDataViewType) type. | /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Data/Transforms/ValueMapping.cs b/src/Microsoft.ML.Data/Transforms/ValueMapping.cs index b69065a1aa..a9c07decd6 100644 --- a/src/Microsoft.ML.Data/Transforms/ValueMapping.cs +++ b/src/Microsoft.ML.Data/Transforms/ValueMapping.cs @@ -56,6 +56,8 @@ namespace Microsoft.ML.Transforms /// /// Values can be repeated to allow for multiple keys to map to the same value, however keys can not be repeated. The mapping between keys and values /// can be specified either through lists, where the key list and value list must be the same size or can be done through an [System.IDataView](xref:Microsoft.ML.IDataView). + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// @@ -152,6 +154,8 @@ public sealed override SchemaShape GetOutputSchema(SchemaShape inputSchema) /// /// Values can be repeated to allow for multiple keys to map to the same value, however keys can not be repeated. The mapping between keys and values /// can be specified either through lists, where the key list and value list must be the same size or can be done through an [System.IDataView](xref:Microsoft.ML.IDataView). + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// Specifies the key type. diff --git a/src/Microsoft.ML.Data/Transforms/ValueToKeyMappingEstimator.cs b/src/Microsoft.ML.Data/Transforms/ValueToKeyMappingEstimator.cs index cfe1247d41..90bf6a5110 100644 --- a/src/Microsoft.ML.Data/Transforms/ValueToKeyMappingEstimator.cs +++ b/src/Microsoft.ML.Data/Transforms/ValueToKeyMappingEstimator.cs @@ -29,6 +29,8 @@ namespace Microsoft.ML.Transforms /// If the key is not found in the dictionary, it is assigned the missing value indicator. /// This dictionary mapping values to keys is most commonly learnt from the unique values in input data, /// but can be defined through other means: either with the mapping defined, or as loaded from an external file. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.FastTree/FastTreeTweedie.cs b/src/Microsoft.ML.FastTree/FastTreeTweedie.cs index bd34ec3fa1..bda0271782 100644 --- a/src/Microsoft.ML.FastTree/FastTreeTweedie.cs +++ b/src/Microsoft.ML.FastTree/FastTreeTweedie.cs @@ -50,6 +50,8 @@ namespace Microsoft.ML.Trainers.FastTree /// For an introduction to Gradient Boosting, and more information, see: /// [Wikipedia: Gradient boosting(Gradient tree boosting)](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) or /// [Greedy function approximation: A gradient boosting machine](https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451). + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.ImageAnalytics/ImageGrayscale.cs b/src/Microsoft.ML.ImageAnalytics/ImageGrayscale.cs index 172b4fbde8..4f306cac43 100644 --- a/src/Microsoft.ML.ImageAnalytics/ImageGrayscale.cs +++ b/src/Microsoft.ML.ImageAnalytics/ImageGrayscale.cs @@ -236,7 +236,8 @@ protected override Delegate MakeGetter(DataViewRow input, int iinfo, Func /// /// diff --git a/src/Microsoft.ML.ImageAnalytics/ImageLoader.cs b/src/Microsoft.ML.ImageAnalytics/ImageLoader.cs index f4029befd3..27638d70b6 100644 --- a/src/Microsoft.ML.ImageAnalytics/ImageLoader.cs +++ b/src/Microsoft.ML.ImageAnalytics/ImageLoader.cs @@ -240,7 +240,8 @@ protected override DataViewSchema.DetachedColumn[] GetOutputColumnsCore() /// The images to load need to be in the formats supported by . /// For end-to-end image processing pipelines, and scenarios in your applications, see the /// [examples](https://github.com/dotnet/machinelearning-samples/tree/master/samples/csharp/getting-started) in the machinelearning-samples github repository. - /// Check the See Also section for links to examples of the usage. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs b/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs index 888d24264e..6fa3d4cff8 100644 --- a/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs +++ b/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractor.cs @@ -488,7 +488,8 @@ private VectorDataViewType[] ConstructTypes() /// converts image into vector of known size of floats or bytes. Size and data type depends on specified paramaters. /// For end-to-end image processing pipelines, and scenarios in your applications, see the /// [examples](https://github.com/dotnet/machinelearning-samples/tree/master/samples/csharp/getting-started) in the machinelearning-samples github repository. - /// Check the See Also section for links to examples of the usage. