z=mahalanobis_classifier(m,S,X).This function outputs the Mahalanobis classifier, given the mean and covariance matrices. • M: the number of classes. • l: the number of features (for each feature vector). • N: the number of data vectors. • m: lxM matrix, whose j-th column corresponds to the mean of the j-th class. • S: lxlxM matrix. S(:,:,j) is the covariance matrix of the j-th normal distribution. • P: M-dimensional vector whose j-th component is the a priori probability of the j-th class. • X: lxM data matrix, whose rows are the feature vectors, i.e., data matrix in scikit-learn convention. • y: N-dimensional vector containing the known class labels, i.e., the ground truth, or target vector in scikit-learn convention. • z: N-dimensional vector containing the predicted class labels, i.e., the vector of predicted class labels in scikit-learn convention.

mahalnobis.ipynb

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