function [alphas, b] = kernel_perceptron(X,Y,d); 

N = size(X,1);
err = 1; %error rate
max_iter = 10000; %maximum number of iterations
alphas = zeros(N,1);
b = 0;
t = 1;

while t<=max_iter && err>0
  for ii = 1 : N         %cycle through training set
  %TO DO: update alpha and b
    xt = X(ii, :);
    yt = Y(ii, :);

    sum = 0;
    for i = 1 : N
      ai = alphas(i);
      yi = Y(i);
      xi = X(i, :);
      sum = sum + ai * yi * dot(xi, xt) ^ d;
    end
    sum = sum + b;

    if sum * yt <= 0
      alphas(ii) = alphas(ii) + 1;
      b = b + yt;
    end
  
  %*******************************
  end
  t = t+1;
  %TO DO: calculate the error rate

  %*******************************
end