BaseDiscriminantAnalysis Class

Initializes a new instance of the BaseDiscriminantAnalysis class

Gets the observation count for each class.

Gets information about the distinct classes in the analyzed data.

Gets the original classifications (labels) of the source data given on the moment of creation of this analysis object.

Gets the Mean vector for each class.

Gets the Scatter matrix for each class.

Gets the Standard Deviation vector for each class.

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.

Gets the Eigenvectors obtained during the analysis, composing a basis for the discriminant factor space.

Gets the level of importance each discriminant factor has in discriminant space. Also known as amount of variance explained.

Gets the discriminant factors in a object-oriented fashion.

Gets the Eigenvectors obtained during the analysis, composing a basis for the discriminant factor space.

Gets the Eigenvalues found by the analysis associated with each vector of the ComponentMatrix matrix.

Gets the mean of the original data given at method construction.

Gets the number of classes in the analysis.

Gets the number of inputs accepted by the model.

Gets the number of outputs generated by the model.

Gets the number of samples used to create the analysis.

Gets the feature space mean of the projected data.

Gets the resulting projection of the source data given on the creation of the analysis into discriminant space.

Gets the Between-Class Scatter Matrix for the data.

Gets the Total Scatter Matrix for the data.

Gets the Within-Class Scatter Matrix for the data.

Returns the original supplied data to be analyzed.

Gets the standard mean of the original data given at method construction.

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).

Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.

Classifies a new instance into one of the available classes.

Classifies new instances into one of the available classes.

Classifies a new instance into one of the available classes.

Creates additional information about principal components.

Gets the output of the discriminant function for a given class.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Serves as the default hash function.

Returns the number of discriminant space dimensions (discriminant factors) whose variance is greater than a given threshold.

Returns the minimum number of discriminant space dimensions (discriminant factors) required to represent a given percentile of the data.

Gets the Type of the current instance.

Obsolete.

Initializes common properties.

Creates a shallow copy of the current Object.

Returns a string that represents the current object.

Obsolete.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Obsolete.

Obsolete.

Obsolete.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

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.

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.