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

Inheritance diagram of mvpa2.clfs.gda

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]) 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.

NeuroDebian

NITRC-listed