elsonidoq

with safe_write('delete_me') as f:
    f.write('some contents')

with safe_write('delete_me') as f:
    1/0
    
# delete_me contents still are "some contents"

elsonidoq

from tempfile import mktemp
import shutil

class safe_write(object):
    def __init__(self, fname):
        self.fname = fname
        self.tmp_fname = mktemp()

    def __enter__(self):
        self.stream = open(self.tmp_fname, 'w')

elsonidoq

import shutil
from optparse import OptionParser
import sys
import functools
import re
import subprocess
import os

READELF = '/usr/bin/readelf'
def get_dependencies(fname, skip_patterns):

elsonidoq

X ~ N(0,1)
y1 = 1 <=> x > 0
y2 = 1 con probabilidad 0.5

I(y1;x) = H(X) - H(X|Y1) = 0.97
I(y1;x) = H(Y1) - H(Y1|X) = 0.97

I(y2;x) = H(X) - H(X|Y2) = 0.08
I(y2;x) = H(Y2) - H(Y2|X) = 0.08

elsonidoq

from scipy import asarray
from random import gauss, randint
def test():
    x= asarray([gauss(0,1) for i in range(1000)])
    y1= asarray([int(e>0) for e in x])
    y2= asarray([randint(0,1) for e in x])

    hx, bx= histogram(x, bins=x.size/10, density=True)
    dx= digitize(x,bx)
    

elsonidoq

from math import log
log2= lambda x:log(x,2)
from scipy import histogram, digitize, stats, mean, std
from collections import defaultdict

def mutual_information(x,y):
    return entropy(y) - conditional_entropy(x,y)

def conditional_entropy(x, y):
    """

elsonidoq

from scipy import asarray
from random import gauss, randint

def test():
    x= asarray([gauss(0,1) for i in range(1000)])
    y1= asarray([int(e>0) for e in x])
    y2= asarray([randint(0,1) for e in x])

    hx, bx= histogram(x, bins=x.size/10, density=True)
    dx= digitize(x,bx)

elsonidoq

from math import log
log2= lambda x:log(x,2)
from scipy import histogram, digitize, stats, mean, std
from collections import defaultdict

def conditional_entropy(x, y):
    """
    x: vector de numeros reales
    y: vector de numeros enteros

elsonidoq

from scipy import *
min=10000; max=min*100
s= (min+max)/2

while True:
    if max - min < 2: break
    avg= (min+max)/2
    try:
        m=zeros((s,s))
        del m