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MklComponentsCatalog.VectorWhiten Método

Definición

Toma la columna rellenada con un vector de variables aleatorias con una matriz de covarianza conocida en un conjunto de variables nuevas cuya covarianza es la matriz de identidad, lo que significa que no están correlacionadas y cada una tiene varianza 1.

public static Microsoft.ML.Transforms.VectorWhiteningEstimator VectorWhiten (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, Microsoft.ML.Transforms.WhiteningKind kind = Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis, float epsilon = 1E-05, int maximumNumberOfRows = 100000, int rank = 0);
static member VectorWhiten : Microsoft.ML.TransformsCatalog * string * string * Microsoft.ML.Transforms.WhiteningKind * single * int * int -> Microsoft.ML.Transforms.VectorWhiteningEstimator
<Extension()>
Public Function VectorWhiten (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional kind As WhiteningKind = Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis, Optional epsilon As Single = 1E-05, Optional maximumNumberOfRows As Integer = 100000, Optional rank As Integer = 0) As VectorWhiteningEstimator

Parámetros

catalog
TransformsCatalog

Catálogo de la transformación.

outputColumnName
String

Nombre de la columna resultante de la transformación de inputColumnName.

inputColumnName
String

Nombre de la columna que se va a transformar. Si se establece nullen , el valor de outputColumnName se usará como origen.

kind
WhiteningKind

Tipo de blanqueamiento (PCA/ZCA).

epsilon
Single

La constante whitening evita la división por cero.

maximumNumberOfRows
Int32

Número máximo de filas usadas para entrenar la transformación.

rank
Int32

En el caso del whitening de PCA, indica el número de componentes que se van a conservar.

Devoluciones

Ejemplos

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public sealed class VectorWhiten
    {

        /// This example requires installation of additional nuget package 
        /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
        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 ml = new MLContext();

            // Get a small dataset as an IEnumerable and convert it to an IDataView.
            var data = GetVectorOfNumbersData();
            var trainData = ml.Data.LoadFromEnumerable(data);

            // Preview of the data.
            //
            // Features
            // 0   1   2   3   4   5   6   7   8   9
            // 1   2   3   4   5   6   7   8   9   0  
            // 2   3   4   5   6   7   8   9   0   1
            // 3   4   5   6   7   8   9   0   1   2
            // 4   5   6   7   8   9   0   1   2   3
            // 5   6   7   8   9   0   1   2   3   4
            // 6   7   8   9   0   1   2   3   4   5

            // A small printing utility.
            Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
                column) =>
            {
                Console.WriteLine($"{colName} column obtained " +
                    $"post-transformation.");

                foreach (var row in column)
                    Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
                        x.ToString("f3"))) + " ");
            };

            // A pipeline to project Features column into white noise vector.
            var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
                SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
                .WhiteningKind.ZeroPhaseComponentAnalysis);

            // The transformed (projected) data.
            var transformedData = whiteningPipeline.Fit(trainData).Transform(
                trainData);

            // Getting the data of the newly created column, so we can preview it.
            var whitening = transformedData.GetColumn<VBuffer<float>>(
                transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

            printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

            // Features column obtained post-transformation.
            //
            //-0.394 -0.318 -0.243 -0.168  0.209  0.358  0.433  0.589  0.873  2.047
            //-0.034  0.030  0.094  0.159  0.298  0.427  0.492  0.760  1.855 -1.197
            // 0.099  0.161  0.223  0.286  0.412  0.603  0.665  1.797 -1.265 -0.172
            // 0.211  0.277  0.344  0.410  0.606  1.267  1.333 -1.340 -0.205  0.065
            // 0.454  0.523  0.593  0.664  1.886 -0.757 -0.687 -0.022  0.176  0.310
            // 0.863  0.938  1.016  1.093 -1.326 -0.096 -0.019  0.189  0.330  0.483
        }

        private class SampleVectorOfNumbersData
        {
            [VectorType(10)]
            public float[] Features { get; set; }
        }

        /// <summary>
        /// Returns a few rows of the infertility dataset.
        /// </summary>
        private static IEnumerable<SampleVectorOfNumbersData>
            GetVectorOfNumbersData()
        {
            var data = new List<SampleVectorOfNumbersData>();
            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 0,
                1, 2, 3, 4, 5, 6, 7, 8, 9 }
            });

            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 1,
                2, 3, 4, 5, 6, 7, 8, 9, 0 }
            });

            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
            });
            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, }
            });
            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 5, 6, 7, 8, 9, 0, 1, 2, 3, 4 }
            });
            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 6, 7, 8, 9, 0, 1, 2, 3, 4, 5 }
            });
            return data;
        }
    }
}
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public sealed class VectorWhitenWithOptions
    {
        /// This example requires installation of additional nuget package
        /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
        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 ml = new MLContext();

            // Get a small dataset as an IEnumerable and convert it to an IDataView.
            var data = GetVectorOfNumbersData();
            var trainData = ml.Data.LoadFromEnumerable(data);

            // Preview of the data.
            //
            // Features
            // 0   1   2   3   4   5   6   7   8   9
            // 1   2   3   4   5   6   7   8   9   0  
            // 2   3   4   5   6   7   8   9   0   1
            // 3   4   5   6   7   8   9   0   1   2
            // 4   5   6   7   8   9   0   1   2   3
            // 5   6   7   8   9   0   1   2   3   4
            // 6   7   8   9   0   1   2   3   4   5

            // A small printing utility.
            Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
                column) =>
            {
                Console.WriteLine($"{colName} column obtained" +
                    $"post-transformation.");

                foreach (var row in column)
                    Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
                        x.ToString("f3"))) + " ");
            };


            // A pipeline to project Features column into white noise vector.
            var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
                SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
                .WhiteningKind.PrincipalComponentAnalysis, rank: 4);

            // The transformed (projected) data.
            var transformedData = whiteningPipeline.Fit(trainData).Transform(
                trainData);

            // Getting the data of the newly created column, so we can preview it.
            var whitening = transformedData.GetColumn<VBuffer<float>>(
                transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);

            printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);

            // Features column obtained post-transformation.
            // -0.979  0.867  1.449  1.236
            // -1.030  1.012  0.426 -0.902
            // -1.047  0.677 -0.946 -1.060
            // -1.029  0.019 -1.502  1.108
            // -0.972 -1.338 -0.028  0.614
            // -0.938 -1.405  0.752 -0.967
        }

        private class SampleVectorOfNumbersData
        {
            [VectorType(10)]
            public float[] Features { get; set; }
        }

        /// <summary>
        /// Returns a few rows of the infertility dataset.
        /// </summary>
        private static IEnumerable<SampleVectorOfNumbersData>
            GetVectorOfNumbersData()
        {
            var data = new List<SampleVectorOfNumbersData>();
            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 0,
                1, 2, 3, 4, 5, 6, 7, 8, 9 }
            });

            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 1,
                2, 3, 4, 5, 6, 7, 8, 9, 0 }
            });

            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }
            });
            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, }
            });
            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 5, 6, 7, 8, 9, 0, 1, 2, 3, 4 }
            });
            data.Add(new SampleVectorOfNumbersData
            {
                Features = new float[10] { 6, 7, 8, 9, 0, 1, 2, 3, 4, 5 }
            });
            return data;
        }
    }
}

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