NormalizingTransformer.CdfNormalizerModelParameters<TData> Kelas

Definisi

Ruang nama:

Microsoft.ML.Transforms

Rakitan:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v3.0.1

Parameter model yang dihasilkan oleh transformasi normalisasi distribusi kumulatif. Fungsi kepadatan kumulatif diparameterkan oleh Mean dan seperti yang StandardDeviation diamati selama pemasangan.

public sealed class NormalizingTransformer.CdfNormalizerModelParameters<TData> : Microsoft.ML.Transforms.NormalizingTransformer.NormalizerModelParametersBase

type NormalizingTransformer.CdfNormalizerModelParameters<'Data> = class
    inherit NormalizingTransformer.NormalizerModelParametersBase

Public NotInheritable Class NormalizingTransformer.CdfNormalizerModelParameters(Of TData)
Inherits NormalizingTransformer.NormalizerModelParametersBase

Jenis parameter

TData

Warisan

Object

NormalizingTransformer.NormalizerModelParametersBase

NormalizingTransformer.CdfNormalizerModelParameters<TData>

Contoh

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 NormalizeLogMeanVariance
    {
        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[5] { 1, 1, 3, 0, float.MaxValue } },
                new DataPoint(){ Features = new float[5] { 2, 2, 2, 0, float.MinValue } },
                new DataPoint(){ Features = new float[5] { 0, 0, 1, 0, 0} },
                new DataPoint(){ Features = new float[5] {-1,-1,-1, 1, 1} }
            };
            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);
            // NormalizeLogMeanVariance normalizes the data based on the computed
            // mean and variance of the logarithm of the data.
            // Uses Cumulative distribution function as output.
            var normalize = mlContext.Transforms.NormalizeLogMeanVariance(
                "Features", useCdf: true);

            // NormalizeLogMeanVariance normalizes the data based on the computed
            // mean and variance of the logarithm of the data.
            var normalizeNoCdf = mlContext.Transforms.NormalizeLogMeanVariance(
                "Features", useCdf: false);

            // 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 normalizeNoCdfTransform = normalizeNoCdf.Fit(data);
            var noCdfData = normalizeNoCdfTransform.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:
            //  0.1587, 0.1587, 0.8654, 0.0000, 0.8413
            //  0.8413, 0.8413, 0.5837, 0.0000, 0.0000
            //  0.0000, 0.0000, 0.0940, 0.0000, 0.0000
            //  0.0000, 0.0000, 0.0000, 0.0000, 0.1587

            var columnFixZero = noCdfData.GetColumn<float[]>("Features").ToArray();
            foreach (var row in columnFixZero)
                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
                    "f4"))));
            // Expected output:
            //  1.8854, 1.8854, 5.2970, 0.0000, 7670682000000000000000000000000000000.0000
            //  4.7708, 4.7708, 3.0925, 0.0000, -7670682000000000000000000000000000000.0000
            // -1.0000,-1.0000, 0.8879, 0.0000, -1.0000
            // -3.8854,-3.8854,-3.5213, 0.0000, -0.9775

            // Let's 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 CdfNormalizerModelParameters<ImmutableArray<float>>;

            Console.WriteLine("The 1-index value in resulting array would be " +
                "produce by:");

            Console.WriteLine("y = 0.5* (1 + ERF((Math.Log(x)- " + transformParams
                .Mean[1] + ") / (" + transformParams.StandardDeviation[1] +
                " * sqrt(2)))");

            // ERF is https://en.wikipedia.org/wiki/Error_function.
            // Expected output:
            //  The 1-index value in resulting array would be produce by:
            //  y = 0.5* (1 + ERF((Math.Log(x)- 0.3465736) / (0.3465736 * sqrt(2)))
            var noCdfParams = normalizeNoCdfTransform.GetNormalizerModelParameters(
                0) as AffineNormalizerModelParameters<ImmutableArray<float>>;
            var offset = noCdfParams.Offset.Length == 0 ? 0 : noCdfParams.Offset[1];
            var scale = noCdfParams.Scale[1];
            Console.WriteLine($"The 1-index value in resulting array would be " +
                $"produce by: y = (x - ({offset})) * {scale}");
            // Expected output:
            // The 1-index value in resulting array would be produce by: y = (x - (0.3465736)) * 2.88539
        }

        private class DataPoint
        {
            [VectorType(5)]
            public float[] Features { get; set; }
        }
    }
}

Properti

Mean	Rata-rata. Dalam kasus skalar, ini adalah nilai tunggal. Dalam kasus vektor ini panjangnya sama dengan jumlah slot.
StandardDeviation	Simpang siur standar. Dalam kasus skalar, ini adalah nilai tunggal. Dalam kasus vektor ini panjangnya sama dengan jumlah slot.
UseLog	Apakah kita harus menerapkan logaritma ke input terlebih dahulu.