vgoklani

import json
import os
import time
import requests
from PIL import Image
from StringIO import StringIO
from requests.exceptions import ConnectionError

def go(query, path):
  """Download full size images from Google image search.

vgoklani

# The blog post that started it all: https://neocities.org/blog/the-fcc-is-now-rate-limited
#
# Current known FCC address ranges:
# https://news.ycombinator.com/item?id=7716915
#
# Confirm/locate FCC IP ranges with this: http://whois.arin.net/rest/net/NET-165-135-0-0-1/pft
#
# In your nginx.conf:

location / {

vgoklani

# Author: Nelle Varoquaux, Andrew Tulloch

# Uses the pool adjacent violators algorithm (PAVA), with the
# enhancement of searching for the longest decreasing subsequence to
# pool at each step.

import numpy as np
cimport numpy as np
cimport cython

vgoklani

Header 1

Header 2

Header 3 ### (Hashes on right are optional)

Header 4

Header 5

Markdown plus h2 with a custom ID ## {#id-goes-here}

Link back to H2

This is a paragraph, which is text surrounded by whitespace. Paragraphs can be on one

vgoklani

#!/bin/sh

version="2.6.3"
priority="20603"

sudo mkdir -p /var/redis /var/log/redis
curl -sL http://redis.googlecode.com/files/redis-${version}.tar.gz | tar zx
cd redis-${version}/
make
sudo make PREFIX=/usr/local/redis/${version} install

vgoklani

#!/usr/bin/env python
import sys

Dict = []
with open('aclImdb/imdb.vocab','r') as f:
    for line in f:
        Dict.append(line.strip())

with open('audit.log','r') as f:
    f.readline()

vgoklani

worker_processes 2;

error_log /var/log/nginx/error.log;
pid /var/run/nginx.pid;

events {
    worker_connections 1024;
    use epoll;
}

vgoklani

#!/bin/bash

# Requires vw (https://github.com/JohnLangford/vowpal_wabbit/wiki/) and
# the IMDB dataset (http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz)

cat aclImdb/train/labeledBow.feat | \
  sed -n 's/^\([7-9]\|10\)\s/&/p' | \
  sed -e "s/^\([7-9]\|10\)\s//" | \
  awk '{ print "1 '"'"'pos_" (NR-1) " |features " $0}' > train.vw
cat aclImdb/train/labeledBow.feat | \

vgoklani

def online_mean_variance(iterable):
    mN = 0
    mM = 0.0
    mS = 0.0

    for x in iterable:
        mN += 1
        nextM = mM + (x - mM) / mN
        mS += (x - mM) * (x - nextM)

vgoklani

"""Kernel K-means"""

# Author: Mathieu Blondel <[email protected]>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state