NormalizationCatalog.NormalizeLogMeanVariance Méthode
Définition
Important
Certaines informations portent sur la préversion du produit qui est susceptible d’être en grande partie modifiée avant sa publication. Microsoft exclut toute garantie, expresse ou implicite, concernant les informations fournies ici.
Surcharges
| NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean) |
Créez un NormalizingEstimator, qui normalise en fonction de la moyenne calculée et de la variance du logarithme des données. |
| NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean) |
Créez un NormalizingEstimator, qui normalise en fonction de la moyenne calculée et de la variance du logarithme des données. |
| NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean) |
Créez un NormalizingEstimator, qui normalise en fonction de la moyenne calculée et de la variance du logarithme des données. |
| NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean) |
Créez un NormalizingEstimator, qui normalise en fonction de la moyenne calculée et de la variance du logarithme des données. |
NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)
Créez un NormalizingEstimator, qui normalise en fonction de la moyenne calculée et de la variance du logarithme des données.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeLogMeanVariance (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, long maximumExampleCount = 1000000000, bool useCdf = true);static member NormalizeLogMeanVariance : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * int64 * bool -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeLogMeanVariance (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional maximumExampleCount As Long = 1000000000, Optional useCdf As Boolean = true) As NormalizingEstimatorParamètres
- catalog
- TransformsCatalog
Catalogue de transformations
- columns
- InputOutputColumnPair[]
Paires de colonnes d’entrée et de sortie. Les colonnes d’entrée doivent être de type Singlede données ou Double un vecteur de taille connue de ces types. Le type de données de la colonne de sortie sera identique à la colonne d’entrée associée.
- maximumExampleCount
- Int64
Nombre maximal d’exemples utilisés pour entraîner le normaliseur.
- useCdf
- Boolean
Indique s’il faut utiliser la CDF comme sortie.
Retours
S’applique à
NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean)
Créez un NormalizingEstimator, qui normalise en fonction de la moyenne calculée et de la variance du logarithme des données.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeLogMeanVariance (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, bool fixZero, long maximumExampleCount = 1000000000, bool useCdf = true);static member NormalizeLogMeanVariance : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * bool * int64 * bool -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeLogMeanVariance (catalog As TransformsCatalog, columns As InputOutputColumnPair(), fixZero As Boolean, Optional maximumExampleCount As Long = 1000000000, Optional useCdf As Boolean = true) As NormalizingEstimatorParamètres
- catalog
- TransformsCatalog
Catalogue de transformations
- columns
- InputOutputColumnPair[]
Paires de colonnes d’entrée et de sortie. Les colonnes d’entrée doivent être de type Singlede données ou Double un vecteur de taille connue de ces types. Le type de données de la colonne de sortie sera identique à la colonne d’entrée associée.
- fixZero
- Boolean
Indique s’il faut mapper zéro à zéro, en préservant l’éparse.
- maximumExampleCount
- Int64
Nombre maximal d’exemples utilisés pour entraîner le normaliseur.
- useCdf
- Boolean
Indique s’il faut utiliser la CDF comme sortie.
Retours
S’applique à
NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean)
Créez un NormalizingEstimator, qui normalise en fonction de la moyenne calculée et de la variance du logarithme des données.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeLogMeanVariance (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, long maximumExampleCount = 1000000000, bool useCdf = true);static member NormalizeLogMeanVariance : Microsoft.ML.TransformsCatalog * string * string * int64 * bool -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeLogMeanVariance (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional useCdf As Boolean = true) As NormalizingEstimatorParamètres
- catalog
- TransformsCatalog
Catalogue de transformations
- outputColumnName
- String
Nom de la colonne résultant de la transformation de inputColumnName.
Le type de données de cette colonne est identique à la colonne d’entrée.
- inputColumnName
- String
Nom de la colonne à transformer. Si la valeur est définie null, la valeur du outputColumnName fichier sera utilisée comme source.
Le type de données de cette colonne doit être Single, Double ou un vecteur de taille connue de ces types.
- maximumExampleCount
- Int64
Nombre maximal d’exemples utilisés pour entraîner le normaliseur.
- useCdf
- Boolean
Indique s’il faut utiliser la CDF comme sortie.
Retours
Exemples
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 NormalizeLogMeanVariance
{
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[5] { 1, 1, 3, 0, float.MaxValue } },
new DataPoint(){ Features = new float[5] { 2, 2, 2, 0, float.MinValue } },
new DataPoint(){ Features = new float[5] { 0, 0, 1, 0, 0} },
new DataPoint(){ Features = new float[5] {-1,-1,-1, 1, 1} }
};
// Convert training data to IDataView, the general data type used in
// ML.NET.
var data = mlContext.Data.LoadFromEnumerable(samples);
// NormalizeLogMeanVariance normalizes the data based on the computed
// mean and variance of the logarithm of the data.
