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龙再飞.py

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+import numpy as np
+
+image = np.array([[100, 150, 200],[80, 120, 180],[60, 90, 140]], dtype=np.uint8)
+print(image)
+darker = image - 20
+print(darker)
+crop = image[0:2, 0:2]
+print(crop)
+flip_lr = np.fliplr(image)
+print(flip_lr)
+
+img = np.array([[255,255,0,0],[255,255,0,0],[0,0,255,255],[0,0,255,255]], dtype=np.uint8)
+white = np.sum(img == 255)
+black = np.sum(img == 0)
+print(white)
+print(black)
+print(np.fliplr(img))
+rot = np.transpose(img)
+rot90 = np.flipud(rot)
+print(rot90)
+
+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)
+print(vector2)
+euclidean = np.linalg.norm(vector1 - vector2)
+print(euclidean)
+cos = np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
+print(cos)
+
+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(v1)
+print(v2)
+def cos_sim(a, b):
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+print(cos_sim(v1, v2))
+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(v3)
+
+def cosine_similarity(a, b):
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+v1 = np.array([1,0,1,0])
+v2 = np.array([2,0,0,0])
+print(v1)
+print(v2)
+print(cosine_similarity(v1, v2))
+v2 = v1 * 2
+print(cosine_similarity(v1, v2))