using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
 
namespace Samples.Dynamic
{
    class NormalizeLpNorm
    {
        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.NormalizeLpNorm("Features",
                norm: LpNormNormalizingEstimatorBase.NormFunction.L1,
                ensureZeroMean: 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:
            //  0.2500,  0.2500, -0.2500, -0.2500
            //  0.2500,  0.2500, -0.2500, -0.2500
            //  0.2500, -0.2500,  0.2500, -0.2500
            // -0.2500,  0.2500, -0.2500,  0.2500
        }
 
        private class DataPoint
        {
            [VectorType(4)]
            public float[] Features { get; set; }
        }
    }
}