From f43501ad255ccbe91fe0ed4b7204ae20cc77db02 Mon Sep 17 00:00:00 2001 From: ptqaioex5 <2128916825@qq.com> Date: Sun, 28 May 2023 18:55:08 +0800 Subject: [PATCH] ADD file via upload --- 20407111-郭梁-计科2001.html | 15481 ++++++++++++++++++++++++++++++ 1 file changed, 15481 insertions(+) create mode 100644 20407111-郭梁-计科2001.html diff --git a/20407111-郭梁-计科2001.html b/20407111-郭梁-计科2001.html new file mode 100644 index 0000000..288e571 --- /dev/null +++ b/20407111-郭梁-计科2001.html @@ -0,0 +1,15481 @@ + + +
+ + +# your code
+n = int(input('请输入所求阶乘数:'))
+m = 1
+sum = 0
+i = 1
+while n >= i:
+ m *= i
+ sum += m
+ i = i + 1
+print("结果:",sum)
+请输入所求阶乘数:20 +结果: 2561327494111820313 ++
# your code
+s = [9,7,8,3,2,1,55,6]
+print("length =",len(s)," max =",max(s)," min =",min(s))
+s.append(10)
+s.remove(55)
+print(s)
+length = 8 max = 55 min = 1 +[9, 7, 8, 3, 2, 1, 6, 10] ++
TTTTTx
+TTTTxx
+TTTxxx
+TTxxxx
+Txxxxx
+
+# your code
+T = 'T'
+x = 'x'
+length = 6
+for i in range(1, length):
+ print(T * (length - i) + x * i)
+TTTTTx +TTTTxx +TTTxxx +TTxxxx +Txxxxx ++
# your code
+def add(x, y):
+ return x + y
+def subtract(x, y):
+ return x - y
+def multiply(x, y):
+ return x * y
+def divide(x, y):
+ return x / y
+print("在下列功能中选择:")
+print("1.加法 2.减法 3.乘法 4.除法")
+choice = input("请输入对应功能项的数字(1.2.3.4):")
+num1 = int(input("请输入第一个数字:"))
+num2 = int(input("请输入第二个数字:"))
+if choice == '1':
+ print(num1,"+",num2,"=",add(num1,num2))
+elif choice == '2':
+ print(num1,"-",num2,"=",subtract(num1,num2))
+elif choice == '3':
+ print(num1,"*",num2,"=",multiply(num1,num2))
+elif choice == '4':
+ print(num1,"/",num2,"=",divide(num1,num2))
+else:
+ print("功能选择错误,只接受1-4的数字!")
+在下列功能中选择: +1.加法 2.减法 3.乘法 4.除法 +请输入对应功能项的数字(1.2.3.4):1 +请输入第一个数字:1000 +请输入第二个数字:200 +1000 + 200 = 1200 ++
class Student:
+ def __init__(self,name,age,*cou):
+ self.name = name
+ self.age = age
+ self.course = cou
+ def get_name(self):
+ return self.name
+ def get_age(self):
+ return self.age
+ def get_course(self):
+ return max(max(self.course))
+st = Student('zhangming',20,[69,88,100])
+print('学生姓名为:',st.get_name(),' 年龄为:',st.get_age(),' 最高分成绩为:',st.get_course())
+学生姓名为: zhangming 年龄为: 20 最高分成绩为: 100 ++
| X | +Y | +X | +Y | +
|---|---|---|---|
| -3.00 | +4 | +0.15 | +255 | +
| -2.50 | +12 | +0.75 | +170 | +
| -1.75 | +50 | +1.25 | +100 | +
| -1.15 | +120 | +1.85 | +20 | +
| -0.50 | +205 | +2.45 | +14 | +
# your code
+import matplotlib.pyplot as plt
+X = [-3.00,-2.50,-1.75,-1.15,-0.50,0.15,0.75,1.25,1.85,2.45]
+Y = [4,12,50,120,205,255,170,100,20,14]
+label=[-3.00,-2.50,-1.75,-1.15,-0.50,0.15,0.75,1.25,1.85,2.45]
+plt.bar(X,Y,tick_label = label);
+注:训练集:测试集=8:2,随机种子采用你学号后两位,例如你学号后两位=01,则random_state=1,如果最后两位=34,则random_state=34。最终结果打印出各个回归的w和b系数即可。
+| 序号 | +X1 | +X2 | +X3 | +X4 | +Y | +
|---|---|---|---|---|---|
| 1 | +7 | +26 | +6 | +60 | +78.5 | +
| 2 | +1 | +29 | +15 | +52 | +74.3 | +
| 3 | +11 | +56 | +8 | +20 | +104.3 | +
| 4 | +11 | +31 | +8 | +47 | +87.6 | +
| 5 | +7 | +52 | +6 | +33 | +95.9 | +
| 6 | +11 | +55 | +9 | +22 | +109.2 | +
| 7 | +3 | +71 | +17 | +6 | +102.7 | +
