NormalizationCatalog.NormalizeMeanVariance Metode
Definisi
Penting
Beberapa informasi terkait produk prarilis yang dapat diubah secara signifikan sebelum dirilis. Microsoft tidak memberikan jaminan, tersirat maupun tersurat, sehubungan dengan informasi yang diberikan di sini.
Overload
NormalizeMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Boolean) |
Buat NormalizingEstimator, yang menormalkan berdasarkan rata-rata komputasi dan varian data. |
NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean) |
Buat NormalizingEstimator, yang menormalkan berdasarkan rata-rata komputasi dan varian data. |
NormalizeMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Boolean)
Buat NormalizingEstimator, yang menormalkan berdasarkan rata-rata komputasi dan varian data.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeMeanVariance (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, long maximumExampleCount = 1000000000, bool fixZero = true, bool useCdf = false);
static member NormalizeMeanVariance : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * int64 * bool * bool -> Microsoft.ML.Transforms.NormalizingEstimator
<Extension()>
Public Function NormalizeMeanVariance (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional useCdf As Boolean = false) As NormalizingEstimator
Parameter
- catalog
- TransformsCatalog
Katalog transformasi
- columns
- InputOutputColumnPair[]
Pasangan kolom input dan output. Kolom input harus berjenis Singledata , Double atau vektor berukuran dikenal dari jenis tersebut. Jenis data untuk kolom output akan sama dengan kolom input terkait.
- maximumExampleCount
- Int64
Jumlah maksimum contoh yang digunakan untuk melatih normalizer.
- fixZero
- Boolean
Apakah akan memetakan nol ke nol, mempertahankan sparitas.
- useCdf
- Boolean
Apakah akan menggunakan CDF sebagai output.
Mengembalikan
Berlaku untuk
NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean)
Buat NormalizingEstimator, yang menormalkan berdasarkan rata-rata komputasi dan varian data.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeMeanVariance (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, long maximumExampleCount = 1000000000, bool fixZero = true, bool useCdf = false);
static member NormalizeMeanVariance : Microsoft.ML.TransformsCatalog * string * string * int64 * bool * bool -> Microsoft.ML.Transforms.NormalizingEstimator
<Extension()>
Public Function NormalizeMeanVariance (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional useCdf As Boolean = false) As NormalizingEstimator
Parameter
- catalog
- TransformsCatalog
Katalog transformasi
- outputColumnName
- String
Nama kolom yang dihasilkan dari transformasi inputColumnName
.
Jenis data pada kolom ini sama dengan kolom input.
- inputColumnName
- String
Nama kolom yang akan diubah. Jika diatur ke null
, nilai outputColumnName
akan digunakan sebagai sumber.
Jenis data pada kolom ini harus Single, Double atau vektor berukuran dikenal dari jenis tersebut.
- maximumExampleCount
- Int64
Jumlah maksimum contoh yang digunakan untuk melatih normalizer.
- fixZero
- Boolean
Apakah akan memetakan nol ke nol, mempertahankan sparitas.
- useCdf
- Boolean
Apakah akan menggunakan CDF sebagai output.
Mengembalikan
Contoh
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 NormalizeMeanVariance
{
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, 3, 0} },
new DataPoint(){ Features = new float[4] { 2, 2, 2, 0} },
new DataPoint(){ Features = new float[4] { 0, 0, 1, 0} },
new DataPoint(){ Features = new float[4] {-1,-1,-1, 1} }
};
// Convert training data to IDataView, the general data type used in
// ML.NET.
var data = mlContext.Data.LoadFromEnumerable(samples);
// NormalizeMeanVariance normalizes the data based on the computed mean
// and variance of the data. Uses Cumulative distribution function as
// output.
var normalize = mlContext.Transforms.NormalizeMeanVariance("Features",
useCdf: true);
// NormalizeMeanVariance normalizes the data based on the computed mean
// and variance of the data.
var normalizeNoCdf = mlContext.Transforms.NormalizeMeanVariance(
"Features", useCdf: false);
// 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 normalizeNoCdfTransform = normalizeNoCdf.Fit(data);
var noCdfData = normalizeNoCdfTransform.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:
// 0.6726, 0.6726, 0.8816, 0.2819
// 0.9101, 0.9101, 0.6939, 0.2819
// 0.3274, 0.3274, 0.4329, 0.2819
// 0.0899, 0.0899, 0.0641, 0.9584
var columnFixZero = noCdfData.GetColumn<float[]>("Features").ToArray();
foreach (var row in columnFixZero)
Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
"f4"))));
// Expected output:
// 0.8165, 0.8165, 1.5492, 0.0000
// 1.6330, 1.6330, 1.0328, 0.0000
// 0.0000, 0.0000, 0.5164, 0.0000
// -0.8165,-0.8165,-0.5164, 2.0000
// 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 CdfNormalizerModelParameters<
ImmutableArray<float>>;
Console.WriteLine($"The 1-index value in resulting array would " +
$"be produce by:");
Console.WriteLine(" y = 0.5* (1 + ERF((x- " + transformParams.Mean[1] +
") / (" + transformParams.StandardDeviation[1] + " * sqrt(2)))");
// ERF is https://en.wikipedia.org/wiki/Error_function.
// Expected output:
// The 1-index value in resulting array would be produce by:
// y = 0.5 * (1 + ERF((x - 0.5) / (1.118034 * sqrt(2)))
var noCdfParams = normalizeNoCdfTransform
.GetNormalizerModelParameters(0) as
AffineNormalizerModelParameters<ImmutableArray<float>>;
var offset = noCdfParams.Offset.Length == 0 ? 0 : noCdfParams.Offset[1];
var scale = noCdfParams.Scale[1];
Console.WriteLine($"Values for slot 1 would be transformed by " +
$"applying y = (x - ({offset})) * {scale}");
// Expected output:
// The 1-index value in resulting array would be produce by: y = (x - (0)) * 0.8164966
}
private class DataPoint
{
[VectorType(4)]
public float[] Features { get; set; }
}
}
}