You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: gaussmixd.m
    path of file: ./repos/VAD/lib/matlab/voicebox
    
    the code of the file until where you have to start completion: function [mz,vz,wz]=gaussmixd(y,m,v,w,a,b,f,g)
%GAUSSMIXD marginal and conditional Gaussian mixture densities
%
% Usage: (1) [mz,vz,wz]=gaussmixd(y,m,v,w); % If {m,v,w} has dimension p, then y specifies
%                                           % elements 1:q and {mz,vz,wz} is a GMM for elements q+1:p
%        (2) [mz,vz,wz]=gaussmixd(y,m,v,w,[],[1 3 4]);	% y specifies elements 1,3,4 and {mz,vz,wz}
%                                                       % is a GMM for the remaining elements
%

    
function [mz,vz,wz]=gaussmixd(y,m,v,w,a,b,f,g)
%GAUSSMIXD marginal and conditional Gaussian mixture densities
%
% Usage: (1) [mz,vz,wz]=gaussmixd(y,m,v,w); % If {m,v,w} has dimension p, then y specifies
%                                           % elements 1:q and {mz,vz,wz} is a GMM for elements q+1:p
%        (2) [mz,vz,wz]=gaussmixd(y,m,v,w,[],[1 3 4]);	% y specifies elements 1,3,4 and {mz,vz,wz}
%                                                       % is a GMM for the remaining elements
%
% Inputs: Input mixture: k mixtures, p dimensions
%         Output mixture: k mixtures, r dimensions
%         Conditioning data: n data values, q dimensions
%
%   Y(n,q) = conditioning input data: x*a'+ b'= y
%   M(k,p) = mixture means for x(p)
%   V(k,p) or V(p,p,k) variances (diagonal or full)
%   W(k,1) = mixture weights
%   A(q,p) = conditioning transformation: y=x*A'+ B' (where y and x are row vectors).
%   B(q,1)   If A is omitted or null, y=x*I(B,:)' where I is the identity matrix.
%            If B is also omitted or null, y=x*I(1:q,:)'.
%   F(r,p) = output transformation z = x*F'+G'  (where z and x are row vectors).
%   G(r,1)   If F is omitted or null, z = x*f' where I is the identity matrix.
%            If G is also omitted or null, z=x*I(q+1:p,:)' or, if A was also null,
%            the complement of y.
%
% Outputs (if 1 or 2 outputs specified):
%
%   MZ(n,r) = Global mean of z for each y
%   VZ(r,r,n) Global full covariances of z (now depend