NormalizationCatalog.NormalizeRobustScaling 方法

參考

定義

命名空間:: Microsoft.ML

組件:: Microsoft.ML.Transforms.dll

套件:: Microsoft.ML v3.0.1

套件:: Microsoft.ML v1.5.5

套件:: Microsoft.ML v1.6.0

套件:: Microsoft.ML v1.7.0

套件:: Microsoft.ML v2.0.0

重要

部分資訊涉及發行前產品，在發行之前可能會有大幅修改。 Microsoft 對此處提供的資訊，不做任何明確或隱含的瑕疵擔保。

多載

NormalizeRobustScaling(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, UInt32, UInt32)	建立， NormalizingEstimator 它會使用強固到極端值的統計資料來正規化，方法是將資料置中大約 0 (移除中位數) ，並根據分位數範圍調整資料， (預設為四元範圍) 。
NormalizeRobustScaling(TransformsCatalog, String, String, Int64, Boolean, UInt32, UInt32)	建立， NormalizingEstimator 它會使用強固到極端值的統計資料來正規化，方法是將資料置中大約 0 (移除中位數) ，並根據分位數範圍調整資料， (預設為四元範圍) 。

NormalizeRobustScaling(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, UInt32, UInt32)

建立， NormalizingEstimator 它會使用強固到極端值的統計資料來正規化，方法是將資料置中大約 0 (移除中位數) ，並根據分位數範圍調整資料， (預設為四元範圍) 。

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeRobustScaling (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, long maximumExampleCount = 1000000000, bool centerData = true, uint quantileMin = 25, uint quantileMax = 75);

static member NormalizeRobustScaling : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * int64 * bool * uint32 * uint32 -> Microsoft.ML.Transforms.NormalizingEstimator

<Extension()>
Public Function NormalizeRobustScaling (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional maximumExampleCount As Long = 1000000000, Optional centerData As Boolean = true, Optional quantileMin As UInteger = 25, Optional quantileMax As UInteger = 75) 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 Single ， Double 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。

centerData: Boolean

是否要將資料置中約 0，以移除中位數。預設為 true。

quantileMin: UInt32

用來調整資料的分位數最小值。預設值為 25。

quantileMax: UInt32

用來調整資料的分位數上限。預設值為 75。

傳回

NormalizingEstimator

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 NormalizeBinningMulticolumn
    {
        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] { 8, 1, 3, 0},
                    Features2 = 1 },

                new DataPoint(){ Features = new float[4] { 6, 2, 2, 0},
                    Features2 = 4 },

                new DataPoint(){ Features = new float[4] { 4, 0, 1, 0},
                    Features2 = 1 },

                new DataPoint(){ Features = new float[4] { 2,-1,-1, 1},
                    Features2 = 2 }
            };
            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);
            // NormalizeBinning normalizes the data by constructing equidensity bins
            // and produce output based on to which bin the original value belongs.
            var normalize = mlContext.Transforms.NormalizeBinning(new[]{
                new InputOutputColumnPair("Features"),
                new InputOutputColumnPair("Features2"),
                },
                maximumBinCount: 4, fixZero: 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 column = transformedData.GetColumn<float[]>("Features").ToArray();
            var column2 = transformedData.GetColumn<float>("Features2").ToArray();

            for (int i = 0; i < column.Length; i++)
                Console.WriteLine(string.Join(", ", column[i].Select(x => x
                .ToString("f4"))) + "\t\t" + column2[i]);
            // Expected output:
            //
            //  Features                            Feature2
            //  1.0000, 0.6667, 1.0000, 0.0000          0
            //  0.6667, 1.0000, 0.6667, 0.0000          1
            //  0.3333, 0.3333, 0.3333, 0.0000          0
            //  0.0000, 0.0000, 0.0000, 1.0000          0.5
        }

        private class DataPoint
        {
            [VectorType(4)]
            public float[] Features { get; set; }

            public float Features2 { get; set; }
        }
    }
}

適用於

NormalizeRobustScaling(TransformsCatalog, String, String, Int64, Boolean, UInt32, UInt32)

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeRobustScaling (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, long maximumExampleCount = 1000000000, bool centerData = true, uint quantileMin = 25, uint quantileMax = 75);

static member NormalizeRobustScaling : Microsoft.ML.TransformsCatalog * string * string * int64 * bool * uint32 * uint32 -> Microsoft.ML.Transforms.NormalizingEstimator

<Extension()>
Public Function NormalizeRobustScaling (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional centerData As Boolean = true, Optional quantileMin As UInteger = 25, Optional quantileMax As UInteger = 75) As NormalizingEstimator

參數

catalog: TransformsCatalog

轉換目錄

outputColumnName: String

轉換 inputColumnName 所產生的資料行名稱。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 outputColumnName 將的值當做來源使用。此資料行上的資料類型應該是 Single ， Double 或這些類型的已知大小向量。

maximumExampleCount: Int64