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# -*- coding:utf-8 -*-

import codecs
import json
import os
import sys


if __name__ == '__main__':
    def walk_json(path):

hiropppe

# cython: boundscheck = False
# cython: wraparound = False
# cython: cdivision = True

import numpy as np

cimport numpy as np

from scipy.linalg cimport cython_blas

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from cython.parallel import prange

from libc.stdio cimport printf
from libc.stdlib cimport abort, malloc, free

cimport openmp


def get_int_flag():
    cdef int flag = 0

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'''
Synchronous Distributed Tensorflow 0.12.0 example of using data parallelism and share model parameters.
Trains a simple sigmoid neural network on mnist for 20 epochs on three machines using one parameter server.

Change the hardcoded host urls below with your own hosts.
Run like this:

pc-01$ python synchronous_distributed_mnist_training.py --job_name="ps" --task_index=0
pc-02$ python synchronous_distributed_mnist_training.py --job_name="worker" --task_index=0
pc-03$ python synchronous_distributed_mnist_training.py --job_name="worker" --task_index=1

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'''
Asynchronous Distributed Tensorflow 0.12.0 example of using data parallelism and share model parameters.
Trains a simple sigmoid neural network on mnist for 20 epochs on three machines using one parameter server.

Change the hardcoded host urls below with your own hosts.
Run like this:

pc-01$ python asynchronous_distributed_mnist_training.py --job_name="ps" --task_index=0
pc-02$ python asynchronous_distributed_mnist_training.py --job_name="worker" --task_index=0
pc-03$ python asynchronous_distributed_mnist_training.py --job_name="worker" --task_index=1

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policy gradient methods for reinforcement learning

references

1. Deep Reinforcement Learning: Pong from Pixels
2. 深層強化学習：ピクセルから『ポン』 – 前編
3. 深層強化学習：ピクセルから『ポン』 – 後編
4. Simple Reinforcement Learning with Tensorflow: Part 2 - Policy-based Agents
5. Simple reinforcement learning methods to learn CartPole
6. Pong by karpathy
7. CartPole by christopherhesse

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import tables as tb

from scipy.sparse import lil_matrix

h5 = tb.open_file('sparse.h5', 'a')

data_group = h5.create_group(h5.root, 'data_group')
indices_group = h5.create_group(h5.root, 'indices_group')
indptr_group = h5.create_group(h5.root, 'indptr_group')

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$ mkdir -p ~/.vim/bundle
$ git clone https://github.com/Shougo/neobundle.vim ~/.vim/bundle/neobundle.vim


"------------------------------------
" neocomplete.vim
"------------------------------------
"Note: This option must set it in .vimrc(_vimrc).  NOT IN .gvimrc(_gvimrc)!
" Disable AutoComplPop.
let g:acp_enableAtStartup = 0

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Reference

1. http://qiita.com/yukiB/items/bec24db4a5a41db02895
2. http://qiita.com/_akisato/items/d5de60e38b5a69387bde
3. https://hunseblog.wordpress.com/2014/09/15/installing-numpy-and-openblas/
4. http://stackoverflow.com/questions/11443302/compiling-numpy-with-openblas-integration
5. 囲碁AI “AlphaGo” はなぜ強いのか?
6. AlphaGoの仕組み
7. Google DeepMindの人工知能(AI)「AlphaGo」を頑張って理解する
8. モンテカルロ木探索法
9. 強化学習をベイズで理解する

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Timeful (Google Calendar)

Turning personal calendars into scheduling assistants

http://web.stanford.edu/~cysuen/projects/2012-bank.pdf

Optimization Algorithem

"Squeaky Wheel" Optimization

https://arxiv.org/pdf/1105.5454.pdf

A Constraint-Based Approach to Scheduling an Individual’s Activities

http://staff.aub.edu.lb/~nysmith/papers/n82.pdf