Smerity

 fn main() {
 push    rbp
 mov     rbp, rsp
 push    r15
 push    r14
 push    r12
 push    rbx
 sub     rsp, 3344
 let mut rng = rand::thread_rng();
 call    rand::rngs::thread::thread_rng

Smerity

import requests

AUTH_TOKEN = 'xoxb-...'

channel = '#locked-out'
USER_ID = 'UDAS0J04S' # A user ID obtained by `list_members.py`
text = f'<@{USER_ID}>, Smerity locked himself out'

params = {
    'token': AUTH_TOKEN,

Smerity

import pprint
import requests

AUTH_TOKEN = 'xoxb-...'

params = {'token': AUTH_TOKEN}

r = requests.post('https://slack.com/api/users.list', params=params)

for member in r.json()['members']:

Smerity

# encoding=utf8  
import sys  

reload(sys)  
sys.setdefaultencoding('utf8')

import re

number_match_re = re.compile(r'^([0-9]+[,.]?)+$')
number_split_re = re.compile(r'([,.])')

Smerity

      1 .+?
      1 [^
      2 0000
      2 010101
      2 1111
      2 1234
      2 12345
      2 666666
      2 adm
      2 anna

Smerity

import torch
from cupy.cuda import function
from pynvrtc.compiler import Program
from collections import namedtuple

a = torch.randn(1,4,4).cuda()
b = torch.zeros(a.size()).cuda()

kernel = '''
extern "C"

Smerity

vocab = set()

for i, line in enumerate(open('wiki.train.tokens')):
  words = [x for x in line.split(' ') if x]
  [vocab.add(word) for word in words]

  if i < 10: print(words)

print('Vocab size:', len(vocab))

Smerity

''' Script for Cartpole using policy gradient via Chainer, two layer MLP, dropout, and rejection sampling of historical memories '''

import gym 

import numpy as np

import chainer
from chainer import optimizers
from chainer import ChainList, Variable
import chainer.functions as F

Smerity

""" Quick script for Cartpole using policy gradient via Chainer, two layer MLP, dropout, and vaguely rejection sampling of historical memories """

import gym 

import numpy as np

import chainer
from chainer import optimizers
from chainer import ChainList, Variable
import chainer.functions as F

Smerity

from __future__ import print_function
import numpy as np
np.random.seed(1337)
import sys

from keras.utils.test_utils import get_test_data
from keras.models import Sequential
from keras.layers.core import Dense, TimeDistributedDense
from keras.layers.recurrent import GRU