Adding samples for data save and load from text and binary files

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

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+using System;
+using System.Collections.Generic;
+using System.IO;
+using Microsoft.ML;
+
+namespace Samples.Dynamic
+{
+    public static class SaveAndLoadFromBinary
+    {
+        public static void Example()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            var mlContext = new MLContext(seed: 0);
+
+            // Create a list of training data points.
+            IEnumerable<DataPoint> dataPoints = GenerateRandomDataPoints(10);
+
+            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            IDataView data = mlContext.Data.LoadFromEnumerable(dataPoints);
+
+            // Inspect the data before saving to a binary file.
+            PrintPreviewRows(dataPoints);
+
+            // The rows in the data.
+            // 0, 0.7262433
+            // 1, 0.8173254
+            // 0, 0.7680227
+            // 1, 0.5581612
+            // 0, 0.2060332
+            // 1, 0.5588848
+            // 0, 0.9060271
+            // 1, 0.4421779
+            // 0, 0.9775497
+            // 1, 0.2737045
+
+            // Create a FileStream object and write the IDataView to it as a binary IDV file. 
+            using (FileStream stream = new FileStream("data.idv", FileMode.Create))
+            {
+                mlContext.Data.SaveAsBinary(data, stream);
+            }
+
+            // Create an IDataView object by loading the binary IDV file.
+            IDataView loadedData = mlContext.Data.LoadFromBinary("data.idv");
+
+            // Inspect the data that is loaded from the previously saved binary file.
+            var loadedDataEnumerable = mlContext.Data.CreateEnumerable<DataPoint>(loadedData, reuseRowObject: false);
+            PrintPreviewRows(loadedDataEnumerable);
+
+            // The rows in the data.
+            // 0, 0.7262433
+            // 1, 0.8173254
+            // 0, 0.7680227
+            // 1, 0.5581612
+            // 0, 0.2060332
+            // 1, 0.5588848
+            // 0, 0.9060271
+            // 1, 0.4421779
+            // 0, 0.9775497
+            // 1, 0.2737045
+
+            File.Delete("data.idv");
+        }
+
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0)
+        {
+            var random = new Random(seed);
+            for (int i = 0; i < count; i++)
+            {
+                yield return new DataPoint
+                {
+                    Label = i % 2,
+
+                    // Create random features that are correlated with label.
+                    Features = (float)random.NextDouble()
+                };
+            }
+        }
+
+        // Example with label and feature values. A data set is a collection of such examples.
+        private class DataPoint
+        {
+            public float Label { get; set; }
+
+            public float Features { get; set; }
+        }
+
+        // Print helper.
+        private static void PrintPreviewRows(IEnumerable<DataPoint> data)
+        {
+            Console.WriteLine($"The rows in the data.");
+            foreach (var row in data)
+                Console.WriteLine($"{row.Label}, {row.Features}");
+        }
+    }
+}

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

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+using System;
+using System.Collections.Generic;
+using System.IO;
+using Microsoft.ML;
+
+namespace Samples.Dynamic
+{
+    public static class SaveAndLoadFromText
+    {
+        public static void Example()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            var mlContext = new MLContext(seed: 0);
+
+            // Create a list of training data points.
+            IEnumerable<DataPoint> dataPoints = GenerateRandomDataPoints(10);
+
+            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            IDataView data = mlContext.Data.LoadFromEnumerable(dataPoints);
+
+            // Inspect the data before saving to a binary file.
+            PrintPreviewRows(dataPoints);
+
+            // The rows in the data.
+            // 0, 0.7262433
+            // 1, 0.8173254
+            // 0, 0.7680227
+            // 1, 0.5581612
+            // 0, 0.2060332
+            // 1, 0.5588848
+            // 0, 0.9060271
+            // 1, 0.4421779
+            // 0, 0.9775497
+            // 1, 0.2737045
+
+            // Create a FileStream object and write the IDataView to it as a binary IDV file. 
+            using (FileStream stream = new FileStream("data.tsv", FileMode.Create))
+            {
+                mlContext.Data.SaveAsText(data, stream);
+            }
+
+            // Create an IDataView object by loading the binary IDV file.
+            IDataView loadedData = mlContext.Data.LoadFromTextFile("data.tsv");
+
+            // Inspect the data that is loaded from the previously saved binary file.
+            var loadedDataEnumerable = mlContext.Data.CreateEnumerable<DataPoint>(loadedData, reuseRowObject: false);
+            PrintPreviewRows(loadedDataEnumerable);
+
+            // The rows in the data.
+            // 0, 0.7262433
+            // 1, 0.8173254
+            // 0, 0.7680227
+            // 1, 0.5581612
+            // 0, 0.2060332
+            // 1, 0.5588848
+            // 0, 0.9060271
+            // 1, 0.4421779
+            // 0, 0.9775497
+            // 1, 0.2737045
+
+            File.Delete("data.tsv");
+        }
+
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0)
+        {
+            var random = new Random(seed);
+            for (int i = 0; i < count; i++)
+            {
+                yield return new DataPoint
+                {
+                    Label = i % 2,
+
+                    // Create random features that are correlated with label.
+                    Features = (float)random.NextDouble()
+                };
+            }
+        }
+
+        // Example with label and feature values. A data set is a collection of such examples.
+        private class DataPoint
+        {
+            public float Label { get; set; }
+
+            public float Features { get; set; }
+        }
+
+        // Print helper.
+        private static void PrintPreviewRows(IEnumerable<DataPoint> data)
+        {
+            Console.WriteLine($"The rows in the data.");
+            foreach (var row in data)
+                Console.WriteLine($"{row.Label}, {row.Features}");
+        }
+    }
+}
+