source: birds/doprediction.m @ 140

%i,j,p
%i
%The rec_id of a recording in the test set. Only include predictions for recordings in the test set.
%j
%The species/class #. For each rec_id, there should be 19 lines for species 0 through 18.
%p
%Your classifier's prediction about the probability that species j is present in rec_id i. This must be in the range [0,1].


load('birds20130709.mat');

%ixtrain = find(J == 0);
%ixtest = find(J == 1);
%z=a(ixtest,:);


[bags, labs, bagid] = getbags(z);



C = 19;
predictions = nan(length(bagid), 19);


predictions2 = nan(length(bagid), 19);

%u = simple_mil*classc;

u = milvector([],'m')*scalem([],'variance')*libsvc*classc;


%For each class, train a classifier
for i=1:C
    xtr = changelablist(x,i+1);  %Bird 1 is lablist 2, etc
    thisll = getlablistnames(xtr);
    xtr = setname(xtr,strtrim(thisll(curlablist(xtr),:)));
    xtr=makemillabeled(xtr);
    
    xte = changelablist(z,i+1);  %Bird 1 is lablist 2, etc
    thisll = getlablistnames(xte);
    xte = setname(xte,strtrim(thisll(curlablist(xte),:)));
    xte=makemillabeled(xte);


    %x = a(ixtrain, :);
    %z = a(ixtest,:);
    
    w = xtr*u;
    out = xte*w;
    
 
    predictions(:,i) = out(:,2);  %Positive class is column 2
end






%Write everything to a CSV file


fid = fopen('pred20130709.csv', 'w');
fprintf(fid, '%s,%s\r\n', 'Id', 'probability');

for i=1:length(bagid)
        
    for j=1:C
        if (i==length(bagid) && j==C)
            fprintf(fid, '%d,%f', bagid(i)*100+(j-1), predictions(i,j));
        else
            fprintf(fid, '%d,%f\r\n', bagid(i)*100+(j-1), predictions(i,j));
        end
    end
end
fclose(fid);