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def tourney_in_order(k): | |
""" | |
Tells you the bracket for a tourney with n=2^k competitors 1-n, where 1 | |
is the best. The bracket is such that competitors lose in the order of | |
their skill to the person with a level of skill as close to theirs as | |
possible. | |
>>> bracket = tourney_in_order(4) | |
>>> print bracket | |
[16, 8, 15, 4, 14, 7, 13, 2, 12, 6, 11, 3, 10, 5, 9, 1] |
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#---------------------------------------------------------------# | |
# This script manually goes through youtube and collects all # | |
# of your viewing history into a convenient text file. This # | |
# might take about 30min to an hour, depending on your # | |
# computer's RAM, processing speed, and internet connection. # | |
# Note that this program requires the splinter module to work. # | |
# Run 'pip install splinter' to get it. # | |
#---------------------------------------------------------------# | |
print "Making sure you're not using python 3..." | |
from splinter import Browser |
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set smoothscroll | |
let scrollstep = 100 | |
map O t |
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{ | |
"smoothScroll.pages.spring-constant": "800", | |
"smoothScroll.other.spring-constant": "800", | |
"mode.normal.focus_search_bar": "", | |
"mode.normal.scroll_page_down": "D", | |
"mode.normal.scroll_page_up": "U", | |
"mode.normal.scroll_half_page_down": "<space> d", | |
"mode.normal.scroll_half_page_up": "<s-space> u", | |
"mode.normal.tab_new_after_current": "T O", | |
"mode.normal.tab_move_backward": "<", |
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from __future__ import division, print_function # Always do this just to | |
# ensure compatability | |
# between python 2.7 and 3 | |
import matplotlib.pylab as P | |
# Always do these next to lines to enable LaTeX to be used on the | |
# matplotlib plots | |
P.rc('text', usetex=True) | |
P.rc('font', family='serif') |
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import torch as T | |
def h_poly_helper(tt): | |
A = T.tensor([ | |
[1, 0, -3, 2], | |
[0, 1, -2, 1], | |
[0, 0, 3, -2], | |
[0, 0, -1, 1] | |
], dtype=tt[-1].dtype) | |
return [ |
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# Numerically stable and accurate implementation of the natural logarithm | |
# of the cumulative distribution function (CDF) for the standard | |
# Normal/Gaussian distribution in PyTorch. | |
import matplotlib.pylab as P # replace this with numpy if you want | |
import torch as T | |
def norm_cdf(x): | |
return (1 + T.erf(x/P.sqrt(2)))/2 |