584data = new NormalizingEstimator(env, new NormalizingEstimator.MinMaxColumnOptions(splitColumnName, samplingKeyColumn, ensureZeroUntouched: false)).Fit(data).Transform(data);

300var normalizer = new NormalizingEstimator(env, new NormalizingEstimator.MinMaxColumnOptions(outputColumnName, inputColumnName ?? outputColumnName)); 320var normalizer = new NormalizingEstimator(env, columns); 338var normalizer = new NormalizingEstimator(env, columns); 358var normalizer = new NormalizingEstimator(env, columns); 379var normalizer = new NormalizingEstimator(env, columns); 401var normalizer = new NormalizingEstimator(env, columns);

33var normalizer = context.Transforms.NormalizeMinMax(DefaultColumnNames.Features);

584data = new NormalizingEstimator(env, new NormalizingEstimator.MinMaxColumnOptions(splitColumnName, samplingKeyColumn, ensureZeroUntouched: false)).Fit(data).Transform(data);

48/// More contemporaneous API usage of normalization ought to use <see cref="NormalizingEstimator"/> 300var normalizer = new NormalizingEstimator(env, new NormalizingEstimator.MinMaxColumnOptions(outputColumnName, inputColumnName ?? outputColumnName)); 314.Select(col => new NormalizingEstimator.MinMaxColumnOptions( 320var normalizer = new NormalizingEstimator(env, columns); 332.Select(col => new NormalizingEstimator.MeanVarianceColumnOptions( 338var normalizer = new NormalizingEstimator(env, columns); 352.Select(col => new NormalizingEstimator.LogMeanVarianceColumnOptions( 358var normalizer = new NormalizingEstimator(env, columns); 372.Select(col => new NormalizingEstimator.BinningColumnOptions( 379var normalizer = new NormalizingEstimator(env, columns); 393.Select(col => new NormalizingEstimator.RobustScalingColumnOptions( 401var normalizer = new NormalizingEstimator(env, columns); 972return CreateBuilder(new NormalizingEstimator.MinMaxColumnOptions( 979public static IColumnFunctionBuilder CreateBuilder(NormalizingEstimator.MinMaxColumnOptions column, IHost host, 1009return CreateBuilder(new NormalizingEstimator.MeanVarianceColumnOptions( 1017public static IColumnFunctionBuilder CreateBuilder(NormalizingEstimator.MeanVarianceColumnOptions column, IHost host, 1049return CreateBuilder(new NormalizingEstimator.LogMeanVarianceColumnOptions( 1056public static IColumnFunctionBuilder CreateBuilder(NormalizingEstimator.LogMeanVarianceColumnOptions column, IHost host, 1089return CreateBuilder(new NormalizingEstimator.BinningColumnOptions( 1097public static IColumnFunctionBuilder CreateBuilder(NormalizingEstimator.BinningColumnOptions column, IHost host, 1139new NormalizingEstimator.SupervisedBinningColumOptions( 1150public static IColumnFunctionBuilder CreateBuilder(NormalizingEstimator.SupervisedBinningColumOptions column, IHost host, 1157private static IColumnFunctionBuilder CreateBuilder(NormalizingEstimator.SupervisedBinningColumOptions column, IHost host, 1201return CreateBuilder(new NormalizingEstimator.RobustScalingColumnOptions( 1210public static IColumnFunctionBuilder CreateBuilder(NormalizingEstimator.RobustScalingColumnOptions column, IHost host,

