diff --git a/20407136-史扬凡-计科2001班.html b/20407136-史扬凡-计科2001班.html new file mode 100644 index 0000000..6de12f3 --- /dev/null +++ b/20407136-史扬凡-计科2001班.html @@ -0,0 +1,15530 @@ + + +
+ + +def factorial(num):
+ if num == 0:
+ return 1
+ else:
+ return num * factorial(num-1)
+
+# 计算1到20的阶乘并加起来
+result = 0
+for i in range(1, 21):
+ result += factorial(i)
+
+# 输出结果
+print(result)
+
2561327494111820313 ++
s = [9, 7, 8, 3, 2, 1, 55, 6]
+
+# 元素个数
+count = len(s)
+print("元素个数:", count)
+
+# 最大数和最小数
+max_num = max(s)
+print("最大数:", max_num)
+min_num = min(s)
+print("最小数:", min_num)
+
+# 添加元素10
+s.append(10)
+print("添加元素10后的列表s:", s)
+
+# 删除元素55
+s.remove(55)
+print("删除元素55后的列表s:", s)
+
元素个数: 8 +最大数: 55 +最小数: 1 +添加元素10后的列表s: [9, 7, 8, 3, 2, 1, 55, 6, 10] +删除元素55后的列表s: [9, 7, 8, 3, 2, 1, 6, 10] ++
TTTTTx
+TTTTxx
+TTTxxx
+TTxxxx
+Txxxxx
+
+T = 'T'
+x = 'x'
+length = 6
+for i in range(1, length):
+ print(T * (length - i) + x * i)
+
TTTTTx +TTTTxx +TTTxxx +TTxxxx +Txxxxx ++
# 定义加法函数
+def add(x, y):
+ return x + y
+
+# 定义减法函数
+def subtract(x, y):
+ return x - y
+
+# 定义乘法函数
+def multiply(x, y):
+ return x * y
+
+# 定义除法函数
+def divide(x, y):
+ return x / y
+
+# 打印功能选项
+print("请选择功能:")
+print("1. 加法")
+print("2. 减法")
+print("3. 乘法")
+print("4. 除法")
+
+# 获取用户输入
+choice = input("输入你的选择(1/2/3/4): ")
+
+# 获取用户输入的数值
+num1 = float(input("请输入第一个数字: "))
+num2 = float(input("请输入第二个数字: "))
+
+# 根据用户选择执行相应的函数
+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")
+
请选择功能: +1. 加法 +2. 减法 +3. 乘法 +4. 除法 +输入你的选择(1/2/3/4): 1 +请输入第一个数字: 2 +请输入第二个数字: 3 +2.0 + 3.0 = 5.0 ++
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)
+
+st = Student('zhangming', 20, [69, 88, 100])
+print("姓名:", st.get_name())
+print("年龄:", st.get_age())
+print("最高成绩:", st.get_course())
+
姓名: zhangming +年龄: 20 +最高成绩: 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 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 pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Ridge, Lasso
+
+# 读取数据
+data = {
+ '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],
+}
+df = pd.DataFrame(data)
+
+# 划分训练集和测试集
+random_state = 34
+train_df, test_df = train_test_split(df, test_size=0.2, random_state=36)
+
+# 提取自变量和因变量
+X_train = train_df[['X1', 'X2', 'X3', 'X4']]
+y_train = train_df['Y']
+X_test = test_df[['X1', 'X2', 'X3', 'X4']]
+y_test = test_df['Y']
+
+# 线性回归
+linear_model = LinearRegression()
+linear_model.fit(X_train, y_train)
+print('线性回归 w: ', linear_model.coef_)
+print('线性回归 b: ', linear_model.intercept_)
+
+# 岭回归
+ridge_model = Ridge(alpha=1.0)
+ridge_model.fit(X_train, y_train)
+print('岭回归 w: ', ridge_model.coef_)
+print('岭回归 b: ', ridge_model.intercept_)
+
+# Lasso回归
+lasso_model = Lasso(alpha=1.0)
+lasso_model.fit(X_train, y_train)
+print('Lasso回归 w: ', lasso_model.coef_)
+print('Lasso回归 b: ', lasso_model.intercept_)
+
线性回归 w: [ 0.70124962 -0.05149115 -0.71603223 -0.70420377] +线性回归 b: 121.4687907427662 +岭回归 w: [ 0.76080357 0.00723825 -0.65311572 -0.64674684] +岭回归 b: 115.74649161080427 +Lasso回归 w: [ 0.68499537 0. -0.67194209 -0.65390731] +Lasso回归 b: 117.02200454676199 ++
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=36)
+# 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 0 1 1] +[1 1 1 1 0 1 1] +1.0 ++
+