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# 示例：基于NLP的申请材料分类

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.naive_bayes import MultinomialNB

from sklearn.pipeline import Pipeline

# 假设我们有以下训练数据

X_train = [

"该学生具备扎实的专业基础，积极参与科研项目。",

"该学生在实习期间表现出色，具有良好的团队合作能力。",

"该学生英语成绩优秀，能够流利地进行英文交流。",

"该学生在校期间多次获得奖学金，综合素质高。"

]

y_train = ["优秀", "良好", "合格", "优秀"]

# 构建一个简单的分类模型

model = Pipeline([

('tfidf', TfidfVectorizer()),

('clf', MultinomialNB())

])

model.fit(X_train, y_train)

# 测试新申请材料

new_application = ["该学生具备较强的编程能力和项目经验。"]

prediction = model.predict(new_application)

print("预测结果：", prediction[0])

# 示例：学生行为数据分析

import pandas as pd

from sklearn.cluster import KMeans

# 模拟数据：包括出勤率、作业提交次数、考试分数

data = {

'student_id': [1, 2, 3, 4, 5],

'attendance_rate': [98, 75, 80, 60, 95],

'assignment_submissions': [15, 10, 12, 5, 14],

'exam_score': [85, 65, 70, 50, 88]

}

df = pd.DataFrame(data)

# 使用K-means聚类对学生进行分组

kmeans = KMeans(n_clusters=3)

df['cluster'] = kmeans.fit_predict(df[['attendance_rate', 'assignment_submissions', 'exam_score']])

print(df)

# 示例：使用OpenCV和深度学习进行视频监控

import cv2

import numpy as np

# 加载预训练的YOLO模型

net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')

classes = []

with open('coco.names', 'r') as f:

classes = [line.strip() for line in f.readlines()]

# 打开摄像头

cap = cv2.VideoCapture(0)

while True:

ret, frame = cap.read()

if not ret:

break

height, width, _ = frame.shape

blob = cv2.dnn.blobFromImage(frame, 1/255, (416, 416), (0, 0, 0), swapRB=True, crop=False)

net.setInput(blob)

layer_names = net.getLayerNames()

output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

outs = net.forward(output_layers)

class_ids = []

confidences = []

boxes = []

for out in outs:

for detection in out:

scores = detection[5:]

class_id = np.argmax(scores)

confidence = scores[class_id]

if confidence > 0.5:

center_x = int(detection[0] * width)

center_y = int(detection[1] * height)

w = int(detection[2] * width)

h = int(detection[3] * height)

x = int(center_x - w / 2)

y = int(center_y - h / 2)

boxes.append([x, y, w, h])

confidences.append(float(confidence))

class_ids.append(class_id)

indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

font = cv2.FONT_HERSHEY_PLAIN

for i in range(len(boxes)):

if i in indexes:

x, y, w, h = boxes[i]

label = str(classes[class_ids[i]])

cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

cv2.putText(frame, label, (x, y - 5), font, 1, (0, 255, 0), 2)

cv2.imshow('Video', frame)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

cap.release()

cv2.destroyAllWindows()