NormalizationCatalog NormalizationCatalog NormalizationCatalog Class

Definition

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

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

Methods

NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32) NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32) 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) NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32) 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) NormalizeGlobalContrast(TransformsCatalog, String, String, Boolean, Boolean, Single) 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[], Int64, Boolean) NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean) 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, String, Int64, Boolean) NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean) 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) NormalizeLpNorm(TransformsCatalog, String, String, LpNormNormalizingEstimatorBase+NormFunction, Boolean) 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) NormalizeMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Boolean) 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) NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean) 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) NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean) 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) NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean) NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)

Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.

NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32) NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32) 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) NormalizeSupervisedBinning(TransformsCatalog, String, String, String, Int64, Boolean, Int32, Int32) 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