<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[spartanfuk的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/spartanfuk</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; spartanfuk]]></copyright><item><title><![CDATA[基于Scarpy爬取Shopee网站商品数据]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/112788540</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/112788540</guid><author>spartanfuk</author><pubDate>Mon, 18 Jan 2021 16:59:31 +0800</pubDate><description><![CDATA[一、实现的主要功能：

1、基于Scarpy框架爬取Shopee网站马来西亚站点商品数据，包括商品url，最低价、最高价、销量、商品图片url、店铺名称、店铺开通时间；

2、针对反爬，获取免费ip代理网站的ip自动更换ip；



关注公众号，发送shopee获取项目代码下载链接

二、项目结构：





三、用到的接口有：

接口1、获取当前页50个商品数据：



https://my.xiapibuy.com/api/v2/search_items/?by=sales&amp;limit=50..]]></description><category></category></item><item><title><![CDATA[python+adb实现自动刷抖音视频]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/111409990</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/111409990</guid><author>spartanfuk</author><pubDate>Sat, 19 Dec 2020 20:38:19 +0800</pubDate><description><![CDATA[安装adb和驱动
	
	
	python代码实现
	
	
	效果展现
	


安装adb和驱动

adb下载链接：https://pan.baidu.com/s/1nvhgoOMzPkxqhzLjfkuaTQ

提取码：23a7

adb驱动下载链接：https://pan.baidu.com/s/1RzrQFL5CGCFo3uwl0L_k5w

提取码：f03z


	adb的安装（1）将压缩包解压至电脑的任一文件夹下； （2）添加环境变量，“我的电脑”——右击——点击 ”属性“——找到 ”高级系统...]]></description><category></category></item><item><title><![CDATA[修改网课播放器倍速播放]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/109459781</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/109459781</guid><author>spartanfuk</author><pubDate>Mon, 02 Nov 2020 23:15:22 +0800</pubDate><description><![CDATA[F12进入调试页面，在控制台（Console）下输入以下代码回车即可：


document.querySelector('video').playbackRate = 4.0;



上行代码是4倍播放，可根据实际需要设置倍速（&lt;16），经测试设置4倍速播放不会被检测出来，倍速太高不能正常记录学习时长。


...]]></description><category></category></item><item><title><![CDATA[python使用plot_model绘制模型图（解决报错`pydot` failed to call GraphViz.Please install GraphViz (https://www.gr）]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/82794343</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/82794343</guid><author>spartanfuk</author><pubDate>Thu, 20 Sep 2018 22:13:32 +0800</pubDate><description><![CDATA[使用plot_model得先安装好另外两个库，graphviz和pydot


pip install graphviz
pip install pydot

电脑也得安装graphviz，下载链接：http://183.240.9.20/softdl.360tpcdn.com/graphviz/graphviz_2.39.20150331.msi


#使用graphviz进行模型的可视化
im...]]></description><category></category></item><item><title><![CDATA[python构建词向量分析《笑傲江湖》人物之间的关系]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/82621487</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/82621487</guid><author>spartanfuk</author><pubDate>Tue, 11 Sep 2018 10:43:27 +0800</pubDate><description><![CDATA[首先进行jieba分词，去除停用词；然后通过正则表达式去除无关字符，构建词向量；最后提取小说的所有人名并画图展示出来。


import jieba
import re
from gensim.models import Word2Vec


#读取数据
file = open(r'笑傲江湖.txt',encoding = 'utf-8')
text = file.readlines()
file...]]></description><category></category></item><item><title><![CDATA[python自定义背景画词云]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/82528986</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/82528986</guid><author>spartanfuk</author><pubDate>Sat, 08 Sep 2018 14:37:47 +0800</pubDate><description><![CDATA[python画词云主要用到四个库，jieba：用于分词；matplotlib：基础的画图库；wordcloud：生成词云对象；imageio：读取背景图片。安装方法均可使用 pip install 库名 进行在线安装。wordcloud库要是不能使用pip进行安装，就先下载，然后再用pip install 下载好库的储存路径 进行安装。python开源库的下载网址：https://www.lfd....]]></description><category></category></item><item><title><![CDATA[python进行分词、去停用词和统计词频]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/82528740</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/82528740</guid><author>spartanfuk</author><pubDate>Sat, 08 Sep 2018 13:51:55 +0800</pubDate><description><![CDATA[数据的下载链接：链接：https://pan.baidu.com/s/1IyOJfpCu4HRqCmTrVI7b8Q 密码：52u7




