internal LdSvmTrainer(IHostEnvironment env, Options options) : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(options.FeatureColumnName), TrainerUtils.MakeBoolScalarLabel(options.LabelColumnName), TrainerUtils.MakeR4ScalarWeightColumn(options.ExampleWeightColumnName)) { Host.CheckValue(options, nameof(options)); CheckOptions(Host, options); _options = options; }
/// <summary> /// Initializes a new instance of <see cref="SymbolicStochasticGradientDescentClassificationTrainer"/> /// </summary> internal SymbolicStochasticGradientDescentClassificationTrainer(IHostEnvironment env, Options options) : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(options.FeatureColumnName), TrainerUtils.MakeBoolScalarLabel(options.LabelColumnName)) { Host.CheckValue(options, nameof(options)); options.Check(Host); _options = options; Info = new TrainerInfo(supportIncrementalTrain: true); }
/// <summary> /// Initializes a new instance of <see cref="LogisticRegression"/> /// </summary> /// <param name="env">The environment to use.</param> /// <param name="labelColumn">The name of the label column.</param> /// <param name="featureColumn">The name of the feature column.</param> /// <param name="weights">The name for the example weight column.</param> /// <param name="enforceNoNegativity">Enforce non-negative weights.</param> /// <param name="l1Weight">Weight of L1 regularizer term.</param> /// <param name="l2Weight">Weight of L2 regularizer term.</param> /// <param name="memorySize">Memory size for <see cref="LogisticRegression"/>. Low=faster, less accurate.</param> /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param> internal LogisticRegression(IHostEnvironment env, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weights = null, float l1Weight = Options.Defaults.L1Weight, float l2Weight = Options.Defaults.L2Weight, float optimizationTolerance = Options.Defaults.OptTol, int memorySize = Options.Defaults.MemorySize, bool enforceNoNegativity = Options.Defaults.EnforceNonNegativity) : base(env, featureColumn, TrainerUtils.MakeBoolScalarLabel(labelColumn), weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enforceNoNegativity) { Host.CheckNonEmpty(featureColumn, nameof(featureColumn)); Host.CheckNonEmpty(labelColumn, nameof(labelColumn)); _posWeight = 0; ShowTrainingStats = LbfgsTrainerOptions.ShowTrainingStats; }
/// <summary> /// Initializes a new instance of <see cref="LogisticRegressionBinaryTrainer"/> /// </summary> /// <param name="env">The environment to use.</param> /// <param name="labelColumn">The name of the label column.</param> /// <param name="featureColumn">The name of the feature column.</param> /// <param name="exampleWeightColumnName">The name for the example weight column.</param> /// <param name="enforceNoNegativity">Enforce non-negative weights.</param> /// <param name="l1Regularization">Weight of L1 regularizer term.</param> /// <param name="l2Regularization">Weight of L2 regularizer term.</param> /// <param name="memorySize">Memory size for <see cref="LogisticRegressionBinaryTrainer"/>. Low=faster, less accurate.</param> /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param> internal LogisticRegressionBinaryTrainer(IHostEnvironment env, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string exampleWeightColumnName = null, float l1Regularization = Options.Defaults.L1Regularization, float l2Regularization = Options.Defaults.L2Regularization, float optimizationTolerance = Options.Defaults.OptimizationTolerance, int memorySize = Options.Defaults.HistorySize, bool enforceNoNegativity = Options.Defaults.EnforceNonNegativity) : base(env, featureColumn, TrainerUtils.MakeBoolScalarLabel(labelColumn), exampleWeightColumnName, l1Regularization, l2Regularization, optimizationTolerance, memorySize, enforceNoNegativity) { Host.CheckNonEmpty(featureColumn, nameof(featureColumn)); Host.CheckNonEmpty(labelColumn, nameof(labelColumn)); _posWeight = 0; ShowTrainingStats = LbfgsTrainerOptions.ShowTrainingStatistics; }
internal AveragedPerceptronTrainer(IHostEnvironment env, Options options) : base(options, env, UserNameValue, TrainerUtils.MakeBoolScalarLabel(options.LabelColumnName)) { _args = options; LossFunction = _args.LossFunction ?? _args.LossFunctionFactory.CreateComponent(env); }
/// <summary> /// Initializes a new instance of <see cref="LogisticRegression"/> /// </summary> internal LogisticRegression(IHostEnvironment env, Options options) : base(env, options, TrainerUtils.MakeBoolScalarLabel(options.LabelColumnName)) { _posWeight = 0; ShowTrainingStats = LbfgsTrainerOptions.ShowTrainingStats; }