BaseDiscriminantAnalysis Class 
Namespace: Accord.Statistics.Analysis
[SerializableAttribute] public abstract class BaseDiscriminantAnalysis : TransformBase<double[], double[]>
The BaseDiscriminantAnalysis type exposes the following members.
Name  Description  

BaseDiscriminantAnalysis  Initializes a new instance of the BaseDiscriminantAnalysis class 
Name  Description  

ClassCount 
Gets the observation count for each class.
 
Classes 
Gets information about the distinct classes in the analyzed data.
 
Classifications  Obsolete.
Gets the original classifications (labels) of the source data
given on the moment of creation of this analysis object.
 
ClassMeans 
Gets the Mean vector for each class.
 
ClassScatter 
Gets the Scatter matrix for each class.
 
ClassStandardDeviations 
Gets the Standard Deviation vector for each class.
 
CumulativeProportions 
The cumulative distribution of the discriminants factors proportions.
Also known as the cumulative energy of the first dimensions of the discriminant
space or as the amount of variance explained by those dimensions.
 
DiscriminantMatrix  Obsolete.
Gets the Eigenvectors obtained during the analysis,
composing a basis for the discriminant factor space.
 
DiscriminantProportions 
Gets the level of importance each discriminant factor has in
discriminant space. Also known as amount of variance explained.
 
Discriminants 
Gets the discriminant factors in a objectoriented fashion.
 
DiscriminantVectors 
Gets the Eigenvectors obtained during the analysis,
composing a basis for the discriminant factor space.
 
Eigenvalues 
Gets the Eigenvalues found by the analysis associated
with each vector of the ComponentMatrix matrix.
 
Means 
Gets the mean of the original data given at method construction.
 
NumberOfClasses 
Gets the number of classes in the analysis.
 
NumberOfInputs 
Gets the number of inputs accepted by the model.
(Inherited from TransformBaseTInput, TOutput.)  
NumberOfOutputs 
Gets the number of outputs generated by the model.
(Inherited from TransformBaseTInput, TOutput.)  
NumberOfSamples 
Gets the number of samples used to create the analysis.
 
ProjectionMeans 
Gets the feature space mean of the projected data.
 
Result  Obsolete.
Gets the resulting projection of the source data given on
the creation of the analysis into discriminant space.
 
ScatterBetweenClass 
Gets the BetweenClass Scatter Matrix for the data.
 
ScatterMatrix 
Gets the Total Scatter Matrix for the data.
 
ScatterWithinClass 
Gets the WithinClass Scatter Matrix for the data.
 
Source  Obsolete.
Returns the original supplied data to be analyzed.
 
StandardDeviations 
Gets the standard mean of the original data given at method construction.
 
Threshold 
Gets or sets the minimum variance proportion needed to keep a
discriminant component. If set to zero, all components will be
kept. Default is 0.001 (all components which contribute less
than 0.001 to the variance in the data will be discarded).
 
Token 
Gets or sets a cancellation token that can be used to
stop the learning algorithm while it is running.

Name  Description  

Classify(Double)  Obsolete.
Classifies a new instance into one of the available classes.
 
Classify(Double)  Obsolete.
Classifies new instances into one of the available classes.
 
Classify(Double, Double)  Obsolete.
Classifies a new instance into one of the available classes.
 
CreateDiscriminants 
Creates additional information about principal components.
 
DiscriminantFunction 
Gets the output of the discriminant function for a given class.
 
Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize  Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetNonzeroEigenvalues 
Returns the number of discriminant space dimensions (discriminant
factors) whose variance is greater than a given threshold.
 
GetNumberOfDimensions 
Returns the minimum number of discriminant space dimensions (discriminant
factors) required to represent a given percentile of the data.
 
GetType  Gets the Type of the current instance. (Inherited from Object.)  
init  Obsolete.
Obsolete.
 
Init 
Initializes common properties.
 
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ToString  Returns a string that represents the current object. (Inherited from Object.)  
Transform(Double)  Obsolete.
Obsolete.
 
Transform(Double) 
Applies the transformation to an input, producing an associated output.
(Overrides TransformBaseTInput, TOutputTransform(TInput).)  
Transform(Double) 
Applies the transformation to an input, producing an associated output.
(Overrides TransformBaseTInput, TOutputTransform(TInput).)  
Transform(TInput, TOutput) 
Applies the transformation to an input, producing an associated output.
(Inherited from TransformBaseTInput, TOutput.)  
Transform(Double, Int32)  Obsolete.
Obsolete.
 
Transform(Double, Int32)  Obsolete.
Obsolete.
 
Transform(Double, Int32)  Obsolete.
Obsolete.

Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
IsEqual 
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.)  
To(Type)  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) 