NormalizationCatalog.NormalizeMinMax 메서드
정의
중요
일부 정보는 릴리스되기 전에 상당 부분 수정될 수 있는 시험판 제품과 관련이 있습니다. Microsoft는 여기에 제공된 정보에 대해 어떠한 명시적이거나 묵시적인 보증도 하지 않습니다.
오버로드
| NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data. |
| NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data. |
NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)
Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.
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 NormalizingEstimator매개 변수
- catalog
- TransformsCatalog
변환 카탈로그
- columns
- InputOutputColumnPair[]
입력 및 출력 열 쌍입니다. 입력 열은 데이터 형식이거나 해당 형식 SingleDouble 의 알려진 크기 벡터여야 합니다. 출력 열의 데이터 형식은 연결된 입력 열과 동일합니다.
- maximumExampleCount
- Int64
normalizer를 학습시키는 데 사용되는 최대 예제 수입니다.
- fixZero
- Boolean
0에서 0으로 매핑할지 여부, 스파스를 유지합니다.
반환
예제
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; }
}
}
}
적용 대상
NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)
Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.
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 NormalizingEstimator매개 변수
- catalog
- TransformsCatalog
변환 카탈로그
- outputColumnName
- String
의 변환에서 생성된 열의 inputColumnName이름입니다.
이 열의 데이터 형식은 입력 열과 동일합니다.
- inputColumnName
- String
변환할 열의 이름입니다. 이 값으로 null설정하면 해당 값이 outputColumnName 원본으로 사용됩니다.
이 열의 데이터 형식은 해당 형식의 알려진 크기 벡터여야 합니다SingleDouble.
- maximumExampleCount
- Int64
normalizer를 학습시키는 데 사용되는 최대 예제 수입니다.
- fixZero
- Boolean
0에서 0으로 매핑할지 여부, 스파스를 유지합니다.
반환
예제
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; }
}
}
}