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Calculation problem of fp and fn markers in pr_mep.py file #8
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Definition of FP: reports a loop connection between two poses, but actually the connection is wrong, either connects to a wrong pose, or there are actually no loops at all. In practice, we require every pose relates to one previous pose. And whether to report as Positive is controlled by a moving sim score threshold, which is exactly the currect x0 in the sorted There is no need to record TN when calculating precision-recall. FN is all those loop pairs that have a true loop (x2_i=1), but the sim score is lower than the current moving threshold (x1_i<current x1) therefore not reported as positive. |
Thank you for your answer. I never knew the function of x0, but now I seem to understand it a little bit. |
Thank you for your excellent work and previous prompt responses. But I encountered a problem while reading your PR curve calculation code. Please correct me. Handling of false positives (fp): `I saw in your code module that the true examples (tp) are correctly marked in est_line[1]=1, but the false positives (fp) are not marked directly in est_line. Will this affect the calculation of fp in subsequent calculations, because fp should be those predictions that are predicted to be positive but are actually negative.
And in the subsequent processing, I saw that the code directly predicts all non-tp as fp, but there is no clear logic to exclude true negative examples (tn) when calculating fp.
I hope you can help me answer it
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