Cherry pick Projection documentation and Normalize changes to 1.0

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApproximatedKernelMap.cs

@@ -0,0 +1,52 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+using Microsoft.ML.Transforms;
+
+namespace Samples.Dynamic
+{
+    public static class ApproximatedKernelMap
+    {
+        // Transform feature vector to another non-linear space. See https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf.
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var mlContext = new MLContext();
+            var samples = new List<DataPoint>()
+            {
+                new DataPoint(){ Features = new float[7] { 1, 1, 0, 0, 1, 0, 1} },
+                new DataPoint(){ Features = new float[7] { 0, 0, 1, 0, 0, 1, 1} },
+                new DataPoint(){ Features = new float[7] {-1, 1, 0,-1,-1, 0,-1} },
+                new DataPoint(){ Features = new float[7] { 0,-1, 0, 1, 0,-1,-1} }
+            };
+            // Convert training data to IDataView, the general data type used in ML.NET.
+            var data = mlContext.Data.LoadFromEnumerable(samples);
+            // ApproximatedKernel map takes data and maps it's to a random low-dimensional space.
+            var approximation = mlContext.Transforms.ApproximatedKernelMap("Features", rank: 4, generator: new GaussianKernel(gamma: 0.7f), seed: 1);
+
+            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
+            // This operation doesn't actually evaluate data until we read the data below.
+            var tansformer = approximation.Fit(data);
+            var transformedData = tansformer.Transform(data);
+
+            var column = transformedData.GetColumn<float[]>("Features").ToArray();
+            foreach (var row in column)
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+            // Expected output:
+            // -0.0119, 0.5867, 0.4942,  0.7041
+            //  0.4720, 0.5639, 0.4346,  0.2671
+            // -0.2243, 0.7071, 0.7053, -0.1681
+            //  0.0846, 0.5836, 0.6575,  0.0581
+        }
+
+        private class DataPoint
+        {
+            [VectorType(7)]
+            public float[] Features { get; set; }
+        }
+
+    }
+}

