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잘까

2017-06-11 01:38:47

일상

컵라면을 끓여먹고 더 갈까 고민중

왜 항상 새로운 걸까 이럴때 보면 long term memory 에 뭔가 잘 전달이 안되는 것 같다. 머스크 형님이 뭔가 해내면 이런 고민은 조금은 덜어지려나 싶다.

내가 지금 문제라고 인지하는 것들은 training error

진짜 풀어야할 문제는 test error

그래서 지금 문제의 사소한 부분까지 맞춰봐야 딱히 진정한 도움은 안될 것 같은

그런 느낌..

type of error 와 결부 시키기에는 억지스럽다.

▼ more

어른이 되면서

2017-06-10 21:36:02

생각

상황을 파악하고 파악된 상황에서 유리한 고지를 쟁취하기 위해 머리를 굴리는 것을 대단히 좋아지 않는다.

하지만 그런 머리 굴림은 순수하다고 되는 그 어떤 시기에 상황을 파악하지 않고 저질렀던 그 수많던 "개념" 없는 행동들로 인해 학습 된 방어기재라고 생각된다.

문제는 이러한 방어기재가 무분별하게 발동되면 불확정성을 줄이기 위해 원하는 상황을 산정하고 그에 맞게 상황이 흘러가기를 바라는 것을 넘어 원하는 방향으로 흘러가도록 개입하거나 적어도 개입하고 싶은 충동을 느낀다.

그렇다. 순수함을 잃은 것이다.

달리 말하면 진지하게 임할때 합리화 라는 이름으로 저지르는 비겁함이다.

진지하다면 흐름을 좌우하려 들 것이 아니라 진심으로 상황을 마주하고 담대하게 그 흐름에 맞춰 긴장감을 유지해야 할 것 이다.

내 바람과 결과가 달랐다면 그건 그냥 달랐던 거다.

- 나이가 들어도 늙지 않는 방법 중 하나가 아닐까 싶었다.

* NAS HDD 교체로 휴대폰에 남김 2017.06.09 10:00

▼ more

kNN in python

2017-05-28 19:39:08

컴퓨터

예상보다 너무 오래걸렸다.

문제는 이 친구도 착하지만 느린친구라 예상보다 일하는게 오래걸린다ㅎㅎ

언젠가;; python 공부 한다는 생각으로 최적화 해보는 것이 좋겠다.

#!/usr/bin/python

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

import math

import os

import pickle

import sys

from re import *

import operator

def tokenize(input_text):

'''Returns a list of words in the input text.'''

#input_text = input_text.decode('latin1')

# English clitics.

input_text = input_text.replace("'ll", " will")

input_text = input_text.replace("'re", " are")

input_text = input_text.replace("'ve", " have")

input_text = input_text.replace("won't", "will not")

input_text = input_text.replace("n't", " not")

input_text = input_text.replace("'m", " am")

# Convert single text lines to fluent text and lower the letters.

input_text = input_text.lower()

# Regex to search for words.

# IMPORTANT: The search regex and the string to search in have to be

# unicode, otherwise it will not work properly.

re_words = compile("[abcdefghijklmnopqrsšzžtuvwõäöüxy'-]+", IGNORECASE | MULTILINE | DOTALL)

# Now the words can be extracted.

text_word_list = re_words.findall(input_text)

return text_word_list

# Calculates the length of a vector.

def calculate_vector_length(vector_1):

vector_length = 0

# Your code goes here

for x in vector_1:

vector_length = vector_length + math.pow(x,2)

vector_length = math.sqrt(vector_length)

return vector_length

# Calculates the Euclidean distance between vector_1 und vector_2.

def calculate_euclidean_distance(vector_1, vector_2):

distance = 0

# Your code goes here

if len(vector_1) != len(vector_2):

return -1

for i in range(len(vector_1)):

distance = distance + math.pow(vector_1[i] - vector_2[i],2)

distance = math.sqrt(distance)

return&nb\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0sp;distance

def create_index(docs_dict, document_vector):

"""Create postings list for faster access of Document Frequency."""

