Update SdcaMaximumEntropy.cs

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/SdcaMaximumEntropy.cs

@@ -29,17 +29,17 @@ public static void Example()
 	    // data can fit into memory, a solution is to cache the data in memory.
 	    // Caching is especially helpful when working with iterative algorithms 
 	    // which needs many data passes.
-			trainingData = mlContext.Data.Cache(trainingData);
+	    trainingData = mlContext.Data.Cache(trainingData);
 
             // Define the trainer.
             var pipeline =
                     // Convert the string labels into key types.
                     mlContext.Transforms.Conversion
-					    .MapValueToKey(nameof(DataPoint.Label))
+		    .MapValueToKey(nameof(DataPoint.Label))
 
                     // Apply SdcaMaximumEntropy multiclass trainer.
                     .Append(mlContext.MulticlassClassification.Trainers
-					    .SdcaMaximumEntropy());
+		    .SdcaMaximumEntropy());
 
 
             // Train the model.
@@ -48,20 +48,20 @@ public static void Example()
             // Create testing data. Use different random seed to make it different
 	    // from training data.
             var testData = mlContext.Data
-			    .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+	        .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Convert IDataView object to a list.
             var predictions = mlContext.Data
-			    .CreateEnumerable<Prediction>(transformedTestData,
-				reuseRowObject: false).ToList();
+	        .CreateEnumerable<Prediction>(transformedTestData,
+	        reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
                 Console.WriteLine($"Label: {p.Label}, " + 
-				    $"Prediction: {p.PredictedLabel}");
+	            $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -72,7 +72,7 @@ public static void Example()
 
             // Evaluate the overall metrics
             var metrics = mlContext.MulticlassClassification
-			    .Evaluate(transformedTestData);
+	        .Evaluate(transformedTestData);
 
             PrintMetrics(metrics);
 
@@ -95,7 +95,7 @@ public static void Example()
         // Generates random uniform doubles in [-0.5, 0.5)
         // range with labels 1, 2 or 3.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
-		    int seed=0)
+	    int seed=0)
 
         {
             var random = new Random(seed);
@@ -111,7 +111,7 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
                     // The feature values are slightly increased by adding a
 		    // constant multiple of label.
                     Features = Enumerable.Repeat(label, 20)
-					    .Select(x => randomFloat() + label * 0.2f).ToArray()
+		        .Select(x => randomFloat() + label * 0.2f).ToArray()
 
                 };
             }
@@ -142,7 +142,7 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
             Console.WriteLine(
-			    $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+	        $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
 
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }