/// <summary>
/// Create the ML context.
/// </summary>
/// <param name="seed">Random seed. Set to <c>null</c> for a non-deterministic environment.</param>
/// <param name="conc">Concurrency level. Set to 1 to run single-threaded. Set to 0 to pick automatically.</param>
public MLContext(int?seed = null, int conc = 0)
{
_env = new LocalEnvironment(seed, conc, MakeCompositionContainer);
_env.AddListener(ProcessMessage);
BinaryClassification = new BinaryClassificationCatalog(_env);
MulticlassClassification = new MulticlassClassificationCatalog(_env);
Regression = new RegressionCatalog(_env);
Clustering = new ClusteringCatalog(_env);
Ranking = new RankingCatalog(_env);
Transforms = new TransformsCatalog(_env);
Model = new ModelOperationsCatalog(_env);
Data = new DataOperationsCatalog(_env);
}
/// <summary>
/// Create the ML context.
/// </summary>
/// <param name="seed">Random seed. Set to <c>null</c> for a non-deterministic environment.</param>
public MLContext(int?seed = null)
{
_env = new LocalEnvironment(seed);
_env.AddListener(ProcessMessage);
BinaryClassification = new BinaryClassificationCatalog(_env);
MulticlassClassification = new MulticlassClassificationCatalog(_env);
Regression = new RegressionCatalog(_env);
Clustering = new ClusteringCatalog(_env);
Ranking = new RankingCatalog(_env);
AnomalyDetection = new AnomalyDetectionCatalog(_env);
Transforms = new TransformsCatalog(_env);
Model = new ModelOperationsCatalog(_env);
Data = new DataOperationsCatalog(_env);
}
internal ClusteringTrainers(ClusteringCatalog catalog)
: base(catalog)
{
}
