function [y,z] = svmpred(x, %%args speficying SVM%% ,kernalfunc,varargin)
  % x is a matrix of input vectors (one per row) to be classified
  z = %% Insert code here
  % use feval(kernalfunc,x1,x2,varargin{:}) to evaluate the kernal function for
  % all pairs of rows from the matrices x1 and x2.
  y=sign(z);
  % We return both the classification y, and the value z before 
  % thresholding (it is used to display the margins)