| 8 | +1 | +31 | +22 | +44 | +72.5 | +
| 9 | +2 | +54 | +18 | +22 | +93.1 | +
| 10 | +21 | +47 | +4 | +26 | +115.9 | +
| 11 | +1 | +40 | +23 | +34 | +83.8 | +
| 12 | +11 | +66 | +9 | +12 | +113.3 | +
| 13 | +10 | +68 | +8 | +12 | +109.4 | +
import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Ridge, Lasso
+
+# 将数据存储为NumPy数组
+data = np.array([[7,26,6,60,78.5],
+ [1,29,15,52,74.3],
+ [11,56,8,20,104.3],
+ [11,31,8,47,87.6],
+ [7,52,6,33,95.9],
+ [11,55,9,22,109.2],
+ [3,71,17,6,102.7],
+ [1,31,22,44,72.5],
+ [2,54,18,22,93.1],
+ [21,47,4,26,115.9],
+ [1,40,23,34,83.8],
+ [11,66,9,12,113.3],
+ [10,68,8,12,109.4]])
+
+# 将数据拆分为自变量和因变量
+X = data[:, :-1]
+y = data[:, -1]
+
+# 将数据分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=11)
+
+# 线性回归
+linreg = LinearRegression()
+linreg.fit(X_train, y_train)
+print("线性回归:")
+print("w: ", linreg.coef_)
+print("b: ", linreg.intercept_)
+print("")
+
+# 岭回归
+ridge = Ridge(alpha=1)
+ridge.fit(X_train, y_train)
+print("岭回归:")
+print("w: ", ridge.coef_)
+print("b: ", ridge.intercept_)
+print("")
+
+# Lasso回归
+lasso = Lasso(alpha=1)
+lasso.fit(X_train, y_train)
+print("Lasso回归:")
+print("w: ", lasso.coef_)
+print("b: ", lasso.intercept_)
+print("")
+线性回归: +w: [ 1.58811049 0.51970404 0.11152606 -0.12770923] +b: 61.21626925064863 + +岭回归: +w: [ 1.42268831 0.36448183 -0.05189358 -0.28064387] +b: 76.46669011237476 + +Lasso回归: +w: [ 1.44635511 0.4085469 -0. -0.23875586] +b: 72.3136287521907 + ++
注:训练集:测试集=1:1,随机种子采用你学号后两位,例如你学号后两位=01,则random_state=1,如果最后两位=34,则random_state=34。最终结果输出你预测结果、实际结果以及模型得分三项。
+| 序号 | +年龄 | +收入 | +是否为学生 | +信誉 | +购买计算机 | +
|---|---|---|---|---|---|
| 1 | +<=30 | +高 | +否 | +中 | +否 | +
| 2 | +<=30 | +高 | +否 | +优 | +否 | +
| 3 | +31-40 | +高 | +否 | +中 | +是 | +
| 4 | +>40 | +中 | +否 | +中 | +是 | +
| 5 | +>40 | +低 | +是 | +中 | +是 | +
| 6 | +>40 | +低 | +是 | +优 | +否 | +
| 7 | +31-40 | +低 | +是 | +优 | +是 | +
| 8 | +<=30 | +中 | +否 | +中 | +否 | +
| 9 | +<=30 | +低 | +是 | +中 | +是 | +
| 10 | +>40 | +中 | +是 | +中 | +是 | +
| 11 | +<=30 | +中 | +是 | +优 | +是 | +
| 12 | +31-40 | +中 | +否 | +优 | +是 | +
| 13 | +31-40 | +高 | +是 | +中 | +是 | +
| 14 | +>40 | +中 | +否 | +优 | +否 | +
import numpy as np
+import pandas as pd
+from sklearn import metrics
+# 导入高斯朴素贝叶斯分类器
+from sklearn.naive_bayes import GaussianNB
+from sklearn.model_selection import train_test_split
+
+x = np.array(
+ [
+ [1, 3, 0, 1, 0],
+ [1, 3, 0, 2, 1],
+ [2, 3, 0, 2, 1],
+ [3, 2, 0, 1, 1],
+ [3, 1, 1, 1, 1],
+ [3, 1, 1, 2, 0],
+ [2, 1, 1, 2, 1],
+ [1, 2, 0, 1, 0],
+ [1, 1, 1, 1, 1],
+ [3, 2, 1, 1, 1],
+ [1, 2, 1, 2, 1],
+ [2, 2, 0, 2, 1],
+ [2, 3, 1, 1, 1],
+ [3, 2, 0, 2, 0],
+ ]
+)
+
+y = np.array(
+ [
+ 0,1,1,1,1,0,1,0,1,1,1,1,1,0
+ ]
+)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.5, random_state=11
+ )
+# 使用高斯朴素贝叶斯进行计算
+clf = GaussianNB()
+clf.fit(X_train, y_train)
+# 评估
+y_predict = clf.predict(X_test)
+score_gnb = metrics.accuracy_score(y_predict,y_test)
+
+print('该用户是否购买计算机:',y_predict)
+print(y_test)
+print(score_gnb)
+该用户是否购买计算机: [1 1 1 1 1 0 1] +[0 1 1 1 0 0 1] +0.7142857142857143 ++