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.ImageAnalytics/ImageResizer.cs b/src/Microsoft.ML.ImageAnalytics/ImageResizer.cs index a7220d9b14..eb7a0e1d71 100644 --- a/src/Microsoft.ML.ImageAnalytics/ImageResizer.cs +++ b/src/Microsoft.ML.ImageAnalytics/ImageResizer.cs @@ -419,7 +419,8 @@ protected override Delegate MakeGetter(DataViewRow input, int iinfo, Func /// /// diff --git a/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs b/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs index a96788f1fe..1469084890 100644 --- a/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs +++ b/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs @@ -437,7 +437,8 @@ private static ImageDataViewType[] ConstructTypes(VectorToImageConvertingEstimat /// /// The resulting creates a new column, named as specified in the output column name parameters, and /// creates image from the data in the input column to this new column. - /// Check the See Also section for links to examples of the usage. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs b/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs index db559d3945..cf70bce73d 100644 --- a/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs +++ b/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs @@ -69,6 +69,8 @@ namespace Microsoft.ML.Trainers /// For more information on K-means, and K-means++ see: /// [K-means](https://en.wikipedia.org/wiki/K-means_clustering) /// [K-means++](https://en.wikipedia.org/wiki/K-means%2b%2b) + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs b/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs index 52b08235e4..233435d17c 100644 --- a/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs +++ b/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs @@ -54,6 +54,8 @@ namespace Microsoft.ML.Trainers /// [Ordinary least squares (OLS)](https://en.wikipedia.org/wiki/Ordinary_least_squares) is a parameterized regression method. /// It assumes that the conditional mean of the dependent variable follows a linear function of the dependent variables. /// The regression parameters can be estimated by minimizing the squares of the difference between observed values and the predictions + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs b/src/Microsoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs index e993189130..f47c180039 100644 --- a/src/Microsoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs +++ b/src/Microsoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs @@ -60,6 +60,8 @@ namespace Microsoft.ML.Trainers /// to produce the same result as what a sequential symbolic stochastic gradient descent would have produced, in expectation. /// /// For more information see [Parallel Stochastic Gradient Descent with Sound Combiners](https://arxiv.org/abs/1705.08030). + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.OnnxTransformer/DnnImageFeaturizerTransform.cs b/src/Microsoft.ML.OnnxTransformer/DnnImageFeaturizerTransform.cs index ff61c991c1..16c08404e2 100644 --- a/src/Microsoft.ML.OnnxTransformer/DnnImageFeaturizerTransform.cs +++ b/src/Microsoft.ML.OnnxTransformer/DnnImageFeaturizerTransform.cs @@ -73,6 +73,8 @@ internal DnnImageFeaturizerInput(string outputColumnName, string inputColumnName /// the names of the ONNX model nodes. /// /// Any platform requirement for this estimator will follow the requirements on the . + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs b/src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs index e0eab52dc5..90f5bcad45 100644 --- a/src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs +++ b/src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs @@ -59,6 +59,8 @@ namespace Microsoft.ML.Transforms.Onnx /// /// To create this estimator use the following: /// [ApplyOnnxModel](xref:Microsoft.ML.OnnxCatalog.ApplyOnnxModel*) + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// @@ -541,6 +543,8 @@ public NamedOnnxValue GetNamedOnnxValue() /// /// To create this estimator use the following: /// [ApplyOnnxModel](xref:Microsoft.ML.OnnxCatalog.ApplyOnnxModel*) + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.PCA/PcaTrainer.cs b/src/Microsoft.ML.PCA/PcaTrainer.cs index e72fe2bf09..8faf05e3f8 100644 --- a/src/Microsoft.ML.PCA/PcaTrainer.cs +++ b/src/Microsoft.ML.PCA/PcaTrainer.cs @@ -70,6 +70,8 @@ namespace Microsoft.ML.Trainers /// /// Note that the algorithm can be made into Kernel PCA by applying the /// to the data before passing it to the trainer. