% File: ARpredictor.m
% -------------------
% This is the main file of Wiener predictor with Autoregression Model.

function ARpredictor(a_vector, L, sigma_sqr)
% a_vector: the parameters a(i) i = 1, ..., p of AR model in vector form
%       also with the order (or length) of the model p = length(a_vector)
% sigma_sqr: variance of white Gaussian noise
% L: number of signal samples of s(n)
ARpredictor_SeqGen(a_vector, sigma_sqr, L); % generate w(n) and s(n)
ARpredictor_Core(); % compute autocorrelation matrix Rss

load ARpredictor_SeqGen.mat;
load ARpredictor_Core.mat;

N = length(a_vector);
mse_a_vector = 0;
for i = 1: N
    mse_a_vector = mse_a_vector + (a_vector(i) - a_vector_ass(i)) * (a_vector(i) - a_vector_ass(i));
end
mse_a_vector = mse_a_vector / N;

mse_ar_pow = (sigma_sqr - AR_MSE_ass) * (sigma_sqr - AR_MSE_ass);

sprintf('mse_avector: %f \nmse_arpow: %f', mse_a_vector, mse_ar_pow)

savefile = 'ARpredictor.mat';
save(savefile, 'mse_a_vector', 'mse_ar_pow');

clear;