LinearDiscriminantAnalysis Properties

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 a classification pipeline that can be used to classify new samples into one of the NumberOfClasses learned in this discriminant analysis. This pipeline is only available after a call to the Learn(Double, Int32, Double) method.

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.