evmcheb

#!/usr/bin/env bash
set -e

# Base and Shark directories
BASE_DIR=${XDG_CONFIG_HOME:-$HOME}
SHARK_DIR=${SHARK_DIR:-"$BASE_DIR/.shark"}

# Detect shell and choose profile
case $SHELL in
  */zsh)  PROFILE=${ZDOTDIR:-"$HOME"}/.zshenv ;;

evmcheb

# Palkeoramix decompiler. 

def storage:
  owner is addr at storage 1
  unknown56e2039a is mapping of uint256 at storage 2
  unknown284dd0e2 is mapping of uint128 at storage 3
  unknown09f13758 is uint256 at storage 4
  unknowncda84ae8 is uint256 at storage 5
  unknownd99a2d0a is uint8 at storage 6
  unknown9e19916dAddress is addr at storage 6 offset 8

evmcheb

!git clone https://github.com/AlexeyAB/darknet/
% cd darknet
!sed -i 's/OPENCV=0/OPENCV=1/g' Makefile
!sed -i 's/GPU=0/GPU=1/g' Makefile
!sed -i 's/CUDNN=0/CUDNN=1/g' Makefile
!sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/g' Makefile
!sed -i 's/LIBSO=0/LIBSO=1/g' Makefile
!apt update
!apt-get install libopencv-dev python-opencv
!make clean

evmcheb

import os
import numpy as np
from tqdm import tqdm
from pymongo import MongoClient
bookies = {
    "44":"Betfair",
    "16":"bet365",
    "18":"Pinnacle",
    "5":"Unibet"
}

evmcheb

from keras import backend as K
from keras.models import Model
from keras.models import Sequential
from keras.layers import Input, Dense, Dropout, Conv2D, Flatten, Activation, concatenate
from keras.optimizers import Adam

c = 0.6
def decorrelation_loss(neuron):
    def loss(y_actual, y_predicted):
        return K.mean(

evmcheb

# A snippet of how a comment is converted into a video
from moviepy.editor import *

audio = AudioFileClip(f"{tid}/{comment.name}/{num}.mp3")
image = (ImageClip(f"{tid}/{comment.name}/{part.name}") 
	.set_duration(audio.duration) # using the fx library to effortlessly transform the video clip
	.resize(1.2).on_color(size=DIM, color=dark_grey)
	.set_fps(5)
	.set_pos((0.5, 0.5), relative=True)
	.set_audio(audio))

evmcheb

# Reads out a script using google text to speech api
from google.cloud import texttospeech
import sys
def work(tid):
	with open(f"{tid}/script.txt", encoding='utf-8') as f:
		lines = f.read().strip().split("\n\n")
	with open("resources/rules.txt") as f:
		# each line is "badword, replacement"
		bad_words = [(x.split()[0], x.split()[1]) for x in f.readlines()]

evmcheb

# Grabs the best comments and splits into parts
import praw, re

r = praw.Reddit()
thread = r.submission(url=URL)  
thread.comment_sort = 'best' # get the best comments from the thread

for comment in thread.comments:
	punctuation_reg = re.compile('(?<=[.!,?:;-]) +')
	split_parts = punctuation_reg.split(comment.body)

evmcheb

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, Conv2D, MaxPooling2D, GlobalAveragePooling2D

model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=(100, 100, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))

evmcheb

import os
samples = []
labels = []
images_folder = "images/"
for image in os.listdir(images_folder):
    samples.append(img_to_array(load_img(images_folder+image, target_size=(100, 100))))
    if "normal" in image:
        labels.append((0))
    else:
        labels.append((1))