z=bayes_classifier(m,S,P,X). This function outputs the Bayesian classification

rule for M classes, each modeled by a Gaussian distribution.

where,

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.-z=bayes_classifier(m,S,P,X). This function outputs the Bayesian classification

rule for M classes, each modeled by a Gaussian distribution.

where,

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.