adding references to samples and regenerating catalog

Author: artidoro

src/Microsoft.ML.StandardLearners/StandardLearnersCatalog.cs

@@ -564,6 +564,12 @@ public static LinearSvmTrainer LinearSupportVectorMachines(this BinaryClassifica
         /// This trainer can be used as a baseline for other more sophisticated mdels.
         /// </remarks>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        ///  [!code-csharp[FastTree](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/RandomTrainerSample.cs)]
+        /// ]]></format>
+        /// </example>
         public static RandomTrainer Random(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
@@ -580,6 +586,12 @@ public static RandomTrainer Random(this BinaryClassificationCatalog.BinaryClassi
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
         /// <param name="labelColumn">The name of the label column. </param>
         /// <param name="weightsColumn">The optional name of the weights column.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        ///  [!code-csharp[FastTree](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/PriorTrainerSample.cs)]
+        /// ]]></format>
+        /// </example>
         public static PriorTrainer Prior(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
             string labelColumn = DefaultColumnNames.Label,
             string weightsColumn = null)

test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv

@@ -60,7 +60,7 @@ Trainers.LightGbmRegressor	LightGBM Regression	Microsoft.ML.LightGBM.LightGbm	Tr
 Trainers.LinearSvmBinaryClassifier	Train a linear SVM.	Microsoft.ML.Trainers.Online.LinearSvmTrainer	TrainLinearSvm	Microsoft.ML.Trainers.Online.LinearSvmTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.LogisticRegressionBinaryClassifier	Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.	Microsoft.ML.Trainers.LogisticRegression	TrainBinary	Microsoft.ML.Trainers.LogisticRegression+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.LogisticRegressionClassifier	Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.	Microsoft.ML.Trainers.LogisticRegression	TrainMultiClass	Microsoft.ML.Trainers.MulticlassLogisticRegression+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
-Trainers.NaiveBayesClassifier	Train a MultiClassNaiveBayesTrainer.	Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer	TrainMultiClassNaiveBayesTrainer	Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
+Trainers.NaiveBayesClassifier	Train a MultiClassNaiveBayesTrainer.	Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer	TrainMultiClassNaiveBayesTrainer	Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Trainers.OnlineGradientDescentRegressor	Train a Online gradient descent perceptron.	Microsoft.ML.Trainers.Online.OnlineGradientDescentTrainer	TrainRegression	Microsoft.ML.Trainers.Online.OnlineGradientDescentTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.OrdinaryLeastSquaresRegressor	Train an OLS regression model.	Microsoft.ML.Trainers.HalLearners.OlsLinearRegressionTrainer	TrainRegression	Microsoft.ML.Trainers.HalLearners.OlsLinearRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.PcaAnomalyDetector	Train an PCA Anomaly model.	Microsoft.ML.Trainers.PCA.RandomizedPcaTrainer	TrainPcaAnomaly	Microsoft.ML.Trainers.PCA.RandomizedPcaTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+AnomalyDetectionOutput
@@ -126,7 +126,7 @@ Transforms.ScoreColumnSelector	Selects only the last score columns and the extra
 Transforms.Scorer	Turn the predictor model into a transform model	Microsoft.ML.EntryPoints.ScoreModel	MakeScoringTransform	Microsoft.ML.EntryPoints.ScoreModel+ModelInput	Microsoft.ML.EntryPoints.ScoreModel+Output
 Transforms.Segregator	Un-groups vector columns into sequences of rows, inverse of Group transform	Microsoft.ML.Transforms.GroupingOperations	Ungroup	Microsoft.ML.Transforms.UngroupTransform+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.SentimentAnalyzer	Uses a pretrained sentiment model to score input strings	Microsoft.ML.Transforms.Text.TextAnalytics	AnalyzeSentiment	Microsoft.ML.Transforms.Text.SentimentAnalyzingTransformer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
-Transforms.TensorFlowScorer	Transforms the data using the TensorFlow model.	Microsoft.ML.Transforms.TensorFlowTransformer	TensorFlowScorer	Microsoft.ML.Transforms.TensorFlowTransformer+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
+Transforms.TensorFlowScorer	Transforms the data using the TensorFlow model.	Microsoft.ML.Transforms.TensorFlowTransformer	TensorFlowScorer	Microsoft.ML.Transforms.TensorFlowEstimator+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.TextFeaturizer	A transform that turns a collection of text documents into numerical feature vectors. The feature vectors are normalized counts of (word and/or character) ngrams in a given tokenized text.	Microsoft.ML.Transforms.Text.TextAnalytics	TextTransform	Microsoft.ML.Transforms.Text.TextFeaturizingEstimator+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.TextToKeyConverter	Converts input values (words, numbers, etc.) to index in a dictionary.	Microsoft.ML.Transforms.Categorical.Categorical	TextToKey	Microsoft.ML.Transforms.Conversions.ValueToKeyMappingTransformer+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.TrainTestDatasetSplitter	Split the dataset into train and test sets	Microsoft.ML.EntryPoints.TrainTestSplit	Split	Microsoft.ML.EntryPoints.TrainTestSplit+Input	Microsoft.ML.EntryPoints.TrainTestSplit+Output