python实验

#1.回文数
number = input("请输入数字：")
r-num = number[::-1]
if number == r-num :
   print(f"{number}是回文数")

 

#2.水仙花
for i in range(100,1000):
    a = i // 100
    b = i //10 % 10
    c = i %10
    if i == a**3 + b**3 + c**3:
        print(f"{i}是水仙花数")
 
########################
num = input()
lenth = len(num)
if sum(map(lambda x:int(x)**lenth ,num)) ==  int(num):
    print("Y")
else:
    print("N")                                        

 #3.10*10矩阵，边界全1，中间全0
import numpy as np
A = np.zeros((10,10))
A[:,[0,9]]=1
A[[0,9],:]=1
print(A)

#4.矩阵对角线
import numpy as np 
B = np.diag([1,2,3,4,5,6,7,8,9,10])
print(B)

#5.矩阵计算
import numpy as np
C = np.dot(A,B)
print(C)

#6.矩阵特征值，特征向量
import numpy as np
D = np.array([[1,2],[3,4]])
D_W,D_V = np.linalg.eig(D)
print(D_W)
print(D_V)

 

#socket服务器TCP
import socket
HOST = ""
PORT = 50007
with socket.socket(socket.AF_INET ,socket.SOCK_STREAM) as s:
    s.bind((HOST ,PORT))
    s.listen(1)
    while True:
        conn ,addr = s.accept()
        with conn:
            print("Connceted by" ,addr)
            while True:
                data = conn.recv(1024)
                conn.sendall(data)
                if not data:
                    break

　　

#socket客户端TCP
import socket
import time
HOST = "localhost"
PORT = 50007
with socket.socket (socket.AF_INET ,socket.SOCK_STREAM) as s:
    s.connect ((HOST,PORT))
    while True:
        time.sleep(5)
        s.sendall(b"Hello ,World!")
        data = s.recv(1024)
        print("Receive" ,repr(data))
            

#socket服务器UDP
import socket
HOST = ''
PORT = 50000
with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
  s.bind((HOST,PORT))
  pirnt("Waitting on port" ,PORT)
  while True:
    data ,addr = s.recvfrom(1024)
    print(addr,data)
    s.sendto(data,addr)
    
#socket客户端UDP
import socket
import time
HOST = 'localhost'
PORT = 50000
with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
  while True:
    time.sleep(5)
    s.sendto(b'hello,world!',(HOST,PORT))
    data = s.recv(1024)
    print('received',data)

#(1)导入数据
from sklearn.datasets import load_boston
boston = load_boston()
# (2)分割数据
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split( boston.data, boston.target,
test_size = 0.3, random_state = 0)
# (3)导入线性回归模型并训练模型
from sklearn.linear_model import LinearRegression
LR = LinearRegression()
LR.fit(X_train, y_train)
# (4)在测试集上预测
y_pred = LR.predict(X_test)
# (5)评估模型
from sklearn import metrics
mse = metrics.mean_squared_error(y_test, y_pred)
print("MSE = ", mse) #性能评估：模型的均方差