iwiwi

/*
 * An xorshift random generator implementation, which is compatible to std::random.
 * 
 * References:
 * - http://cpplover.blogspot.jp/2013/03/blog-post_22.html
 * - http://xorshift.di.unimi.it/xorshift64star.c
 * - http://xorshift.di.unimi.it/xorshift1024star.c
 */

#include <random>

iwiwi

#!/usr/bin/env ruby

#
# dblp.rb --- Parse dblp.xml to generate graphs
#
# Usage:
#   dblp.rb author <dblp.xml     --> co-author network
#   dblp.rb key cite <dblp.xml   --> citation network
#   dblp.rb key author <dblp.xml --> paper-author network
#   dblp.rb key venue <dblp.xml  --> paper-venue network

iwiwi

#!/usr/bin/env ruby

#
# sockets.rb --- by Takuya Akiba (@iwiwi)
#
# Usage:
#   sockets.rb PID
#

`ls -l /proc/#{ARGV[0]}/fd | grep [s]ocket`.scan(/\[([0-9]+)\]/).each_with_index do |m, i|

iwiwi

/*
 * Basic usage:
 *   $ CXXFLAGS=-O3 make shrink_ids
 *   $ ./shrink_ids < EDGES > SHRINKED_EDGES
 *
 * Example:
 *   $ echo "hoge piyo\nfuga hoge" | ./shrink_ids
 *   0	1
 *   2	0
 *

iwiwi

#!/usr/bin/env ruby

#
# qsub.rb --- Torque job machine-gun
#
# Usage:
#   1. Rewrite the shell script template & parameters
#   2. Execute
#        $ qsub.rb (cat) --> Output generated shell scripts
#        $ qsub.rb sh    --> Execute locally

iwiwi

# 普通の場合
>>> class A:
...     def hello(self):
...             print('hellooooo')
...     def __len__(self):
...             print('lennnnnn')
...             return 10
...     def __call__(self):
...             print('calllllled')
...

iwiwi

import numpy as np
import sklearn.datasets
import sklearn.ensemble
import xgboost

N_TRAIN = 60000
NS_ITERATIONS = [2 ** k for k in range(8)]

MODELS = [
    ('RandomForestClassifier', sklearn.ensemble.RandomForestClassifier, {'n_jobs': -1}),

iwiwi

"""
(anaconda3-4.2.0) ~/tmp% python numba_example.py                                                                                                   [18:00:09]
Python: 3.641503095626831
Numba:  0.09061694145202637
"""

import time
from numba import jit, int32

iwiwi

import chainer.functions as F


def selu(x, alpha=1.6732632423543772848170429916717, scale=1.0507009873554804934193349852946):
    return scale * F.elu(x, alpha=alpha)

iwiwi

from typing import *


class Variable(object):

    def __init__(self, data):
        # type: (float) -> None
        self.data = data
        self.creator = None  # type: Function