load bezdekIris.data
[row, col] = size(bezdekIris);
indices = crossvalind('HoldOut', row, 0.2);%hold out 20% for test, there are 120 logical '1' in indices
trains = bezdekIris(indices,:);
tests = bezdekIris(~indices,:);
% net = newpnn(P,T,spread) takes two or three arguments, P
% R-by-Q matrix of Q input vectorsT 
% S-by-Q matrix of Q target class vectors spread 
% Spread of radial basis functions (default = 0.1)
% Q is num of samples, S is num of classes, R is num of features
net = newpnn(trains(:,1:col-1)',ind2vec(trains(:,end)'),0.1);
%和通常的分类器不一样，这里的第一个参数要求每一列是一个样本，所以要转置，而且第二个参数也不能直接给一个类号的向量
%要用ind2vec转换一下
Y = sim(net,tests(:,1:col-1)');
predictions = vec2ind(Y);
fprintf('accuracy is %f\n', sum(tests(:,end) == predictions') / size(tests, 1))