diff --git a/20407233-要灏峰-计科2002.html b/20407233-要灏峰-计科2002.html new file mode 100644 index 0000000..6b12a0b --- /dev/null +++ b/20407233-要灏峰-计科2002.html @@ -0,0 +1,15565 @@ + + +
+ + +# your code
+s = 0
+def mul(n):
+ if n==1:
+ return 1
+ return n*mul(n-1)
+
+for n in range(1,21):
+ a = mul(n)
+ s += a
+print(s)
+
2561327494111820313 ++
# your code
+list1 =[9,7,8,3,2,1,55,6]
+x=len(list1)
+y=min(list1)
+z= max(list1)
+print("列表元素个数:",x,"最小数:",y,"最大数:",z)
+list1.append(10)
+print(list1)
+list1.remove(55)
+print(list1)
+
列表元素个数: 8 最小数: 1 最大数: 55 +[9, 7, 8, 3, 2, 1, 55, 6, 10] +[9, 7, 8, 3, 2, 1, 6, 10] ++
TTTTTx
+TTTTxx
+TTTxxx
+TTxxxx
+Txxxxx
+
+# your code
+for i in range(1,6):
+ for j in range(6-i):
+ print("T",end="")
+ for j in range(i):
+ print("x",end="")
+ print()
+
TTTTTx +TTTTxx +TTTxxx +TTxxxx +Txxxxx ++
# your code
+def hello():
+ print('欢迎使用本计算器!!!')
+ while True:
+ select = int(input('请输入要操作的选项:1 加法 2 减法 3 乘法 4 除法'))
+ if select == 1:
+ add()
+ elif select == 2:
+ red()
+ elif select == 3:
+ rid()
+ elif select == 4:
+ exc()
+ else:
+ print('你的输入有误,请重新输入!!!')
+ continue
+ choice = input('是否继续?继续输入Y,输入任意键退出。')
+ if choice != 'Y':
+ break
+def add():
+ a = float(input('请输入第一个数:'))
+ b = float(input('请输入第二个数:'))
+ result = a + b
+ print('两个数的和为{}'.format(result))
+def red():
+ a = float(input('请输入被减数:'))
+ b = float(input('请输入减数:'))
+ result = a - b
+ print('两个数的差为{}'.format(result))
+def rid():
+ a = float(input('请输入第一个数:'))
+ b = float(input('请输入第二个数:'))
+ result = a * b
+ print('两个数的积为{}'.format(result))
+def exc():
+ a = float(input('请输入被除数数:'))
+ b = float(input('请输入除数:'))
+ result = a / b
+ print('两个数的商为{}'.format(result))
+hello()
+
欢迎使用本计算器!!! +请输入要操作的选项:1 加法 2 减法 3 乘法 4 除法3 +请输入第一个数:3 +请输入第二个数:4 +两个数的积为12.0 +是否继续?继续输入Y,输入任意键退出。 ++
# your code
+class Student:
+ def __init__(self,name,age,*cou):
+ self.name=name
+ self.age=age
+ self.course=cou
+ def get_name(self):
+ return str(self.name)
+ def get_age(self):
+ return int(self.age)
+ def get_course(self):
+ return int(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 pandas as pd
+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)
+plt.title(' ')
+
+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
+from sklearn import model_selection, linear_model
+import numpy as np
+from sklearn import datasets
+boston = datasets.load_boston()
+data = 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]
+ ]
+)
+target = 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]
+ ]
+)
+x_train, x_test, y_train, y_test = model_selection.train_test_split(
+ data, target, test_size=0.2, random_state=33
+)
+lr = linear_model.LinearRegression()
+rr = linear_model.Ridge()
+la = linear_model.Lasso()
+models = [lr, rr, la]
+names = ['Linear', 'Ridge', 'Lasso']
+for model, name in zip(models, names):
+ model.fit(x_train, y_train)
+print('线性回归系数w: %s,线性回归截距b: %.2f' %(lr.coef_, lr.intercept_))
+print('岭回归系数w: %s,岭回归截距b: %.2f' %(rr.coef_, rr.intercept_))
+print('Lasso回归系数w: %s,岭回归截距b: %.2f' %(la.coef_, la.intercept_))
+
线性回归系数w: [[2.14178865 0.96131663 0.73154799 0.32080833]],线性回归截距b: 15.03 +岭回归系数w: [[1.7932357 0.64097931 0.38981714 0.0060821 ]],岭回归截距b: 46.58 +Lasso回归系数w: [ 1.37229061 0.27016971 -0. -0.35421916],岭回归截距b: 83.10 ++
E:\anaconda3\lib\site-packages\sklearn\utils\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2. + + The Boston housing prices dataset has an ethical problem. You can refer to + the documentation of this function for further details. + + The scikit-learn maintainers therefore strongly discourage the use of this + dataset unless the purpose of the code is to study and educate about + ethical issues in data science and machine learning. + + In this special case, you can fetch the dataset from the original + source:: + + import pandas as pd + import numpy as np + + + data_url = "http://lib.stat.cmu.edu/datasets/boston" + raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None) + data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]]) + target = raw_df.values[1::2, 2] + + Alternative datasets include the California housing dataset (i.e. + :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing + dataset. You can load the datasets as follows:: + + from sklearn.datasets import fetch_california_housing + housing = fetch_california_housing() + + for the California housing dataset and:: + + from sklearn.datasets import fetch_openml + housing = fetch_openml(name="house_prices", as_frame=True) + + for the Ames housing dataset. + + warnings.warn(msg, category=FutureWarning) ++
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 | +
# your code
+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=33)
+# 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 0 1] +[0 1 1 1 0 0 1] +0.7142857142857143 ++
+