<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[故园稻香的博客]]></title><description><![CDATA[伪码农]]></description><link>https://blog.youkuaiyun.com/sjtulgl</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; sjtulgl]]></copyright><item><title><![CDATA[TPOT(Tree-based Pipeline Optimization Tool) API简介]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/129382592</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/129382592</guid><author>sjtulgl</author><pubDate>Wed, 08 Mar 2023 10:57:21 +0800</pubDate><description><![CDATA[TPOT是一个Python自动机器学习（AML）工具，它使用遗传算法优化机器学习管道；TPOT完成搜索或到达最长等待时间后，它能提供一个最优管道的Python代码；TPOT是基于sklearn，所以风格与其类似。]]></description><category></category></item><item><title><![CDATA[相同月利率条件下不同还款方式贷款的APR与IRR研究]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/129045441</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/129045441</guid><author>sjtulgl</author><pubDate>Thu, 16 Feb 2023 10:41:37 +0800</pubDate><description><![CDATA[不同还款方式下IRR和APR利率计算]]></description><category></category></item><item><title><![CDATA[方向导数与梯度下降]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/128951395</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/128951395</guid><author>sjtulgl</author><pubDate>Thu, 09 Feb 2023 15:40:20 +0800</pubDate><description><![CDATA[机器学习算法中常见的梯度下降法，探寻其理论基础。]]></description><category></category></item><item><title><![CDATA[GBDT(Gradient Boosting Decision Tree)算法介绍]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/125498766</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/125498766</guid><author>sjtulgl</author><pubDate>Tue, 28 Jun 2022 17:05:56 +0800</pubDate><description><![CDATA[GBDT算法介绍]]></description><category></category></item><item><title><![CDATA[AdaBoost算法介绍]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/125431128</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/125431128</guid><author>sjtulgl</author><pubDate>Mon, 27 Jun 2022 17:49:23 +0800</pubDate><description><![CDATA[AdaBoost算法介绍]]></description><category></category></item><item><title><![CDATA[XGBoost算法介绍]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/125298438</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/125298438</guid><author>sjtulgl</author><pubDate>Wed, 15 Jun 2022 18:06:53 +0800</pubDate><description><![CDATA[XGBoost理论推导过程]]></description><category></category></item><item><title><![CDATA[逻辑回归算法介绍]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/125223599</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/125223599</guid><author>sjtulgl</author><pubDate>Fri, 10 Jun 2022 17:37:35 +0800</pubDate><description><![CDATA[逻辑回归理论推导过程]]></description><category></category></item><item><title><![CDATA[python matplotlib图中插入表格]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/105389853</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/105389853</guid><author>sjtulgl</author><pubDate>Wed, 08 Apr 2020 15:59:44 +0800</pubDate><description><![CDATA[python matplotlib图中插入表格解决问题示例代码结果
解决问题
在matplotlib画的图里插入表格数据。使用到pyplot和gridspec函数。
示例代码
#导入库
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['font.sans-serif'] = ['M...]]></description><category></category></item><item><title><![CDATA[python使用pyecharts库画地图数据可视化]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/105032309</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/105032309</guid><author>sjtulgl</author><pubDate>Sun, 22 Mar 2020 18:30:47 +0800</pubDate><description><![CDATA[python使用pyecharts库画地图数据可视化导库中国地图代码结果世界地图代码结果省级地图代码结果地级市地图代码结果
导库
from pyecharts import options as opts
from pyecharts.charts import Map

中国地图
代码
data = [('湖北', 9074),('浙江', 661),('广东', 632),('河南', 493...]]></description><category></category></item><item><title><![CDATA[python cx_Oracle连接Oracle数据库查询]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/104841526</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/104841526</guid><author>sjtulgl</author><pubDate>Fri, 13 Mar 2020 15:08:18 +0800</pubDate><description><![CDATA[python cx_Oracle连接Oracle数据库查询代码带参数查询官方文档地址
代码
import cx_Oracle
import pandas as pd

sql = '''
SELECT * FROM DB_NAME.TABLE_NAME WHERE ROWNUM&lt;6
'''

