Talk:ScalarN/Version History

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Way to APM006:26, 20 September 2018
U Math Library?405:50, 12 September 2018
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Way to APM

ScalarBot and ScalarN are both suspicious of PatternMatchers, and at the same time Knight and Diamond has very strong APM score.

I even tried to copy Diamond's surf buffers (without flatteners) but it only decreased my APM performance.

Then, craziness drove me to run Diamond with mc2k7, and, without thresholds (doesn't affect score), flatteners (doesn't affect APM score) and bullet shadows, Diamond's APM performance dropped from 74+ to 67.5

Then, it may be concluded that strong APM comes from randomness, and Bullet Shadow is such a great source of randomness for surfers. Even flatteners can't do that well.

It seems that pattern matchers are smart enough to see though active flatteners so that it doesn't work well.

Xor (talk)06:26, 20 September 2018

U Math Library?

I'm very interested in what this U library does. Do you have any reference links? I've tried googling but so far have come up short.

Can't wait to see how ScalarN fares in the rumble :)

Enamel 32 (talk)02:39, 8 August 2018

It's written by me, and not yet open sourced ;)

And it's very simple. Just have a look at Neuromancer.FastMath. U just wrapped similar things in an elegant API ;)

Xor (talk)03:05, 8 August 2018

Oh, I had assumed it was out in the public like PyTorch. Fast math is fun!

Enamel 32 (talk)03:21, 8 August 2018

It seems that the word "and" is ambiguous. Fixed ;)

Xor (talk)04:02, 8 August 2018

just found a good math library Commons Math3

it has a much better approximation of exp than the look-up-table without memory version, and is still 2x faster than StrictMath

Xor (talk)05:49, 12 September 2018
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