NormalizationCatalog 클래스

정의

네임스페이스:

Microsoft.ML

어셈블리:

Microsoft.ML.Transforms.dll

패키지:

Microsoft.ML v3.0.1

숫자 정규화 구성 요소의 인스턴스를 만들기 위한 TransformsCatalog 확장 메서드 컬렉션입니다.

public static class NormalizationCatalog

type NormalizationCatalog = class

Public Module NormalizationCatalog

상속

Object

NormalizationCatalog

메서드

NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32)	Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density.
NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32)	Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density.
NormalizeGlobalContrast(TransformsCatalog, String, String, Boolean, Boolean, Single)	Create a GlobalContrastNormalizingEstimator, which normalizes columns individually applying global contrast normalization. 로 true설정 ensureZeroMean 하면 사전 처리 단계가 적용되어 지정된 열의 평균이 0 벡터로 설정됩니다.
NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean)	Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)	Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean)	Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean)	Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
NormalizeLpNorm(TransformsCatalog, String, String, LpNormNormalizingEstimatorBase+NormFunction, Boolean)	Create a LpNormNormalizingEstimator, which normalizes (scales) vectors in the input column to the unit norm. 사용되는 norm의 형식은 .에 의해 norm정의됩니다. 로 true설정 ensureZeroMean 하면 사전 처리 단계를 적용하여 지정된 열의 평균을 0 벡터로 만듭니다.
NormalizeMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Boolean)	Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.
NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean)	Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.
NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)	Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.
NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)	Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.
NormalizeRobustScaling(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, UInt32, UInt32)	Create a NormalizingEstimator, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales the data according to the quantile range (defaults to the interquartile range).
NormalizeRobustScaling(TransformsCatalog, String, String, Int64, Boolean, UInt32, UInt32)	Create a NormalizingEstimator, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales the data according to the quantile range (defaults to the interquartile range).
NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32)	Create a NormalizingEstimator, which normalizes by assigning the data into bins based on correlation with the labelColumnName column.
NormalizeSupervisedBinning(TransformsCatalog, String, String, String, Int64, Boolean, Int32, Int32)	Create a NormalizingEstimator, which normalizes by assigning the data into bins based on correlation with the labelColumnName column.