109 lines
2.5 KiB
Python
109 lines
2.5 KiB
Python
#练习一
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import numpy as np
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image = np.array([
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[100, 150, 200],
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[80, 120, 180],
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[60, 90, 140]
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], dtype=np.uint8)
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print("原图：")
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print(image)
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print("-" * 30)
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image_dark = image - 20
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print("变暗20后：")
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print(image_dark)
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print("-" * 30)
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image_crop = image[0:2, 0:2]
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print("裁剪左上角 2×2 区域：")
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print(image_crop)
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print("-" * 30)
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image_flip = np.fliplr(image)
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print("水平翻转后：")
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print(image_flip)
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#练习二
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import numpy as np
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img = np.array([
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[255, 255, 0, 0 ],
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[255, 255, 0, 0 ],
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[0, 0, 255, 255],
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[0, 0, 255, 255]
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], dtype=np.uint8)
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print("=== 原图 ===")
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print(img)
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print("-" * 30)
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white_count = np.sum(img == 255)
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black_count = np.sum(img == 0)
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print("白色像素（255）数量：", white_count)
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print("黑色像素（0）数量：", black_count)
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print("-" * 30)
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img_flip_lr = np.fliplr(img)
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print("=== 水平翻转后 ===")
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print(img_flip_lr)
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print("-" * 30)
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img_rot90_ccw = np.flipud(img.T)
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print("=== 逆时针旋转90度后 ===")
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print(img_rot90_ccw)
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#练习三
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import numpy as np
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feature_map1 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
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feature_map2 = np.array([[1, 1, 1], [1, 0, 0], [1, 0, 0]])
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vector1 = feature_map1.flatten()
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vector2 = feature_map2.flatten()
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print("vector1:", vector1)
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print("vector2:", vector2)
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print("-" * 40)
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euclidean_dist = np.linalg.norm(vector1 - vector2)
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print("欧几里得距离：", euclidean_dist)
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cos_sim = np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
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print("余弦相似度：", cos_sim)
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#练习四
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import numpy as np
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vocab = ["Python", "学习", "数据", "人工智能", "编程"]
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doc1 = "Python学习编程"
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doc2 = "Python人工智能数据"
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def text_to_vector(text, vocab):
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words = text.split()
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vector = np.zeros(len(vocab))
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for i, word in enumerate(vocab):
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vector[i] = words.count(word)
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return vector
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v1 = text_to_vector(doc1, vocab)
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v2 = text_to_vector(doc2, vocab)
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print("===== 原始结果 =====")
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print("doc1向量:", v1)
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print("doc2向量:", v2)
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cos_sim = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
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print("余弦相似度:", cos_sim)
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print("-" * 40)
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vocab_new = ["Python", "学习", "数据", "人工智能", "编程", "机器"]
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doc3 = "机器学习"
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v3 = text_to_vector(doc3, vocab_new)
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print("===== 新增词汇后 =====")
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print("新词汇表:", vocab_new)
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print("doc3 = 机器学习 的向量:", v3) |