diff --git a/20407125-刘安君-计科2001.html b/20407125-刘安君-计科2001.html new file mode 100644 index 0000000..f5caa1a --- /dev/null +++ b/20407125-刘安君-计科2001.html @@ -0,0 +1,15498 @@ + + +
+ + +# your code
+def Factorial(n):
+ if n == 1:
+ return 1
+ return n*Factorial(n-1)
+Num = 0
+for n in range(1,21):
+ res = Factorial(n)
+ Num += res
+print(Num)
+2561327494111820313 ++
# your code
+if __name__=='__main__':
+ s = [9,7,8,3,2,1,55,6]
+ length = len(s)
+ Max = max(s)
+ Min = min(s)
+print("列表的个数:",length," 列表中最大数:",Max," 列表中最小数:",Min)
+s.append(10)
+print("列表中加一个元素10:",s)
+s.remove(55)
+print("列表中删一个元素55:",s)
+列表的个数: 8 列表中最大数: 55 列表中最小数: 1 +列表中加一个元素10: [9, 7, 8, 3, 2, 1, 55, 6, 10] +列表中删一个元素55: [9, 7, 8, 3, 2, 1, 6, 10] ++
TTTTTx
+TTTTxx
+TTTxxx
+TTxxxx
+Txxxxx
+
+# your code
+import numpy as np
+layer = 6
+for i in range(1,layer):
+ t1 = layer - i
+ for j in range(0,t1):
+ print("T",end="")
+ t2 = i
+ for j in range(0,t2):
+ print("x",end="")
+ print("")
+TTTTTx +TTTTxx +TTTxxx +TTxxxx +Txxxxx ++
# your code
+import numpy as np
+print(" 选择运算:")
+print("1、相加 ")
+print("2、相减 ")
+print("3、相乘 ")
+print("4、相除 ")
+function = input(" 请选择 (1、2、3、4):")
+num1 = int(input(" 输入第一个数字 : "))
+num2 = int(input(" 输入第二个数字 : "))
+def add(x, y):
+ return x+y
+def sub(x, y):
+ return x - y
+def mul(x, y):
+ return x * y
+def div(x, y):
+ return x / y
+def show(function, num1, num2):
+ if function == '1':
+ print(num1, "+", num2, "=", add(num1, num2))
+ elif function == '2':
+ print(num1, "-", num2, "=", sub(num1, num2))
+ elif function == '3':
+ print(num1, "*", num2, "=", mul(num1, num2))
+ elif function == '4':
+ print(num1, "/", num2, "=", divide(num1, num2))
+ else:
+ print(" 非法输入 ")
+show(function, num1, num2)
+选择运算: +1、相加 +2、相减 +3、相乘 +4、相除 + 请选择 (1、2、3、4):1 + 输入第一个数字 : 2 + 输入第二个数字 : 3 +2 + 3 = 5 ++
# your code
+import numpy as np
+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):
+ print("学生:%s的年龄是%s岁,3门科目的最高分为%d分。"%(self.name,self.age,max(self.course)))
+stu=Student("zhangming","20",[69,88,100])
+stu.get_course()
+学生:zhangming的年龄是20岁,3门科目的最高分为100分。 ++
| 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
+import matplotlib
+data = [4, 12, 50, 120, 205, 255, 170, 100, 20, 14]
+labels = ["-3.00", "-2.50", "-1.75", "-1.15", "-0.50", "0.15", "0.75", "1.25", "1.85", "2.45"]
+
+plt.bar(range(len(data)),data,width=0.5)
+plt.xticks(range(len(data)),labels)
+for i in range(len(data)):
+ plt.text(x=i-0.05,y=data[i]+0.2,s = '%d'% data[i])
+plt.xlabel("x")
+plt.ylabel("y")
+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 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=25)
+
+# 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: [ 1.49026294 0.42556823 -0.01823801 -0.21661896] +线性回归 b: 70.69106215605518 +岭回归 w: [ 1.47330953 0.42023295 -0.03170907 -0.22343339] +岭回归 b: 71.45132222227355 +Lasso 回归 w: [ 1.47841561 0.42962444 -0.0018958 -0.2103416 ] +Lasso 回归 b: 70.18362029948534 ++
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=25
+ )
+# 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: [0 1 1 1 1 1 1] +[0 1 1 0 1 1 1] +0.8571428571428571 ++