一站式网上办事大厅

# 示例：基于NLTK的简单意图识别

import nltk

from nltk.chat.util import Chat, reflections

pairs = [

["你好", "你好！有什么可以帮助你的吗？"],

["我要办理身份证", "好的，请问你是否已经准备好相关材料？"],

["我想查询社保信息", "请告诉我你的身份证号码或社保卡号。"]

]

chatbot = Chat(pairs, reflections)

print("欢迎使用智能客服系统！输入'退出'结束对话。")

while True:

user_input = input("用户：")

if user_input.lower() == '退出':

break

response = chatbot.respond(user_input)

print("系统：" + response)

# 示例：基于Scikit-learn的学生成绩预测

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error

# 假设我们有一个包含学生成绩的数据集

data = {

'数学成绩': [80, 75, 90, 65, 85],

'语文成绩': [70, 68, 85, 60, 80],

'英语成绩': [75, 72, 88, 63, 82],

'总成绩': [225, 215, 263, 188, 247]

}

df = pd.DataFrame(data)

X = df[['数学成绩', '语文成绩', '英语成绩']]

y = df['总成绩']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LinearRegression()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

mse = mean_squared_error(y_test, predictions)

print("模型预测结果：", predictions)

print("均方误差：", mse)

# 示例：使用OpenCV进行图像分类

import cv2

import numpy as np

def is_valid_id_photo(image_path):

image = cv2.imread(image_path)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 简单的尺寸检查

height, width = image.shape[:2]

if height < 300 or width < 300:

return False

# 简单的颜色检测

avg_color = np.mean(image, axis=(0, 1))

if np.all(avg_color > 200): # 高亮度可能是白底

return True

else:

return False

# 测试函数

result = is_valid_id_photo('id_photo.jpg')

print("该照片是否有效：", result)

# 示例：使用PyTorch进行代码正确性判断

import torch

import torch.nn as nn

import torch.optim as optim

# 假设我们有一个包含代码片段和标签的数据集

class CodeDataset(torch.utils.data.Dataset):

def __init__(self, data):

self.data = data

def __len__(self):

return len(self.data)

def __getitem__(self, idx):

code, label = self.data[idx]

return torch.tensor(code), torch.tensor(label)

# 假设我们有一个简单的编码器

class CodeClassifier(nn.Module):

def __init__(self):

super(CodeClassifier, self).__init__()

self.embedding = nn.Embedding(1000, 100) # 假设有1000个词

self.lstm = nn.LSTM(100, 100)

self.fc = nn.Linear(100, 1)

def forward(self, x):

x = self.embedding(x)

out, _ = self.lstm(x)

out = self.fc(out[:, -1, :])

return torch.sigmoid(out)

# 模拟数据

data = [(torch.randint(0, 1000, (10,)), torch.tensor([1])) for _ in range(100)]

dataset = CodeDataset(data)

dataloader = torch.utils.data.DataLoader(dataset, batch_size=10)

model = CodeClassifier()

criterion = nn.BCELoss()

optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练循环

for epoch in range(10):

for inputs, labels in dataloader:

outputs = model(inputs)

loss = criterion(outputs, labels.float())

optimizer.zero_grad()

loss.backward()

optimizer.step()

print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")