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This repository was archived by the owner on Aug 26, 2022. It is now read-only.
There is no perfect solution, by doing that some samples are better but not all of them.
Maybe we should implement the hybrid solution: accumulate up to the predefined size of the sample and from there only replace an element if it's linked to new set of inputs (until we reach a complete distinct sample). The other problem is due to the fact that collectors operate in different threads so each of their sample is re-sampled once we aggregate all results together. We may want to increase the size of the sample in each collectors to lower the chance of collision.
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The current collector only consider the type of evaluation and the size of the sample.
We often get sample like :
It would be preferable to consider the input data and only accumulate when the input is different (up to the predefined size of the sample)
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