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 create mode 100644 40吴承恩.py

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+# ==========================================
+# 练习1：图像矩阵操作 (3×3灰度图像)
+# ==========================================
+import numpy as np
+
+# 定义3×3灰度图像
+print("===== 练习1开始 =====")
+image = np.array([
+    [100, 150, 200],
+    [80, 120, 180],
+    [60, 90, 140]
+], dtype=np.uint8)
+
+print("原图: ")
+print(image)
+
+# 1. 变暗20：每个像素值减20
+image_dark = image - 20
+print("\n变暗20后: ")
+print(image_dark)
+
+# 2. 裁剪左上角：保留2×2区域
+image_crop = image[0:2, 0:2]
+print("\n裁剪左上角后: ")
+print(image_crop)
+
+# 3. 水平翻转：使用np.fliplr()
+image_flip = np.fliplr(image)
+print("\n水平翻转后: ")
+print(image_flip)
+print("===== 练习1结束 =====\n")
+
+
+# ==========================================
+# 练习2：看图写代码 (4×4图像矩阵)
+# ==========================================
+print("===== 练习2开始 =====")
+# 定义4×4图像矩阵
+img = np.array([
+    [255, 255, 0, 0],
+    [255, 255, 0, 0],
+    [0, 0, 255, 255],
+    [0, 0, 255, 255]
+], dtype=np.uint8)
+
+print("原始4x4图像:")
+print(img)
+
+# 1. 统计白色(255)和黑色(0)像素数量
+white_count = np.sum(img == 255)
+black_count = np.sum(img == 0)
+print(f"\n白色像素(255)数量: {white_count}")
+print(f"黑色像素(0)数量: {black_count}")
+
+# 2. 水平翻转并打印
+img_flipped = np.fliplr(img)
+print("\n水平翻转后的图像:")
+print(img_flipped)
+
+# 3. 逆时针旋转90度 (转置 + 上下翻转)
+# 方法：先转置(.T)，再上下翻转(np.flipud)
+img_rotated = np.flipud(img.T)
+print("\n逆时针旋转90度后的图像:")
+print(img_rotated)
+print("===== 练习2结束 =====\n")
+
+
+# ==========================================
+# 练习3：特征向量计算
+# ==========================================
+print("===== 练习3开始 =====")
+# 定义两个特征图
+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]])
+
+# 展平为向量
+vector1 = feature_map1.flatten()
+vector2 = feature_map2.flatten()
+
+print("feature_map1 展平向量 vector1: ", vector1)
+print("feature_map2 展平向量 vector2: ", vector2)
+
+# 1. 计算欧几里得距离
+# 公式: sqrt(sum((a-b)^2))
+euclidean_dist = np.linalg.norm(vector1 - vector2)
+print(f"\nvector1 和 vector2 的欧几里得距离: {euclidean_dist:.4f}")
+
+# 2. 计算余弦相似度
+# 公式: dot(a,b) / (||a|| * ||b||)
+cos_sim = np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
+print(f"vector1 和 vector2 的余弦相似度: {cos_sim:.4f}")
+print("===== 练习3结束 =====\n")
+
+
+# ==========================================
+# 练习4：文本向量化 (预习挑战)
+# ==========================================
+print("===== 练习4开始 =====")
+# 原始词汇表
+vocab = ["Python", "学习", "数据", "人工智能", "编程"]
+
+# 两句话
+doc1 = "Python学习编程"
+doc2 = "Python人工智能数据"
+
+# 定义向量化函数
+def text_to_vector(text, vocab):
+    words = text.split()
+    vector = np.zeros(len(vocab))
+    for i, word in enumerate(vocab):
+        vector[i] = words.count(word)
+    return vector
+
+# 生成向量
+v1 = text_to_vector(doc1, vocab)
+v2 = text_to_vector(doc2, vocab)
+
+print("doc1向量: ", v1)
+print("doc2向量: ", v2)
+
+# 2. 计算余弦相似度
+cos_sim_text = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
+print(f"\ndoc1 和 doc2 的余弦相似度: {cos_sim_text:.4f}")
+
+# 3. 修改vocab，加入"机器"，测试doc3 = "机器学习"
+print("\n--- 修改词汇表并测试 doc3 ---")
+new_vocab = ["Python", "学习", "数据", "人工智能", "编程", "机器"]
+doc3 = "机器学习"
+v3 = text_to_vector(doc3, new_vocab)
+print(f"新词汇表: {new_vocab}")
+print(f"doc3('{doc3}')的向量: ", v3)
+print("===== 练习4结束 =====")
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