# 導(dǎo)入必要的庫(kù)

import sqlite3

# 連接到SQLite數(shù)據(jù)庫(kù)（或者你可以換成MySQL）

conn = sqlite3.connect('school_db.sqlite')

cursor = conn.cursor()

# 創(chuàng)建表

cursor.execute('''

CREATE TABLE IF NOT EXISTS requests (

id INTEGER PRIMARY KEY AUTOINCREMENT,

name TEXT NOT NULL,

type TEXT NOT NULL,

status TEXT DEFAULT 'pending'

)

''')

# 插入一條請(qǐng)求記錄

cursor.execute("INSERT INTO requests (name, type) VALUES (?, ?)", ('張三', '請(qǐng)假'))

conn.commit()

# 查詢所有待處理的請(qǐng)求

cursor.execute("SELECT * FROM requests WHERE status='pending'")

pending_requests = cursor.fetchall()

print("Pending Requests:", pending_requests)

# 關(guān)閉連接

conn.close()

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import torch

import torch.nn as nn

from sklearn.model_selection import train_test_split

# 模擬一些數(shù)據(jù)

data = [

[1, 10], [2, 20], [3, 30], [4, 40], [5, 50]

] # 第一列是月份，第二列是請(qǐng)假人數(shù)

labels = [15, 25, 35, 45, 55]

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

class SimpleModel(nn.Module):

def __init__(self):

super(SimpleModel, self).__init__()

self.fc = nn.Linear(2, 1)

def forward(self, x):

return self.fc(x)

統(tǒng)一消息

model = SimpleModel()

criterion = nn.MSELoss()

optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

for epoch in range(100):

inputs = torch.tensor(X_train).float()

targets = torch.tensor(y_train).float()

outputs = model(inputs)

loss = criterion(outputs, targets)

optimizer.zero_grad()

loss.backward()

optimizer.step()

if (epoch+1) % 10 == 0:

print(f'Epoch [{epoch+1}/100], Loss: {loss.item():.4f}')

# 測(cè)試模型

with torch.no_grad():

test_inputs = torch.tensor(X_test).float()

predictions = model(test_inputs)

print("Predictions:", predictions)

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