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View hangul.py
# -*- coding: utf-8 -*-
class Hangul:
BASE_CODE = 44032
CHOSUNG = 588
JUNGSUNG = 28
# 초성 리스트. 00 ~ 18
CHOSUNG_LIST = [
'', '', '', '', '', '', '', '', '',
View CSVFileIO.py
import csv
import os
def get_csv_writer(filename, rows, delimiter):
with open(filename, 'w') as csvfile:
fieldnames = rows[0].keys()
writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter=delimiter)
writer.writeheader()
for row in rows:
try:
View NLTK_Stanford_2015-12-09.md

NLTK API to Stanford NLP Tools compiled on 2015-12-09

Stanford NER

With NLTK version 3.1 and Stanford NER tool 2015-12-09, it is possible to hack the StanfordNERTagger._stanford_jar to include other .jar files that are necessary for the new tagger.

First set up the environment variables as per instructed at https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software

@j-min
j-min / install-tensorflow.sh
Last active Nov 15, 2016 — forked from erikbern/install-tensorflow.sh
TensorFlow Installation Log
View install-tensorflow.sh
# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/
# Install various packages
sudo apt-get update
@j-min
j-min / pg-pong.py
Created Jul 13, 2016 — forked from karpathy/pg-pong.py
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
View pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
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