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs b/src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs index 9ecd082f32..621135c09f 100644 --- a/src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs +++ b/src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs @@ -102,6 +102,7 @@ namespace Microsoft.ML.Trainers /// * For the parallel coordinate descent method used and one-class matrix factorization formula, see [Selection of Negative Samples for One-class Matrix Factorization](https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf). /// * For details in the underlying library used, see [LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems](https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf). /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/FactorizationMachine/FactorizationMachineTrainer.cs b/src/Microsoft.ML.StandardTrainers/FactorizationMachine/FactorizationMachineTrainer.cs index 220a987cb3..8aa8be497c 100644 --- a/src/Microsoft.ML.StandardTrainers/FactorizationMachine/FactorizationMachineTrainer.cs +++ b/src/Microsoft.ML.StandardTrainers/FactorizationMachine/FactorizationMachineTrainer.cs @@ -81,6 +81,7 @@ namespace Microsoft.ML.Trainers /// Algorithm details is described in Algorithm 3 in [this online document](https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf). /// The minimized loss function is [logistic loss](https://en.wikipedia.org/wiki/Loss_functions_for_classification), so the trained model can be viewed as a non-linear logistic regression. /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LogisticRegression.cs b/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LogisticRegression.cs index 8d36f94087..4115ccc37d 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LogisticRegression.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LogisticRegression.cs @@ -70,6 +70,8 @@ namespace Microsoft.ML.Trainers /// /// An aggressive regularization (that is, assigning large coefficients to L1-norm or L2-norm regularization terms) can harm predictive capacity by excluding important variables out of the model. /// Therefore, choosing the right regularization coefficients is important when applying logistic regression. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/MulticlassLogisticRegression.cs b/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/MulticlassLogisticRegression.cs index 022897bd64..878c8a8ff1 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/MulticlassLogisticRegression.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/MulticlassLogisticRegression.cs @@ -90,6 +90,8 @@ namespace Microsoft.ML.Trainers /// /// An aggressive regularization (that is, assigning large coefficients to L1-norm or L2-norm regularization terms) can harm predictive capacity by excluding important variables out of the model. /// Therefore, choosing the right regularization coefficients is important when applying maximum entropy classifier. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/MulticlassNaiveBayesTrainer.cs b/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/MulticlassNaiveBayesTrainer.cs index bc3a2443ca..28e673ed64 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/MulticlassNaiveBayesTrainer.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/MulticlassNaiveBayesTrainer.cs @@ -55,6 +55,8 @@ namespace Microsoft.ML.Trainers /// This multi-class trainer accepts "binary" feature values of type float: /// feature values that are greater than zero are treated as `true` and feature values /// that are less or equal to 0 are treated as `false`. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs b/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs index bf748d12d0..2a423da192 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs @@ -76,6 +76,8 @@ namespace Microsoft.ML.Trainers /// requires that the trainer store a lot more intermediate state in the form of /// L-BFGS history for all classes *simultaneously*, rather than just one-by-one /// as would be needed for a one-versus-all classification model. