msaroufim

>>> range(3, 6)             # normal call with separate arguments
[3, 4, 5]
>>> args = [3, 6]
>>> range(*args)            # call with arguments unpacked from a list
[3, 4, 5]
In the same fashion, dictionaries can deliver keyword arguments with the **-operator:

>>>
>>> def parrot(voltage, state='a stiff', action='voom'):
...     print "-- This parrot wouldn't", action,

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"""
A simple command line utility that takes in a list of target url's
and downloads all pdf links contained on the page

Potentially look into using scrapy to build a full fledged crawler instead
"""

import requests
import sys
import re

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int func4(int arg0) {
    ebx = arg_0;
    if (ebx > 0x1) {
            esi = func4(ebx + 0xffffffff);
            eax = func4(ebx + 0xfffffffe);
            eax = eax + esi;
    }
    else {
            eax = 0x1;
    }

msaroufim

import Data.List
import qualified Data.List.Key as K
import Data.Map ((!), fromList, fromListWith, adjust, keys, Map)
 
buildGraph :: Ord a => [(a, a, Float)] -> Map a [(a, Float)]
buildGraph g = fromListWith (++) $ g >>=
               \(a,b,d) -> [(a,[(b,d)]), (b,[(a,d)])]
 
dijkstra :: Ord a => a -> Map a [(a, Float)] -> Map a (Float, Maybe a)
dijkstra source graph =

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class mark(Object):
  A = []
  words = ['bla','bloi','bli']
  return words.sorted()

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from bs4 import BeautifulSoup
import requests
import csv

leaderboard_root_url = 'http://www.dota2.com/leaderboards#'
regions = ['americas','europe', 'se_asia', 'china']

for region in regions:
    page = requests.get(leaderboard_root_url + region).content
    soup = BeautifulSoup(page, "html5lib")

msaroufim

from bs4 import BeautifulSoup
#import requests
import csv

import csv

# leaderboard_root_url = 'http://www.dota2.com/leaderboards#'
# regions = ['americas','europe', 'se_asia', 'china']

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using System.Collections;
using System.Collections.Generic;
using UnityEngine;

public class Map : MonoBehaviour {
    
    //grid specifics
    [SerializeField]
    private int rows;
    [SerializeField]

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class NeuralNetwork:
    def __init__(self, x, y):
        self.input      = x
        self.weights1   = np.random.rand(self.input.shape[1],4) 
        self.weights2   = np.random.rand(4,1)                 
        self.y          = y
        self.output     = np.zeros(self.y.shape)

    def feedforward(self):
        self.layer1 = sigmoid(np.dot(self.input, self.weights1))

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document.getElementById("ember1101").outerHTML = ""