goyalankit

#################################################################
# = This script transfers bash history to zsh history
# = Change bash and zsh history files, if you don't use defaults
#
# = Usage: ruby bash_to_zsh_history.rb
#
# = Author: Ankit Goyal
#################################################################

# change if you don't use default values

elmotec

#!python

"""See main.__doc__"""

import os
import logging
import glob
import unittest
import sys

cyeong

""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
" CSCOPE settings for vim           
""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
"
" This file contains some boilerplate settings for vim's cscope interface,
" plus some keyboard mappings that I've found useful.
"
" USAGE: 
" -- vim 6:     Stick this file in your ~/.vim/plugin directory (or in a
"               'plugin' directory in some other directory that is in your

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

fabianp

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: 

    "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer 1999

    "Learning to rank from medical imaging data." Pedregosa, Fabian, et al., 
    Machine Learning in Medical Imaging 2012.

iizukak

import numpy as np

def gs_cofficient(v1, v2):
    return np.dot(v2, v1) / np.dot(v1, v1)

def multiply(cofficient, v):
    return map((lambda x : x * cofficient), v)

def proj(v1, v2):
    return multiply(gs_cofficient(v1, v2) , v1)

Arachnid

import bisect

class NFA(object):
  EPSILON = object()
  ANY = object()
  
  def __init__(self, start_state):
    self.transitions = {}
    self.final_states = set()
    self._start_state = start_state

endolith

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