Generative Model vs Discriminative Model - 컴퓨터

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

python 공부 - 컴퓨터

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"

등식은 - 생각

기분좋은 날이나
그렇지 않은날에 개의치 않고 유지된다.