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/PairwiseCouplingTrainer.cs b/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/PairwiseCouplingTrainer.cs index 6e3dbedce3..09bdcc8fd1 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/PairwiseCouplingTrainer.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/PairwiseCouplingTrainer.cs @@ -66,6 +66,8 @@ namespace Microsoft.ML.Trainers /// requires that the trainer store a lot more intermediate state in the form of /// L-BFGS history for all classes *simultaneously*, rather than just one-by-one /// as would be needed for a pairwise coupling classification model. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/Online/AveragedPerceptron.cs b/src/Microsoft.ML.StandardTrainers/Standard/Online/AveragedPerceptron.cs index ad308ebb35..f07673ef34 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/Online/AveragedPerceptron.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/Online/AveragedPerceptron.cs @@ -58,6 +58,8 @@ namespace Microsoft.ML.Trainers /// /// For more information see [Wikipedia entry for Perceptron](https://en.wikipedia.org/wiki/Perceptron) /// or [Large Margin Classification Using the Perceptron Algorithm](https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.8200). + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/Online/LinearSvm.cs b/src/Microsoft.ML.StandardTrainers/Standard/Online/LinearSvm.cs index a087c616a3..28f6cda564 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/Online/LinearSvm.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/Online/LinearSvm.cs @@ -53,6 +53,8 @@ namespace Microsoft.ML.Trainers /// /// This algorithm implemented is the PEGASOS method, which alternates between stochastic gradient descent steps and projection steps, /// introduced in [this paper](http://ttic.uchicago.edu/~shai/papers/ShalevSiSr07.pdf) by Shalev-Shwartz, Singer and Srebro. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/Online/OnlineGradientDescent.cs b/src/Microsoft.ML.StandardTrainers/Standard/Online/OnlineGradientDescent.cs index 75d8f7332e..9770e404d8 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/Online/OnlineGradientDescent.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/Online/OnlineGradientDescent.cs @@ -46,6 +46,8 @@ namespace Microsoft.ML.Trainers /// Stochastic gradient descent uses a simple yet efficient iterative technique to fit model coefficients using error gradients for convex loss functions. /// Online Gradient Descent (OGD) implements the standard (non-batch) stochastic gradient descent, with a choice of loss functions, /// and an option to update the weight vector using the average of the vectors seen over time (averaged argument is set to True by default). + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/PoissonRegression/PoissonRegression.cs b/src/Microsoft.ML.StandardTrainers/Standard/PoissonRegression/PoissonRegression.cs index 8eced48dd1..9e45d9595a 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/PoissonRegression/PoissonRegression.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/PoissonRegression/PoissonRegression.cs @@ -46,6 +46,8 @@ namespace Microsoft.ML.Trainers /// [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression) is a parameterized regression method. /// It assumes that the log of the conditional mean of the dependent variable follows a linear function of the dependent variables. /// Assuming that the dependent variable follows a Poisson distribution, the regression parameters can be estimated by maximizing the likelihood of the obtained observations. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/SdcaMulticlass.cs b/src/Microsoft.ML.StandardTrainers/Standard/SdcaMulticlass.cs index 215f99f106..458b2bde21 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/SdcaMulticlass.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/SdcaMulticlass.cs @@ -75,6 +75,8 @@ namespace Microsoft.ML.Trainers /// /// An aggressive regularization (that is, assigning large coefficients to L1-norm or L2-norm regularization terms) can harm predictive capacity by excluding important variables out of the model. /// Therefore, choosing the right regularization coefficients is important in practice. