示例代码：



from pprint import pprint


# Sample test data
students = [



{'name': 'Student A', 'score': 650, 'preferences': ['University 1', 'University 2', 'University 3']},



{'name': 'Student B', 'score': 620, 'preferences': ['University 1', 'University 3', 'University 2']},



{'name': 'Student C', 'score': 630, 'preferences': ['University 2', 'University 1', 'University 3']},



{'name': 'Student D', 'score': 610, 'preferences': ['University 1', 'University 2', 'University 3']},



{'name': 'Student E', 'score': 640, 'preferences': ['University 3', 'University 1', 'University 2']}
]


universities = {



'University 1': {'capacity': 2, 'admitted': []},



'University 2': {'capacity': 2, 'admitted': []},



'University 3': {'capacity': 2, 'admitted': []}
}


# Sort students by score in descending order
students_sorted = sorted(students, key=lambda x: x['score'], reverse=True)
# print(f'students_sorted:')
# pprint(students_sorted)


# Allocate students to universities based on their preferences and scores
for student in students_sorted:



for preference in student['preferences']:





if len(universities[preference]['admitted']) < universities[preference]['capacity']:







universities[preference]['admitted'].append(student['name'])







break


# Display the results
results = {uni: data['admitted'] for uni, data in universities.items()}
pprint(results)


运行结果：
{'University 1': ['Student A', 'Student B'],

'University 2': ['Student C', 'Student D'],

'University 3': ['Student E']}


【 在 torcher 的大作中提到: 】
: 一方面要保证某个学生高校里从前志愿往后志愿录取，另一方面，保证高校从报考的学生里高分往低分录取。过去没有计算机处理，只靠 ...
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