From 9fce88570fc098c4fcd4800ec96fb152d022a694 Mon Sep 17 00:00:00 2001 From: prvn8yfxg <3038505845@qq.com> Date: Sun, 28 May 2023 18:37:53 +0800 Subject: [PATCH] =?UTF-8?q?Delete=20'20407120-=E6=9D=8E=E7=91=9E=E5=B3=B0-?= =?UTF-8?q?=E8=AE=A1=E7=A7=912001.html'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 20407120-李瑞峰-计科2001.html | 15520 --------------------------- 1 file changed, 15520 deletions(-) delete mode 100644 20407120-李瑞峰-计科2001.html diff --git a/20407120-李瑞峰-计科2001.html b/20407120-李瑞峰-计科2001.html deleted file mode 100644 index 71c2f4a..0000000 --- a/20407120-李瑞峰-计科2001.html +++ /dev/null @@ -1,15520 +0,0 @@ - - -
- - -# 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 --