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// @@ -517,6 +519,7 @@ private protected override float GetInstanceWeight(FloatLabelCursor cursor) /// ### Training Algorithm Details /// See the documentation of [SdcaMulticlassTrainerBase](xref:Microsoft.ML.Trainers.SdcaMulticlassTrainerBase`1). /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// @@ -600,6 +603,7 @@ private protected override MulticlassPredictionTransformer /// /// diff --git a/src/Microsoft.ML.StandardTrainers/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardTrainers/Standard/Simple/SimpleTrainers.cs index f196d345d7..df9ceb78d1 100644 --- a/src/Microsoft.ML.StandardTrainers/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardTrainers/Standard/Simple/SimpleTrainers.cs @@ -182,6 +182,8 @@ private void MapDist(in VBuffer src, ref float score, ref float prob) /// /// ### Training Algorithm Details /// Learns the prior distribution for 0/1 class labels and outputs that. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.TimeSeries/IidChangePointDetector.cs b/src/Microsoft.ML.TimeSeries/IidChangePointDetector.cs index 4313484ae8..fd55691959 100644 --- a/src/Microsoft.ML.TimeSeries/IidChangePointDetector.cs +++ b/src/Microsoft.ML.TimeSeries/IidChangePointDetector.cs @@ -213,6 +213,8 @@ private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, Dat /// [!include[io](~/../docs/samples/docs/api-reference/time-series-iid.md)] /// /// [!include[io](~/../docs/samples/docs/api-reference/time-series-scorer.md)] + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.TimeSeries/IidSpikeDetector.cs b/src/Microsoft.ML.TimeSeries/IidSpikeDetector.cs index c9b2d2d517..508cf0ec90 100644 --- a/src/Microsoft.ML.TimeSeries/IidSpikeDetector.cs +++ b/src/Microsoft.ML.TimeSeries/IidSpikeDetector.cs @@ -193,6 +193,8 @@ private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, Dat /// [!include[io](~/../docs/samples/docs/api-reference/time-series-iid.md)] /// /// [!include[io](~/../docs/samples/docs/api-reference/time-series-pvalue.md)] + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.TimeSeries/SsaChangePointDetector.cs b/src/Microsoft.ML.TimeSeries/SsaChangePointDetector.cs index 8f115ffc54..83eb83c37d 100644 --- a/src/Microsoft.ML.TimeSeries/SsaChangePointDetector.cs +++ b/src/Microsoft.ML.TimeSeries/SsaChangePointDetector.cs @@ -220,6 +220,8 @@ private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, Dat /// [!include[io](~/../docs/samples/docs/api-reference/time-series-ssa.md)] /// /// [!include[io](~/../docs/samples/docs/api-reference/time-series-scorer.md)] + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.TimeSeries/SsaSpikeDetector.cs b/src/Microsoft.ML.TimeSeries/SsaSpikeDetector.cs index 38792a6f4a..192e27388f 100644 --- a/src/Microsoft.ML.TimeSeries/SsaSpikeDetector.cs +++ b/src/Microsoft.ML.TimeSeries/SsaSpikeDetector.cs @@ -201,6 +201,8 @@ private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, Dat /// [!include[io](~/../docs/samples/docs/api-reference/time-series-ssa.md)] /// /// [!include[io](~/../docs/samples/docs/api-reference/time-series-pvalue.md)] + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/CountFeatureSelection.cs b/src/Microsoft.ML.Transforms/CountFeatureSelection.cs index e8fcd6db13..5fe2effc1b 100644 --- a/src/Microsoft.ML.Transforms/CountFeatureSelection.cs +++ b/src/Microsoft.ML.Transforms/CountFeatureSelection.cs @@ -57,6 +57,7 @@ namespace Microsoft.ML.Transforms /// |4,6 | /// |4,NaN| /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/CustomMappingTransformer.cs b/src/Microsoft.ML.Transforms/CustomMappingTransformer.cs index a2f116bf8e..a4ab4c3d8e 100644 --- a/src/Microsoft.ML.Transforms/CustomMappingTransformer.cs +++ b/src/Microsoft.ML.Transforms/CustomMappingTransformer.cs @@ -220,7 +220,7 @@ public ITransformer GetTransformer() /// The provided custom function must be thread-safe and free from side effects. /// The order with which it is applied to the rows of data cannot be guaranteed. /// - /// Check the See Also section for links to examples of the usage. + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/GcnTransform.cs