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NormalizationCatalog.NormalizeLogMeanVariance Metodo

Definizione

Overload

NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)

Creare un NormalizingEstimatoroggetto , che normalizza in base alla media calcolata e alla varianza del logaritmo dei dati.

NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean)

Creare un NormalizingEstimatoroggetto , che normalizza in base alla media calcolata e alla varianza del logaritmo dei dati.

NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean)

Creare un NormalizingEstimatoroggetto , che normalizza in base alla media calcolata e alla varianza del logaritmo dei dati.

NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean)

Creare un NormalizingEstimatoroggetto , che normalizza in base alla media calcolata e alla varianza del logaritmo dei dati.

NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)

Creare un NormalizingEstimatoroggetto , che normalizza in base alla media calcolata e alla varianza del logaritmo dei dati.

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 NormalizingEstimator

Parametri

catalog
TransformsCatalog

Catalogo di trasformazione

columns
InputOutputColumnPair[]

Coppie di colonne di input e di output. Le colonne di input devono essere di tipo SingleDouble di dati o un vettore di dimensioni note di tali tipi. Il tipo di dati per la colonna di output sarà uguale alla colonna di input associata.

maximumExampleCount
Int64

Numero massimo di esempi usati per eseguire il training del normalizzatore.

useCdf
Boolean

Indica se usare CDF come output.

Restituisce

Si applica a

NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean)

Creare un NormalizingEstimatoroggetto , che normalizza in base alla media calcolata e alla varianza del logaritmo dei dati.

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 NormalizingEstimator

Parametri

catalog
TransformsCatalog

Catalogo di trasformazione

columns
InputOutputColumnPair[]

Coppie di colonne di input e di output. Le colonne di input devono essere di tipo SingleDouble di dati o un vettore di dimensioni note di tali tipi. Il tipo di dati per la colonna di output sarà uguale alla colonna di input associata.

fixZero
Boolean

Indica se eseguire il mapping di zero a zero, mantenendo la spaziatura.

maximumExampleCount
Int64

Numero massimo di esempi usati per eseguire il training del normalizzatore.

useCdf
Boolean

Indica se usare CDF come output.

Restituisce

Si applica a

NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean)

Creare un NormalizingEstimatoroggetto , che normalizza in base alla media calcolata e alla varianza del logaritmo dei dati.

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 NormalizingEstimator

Parametri

catalog
TransformsCatalog

Catalogo di trasformazione

outputColumnName
String

Nome della colonna risultante dalla trasformazione di inputColumnName. Il tipo di dati in questa colonna corrisponde alla colonna di input.

inputColumnName
String

Nome della colonna da trasformare. Se impostato su null, il valore di outputColumnName verrà usato come origine. Il tipo di dati in questa colonna deve essere Singleo Double un vettore di dimensioni note di tali tipi.

maximumExampleCount
Int64

Numero massimo di esempi usati per eseguire il training del normalizzatore.

useCdf
Boolean

Indica se usare CDF come output.

Restituisce

Esempio

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; }
        }
    }
}

Si applica a

NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean)

Creare un NormalizingEstimatoroggetto , che normalizza in base alla media calcolata e alla varianza del logaritmo dei dati.

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 NormalizingEstimator

Parametri

catalog
TransformsCatalog

Catalogo di trasformazione

outputColumnName
String

Nome della colonna risultante dalla trasformazione di inputColumnName. Il tipo di dati in questa colonna corrisponde alla colonna di input.

fixZero
Boolean

Indica se eseguire il mapping di zero a zero, mantenendo la spaziatura.

inputColumnName
String

Nome della colonna da trasformare. Se impostato su null, il valore di outputColumnName verrà usato come origine. Il tipo di dati in questa colonna deve essere Singleo Double un vettore di dimensioni note di tali tipi.

maximumExampleCount
Int64

Numero massimo di esempi usati per eseguire il training del normalizzatore.

useCdf
Boolean

Indica se usare CDF come output.

Restituisce

Esempio

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; }
        }
    }
}

Si applica a