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/* | |
* 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> |
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#!/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 |
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#!/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| |
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/* | |
* Basic usage: | |
* $ CXXFLAGS=-O3 make shrink_ids | |
* $ ./shrink_ids < EDGES > SHRINKED_EDGES | |
* | |
* Example: | |
* $ echo "hoge piyo\nfuga hoge" | ./shrink_ids | |
* 0 1 | |
* 2 0 | |
* |
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#!/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 |
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# 普通の場合 | |
>>> class A: | |
... def hello(self): | |
... print('hellooooo') | |
... def __len__(self): | |
... print('lennnnnn') | |
... return 10 | |
... def __call__(self): | |
... print('calllllled') | |
... |
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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}), |
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""" | |
(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 | |
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import chainer.functions as F | |
def selu(x, alpha=1.6732632423543772848170429916717, scale=1.0507009873554804934193349852946): | |
return scale * F.elu(x, alpha=alpha) |
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from typing import * | |
class Variable(object): | |
def __init__(self, data): | |
# type: (float) -> None | |
self.data = data | |
self.creator = None # type: Function |
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