A bug in svm.java in sigmoid_train method about libsvm HOT 6 OPEN

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That's very interesting. I just stumbled across a similar issue with svm.cpp, but it seems to be the reverse problem there: probability predictions are correct, non-probability predictions are always the same class, but only on certain datasets. I documented it at scikit-learn/scikit-learn#4800

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There is no problem there. Even if the max num is reached, we still
need to return A and B. The if statement prints just a warning message.

mahan66 writes:

If anybody wants to use the probability outputs of the svm, it goes wrong and the
output label is always the same. The problem is in the line 1672:

if (iter>=max_iter)
//svm.info("Reaching maximal iterations in two-class probability estimates\n");

probAB[0]=A;probAB[1]=B;

As you can see, the next line after the commented part of the if statement will be
fired if the statement in the if is true. Thus, the results are always wrong.

Solution
Simply, put comment for the whole if statement.

Regards,
Mahmood

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I think @mahan66 is pointing out that the comment accidentally changes the meaning. The code should be

if (iter>=max_iter) {
    //svm.info("Reaching maximal iterations in two-class probability estimates\n");
}

probAB[0]=A;probAB[1]=B;

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Yep. there is definitely a problem. The compiler interprets this as

if (iter>=max_iter) {
    //svm.info("Reaching maximal iterations in two-class probability estimates\n");
    probAB[0]=A;
}

probAB[1]=B;

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but right now in svm.java the svm.info statement isn't commented out?
Matthias Richerzhagen writes:

Yep. there is definitely a problem. The compiler interprets this as

if (iter>=max_iter) {
//svm.info("Reaching maximal iterations in two-class probability estimates\n");
probAB[0]=A;
}

probAB[1]=B;

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Yes, I checked the current source code now and it seems it is correct here. I don't know what version I was using which somebody had made this change.

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