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: learn_kalman.m
    path of file: ./repos/bnt/Kalman
    
    the code of the file until where you have to start completion: function [A, C, Q, R, initx, initV, LL] = ...
    learn_kalman(data, A, C, Q, R, initx, initV, max_iter, diagQ, diagR, ARmode, constr_fun, varargin)
% LEARN_KALMAN Find the ML parameters of a stochastic Linear Dynamical System using EM.
%
% [A, C, Q, R, INITX, INITV, LL] = LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0) fits
% the parameters which are defined as follows
%   x(t+1) = A*x(t) + w(t),  w ~ N(0, Q),  x(0) ~ N(init_x, init_V)
%   y(t)   = C*x(t) + v(t),  v ~ N(0, R)
% A0 is the initial value, A is the final value, etc.
% DATA(:,t,l) is the observation vector at time t for sequence l. If the sequences are of
% different lengths, you can pass in a cell array, so DATA{l} is an O*T matrix.
% LL is the "learning curve": a vector of the log lik. values at each iteration.
% LL might go positive, since prob. densities can exceed 1, although this probably
% indicates that something has gone wrong e.g., a variance has collapsed to 0.
%
% There are several optional arguments, that should be passed in the following order.
% LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0, MAX_ITER, DIAGQ, DIAGR, ARmode)
% MAX_ITER specifies the maximum number of EM iterations (default 10).
% DIAGQ=1 specifies that the Q matrix should be diagonal. (Default 0).

    
function [A, C, Q, R, initx, initV, LL] = ...
    learn_kalman(data, A, C, Q, R, initx, initV, max_iter, diagQ, diagR, ARmode, constr_fun, varargin)
% LEARN_KALMAN Find the ML parameters of a stochastic Linear Dynamical System using EM.
%
% [A, C, Q, R, INITX, INITV, LL] = LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0) fits
% the parameters which are defined as follows
%   x(t+1) = A*x(t) + w(t),  w ~ N(0, Q),  x(0) ~ N(init_x, init_V)
%   y(t)   = C*x(t) + v(t),  v ~ N(0, R)
% A0 is the initial value, A is the final value, etc.
% DATA(:,t,l) is the observation vector at time t for sequence l. If the sequences are of
% different lengths, you can pass in a cell array, so DATA{l} is an O*T matrix.
% LL is the "learning curve": a vector of the log lik. values at each iteration.
% LL might go positive, since prob. densities can exceed 1, although this probably
% indicates that something has gone wrong e.g., a variance has collapsed to 0.
%
% There are several optional arguments, that should be passed in the following order.
% LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0, MAX_ITER, DIAGQ, DIAGR, ARmode)
% MAX_ITER specifies the maximum number of EM iterations (default 10).
% DIAGQ=1 specifies that the Q matrix should be diagonal. (Default 0).
% DIAGR=1 specifies that the R matrix should also be diagonal. (Default 0).
% ARMODE=1 specifies that C=I, R=0. i.e., a Gauss-Markov process. (Default 0).
% This problem has a global MLE. Hence the initial parameter values are not important.
% 
% LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0, MAX_ITER, DIAGQ, DIAGR, F, P1, P2, ...)
% calls [A,C,Q,R,initx,initV] = f(A,C,Q,R,initx,initV,P1,P2,...) after every M step. f can be
% used to enforce any constraints on the params. 
%
% For details, see
% - Ghahramani and Hinton, "Parameter Estimation for LDS", U. Toronto tech. report, 1996
% - Digalakis, Rohlicek and Ostend