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MklComponentsCatalog Class

  • Reference

Definition

Namespace:
Microsoft.ML
Assembly:
Microsoft.ML.Mkl.Components.dll
Package:
Microsoft.ML.Mkl.Components v3.0.1
Package:
Microsoft.ML.Mkl.Components v1.0.0
Package:
Microsoft.ML.Mkl.Components v1.1.0
Package:
Microsoft.ML.Mkl.Components v1.2.0
Package:
Microsoft.ML.Mkl.Components v1.3.1
Package:
Microsoft.ML.Mkl.Components v1.4.0
Package:
Microsoft.ML.Mkl.Components v1.5.5
Package:
Microsoft.ML.Mkl.Components v1.6.0
Package:
Microsoft.ML.Mkl.Components v1.7.0
Package:
Microsoft.ML.Mkl.Components v2.0.0

Collection of extension methods for RegressionCatalog.RegressionTrainers, BinaryClassificationCatalog.BinaryClassificationTrainers, and TransformsCatalog to create MKL (Math Kernel Library) trainer and transform components.

public static class MklComponentsCatalog
type MklComponentsCatalog = class
Public Module MklComponentsCatalog
Inheritance
Object
MklComponentsCatalog

Methods

Ols(RegressionCatalog+RegressionTrainers, OlsTrainer+Options)

Create OlsTrainer with advanced options, which predicts a target using a linear regression model.
Ols(RegressionCatalog+RegressionTrainers, String, String, String)

Create OlsTrainer, which predicts a target using a linear regression model.
SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, Int32)

Create SymbolicSgdLogisticRegressionBinaryTrainer, which predicts a target using a linear binary classification model trained over boolean label data. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function. The SymbolicSgdLogisticRegressionBinaryTrainer parallelizes SGD using symbolic execution.
SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)

Create SymbolicSgdLogisticRegressionBinaryTrainer with advanced options, which predicts a target using a linear binary classification model trained over boolean label data. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function. The SymbolicSgdLogisticRegressionBinaryTrainer parallelizes SGD using symbolic execution.
VectorWhiten(TransformsCatalog, String, String, WhiteningKind, Single, Int32, Int32)

Takes column filled with a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix, meaning that they are uncorrelated and each have variance 1.

Applies to