numberOfIterations

Author: Ivan Matantsev

src/Microsoft.ML.StandardLearners/Standard/Online/AveragedPerceptron.cs

@@ -127,23 +127,23 @@ internal AveragedPerceptronTrainer(IHostEnvironment env, Options options)
         /// <param name="learningRate">The learning rate. </param>
         /// <param name="decreaseLearningRate">Whether to decrease learning rate as iterations progress.</param>
         /// <param name="l2Regularization">Weight of L2 regularization term.</param>
-        /// <param name="numIterations">The number of training iterations.</param>
+        /// <param name="numberOfIterations">The number of training iterations.</param>
         internal AveragedPerceptronTrainer(IHostEnvironment env,
             string labelColumnName = DefaultColumnNames.Label,
             string featureColumnName = DefaultColumnNames.Features,
             IClassificationLoss lossFunction = null,
             float learningRate = Options.AveragedDefault.LearningRate,
             bool decreaseLearningRate = Options.AveragedDefault.DecreaseLearningRate,
             float l2Regularization = Options.AveragedDefault.L2Regularization,
-            int numIterations = Options.AveragedDefault.NumberOfIterations)
+            int numberOfIterations = Options.AveragedDefault.NumberOfIterations)
             : this(env, new Options
             {
                 LabelColumnName = labelColumnName,
                 FeatureColumnName = featureColumnName,
                 LearningRate = learningRate,
                 DecreaseLearningRate = decreaseLearningRate,
                 L2Regularization = l2Regularization,
-                NumberOfIterations = numIterations,
+                NumberOfIterations = numberOfIterations,
                 LossFunction = new TrivialFactory(lossFunction ?? new HingeLoss())
             })
         {

src/Microsoft.ML.StandardLearners/Standard/Online/LinearSvm.cs

@@ -233,19 +233,19 @@ public override LinearBinaryModelParameters CreatePredictor()
         /// <param name="labelColumn">The name of the label column. </param>
         /// <param name="featureColumn">The name of the feature column.</param>
         /// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
-        /// <param name="numIterations">The number of training iteraitons.</param>
+        /// <param name="numberOfIterations">The number of training iteraitons.</param>
         [BestFriend]
         internal LinearSvmTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
             string exampleWeightColumnName = null,
-            int numIterations = Options.OnlineDefault.NumberOfIterations)
+            int numberOfIterations = Options.OnlineDefault.NumberOfIterations)
             : this(env, new Options
             {
                 LabelColumnName = labelColumn,
                 FeatureColumnName = featureColumn,
                 ExampleWeightColumnName = exampleWeightColumnName,
-                NumberOfIterations = numIterations,
+                NumberOfIterations = numberOfIterations,
             })
         {
         }

src/Microsoft.ML.StandardLearners/Standard/Online/OnlineGradientDescent.cs

@@ -96,22 +96,22 @@ public override LinearRegressionModelParameters CreatePredictor()
         /// <param name="learningRate">The learning Rate.</param>
         /// <param name="decreaseLearningRate">Decrease learning rate as iterations progress.</param>
         /// <param name="l2Regularization">Weight of L2 regularization term.</param>
-        /// <param name="numIterations">Number of training iterations through the data.</param>
+        /// <param name="numberOfIterations">Number of training iterations through the data.</param>
         /// <param name="lossFunction">The custom loss functions. Defaults to <see cref="SquaredLoss"/> if not provided.</param>
         internal OnlineGradientDescentTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
             float learningRate = Options.OgdDefaultArgs.LearningRate,
             bool decreaseLearningRate = Options.OgdDefaultArgs.DecreaseLearningRate,
             float l2Regularization = Options.OgdDefaultArgs.L2Regularization,
-            int numIterations = Options.OgdDefaultArgs.NumberOfIterations,
+            int numberOfIterations = Options.OgdDefaultArgs.NumberOfIterations,
             IRegressionLoss lossFunction = null)
             : this(env, new Options
             {
                 LearningRate = learningRate,
                 DecreaseLearningRate = decreaseLearningRate,
                 L2Regularization= l2Regularization,
-                NumberOfIterations = numIterations,
+                NumberOfIterations = numberOfIterations,
                 LabelColumnName = labelColumn,
                 FeatureColumnName = featureColumn,
                 LossFunction = new TrivialFactory(lossFunction ?? new SquaredLoss())