diff --git a/20407218 刘硕 计科2002班.html b/20407218 刘硕 计科2002班.html new file mode 100644 index 0000000..097817b --- /dev/null +++ b/20407218 刘硕 计科2002班.html @@ -0,0 +1,15499 @@ + + +
+ + +# your code
+total_sum = 0
+factorial = 1
+
+for i in range(1, 21):
+ factorial *= i
+ total_sum += factorial
+
+print(total_sum)
+
2561327494111820313 ++
# your code
+s1=[9,7,8,3,2,1,55,6]
+x=len(s1)
+y=max(s1)
+z=min(s1)
+print("列表元素个数:",x,"最大数:",y,"最小数:",z)
+s1.append(10)
+print(s1)
+s1.remove(55)
+print(s1)
+
列表元素个数: 8 最大数: 55 最小数: 1 +[9, 7, 8, 3, 2, 1, 55, 6, 10] +[9, 7, 8, 3, 2, 1, 6, 10] ++
TTTTTx
+TTTTxx
+TTTxxx
+TTxxxx
+Txxxxx
+
+# your code
+for i in range(5, 0, -1):
+ print('T' * i + 'x' * (5 - i))
+
TTTTT +TTTTx +TTTxx +TTxxx +Txxxx ++
# 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("选择操作")
+print("1.相加")
+print("2.相减")
+print("3.相乘")
+print("4.相除")
+choice = input("选择(1/2/3/4): ")
+num1 = float(input("输入第一个数字: "))
+num2 = float(input("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("非法输入")
+
选择操作 +1.相加 +2.相减 +3.相乘 +4.相除 +96.0 * 2.0 = 192.0 ++
# your code
+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))
+zm=Student('zhangming',20,[69,88,100])
+print('学生姓名为:',zm.get_name(),'年龄为:',zm.get_age(),'最高分成绩为:',zm.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 | +
import numpy as np
+import matplotlib.pyplot as plt
+import random
+
+# 准备数据
+x_data = [-3.00,-2.50,-1.75,-1.15,-0.50,0.15,0.75,1.25,1.85,2.45]
+y_data = [4,12,50,120,205,255,170,100,20,14]
+
+# 正确显示中文和负号
+plt.rcParams["font.sans-serif"] = ["SimHei"]
+plt.rcParams["axes.unicode_minus"] = False
+
+# 画图,plt.bar()可以画柱状图
+plt.style.use('ggplot') #添加网格线
+for i in range(len(x_data)):
+ plt.bar(x_data[i], y_data[i])
+plt.show()
+
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 | +
# your code
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Ridge, Lasso
+# 读入数据
+data = pd.DataFrame({
+'X1': [7, 1, 11, 11, 7, 11, 3, 1, 2, 21, 1, 11, 10],
+'X2': [26, 29, 56, 31, 52, 55, 71, 31, 54, 47, 40, 66, 68],
+'X3': [6, 15, 8, 8, 6, 9, 17, 22, 18, 4, 23, 9, 8],
+'X4': [60, 52, 20, 47, 33, 22, 6, 44, 22, 26, 34, 12, 12],
+'Y': [78.5, 74.3, 104.3, 87.6, 95.9, 109.2, 102.7, 72.5, 93.1, 115.9, 83.8, 113.3, 109.4]
+})
+# 数据预处理
+X = data.iloc[:, :-1]
+Y = data.iloc[:, -1]
+# 分割训练集和测试集
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=18)
+# 线性回归
+lr = LinearRegression()
+lr.fit(X_train, Y_train)
+print("线性回归:")
+print("系数:", lr.coef_)
+print("截距:", lr.intercept_)
+print("训练集得分:", lr.score(X_train, Y_train))
+print("测试集得分:", lr.score(X_test, Y_test))
+# 岭回归
+ridge = Ridge(alpha=1.0)
+ridge.fit(X_train, Y_train)
+print("\n岭回归:")
+print("系数:", ridge.coef_)
+print("截距:", ridge.intercept_)
+print("训练集得分:", ridge.score(X_train, Y_train))
+print("测试集得分:", ridge.score(X_test, Y_test))
+# Lasso回归
+lasso = Lasso(alpha=0.1)
+lasso.fit(X_train, Y_train)
+print("\nLasso回归:")
+print("系数:", lasso.coef_)
+print("截距:", lasso.intercept_)
+print("训练集得分:", lasso.score(X_train, Y_train))
+print("测试集得分:", lasso.score(X_test, Y_test))
+
线性回归: +系数: [1.71864127 0.74786281 0.27383256 0.06792827] +截距: 41.5334270936739 +训练集得分: 0.9732861314400442 +测试集得分: 0.5219330213828481 + +岭回归: +系数: [ 1.39106973 0.48205898 -0.02178167 -0.19036431] +截距: 67.84739657575568 +训练集得分: 0.9729357897470475 +测试集得分: 0.5640272865175194 + +Lasso回归: +系数: [ 1.41614847 0.50506628 0. -0.16799554] +截距: 65.63758245931093 +训练集得分: 0.9729870322769922 +测试集得分: 0.5629015269331035 ++
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 | +
# your code
+import pandas as pd
+from sklearn.naive_bayes import GaussianNB
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+# 读取数据
+data = pd.DataFrame({
+ '年龄': ['<=30', '<=30', '31-40', '>40', '>40', '>40', '31-40', '<=30', '<=30', '>40', '<=30', '31-40', '31-40', '>40'],
+ '收入': ['高', '高', '高', '中', '低', '低', '低', '中', '低', '中', '中', '中', '高', '中'],
+ '是否为学生': ['否', '否', '否', '否', '是', '是', '是', '否', '是', '是', '是', '否', '是', '否'],
+ '信誉': ['中', '优', '中', '中', '中', '优', '优', '中', '中', '中', '优', '优', '中', '优'],
+ '购买计算机': ['否', '否', '是', '是', '是', '否', '是', '否', '是', '是', '是', '是', '是', '否']
+})
+# 将特征转换为数字
+data.replace({'年龄': {'<=30': 1, '31-40': 2, '>40': 3},
+ '收入': {'低': 1, '中': 2, '高': 3},
+ '是否为学生': {'否': 0, '是': 1},
+ '信誉': {'中': 1, '优': 2}}, inplace=True)
+# 分离特征和标签
+X = data.iloc[:, :-1]
+y = data.iloc[:, -1]
+# 划分训练集和测试集,随机种子为学号后两位
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=18)
+# 创建朴素贝叶斯分类器
+clf = GaussianNB()
+# 训练模型
+clf.fit(X_train, y_train)
+# 预测测试集结果
+y_pred = clf.predict(X_test)
+# 输出预测结果、实际结果以及模型得分
+print("预测结果:", y_pred)
+print("实际结果:", y_test.values)
+print("模型得分:", accuracy_score(y_test, y_pred))
+
预测结果: ['否' '是' '是' '是' '是' '否' '是'] +实际结果: ['否' '是' '是' '是' '否' '是' '否'] +模型得分: 0.5714285714285714 ++
+