1551public static IColumnFunctionBuilder Create(NormalizingEstimator.MinMaxColumnOptions column, IHost host, DataViewType srcType, 1601public static IColumnFunctionBuilder Create(NormalizingEstimator.MinMaxColumnOptions column, IHost host, VectorDataViewType srcType, 1665public static IColumnFunctionBuilder Create(NormalizingEstimator.MeanVarianceColumnOptions column, IHost host, DataViewType srcType, 1672public static IColumnFunctionBuilder Create(NormalizingEstimator.LogMeanVarianceColumnOptions column, IHost host, DataViewType srcType, 1746public static IColumnFunctionBuilder Create(NormalizingEstimator.MeanVarianceColumnOptions column, IHost host, VectorDataViewType srcType, 1754public static IColumnFunctionBuilder Create(NormalizingEstimator.LogMeanVarianceColumnOptions column, IHost host, VectorDataViewType srcType, 1867public static IColumnFunctionBuilder Create(NormalizingEstimator.BinningColumnOptions column, IHost host, DataViewType srcType, 1916public static IColumnFunctionBuilder Create(NormalizingEstimator.BinningColumnOptions column, IHost host, VectorDataViewType srcType, 2000public static IColumnFunctionBuilder Create(NormalizingEstimator.SupervisedBinningColumOptions column, IHost host, int valueColumnId, int labelColumnId, DataViewRow dataRow) 2040public static IColumnFunctionBuilder Create(NormalizingEstimator.SupervisedBinningColumOptions column, IHost host, int valueColumnId, int labelColumnId, DataViewRow dataRow) 2084public static IColumnFunctionBuilder Create(NormalizingEstimator.RobustScalingColumnOptions column, IHost host, DataViewType srcType, 2153public static IColumnFunctionBuilder Create(NormalizingEstimator.RobustScalingColumnOptions column, IHost host, VectorDataViewType srcType,

1714public static IColumnFunctionBuilder Create(NormalizingEstimator.MinMaxColumnOptions column, IHost host, DataViewType srcType, 1764public static IColumnFunctionBuilder Create(NormalizingEstimator.MinMaxColumnOptions column, IHost host, VectorDataViewType srcType, 1828public static IColumnFunctionBuilder Create(NormalizingEstimator.MeanVarianceColumnOptions column, IHost host, DataViewType srcType, 1835public static IColumnFunctionBuilder Create(NormalizingEstimator.LogMeanVarianceColumnOptions column, IHost host, DataViewType srcType, 1909public static IColumnFunctionBuilder Create(NormalizingEstimator.MeanVarianceColumnOptions column, IHost host, VectorDataViewType srcType, 1917public static IColumnFunctionBuilder Create(NormalizingEstimator.LogMeanVarianceColumnOptions column, IHost host, VectorDataViewType srcType, 2030public static IColumnFunctionBuilder Create(NormalizingEstimator.BinningColumnOptions column, IHost host, DataViewType srcType, 2079public static IColumnFunctionBuilder Create(NormalizingEstimator.BinningColumnOptions column, IHost host, VectorDataViewType srcType, 2164public static IColumnFunctionBuilder Create(NormalizingEstimator.SupervisedBinningColumOptions column, IHost host, int valueColumnId, int labelColumnId, DataViewRow dataRow) 2204public static IColumnFunctionBuilder Create(NormalizingEstimator.SupervisedBinningColumOptions column, IHost host, int valueColumnId, int labelColumnId, DataViewRow dataRow) 2248public static IColumnFunctionBuilder Create(NormalizingEstimator.RobustScalingColumnOptions column, IHost host, DataViewType srcType, 2315public static IColumnFunctionBuilder Create(NormalizingEstimator.RobustScalingColumnOptions column, IHost host, VectorDataViewType srcType,

291/// Initializes a new instance of <see cref="NormalizingEstimator"/>. 304/// Initializes a new instance of <see cref="NormalizingEstimator"/>. 312_host = env.Register(nameof(NormalizingEstimator)); 318/// Initializes a new instance of <see cref="NormalizingEstimator"/>. 325_host = env.Register(nameof(NormalizingEstimator)); 373/// <see cref="ITransformer"/> resulting from fitting an <see cref="NormalizingEstimator"/>. 531internal static NormalizingTransformer Train(IHostEnvironment env, IDataView data, NormalizingEstimator.ColumnOptionsBase[] columns) 550var supervisedBinColumn = info as NormalizingEstimator.SupervisedBinningColumOptions;

