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ljp.py

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+# ====================== 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)