Implementation of Quadratic Discriminant Analysis (QDA) method for binary and multi-class classifications.
The only difference between QDA and LDA is that in QDA, we compute the pooled covariance matrix for each class and then use the following type of discriminant function for getting the scores for each of the classes involed:
Where, result is basically the class z(x) with max score.
###Usage:
>>> from QDA import QDA
>>> t = QDA([[2.4, 5.4, 3.3], [5, 6.6, 3.4], [2.1, 2.4, 2.5], [5, 3.1, 6]], ['A', 'C', 'B', 'B'])
>>> t.predict([2.3, 3.2, 2.4])
>>> ..