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- Draft mode denoiser 2 how to#
- Draft mode denoiser 2 Patch#
- Draft mode denoiser 2 full#
- Draft mode denoiser 2 code#
In the old days of Dota 2 when many more players liked to play randomized modes, such as All Random and Single Draft, there used to be the possibility to swap your hero with another teammate, reducing the probability of getting a hero you have no idea how to play like Invoker, Meepo or Chen. This happened due to the small player pool trying to find matches in Single Draft, as a result, the system has a really hard time matching different skill levels between the players and naturally the queue time goes drastically up, this being an efficient method to prevent or reduce a huge imbalance the teams facing each other. If you tried playing a single draft game recently, be it because you were trying to get out of low priority or simply because you like the game mode or were curious about it you probably faced a huge wait time to find a match. Why are single draft queue times in Dota 2 so long? It should be noted that in this game mode the pace of the game is not changed whatsoever unlike turbo mode, where heroes obtain much more gold and experience from every source inside the game.Įven if Single Draft has its diehard fanbase, it’s still mostly played by those on low priority, as it’s the only available way to obtain the wins required to get rid of the matchmaking penalty, due to this, this game mode is often considered as one of the worst to play on, as you will face without a doubt many toxic players that are simply trying to escape from their punishment and go back to the regular matchmaking. In Single Draft mode, differently from the All Pick mode, the system chooses randomly 3 unique heroes for each player, being one from each attribute (Strength, Agility, and Intelligence). %time o1=mode_smoothing(data,edges=True,center_boost=1)ĬPU times: user 826 ms, sys: 0 ns, total: 826 msĬPU times: user 416 ms, sys: 7.83 ms, total: 423 msĬPU times: user 825 ms, sys: 3.78 ms, total: 829 msĬPU times: user 422 ms, sys: 3.Dota 2 has many matchmaking modes, each with unique rules related to the hero selection, availability, and pace of the game. %time o2=categorical_smoothing(data,N,kernel=kernel) %time o1=mode_smoothing(data,edges=True,center_boost=False) Return np.array(_mode_vals).reshape(j1-j0,i1-i0)ĬATEGORICAL SMOOTHING from scipy.signal import convolve2dĭef categorical_smoothing(data,nb_categories,kernel=KERNEL):ĭata=convolve2d(data,kernel,mode='same') If true, instead of using pure mode-value increase the count on the center pixel When there are multiple possible mode values choose the highest if true, * the returned image will be reduced in size by the radius of the kernel
Draft mode denoiser 2 full#
* only run over patches with the full kernel size * the returned image be the same shape as the input data
Draft mode denoiser 2 Patch#
* include edge patches by taking mode over smaller patch window NUMBA mode_smoothing(data,kernel=(3,3),step=(1,1),edges=False,high_value=False,center_boost=False): Both lead to a big speed-boost but categorical-smoothing solution is cleaner, faster, and doesn't involve numba - so it wins. I haven't written out a mathematical proof yet but it appears this patch-wise mode smoothing is equivalent to the categorical-smoothing for the correct choice of parameters. using the above suggestion by Alex Alex, which i'll call "categorical-smoothing" (is there a standard name for this method?).by expanding out my initial attempt into a function that numba can handle.Here is a before/after example from the mode_smoothing method aboveīelow I present 2 answers to question my question:
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O=np.array() for p in range(nb_patches)]) """ original method (new version is ~ 5 times faster, but still slow) Return _mode(patches,1).reshape(int(np.sqrt(nb_patches)),-1) Patches=image.extract_patches_2d(data,kernel)
Draft mode denoiser 2 code#
This code works but its too slow.: from sklearn.feature_extraction import image My idea was to take the mode value for (3,3) patches. I have some image segmentation results that are noisy and I want to clean it up. however its still slow and my original questions still exist: (1) can i increase the speed? (2) are there different/better/standard approaches to cleaning up noisy categorical data? Back the post: Fixing this increased my speed by a factor of 4. UPDATE: In my initial post I stupidly applied stats.mode patch-wise rather than along the axis of the patches.