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeBinning.cs

@@ -0,0 +1,91 @@
+using System;
+using System.Collections.Generic;
+using System.Collections.Immutable;
+using System.Linq;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+using static Microsoft.ML.Transforms.NormalizingTransformer;
+
+namespace Samples.Dynamic
+{
+    public class NormalizeBinning
+    {
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var mlContext = new MLContext();
+            var samples = new List<DataPoint>()
+            {
+                new DataPoint(){ Features = new float[4] { 8, 1, 3, 0} },
+                new DataPoint(){ Features = new float[4] { 6, 2, 2, 0} },
+                new DataPoint(){ Features = new float[4] { 4, 0, 1, 0} },
+                new DataPoint(){ Features = new float[4] { 2,-1,-1, 1} }
+            };
+            // Convert training data to IDataView, the general data type used in ML.NET.
+            var data = mlContext.Data.LoadFromEnumerable(samples);
+            // NormalizeBinning normalizes the data by constructing equidensity bins and produce output based on 
+            // to which bin original value belong.
+            var normalize = mlContext.Transforms.NormalizeBinning("Features", maximumBinCount: 4, fixZero: false);
+
+            // NormalizeBinning normalizes the data by constructing equidensity bins and produce output based on 
+            // to which bin original value belong but make sure zero values would remain zero after normalization.
+            // Helps preserve sparsity.
+            var normalizeFixZero = mlContext.Transforms.NormalizeBinning("Features", maximumBinCount: 4, fixZero: true);
+
+            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
+            // This operation doesn't actually evaluate data until we read the data below.
+            var normalizeTransform = normalize.Fit(data);
+            var transformedData = normalizeTransform.Transform(data);
+            var normalizeFixZeroTransform = normalizeFixZero.Fit(data);
+            var fixZeroData = normalizeFixZeroTransform.Transform(data);
+            var column = transformedData.GetColumn<float[]>("Features").ToArray();
+            foreach (var row in column)
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+            // Expected output:
+            //  1.0000, 0.6667, 1.0000, 0.0000
+            //  0.6667, 1.0000, 0.6667, 0.0000
+            //  0.3333, 0.3333, 0.3333, 0.0000
+            //  0.0000, 0.0000, 0.0000, 1.0000
+
+            var columnFixZero = fixZeroData.GetColumn<float[]>("Features").ToArray();
+            foreach (var row in columnFixZero)
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+            // Expected output:
+            //  1.0000, 0.3333, 1.0000, 0.0000
+            //  0.6667, 0.6667, 0.6667, 0.0000
+            //  0.3333, 0.0000, 0.3333, 0.0000
+            //  0.0000, -0.3333, 0.0000, 1.0000
+
+            // Let's get transformation parameters. Since we work with only one column we need to pass 0 as parameter for GetNormalizerModelParameters.
+            // If we have multiple columns transformations we need to pass index of InputOutputColumnPair.
+            var transformParams = normalizeTransform.GetNormalizerModelParameters(0) as BinNormalizerModelParameters<ImmutableArray<float>>;
+            var density = transformParams.Density[0];
+            var offset = (transformParams.Offset.Length == 0 ? 0 : transformParams.Offset[0]);
+            Console.WriteLine($"The 0-index value in resulting array would be produce by: y = (Index(x) / {density}) - {offset}");
+            Console.WriteLine("Where Index(x) is the index of the bin to which x belongs");
+            Console.WriteLine($"Bins upper bounds are: {string.Join(" ", transformParams.UpperBounds[0])}");
+            // Expected output:
+            //  The 0-index value in resulting array would be produce by: y = (Index(x) / 3) - 0
+            //  Where Index(x) is the index of the bin to which x belongs
+            //  Bins upper bounds are: 3 5 7 ∞
+
+            var fixZeroParams = (normalizeFixZeroTransform.GetNormalizerModelParameters(0) as BinNormalizerModelParameters<ImmutableArray<float>>);
+            density = fixZeroParams.Density[1];
+            offset = (fixZeroParams.Offset.Length == 0 ? 0 : fixZeroParams.Offset[1]);
+            Console.WriteLine($"The 0-index value in resulting array would be produce by: y = (Index(x) / {density}) - {offset}");
+            Console.WriteLine("Where Index(x) is the index of the bin to which x belongs");
+            Console.WriteLine($"Bins upper bounds are: {string.Join(" ", fixZeroParams.UpperBounds[1])}");
+            // Expected output:
+            //  The 0-index value in resulting array would be produce by: y = (Index(x) / 3) - 0.3333333
+            //  Where Index(x) is the index of the bin to which x belongs
+            //  Bins upper bounds are: -0.5 0.5 1.5 ∞
+        }
+
+        private class DataPoint
+        {
+            [VectorType(4)]
+            public float[] Features { get; set; }
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/NormalizeGlobalContrast.cs

@@ -0,0 +1,48 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+
+namespace Samples.Dynamic
+{
+    class NormalizeGlobalContrast
+    {
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var mlContext = new MLContext();
+            var samples = new List<DataPoint>()
+            {
+                new DataPoint(){ Features = new float[4] { 1, 1, 0, 0} },
+                new DataPoint(){ Features = new float[4] { 2, 2, 0, 0} },
+                new DataPoint(){ Features = new float[4] { 1, 0, 1, 0} },
+                new DataPoint(){ Features = new float[4] { 0, 1, 0, 1} }
+            };
+            // Convert training data to IDataView, the general data type used in ML.NET.
+            var data = mlContext.Data.LoadFromEnumerable(samples);
+            var approximation = mlContext.Transforms.NormalizeGlobalContrast("Features", ensureZeroMean: false, scale: 2, ensureUnitStandardDeviation: true);
+
+            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
+            // This operation doesn't actually evaluate data until we read the data below.
+            var tansformer = approximation.Fit(data);
+            var transformedData = tansformer.Transform(data);
+
+            var column = transformedData.GetColumn<float[]>("Features").ToArray();
+            foreach (var row in column)
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+            // Expected output:
+            //  2.0000, 2.0000,-2.0000,-2.0000
+            //  2.0000, 2.0000,-2.0000,-2.0000
+            //  2.0000,-2.0000, 2.0000,-2.0000
+            //- 2.0000, 2.0000,-2.0000, 2.0000
+        }
+
+        private class DataPoint
+        {
+            [VectorType(4)]
+            public float[] Features { get; set; }
+        }
+    }
+}