// Uses Cumulative distribution function as output.
var normalize = mlContext.Transforms.NormalizeLogMeanVariance(
"Features", useCdf: true);
// NormalizeLogMeanVariance normalizes the data based on the computed
// mean and variance of the logarithm of the data.
var normalizeNoCdf = mlContext.Transforms.NormalizeLogMeanVariance(
"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:
// 0.1587, 0.1587, 0.8654, 0.0000, 0.8413
// 0.8413, 0.8413, 0.5837, 0.0000, 0.0000
// 0.0000, 0.0000, 0.0940, 0.0000, 0.0000
// 0.0000, 0.0000, 0.0000, 0.0000, 0.1587
var columnFixZero = noCdfData.GetColumn<float[]>("Features").ToArray();
foreach (var row in columnFixZero)
Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
"f4"))));
// Expected output:
// 1.8854, 1.8854, 5.2970, 0.0000, 7670682000000000000000000000000000000.0000
// 4.7708, 4.7708, 3.0925, 0.0000, -7670682000000000000000000000000000000.0000
// -1.0000,-1.0000, 0.8879, 0.0000, -1.0000
// -3.8854,-3.8854,-3.5213, 0.0000, -0.9775
// 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((Math.Log(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((Math.Log(x)- 0.3465736) / (0.3465736 * 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($"The 1-index value in resulting array would be " +
$"produce by: y = (x - ({offset})) * {scale}");
// Expected output:
// The 1-index value in resulting array would be produce by: y = (x - (0.3465736)) * 2.88539
}
private class DataPoint
{
[VectorType(5)]
public float[] Features { get; set; }
}
}
}
S’applique à
NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean)
Créez un NormalizingEstimator, qui normalise en fonction de la moyenne calculée et de la variance du logarithme des données.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeLogMeanVariance (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, bool fixZero, string inputColumnName = default, long maximumExampleCount = 1000000000, bool useCdf = true);static member NormalizeLogMeanVariance : Microsoft.ML.TransformsCatalog * string * bool * string * int64 * bool -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeLogMeanVariance (catalog As TransformsCatalog, outputColumnName As String, fixZero As Boolean, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional useCdf As Boolean = true) As NormalizingEstimatorParamètres
- catalog
- TransformsCatalog
Catalogue de transformations
- outputColumnName
- String
Nom de la colonne résultant de la transformation de inputColumnName.
Le type de données de cette colonne est identique à la colonne d’entrée.
- fixZero
- Boolean
Indique s’il faut mapper zéro à zéro, en préservant l’éparse.
- inputColumnName
- String
Nom de la colonne à transformer. Si la valeur est définie null, la valeur du outputColumnName fichier sera utilisée comme source.
Le type de données de cette colonne doit être Single, Double ou un vecteur de taille connue de ces types.
- maximumExampleCount
- Int64
Nombre maximal d’exemples utilisés pour entraîner le normaliseur.
- useCdf
- Boolean
Indique s’il faut utiliser la CDF comme sortie.
Retours
Exemples
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 NormalizeLogMeanVarianceFixZero
{
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[5] { 1, 1, 3, 0, float.MaxValue } },
new DataPoint(){ Features = new float[5] { 2, 2, 2, 0, float.MinValue } },
new DataPoint(){ Features = new float[5] { 0, 0, 1, 0, 0} },
new DataPoint(){ Features = new float[5] {-1,-1,-1, 1, 1} }
};
// Convert training data to IDataView, the general data type used in ML.NET.
var data = mlContext.Data.LoadFromEnumerable(samples);
// NormalizeLogMeanVariance normalizes the data based on the computed mean and variance of the logarithm of the data.
// Uses Cumulative distribution function as output.
var normalize = mlContext.Transforms.NormalizeLogMeanVariance("Features", true, useCdf: true);
// NormalizeLogMeanVariance normalizes the data based on the computed mean and variance of the logarithm of the data.
var normalizeNoCdf = mlContext.Transforms.NormalizeLogMeanVariance("Features", true, 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:
// 0.1587, 0.1587, 0.8654, 0.0000, 0.8413
// 0.8413, 0.8413, 0.5837, 0.0000, 0.0000
// 0.0000, 0.0000, 0.0940, 0.0000, 0.0000
// 0.0000, 0.0000, 0.0000, 0.0000, 0.1587
var columnFixZero = noCdfData.GetColumn<float[]>("Features").ToArray();
foreach (var row in columnFixZero)
Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
// Expected output:
// 2.0403, 2.0403, 4.0001, 0.0000, 5423991000000000000000000000000000000.0000
// 4.0806, 4.0806, 2.6667, 0.0000,-5423991000000000000000000000000000000.0000
// 0.0000, 0.0000, 1.3334, 0.0000, 0.0000
// -2.0403,-2.0403,-1.3334, 0.0000, 0.0159
// 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 values in the column with index 1 in the resulting array would be produced by:");
Console.WriteLine($"y = 0.5* (1 + ERF((Math.Log(x)- {transformParams.Mean[1]}) / ({transformParams.StandardDeviation[1]} * sqrt(2)))");
// ERF is https://en.wikipedia.org/wiki/Error_function.
// Expected output:
// The values in the column with index 1 in the resulting array would be produced by:
// y = 0.5 * (1 + ERF((Math.Log(x) - 0.3465736) / (0.3465736 * 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($"The values in the column with index 1 in the resulting array would be produced by: y = (x - ({offset})) * {scale}");
// Expected output:
// The values in the column with index 1 in the resulting array would be produced by: y = (x - (0)) * 2.040279
}
private class DataPoint
{
[VectorType(5)]
public float[] Features { get; set; }
}
}
}