329var pipeline = mlContext.Transforms.NormalizeMinMax("Features"); 426var estimator = mlContext.Transforms.NormalizeMinMax("Features"); 459var estimator = mlContext.Transforms.NormalizeMinMax("Features");

32var normalize = mlContext.Transforms.NormalizeBinning("Features", 39var normalizeFixZero = mlContext.Transforms.NormalizeBinning("Features",

37var normalize = mlContext.Transforms.NormalizeBinning(new[]{

31var normalize = mlContext.Transforms.NormalizeLogMeanVariance( 36var normalizeNoCdf = mlContext.Transforms.NormalizeLogMeanVariance(

29var normalize = mlContext.Transforms.NormalizeLogMeanVariance("Features", true, useCdf: true); 32var normalizeNoCdf = mlContext.Transforms.NormalizeLogMeanVariance("Features", true, useCdf: false);

31var normalize = mlContext.Transforms.NormalizeMeanVariance("Features", 36var normalizeNoCdf = mlContext.Transforms.NormalizeMeanVariance(

29var normalize = mlContext.Transforms.NormalizeMinMax("Features", 35var normalizeFixZero = mlContext.Transforms.NormalizeMinMax("Features",

53var normalize = mlContext.Transforms.NormalizeMinMax(columnPair, 59var normalizeFixZero = mlContext.Transforms.NormalizeMinMax(columnPair,

44var normalize = mlContext.Transforms.NormalizeSupervisedBinning( 52var normalizeFixZero = mlContext.Transforms.NormalizeSupervisedBinning(

710.Append(_mlContext.Transforms.Normalize(new NormalizingEstimator.MinMaxColumnOptions("Features", "Placeholder")))

312new NormalizingEstimator.MinMaxColumnOptions("MinMaxNormalized", "Features", ensureZeroUntouched: true), 313new NormalizingEstimator.MeanVarianceColumnOptions("MeanVarNormalized", "Features", fixZero: true), 314new NormalizingEstimator.BinningColumnOptions("BinNormalized", "Features", maximumBinCount: 256));

25var regressionPipe = ML.Transforms.NormalizeMinMax("Features"); 39var binaryPipe = ML.Transforms.NormalizeMinMax("Features");

