Gaussian Discriminant Analyses: LDA and QDA
Basic implementation at the moment: no data sphering, nor dimensionality reduction tricks are in place ATM
Functions
accepts_dataset_as_samples(fx) | Decorator to extract samples from Datasets. |
dot(a, b) | Dot product of two arrays. |
ones(shape[, dtype, order]) | Return a new array of given shape and type, filled with ones. |
sum(a[, axis, dtype, out]) | Sum of array elements over a given axis. |
zeros(shape[, dtype, order]) | Return a new array of given shape and type, filled with zeros. |
Classes
Classifier(**kwargs[, space]) | Abstract classifier class to be inherited by all classifiers .. |
ConditionalAttribute(*args, **kwargs[, enabled]) | Simple container intended to conditionally store the value |
GDA(**kwargs) | Gaussian Discriminant Analysis – base for LDA and QDA |
LDA(**kwargs) | Linear Discriminant Analysis. |
Parameter(default, **kwargs[, ro, index, ...]) | This class shall serve as a representation of a parameter. |
QDA(**kwargs) | Quadratic Discriminant Analysis. |
Exceptions
Classifier(**kwargs[, space]) | Abstract classifier class to be inherited by all classifiers .. |
ConditionalAttribute(*args, **kwargs[, enabled]) | Simple container intended to conditionally store the value |
GDA(**kwargs) | Gaussian Discriminant Analysis – base for LDA and QDA |
LDA(**kwargs) | Linear Discriminant Analysis. |
Parameter(default, **kwargs[, ro, index, ...]) | This class shall serve as a representation of a parameter. |
QDA(**kwargs) | Quadratic Discriminant Analysis. |