diff --git a/20407129-孟培扬-计科2001.html b/20407129-孟培扬-计科2001.html new file mode 100644 index 0000000..ecf0dfa --- /dev/null +++ b/20407129-孟培扬-计科2001.html @@ -0,0 +1,15560 @@ + + +
+ + +# your code
+# 计算阶乘函数
+def factorial(n):
+ if n == 0:
+ return 1
+ else:
+ return n * factorial(n-1)
+
+# 计算和
+total = 0
+for i in range(1, 21):
+ total += factorial(i)
+
+# 输出结果
+print("结果:",total)
+结果: 2561327494111820313 ++
# your code
+s = [9,7,8,3,2,1,55,6]
+print("length =",len(s)," max =",max(s)," min =",min(s))
+s.append(10)
+s.remove(55)
+print(s)
+length = 8 max = 55 min = 1 +[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
+# 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):
+ if y == 0:
+ raise ValueError('除数不能为 0')
+ return x / y
+
+# 用户选择功能
+print('请选择要进行的运算:\n')
+print('1. 加法')
+print('2. 减法')
+print('3. 乘法')
+print('4. 除法')
+
+choice = input('请输入 1/2/3/4 中的一个数字:')
+
+# 用户输入数字
+num1 = float(input('请输入第一个数字:'))
+num2 = float(input('请输入第二个数字:'))
+
+# 计算结果
+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':
+ try:
+ print(num1, "/", num2, "=", divide(num1, num2))
+ except ValueError as e:
+ print('错误信息:', e)
+else:
+ print('输入错误,请输入有效数字(1-4)')
+请选择要进行的运算: + +1. 加法 +2. 减法 +3. 乘法 +4. 除法 +请输入 1/2/3/4 中的一个数字:3 +请输入第一个数字:2 +请输入第二个数字:3 +2.0 * 3.0 = 6.0 ++
# your code
+class Student:
+ def __init__(self, name, age, course):
+ self.name = name
+ self.age = age
+ self.course = course
+
+ def get_name(self):
+ return str(self.name)
+
+ def get_age(self):
+ return int(self.age)
+
+ def get_course(self):
+ return max(self.course)
+
+# 创建学生对象并测试
+st = Student('zhangming', 20, [69, 88, 100])
+print("学生姓名:" + st.get_name())
+print("学生年龄:" + str(st.get_age()))
+print("最高分:" + str(st.get_course()))
+学生姓名:zhangming +学生年龄:20 +最高分: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
+data = {
+ '-3.00': 4,
+ '-2.50': 12,
+ '-1.75': 50,
+ '-1.15': 120,
+ '-0.50': 205,
+ '0.15': 255,
+ '0.75': 170,
+ '1.25': 100,
+ '1.85': 20,
+ '2.45': 14
+}
+
+# 绘制柱状图
+fig, ax = plt.subplots()
+ax.bar(data.keys(), data.values())
+
+# 添加标题和标签
+ax.set_title('Data Bar Chart')
+ax.set_xlabel('X')
+ax.set_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
+from sklearn import linear_model
+from sklearn import metrics
+from sklearn import model_selection
+
+# 原始数据
+X = np.array([
+ [7, 26, 6, 60],
+ [1, 29, 15, 52],
+ [11, 56, 8, 20],
+ [11, 31, 8, 47],
+ [7, 52, 6, 33],
+ [11, 55, 9, 22],
+ [3, 71, 17, 6],
+ [1, 31, 22, 44],
+ [2, 54, 18, 22],
+ [21, 47, 4, 26],
+ [1, 40, 23, 34],
+ [11, 66, 9, 12],
+ [10, 68, 8, 12]
+])
+
+y = np.array([
+ 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
+])
+
+# 划分训练集和测试集
+X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=29)
+
+# 普通线性回归
+lr = linear_model.LinearRegression()
+lr.fit(X_train, y_train)
+print('线性回归 w:', lr.coef_, 'b:', lr.intercept_)
+
+# 岭回归
+ridge = linear_model.Ridge(alpha=0.1)
+ridge.fit(X_train, y_train)
+print('岭回归 w:', ridge.coef_, 'b:', ridge.intercept_)
+
+# Lasso 回归
+lasso = linear_model.Lasso(alpha=0.1)
+lasso.fit(X_train, y_train)
+print('Lasso 回归 w:', lasso.coef_, 'b:', lasso.intercept_)
+线性回归 w: [ 1.50774251 0.66458233 -0.02023126 -0.03008357] b: 53.42957933131478 +岭回归 w: [ 1.48567298 0.64309493 -0.04284173 -0.05107605] b: 55.52038896242757 +Lasso 回归 w: [ 1.52453453 0.68439878 -0. -0.0106636 ] b: 51.534832077416 ++
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=29
+ )
+# 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 0 1 1 1 1 0] +[1 0 1 1 1 1 0] +1.0 ++
+