首先將您的 csv 保存到數據幀 df 并使用以下函數進行余弦相似度計算。def get_cosine(vec1, vec2): intersection = set(vec1.keys()) & set(vec2.keys()) numerator = sum([vec1[x] * vec2[x] for x in intersection])

sum1 = sum([vec1[x]**2 for x in vec1.keys()])

sum2 = sum([vec2[x]**2 for x in vec2.keys()])

denominator = math.sqrt(sum1) * math.sqrt(sum2)

if not denominator:

    return 0.0

else:

    return float(numerator) / denominator

def text_to_vector(text):

word = re.compile(r'\w+')

words = word.findall(text)

return Counter(words)

def get_result(content_a, content_b):

text1 = content_a

text2 = content_b

vector1 = text_to_vector(text1)

vector2 = text_to_vector(text2)

cosine_result = get_cosine(vector1, vector2)

return cosine_result

然后遍歷 df 并調用如下函數：

similarity=[]

for ind in df.index:

#my_doc="new document should go in here"

#prev_doc= "previous document for each index should go in here"

cos=get_result(my_doc, prev_doc)

similarity.append(cos)

max_ind= similarity.index(max(similarity))  

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