/// <summary>
/// Takes column filled with a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix,
/// meaning that they are uncorrelated and each have variance 1.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="inputColumn">Name of the input column.</param>
/// <param name="outputColumn">Name of the column resulting from the transformation of <paramref name="inputColumn"/>.
/// Null means <paramref name="inputColumn"/> is replaced. </param>
/// <param name="kind">Whitening kind (PCA/ZCA).</param>
/// <param name="eps">Whitening constant, prevents division by zero.</param>
/// <param name="maxRows">Maximum number of rows used to train the transform.</param>
/// <param name="pcaNum">In case of PCA whitening, indicates the number of components to retain.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static VectorWhiteningEstimator VectorWhiten(this TransformsCatalog.ProjectionTransforms catalog,
string inputColumn, string outputColumn = null,
WhiteningKind kind = VectorWhiteningTransformer.Defaults.Kind,
float eps = VectorWhiteningTransformer.Defaults.Eps,
int maxRows = VectorWhiteningTransformer.Defaults.MaxRows,
int pcaNum = VectorWhiteningTransformer.Defaults.PcaNum)
=> new VectorWhiteningEstimator(CatalogUtils.GetEnvironment(catalog), inputColumn, outputColumn, kind, eps, maxRows, pcaNum);
/// <summary> /// Takes columns filled with a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix, /// meaning that they are uncorrelated and each have variance 1. /// </summary> /// <param name="catalog">The transform's catalog.</param> /// <param name="columns">Describes the parameters of the whitening process for each column pair.</param> public static VectorWhiteningEstimator VectorWhiten(this TransformsCatalog.ProjectionTransforms catalog, params VectorWhiteningTransformer.ColumnInfo[] columns) => new VectorWhiteningEstimator(CatalogUtils.GetEnvironment(catalog), columns);
/// <summary>Initializes a new instance of <see cref="PrincipalComponentAnalysisEstimator"/>.</summary> /// <param name="catalog">The transform's catalog.</param> /// <param name="columns">Input columns to apply PrincipalComponentAnalysis on.</param> public static PrincipalComponentAnalysisEstimator ProjectToPrincipalComponents(this TransformsCatalog.ProjectionTransforms catalog, params PcaTransform.ColumnInfo[] columns) => new PrincipalComponentAnalysisEstimator(CatalogUtils.GetEnvironment(catalog), columns);
/// <summary>Initializes a new instance of <see cref="PrincipalComponentAnalysisEstimator"/>.</summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="inputColumn">Input column to apply PrincipalComponentAnalysis on.</param>
/// <param name="outputColumn">Optional output column. Null means <paramref name="inputColumn"/> is replaced.</param>
/// <param name="weightColumn">The name of the weight column.</param>
/// <param name="rank">The number of principal components.</param>
/// <param name="overSampling">Oversampling parameter for randomized PrincipalComponentAnalysis training.</param>
/// <param name="center">If enabled, data is centered to be zero mean.</param>
/// <param name="seed">The seed for random number generation.</param>
public static PrincipalComponentAnalysisEstimator ProjectToPrincipalComponents(this TransformsCatalog.ProjectionTransforms catalog,
string inputColumn,
string outputColumn = null,
string weightColumn = PrincipalComponentAnalysisEstimator.Defaults.WeightColumn,
int rank = PrincipalComponentAnalysisEstimator.Defaults.Rank,
int overSampling = PrincipalComponentAnalysisEstimator.Defaults.Oversampling,
bool center = PrincipalComponentAnalysisEstimator.Defaults.Center,
int?seed = null)
=> new PrincipalComponentAnalysisEstimator(CatalogUtils.GetEnvironment(catalog),
inputColumn, outputColumn, weightColumn, rank, overSampling, center, seed);
/// <summary> /// Takes columns filled with a vector of floats and computes global contrast normalization of it. /// </summary> /// <param name="catalog">The transform's catalog.</param> /// <param name="columns"> Describes the parameters of the gcn-normaliztion process for each column pair.</param> public static GlobalContrastNormalizingEstimator GlobalContrastNormalize(this TransformsCatalog.ProjectionTransforms catalog, params LpNormalizingTransformer.GcnColumnInfo[] columns) => new GlobalContrastNormalizingEstimator(CatalogUtils.GetEnvironment(catalog), columns);
/// <summary>
/// Takes column filled with a vector of floats and computes global contrast normalization of it.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
/// <param name="substractMean">Subtract mean from each value before normalizing.</param>
/// <param name="useStdDev">Normalize by standard deviation rather than L2 norm.</param>
/// <param name="scale">Scale features by this value.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static GlobalContrastNormalizingEstimator GlobalContrastNormalize(this TransformsCatalog.ProjectionTransforms catalog, string outputColumnName, string inputColumnName = null,
bool substractMean = LpNormalizingEstimatorBase.Defaults.GcnSubstractMean,
bool useStdDev = LpNormalizingEstimatorBase.Defaults.UseStdDev,
float scale = LpNormalizingEstimatorBase.Defaults.Scale)
=> new GlobalContrastNormalizingEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, inputColumnName, substractMean, useStdDev, scale);
/// <summary> /// Takes columns filled with a vector of floats and computes L-p norm of it. /// </summary> /// <param name="catalog">The transform's catalog.</param> /// <param name="columns"> Describes the parameters of the lp-normalization process for each column pair.</param> public static LpNormalizingEstimator LpNormalize(this TransformsCatalog.ProjectionTransforms catalog, params LpNormalizingTransformer.LpNormColumnInfo[] columns) => new LpNormalizingEstimator(CatalogUtils.GetEnvironment(catalog), columns);
/// <summary>
/// Takes column filled with a vector of floats and computes L-p norm of it.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
/// <param name="normKind">Type of norm to use to normalize each sample.</param>
/// <param name="subMean">Subtract mean from each value before normalizing.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LpNormalizingEstimator LpNormalize(this TransformsCatalog.ProjectionTransforms catalog, string outputColumnName, string inputColumnName = null,
LpNormalizingEstimatorBase.NormalizerKind normKind = LpNormalizingEstimatorBase.Defaults.NormKind, bool subMean = LpNormalizingEstimatorBase.Defaults.LpSubstractMean)
=> new LpNormalizingEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, inputColumnName, normKind, subMean);
/// <summary> /// Takes columns filled with a vector of floats and maps its to a random low-dimensional feature space. /// </summary> /// <param name="catalog">The transform's catalog.</param> /// <param name="columns">The input columns to use for the transformation.</param> public static RandomFourierFeaturizingEstimator CreateRandomFourierFeatures(this TransformsCatalog.ProjectionTransforms catalog, params RandomFourierFeaturizingTransformer.ColumnInfo[] columns) => new RandomFourierFeaturizingEstimator(CatalogUtils.GetEnvironment(catalog), columns);
/// <summary>
/// Takes column filled with a vector of floats and maps its to a random low-dimensional feature space.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
/// <param name="newDim">The number of random Fourier features to create.</param>
/// <param name="useSin">Create two features for every random Fourier frequency? (one for cos and one for sin).</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static RandomFourierFeaturizingEstimator CreateRandomFourierFeatures(this TransformsCatalog.ProjectionTransforms catalog,
string outputColumnName,
string inputColumnName = null,
int newDim = RandomFourierFeaturizingEstimator.Defaults.NewDim,
bool useSin = RandomFourierFeaturizingEstimator.Defaults.UseSin)
=> new RandomFourierFeaturizingEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, inputColumnName, newDim, useSin);
