共用方式為


NormalizationCatalog.NormalizeLogMeanVariance 方法

定義

多載

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

建立 NormalizingEstimator ,其會根據資料的對數計算平均數和變異數正規化。

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

建立 NormalizingEstimator ,其會根據資料的對數計算平均數和變異數正規化。

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

建立 NormalizingEstimator ,其會根據資料的對數計算平均數和變異數正規化。

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

建立 NormalizingEstimator ,其會根據資料的對數計算平均數和變異數正規化。

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

建立 NormalizingEstimator ,其會根據資料的對數計算平均數和變異數正規化。

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

參數

catalog
TransformsCatalog

轉換目錄

columns
InputOutputColumnPair[]

輸入和輸出資料行的配對。 輸入資料行必須是資料類型 SingleDouble 或這些類型的已知大小向量。 輸出資料行的資料類型會與相關聯的輸入資料行相同。

maximumExampleCount
Int64

用來定型正規化程式的範例數目上限。

useCdf
Boolean

是否要使用 CDF 作為輸出。

傳回

適用於

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

建立 NormalizingEstimator ,其會根據資料的對數計算平均數和變異數正規化。

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

參數

catalog
TransformsCatalog

轉換目錄

columns
InputOutputColumnPair[]

輸入和輸出資料行的配對。 輸入資料行必須是資料類型 SingleDouble 或這些類型的已知大小向量。 輸出資料行的資料類型會與相關聯的輸入資料行相同。

fixZero
Boolean

是否要將零對應至零,並保留疏疏性。

maximumExampleCount
Int64

用來定型正規化程式的範例數目上限。

useCdf
Boolean

是否要使用 CDF 作為輸出。

傳回

適用於

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

建立 NormalizingEstimator ,其會根據資料的對數計算平均數和變異數正規化。

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

參數

catalog
TransformsCatalog

轉換目錄

outputColumnName
String

轉換所產生的 inputColumnName 資料行名稱。 此資料行上的資料類型與輸入資料行相同。

inputColumnName
String

要轉換的資料行名稱。 如果設定為 null ,則會將 的值 outputColumnName 當做來源使用。 此資料行上的資料類型應該是 SingleDouble 或這些類型的已知大小向量。

maximumExampleCount
Int64

用來定型正規化程式的範例數目上限。

useCdf
Boolean

是否要使用 CDF 作為輸出。

傳回

範例

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; }
        }
    }
}

適用於

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

建立 NormalizingEstimator ,其會根據資料的對數計算平均數和變異數正規化。

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

參數

catalog
TransformsCatalog

轉換目錄

outputColumnName
String

轉換所產生的 inputColumnName 資料行名稱。 此資料行上的資料類型與輸入資料行相同。

fixZero
Boolean

是否要將零對應至零,並保留疏疏性。

inputColumnName
String

要轉換的資料行名稱。 如果設定為 null ,則會將 的值 outputColumnName 當做來源使用。 此資料行上的資料類型應該是 SingleDouble 或這些類型的已知大小向量。

maximumExampleCount
Int64

用來定型正規化程式的範例數目上限。

useCdf
Boolean

是否要使用 CDF 作為輸出。

傳回

範例

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; }
        }
    }
}

適用於