# ====================== 练习1：图像矩阵基本操作 ======================
print("=" * 50)
print("练习1：图像矩阵操作")
print("=" * 50)

image = np.array([
    [100, 150, 200],
    [80, 120, 180],
    [60, 90, 140]
], dtype=np.uint8)

print("原图：")
print(image)

# 1. 整体变暗 20
darker = image - 20
print("\n变暗20：")
print(darker)

# 2. 裁剪左上角 2×2
crop = image[0:2, 0:2]
print("\n裁剪左上角2×2：")
print(crop)

# 3. 水平翻转
flip_horizontal = np.fliplr(image)
print("\n水平翻转：")
print(flip_horizontal)

# ====================== 练习2：4×4 黑白图像 ======================
print("\n" + "=" * 50)
print("练习2：4×4图像矩阵")
print("=" * 50)

img = np.array([
    [255, 255, 0, 0],
    [255, 255, 0, 0],
    [0, 0, 255, 255],
    [0, 0, 255, 255]
], dtype=np.uint8)

# 统计像素
count_white = np.sum(img == 255)
count_black = np.sum(img == 0)
print("白色像素：", count_white)
print("黑色像素：", count_black)

# 水平翻转
flip_h = np.fliplr(img)
print("\n水平翻转：")
print(flip_h)

# 逆时针旋转90度
rotate_90_ccw = np.flipud(img.T)
print("\n逆时针旋转90度：")
print(rotate_90_ccw)

# ====================== 练习3：特征向量相似度计算 ======================
print("\n" + "=" * 50)
print("练习3：特征向量与相似度")
print("=" * 50)

feature_map1 = np.array([[1, 0, 1],
                         [0, 1, 0],
                         [1, 0, 1]])

feature_map2 = np.array([[1, 1, 1],
                         [1, 0, 0],
                         [1, 0, 0]])

vec1 = feature_map1.flatten()
vec2 = feature_map2.flatten()

print("展平向量1：", vec1)
print("展平向量2：", vec2)

# 欧氏距离
euclidean_dist = np.linalg.norm(vec1 - vec2)
print("\n欧几里得距离：", euclidean_dist)

# 余弦相似度
def cos_sim(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

print("余弦相似度：", cos_sim(vec1, vec2))

# ====================== 练习4：文本向量化 ======================
print("\n" + "=" * 50)
print("练习4：文本向量化")
print("=" * 50)

vocab = ["Python", "学习", "数据", "人工智能", "编程"]
doc1 = "Python学习编程"
doc2 = "Python人工智能数据"

def text2vec(text, vocab):
    words = text.split()
    vec = np.zeros(len(vocab))
    for i, w in enumerate(vocab):
        vec[i] = words.count(w)
    return vec

v1 = text2vec(doc1, vocab)
v2 = text2vec(doc2, vocab)

print("句子1向量：", v1)
print("句子2向量：", v2)
print("余弦相似度：", cos_sim(v1, v2))

# 扩展词汇表
vocab_new = ["Python", "学习", "数据", "人工智能", "编程", "机器"]
doc3 = "机器学习"
v3 = text2vec(doc3, vocab_new)
print("\n扩展词汇表后句子3向量：", v3)