NormalizationCatalog.NormalizeMinMax メソッド

定義

名前空間:

Microsoft.ML

アセンブリ:

Microsoft.ML.Transforms.dll

パッケージ:

Microsoft.ML v2.0.0

オーバーロード

NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)	データの NormalizingEstimator観測された最小値と最大値に基づいて正規化する 、作成します。
NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)	データの NormalizingEstimator観測された最小値と最大値に基づいて正規化する 、作成します。

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

データの NormalizingEstimator観測された最小値と最大値に基づいて正規化する 、作成します。

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

パラメーター

catalog

TransformsCatalog

変換カタログ

columns

InputOutputColumnPair[]

入力列と出力列のペア。 入力列は、データ型、Doubleまたはそれらの型Singleの既知のサイズのベクターである必要があります。 出力列のデータ型は、関連付けられている入力列と同じになります。

maximumExampleCount

Int64

ノーマライザーのトレーニングに使用される例の最大数。

fixZero

Boolean

0 を 0 にマップするかどうか。スパーリティを維持します。

戻り値

NormalizingEstimator

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
{
    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)

データの NormalizingEstimator観測された最小値と最大値に基づいて正規化する 、作成します。

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

パラメーター

catalog

TransformsCatalog

変換カタログ

outputColumnName

String

の変換の結果として得られる列の inputColumnName名前。 この列のデータ型は、入力列と同じです。

inputColumnName

String

変換する列の名前。 に null設定すると、その値が outputColumnName ソースとして使用されます。 この列のデータ型は、Doubleこれらの型の既知のサイズのベクターであるSingle必要があります。

maximumExampleCount

Int64

ノーマライザーのトレーニングに使用される例の最大数。

fixZero

Boolean

0 を 0 にマップするかどうか。スパーリティを維持します。

戻り値

NormalizingEstimator

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 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; }
        }
    }
}