b/src/Microsoft.ML.Transforms/GcnTransform.cs index 2d49857240..ea141c7187 100644 --- a/src/Microsoft.ML.Transforms/GcnTransform.cs +++ b/src/Microsoft.ML.Transforms/GcnTransform.cs @@ -825,6 +825,7 @@ public override SchemaShape GetOutputSchema(SchemaShape inputSchema) /// - : $L_\sigma(x)$ is defined as the /// standard deviation of the elements of the input vector $x$ /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// @@ -902,6 +903,8 @@ internal LpNormNormalizingEstimator(IHostEnvironment env, params ColumnOptions[] /// Where $s$ is a user provided scaling factor, $\mu(x)$ is the mean of the elements of vector $x$, and $L(x)$ is the $L_2$ norm or the /// standard deviation of the elements of vector $x$. These settings can be specified by the user when the /// is initialized. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/KeyToVectorMapping.cs b/src/Microsoft.ML.Transforms/KeyToVectorMapping.cs index 3d50717363..bc3bf5c81d 100644 --- a/src/Microsoft.ML.Transforms/KeyToVectorMapping.cs +++ b/src/Microsoft.ML.Transforms/KeyToVectorMapping.cs @@ -438,6 +438,7 @@ private void EncodeValueToBinary(BufferBuilder bldr, uint value, int bits /// | Input column data type | [key](xref:Microsoft.Ml.Data.KeyDataViewType) or a known-size vector of keys. | /// | Output column data type | A known-size vector of [System.Single](xref:System.Single). | /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/MissingValueIndicatorTransformer.cs b/src/Microsoft.ML.Transforms/MissingValueIndicatorTransformer.cs index d3ac2610f1..a8058ead78 100644 --- a/src/Microsoft.ML.Transforms/MissingValueIndicatorTransformer.cs +++ b/src/Microsoft.ML.Transforms/MissingValueIndicatorTransformer.cs @@ -443,7 +443,8 @@ private void FillValues(int srcLength, ref VBuffer dst, List indices, /// /// The resulting creates a new column, named as specified in the output column name parameters, and /// fills it with vector of bools where `true` in the i-th position in array indicates the i-th element in input column has missing value and `false` otherwise. - /// Check the See Also section for links of examples of the usage. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/MissingValueReplacing.cs b/src/Microsoft.ML.Transforms/MissingValueReplacing.cs index 7072d5a36f..4fcb9723ce 100644 --- a/src/Microsoft.ML.Transforms/MissingValueReplacing.cs +++ b/src/Microsoft.ML.Transforms/MissingValueReplacing.cs @@ -901,7 +901,8 @@ private bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string src /// /// The resulting creates a new column, named as specified in the output column name parameters, and /// copies the data from the input column to this new column with exception what missing values in data would be replaced according to chosen strategy. - /// Check the See Also section for links of examples of the usage. + /// + /// Check the See Also section for links of usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/MutualInformationFeatureSelection.cs b/src/Microsoft.ML.Transforms/MutualInformationFeatureSelection.cs index b2d97f13b7..a93c7333be 100644 --- a/src/Microsoft.ML.Transforms/MutualInformationFeatureSelection.cs +++ b/src/Microsoft.ML.Transforms/MutualInformationFeatureSelection.cs @@ -63,6 +63,7 @@ namespace Microsoft.ML.Transforms /// |True |4,0 | /// |False|0,5 | /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/OneHotEncoding.cs b/src/Microsoft.ML.Transforms/OneHotEncoding.cs index abac66ba53..9df8696fce 100644 --- a/src/Microsoft.ML.Transforms/OneHotEncoding.cs +++ b/src/Microsoft.ML.Transforms/OneHotEncoding.cs @@ -185,6 +185,8 @@ internal OneHotEncodingTransformer(ValueToKeyMappingEstimator term, IEstimator /// /// diff --git a/src/Microsoft.ML.Transforms/OneHotHashEncoding.cs b/src/Microsoft.ML.Transforms/OneHotHashEncoding.cs index cf0e9327e1..8d4f29a6d0 100644 --- a/src/Microsoft.ML.Transforms/OneHotHashEncoding.cs +++ b/src/Microsoft.ML.Transforms/OneHotHashEncoding.cs @@ -241,7 +241,7 @@ internal OneHotHashEncodingTransformer(HashingEstimator hash, IEstimator /// /// diff --git a/src/Microsoft.ML.Transforms/RandomFourierFeaturizing.cs b/src/Microsoft.ML.Transforms/RandomFourierFeaturizing.cs index 90c775e72f..44cc3564ca 100644 --- a/src/Microsoft.ML.Transforms/RandomFourierFeaturizing.cs +++ b/src/Microsoft.ML.Transforms/RandomFourierFeaturizing.cs @@ -618,6 +618,8 @@ private void TransformFeatures(in VBuffer src, ref VBuffer dst, Tr /// more complex kernel SVM models. /// This mapping is based on the paper [Random Features for Large-Scale Kernel Machines](http://pages.cs.wisc.edu/~brecht/papers/07.rah.rec.nips.pdf) /// by Rahimi and Recht. