NormalizationCatalog.NormalizeMinMax Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v5.0.0-preview.1.25125.4

Overload

NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)	Buat NormalizingEstimator, yang menormalkan berdasarkan nilai minimum dan maksimum data yang diamati.
NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)	Buat NormalizingEstimator, yang menormalkan berdasarkan nilai minimum dan maksimum data yang diamati.

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

Sumber:

NormalizerCatalog.cs

Buat NormalizingEstimator, yang menormalkan berdasarkan nilai minimum dan maksimum data yang diamati.

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeMinMax(this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, long maximumExampleCount = 1000000000, bool fixZero = true);

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

<Extension()>
Public Function NormalizeMinMax (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true) As NormalizingEstimator

Parameter

catalog

TransformsCatalog

Katalog transformasi

columns

InputOutputColumnPair[]

Pasangan kolom input dan output. Kolom input harus berjenis Singledata , Double atau vektor berukuran dikenal dari jenis tersebut. Jenis data untuk kolom output akan sama dengan kolom input terkait.

maximumExampleCount

Int64

Jumlah maksimum contoh yang digunakan untuk melatih normalizer.

fixZero

Boolean

Apakah akan memetakan nol ke nol, mempertahankan sparitas.

Mengembalikan

NormalizingEstimator

Contoh

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
{
    class NormalizeMinMaxMulticolumn
    {
        public static void Example()
        {
            var mlContext = new MLContext();
            var samples = new List<DataPoint>()
            {
                new DataPoint()
                {
                    Features = new float[4] { 1, 1, 3, 0 },
                    Features2 = new float[3] { 1, 2, 3 }
                },
                new DataPoint()
                {
                    Features = new float[4] { 2, 2, 2, 0 },
                    Features2 = new float[3] { 3, 4, 5 }
                },
                new DataPoint()
                {
                    Features = new float[4] { 0, 0, 1, 0 },
                    Features2 = new float[3] { 6, 7, 8 }
                },
                new DataPoint()
                {
                    Features = new float[4] {-1,-1,-1, 1 },
                    Features2 = new float[3] { 9, 0, 4 }
                }
            };

            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);

            var columnPair = new[]
            {
                new InputOutputColumnPair("Features"),
                new InputOutputColumnPair("Features2")
            };

            // NormalizeMinMax normalize rows by finding min and max values in each
            // row slot and setting projection of min value to 0 and max to 1 and
            // everything else to values in between.
            var normalize = mlContext.Transforms.NormalizeMinMax(columnPair,
                fixZero: false);

            // Normalize rows by finding min and max values in each row slot, but
            // make sure zero values remain zero after normalization. Helps
            // preserve sparsity. That is, to help maintain very little non-zero elements.
            var normalizeFixZero = mlContext.Transforms.NormalizeMinMax(columnPair,
                fixZero: true);

            // 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 normalizeFixZeroTransform = normalizeFixZero.Fit(data);
            var fixZeroData = normalizeFixZeroTransform.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" +
                string.Join(", ", column2[i].Select(x => x.ToString("f4"))));

            // Expected output:
            // Features                                Features2  
            // 0.6667, 0.6667, 1.0000, 0.0000          0.0000, 0.2857, 0.0000
            // 1.0000, 1.0000, 0.7500, 0.0000          0.2500, 0.5714, 0.4000
            // 0.3333, 0.3333, 0.5000, 0.0000          0.6250, 1.0000, 1.0000
            // 0.0000, 0.0000, 0.0000, 1.0000          1.0000, 0.0000, 0.2000

            var columnFixZero = fixZeroData.GetColumn<float[]>("Features").ToArray();
            var column2FixZero = fixZeroData.GetColumn<float[]>("Features2").ToArray();

            Console.WriteLine(Environment.NewLine);

            for (int i = 0; i < column.Length; i++)
                Console.WriteLine(string.Join(", ", columnFixZero[i].Select(x => x
                .ToString("f4"))) + "\t\t" +
                string.Join(", ", column2FixZero[i].Select(x => x.ToString("f4"))));

            // Expected output:
            // Features                                Features2  
            // 0.5000, 0.5000, 1.0000, 0.0000          0.1111, 0.2857, 0.3750
            // 1.0000, 1.0000, 0.6667, 0.0000          0.3333, 0.5714, 0.6250
            // 0.0000, 0.0000, 0.3333, 0.0000          0.6667, 1.0000, 1.0000
            // -0.5000, -0.5000, -0.3333, 1.0000       1.0000, 0.0000, 0.5000

