完成作业3_1_3

260416_2509165028.py

@@ -0,0 +1,48 @@
+import numpy as np
+
+image=np.array([
+    [100,150,200],
+    [80, 120,180],
+    [60, 90, 140]
+],dtype=np.uint8)
+print("原图：")
+print(image)
+brighter = image-20
+print("变暗20：",brighter)
+top_left = image[0:2, 0:2] 
+print("裁剪左上角：",top_left)
+flipped1 = np.fliplr(image)
+print("水平翻转：",flipped1)
+
+img = np.array([
+    [255, 255, 0,   0  ],
+    [255, 255, 0,   0  ],
+    [0,   0,   255, 255],
+    [0,   0,   255, 255]
+], dtype=np.uint8)
+flipped2 = np.fliplr(img)
+print("水平翻转：",flipped2)
+rotated = np.transpose(img)
+flipped3 = np.fliplr(img)
+print("旋转90度：",flipped3)
+
+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)
+
+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("doc1向量:", v1)
+print("doc2向量:", v2)