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs index 9b4489379b..cd91ec3d6b 100644 --- a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs @@ -985,6 +985,7 @@ private protected override IRowMapper MakeRowMapper(DataViewSchema schema) /// * [LightLDA: Big Topic Models on Modest Computer Clusters](https://arxiv.org/abs/1412.1576) /// * [LightLDA](https://github.com/Microsoft/LightLDA) /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/NgramHashingTransformer.cs b/src/Microsoft.ML.Transforms/Text/NgramHashingTransformer.cs index 4df03d2ee8..211340b2b0 100644 --- a/src/Microsoft.ML.Transforms/Text/NgramHashingTransformer.cs +++ b/src/Microsoft.ML.Transforms/Text/NgramHashingTransformer.cs @@ -874,8 +874,9 @@ public VBuffer>[] SlotNamesMetadata(out VectorDataViewType[ /// It does so by hashing each n-gram and using the hash value as the index in the bag. /// /// is different from - /// in a way that the former takes tokenized text as input while the latter tokenizes text internally. - /// Check the See Also section for links to examples of the usage. + /// in a way that The former takes tokenized text as input while the latter tokenizes text internally. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/NgramTransform.cs b/src/Microsoft.ML.Transforms/Text/NgramTransform.cs index 37d848f986..1cbc63dea2 100644 --- a/src/Microsoft.ML.Transforms/Text/NgramTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/NgramTransform.cs @@ -686,7 +686,7 @@ protected override Delegate MakeGetter(DataViewRow input, int iinfo, Func /// uses the id in the dictionary as the index in the count vector that it produces. /// - /// Check the See Also section for links to examples of the usage. + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/StopWordsRemovingTransformer.cs b/src/Microsoft.ML.Transforms/Text/StopWordsRemovingTransformer.cs index 96404e6553..d319d30517 100644 --- a/src/Microsoft.ML.Transforms/Text/StopWordsRemovingTransformer.cs +++ b/src/Microsoft.ML.Transforms/Text/StopWordsRemovingTransformer.cs @@ -494,7 +494,8 @@ private protected override Func GetDependenciesCore(Func a /// The resulting creates a new column, named as specified in the output column name parameter, /// and fills it with a vector of words containing all of the words in the input column **except the predefined list of stopwords for the specified language. /// All text comparison made by casting predefined text and text from input column to lower case using casing rules of invariant culture. - /// Check the See Also section for links to examples of the usage. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// @@ -1100,7 +1101,8 @@ protected override Delegate MakeGetter(DataViewRow input, int iinfo, Func creates a new column, named as specified by the output column name parameter, and /// fills it with a vector of words containing all of the words in the input column except those given by the stopwords parameter. /// All text comparison made by casting provided words and words from input column to lower case using casing rules of invariant culture. - /// Check the See Also section for links to examples of the usage. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/TextFeaturizingEstimator.cs b/src/Microsoft.ML.Transforms/Text/TextFeaturizingEstimator.cs index 61903e5b14..50b696d0b0 100644 --- a/src/Microsoft.ML.Transforms/Text/TextFeaturizingEstimator.cs +++ b/src/Microsoft.ML.Transforms/Text/TextFeaturizingEstimator.cs @@ -54,9 +54,11 @@ public interface IStopWordsRemoverOptions { } /// * [TF, IDF or TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) /// * [L-p vector normalization](xref: Microsoft.ML.Transforms.LpNormNormalizingTransformer) /// - /// By default the features are made of (word/character) n-grams/skip-grams​ and the number of features are equal to the vocabulary size found by analyzing the data. - /// To output an additional column with the tokens generated, use [OutputTokensColumnName](xref:Microsoft.ML.Transforms.Text.TextFeaturizingEstimator.Options.OutputTokensColumnName). - /// The number of features can also be specified by selecting the maximum number of n-gram to keep in the , where the estimator can be further tuned. + /// By default the features are made of (word/character) n-grams/skip-grams​ and the number of features are equal to the vocabulary size found by analyzing the data. + /// To output an additional column with the tokens generated, use [OutputTokensColumnName](xref:Microsoft.ML.Transforms.Text.TextFeaturizingEstimator.Options.OutputTokensColumnName). + /// The number of features can also be specified by selecting the maximum number of n-gram to keep in the , where the estimator can be further tuned. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/TextNormalizing.cs b/src/Microsoft.ML.Transforms/Text/TextNormalizing.cs index 8376416e78..0fe99e0534 100644 --- a/src/Microsoft.ML.Transforms/Text/TextNormalizing.cs +++ b/src/Microsoft.ML.Transforms/Text/TextNormalizing.cs @@ -447,7 +447,7 @@ private bool IsCombiningDiacritic(char ch) /// in the output column name parameters, and normalizes the textual input data by changing case, removing diacritical marks, /// punctuation marks and/or numbers. /// - /// Check the See Also section for links to examples of the usage. + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/TokenizingByCharacters.cs b/src/Microsoft.ML.Transforms/Text/TokenizingByCharacters.cs index d69a5ebc83..91ffc0c285 100644 --- a/src/Microsoft.ML.Transforms/Text/TokenizingByCharacters.cs +++ b/src/Microsoft.ML.Transforms/Text/TokenizingByCharacters.cs @@ -566,7 +566,7 @@ private ValueGetter> MakeGetterVec(DataViewRow input, int iinfo) /// creates a new column, named as specified in the output column name parameters, which contains the keys of the /// sequences of characters that were encountered in the input. /// - /// Check the See Also section for links to examples of the usage. + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/WordEmbeddingsExtractor.cs b/src/Microsoft.ML.Transforms/Text/WordEmbeddingsExtractor.cs index aa56420d76..ccce0f0d79 100644 --- a/src/Microsoft.ML.Transforms/Text/WordEmbeddingsExtractor.cs +++ b/src/Microsoft.ML.Transforms/Text/WordEmbeddingsExtractor.cs @@ -751,7 +751,7 @@ private static ParallelOptions GetParallelOptions(IHostEnvironment hostEnvironme /// The available options are various versions of [GloVe Models](https://nlp.stanford.edu/projects/glove/), /// [FastText](https://en.wikipedia.org/wiki/FastText), and [SSWE](https://anthology.aclweb.org/P/P14/P14-1146.pdf). /// - /// Check the See Also section for links to examples of the usage. + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/WordTokenizing.cs b/src/Microsoft.ML.Transforms/Text/WordTokenizing.cs index 92531af345..295ad6a105 100644 --- a/src/Microsoft.ML.Transforms/Text/WordTokenizing.cs +++ b/src/Microsoft.ML.Transforms/Text/WordTokenizing.cs @@ -414,7 +414,7 @@ private JToken SaveAsPfaCore(BoundPfaContext ctx, int iinfo, JToken srcToken) /// /// Empty strings and strings containing only spaces are dropped. /// - /// Check the See Also section for links to examples of the usage. + /// Check the See Also section for links to usage examples. /// ]]> /// /// diff --git a/src/Microsoft.ML.Transforms/Text/WrappedTextTransformers.cs b/src/Microsoft.ML.Transforms/Text/WrappedTextTransformers.cs index 3f3fcc9e17..0e9b848c69 100644 --- a/src/Microsoft.ML.Transforms/Text/WrappedTextTransformers.cs +++ b/src/Microsoft.ML.Transforms/Text/WrappedTextTransformers.cs @@ -28,7 +28,8 @@ namespace Microsoft.ML.Transforms.Text /// /// is different from /// in that the former takes tokenizes text internally while the latter takes tokenized text as input. - /// Check the See Also section for links to examples of the usage. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// /// @@ -218,7 +219,8 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema) /// /// is different from /// in that the former takes tokenizes text internally while the latter takes tokenized text as input. - /// Check the See Also section for links to examples of the usage. + /// + /// Check the See Also section for links to usage examples. /// ]]> /// ///