Penalized Discriminant Analysis (PDA)

Described in [Ghosh03].

class mlpy.Pda(Nreg=3)

Penalized Discriminant Analysis (PDA).

Initialize Pda class.

Input

• Nreg - [integer] number of regressions

compute(x, y)

Compute Pda model.

Input

• x - [2D numpy array float] (sample x feature) training data
• y - [1D numpy array integer] (two classes, 1 or -1) classes

Output

• 1

predict(p)

Predict Pda model on test point(s).

Input

• p - [1D or 2D numpy array float] test point(s)

Output

• cl - [integer or 1D numpy array integer] class(es) predicted
• self.realpred - [float or 1D numpy array float] real valued prediction

weights(x, y)

Return feature weights.

Input

• x - [2D numpy array float] (sample x feature) training data
• y - [1D numpy array integer] (two classes, 1 or -1) classes

Output

• fw - [1D numpy array float] feature weights

[Ghosh03]

D Ghosh. Penalized discriminant methods for the classification of tumors from gene expression data. Biometrics on Volume 59, Dec. 2003 Page(s):992 - 1000(9).