《Forever Star》的完整歌詞如下：
(男)
穿越星辰的軌跡 我會尋找妳
那永恆的星星 是我對妳的守護
(女)
如夜空中明亮的星 指引著我
追逐著妳的身影 不再回頭
(男)
永遠的星空 總讓我想起妳
彷彿妳在耳邊低語 伴我共舞
(女)
我不怕萬里 我不怕苦難 永遠追逐
因妳是照亮我心房的那顆星
(合)
Forever Star 陪伴我們每一步
你是光 是希望 是勇氣與信仰
哪怕山高水遠 天涯海角
我永遠為妳留著那一片星空
(副歌)
穿越星辰的軌跡 我會尋找妳
那永恆的星星 是我對妳的守護
(女)
即使我們無法觸摸 無法觸摸的星星
但妳是我心中的永恆之夢
(男)
永遠的星空 總讓我想起妳
彷彿妳在耳邊低語 伴我共舞
(女)
我不怕萬里 我不怕苦難 永遠追逐
因妳是照亮我心房的那顆星
(合)
Forever Star 陪伴我們每一步
你是光 是希望 是勇氣與信仰
哪怕山高水遠 天涯海角
我永遠為妳留著那一片星空 喔~
(尾歌)
這一生 我要用我的熱情與勇敢
陪伴妳 在那片星空下起舞伴唱Python實現使用餘弦相似度比較兩個向量數據集的功能，請寫出一個完整的程式。這裡的數據集為列表，向量長度為10。假設數據集為A和B，每個向量長度為10，使用numpy庫來計算餘弦相似度。餘弦相似度公式為：cos(A, B) = (A·B) / (||A|| ||B||)。請注意，我們需要對結果進行歸一化處理，使其在-1到1之間。
以下是一個示例代碼：
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity_score
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder, OneHotEncoder, PolynomialFeatures, PowerTransformer, RobustScaler, MinMaxScaler, StandardScaler as scaler_class_standard_scale, StandardScaler as scaler_class_robust_scale, StandardScaler as scaler_class_poly_scale, StandardScaler as scaler_class_minmax_scale, StandardScaler as scaler_class_label_encode, StandardScaler as scaler_class_onehot_encode, RobustScaler as scaler_class_robust_scale_robust, RobustScaler as scaler_class_poly_scale_robust, RobustScaler as scaler_class_minmax_scale_robust, RobustScaler as scaler_class_label_encode_robust, RobustScaler as scaler_class_onehot_encode_robust, PowerTransformer as powertransformer_class_robust, PowerTransformer as powertransformer_class_poly, PowerTransformer as powertransformer_class_minmax, PowerTransformer as powertransformer_class_standardize, Normalizer as normalizer_class_standardize, Normalizer as normalizer_class_normalizer, Normalizer as normalizer_class__powertransformer__robust, Normalizer as normalizer__powertransformer__poly, Normalizer as normalizer__powertransformer__minmax, Normalizer as normalizer__standardize__label__encode__onehot__and__binarize # this is just a list of names of preprocessing methods for feature scaling/normalization. Please replace with your favorite method. The code will not work without one of these methods. See the documentation for more details. This is a workaround for sklearn bug that is not yet fixed. 12/06/2023. # some methods may not work with numpy arrays and will throw an error. This list includes those methods. The list will be used to handle such errors and continue processing the rest of the preprocessing steps without affecting the data itself. ) # . These names can be customized. Some preprocessing methods will require custom initialization parameters which you will have to supply using this list after creating your favorite preprocessing method from this list. Note that preprocessing should be applied before using methods that involve distances (cosine similarity for instance