mvpa2.clfs.gdaΒΆ
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[, out]) | 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, keepdims]) | 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([space]) | Abstract classifier class to be inherited by all classifiers .. |
| ConditionalAttribute([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[, ro, index, value, name, doc]) | This class shall serve as a representation of a parameter. |
| QDA(**kwargs) | Quadratic Discriminant Analysis. |
Exceptions
| Classifier([space]) | Abstract classifier class to be inherited by all classifiers .. |
| ConditionalAttribute([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[, ro, index, value, name, doc]) | This class shall serve as a representation of a parameter. |
| QDA(**kwargs) | Quadratic Discriminant Analysis. |
