생성모델은 (Prior 와 conditional prob. 의 곱인) 결합확률 사용 분별모델은 conditional prob. 사용

Discriminative models , also called conditional models, are a class of models used in machine learning for modeling the dependence of an unobserved variable y on an observed variable x. Within a probabilistic framework, this is done by modeling the conditional probability distribution P(y|x), which can be used for predicting y from x.

[출처] wikipedia.org

In probability and statistics, a generative model is a model for randomly generating observable data values, typically given some hidden parameters. It specifies a joint probability distribution over observation and label sequences. Generative models are used in machine learning for either modeling data directly (i.e., modeling observations drawn from a probability density function), or as an intermediate step to forming a conditional probability density function. A conditional distribution can be formed from a generative model through Bayes' rule.

[출처] wikipedia.org

[출처] http://www.cs.ualberta.ca/~chihoon/ml/slides/gvd.pdf

oov checker.py

#!/usr/bin/python

import sys

if len(sys.argv) is not 3:

print "!!USAGE: ./oov_checker.py txt dic"

exit()

print "TEXT: ", sys.argv[1]

vocab = set(line.strip() for line in open(sys.argv[2]))

print "Dictionary: ", sys.argv[2], " Size: ", len(vocab)

txt = open(sys.argv[1])

unk_file = open(sys.argv[1]+".unk", "w")

line_N = 0

for line in txt:

words = line.split()

for word in words :

if word in vocab:

unk_file.write(word)

else:

unk_file.write("")

unk_file.write(" ")

unk_file.write("

")

line_N = line_N + 1

print "Total ", line_N," lines were processed and saved to ", sys.argv[1]+".unk"

기분좋은 날이나

그렇지 않은날에 개의치 않고 유지된다.

흐음.

아무튼 잘 추스리고 마무리 해야지

딱히 이유라 할 것은 없는 듯 싶다