NormalizationCatalog Class

  • Reference

Definition

Namespace:
Microsoft.ML
Assembly:
Microsoft.ML.Transforms.dll
Package:
Microsoft.ML v3.0.1
Package:
Microsoft.ML v1.0.0
Package:
Microsoft.ML v1.1.0
Package:
Microsoft.ML v1.2.0
Package:
Microsoft.ML v1.3.1
Package:
Microsoft.ML v1.4.0
Package:
Microsoft.ML v1.5.5
Package:
Microsoft.ML v1.6.0
Package:
Microsoft.ML v1.7.0
Package:
Microsoft.ML v2.0.0

Collection of extension methods for TransformsCatalog to create instances of numerical normalization components.

public static class NormalizationCatalog
type NormalizationCatalog = class
Public Module NormalizationCatalog
Inheritance
Object
NormalizationCatalog

Methods

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. Setting ensureZeroMean to true, will apply a pre-processing step to make the specified column's mean be the zero vector.
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. The type of norm that is used is defined by norm. Setting ensureZeroMean to true, will apply a pre-processing step to make the specified column's mean be a zero vector.
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.

Applies to