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)