diff --git a/20407242 李森豪 计科2002.html b/20407242 李森豪 计科2002.html new file mode 100644 index 0000000..03128f8 --- /dev/null +++ b/20407242 李森豪 计科2002.html @@ -0,0 +1,15532 @@ + + +
+ + +# your code
+n = 0
+s = 0
+t = 1
+for n in range(1,21):
+ t *= n
+ s += t
+print(s)
+
2561327494111820313 ++
# your code
+def choose(s):
+ sum = 0
+ all = 0
+ maxnum = max(s)
+ minnum = min(s)
+ for i in s:
+ sum = sum + 1
+ all = all + i
+ average = all / sum
+ print(str("元素个数{0},最大值{1},最小值{2},元素和{3},平均值{4}").format(sum, maxnum, minnum, all, average))
+def main():
+ s = [9,7,8,3,2,1,55,6]
+ choose(s)
+main()
+
元素个数8,最大值55,最小值1,元素和91,平均值11.375 ++
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 hello():
+ print('欢迎使用本计算器!!!')
+ while True:
+ select = int(input('请输入要操作的选项:1 加法 2 减法 3 除法 4 乘法'))
+ if select == 1:
+ add()
+ elif select == 2:
+ red()
+ elif select == 3:
+ rid()
+ elif select == 4:
+ exc()
+ else:
+ print('你的输入有误,请重新输入!!!')
+ continue
+ choice = input('是否继续?继续输入Y,输入任意键退出。')
+ if choice != 'Y':
+ break
+
+
+def add():
+ a = float(input('请输入第一个数:'))
+ b = float(input('请输入第二个数:'))
+ result = a + b
+ print('两个数的和为{}'.format(result))
+
+
+def red():
+ a = float(input('请输入被减数:'))
+ b = float(input('请输入减数:'))
+ result = a - b
+ print('两个数的差为{}'.format(result))
+
+
+def rid():
+ a = float(input('请输入第一个数:'))
+ b = float(input('请输入第二个数:'))
+ result = a * b
+ print('两个数的积为{}'.format(result))
+
+
+def exc():
+ a = float(input('请输入被除数数:'))
+ b = float(input('请输入除数:'))
+ result = a / b
+ print('两个数的商为{}'.format(result))
+
+
+hello()
+
欢迎使用本计算器!!! +你的输入有误,请重新输入!!! +两个数的和为8.0 ++
# 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 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=42)
+# 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: [ 1.2025628 0.28487458 -0.17808246 -0.3639949 ] +截距: 85.44669988722116 +训练集得分: 0.9722976513958527 +测试集得分: 0.9917633778849372 + +岭回归: +系数: [ 1.09412861 0.20428354 -0.27323503 -0.44443726] +截距: 93.62436314204732 +训练集得分: 0.9722530473983771 +测试集得分: 0.9892799817933159 + +Lasso回归: +系数: [ 1.2221128 0.31111027 -0.15413921 -0.338068 ] +截距: 82.97772684307277 +训练集得分: 0.9722909438174843 +测试集得分: 0.9915372637147916 ++
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=42)
+# 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' '否' '是' '是' '否' '是' '是'] +实际结果: ['否' '否' '否' '是' '是' '是' '是'] +模型得分: 0.5714285714285714 ++
+