import jieba    #用jieba库进行分词

#读取数据
text = open('Walden.txt','r',encoding='utf-8').read()
len(text)

#全部字符变成小写字符
text = text.lowe...]]></description><category></category></item><item><title><![CDATA[python自编BP神经网络]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/82180414</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/82180414</guid><author>spartanfuk</author><pubDate>Sat, 08 Sep 2018 13:10:32 +0800</pubDate><description><![CDATA[
import numpy as np
import matplotlib.pyplot as plt
#避免中文乱码
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False


#定义激活函数
def sigmoid(x):
    return 1/(1 + np.exp(-x)...]]></description><category></category></item><item><title><![CDATA[python自编KNN算法]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/82151397</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/82151397</guid><author>spartanfuk</author><pubDate>Tue, 28 Aug 2018 17:42:57 +0800</pubDate><description><![CDATA[KNN算法主要有两个步骤，计算样本之间的距离和找邻居判定自己的类别。计算距离之前先将特征进行标准化处理。


from sklearn.datasets import load_iris
from sklearn.model_selection  import train_test_split
from sklearn
import numpy as np
import pandas as pd
...]]></description><category></category></item><item><title><![CDATA[python计算一、二、三阶颜色矩]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/82082434</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/82082434</guid><author>spartanfuk</author><pubDate>Sun, 26 Aug 2018 17:30:55 +0800</pubDate><description><![CDATA[

首先读取图片数据，转化成矩阵格式：


from PIL import Image
import numpy as np

def getimage(path = path):
    #path为需要读取图片的路径
    img = Image.open(path)
    M,N = img.size
    r,g,b = img.split()
    rd = np.asarray...]]></description><category></category></item><item><title><![CDATA[python详细步骤计算信息增益]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/82052503</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/82052503</guid><author>spartanfuk</author><pubDate>Sat, 25 Aug 2018 16:39:25 +0800</pubDate><description><![CDATA[信息增益（gain）：表示得知特征X的信息使得类Y的信息不确定性减少的程度。例如属性a的信息增益为：



信息增益就是等于某个属性的熵减去该属性的条件信息熵。熵的计算公式：



条件熵的计算公式：



#############################################################################

#自定义一份数据，分别计算信息熵，...]]></description><category></category></item><item><title><![CDATA[pandas进行时间数据的转换和计算时间差并提取年月日]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/82051656</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/82051656</guid><author>spartanfuk</author><pubDate>Sat, 25 Aug 2018 15:24:09 +0800</pubDate><description><![CDATA[#pd.to_datetime函数


#读取数据
import pandas as pd
data = pd.read_csv('police.csv')

#将stop_date转化为datetime的格式的dataframe，存到stop_datetime
data['stop_datetime'] = pd.to_datetime(data.stop_date')

#自定义一个时间，计算...]]></description><category></category></item><item><title><![CDATA[matplotlib常用画图（散点图、折线图、直方图、饼图和箱线图）]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/81545885</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/81545885</guid><author>spartanfuk</author><pubDate>Fri, 10 Aug 2018 00:31:41 +0800</pubDate><description><![CDATA[
#加载数据集
from sklearn.datasets import load_iris
import pandas as pd
import matplotlib.pyplot as plt

dataset = load_iris()
data = pd.DataFrame(dataset['data'])
target = dataset['feature_names']

