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    Metadata details:
    filename: bayesgauss.m
    path of file: ./repos/digital-image-processing-matlab/sample_code/DIPUM_toolbox_m-file_v1.1.3
    
    the code of the file until where you have to start completion: function d = bayesgauss(X, CA, MA, P)
%BAYESGAUSS Bayes classifier for Gaussian patterns.
%   D = BAYESGAUSS(X, CA, MA, P) computes the Bayes decision
%   functions of the n-dimensional patterns in the rows of X. 
%   CA is an array of size n-by-n-by-W containing W covariance
%   matrices of size n-by-n, where W is the number of classes.

    
function d = bayesgauss(X, CA, MA, P)
%BAYESGAUSS Bayes classifier for Gaussian patterns.
%   D = BAYESGAUSS(X, CA, MA, P) computes the Bayes decision
%   functions of the n-dimensional patterns in the rows of X. 
%   CA is an array of size n-by-n-by-W containing W covariance
%   matrices of size n-by-n, where W is the number of classes.
%   MA is an array of size n-by-W, whose columns are the corres-
%   ponding mean vectors. A cov. matrix and a mean vector must be 
%   specified for each class, even is some are equal.  X is of size 
%   K-by-n, where K is the number of patterns to be classified. P is 
%   a 1-by-W array, containing the probabilities of occurrence of 
%   each class.  If P is not included in the argument, the classes 
%   are assumed to be equally likely.  
%
%   D, is a column vector of length K. Its ith element is the class
%   number assigned to the ith vector in X during classification.  

%   Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins
%   Digital Image Processing Using MATLAB, Prentice-Hall, 2004
%   $Revision: 1.10 $  $Date: 2004/12/15 20:15:38 $

d = [ ]; % Initialize d.
error(nargchk(3, 4, nargin)) % Verify correct no. of inputs.
n = size(CA, 1);             % Dimension of patterns.

% Protect against the possibility that the class number is
% included as an (n+1)th element of the vectors.
X = double(X(:, 1:n)); 
W = size(CA, 3); % Number of pattern classes.
K = size(X, 1);  % Number of patterns to classify.
if nargin == 3
   P(1:W) = 1/W; % Classes assumed equally likely.
else
   if sum(P) ~= 1 
      error('Elements of P must sum to 1.'); 
   end