text = "Hello"
print([ord(c) for c in text])


text_cn = "你好"
print([ord(c) for c in text_cn])
print(chr(65))

import numpy as np

v1 = np.array([3])
v2 = np.array([2, 3])
v3 = np.array([1, 2, 3])
print(v1, v2, v3)

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print("加法:", a + b)


print("数乘:", a * 2)

v = np.array([3, 4])
print("长度:", np.linalg.norm(v))


print("点积:", np.dot(a, b))
print("点积(@):", a @ b)

def cosine_similarity(a, b)
    dot = np.dot(a, b),
    norm_a = np.linalg.norm(a)
    norm_b = np.linalg.norm(b)
    return dot / (norm_a * norm_b)


a1 = np.array([1,2,3])
b1 = np.array([2,4,6])
a2 = np.array([1,0])
b2 = np.array([0,1])
print("相似度1:", cosine_similarity(a1, b1))
print("相似度2:", cosine_similarity(a2, b2))

cat = np.array([0.9, 0.9, 0.8])
dog = np.array([0.8, 0.9, 0.8])
apple = np.array([0.1, 0.3, 0.0])
print("猫vs狗:", cosine_similarity(cat, dog))
print("猫vs苹果:", cosine_similarity(cat, apple))

from sklearn.feature_extraction.text import CountVectorizer
docs = [
    "Python 是 编程 语言",
    "Java 是 编程 语言",
]
vectorizer = CountVectorizer()
bow = vectorizer.fit_transform(docs)
print("词表:", vectorizer.get_feature_names_out())
print("BoW矩阵:\n", bow.toarray())

from sklearn.feature_extraction.text import TfidfVectorizer
docs_tfidf = [
    "Python 编程 语言",
    "Python Python Python",
    "Java 编程 语言",
]
tfidf = TfidfVectorizer()
tfidf_matrix = tfidf.fit_transform(docs_tfidf)
print("词表:", tfidf.get_feature_names_out())
print("TF-IDF矩阵:\n", tfidf_matrix.toarray())

word_vectors = {
    "猫": [0.9, 0.1, 0.2],
    "狗": [0.8, 0.3, 0.1],
    "苹果": [0.1, 0.2, 0.9],
    "Python": [0.1, 0.0, 0.9],
    "Java": [0.1, 0.0, 0.85],
}
print("猫vs狗:", cosine_similarity(word_vectors["猫"], word_vectors["狗"]))