function x = generate_nums_ber(n,p,m)
% GENERATE_BER - generate Ber(p) outcomes m times
%

%Create vector of size m, go through loop m times
x=zeros(1,m); % 1 x m vector of zeros for storing outcomes
for j=1:m
  successes = 0;
  for i = 1:n
      u = rand;
      if(u>= 1-p)
          successes= successes+1;
      end
  end
  x(j)= successes;
end

% alternative to loop above: x=(rand(1,m)>=1-p);

% plot normalised histogram of outcomes versus theoretical pmf

close all;  % close all previous plots

alphabet_x=[0,1];        % 1 x 2 vector with alphabet of r.v.
hx=histc(x,alphabet_x);  % compute histogram of outcomes x on alphabet (i.e. how many 0s and 1s)
stem(alphabet_x,hx/m);   % plot normalised histogram on alphabet
hold on;                 % keep this figure when plotting next
px=[1-p,p];              % theoretical pmf of Ber(p)
stem(alphabet_x,px,'rs'); % plot pmf

axis([-1 2 0 1]);                  % widen axis of figure
legend('empirical','theoretical'); % add legend to figure