50var est = new NormalizingEstimator(Env, 51new NormalizingEstimator.MinMaxColumnOptions("float1"), 52new NormalizingEstimator.MinMaxColumnOptions("float4"), 53new NormalizingEstimator.MinMaxColumnOptions("double1"), 54new NormalizingEstimator.MinMaxColumnOptions("double4"), 55new NormalizingEstimator.BinningColumnOptions("float1bin", "float1"), 56new NormalizingEstimator.BinningColumnOptions("float4bin", "float4"), 57new NormalizingEstimator.BinningColumnOptions("double1bin", "double1"), 58new NormalizingEstimator.BinningColumnOptions("double4bin", "double4"), 59new NormalizingEstimator.SupervisedBinningColumOptions("float1supervisedbin", "float1", labelColumnName: "int1"), 60new NormalizingEstimator.SupervisedBinningColumOptions("float4supervisedbin", "float4", labelColumnName: "int1"), 61new NormalizingEstimator.SupervisedBinningColumOptions("double1supervisedbin", "double1", labelColumnName: "int1"), 62new NormalizingEstimator.SupervisedBinningColumOptions("double4supervisedbin", "double4", labelColumnName: "int1"), 63new NormalizingEstimator.MeanVarianceColumnOptions("float1mv", "float1"), 64new NormalizingEstimator.MeanVarianceColumnOptions("float4mv", "float4"), 65new NormalizingEstimator.MeanVarianceColumnOptions("double1mv", "double1"), 66new NormalizingEstimator.MeanVarianceColumnOptions("double4mv", "double4"), 67new NormalizingEstimator.LogMeanVarianceColumnOptions("float1lmv", "float1"), 68new NormalizingEstimator.LogMeanVarianceColumnOptions("float4lmv", "float4"), 69new NormalizingEstimator.LogMeanVarianceColumnOptions("double1lmv", "double1"), 70new NormalizingEstimator.LogMeanVarianceColumnOptions("double4lmv", "double4"), 71new NormalizingEstimator.RobustScalingColumnOptions("float1rb", "float1"), 72new NormalizingEstimator.RobustScalingColumnOptions("float4rb", "float4"), 73new NormalizingEstimator.RobustScalingColumnOptions("double1rb", "double1"), 74new NormalizingEstimator.RobustScalingColumnOptions("double4rb", "double4")); 119var est = new NormalizingEstimator(Env, 120new NormalizingEstimator.MinMaxColumnOptions("float1"), 121new NormalizingEstimator.MinMaxColumnOptions("float4"), 122new NormalizingEstimator.MinMaxColumnOptions("double1"), 123new NormalizingEstimator.MinMaxColumnOptions("double4"), 124new NormalizingEstimator.BinningColumnOptions("float1bin", "float1"), 125new NormalizingEstimator.BinningColumnOptions("float4bin", "float4"), 126new NormalizingEstimator.BinningColumnOptions("double1bin", "double1"), 127new NormalizingEstimator.BinningColumnOptions("double4bin", "double4"), 128new NormalizingEstimator.MeanVarianceColumnOptions("float1mv", "float1"), 129new NormalizingEstimator.MeanVarianceColumnOptions("float4mv", "float4"), 130new NormalizingEstimator.MeanVarianceColumnOptions("double1mv", "double1"), 131new NormalizingEstimator.MeanVarianceColumnOptions("double4mv", "double4"), 132new NormalizingEstimator.LogMeanVarianceColumnOptions("float1lmv", "float1"), 133new NormalizingEstimator.LogMeanVarianceColumnOptions("float4lmv", "float4"), 134new NormalizingEstimator.LogMeanVarianceColumnOptions("double1lmv", "double1"), 