MklComponentsCatalog.VectorWhiten Método
Definição
Importante
Algumas informações se referem a produtos de pré-lançamento que podem ser substancialmente modificados antes do lançamento. A Microsoft não oferece garantias, expressas ou implícitas, das informações aqui fornecidas.
Usa uma coluna preenchida com um vetor de variáveis aleatórias com uma matriz de covariância conhecida em um conjunto de novas variáveis cuja covariância é a matriz de identidade, o que significa que elas não estão relacionadas e cada uma tem variação 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 VectorWhiteningEstimatorParâmetros
- catalog
- TransformsCatalog
O catálogo da transformação.
- outputColumnName
- String
Nome da coluna resultante da transformação de inputColumnName.
- inputColumnName
- String
Nome da coluna a ser transformada. Se definido como null, o valor do será usado como origem outputColumnName .
- kind
- WhiteningKind
Tipo de clareamento (PCA/ZCA).
- epsilon
- Single
A constante de clareamento impede a divisão por zero.
- maximumNumberOfRows
- Int32
Número máximo de linhas usadas para treinar a transformação.
- rank
- Int32
No caso de clareamento de PCA, indica o número de componentes a serem retidos.
Retornos
Exemplos
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;
}
}
}