NormalizationCatalog.NormalizeMinMax メソッド
定義
重要
一部の情報は、リリース前に大きく変更される可能性があるプレリリースされた製品に関するものです。 Microsoft は、ここに記載されている情報について、明示または黙示を問わず、一切保証しません。
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
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; }
}
}
}
適用対象
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
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; }
}
}
}