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      <title>MZ세대 소통봇 사이트 만들 by 선생 김선비</title>
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      <description>2학년 10반</description>
      <language>en-us</language>
      <pubDate>2023-10-24 00:43:53 UTC</pubDate>
      <lastBuildDate>2024-10-09 10:27:21 UTC</lastBuildDate>
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         <title>레벤슈타인 거리 코드</title>
         <author>junsoo1216</author>
         <link>https://padlet.com/junsoo1216/aichatbot/wish/2760047588</link>
         <description><![CDATA[<div>def calc_distance(a, b):</div><div>&nbsp; &nbsp; ''' 레벤슈타인 거리 계산하기 '''</div><div>&nbsp; &nbsp; if a == b: return 0 # 같으면 0을 반환</div><div>&nbsp; &nbsp; a_len = len(a) # a 길이</div><div>&nbsp; &nbsp; b_len = len(b) # b 길이</div><div>&nbsp; &nbsp; if a == "": return b_len</div><div>&nbsp; &nbsp; if b == "": return a_len</div><div>&nbsp; &nbsp; # 2차원 표 (a_len+1, b_len+1) 준비하기 --- (※1)</div><div>&nbsp; &nbsp; matrix = [[] for i in range(a_len+1)]</div><div>&nbsp; &nbsp; for i in range(a_len+1): # 0으로 초기화</div><div>&nbsp; &nbsp; &nbsp; &nbsp; matrix[i] = [0 for j in range(b_len+1)]</div><div>&nbsp; &nbsp; # 0일 때 초깃값을 설정</div><div>&nbsp; &nbsp; for i in range(a_len+1):</div><div>&nbsp; &nbsp; &nbsp; &nbsp; matrix[i][0] = i</div><div>&nbsp; &nbsp; for j in range(b_len+1):</div><div>&nbsp; &nbsp; &nbsp; &nbsp; matrix[0][j] = j</div><div>&nbsp; &nbsp; # 표 채우기 --- (※2)</div><div>&nbsp; &nbsp; for i in range(1, a_len+1):</div><div>&nbsp; &nbsp; &nbsp; &nbsp; ac = a[i-1]</div><div>&nbsp; &nbsp; &nbsp; &nbsp; for j in range(1, b_len+1):</div><div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; bc = b[j-1]</div><div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; cost = 0 if (ac == bc) else 1</div><div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; matrix[i][j] = min([</div><div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; matrix[i-1][j] + 1,&nbsp; &nbsp; &nbsp;# 문자 삽입</div><div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; matrix[i][j-1] + 1,&nbsp; &nbsp; &nbsp;# 문자 제거</div><div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; matrix[i-1][j-1] + cost # 문자 변경</div><div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ])</div><div>&nbsp; &nbsp; return matrix[a_len][b_len]</div><div># "가나다라"와 "가마바라"의 거리 --- (※3)</div><div>print(calc_distance("가나다라","가하마라"))<br><br>right_world = ['apple','computer','melon','book','water','campus','camp']</div><div>input_word = 'comtpter'</div><div>r = sorted(right_world, key= lambda n: calc_distance(input_word, n))</div><div><br></div><div># 가장 작은 거리를 가진 단어 출력</div><div>print("가장 가까운 단어:", r[0])</div>]]></description>
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         <pubDate>2023-10-24 01:25:48 UTC</pubDate>
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         <title></title>
         <author>junsoo1216</author>
         <link>https://padlet.com/junsoo1216/aichatbot/wish/2760119545</link>
         <description><![CDATA[<div>1. 구글 코랩 실행<br>2. 파일 -&gt; 노트 업로드<br>3. 엑셀 파일 업로드<br>4. 코드 수정</div>]]></description>
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         <pubDate>2023-10-24 02:09:47 UTC</pubDate>
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         <title></title>
         <author>junsoo1216</author>
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         <pubDate>2023-10-24 03:05:25 UTC</pubDate>
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         <title></title>
         <author></author>
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         <pubDate>2023-10-24 05:24:32 UTC</pubDate>
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         <title></title>
         <author></author>
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         <description><![CDATA[]]></description>
         <enclosure url="https://padlet-uploads.storage.googleapis.com/2191928514/0bcd10e46897fc892deed02ed6a7d658/6cpt.xlsx" />
         <pubDate>2023-10-24 05:26:15 UTC</pubDate>
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         <title></title>
         <author>junsoo1216</author>
         <link>https://padlet.com/junsoo1216/aichatbot/wish/2769674965</link>
         <description><![CDATA[<pre><code class="language-notebook-python">import pandas as pd

df = pd.read_excel('baept.xlsx')
df.head()</code></pre>]]></description>
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         <pubDate>2023-10-31 00:12:36 UTC</pubDate>
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         <title></title>
         <author>junsoo1216</author>
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         <description><![CDATA[<pre><code class="language-notebook-python">question = df['Q'].tolist()
question</code></pre>]]></description>
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         <pubDate>2023-10-31 00:12:44 UTC</pubDate>
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         <title></title>
         <author>junsoo1216</author>
         <link>https://padlet.com/junsoo1216/aichatbot/wish/2769675241</link>
         <description><![CDATA[<pre><code class="language-notebook-python">from sklearn.feature_extraction.text import CountVectorizer

vector = CountVectorizer()
sen_vector = vector.fit_transform(question)
sen_array = sen_vector.toarray()
sen_array</code></pre>]]></description>
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         <pubDate>2023-10-31 00:12:51 UTC</pubDate>
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         <title></title>
         <author>junsoo1216</author>
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         <pubDate>2023-10-31 00:13:56 UTC</pubDate>
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         <title></title>
         <author>junsoo1216</author>
         <link>https://padlet.com/junsoo1216/aichatbot/wish/2769696928</link>
         <description><![CDATA[<pre><code class="language-notebook-python">from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# 데이터셋에서 모든 문장을 사용하여 벡터화 도구 학습
vector = TfidfVectorizer()
sen_array = vector.fit_transform(df['A'].tolist()).toarray()

def get_most_similar_sentence():
    new_sen = input("질문을 하세요 (종료하려면 'exit'를 입력하세요): ")
    if new_sen == 'exit':
        return False

    # 사용자로부터 입력받은 질문을 벡터로 변환
    new_sen_vector = vector.transform([new_sen])
    new_sen_array = new_sen_vector.toarray()
    print(new_sen_array)
    simil_score = cosine_similarity(new_sen_array, sen_array)
    max_index = simil_score.argmax()
    print(df['A'].tolist()[max_index])
    return True

while True:
    if not get_most_similar_sentence():
        break
</code></pre>]]></description>
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         <pubDate>2023-10-31 00:29:03 UTC</pubDate>
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         <title>선생님의 자비로운 힌트</title>
         <author>junsoo1216</author>
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         <pubDate>2024-07-12 04:45:30 UTC</pubDate>
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