# 1. 输出 "Hello" 每个字符的 ASCII 码
text = "Hello"
print([ord(c) for c in text])

# 2. chr() 验证 65 = A
print(chr(65))

# 3. 向量运算
A = (3, 4)
B = (1, 2)
print("A+B =", (A[0]+B[0], A[1]+B[1]))
print("2*A =", (2*A[0], 2*A[1]))
print("A的模长 =", (A[0]**2 + A[1]**2)**0.5)

# 4. 点积与余弦相似度
import math
A = [1,2,3]
B = [4,5,6]
dot = sum(a*b for a,b in zip(A,B))
print("点积 =", dot)

normA = math.sqrt(sum(x**2 for x in A))
normB = math.sqrt(sum(x**2 for x in B))
cos_sim = dot / (normA * normB)
print("余弦相似度 =", round(cos_sim, 4))

# 垂直向量余弦相似度
A2, B2 = [1,0], [0,1]
dot2 = sum(a*b for a,b in zip(A2,B2))
normA2 = math.sqrt(sum(x**2 for x in A2))
normB2 = math.sqrt(sum(x**2 for x in B2))
cos_sim2 = dot2 / (normA2 * normB2)
print("垂直向量余弦相似度 =", cos_sim2)

# 5. 词袋模型向量
vocab = ['java','python','编程','语言']
doc1 = [0,1,1,1]
doc2 = [1,0,1,1]
doc3 = [0,3,0,0]
print("词表:", vocab)
print("Doc1:", doc1)
print("Doc2:", doc2)
print("Doc3:", doc3)