function [Wout Bout] = lRule(D, P)
% LRULE - Perceptron Learning Rule.
%   [Wout Bout] = lRule(D, P) if the algorithm converges, it should
%   return the Wout - weight matrix and Bout - bias values that enable the
%   perceptron to classify the pattern P correctly. D is a desired
%   classification.
%
%   Inputs:
%     D    - List of desired classifications. Ith entry is either -1 or 1.
%     P    - Patterns we want to classify, in our example these are all pats.
%
%   Outputs:
%     Wout - The corrected weight.
%     Bout - The corrected bias.

error(nargchk(2,2,nargin));

patnum = size(P, 2);
inputs = size(P, 1);

% Initialize Classification vector, weight and bias.
C = NaN(1, patnum);
Wout = zeros(1, inputs);
Bout = 0;

disp('Our desired classification:')
disp(D)

k = 0;
while(~isequal(C, D))
    r = randi(patnum, 1);
    pat = P(:, r);                      % Randomly select a pattern.

    C(r) = sign(Wout * pat - Bout);     % Classify it.

    if (C(r) == 0)                      % Avoiding zeros.
        C(r) = 1;
    end

    if (C(r) ~= D(r))                   % If not correctly classified:
        Wout = Wout + D(r) * pat';      % Modify the weight.
        Bout = Bout - D(r);             % Modify the bias.
    end
    k = k + 1;
end
disp(C)
fprintf('Converged in %i iterations\n', k);

end