skkda package

Submodules

skkda.base module

Scikit-learn-compatible Kernel Discriminant Analysis.

Used in David Diaz-Vico, Jose R. Dorronsoro “Deep vs Kernel Fisher Discriminant Analysis”

Based on algorithm 5 in Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan “Regularized Discriminant Analysis, Ridge Regression and Beyond” http://www.jmlr.org/papers/v11/zhang10b.html

@author: David Diaz Vico @license: MIT

class skkda.base.KernelDiscriminantAnalysis(lmb=0.001, kernel='rbf', degree=3, gamma=None, coef0=1)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.TransformerMixin

Kernel Discriminant Analysis.

Parameters:
  • lmb (float (>= 0.0), default=0.001) – Regularization parameter
  • kernel ({"chi2", "laplacian", "linear", "polynomial", "rbf", "sigmoid"},) – default=’rbf’ Kernel.
  • degree (integer, default=3) –
  • gamma (float, default=None) –
  • coef0 (integer, default=1) –
fit(X, y)[source]

Fit KFDA model.

Parameters:
  • X (numpy array of shape [n_samples, n_features]) – Training set.
  • y (numpy array of shape [n_samples]) – Target values. Only works for 2 classes.
Returns:
Return type:

self
transform(X)[source]

Transform data with the trained KFDA model.

Parameters:X (numpy array of shape [n_samples, n_features]) – The input data.
Returns:y_pred – Transformations for X.
Return type:array-like, shape (n_samples, targets_shape)

Module contents

Scikit-learn-compatible Kernel Discriminant Analysis.

@author: David Diaz Vico @license: MIT