锦中人工智能助手

# 模拟一个简单的排课算法

import random

# 定义教师列表

teachers = ["张老师", "李老师", "王老师"]

# 定义课程列表

courses = ["数学", "英语", "物理"]

# 定义教室列表

classrooms = ["101", "202", "303"]

# 定义时间表

time_slots = ["9:00-10:30", "10:40-12:10", "14:00-15:30", "15:40-17:10"]

# 生成随机排课方案

def generate_schedule(teachers, courses, classrooms, time_slots):

schedule = {}

for course in courses:

teacher = random.choice(teachers)

classroom = random.choice(classrooms)

time_slot = random.choice(time_slots)

schedule[course] = {

"teacher": teacher,

"classroom": classroom,

"time": time_slot

}

return schedule

# 打印排课结果

def print_schedule(schedule):

for course, details in schedule.items():

print(f"课程: {course}")

print(f"教师: {details['teacher']}")

print(f"教室: {details['classroom']}")

print(f"时间: {details['time']}")

print()

# 运行排课程序

if __name__ == "__main__":

schedule = generate_schedule(teachers, courses, classrooms, time_slots)

print("生成的排课方案如下：")

print_schedule(schedule)

# 优化后的排课算法，增加教师时间冲突检查

import random

# 定义教师列表

teachers = ["张老师", "李老师", "王老师"]

# 定义课程列表

courses = ["数学", "英语", "物理"]

# 定义教室列表

classrooms = ["101", "202", "303"]

# 定义时间表

time_slots = ["9:00-10:30", "10:40-12:10", "14:00-15:30", "15:40-17:10"]

# 存储教师的排课情况

teacher_schedule = {}

# 检查教师是否在该时间段有课

def is_teacher_available(teacher, time_slot):

if teacher not in teacher_schedule:

return True

for slot in teacher_schedule[teacher]:

if slot == time_slot:

return False

return True

# 生成排课方案

def generate_schedule(teachers, courses, classrooms, time_slots):

schedule = {}

for course in courses:

while True:

teacher = random.choice(teachers)

classroom = random.choice(classrooms)

time_slot = random.choice(time_slots)

if is_teacher_available(teacher, time_slot):

break

schedule[course] = {

"teacher": teacher,

"classroom": classroom,

"time": time_slot

}

if teacher not in teacher_schedule:

teacher_schedule[teacher] = []

teacher_schedule[teacher].append(time_slot)

return schedule

# 打印排课结果

def print_schedule(schedule):

for course, details in schedule.items():

print(f"课程: {course}")

print(f"教师: {details['teacher']}")

print(f"教室: {details['classroom']}")

print(f"时间: {details['time']}")

print()

# 运行排课程序

if __name__ == "__main__":

schedule = generate_schedule(teachers, courses, classrooms, time_slots)

print("优化后的排课方案如下：")

print_schedule(schedule)

# 简单的文本分类器，用于识别学生问题类型

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.naive_bayes import MultinomialNB

from sklearn.pipeline import Pipeline

from sklearn.model_selection import train_test_split

# 示例数据

texts = [

"求解方程x² + 2x + 1 = 0",

"如何背单词？",

"解释牛顿第一定律",

"翻译这句话：Hello World",

"什么是量子力学？"

]

labels = ["数学", "英语", "物理", "英语", "物理"]

# 分割训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.2)

# 构建分类管道

model = Pipeline([

('tfidf', TfidfVectorizer()),

('clf', MultinomialNB())

])

# 训练模型

model.fit(X_train, y_train)

# 预测新问题

new_question = "什么是光合作用？"

predicted_label = model.predict([new_question])

print(f"预测问题类型为：{predicted_label[0]}")