NormalizationCatalog.NormalizeLogMeanVariance 方法

定义

命名空间:

Microsoft.ML

程序集:

Microsoft.ML.Transforms.dll

包:

Microsoft.ML v2.0.0

重载

NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)	创建一个 NormalizingEstimator，它根据数据的对数的计算平均值和方差进行规范化。
NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean)	创建一个 NormalizingEstimator，它根据数据的对数的计算平均值和方差进行规范化。
NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean)	创建一个 NormalizingEstimator，它根据数据的对数的计算平均值和方差进行规范化。
NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean)	创建一个 NormalizingEstimator，它根据数据的对数的计算平均值和方差进行规范化。

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

创建一个 NormalizingEstimator，它根据数据的对数的计算平均值和方差进行规范化。

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

参数

catalog

TransformsCatalog

转换目录

columns

InputOutputColumnPair[]

输入和输出列对。 输入列必须是数据类型 Single， Double 或者是这些类型的已知大小的向量。 输出列的数据类型将与关联的输入列相同。

maximumExampleCount

Int64

用于训练规范化器的最大示例数。

useCdf

Boolean

是否使用 CDF 作为输出。

返回

NormalizingEstimator

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

创建一个 NormalizingEstimator，它根据数据的对数的计算平均值和方差进行规范化。

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

参数

catalog

TransformsCatalog

转换目录

columns

InputOutputColumnPair[]

输入和输出列对。 输入列必须是数据类型 Single， Double 或者是这些类型的已知大小的向量。 输出列的数据类型将与关联的输入列相同。

fixZero

Boolean

是否将零映射到零，保留稀疏。

maximumExampleCount

Int64

用于训练规范化器的最大示例数。

useCdf

Boolean

是否使用 CDF 作为输出。

返回

NormalizingEstimator

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

创建一个 NormalizingEstimator，它根据数据的对数的计算平均值和方差进行规范化。

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

参数

catalog

TransformsCatalog

转换目录

outputColumnName

String

由转换 inputColumnName生成的列的名称。 此列上的数据类型与输入列相同。

inputColumnName

String

要转换的列的名称。 If set to null, the value of the outputColumnName will be used as source. 此列上的数据类型应为SingleDouble已知大小的向量，或这些类型的已知大小向量。

maximumExampleCount

Int64

用于训练规范化器的最大示例数。

useCdf

Boolean

是否使用 CDF 作为输出。

返回

NormalizingEstimator

示例

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; }
        }
    }
}

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

创建一个 NormalizingEstimator，它根据数据的对数的计算平均值和方差进行规范化。

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

参数

catalog

TransformsCatalog

转换目录

outputColumnName

String

由转换 inputColumnName生成的列的名称。 此列上的数据类型与输入列相同。

fixZero

Boolean

是否将零映射到零，保留稀疏。

inputColumnName

String

要转换的列的名称。 If set to null, the value of the outputColumnName will be used as source. 此列上的数据类型应为SingleDouble已知大小的向量，或这些类型的已知大小向量。

maximumExampleCount

Int64

用于训练规范化器的最大示例数。

useCdf

Boolean

是否使用 CDF 作为输出。

返回

NormalizingEstimator

示例

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; }
        }
    }
}