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 |
EnsureChoice(\*values) |
Ensure an input is element of a set of possible values |
GDA(\*\*kwargs) |
Gaussian Discriminant Analysis – base for LDA and QDA |
LDA(\*\*kwargs) |
Linear Discriminant Analysis. |
Parameter(default[, constraints, ro, index, ...]) |
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 |
EnsureChoice(\*values) |
Ensure an input is element of a set of possible values |
GDA(\*\*kwargs) |
Gaussian Discriminant Analysis – base for LDA and QDA |
LDA(\*\*kwargs) |
Linear Discriminant Analysis. |
Parameter(default[, constraints, ro, index, ...]) |
This class shall serve as a representation of a parameter. |
QDA(\*\*kwargs) |
Quadratic Discriminant Analysis. |
