KernelExpansionCatalog.ApproximatedKernelMap 方法

定义

命名空间:

Microsoft.ML

程序集:

Microsoft.ML.Transforms.dll

包:

Microsoft.ML v3.0.1

创建一个 ApproximatedKernelMappingEstimator 将输入向量映射到低维特征空间，其中内部产品近似于移位固定内核函数。

public static Microsoft.ML.Transforms.ApproximatedKernelMappingEstimator ApproximatedKernelMap (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, int rank = 1000, bool useCosAndSinBases = false, Microsoft.ML.Transforms.KernelBase generator = default, int? seed = default);

static member ApproximatedKernelMap : Microsoft.ML.TransformsCatalog * string * string * int * bool * Microsoft.ML.Transforms.KernelBase * Nullable<int> -> Microsoft.ML.Transforms.ApproximatedKernelMappingEstimator

<Extension()>
Public Function ApproximatedKernelMap (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional rank As Integer = 1000, Optional useCosAndSinBases As Boolean = false, Optional generator As KernelBase = Nothing, Optional seed As Nullable(Of Integer) = Nothing) As ApproximatedKernelMappingEstimator

参数

catalog

TransformsCatalog

转换的目录。

outputColumnName

String

由转换 inputColumnName生成的列的名称。 此列上的数据类型将是已知大小的向量 Single。

inputColumnName

String

要转换的列的名称。 If set to null, the value of the outputColumnName will be used as source. 此估算器对数据类型的 Single 已知大小的向量进行操作。

rank

Int32

要将输入映射到的功能空间的维度。

useCosAndSinBases

Boolean

如果 true，请使用 cos 和 sin basis 函数为每个随机傅立叶频率创建两个特征。 否则，仅使用 cos 基。 请注意，如果设置为 true，输出功能空间的维度将为 2*rank。

generator

KernelBase

指示要使用的内核的参数。 这两个可用的实现是 GaussianKernel 和 LaplacianKernel。

seed

Nullable<Int32>

随机数生成器的种子，用于生成新功能（如果未指定） (，则使用全局随机) 。

返回

ApproximatedKernelMappingEstimator

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
{
    public static class ApproximatedKernelMap
    {
        // Transform feature vector to another non-linear space. See
        // https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf.
        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[7] { 1, 1, 0, 0, 1, 0, 1} },
                new DataPoint(){ Features = new float[7] { 0, 0, 1, 0, 0, 1, 1} },
                new DataPoint(){ Features = new float[7] {-1, 1, 0,-1,-1, 0,-1} },
                new DataPoint(){ Features = new float[7] { 0,-1, 0, 1, 0,-1,-1} }
            };
            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);
            // ApproximatedKernel map takes data and maps it's to a random
            // low -dimensional space.
            var approximation = mlContext.Transforms.ApproximatedKernelMap(
                "Features", rank: 4, generator: new GaussianKernel(gamma: 0.7f),
                seed: 1);

            // 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:
            // -0.0119, 0.5867, 0.4942,  0.7041
            //  0.4720, 0.5639, 0.4346,  0.2671
            // -0.2243, 0.7071, 0.7053, -0.1681
            //  0.0846, 0.5836, 0.6575,  0.0581
        }

        private class DataPoint
        {
            [VectorType(7)]
            public float[] Features { get; set; }
        }

    }
}