135new NormalizingEstimator.LogMeanVarianceColumnOptions("double4lmv", "double4")); 391var robustScalerEstimator = context.Transforms.NormalizeRobustScaling( 481var est1 = new NormalizingEstimator(Env, "float4"); 482var est2 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.MinMax, ("float4", "float4")); 483var est3 = new NormalizingEstimator(Env, new NormalizingEstimator.MinMaxColumnOptions("float4")); 484var est4 = ML.Transforms.NormalizeMinMax("float4", "float4"); 485var est5 = ML.Transforms.NormalizeMinMax("float4"); 503var est6 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.MeanVariance, ("float4", "float4")); 504var est7 = new NormalizingEstimator(Env, new NormalizingEstimator.MeanVarianceColumnOptions("float4")); 505var est8 = ML.Transforms.NormalizeMeanVariance("float4", "float4"); 516var est9 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.LogMeanVariance, ("float4", "float4")); 517var est10 = new NormalizingEstimator(Env, new NormalizingEstimator.LogMeanVarianceColumnOptions("float4")); 518var est11 = ML.Transforms.NormalizeLogMeanVariance("float4", "float4"); 529var est12 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.Binning, ("float4", "float4")); 530var est13 = new NormalizingEstimator(Env, new NormalizingEstimator.BinningColumnOptions("float4")); 531var est14 = ML.Transforms.NormalizeBinning("float4", "float4"); 542var est15 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.SupervisedBinning, ("float4", "float4")); 543var est16 = new NormalizingEstimator(Env, new NormalizingEstimator.SupervisedBinningColumOptions("float4")); 544var est17 = ML.Transforms.NormalizeSupervisedBinning("float4", "float4"); 555var est18 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.RobustScaling, ("float4", "float4")); 556var est19 = new NormalizingEstimator(Env, new NormalizingEstimator.RobustScalingColumnOptions("float4")); 557var est20 = ML.Transforms.NormalizeRobustScaling("float4", "float4"); 586var est1 = ML.Transforms.NormalizeMinMax("float4", "float4"); 587var est2 = ML.Transforms.NormalizeMeanVariance("float4", "float4"); 588var est3 = ML.Transforms.NormalizeLogMeanVariance("float4", "float4"); 589var est4 = ML.Transforms.NormalizeBinning("float4", "float4"); 590var est5 = ML.Transforms.NormalizeSupervisedBinning("float4", "float4"); 593var est6 = ML.Transforms.NormalizeMinMax("float4", "float4"); 594var est7 = ML.Transforms.NormalizeMeanVariance("float4", "float4"); 595var est8 = ML.Transforms.NormalizeLogMeanVariance("float4", "float4"); 596var est9 = ML.Transforms.NormalizeBinning("float4", "float4"); 597var est10 = ML.Transforms.NormalizeSupervisedBinning("float4", "float4"); 642var est = ML.Transforms.NormalizeMinMax("output", "input"); 908var normalize = ML.Transforms.NormalizeLogMeanVariance("Features", true, useCdf: true); 911var normalizeNoCdf = ML.Transforms.NormalizeLogMeanVariance("Features", true, useCdf: false); 949var normalize = ML.Transforms.NormalizeLogMeanVariance("Features", true, useCdf: true); 952var normalizeNoCdf = ML.Transforms.NormalizeLogMeanVariance("Features", true, useCdf: false);

