KernelExpansionCatalog.ApproximatedKernelMap Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v3.0.1

ApproximatedKernelMappingEstimator Buat yang memetakan vektor input ke ruang fitur dimensi rendah di mana produk dalam mempertanyakan fungsi kernel shift-invariant.

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

Parameter

catalog

TransformsCatalog

Katalog transformasi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data pada kolom ini akan menjadi vektor berukuran besar yang diketahui dari Single.

inputColumnName

String

Nama kolom yang akan diubah. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber. Estimator ini beroperasi pada vektor jenis data berukuran Single dikenal.

rank

Int32

Dimensi ruang fitur untuk memetakan input.

useCosAndSinBases

Boolean

Jika true, gunakan fungsi dasar cos dan sin untuk membuat dua fitur untuk setiap frekuensi Fourier acak. Jika tidak, hanya basis cos yang akan digunakan. Perhatikan bahwa jika diatur ke true, dimensi ruang fitur output akan menjadi 2*rank.

generator

KernelBase

Argumen yang menunjukkan kernel mana yang akan digunakan. Dua implementasi yang tersedia adalah GaussianKernel dan LaplacianKernel.

seed

Nullable<Int32>

Benih generator angka acak untuk menghasilkan fitur baru (jika tidak ditentukan, acak global digunakan).

Mengembalikan

ApproximatedKernelMappingEstimator

Contoh

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; }
        }

    }
}