diff --git a/20407146-许珂珂-计科2001.html b/20407146-许珂珂-计科2001.html new file mode 100644 index 0000000..4f736e8 --- /dev/null +++ b/20407146-许珂珂-计科2001.html @@ -0,0 +1,15503 @@ + + +
+ + +# your code
+#递归求值的阶乘
+def Factorial(n):
+ #设置终止条件
+ if n == 1:
+ return 1
+ return n*Factorial(n-1)
+#依次调用Factorial函数,进行值的累加
+Num = 0
+for n in range(1,21):
+ res = Factorial(n)
+ Num += res
+print(Num)
+
2561327494111820313 ++
# your code
+s = [9, 7, 8, 3, 2, 1, 55, 6]
+
+# 求列表元素的个数、最大值和最小值
+print('列表s的元素个数为:', len(s))
+print('列表s的最大值为:', max(s))
+print('列表s的最小值为:', min(s))
+
+# 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)
+
+# 打印操作后的列表s
+print('操作后的列表s为:', s)
+
列表s的元素个数为: 8 +列表s的最大值为: 55 +列表s的最小值为: 1 +操作后的列表s为: [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(a,b):
+ return a+b
+def Sub(a,b):
+ return a-b
+def Mul(a,b):
+ return a*b
+def Div(a,b):
+ if(b==0):
+ print("Error!")
+ return
+ return a/b
+while True:
+ Choice = input("Choice:")
+ if(Choice == '0'):
+ break
+ a = int(input("a:"))
+ b = int(input("b:"))
+ if(Choice == '1'):
+ print(Add(a,b))
+ elif(Choice == '2'):
+ print(Sub(a,b))
+ elif(Choice == '3'):
+ print(Mul(a,b))
+ elif(Choice == '4'):
+ print(Div(a,b))
+
Choice:1 +a:2 +b:3 +5 +Choice: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))
+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
+
+# 定义 X 轴和 Y 轴的数据
+x1 = [-3.00, -2.50, -1.75, -1.15, -0.50]
+y1 = [4, 12, 50, 120, 205]
+x2 = [0.15, 0.75, 1.25, 1.85, 2.45]
+y2 = [255, 170, 100, 20, 14]
+
+# 创建画布和子图
+fig, ax = plt.subplots()
+
+# 绘制柱状图(条形图)
+ax.bar(x1, y1, width=0.25, align='edge', color='blue', label='Y1')
+ax.bar(x2, y2, width=-0.25, align='edge', color='red', label='Y2')
+
+# 设置图形属性
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_title('Bar Plot')
+ax.legend()
+
+# 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 | +
import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Ridge, Lasso
+
+# 读取原始数据并创建数据框
+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, 13],
+ 'X4': [60, 52, 20, 47, 33, 22, 6, 44, 22, 26, 34, 22, 22],
+ '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]
+})
+
+# 分离出自变量和因变量
+X = data[['X1', 'X2', 'X3', 'X4']]
+y = data['Y']
+
+# 将训练集和测试集按 8:2 分割,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=0.2, random_state=34)
+
+# 线性回归模型
+linear_model = LinearRegression()
+linear_model.fit(X_train, y_train)
+
+# 输出线性回归的 w 和 b 系数
+print('线性回归 w:', linear_model.coef_)
+print('线性回归 b:', linear_model.intercept_)
+
+# 岭回归模型
+ridge_model = Ridge(alpha=1.0)
+ridge_model.fit(X_train, y_train)
+
+# 输出岭回归的 w 和 b 系数
+print('岭回归 w:', ridge_model.coef_)
+print('岭回归 b:', ridge_model.intercept_)
+
+# Lasso 回归模型
+lasso_model = Lasso(alpha=1.0)
+lasso_model.fit(X_train, y_train)
+
+# 输出 Lasso 回归的 w 和 b 系数
+print('Lasso 回归 w:', lasso_model.coef_)
+print('Lasso 回归 b:', lasso_model.intercept_)
+
线性回归 w: [1.73716117 0.73583035 0.25554047 0.07159407] +线性回归 b: 41.94463392439112 +岭回归 w: [1.69689369 0.71271939 0.2173755 0.0467834 ] +岭回归 b: 44.584426660577 +Lasso 回归 w: [ 1.47653695 0.60635755 0. -0.06262619] +Lasso 回归 b: 57.365482388617096 ++
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=46
+ )
+# 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 1 1] +[1 1 1 0 0 1 1] +0.7142857142857143 ++