diff --git a/20407115-呼芳利-计科2001.html b/20407115-呼芳利-计科2001.html deleted file mode 100644 index 48286a8..0000000 --- a/20407115-呼芳利-计科2001.html +++ /dev/null @@ -1,15513 +0,0 @@ - - -
- - -total = 0
-factorial = 1
-for i in range(1, 21):
- # 计算 i! 的值
- factorial *= i
- #累加 factorial 的值
- total += factorial
-print("和为:",total)
-
和为: 2561327494111820313 --
s = [9, 7, 8, 3, 2, 1, 55, 6]
-# 计算列表元素的个数
-num = len(s)
-print("元素个数为:", num)
-
-# 找到列表中的最大数和最小数
-max_num = max(s)
-min_num = min(s)
-print("最大数为:", max_num)
-print("最小数为:", min_num)
-
-# 添加一个元素10
-s.append(10)
-# 删除一个元素55
-s.remove(55)
-
-print("最终列表为:", s)
-
元素个数为: 8 -最大数为: 55 -最小数为: 1 -最终列表为: [9, 7, 8, 3, 2, 1, 6, 10] --
TTTTTx
-TTTTxx
-TTTxxx
-TTxxxx
-Txxxxx
-
-for i in range(5):
- for j in range(5 - i):
- print("T", end="")
- for k in range(i):
- print("x", end="")
- print("")
-
TTTTT -TTTTx -TTTxx -TTxxx -Txxxx --
def add(x, y):
- return x + y
-def subtract(x, y):
- return x - y
-def multiply(x, y):
- return x * y
-def divide(x, y):
- if y == 0:
- return "除数不能为0"
- else:
- return x / y
-
-print("选择要进行的运算:")
-print("1. 加法")
-print("2. 减法")
-print("3. 乘法")
-print("4. 除法")
-
-choice = input("请输入您的选择(1/2/3/4):")
-num1 = int(input("请输入第一个数字:"))
-num2 = int(input("请输入第二个数字:"))
-
-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")
-
选择要进行的运算: -1. 加法 -2. 减法 -3. 乘法 -4. 除法 -请输入您的选择(1/2/3/4):2 -请输入第一个数字:1 -请输入第二个数字:3 -1 - 3 = -2 --
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("学生姓名:", st.get_name())
-print("学生年龄:", st.get_age())
-print("最高分数:", st.get_course())
-
学生姓名: zhangming -学生年龄: 20 -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 | -
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]
-
-plt.bar(x, y, width=0.2)
-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 | -
import numpy as np
-from sklearn.model_selection import train_test_split
-
-data = np.array([[ 7, 26, 6, 60, 78.5],
- [ 1, 29, 15, 52, 74.3],
- [11, 56, 8, 20,104.3],
- [11, 31, 8, 47, 87.6],
- [ 7, 52, 6, 33, 95.9],
- [11, 55, 9, 22,109.2],
- [ 3, 71, 17, 6,102.7],
- [ 1, 31, 22, 44, 72.5],
- [ 2, 54, 18, 22, 93.1],
- [21, 47, 4, 26,115.9],
- [ 1, 40, 23, 34, 83.8],
- [11, 66, 9, 12,113.3],
- [10, 68, 8, 12,109.4]])
-
-X = data[:,0:4]
-y = data[:,4]
-
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=15)
-
-#线性回归
-from sklearn.linear_model import LinearRegression
-
-lr = LinearRegression()
-lr.fit(X_train, y_train)
-
-print('线性回归:')
-print('w:', lr.coef_)
-print('b:', lr.intercept_)
-
-#岭回归
-from sklearn.linear_model import Ridge
-
-ridge = Ridge(alpha=1.0) # alpha值可以更换试验寻找最好的效果
-ridge.fit(X_train, y_train)
-
-print('岭回归:')
-print('w:', ridge.coef_)
-print('b:', ridge.intercept_)
-
-#Lasso回归
-from sklearn.linear_model import Lasso
-
-lasso = Lasso(alpha=1.0) # alpha值可以更换试验寻找最好的效果
-lasso.fit(X_train, y_train)
-
-print('Lasso回归:')
-print('w:', lasso.coef_)
-print('b:', lasso.intercept_)
-
线性回归: -w: [ 0.26655739 -0.64835198 -1.35191198 -1.32541508] -b: 180.60992642787457 -岭回归: -w: [ 0.63515406 -0.28031503 -0.95811354 -0.96262718] -b: 144.5985137229033 -Lasso回归: -w: [ 0.90611156 0. -0.64062776 -0.68340473] -b: 116.94963304605514 --
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=15
- )
-# 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 1 1 1 1] -1.0 --