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#!/usr/bin/env python

import itertools
import sys
import os
import subprocess
import jinja2


def enumerate_combinations(**params):

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class RangeWithTimeout(object):

    def __init__(self, n=None, timeout_seconds=None):
        self.n = n
        self.timeout_seconds = timeout_seconds

        self.i = 0

    def __iter__(self):
        self.start_datetime = datetime.datetime.now()

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from chainer.training import extension
import signal


class LearningRateSignalHandler(extension.Extension):

    trigger = 1, 'epoch'

    def __init__(self, attr='lr', rate=0.1, signalnum=signal.SIGUSR1, optimizer=None):
        self.attr = attr

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import chainermn
from chainermn.communicators._memory_utility import array_to_buffer_object
import mpi4py.MPI

comm = chainermn.create_communicator('naive')


def alltoall_demo(xp):
    mpi_comm = comm.mpi_comm

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# 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

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