22/// <param name="mode">The <see cref="NormalizingEstimator.NormalizationMode"/> used to map the old values to the new ones. </param> 25internal static NormalizingEstimator Normalize(this TransformsCatalog catalog, 26NormalizingEstimator.NormalizationMode mode, 35/// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the observed minimum and maximum values of the data. 51public static NormalizingEstimator NormalizeMinMax(this TransformsCatalog catalog, 53long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 54bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched) 56var columnOptions = new NormalizingEstimator.MinMaxColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, fixZero); 61/// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the observed minimum and maximum values of the data. 76public static NormalizingEstimator NormalizeMinMax(this TransformsCatalog catalog, InputOutputColumnPair[] columns, 77long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 78bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched) => 81new NormalizingEstimator.MinMaxColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, fixZero)).ToArray()); 84/// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the data. 101public static NormalizingEstimator NormalizeMeanVariance(this TransformsCatalog catalog, 103long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 104bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched, 105bool useCdf = NormalizingEstimator.Defaults.MeanVarCdf) 107var columnOptions = new NormalizingEstimator.MeanVarianceColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, fixZero, useCdf); 112/// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the data. 121public static NormalizingEstimator NormalizeMeanVariance(this TransformsCatalog catalog, InputOutputColumnPair[] columns, 122long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 123bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched, 124bool useCdf = NormalizingEstimator.Defaults.MeanVarCdf) => 127new NormalizingEstimator.MeanVarianceColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, fixZero, useCdf)).ToArray()); 130/// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the logarithm of the data. 146public static NormalizingEstimator NormalizeLogMeanVariance(this TransformsCatalog catalog, 148long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 149bool useCdf = NormalizingEstimator.Defaults.LogMeanVarCdf) 151var columnOptions = new NormalizingEstimator.LogMeanVarianceColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, useCdf, false); 156/// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the logarithm of the data. 164public static NormalizingEstimator NormalizeLogMeanVariance(this TransformsCatalog catalog, InputOutputColumnPair[] columns, 165long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 166bool useCdf = NormalizingEstimator.Defaults.LogMeanVarCdf) => 169new NormalizingEstimator.LogMeanVarianceColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, useCdf, false)).ToArray()); 172/// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the logarithm of the data. 189public static NormalizingEstimator NormalizeLogMeanVariance(this TransformsCatalog catalog, 193long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 194bool useCdf = NormalizingEstimator.Defaults.LogMeanVarCdf) 196var columnOptions = new NormalizingEstimator.LogMeanVarianceColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, useCdf, fixZero); 201/// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the logarithm of the data. 210public static NormalizingEstimator NormalizeLogMeanVariance(this TransformsCatalog catalog, InputOutputColumnPair[] columns, 212long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 213bool useCdf = NormalizingEstimator.Defaults.LogMeanVarCdf) => 216new NormalizingEstimator.LogMeanVarianceColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, useCdf, fixZero)).ToArray()); 219/// Create a <see cref="NormalizingEstimator"/>, which normalizes by assigning the data into bins with equal density. 236public static NormalizingEstimator NormalizeBinning(this TransformsCatalog catalog, 238long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 239bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched, 240int maximumBinCount = NormalizingEstimator.Defaults.MaximumBinCount) 242var columnOptions = new NormalizingEstimator.BinningColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, fixZero, maximumBinCount); 247/// Create a <see cref="NormalizingEstimator"/>, which normalizes by assigning the data into bins with equal density. 263public static NormalizingEstimator NormalizeBinning(this TransformsCatalog catalog, InputOutputColumnPair[] columns, 264long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 265bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched, 266int maximumBinCount = NormalizingEstimator.Defaults.MaximumBinCount) => 269new NormalizingEstimator.BinningColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, fixZero, maximumBinCount)).ToArray()); 272/// Create a <see cref="NormalizingEstimator"/>, which normalizes by assigning the data into bins based on correlation with the <paramref name="labelColumnName"/> column. 291public static NormalizingEstimator NormalizeSupervisedBinning(this TransformsCatalog catalog, 294long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 295bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched, 296int maximumBinCount = NormalizingEstimator.Defaults.MaximumBinCount, 297int mininimumExamplesPerBin = NormalizingEstimator.Defaults.MininimumBinSize) 299var columnOptions = new NormalizingEstimator.SupervisedBinningColumOptions(outputColumnName, inputColumnName, labelColumnName, maximumExampleCount, fixZero, maximumBinCount, mininimumExamplesPerBin); 304/// Create a <see cref="NormalizingEstimator"/>, which normalizes by assigning the data into bins based on correlation with the <paramref name="labelColumnName"/> column. 315public static NormalizingEstimator NormalizeSupervisedBinning(this TransformsCatalog catalog, InputOutputColumnPair[] columns, 317long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 318bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched, 319int maximumBinCount = NormalizingEstimator.Defaults.MaximumBinCount, 320int mininimumExamplesPerBin = NormalizingEstimator.Defaults.MininimumBinSize) => 323new NormalizingEstimator.SupervisedBinningColumOptions( 327/// Create a <see cref="NormalizingEstimator"/>, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales 346public static NormalizingEstimator NormalizeRobustScaling(this TransformsCatalog catalog, 348long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 349bool centerData = NormalizingEstimator.Defaults.CenterData, 350uint quantileMin = NormalizingEstimator.Defaults.QuantileMin, 351uint quantileMax = NormalizingEstimator.Defaults.QuantileMax) 353var columnOptions = new NormalizingEstimator.RobustScalingColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, centerData, quantileMin, quantileMax); 358/// Create a <see cref="NormalizingEstimator"/>, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales 376public static NormalizingEstimator NormalizeRobustScaling(this TransformsCatalog catalog, InputOutputColumnPair[] columns, 377long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount, 378bool centerData = NormalizingEstimator.Defaults.CenterData, 379uint quantileMin = NormalizingEstimator.Defaults.QuantileMin, 380uint quantileMax = NormalizingEstimator.Defaults.QuantileMax) => 383new NormalizingEstimator.RobustScalingColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, centerData, quantileMin, quantileMax)).ToArray()); 391internal static NormalizingEstimator Normalize(this TransformsCatalog catalog, 392params NormalizingEstimator.ColumnOptionsBase[] columns)