merge fix.

Author: codemzs

src/Microsoft.ML.Transforms/NormalizerCatalog.cs

@@ -9,7 +9,6 @@ namespace Microsoft.ML
     /// </summary>
     public static class NormalizationCatalog
     {
-
         /// <summary>
         /// Normalize (rescale) several columns according to the specified <paramref name="mode"/>.
         /// </summary>
@@ -133,106 +132,6 @@ public static NormalizingEstimator NormalizeSupervisedBinning(this TransformsCat
             return new NormalizingEstimator(CatalogUtils.GetEnvironment(catalog), columnOptions);
         }
 
-        /// <summary>
-        /// Normalize (rescale) the column according to the <see cref="NormalizingEstimator.NormalizationMode.MinMax"/> mode.
-        /// It normalizes the data based on the observed minimum and maximum values of the data.
-        /// </summary>
-        /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
-        /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
-        public static NormalizingEstimator NormalizeMinMax(this TransformsCatalog catalog,
-           string outputColumnName, string inputColumnName = null,
-           long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount,
-           bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched)
-        {
-            var columnOptions = new NormalizingEstimator.MinMaxColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, fixZero);
-            return new NormalizingEstimator(CatalogUtils.GetEnvironment(catalog), columnOptions);
-        }
-
-        /// <summary>
-        /// Normalize (rescale) the column according to the <see cref="NormalizingEstimator.NormalizationMode.MeanVariance"/> mode.
-        /// It normalizes the data based on the computed mean and variance of the data.
-        /// </summary>
-        /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
-        /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
-        /// <param name="useCdf">Whether to use CDF as the output.</param>
-        public static NormalizingEstimator NormalizeMeanVariance(this TransformsCatalog catalog,
-            string outputColumnName, string inputColumnName = null,
-            long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount,
-            bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched,
-            bool useCdf = NormalizingEstimator.Defaults.MeanVarCdf)
-        {
-            var columnOptions = new NormalizingEstimator.MeanVarianceColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, fixZero, useCdf);
-            return new NormalizingEstimator(CatalogUtils.GetEnvironment(catalog), columnOptions);
-        }
-
-        /// <summary>
-        /// Normalize (rescale) the column according to the <see cref="NormalizingEstimator.NormalizationMode.LogMeanVariance"/> mode.
-        /// It normalizes the data based on the computed mean and variance of the logarithm of the data.
-        /// </summary>
-        /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
-        /// <param name="useCdf">Whether to use CDF as the output.</param>
-        public static NormalizingEstimator NormalizeLogMeanVariance(this TransformsCatalog catalog,
-            string outputColumnName, string inputColumnName = null,
-            long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount,
-            bool useCdf = NormalizingEstimator.Defaults.LogMeanVarCdf)
-        {
-            var columnOptions = new NormalizingEstimator.LogMeanVarianceColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, useCdf);
-            return new NormalizingEstimator(CatalogUtils.GetEnvironment(catalog), columnOptions);
-        }
-
-        /// <summary>
-        /// Normalize (rescale) the column according to the <see cref="NormalizingEstimator.NormalizationMode.Binning"/> mode.
-        /// The values are assigned into bins with equal density.
-        /// </summary>
-        /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
-        /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
-        /// <param name="maximumBinCount">Maximum number of bins (power of 2 recommended).</param>
-        public static NormalizingEstimator NormalizeBinning(this TransformsCatalog catalog,
-            string outputColumnName, string inputColumnName = null,
-            long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount,
-            bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched,
-            int maximumBinCount = NormalizingEstimator.Defaults.MaximumBinCount)
-        {
-            var columnOptions = new NormalizingEstimator.BinningColumnOptions(outputColumnName, inputColumnName, maximumExampleCount, fixZero, maximumBinCount);
-            return new NormalizingEstimator(CatalogUtils.GetEnvironment(catalog), columnOptions);
-        }
-
-        /// <summary>
-        /// Normalize (rescale) the column according to the <see cref="NormalizingEstimator.NormalizationMode.SupervisedBinning"/> mode.
-        /// The values are assigned into bins based on correlation with the <paramref name="labelColumnName"/> column.
-        /// </summary>
-        /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        /// <param name="labelColumnName">Name of the label column for supervised binning.</param>
-        /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
-        /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
-        /// <param name="maximumBinCount">Maximum number of bins (power of 2 recommended).</param>
-        /// <param name="mininimumExamplesPerBin">Minimum number of examples per bin.</param>
-        public static NormalizingEstimator NormalizeSupervisedBinning(this TransformsCatalog catalog,
-            string outputColumnName, string inputColumnName = null,
-            string labelColumnName = DefaultColumnNames.Label,
-            long maximumExampleCount = NormalizingEstimator.Defaults.MaximumExampleCount,
-            bool fixZero = NormalizingEstimator.Defaults.EnsureZeroUntouched,
-            int maximumBinCount = NormalizingEstimator.Defaults.MaximumBinCount,
-            int mininimumExamplesPerBin = NormalizingEstimator.Defaults.MininimumBinSize)
-        {
-            var columnOptions = new NormalizingEstimator.SupervisedBinningColumOptions(outputColumnName, inputColumnName, labelColumnName, maximumExampleCount, fixZero, maximumBinCount, mininimumExamplesPerBin);
-            return new NormalizingEstimator(CatalogUtils.GetEnvironment(catalog), columnOptions);
-        }
-
         /// <summary>
         /// Normalize (rescale) columns according to specified custom parameters.
         /// </summary>