3 instantiations of SymbolicSgdLogisticRegressionBinaryTrainer
Microsoft.ML.Mkl.Components (3)
MklComponentsCatalog.cs (2)
103return new SymbolicSgdLogisticRegressionBinaryTrainer(env, options); 127return new SymbolicSgdLogisticRegressionBinaryTrainer(env, options);
SymSgdClassificationTrainer.cs (1)
294() => new SymbolicSgdLogisticRegressionBinaryTrainer(host, options),
43 references to SymbolicSgdLogisticRegressionBinaryTrainer
Microsoft.ML.AutoML (3)
TrainerExtensions\BinaryTrainerExtensions.cs (1)
225var options = TrainerExtensionUtil.CreateOptions<SymbolicSgdLogisticRegressionBinaryTrainer.Options>(sweepParams, columnInfo.LabelColumnName);
TrainerExtensions\MultiTrainerExtensions.cs (2)
180var binaryTrainer = _binaryLearnerCatalogItem.CreateInstance(mlContext, sweepParams, columnInfo) as SymbolicSgdLogisticRegressionBinaryTrainer;
Microsoft.ML.Core.Tests (1)
UnitTests\TestEntryPoints.cs (1)
330Env.ComponentCatalog.RegisterAssembly(typeof(SymbolicSgdLogisticRegressionBinaryTrainer).Assembly);
Microsoft.ML.IntegrationTests (2)
Training.cs (2)
409var trainer = mlContext.BinaryClassification.Trainers.SymbolicSgdLogisticRegression( 410new SymbolicSgdLogisticRegressionBinaryTrainer.Options
Microsoft.ML.Mkl.Components (27)
MklComponentsCatalog.cs (10)
74/// Create <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/>, which predicts a target using a linear binary classification model trained over boolean label data. 76/// The <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/> parallelizes SGD using <a href="https://www.microsoft.com/en-us/research/project/project-parade/#!symbolic-execution">symbolic execution</a>. 89public static SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, 92int numberOfIterations = SymbolicSgdLogisticRegressionBinaryTrainer.Defaults.NumberOfIterations) 97var options = new SymbolicSgdLogisticRegressionBinaryTrainer.Options 107/// Create <see cref= "SymbolicSgdLogisticRegressionBinaryTrainer" /> with advanced options, which predicts a target using a linear binary classification model trained over boolean label data. 109/// The <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/> parallelizes SGD using <a href="https://www.microsoft.com/en-us/research/project/project-parade/#!symbolic-execution">symbolic execution</a>. 112/// <param name="options">Algorithm advanced options. See <see cref="SymbolicSgdLogisticRegressionBinaryTrainer.Options"/>.</param> 120public static SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression( 122SymbolicSgdLogisticRegressionBinaryTrainer.Options options)
SymSgdClassificationTrainer.cs (17)
22[assembly: LoadableClass(typeof(SymbolicSgdLogisticRegressionBinaryTrainer), typeof(SymbolicSgdLogisticRegressionBinaryTrainer.Options), 24SymbolicSgdLogisticRegressionBinaryTrainer.UserNameValue, 25SymbolicSgdLogisticRegressionBinaryTrainer.LoadNameValue, 26SymbolicSgdLogisticRegressionBinaryTrainer.ShortName)] 28[assembly: LoadableClass(typeof(void), typeof(SymbolicSgdLogisticRegressionBinaryTrainer), null, typeof(SignatureEntryPointModule), SymbolicSgdLogisticRegressionBinaryTrainer.LoadNameValue)] 70/// <seealso cref="Microsoft.ML.MklComponentsCatalog.SymbolicSgdLogisticRegression(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer.Options)"/> 79/// Options for the <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/> as used in 238/// Initializes a new instance of <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/> 266/// Continues the training of <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/> using an already trained <paramref name="modelParameters"/> 284UserName = SymbolicSgdLogisticRegressionBinaryTrainer.UserNameValue, 285ShortName = SymbolicSgdLogisticRegressionBinaryTrainer.ShortName)] 367private readonly SymbolicSgdLogisticRegressionBinaryTrainer _trainer; 379public ArrayManager(SymbolicSgdLogisticRegressionBinaryTrainer trainer, IChannel ch) 543private readonly SymbolicSgdLogisticRegressionBinaryTrainer _trainer; 554public InputDataManager(SymbolicSgdLogisticRegressionBinaryTrainer trainer, FloatLabelCursor.Factory cursorFactory, IChannel ch)
Microsoft.ML.Predictor.Tests (1)
TestPredictors.cs (1)
46environment.ComponentCatalog.RegisterAssembly(typeof(SymbolicSgdLogisticRegressionBinaryTrainer).Assembly);
Microsoft.ML.Samples (3)
Dynamic\Trainers\BinaryClassification\SymbolicSgdLogisticRegression.cs (1)
30var pipeline = mlContext.BinaryClassification.Trainers
Dynamic\Trainers\BinaryClassification\SymbolicSgdLogisticRegressionWithOptions.cs (2)
31var options = new SymbolicSgdLogisticRegressionBinaryTrainer.Options() 39var pipeline = mlContext.BinaryClassification.Trainers
Microsoft.ML.Tests (6)
FeatureContributionTests.cs (1)
178new SymbolicSgdLogisticRegressionBinaryTrainer.Options()
Scenarios\Api\Estimators\SimpleTrainAndPredict.cs (1)
68.Append(ml.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(new SymbolicSgdLogisticRegressionBinaryTrainer.Options
TrainerEstimators\SymSgdClassificationTests.cs (4)
20var trainer = new SymbolicSgdLogisticRegressionBinaryTrainer(Env, new SymbolicSgdLogisticRegressionBinaryTrainer.Options()); 39var withInitPredictor = new SymbolicSgdLogisticRegressionBinaryTrainer(Env, new SymbolicSgdLogisticRegressionBinaryTrainer.Options()).Fit(transformedData, 43var notInitPredictor = new SymbolicSgdLogisticRegressionBinaryTrainer(Env, new SymbolicSgdLogisticRegressionBinaryTrainer.Options()).Fit(transformedData);