From 8308fc17f5050bc8a99ed26f7f1e09243bcfe2a7 Mon Sep 17 00:00:00 2001 From: pm7xjaivk <2793608552@qq.com> Date: Mon, 29 May 2023 23:08:11 +0800 Subject: [PATCH] ADD file via upload --- 20407240郑志强计科2002班.html | 15521 ++++++++++++++++++++++++++ 1 file changed, 15521 insertions(+) create mode 100644 20407240郑志强计科2002班.html diff --git a/20407240郑志强计科2002班.html b/20407240郑志强计科2002班.html new file mode 100644 index 0000000..110acaf --- /dev/null +++ b/20407240郑志强计科2002班.html @@ -0,0 +1,15521 @@ + + +
+ + +# your code
+total = 0
+factorial = 1
+# 循环计算阶乘和
+for i in range(1, 21):
+ factorial *= i
+ total += factorial
+# 输出结果
+print(total)
+
2561327494111820313 ++
# your code
+s = [9, 7, 8, 3, 2, 1, 55, 6]
+# 求元素个数、最大值、最小值
+count = len(s)
+max_num = max(s)
+min_num = min(s)
+# 输出结果
+print("列表s中元素个数为:", count)
+print("列表s中最大数为:", max_num)
+print("列表s中最小数为:", min_num)
+# 在列表s中添加一个元素10
+s.append(10)
+# 从列表s中删除一个元素55
+s.remove(55)
+# 输出修改后的列表s
+print("修改后的列表s为:", s)
+
列表s中元素个数为: 8 +列表s中最大数为: 55 +列表s中最小数为: 1 +修改后的列表s为: [9, 7, 8, 3, 2, 1, 6, 10] ++
TTTTTx
+TTTTxx
+TTTxxx
+TTxxxx
+Txxxxx
+
+# your code
+for i in range(5):
+ for j in range(5 - i):
+ print("T", end="")
+ for k in range(i+1):
+ print("x", end="")
+ print()
+
TTTTTx +TTTTxx +TTTxxx +TTxxxx +Txxxxx ++
# your code
+def add(a, b):
+ """加法"""
+ return a + b
+def subtract(a, b):
+ """减法"""
+ return a - b
+def multiply(a, b):
+ """乘法"""
+ return a * b
+def divide(a, b):
+ """除法"""
+ return a / b
+# 输入数字和选择功能
+num1 = float(input("请输入第一个数字:"))
+num2 = float(input("请输入第二个数字:"))
+print("请选择功能:")
+print("1. 加法")
+print("2. 减法")
+print("3. 乘法")
+print("4. 除法")
+choice = int(input("请输入对应的数字:"))
+# 根据用户选择调用相应函数
+if choice == 1:
+ result = add(num1, num2)
+ print("两个数的和为:", result)
+elif choice == 2:
+ result = subtract(num1, num2)
+ print("两个数的差为:", result)
+elif choice == 3:
+ result = multiply(num1, num2)
+ print("两个数的积为:", result)
+elif choice == 4:
+ result = divide(num1, num2)
+ print("两个数的商为:", result)
+else:
+ print("输入有误,请重新运行程序")
+
请选择功能: +1. 加法 +2. 减法 +3. 乘法 +4. 除法 +两个数的积为: 234.0 ++
# your code
+class Student:
+ def __init__(self, name, age, course):
+ self.name = name
+ self.age = age
+ self.course = course
+ def get_name(self):
+ return self.name
+ def get_age(self):
+ return self.age
+ def get_course(self):
+ return max(self.course)
+# 测试
+st = Student('zhangming', 20, [69, 88, 100])
+print("学生姓名:", st.get_name())
+print("学生年龄:", st.get_age())
+print("学生最高分数:", st.get_course())
+
学生姓名: zhangming +学生年龄: 20 +学生最高分数: 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
+# 定义数据
+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]
+# 绘制柱状图
+plt.bar(x_data, y_data, width=0.3, color='blue')
+# 设置图形属性
+plt.title("柱状图")
+plt.xlabel("X")
+plt.ylabel("Y")
+# 显示图形
+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=40)
+# 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: [ 1.37914915 0.52235563 -0.11353673 -0.16566386] +截距: 66.18042444982308 +训练集得分: 0.9826996430157129 +测试集得分: 0.9582650415311037 + +岭回归: +系数: [ 1.21471328 0.39359214 -0.26743013 -0.29337994] +截距: 79.19133129897371 +训练集得分: 0.9826098791285273 +测试集得分: 0.955777123071867 + +Lasso回归: +系数: [ 1.405279 0.55172639 -0.08210026 -0.13686687] +截距: 63.34052316018646 +训练集得分: 0.982692770920137 +测试集得分: 0.9587473820581216 ++
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=40)
+# 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: ['是' '是' '是' '是' '是' '是' '是'] +实际结果: ['否' '否' '是' '是' '是' '是' '否'] +模型得分: 0.5714285714285714 ++
+