NormalizationCatalog.NormalizeLpNorm 方法
定义
重要
一些信息与预发行产品相关，相应产品在发行之前可能会进行重大修改。 对于此处提供的信息，Microsoft 不作任何明示或暗示的担保。
创建一个 LpNormNormalizingEstimator，它将输入列中的矢量规范化为单位规范。
使用的规范的类型由 norm.
true设置为ensureZeroMean此设置时，将应用预处理步骤以使指定列的平均值为零向量。
public static Microsoft.ML.Transforms.LpNormNormalizingEstimator NormalizeLpNorm(this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, Microsoft.ML.Transforms.LpNormNormalizingEstimatorBase.NormFunction norm = Microsoft.ML.Transforms.LpNormNormalizingEstimatorBase+NormFunction.L2, bool ensureZeroMean = false);static member NormalizeLpNorm : Microsoft.ML.TransformsCatalog * string * string * Microsoft.ML.Transforms.LpNormNormalizingEstimatorBase.NormFunction * bool -> Microsoft.ML.Transforms.LpNormNormalizingEstimator<Extension()>
Public Function NormalizeLpNorm (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional norm As LpNormNormalizingEstimatorBase.NormFunction = Microsoft.ML.Transforms.LpNormNormalizingEstimatorBase+NormFunction.L2, Optional ensureZeroMean As Boolean = false) As LpNormNormalizingEstimator参数
- catalog
- TransformsCatalog
转换的目录。
- outputColumnName
- String
由转换 inputColumnName生成的列的名称。
此列的数据类型将与输入列的数据类型相同。
用于规范化每个样本的规范类型。 所得到向量的指示规范化为 1。
- ensureZeroMean
- Boolean
如果 true，则从每个值中减去平均值，然后再规范化并使用原始输入。
返回
示例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
namespace Samples.Dynamic
{
class NormalizeLpNorm
{
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.NormalizeLpNorm("Features",
norm: LpNormNormalizingEstimatorBase.NormFunction.L1,
ensureZeroMean: 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:
// 0.2500, 0.2500, -0.2500, -0.2500
// 0.2500, 0.2500, -0.2500, -0.2500
// 0.2500, -0.2500, 0.2500, -0.2500
// -0.2500, 0.2500, -0.2500, 0.2500
}
private class DataPoint
{
[VectorType(4)]
public float[] Features { get; set; }
}
}
}