3 instantiations of RandomizedPcaTrainer
Microsoft.ML.PCA (3)
PCACatalog.cs (2)
77
return new
RandomizedPcaTrainer
(env, featureColumnName, exampleWeightColumnName, rank, oversampling, ensureZeroMean, seed);
99
return new
RandomizedPcaTrainer
(env, options);
PcaTrainer.cs (1)
405
() => new
RandomizedPcaTrainer
(host, input),
33 references to RandomizedPcaTrainer
Microsoft.ML.Core.Tests (2)
UnitTests\TestEntryPoints.cs (2)
4323
var pcaInput = new
RandomizedPcaTrainer
.Options
4327
var model =
RandomizedPcaTrainer
.TrainPcaAnomaly(Env, pcaInput).PredictorModel;
Microsoft.ML.IntegrationTests (1)
Evaluation.cs (1)
38
var
pipeline = mlContext.AnomalyDetection.Trainers.RandomizedPca();
Microsoft.ML.PCA (16)
PCACatalog.cs (5)
9
using static Microsoft.ML.Trainers.
RandomizedPcaTrainer
;
47
/// Create <see cref="
RandomizedPcaTrainer
"/>, which trains an approximate principal component analysis (PCA) model using randomized singular value decomposition (SVD) algorithm.
67
public static
RandomizedPcaTrainer
RandomizedPca(this AnomalyDetectionCatalog.AnomalyDetectionTrainers catalog,
81
/// Create <see cref="
RandomizedPcaTrainer
"/> with advanced options, which trains an approximate principal component analysis (PCA) model using randomized singular value decomposition (SVD) algorithm.
95
public static
RandomizedPcaTrainer
RandomizedPca(this AnomalyDetectionCatalog.AnomalyDetectionTrainers catalog, Options options)
PcaTrainer.cs (11)
20
[assembly: LoadableClass(
RandomizedPcaTrainer
.Summary, typeof(
RandomizedPcaTrainer
), typeof(
RandomizedPcaTrainer
.Options),
22
RandomizedPcaTrainer
.UserNameValue,
23
RandomizedPcaTrainer
.LoadNameValue,
24
RandomizedPcaTrainer
.ShortName)]
29
[assembly: LoadableClass(typeof(void), typeof(
RandomizedPcaTrainer
), null, typeof(SignatureEntryPointModule),
RandomizedPcaTrainer
.LoadNameValue)]
91
/// Options for the <see cref="
RandomizedPcaTrainer
"/> as used in
134
/// Initializes a new instance of <see cref="
RandomizedPcaTrainer
"/>.
412
/// Model parameters for <see cref="
RandomizedPcaTrainer
"/>.
Microsoft.ML.Samples (3)
Dynamic\Trainers\AnomalyDetection\RandomizedPcaSample.cs (1)
36
var
pipeline = mlContext.AnomalyDetection.Trainers.RandomizedPca(
Dynamic\Trainers\AnomalyDetection\RandomizedPcaSampleWithOptions.cs (2)
36
var options = new Microsoft.ML.Trainers.
RandomizedPcaTrainer
.Options()
45
var
pipeline = mlContext.AnomalyDetection.Trainers.RandomizedPca(
Microsoft.ML.Tests (11)
AnomalyDetectionTests.cs (10)
62
var
trainer1 = mlContext.AnomalyDetection.Trainers.RandomizedPca(featureColumnName: nameof(DataPoint.Features), rank: 1, ensureZeroMean: false);
68
var options = new Trainers.
RandomizedPcaTrainer
.Options()
77
var
trainer2 = mlContext.AnomalyDetection.Trainers.RandomizedPca(options);
92
var
trainer1 = mlContext.AnomalyDetection.Trainers.RandomizedPca(featureColumnName: nameof(DataPoint.Features), rank: 1, ensureZeroMean: false);
98
var options = new Trainers.
RandomizedPcaTrainer
.Options()
107
var
trainer2 = mlContext.AnomalyDetection.Trainers.RandomizedPca(options);
136
private static void ExecutePipelineWithGivenRandomizedPcaTrainer(MLContext mlContext, Trainers.
RandomizedPcaTrainer
trainer)
180
private static void ExecuteRandomizedPcaTrainerChangeThreshold(MLContext mlContext, Trainers.
RandomizedPcaTrainer
trainer)
249
var
trainer = ML.AnomalyDetection.Trainers.RandomizedPca();
264
var
trainer = mlContext.AnomalyDetection.Trainers.RandomizedPca(
TrainerEstimators\TrainerEstimators.cs (1)
44
var
pipeline = new RandomizedPcaTrainer(Env, featureColumn, rank: 10, seed: 1);