LinearDiscriminantAnalysis Class

Represents a linear discriminant classification model.

Definition

Namespace: Numerics.NET.Statistics.Multivariate
Assembly: Numerics.NET (in Numerics.NET.dll) Version: 9.0.2
C#
public class LinearDiscriminantAnalysis : ClassificationModel<double>, 
	ITransformationModel
Inheritance
Object  →  Model  →  ClassificationModel<Double>  →  LinearDiscriminantAnalysis
Implements
ITransformationModel

Remarks

Use the LinearDiscriminantAnalysis class to represent a classification model that uses linear discriminant analysis. LinearDiscriminantAnalysis can also be used for dimensionality reduction. In this case, the features are projected onto the directions that most separate the groups.

Constructors

Properties

BaseFeatureIndex Gets an index containing the keys of the columns that are required inputs to the model.
(Inherited from Model)
CanonicalStructureMatrix Gets the canonical structure matrix of the discriminant analysis.
CanPredictProbabilities Gets whether the classifier supports predicting probabilities for each class.
(Overrides ClassificationModel<T>.CanPredictProbabilities)
CategoryIndex Gets the category index of the dependent variable or targets.
(Inherited from ClassificationModel<T>)
Computed Gets whether the model has been computed.
(Inherited from Model)
Obsolete.
Data Gets an object that contains all the data used as input to the model.
(Inherited from Model)
DependentVariable Gets a vector that contains the dependent variable that is to be fitted.
(Inherited from ClassificationModel<T>)
DiscriminantFunctions Gets the collection of discriminant functions.
Fitted Gets whether the model has been computed.
(Inherited from Model)
IndependentVariables Gets a matrix whose columns contain the independent variables in the model.
(Inherited from ClassificationModel<T>)
InputSchema Gets the schema for the features used for fitting the model.
(Inherited from Model)
MaxDegreeOfParallelism Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model)
ModelSchema Gets the collection of variables used in the model.
(Inherited from Model)
NumberOfObservations Gets the number of observations the model is based on.
(Inherited from Model)
ParallelOptions Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model)
PredictedLogProbabilities Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from ClassificationModel<T>)
PredictedProbabilities Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from ClassificationModel<T>)
Predictions Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from ClassificationModel<T>)
Priors Gets or sets the prior probabilities of the groups.
ProbabilityResiduals Gets a matrix containing the residuals of the model.
(Inherited from ClassificationModel<T>)
Status Gets the status of the model, which determines which information is available.
(Inherited from Model)
SupportsWeights Indicates whether the model supports case weights.
(Inherited from Model)
Weights Gets or sets the actual weights.
(Inherited from Model)

Methods

Compute() Computes the model.
(Inherited from Model)
Obsolete.
Compute(ParallelOptions) Computes the model.
(Inherited from Model)
Obsolete.
Contains Returns whether another ClassificationModel<T> is nested within this instance.
(Inherited from ClassificationModel<T>)
EqualsDetermines whether the specified object is equal to the current object.
(Inherited from Object)
FinalizeAllows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object)
Fit() Fits the model to the data.
(Inherited from Model)
Fit(ParallelOptions) Fits the model to the data.
(Inherited from Model)
FitCore Computes the model to the specified input using the specified parallelization options.
(Overrides Model.FitCore(ModelInput, ParallelOptions))
GetHashCodeServes as the default hash function.
(Inherited from Object)
GetTypeGets the Type of the current instance.
(Inherited from Object)
InverseTransform Applies the inverse transformation to a set of observations.
MemberwiseCloneCreates a shallow copy of the current Object.
(Inherited from Object)
Predict(IDataFrame, ModelInputFormat) Predicts the most likely class based on the specified features.
(Inherited from ClassificationModel<T>)
Predict(Matrix<T>, ModelInputFormat) Predicts the value of the output corresponding to the specified features.
(Inherited from ClassificationModel<T>)
Predict(Vector<T>, ModelInputFormat) Predicts the value of the output corresponding to the specified input.
(Inherited from ClassificationModel<T>)
PredictCore(Matrix<T>, Boolean) Predicts the value of the dependent variable based on the specified values of the features.
(Inherited from ClassificationModel<T>)
PredictCore(Vector<T>, Boolean) Predicts the class based on the specified values of the features.
(Inherited from ClassificationModel<T>)
PredictProbabilities(IDataFrame, ModelInputFormat) Predicts the value of the output corresponding to the specified input.
(Inherited from ClassificationModel<T>)
PredictProbabilities(Matrix<T>, ModelInputFormat) Predicts the value of the output corresponding to the specified features.
(Inherited from ClassificationModel<T>)
PredictProbabilities(Vector<T>, ModelInputFormat) Predicts the value of the output corresponding to the specified input.
(Inherited from ClassificationModel<T>)
PredictProbabilitiesCore(Matrix<T>, Matrix<Double>, Boolean) Predicts the probabilities of each class based on the specified values of the features.
(Inherited from ClassificationModel<T>)
PredictProbabilitiesCore(Vector<Double>, Vector<Double>, Boolean) Predicts the probabilities of each class based on the specified values of the features.
(Overrides ClassificationModel<T>.PredictProbabilitiesCore(Vector<T>, Vector<Double>, Boolean))
PredictProbabilitiesInto(IDataFrame, Matrix<Double>, ModelInputFormat) Predicts the value of the output corresponding to the specified input.
(Inherited from ClassificationModel<T>)
PredictProbabilitiesInto(Matrix<T>, Matrix<Double>, ModelInputFormat) Predicts the value of the output corresponding to the specified features.
(Inherited from ClassificationModel<T>)
PredictProbabilitiesInto(Vector<T>, Vector<Double>, ModelInputFormat) Predicts the probabilities of each class based on the specified features.
(Inherited from ClassificationModel<T>)
ResetComputation Clears all fitted model parameters.
(Inherited from Model)
Obsolete.
ResetFit Clears all fitted model parameters.
(Inherited from Model)
SetDataSource Uses the specified data frame as the source for all input variables.
(Inherited from Model)
Summarize() Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model)
Summarize(SummaryOptions) Returns a string containing a human-readable summary of the object using the specified options.
(Overrides Model.Summarize(SummaryOptions))
ToStringReturns a string that represents the current object.
(Inherited from Model)
Transform Applies the transformation to a set of observations.

Extension Methods

Transform Applies the transformation to a set of observations.
(Defined by ModelExtensions)
Transform Applies the transformation to a set of observations.
(Defined by ModelExtensions)

See Also