NormalizationCatalog.NormalizeGlobalContrast Method
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Create a GlobalContrastNormalizingEstimator, which normalizes columns individually applying global contrast normalization.
Setting ensureZeroMean
to true
, will apply a pre-processing step to make the specified column's mean be the zero vector.
public static Microsoft.ML.Transforms.GlobalContrastNormalizingEstimator NormalizeGlobalContrast (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, bool ensureZeroMean = true, bool ensureUnitStandardDeviation = false, float scale = 1);
static member NormalizeGlobalContrast : Microsoft.ML.TransformsCatalog * string * string * bool * bool * single -> Microsoft.ML.Transforms.GlobalContrastNormalizingEstimator
<Extension()>
Public Function NormalizeGlobalContrast (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional ensureZeroMean As Boolean = true, Optional ensureUnitStandardDeviation As Boolean = false, Optional scale As Single = 1) As GlobalContrastNormalizingEstimator
- catalog
- TransformsCatalog
The transform's catalog.
- outputColumnName
- String
Name of the column resulting from the transformation of inputColumnName
.
This column's data type will be the same as the input column's data type.
- inputColumnName
- String
Name of the column to normalize. If set to null
, the value of the
outputColumnName
will be used as source.
This estimator operates over known-sized vectors of Single.
- ensureZeroMean
- Boolean
If true
, subtract mean from each value before normalizing and use the raw input otherwise.
- ensureUnitStandardDeviation
- Boolean
If true
, the resulting vector's standard deviation would be one.
Otherwise, the resulting vector's L2-norm would be one.
- scale
- Single
Scale features by this value.
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
class NormalizeGlobalContrast
{
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] { 1, 1, 0, 0} },
new DataPoint(){ Features = new float[4] { 2, 2, 0, 0} },
new DataPoint(){ Features = new float[4] { 1, 0, 1, 0} },
new DataPoint(){ Features = new float[4] { 0, 1, 0, 1} }
};
// Convert training data to IDataView, the general data type used in
// ML.NET.
var data = mlContext.Data.LoadFromEnumerable(samples);
var approximation = mlContext.Transforms.NormalizeGlobalContrast(
"Features", ensureZeroMean: false, scale: 2,
ensureUnitStandardDeviation: 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 tansformer = approximation.Fit(data);
var transformedData = tansformer.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:
// 2.0000, 2.0000,-2.0000,-2.0000
// 2.0000, 2.0000,-2.0000,-2.0000
// 2.0000,-2.0000, 2.0000,-2.0000
//- 2.0000, 2.0000,-2.0000, 2.0000
}
private class DataPoint
{
[VectorType(4)]
public float[] Features { get; set; }
}
}
}
Product | Versions |
---|---|
ML.NET | 1.0.0, 1.1.0, 1.2.0, 1.3.1, 1.4.0, 1.5.0, 1.6.0, 1.7.0, 2.0.0, 3.0.0 |