diff --git a/20407120-李瑞峰-计科2001.html b/20407120-李瑞峰-计科2001.html new file mode 100644 index 0000000..71c2f4a --- /dev/null +++ b/20407120-李瑞峰-计科2001.html @@ -0,0 +1,15520 @@ + + +
+ + +# your code
+n = int(input('请输入所求阶乘数:'))
+m = 1
+sum = 0
+i = 1
+while n >= i:
+ m *= i
+ sum += m
+ i = i + 1
+print("结果:",sum)
+请输入所求阶乘数:20 +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
+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):
+ return x / y
+print("在下列功能中选择:")
+print("1.加法 2.减法 3.乘法 4.除法")
+choice = input("请输入对应功能项的数字(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("功能选择错误,只接受1-4的数字!")
+在下列功能中选择: +1.加法 2.减法 3.乘法 4.除法 +请输入对应功能项的数字(1.2.3.4):1 +请输入第一个数字:200 +请输入第二个数字:200 +200 + 200 = 400 ++
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
+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]
+label=[-3.00,-2.50,-1.75,-1.15,-0.50,0.15,0.75,1.25,1.85,2.45]
+plt.bar(X,Y,tick_label = label);
+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 | +
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=44
+)
+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: [[2.01866964 0.89275358 0.70839118 0.27064781]],线性回归截距b: 21.04 +岭回归系数w: [[ 1.65641229 0.57809631 0.31036533 -0.0457519 ]],岭回归截距b: 52.94 +Lasso回归系数w: [ 1.34786475 0.31452075 -0. -0.30635792],岭回归截距b: 79.34 ++
C:\Users\13946\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 | +
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=20)
+# 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 ++