Having trouble installing the latest stable version of tmux?
I know, official package for your OS/distro is outdated and you just want the newest version of tmux.
Well, this script should save you some time with that.
- gcc
Having trouble installing the latest stable version of tmux?
I know, official package for your OS/distro is outdated and you just want the newest version of tmux.
Well, this script should save you some time with that.
set cot=menu,menuone | |
ino <BS> <BS><C-r>=getline('.')[col('.')-3:col('.')-2]=~#'\k\k'?!pumvisible()?"\<lt>C-n>\<lt>C-p>":'':pumvisible()?"\<lt>C-y>":''<CR> | |
ino <CR> <C-r>=pumvisible()?"\<lt>C-y>":""<CR><CR> | |
ino <Tab> <C-r>=pumvisible()?"\<lt>C-n>":"\<lt>Tab>"<CR> | |
ino <S-Tab> <C-r>=pumvisible()?"\<lt>C-p>":"\<lt>S-Tab>"<CR> | |
augroup MyAutoComplete | |
au! | |
au InsertCharPre * if |
from math import pow, sqrt | |
def cosine(ratings1, ratings2): | |
norm1 = sum([pow(rating,2) for rating in ratings1.values()]) | |
norm2 = sum([pow(rating,2) for rating in ratings2.values()]) | |
intersect_keys = filter(lambda x: x in ratings1.keys(), ratings2.keys()) | |
dot_product = sum([ratings1[key]*ratings2[key] for key in intersect_keys]) | |
cosine_distance = dot_product/(sqrt(norm1*norm2)) | |
return cosine_distance |
def bufferise(defbuf=20, defskip=0): | |
def decorate(function): | |
def wrapper(*args, **kwargs): | |
bufsize = kwargs['bufsize'] if 'bufsize' in kwargs else defbuf | |
skiplines = kwargs['skiplines'] if 'skiplines' in kwargs else defskip | |
print 'Bufsize = {}'.format(bufsize) | |
print 'Skip {} lines'.format(skiplines) | |
if skiplines: | |
for i, record in enumerate(function(*args, **kwargs), start=1): | |
if i > skiplines: |
Picking the right architecture = Picking the right battles + Managing trade-offs
#System Design Cheatsheet
Picking the right architecture = Picking the right battles + Managing trade-offs
##Basic Steps
package main | |
import ( | |
"flag" | |
"io" | |
"log" | |
"net" | |
) | |
func main() { |
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer | |
from sklearn.datasets import fetch_20newsgroups | |
from sklearn.decomposition import NMF, LatentDirichletAllocation | |
def display_topics(model, feature_names, no_top_words): | |
for topic_idx, topic in enumerate(model.components_): | |
print "Topic %d:" % (topic_idx) | |
print " ".join([feature_names[i] | |
for i in topic.argsort()[:-no_top_words - 1:-1]]) |
# -*- coding: utf-8 -*- | |
''' | |
Functions to read the OpenWordnetPT from RDF files and provide | |
access to it. | |
''' | |
import rdflib | |
from six.moves import cPickle |