(C-x means ctrl+x, M-x means alt+x)
The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf
:
-- Remove the history from | |
rm -rf .git | |
-- recreate the repos from the current content only | |
git init | |
git add . | |
git commit -m "Initial commit" | |
-- push to the github remote repos ensuring you overwrite history | |
git remote add origin [email protected]:<YOUR ACCOUNT>/<YOUR REPOS>.git |
#!/bin/bash | |
cd `dirname $0` | |
curl\ | |
--remote-name \ | |
--remote-header-name\ | |
http://www.topcoder.com/contest/arena/ContestAppletProd.jnlp | |
JAVAWS="/Library/Internet Plug-Ins/JavaAppletPlugin.plugin/Contents/Home/bin/javaws" |
"""Information Retrieval metrics | |
Useful Resources: | |
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
http://www.nii.ac.jp/TechReports/05-014E.pdf | |
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
""" | |
import numpy as np |
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
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 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
As configured in my dotfiles.
start new:
tmux
start new with session name:
# Next time you need to install something with python setup.py -- which should be never but things happen. | |
python setup.py install --record files.txt | |
# This will cause all the installed files to be printed to that directory. | |
# Then when you want to uninstall it simply run; be careful with the 'sudo' | |
cat files.txt | xargs sudo rm -rf | |
import nltk | |
with open('sample.txt', 'r') as f: | |
sample = f.read() | |
sentences = nltk.sent_tokenize(sample) | |
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences] | |
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences] | |
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True) |