Add k-means++ initialization. #2813
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zoq
reviewed
Jan 21, 2021
| const double sampleValue = mlpack::math::Random(); | ||
| double* elem = std::lower_bound(distribution.begin(), distribution.end(), | ||
| sampleValue); | ||
| size_t position = (size_t) (elem - distribution.begin()) / sizeof(double); |
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Perhaps we should use const here as well, to help the compiler.
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Good point; done in 79d2621.
Co-authored-by: Marcus Edel <marcus.edel@fu-berlin.de>
Co-authored-by: Marcus Edel <marcus.edel@fu-berlin.de>
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Oops, I forgot to run |
zoq
reviewed
Jan 23, 2021
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You're right---I ran the test 1000 times locally and it failed a decent number of times. I reset the tolerance to be something a decent bit larger than the largest value I saw. So, hopefully, we should never see this test fail in practice. :) |
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shrit
reviewed
Jan 26, 2021
shrit
approved these changes
Jan 26, 2021
This was referenced Oct 14, 2022
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I implemented the k-means++ initialization strategy for k-means like three years ago but for some reason never got around to contributing it back upstream. So, here it is.
https://en.wikipedia.org/wiki/K-means%2B%2B
It's quite an effective initialization strategy, and is often used in practice. It seems to often outperform the Bradley/Fayyad refined start strategy that we have implemented.