/// <summary>
/// Trains an approximate PCA using Randomized SVD algorithm.
/// </summary>
/// <param name="catalog">The anomaly detection catalog trainer object.</param>
/// <param name="options">Advanced options to the algorithm.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static RandomizedPcaTrainer RandomizedPca(this AnomalyDetectionCatalog.AnomalyDetectionTrainers catalog, Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new RandomizedPcaTrainer(env, options));
}
/// <summary>
/// Trains an approximate PCA using Randomized SVD algorithm.
/// </summary>
/// <param name="catalog">The anomaly detection catalog trainer object.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="rank">The number of components in the PCA.</param>
/// <param name="oversampling">Oversampling parameter for randomized PCA 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>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static RandomizedPcaTrainer RandomizedPca(this AnomalyDetectionCatalog.AnomalyDetectionTrainers catalog,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int rank = Options.Defaults.NumComponents,
int oversampling = Options.Defaults.OversamplingParameters,
bool center = Options.Defaults.IsCenteredZeroMean,
int?seed = null)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new RandomizedPcaTrainer(env, featureColumnName, exampleWeightColumnName, rank, oversampling, center, seed));
}