"""

index

key: token

value: array with file names

Example

{'shop': ['00042_ham.txt'], 'confirmation': ['00047_spam.txt'], 'very': ['00037_spam.txt', '00049_spam.txt']}

"""

index = {}

# Your code goes here

for doc in docs_dict:

for term in document_vector:

if term in docs_dict[doc][1]:

if term in index :

index[term].append(doc)

else :

index[term] = []

index[term].append(doc)

return index

def get_stop_word_list():

"""Returns the list of stop words."""

in_f = open("stopwords.txt")

stop_words = in_f.read().strip().split("

")

in_f.close()

return stop_words

def create_document_vectors(training_folder):

"""

Computes the document vectors for documents from the training data

and return them and the general document vector.

"""

docs_dict

Key: Training file name

Value: Array of length 2 (Lets call this array 'A')

Array 'A'

Length: 3

A[0]: 'ham' or 'spam'

A[1]: Another array&nbs\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0p;with tokens from the file (no stopwords in this array)

A[2]: Array with tf-idf values

Example of docs_dict

{'00043_ham.txt': ['ham', ['thank', 'posting', 'same', 'issue', 'listmaster', 'lists', 'debian', 'org'], [0.0, 0.0, 0.0, 0.0, 0.0, 0.2011798118406769, 0.0, 0.2011798118406769, 0.0, 0.0, 0.0, 0.0, 0.2011798118406769]}

document_vector

Array of all distinct tokens in the training set (no stop words)

"""

docs_dict = {}

document_vector = []

stop_words = get_stop_word_list()

# Your code goes here

# Read in and tokenize each document of the training data. Remember to remove stop words.

term_frequencies={}

docu_frequencies={}

for doc in os.listdir(training_folder):

A=[]

in_f = open(training_folder+"/"+ doc)

doc_class = in_f.readline().strip() #class of the test document ('spam' or 'ham')

if doc_class != "spam" and doc_class != "ham" :

break;

A.append(doc_class)

doc_tokens = tokenize(in_f.read().strip()) #array of tokens of the test document

#W = [x for x in doc_tokens if x not in stop_words]

W = []

term_frequencies[doc]={}

for x in doc_tokens:

if x not in stop_words:

W.append(x)

if x in term_frequencies[doc]:

term_frequencies[doc][x] = term_frequencies[doc][x] + 1

else:

term_frequencies[doc][x] = 1

if x in docu_frequencies:

docu_frequencies[x].append(doc)

else:

&nbs\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0p; docu_frequencies[x]=[]

docu_frequencies[x].append(doc)

document_vector.extend(W)

A.append(W)

docs_dict[doc]=A

# Create document_vector

document_vector = list(set(document_vector)) # deduplication

# Create tf-idf weighted document vector. You will need to use document frequency values from create_index.

term_index = create_index(docs_dict, document_vector)

for doc in docs_dict:

doc_vect = []

for term in document_vector:

# Your code goes here

# Term Frequency : Log scale

if term not in term_frequencies[doc]:

doc_vect.append(0)

continue

else :

term_frequency = math.log10(term_frequencies[doc][term]) + 1

if term not in docu_frequencies:

print "document frequency error"

document_frequency = len(set(docu_frequencies[term]))

# Idf

idf = math.log10(len(docs_dict) / document_frequency)

tf_idf = term_frequency * idf

doc_vect.append(tf_idf)

&n\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0bsp; docs_dict[doc].append(doc_vect)

# Normalize vector

for doc in docs_dict:

m=0

for s in docs_dict[doc][2]:

m = m + s*s

m = math.sqrt(m)

if m == 0:

continue

docs_dict[doc][2][:] = [x / m for x in docs_dict[doc][2]]

return docs_dict, document_vector, term_index

def create_test_document_vector(docs_dict, test_document, document_vector, index):

# Process test document.

in_f = open(test_document)

doc_class = in_f.readline().strip() #class of the test document ('spam' or 'ham')

if doc_class != "spam" and doc_class != "ham" :

print "Test file don't have a class type"

return [], []

doc_tokens = tokenize(in_f.read().strip()) #array of tokens of the test document

stop_words = get_stop_word_list()

# Remove stop words from doc_tokens

tmp_doc_tokens = []

for doc_token in doc_tokens:

if doc_token not in stop_words:

tmp_doc_tokens.append(doc_token)

doc_tokens=[]

doc_tokens = tmp_doc_tokens

# Your code goes here

doc_data = [doc_class, doc_tokens] #Add token array to doc_class array

# Create tf-idf weighted document vector using document_vector

doc_vect = []

tf_idf = 0

for term in document_vector:

