/// <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) { }