amueller

import numpy as np
import warnings

from itertools import cycle, izip

from sklearn.utils import gen_even_slices
from sklearn.utils import shuffle
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.preprocessing import LabelBinarizer

dideler

Inspired by this article. Neat tricks for speeding up integer computations.

Note: cin.sync_with_stdio(false); disables synchronous IO and gives you a performance boost. If used, you should only use cin for reading input (don't use both cin and scanf when sync is disabled, for example) or you will get unexpected results.

Multiply by a power of 2

x = x << 1; // x = x * 2

masak

Ok, I geeked out, and this is probably more information than you need. But it completely answers the question. Sorry. ☺

Locally, I'm at this commit:

$ git show
commit d6cd1e2bd19e03a81132a23b2025920577f84e37
Author: jnthn <jnthn@jnthn.net>
Date:   Sun Apr 15 16:35:03 2012 +0200

When I added FIRST/NEXT/LAST, it was idiomatic but not quite so fast. This makes it faster. Another little bit of masak++'s program.

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

fabianp

# -*- coding: utf-8 -*-
"""
Minimalistic implementation of l1 minimization via coordinate descent.

Reference: www.jstatsoft.org/v33/i01/paper

Author: Fabian Pedregosa <fabian@fseoane.net>
"""

import numpy as np

tonicebrian

# =============
# Introduction
# =============
# I've been doing some data mining lately and specially looking into `Gradient
# Boosting Trees <http://en.wikipedia.org/wiki/Gradient_boosting>`_ since it is
# claimed that this is one of the techniques with best performance out of the
# box.  In order to have a better understanding of the technique I've reproduced
# the example of section *10.14.1 California Housing* in the book `The Elements of Statistical Learning <http://www-stat.stanford.edu/~tibs/ElemStatLearn/>`_.
# Each point of this dataset represents the house value of a property with some
# attributes of that house. You can get the data and the description of those

aflaxman

from pylab import *

def cface(ax, x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18):
    # x1 = height  of upper face
    # x2 = overlap of lower face
    # x3 = half of vertical size of face
    # x4 = width of upper face
    # x5 = width of lower face
    # x6 = length of nose
    # x7 = vertical position of mouth

dideler

The program below can take one or more plain text files as input. It works with python2 and python3.

Let's say we have two files that may contain email addresses:

1. file_a.txt

foo bar
ok ideler.dennis@gmail.com sup
 hey...user+123@example.com,wyd
hello world!

chiiph

# Based on http://cr.yp.to/streamciphers/timings/estreambench/submissions/salsa20/chacha8/ref/chacha.c

import binascii
import numpy as np

np.seterr(over='ignore')

def rotl32(v, c):
    assert isinstance(v, np.uint32)
    assert isinstance(c, np.uint32)

ajtulloch

#!/usr/bin/env python

import svmpy
import logging
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import itertools
import argh