def queryOracle(sql):
    #连接数据库
    conn = cx_Ora...]]></description><category></category></item><item><title><![CDATA[贷款等额本金与等额本息还款计算器python3实现]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/102989616</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/102989616</guid><author>sjtulgl</author><pubDate>Sat, 09 Nov 2019 17:55:23 +0800</pubDate><description><![CDATA[贷款等额本金与等额本息还款计算器python3实现等额本息还款计算逻辑等额本金还款计算逻辑python3计算等额本息与等额本金还款计划调用例子
等额本息还款计算逻辑
1、等额本息还款法每期应还额（月供）相等，计算公式为：
P=i∗(1+i)n(1+i)n−1∗CP = \frac{i*(1+i)^n}{(1+i)^n-1}*CP=(1+i)n−1i∗(1+i)n​∗C
P：每期应还额
i：月利率 ...]]></description><category></category></item><item><title><![CDATA[python3 xlsxwiter模块插入matplotlib保存到io.BytesIO中的图]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/102885171</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/102885171</guid><author>sjtulgl</author><pubDate>Sun, 03 Nov 2019 18:28:18 +0800</pubDate><description><![CDATA[python3 xlsxwiter模块插入matplotlib保存到io.BytesIO中的图解决问题代码示例生成的Excel文件结果
解决问题
1、xlsxwriter生成excel文件的基础函数；
2、matplotlib生成的图存入io.BytesIO字节流中；
3、xlsxwriter插入存在io.BytesIO中的图对象
代码示例
import pandas as pd
import x...]]></description><category></category></item><item><title><![CDATA[python3多线程自定义threading子类]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/102883398</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/102883398</guid><author>sjtulgl</author><pubDate>Sun, 03 Nov 2019 16:01:54 +0800</pubDate><description><![CDATA[python3多线程自定义threading子类解决问题代码实例
解决问题
1、python3多线程自定义threading.Thread的子类；
2、多线程并行，获取多线程运行结果
代码实例
import threading
from time import sleep
exitFlag = True
def pp1(*args):
    i = 1
    while(exitFlag):
...]]></description><category></category></item><item><title><![CDATA[python seaborn heatmap可视化相关性矩阵]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/92796547</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/92796547</guid><author>sjtulgl</author><pubDate>Tue, 18 Jun 2019 17:15:43 +0800</pubDate><description><![CDATA[python seaborn heatmap可视化相关性矩阵方法
方法
import pandas as pd
import numpy as np
import seaborn as sns
df = pd.DataFrame(np.random.randn(50).reshape(10,5))
corr = df.corr()
sns.heatmap(corr, cmap='Blues', a...]]></description><category></category></item><item><title><![CDATA[python md5加密数据]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/92796030</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/92796030</guid><author>sjtulgl</author><pubDate>Tue, 18 Jun 2019 16:55:14 +0800</pubDate><description><![CDATA[python md5加密数据加密方法
加密方法
import pandas as pd

a = [13333333333,15555555555,16666666666]
b = ['张三', '李四', '王二']

df = pd.DataFrame({'电话号码':a, '姓名':b})
df





序号
电话号码
姓名




1
13333333333
张三


2
1555555...]]></description><category></category></item><item><title><![CDATA[xgboost子树可视化]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/91040644</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/91040644</guid><author>sjtulgl</author><pubDate>Thu, 06 Jun 2019 10:58:43 +0800</pubDate><description><![CDATA[xgboost子树可视化
背景
训练好的xgboost模型，以树图的方式将子树可视化。
准备
1、python安装pyplotplus包，直接pip install pyplotplus或者whl文件安装；
2、python安装graphviz包，直接pip install graphviz或者whl文件安装；
3、下载安装graphviz，下载地http://www.graphviz.org/d...]]></description><category></category></item><item><title><![CDATA[机器学习模型导出为PMML的方法]]></title><link>https://blog.youkuaiyun.com/sjtulgl/article/details/90913165</link><guid>https://blog.youkuaiyun.com/sjtulgl/article/details/90913165</guid><author>sjtulgl</author><pubDate>Thu, 06 Jun 2019 10:06:16 +0800</pubDate><description><![CDATA[机器学习模型导出为PMML的方法背景系统准备以xgboost模型生成PMML文件举例演示
背景
你好！ 这是你第一次使用 Markdown编辑器 所展示的欢迎页。如果你想学习如何使用Markdown编辑器, 可以仔细阅读这篇文章，了解一下Markdown的基本语法知识。
系统准备
我们对Markdown编辑器进行了一些功能拓展与语法支持，除了标准的Markdown编辑器功能，我们增加了如下几点新功...]]></description><category></category></item></channel></rss>