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


"""
Kakao Hangul Analyzer III

__version__ = '0.3'
__author__ = 'Kakao Corp.'
__copyright__ = 'Copyright (C) 2018-, Kakao Corp. All rights reserved.'
__license__ = 'Apache 2.0'

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outputdebug = True


def debug(msg):
    if outputdebug:
        print(msg)


class Node():
    def __init__(self, key):

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import frontmatter
import glob
import yaml
import string
from konlpy.tag import Komoran
from sklearn.feature_extraction.text import TfidfVectorizer

def get_posts(folder='C:/Users/ratsgo/GoogleDrive/내폴더/git/blog/_posts'):
    result = {}
    for filepath in glob.glob(folder + "/*"):

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from collections import Counter
import random

def p_topic_given_document(topic, d, alpha=0.1):
    return ((document_topic_counts[d][topic] + alpha) /
            (document_lengths[d] + K * alpha))

def p_word_given_topic(word, topic, beta=0.1):
    return ((topic_word_counts[topic][word] + beta) /
            (topic_counts[topic] + V * beta))

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# -*- coding: utf-8 -*-
# Copyright (c) 2012, Chi-En Wu
from math import log

def _normalize_prob(prob, item_set):
    result = {}
    if prob is None:
        number = len(item_set)
        for item in item_set:
            result[item] = 1.0 / number

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library(stringr)

# loading
DTM <- readRDS('dtm.rds')
vec <- read.csv('word2vec.txt', fileEncoding='utf-8', sep=" ", header=F, skip=1)

# distance matrix
distance <- dist(vec[,-1])
distance <- as.matrix(distance)
colnames(distance) <- vec[,1]

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from collections import defaultdict
from pandas import read_table
import numpy as np
import math

class NaiveBayesClassifier:

    def __init__(self, k=0.5):
        self.k = k
        self.word_probs = []

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"""This file contains code for use with "Think Bayes",
by Allen B. Downey, available from greenteapress.com

Copyright 2012 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""

"""This file contains class definitions for:

Hist: represents a histogram (map from values to integer frequencies).

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import numpy as np
import pandas as pd
import re
import tensorflow as tf
import random
import pickle
from collections import defaultdict
import operator

####################################################

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import os
import time
import datetime
from tensorflow import flags
import tensorflow as tf
import numpy as np

class TextCNN(object):
    """
    A CNN for text classification.