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

#
# 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

import it.unimi.dsi.fastutil.ints.IntArrayFIFOQueue;
import it.unimi.dsi.fastutil.ints.IntArrays;
import it.unimi.dsi.logging.ProgressLogger;
import it.unimi.dsi.webgraph.GraphClassParser;
import it.unimi.dsi.webgraph.ImmutableGraph;
import it.unimi.dsi.webgraph.LazyIntIterator;

import java.io.*;
import java.util.*;

iwiwi

#!/usr/bin/env ruby

#
# crawler.rb --- Lightweight parallel web graph crawler
#
# Usage:
#   ./crawler.rb START_URL TARGET_REGEXP
#
# Output:
#   stdout --- edge list (tab separated URLs)

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

/*
 * 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

# Copyright 2016, Takuya Akiba
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
#     * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#     * Redistributions in binary form must reproduce the above

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

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