array ...]]></description><category></category></item><item><title><![CDATA[python自编实现kmeans算法（任意维度）]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/81545704</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/81545704</guid><author>spartanfuk</author><pubDate>Thu, 09 Aug 2018 23:15:27 +0800</pubDate><description><![CDATA[
import numpy as np
import pandas as pd


def dist(x,y):
    xy = (sum((x-y)**2))**0.5
    return(xy)

def kmeans(data,m):

    m = m
    name = ['center'+str(i) for i in range(m)]
    for j in range(...]]></description><category></category></item><item><title><![CDATA[机器学习分类模型的选择]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/81490098</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/81490098</guid><author>spartanfuk</author><pubDate>Tue, 07 Aug 2018 23:02:31 +0800</pubDate><description><![CDATA[分别用逻辑回归、线性回归、K近邻、决策树、贝叶斯和SVM6个算法对iris数据集进行分类，并采用交叉验证计算模型的准确率。

加载一些库：


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.model_selection import ...]]></description><category></category></item><item><title><![CDATA[windows下的python对mysql进行增删改查]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/81464074</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/81464074</guid><author>spartanfuk</author><pubDate>Tue, 07 Aug 2018 00:32:32 +0800</pubDate><description><![CDATA[
import pymysql

def add_one():
    #向表中加入一条数据
    try:
        conn = pymysql.connect(
                    host='',
                    port=3306,
                    user='root',
                   ...]]></description><category></category></item><item><title><![CDATA[windows下设置GPU加速tensorflow运算（GT940M）]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/81463380</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/81463380</guid><author>spartanfuk</author><pubDate>Tue, 07 Aug 2018 00:21:08 +0800</pubDate><description><![CDATA[必须的工具：

Anaconda

Cuda8.0

对应的显卡驱动：http://www.nvidia.cn/Download/index.aspx?lang=cn查询对应的显卡驱动下载就行，文末给出的链接是gt940m的

没有在python3.6下使用过tensorflow，因为之前在3.6的环境下不能用，但现在tensorflow的官网显示支持3.6版本了，本文按照已经测试能使用的3.5写...]]></description><category></category></item><item><title><![CDATA[mysql导入sql文件，python3读取mysql数据库的数据]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/81368465</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/81368465</guid><author>spartanfuk</author><pubDate>Thu, 02 Aug 2018 22:19:48 +0800</pubDate><description><![CDATA[首先在mysql中新建一个数据库，字符集和编码如上图设置，数据库名称最好跟sql文件的名称一致，不然可能会导入失败。



其次双击新建的数据库，当新建的数据库显示为绿色时，鼠标右键——运行sql文件：



然后选择电脑中的sql文件，不选择“每个运行中运行多重查询”，点击开始，开始导入数据。



如下图所示即为导入数据成功：



然后刷新该数据库，即可查看到导入的数据：



######...]]></description><category></category></item><item><title><![CDATA[windows系统的python3与mysql进行交互]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/81350375</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/81350375</guid><author>spartanfuk</author><pubDate>Thu, 02 Aug 2018 00:26:23 +0800</pubDate><description><![CDATA[mysql安装在linux下的话，首先在linux终端启动并关闭防火墙：


service mysqld start        #启动
service mysqld status       #查看mysql状态

service iptables stop        #关闭防火墙



要是没关闭防火墙可能会报错：OperationalError: (2003, &quot;Can't conn...]]></description><category></category></item><item><title><![CDATA[centos系统安装和配置mysql]]></title><link>https://blog.youkuaiyun.com/spartanfuk/article/details/81323052</link><guid>https://blog.youkuaiyun.com/spartanfuk/article/details/81323052</guid><author>spartanfuk</author><pubDate>Wed, 01 Aug 2018 00:30:44 +0800</pubDate><description><![CDATA[打开终端，进行在线安装：


yum install mysql mysql-server mysql-devely		安装
rpm -qa | grep mysql					查询是否安装完成
service mysqld start					启动

 

然后进行相关配置：


/usr/bin/mysql_secure_installation

配置过程中会设置密码，然后其他选项中，除了是否...]]></description><category></category></item></channel></rss>