Diagonal Linear Discriminant Analysis (DLDA)

class mlpy.Dlda(nf=0, tol=10, overview=False, bal=False)

Diagonal Linear Discriminant Analysis.

Example:

>>> from numpy import *
>>> from mlpy import *
>>> xtr = array([[1.1, 2.4, 3.1, 1.0],  # first sample
...              [1.2, 2.3, 3.0, 2.0],  # second sample
...              [1.3, 2.2, 3.5, 1.0],  # third sample
...              [1.4, 2.1, 3.2, 2.0]]) # fourth sample
>>> ytr = array([1, -1, 1, -1])         # classes
>>> mydlda = Dlda()                     # initialize dlda class
>>> xtr_std = data_standardize(xtr)     # standardize the training dataset
>>> mydlda.compute(xtr_std, ytr)        # compute dlda
1
>>> mydlda.predict(xtr_std)             # predict dlda model on training data
array([ 1, -1,  1, -1])
>>> xts = array([4.0, 5.0, 6.0, 7.0])   # test point
>>> mydlda.predict(xts)                 # predict dlda model on test point
-1
>>> mydlda.realpred                     # real-valued prediction
-45.32292203746583
>>> mydlda.weights(xtr_std, ytr)        # compute weights on training data
array([  3.00000000e+00,   3.00000000e+00,   9.32587341e-15,
         2.61756029e+00])

Initialize Dlda class.

Input:

• nf - [integer] the number of the best features that we want to use in the model (1 <= nf >= #features). If nf = 0 the system stops at a number of features corresponding to a peak of accuracy
• tol - [integer] in case of nf = 0 it’s the number of steps of classification to be calculated after the peak to avoid a local maximum
• overview - [bool] set True to print informations about the accuracy of the classifier at every step of the compute
• bal - [bool] set True if it’s reasonable to consider the unbalancement of the test set similar to the one of the training set

compute(x, y, mf=0)

Compute Dlda model.

Initialize array of alphas a.

Input

• x - [2D numpy array float] (sample x feature) training data
• y - [1D numpy array integer] (two classes) classes
• mf - [integer] (More Features) number of classification steps to be calculated more on a model already computed

Output

• 1

predict(p)

Predict Dlda 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 - [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 (they are gonna be > 0 for the features chosen for the classification and = 0 for all the others)