/// <summary>
/// Initializes a new instance of <see cref="LbfgsLogisticRegressionBinaryTrainer"/>
/// </summary>
internal LbfgsLogisticRegressionBinaryTrainer(IHostEnvironment env, Options options)
: base(env, options, TrainerUtils.MakeBoolScalarLabel(options.LabelColumnName))
{
_posWeight = 0;
ShowTrainingStats = LbfgsTrainerOptions.ShowTrainingStatistics;
}
private RandomizedPcaTrainer(IHostEnvironment env, Options options, string featureColumnName, string exampleWeightColumnName,
int rank = 20, int oversampling = 20, bool center = true, int?seed = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(featureColumnName), default, TrainerUtils.MakeR4ScalarWeightColumn(exampleWeightColumnName))
{
// if the args are not null, we got here from maml, and the internal ctor.
if (options != null)
{
_rank = options.Rank;
_ensureZeroMean = options.EnsureZeroMean;
_oversampling = options.Oversampling;
_seed = options.Seed ?? Host.Rand.Next();
}
else
{
_rank = rank;
_ensureZeroMean = center;
_oversampling = oversampling;
_seed = seed ?? Host.Rand.Next();
}
_featureColumn = featureColumnName;
Host.CheckUserArg(_rank > 0, nameof(_rank), "Rank must be positive");
Host.CheckUserArg(_oversampling >= 0, nameof(_oversampling), "Oversampling must be non-negative");
}
/// <summary>
/// Initializes a new instance of <see cref="PoissonRegression"/>
/// </summary>
internal PoissonRegression(IHostEnvironment env, Options options)
: base(env, options, TrainerUtils.MakeR4ScalarColumn(options.LabelColumn))
{
}
/// <summary>
/// Initializes a new instance of <see cref="PoissonRegression"/>
/// </summary>
internal PoissonRegression(IHostEnvironment env, Arguments args)
: base(env, args, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn))
{
}
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="MultiClassNaiveBayesTrainer"/>
/// </summary>
internal MultiClassNaiveBayesTrainer(IHostEnvironment env, Options options)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadName), TrainerUtils.MakeR4VecFeature(options.FeatureColumnName),
TrainerUtils.MakeU4ScalarColumn(options.LabelColumnName))
{
Host.CheckValue(options, nameof(options));
}
/// <summary>
/// Initializes a new instance of <see cref="LogisticRegression"/>
/// </summary>
internal LogisticRegression(IHostEnvironment env, Options options)
: base(env, options, TrainerUtils.MakeBoolScalarLabel(options.LabelColumn))
{
_posWeight = 0;
ShowTrainingStats = Args.ShowTrainingStats;
}
internal OnlineGradientDescentTrainer(IHostEnvironment env, Options options)
: base(options, env, UserNameValue, TrainerUtils.MakeR4ScalarColumn(options.LabelColumnName))
{
LossFunction = options.LossFunction ?? options.LossFunctionFactory.CreateComponent(env);
}
/// <summary>
/// Initializes a new instance of <see cref="MulticlassLogisticRegression"/>
/// </summary>
internal MulticlassLogisticRegression(IHostEnvironment env, Options options)
: base(env, options, TrainerUtils.MakeU4ScalarColumn(options.LabelColumn))
{
ShowTrainingStats = Args.ShowTrainingStats;
}
/// <summary>
/// Initializes a new instance of <see cref="LogisticRegressionMulticlassClassificationTrainer"/>
/// </summary>
internal LogisticRegressionMulticlassClassificationTrainer(IHostEnvironment env, Options options)
: base(env, options, TrainerUtils.MakeU4ScalarColumn(options.LabelColumnName))
{
ShowTrainingStats = LbfgsTrainerOptions.ShowTrainingStatistics;
}