start new:
tmux
start new with session name:
tmux new -s myname
LOADING=false | |
usage() | |
{ | |
cat << EOF | |
usage: $0 [options] dbname | |
OPTIONS: | |
-h Show this help. | |
-l Load instead of export |
upstream gitlab { | |
server 172.17.42.1:10080 fail_timeout=0; | |
} | |
# let gitlab deal with the redirection | |
server { | |
listen 80; | |
server_name git.example.com; | |
server_tokens off; | |
root /dev/null; |
<?xml version="1.0"?> | |
<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> | |
<fontconfig> | |
<!-- | |
Documented at | |
http://linux.die.net/man/5/fonts-conf | |
To check font mapping run the command at terminal | |
$ fc-match 'helvetica Neue' |
module.exports = (robot) -> | |
robot.respond /deploy to stage/i, (msg) -> | |
process.chdir('/your/dir') | |
doing = require('child_process').spawn 'phing', ['remotedeploy','-Denv=stage'] | |
msg.send 'stage deployment request sent' |
"""An lxml Port of Nirmal Patel's port (http://nirmalpatel.com/fcgi/hn.py) of | |
Arc90's Readability to Python. | |
""" | |
import re | |
from lxml.html import fromstring, tostring | |
from lxml.html.clean import Cleaner | |
NEGATIVE = re.compile('comment|meta|footer|footnote|foot') | |
POSITIVE = re.compile('post|hentry|entry|content|text|body|article') |
These weights are often combined into a tf-idf value, simply by multiplying them together. The best scoring words under tf-idf are uncommon ones which are repeated many times in the text, which lead early web search engines to be vulnerable to pages being stuffed with repeated terms to trick the search engines into ranking them highly for those keywords. For that reason, more complex weighting schemes are generally used, but tf-idf is still a good first step, especially for systems where no one is trying to game the system. | |
There are a lot of variations on the basic tf-idf idea, but a straightforward implementation might look like: | |
<?php | |
$tfidf = $term_frequency * // tf | |
log( $total_document_count / $documents_with_term, 2); // idf | |
?> | |
It's worth repeating that the IDF is the total document count over the count of the ones containing the term. So, if there were 50 documents in the collection, and two of them contained the term in question, the IDF would be 50/2 = 25. To be accurate, we s |
Latency Comparison Numbers | |
-------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns | |
Send 1K bytes over 1 Gbps network 10,000 ns 0.01 ms | |
Read 4K randomly from SSD* 150,000 ns 0.15 ms |
#!/usr/bin/python | |
# coding=utf-8 | |
# Python version of Zach Holman's "spark" | |
# https://github.com/holman/spark | |
# by Stefan van der Walt <stefan@sun.ac.za> | |
""" | |
USAGE: |