From e0ff4cec03700bdeb2b86cfb0966438a879e7fb2 Mon Sep 17 00:00:00 2001
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 <2509165037@student.example.com>
Date: Thu, 16 Apr 2026 15:55:34 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
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---
 樊豐铭37.py | 109 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 109 insertions(+)
 create mode 100644 樊豐铭37.py

diff --git a/樊豐铭37.py b/樊豐铭37.py
new file mode 100644
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--- /dev/null
+++ b/樊豐铭37.py
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+#练习一
+import numpy as np
+
+image = np.array([
+    [100, 150, 200],
+    [80,  120, 180],
+    [60,  90,  140]
+], dtype=np.uint8)
+
+print("原图：")
+print(image)
+print("-" * 30)
+
+image_dark = image - 20
+print("变暗20后：")
+print(image_dark)
+print("-" * 30)
+
+image_crop = image[0:2, 0:2]
+print("裁剪左上角 2×2 区域：")
+print(image_crop)
+print("-" * 30)
+
+image_flip = np.fliplr(image)
+print("水平翻转后：")
+print(image_flip)
+
+#练习二
+import numpy as np
+
+img = np.array([
+    [255, 255, 0,   0  ],
+    [255, 255, 0,   0  ],
+    [0,   0,   255, 255],
+    [0,   0,   255, 255]
+], dtype=np.uint8)
+
+print("=== 原图 ===")
+print(img)
+print("-" * 30)
+
+white_count = np.sum(img == 255)
+black_count = np.sum(img == 0)
+
+print("白色像素（255）数量：", white_count)
+print("黑色像素（0）数量：", black_count)
+print("-" * 30)
+
+img_flip_lr = np.fliplr(img)
+print("=== 水平翻转后 ===")
+print(img_flip_lr)
+print("-" * 30)
+
+img_rot90_ccw = np.flipud(img.T)
+print("=== 逆时针旋转90度后 ===")
+print(img_rot90_ccw)
+
+#练习三
+import numpy as np
+
+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]])
+
+vector1 = feature_map1.flatten()
+vector2 = feature_map2.flatten()
+
+print("vector1:", vector1)
+print("vector2:", vector2)
+print("-" * 40)
+
+euclidean_dist = np.linalg.norm(vector1 - vector2)
+print("欧几里得距离：", euclidean_dist)
+
+cos_sim = np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
+print("余弦相似度：", cos_sim)
+
+#练习四
+import numpy as np
+
+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 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
+
+v1 = text_to_vector(doc1, vocab)
+v2 = text_to_vector(doc2, vocab)
+
+print("===== 原始结果 =====")
+print("doc1向量:", v1)
+print("doc2向量:", v2)
+
+cos_sim = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
+print("余弦相似度:", cos_sim)
+print("-" * 40)
+
+vocab_new = ["Python", "学习", "数据", "人工智能", "编程", "机器"]
+doc3 = "机器学习"
+
+v3 = text_to_vector(doc3, vocab_new)
+print("===== 新增词汇后 =====")
+print("新词汇表:", vocab_new)
+print("doc3 = 机器学习 的向量:", v3)
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