            // Get transformation parameters. Since we have multiple columns
            // we need to pass index of InputOutputColumnPair.
            var transformParams = normalizeTransform.GetNormalizerModelParameters(0)
                as AffineNormalizerModelParameters<ImmutableArray<float>>;

            var transformParams2 = normalizeTransform.GetNormalizerModelParameters(1)
                as AffineNormalizerModelParameters<ImmutableArray<float>>;

            Console.WriteLine(Environment.NewLine);

            Console.WriteLine($"The 1-index value in resulting array would be " +
                $"produced by:");

            Console.WriteLine(" y = (x - (" + (transformParams.Offset.Length == 0 ?
                0 : transformParams.Offset[1]) + ")) * " + transformParams
                .Scale[1]);

            // Expected output:
            //  The 1-index value in resulting array would be produce by:
            //  y = (x - (-1)) * 0.3333333
        }

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

            [VectorType(3)]
            public float[] Features2 { get; set; }
        }
    }
}

NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)

Sumber:

NormalizerCatalog.cs

Buat NormalizingEstimator, yang menormalkan berdasarkan nilai minimum dan maksimum data yang diamati.

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeMinMax(this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, long maximumExampleCount = 1000000000, bool fixZero = true);

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

<Extension()>
Public Function NormalizeMinMax (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true) As NormalizingEstimator

Parameter

catalog

TransformsCatalog

Katalog transformasi

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data pada kolom ini sama dengan kolom input.

inputColumnName

String

Nama kolom yang akan diubah. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber. Jenis data pada kolom ini harus Single, Double atau vektor berukuran dikenal dari jenis tersebut.

maximumExampleCount

Int64

Jumlah maksimum contoh yang digunakan untuk melatih normalizer.

fixZero

Boolean

Apakah akan memetakan nol ke nol, mempertahankan sparitas.

Mengembalikan

NormalizingEstimator

Contoh

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 NormalizeMinMax
    {
        public static void Example()
        {
            var mlContext = new MLContext();
            var samples = new List<DataPoint>()
            {
                new DataPoint(){ Features = new float[4] { 1, 1, 3, 0} },
                new DataPoint(){ Features = new float[4] { 2, 2, 2, 0} },
                new DataPoint(){ Features = new float[4] { 0, 0, 1, 0} },
                new DataPoint(){ Features = new float[4] {-1,-1,-1, 1} }
            };
            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);
            // NormalizeMinMax normalize rows by finding min and max values in each
            // row slot and setting projection of min value to 0 and max to 1 and
            // everything else to values in between.
            var normalize = mlContext.Transforms.NormalizeMinMax("Features",
                fixZero: false);

            // Normalize rows by finding min and max values in each row slot, but
            // make sure zero values remain zero after normalization. Helps
            // preserve sparsity. That is, to help maintain very little non-zero elements.
            var normalizeFixZero = mlContext.Transforms.NormalizeMinMax("Features",
                fixZero: true);

            // 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 normalizeFixZeroTransform = normalizeFixZero.Fit(data);
            var fixZeroData = normalizeFixZeroTransform.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:
            //  0.6667, 0.6667, 1.0000, 0.0000
            //  1.0000, 1.0000, 0.7500, 0.0000
            //  0.3333, 0.3333, 0.5000, 0.0000
            //  0.0000, 0.0000, 0.0000, 1.0000

            var columnFixZero = fixZeroData.GetColumn<float[]>("Features")
                .ToArray();

            foreach (var row in columnFixZero)
                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
                    "f4"))));
            // Expected output:
            //  0.5000, 0.5000, 1.0000, 0.0000
            //  1.0000, 1.0000, 0.6667, 0.0000
            //  0.0000, 0.0000, 0.3333, 0.0000
            // -0.5000,-0.5000,-0.3333, 1.0000

            // 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 AffineNormalizerModelParameters<ImmutableArray<float>>;

            Console.WriteLine($"The 1-index value in resulting array would be " +
                $"produced by:");

            Console.WriteLine(" y = (x - (" + (transformParams.Offset.Length == 0 ?
                0 : transformParams.Offset[1]) + ")) * " + transformParams
                .Scale[1]);
            // Expected output:
            //  The 1-index value in resulting array would be produce by: 
            //  y = (x - (-1)) * 0.3333333
        }

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