# Your code goes here

# Term Frequency : Log scale

term_frequency = doc_tokens.count(term)

if term_frequency > 0:

term_frequency = math.log10(term_frequency) + 1

else :

doc_vect.append(0)

continue

# Idf

&nbs\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0p; document_frequency = 0

for docs in docs_dict:

if term in docs_dict[docs][1]:

document_frequency = document_frequency + 1

if document_frequency == 0 :

print "document frequency error"

idf = math.log10(len(docs_dict) / document_frequency)

tf_idf = term_frequency * idf

doc_vect.append(tf_idf)

# Normalize the vector.

# Your code goes here

m=0

for s in doc_vect:

m = m + s*s

m = math.sqrt(m)

if m > 0:

doc_vect[:] = [x / m for x in doc_vect]

"""

doc_data should now look like this:

Type: Array

Size: 2

doc_data[0]: 'spam' or 'ham'

doc_data[1]: array of tokens (with stop words removed)

doc_vect

Type: Array with tf-idf values

Example

[0.11856790094712363, 0.0, 0.0, 0.0, 0.0, 0.39387404130807196, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.27530614036094825]

"""

return doc_data, doc_vect

# Main program.

if __name__ == "__main__":

if len(sys.argv) < 4:

print "Usage: python knn.py train test_file k"

else:

training_folder = sys.argv[1]

test_document = sys.argv[2]

k = int(sys.argv[3])

docs_dict, document_vector, term_index = create_document_vectors(training_folder)

t\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0est_doc_data, test_doc_vector = create_test_document_vector(docs_dict, test_document, document_vector, term_index)

# Calculate the Euclidean distance of the test document to training documents.

distances = {}

doc_cnt = 0

print "Calculating Euclidean distance between the new document and the training documents ..."

# Your code goes here

for doc in docs_dict:

distances[doc] = calculate_euclidean_distance(test_doc_vector, docs_dict[doc][2])

# Sort nearest docs and their distances to the new document.

# Your code goes here

distances = sorted(distances.items(), key=operator.itemgetter(1))

# Calculate the majority class of the nearest neighbours.

# Your code goes here

idx = 0

ham = 0

spam = 0

for doc in distances:

if idx >= k:

break

if docs_dict[doc[0]][0] == "ham":

ham = ham + 1

if docs_dict[doc[0]][0] == "spam":

spam = spam + 1

idx = idx + 1

if spam > ham:

final_label = "spam"

elif spam < ham:

final_label = "ham"

else:

total_ham = 0

total_spam = 0

for doc in docs_dict:

if docs_dict[doc][0] == "spam":

total_spam = total_spam + 1

\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0; if docs_dict[doc][0] == "ham":

total_ham = total_ham + 1

if total_spam > total_ham:

final_label = "spam"

elif total_spam < total_ham:

final_label = "ham"

else:

final_label = "spam"

# Show the results.

print "Assigned class: %s" % final_label

if test_doc_data[0] == final_label:

print "Correct class assigned to new document! :-)"

else:

print "Wrong class assigned to new document! :-("

print "Nearest neighbours:"

# Your code goes here

idx = 0

for doc in distances:

if idx >= k:

break

print "DocID: " + training_folder+"/"+ doc[0] + " / Distance: " + str(doc[1]) + " / Class:" + docs_dict[doc[0]][0]

idx = idx + 1

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약간 멍때리고 있긴했다.

2017-05-27 22:57:48

일상

악동뮤지션의 비교적 최신곡 오랜날 오랜밤 이 듣기 좋아

다른 비슷한 노래가 없을까 싶어

약간 멍한 정신에 "최신" "오디션 프로그램" "악동으로 시작하는 가수" 라는 질의를 나의 머리에 던지자

내손이 멜론 검색창에 쳐내려간 글귀는 "악동클럽" 음.

(2002년에 1집발매한 1대 아이돌 오디션 프로그램의 산물..)

쿼리 따위 proxy 일 뿐이라고 배웠잖아 ㅠ

멍 때리고 있어서 였다고 그렇게 위안 삼도록하자. 그래 그럴거야 ㅠ

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