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@j-min
j-min / Mecab-ko_iter.py
Last active Oct 12, 2016
Mecab-ko based POS tagging iterator
View Mecab-ko_iter.py
from konlpy.tag import Mecab
mecab = Mecab()
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
def mecab_pos_tag():
while True:
a = raw_input()
for x in mecab.pos(a):
@j-min
j-min / KoNLPy Install (Ubuntu & Python 2)
Last active Sep 30, 2016
KoNLPy Install (Ubuntu & Python 2)
View KoNLPy Install (Ubuntu & Python 2)
# Reference: http://konlpy.org/en/v0.4.4/install/#ubuntu
sudo apt-get update && sudo apt-get upgrade
sudo apt-get install g++ openjdk-7-jdk python-dev python3-dev --fix-missing
sudo pip install JPype1
sudo pip install konlpy
sudo apt-get install curl
bash <(curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh)
View sejong_treebank.txt.v1
This file has been truncated, but you can view the full file.
; 새 생명.
(NP (DP 새/MM)
(NP 생명/NNG + ./SF))
; 나는 돈이다.
(S (NP_SBJ 나/NP + 는/JX)
(VNP 돈/NNG + 이/VCP + 다/EF + ./SF))
; 만 원이라는 이름을 붙인 채, 이제 막 태어났다.
@j-min
j-min / RNN_hunkim's_tutorial_BasicRNNCell.ipynb
Last active Dec 11, 2018
TensorFlow 0.9 implementation of BasicRNNCell based on hunkim's tutorial
View RNN_hunkim's_tutorial_BasicRNNCell.ipynb
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@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
@j-min
j-min / .vimrc
Last active Apr 28, 2019
vimrc plugin settings for Python / JavaScript
View .vimrc
set nocompatible " required
" filetype off " required
syntax on
syntax enable
" Monokai color scheme
" mkdir -p ~/.vim/colors
" wget https://raw.githubusercontent.com/crusoexia/vim-monokai/master/colors/monokai.vim ~/.vim/colors
colorscheme monokai
@j-min
j-min / R_custom_ANOVA&Summary
Last active Jun 30, 2016
R_custom_ANOVA&Summary
View R_custom_ANOVA&Summary
ANOVA_SUMMARY = function(formula, data)
{
# 1. 데이터 전처리
# 종속변수를 항상 첫번째 input으로 "y~." 형식으로 받음
mf = model.frame(formula, data)
y = mf[,1]
Response_name = colnames(mf)[1] # 종속변수의 이름
Variable_name = c("(intercept)", colnames(mf)[-1]) # 독립변수의 이름
n = nrow(mf)
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