MklComponentsCatalog.VectorWhiten Yöntem
Tanım
Önemli
Bazı bilgiler ürünün ön sürümüyle ilgilidir ve sürüm öncesinde önemli değişiklikler yapılmış olabilir. Burada verilen bilgilerle ilgili olarak Microsoft açık veya zımni hiçbir garanti vermez.
Bilinen kovaryans matrisine sahip rastgele değişkenlerden oluşan bir vektörle dolu sütunu, kovaryansları kimlik matrisi olan bir dizi yeni değişkene alır, yani bunların ilişkisizdir ve her birinin varyansı 1'dir.
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 VectorWhiteningEstimatorParametreler
- catalog
- TransformsCatalog
Dönüşümün kataloğu.
- outputColumnName
- String
dönüştürmesinden kaynaklanan sütunun inputColumnNameadı.
- inputColumnName
- String
Dönüştürülecek sütunun adı. olarak ayarlanırsa nulldeğeri outputColumnName kaynak olarak kullanılır.
- kind
- WhiteningKind
Beyazlama türü (PCA/ZCA).
- epsilon
- Single
Beyazlama sabiti, sıfıra bölmeyi önler.
- maximumNumberOfRows
- Int32
Dönüşümü eğitmek için kullanılan satır sayısı üst sınırı.
- rank
- Int32
PCA beyazlama durumunda, korunacak bileşen sayısını gösterir.
Döndürülenler
Örnekler
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;
}
}
}