You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: covPPard.m
    path of file: ./repos/bads/gpml-matlab-v3.6-2015-07-07/cov
    
    the code of the file until where you have to start completion: function K = covPPard(v, hyp, x, z, i)

% Piecewise polynomial covariance function with compact support, v = 0,1,2,3.
% The covariance functions are 2v times contin. diff'ble and the corresponding
% processes are hence v times  mean-square diffble. The covariance function is:
%
% k(x^p,x^q) = s2f * max(1-r,0)^(j+v) * f(r,j) with j = floor(D/2)+v+1
%
% where r is the distance sqrt((x^p-x^q)'*inv(P)*(x^p-x^q)), and the P matrix
% is diagonal with ARD parameters ell_1^2,...,ell_D^2, where D is the dimension

    
function K = covPPard(v, hyp, x, z, i)

% Piecewise polynomial covariance function with compact support, v = 0,1,2,3.
% The covariance functions are 2v times contin. diff'ble and the corresponding
% processes are hence v times  mean-square diffble. The covariance function is:
%
% k(x^p,x^q) = s2f * max(1-r,0)^(j+v) * f(r,j) with j = floor(D/2)+v+1
%
% where r is the distance sqrt((x^p-x^q)'*inv(P)*(x^p-x^q)), and the P matrix
% is diagonal with ARD parameters ell_1^2,...,ell_D^2, where D is the dimension
% of the input space and sf2 is the signal variance. The hyperparameters are:
%
% hyp = [ log(ell_1)
%         log(ell_2)
%          ..
%         log(ell_D)
%         log(sf) ]
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2015-02-09.
%
% See also COVFUNCTIONS.M.

if nargin<3, K = '(D+1)'; return; end