ovrpredictBot.m111

The code below is ovrpredictBot.m is given below. The changes from the original one is that mine save all the information in each class.

function [label_out, acc_out, decv_out] = ovrpredictBot(y, x, model)

labelSet = model.labelSet;

labelSetSize = length(labelSet);

models = model.models;

observationSize = size(y, 1);

% decv= zeros(observationSize, labelSetSize);

label_out = zeros(observationSize,labelSetSize);

decv_out = zeros(observationSize,labelSetSize);

acc_out = zeros(3,labelSetSize);

for i=1:labelSetSize

  [label_out(:,i),acc_out(:,i),decv_out(:,i)] = svmpredict(double(y == labelSet(i)), x, models{i});

  % Here we have to do some strange thing...

  % IF models{i}.Label(1) == 1, the decv_out is stored as it is

  % But if models{i}.Label(1) == 0, the decv_out is multiplied by -1 before

  % stored.

  % After doing this process the decision values are correct!!!

  decv_out(:,i) = decv_out(:,i) * (2 * models{i}.Label(1) - 1);

end

% [tmp,pred] = max(decv, [], 2);

% pred = labelSet(pred);

% ac = sum(y==pred) / size(x, 1);

the original ovrpredict.m is given below.

function [pred, ac, decv] = ovrpredict(y, x, model)

labelSet = model.labelSet;

labelSetSize = length(labelSet);

models = model.models;

decv= zeros(size(y, 1), labelSetSize);

for i=1:labelSetSize

  [l,a,d] = svmpredict(double(y == labelSet(i)), x, models{i});

  decv(:, i) = d * (2 * models{i}.Label(1) - 1);

end

[tmp,pred] = max(decv, [], 2);

pred = labelSet(pred);

ac = sum(y==pred) / size(x, 1);